diff --git a/datasets/alpi/__init__.py b/datasets/alpi/__init__.py index 15004ec..8309276 100644 --- a/datasets/alpi/__init__.py +++ b/datasets/alpi/__init__.py @@ -121,6 +121,7 @@ def create_params_list(data_path, params, verbose=True): # PHASE 1 + # current_offset must be an integer and it must indicate minutes def generate_dataset_by_serial_offset(data, params, current_offset): data["current_offset"] = current_offset diff --git a/datasets/alpi/dataset.py b/datasets/alpi/dataset.py index f41a718..6b3f5ba 100644 --- a/datasets/alpi/dataset.py +++ b/datasets/alpi/dataset.py @@ -121,6 +121,7 @@ def create_params_list(data_path, params, verbose=True): # PHASE 1 + # current_offset must be an integer and it must indicate minutes def generate_dataset_by_serial_offset(data, params, current_offset): data["current_offset"] = current_offset diff --git a/datasets/cbm/__init__.py b/datasets/cbm/__init__.py index 52e8974..40fc95c 100644 --- a/datasets/cbm/__init__.py +++ b/datasets/cbm/__init__.py @@ -7,7 +7,6 @@ def parse_feature_names(fn): - with open(fn) as f: names, lines = [], f.readlines() for line in lines: @@ -17,7 +16,6 @@ def parse_feature_names(fn): def load_data(shorten_feature_names=True): - fp = os.path.dirname(__file__) raw_data = np.loadtxt(fp + "/data.txt.gz") features = parse_feature_names(fp + "/Features.txt") @@ -51,7 +49,6 @@ def load_clean_data(): def gen_summary(wd=400, outdir=None): - if outdir is None: outdir = os.path.dirname(__file__) diff --git a/datasets/cmapss/__init__.py b/datasets/cmapss/__init__.py index cff21e4..ded28cd 100644 --- a/datasets/cmapss/__init__.py +++ b/datasets/cmapss/__init__.py @@ -2,6 +2,7 @@ import numpy as np import pandas as pd import tqdm + # from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold @@ -86,6 +87,13 @@ def load_clean_data_rul( ): train, test, labels = load_mesurement_list(index=index, features=features) label = "RUL" + class_label_name = "Class" + if index=="FD001" or index=="FD002": + class_=1 + elif index=="FD003" or index=="FD004": + class_=2 + else: + class_=0 # train train_df_list = [] @@ -93,6 +101,7 @@ def load_clean_data_rul( max_rul = len(tt) rul_array = np.array(range(max_rul))[::-1] tt[label] = rul_array + tt[class_label_name] = class_ train_df_list.append(tt) # test @@ -102,6 +111,7 @@ def load_clean_data_rul( rul_array = np.array(range(max_rul))[::-1] rul_array += int(la) tt[label] = rul_array + tt[class_label_name] = class_ test_df_list.append(tt) return train_df_list, test_df_list @@ -109,31 +119,47 @@ def load_clean_data_rul( def load_clean_data_rul_k_folds( split_ind, - indices=["FD004",], + indices=[ + "FD004", + ], k=5, features=[2, 3, 4, 7, 11, 12, 15], random_state=0, + use_test=True, + val=0.1, ): df_list = [] for index in indices: - train_df_list, test_df_list = load_clean_data_rul(index=index, features=features,) + train_df_list, test_df_list = load_clean_data_rul( + index=index, + features=features, + ) df_list.extend(train_df_list) - df_list.extend(test_df_list) - + if use_test: + df_list.extend(test_df_list) + data_index = range(len(df_list)) - + kf = KFold( n_splits=k, random_state=random_state, - shuffle=True,) + shuffle=True, + ) - train_idx,test_idx = list(kf.split(data_index))[split_ind] + train_idx, test_idx = list(kf.split(data_index))[split_ind] + len(train_idx) + print(f"all index:{len(data_index)}") + print(train_idx) + print(test_idx) new_train_df_list = [df_list[i] for i in train_idx] new_test_df_list = [df_list[i] for i in test_idx] return new_train_df_list, new_test_df_list + + + def load_mesurement_list( index="FD004", features=[2, 3, 4, 7, 11, 12, 15], @@ -168,7 +194,6 @@ def load_mesurement_list( def run_to_failure_aux(df, lifetime, unit_number): - assert lifetime <= df.shape[0] broken = 0 if lifetime < df.shape[0] else 1 sample = pd.DataFrame( @@ -187,7 +212,6 @@ def run_to_failure_aux(df, lifetime, unit_number): def censoring_augmentation(raw_data, n_samples=10, seed=123): - np.random.seed(seed) datasets = [g for _, g in raw_data.groupby("unit_number")] timeseries = raw_data.groupby("unit_number").size() @@ -206,7 +230,6 @@ def censoring_augmentation(raw_data, n_samples=10, seed=123): def generate_run_to_failure(df, health_censor_aug=0, seed=123): - samples = [] for unit_id, timeseries in tqdm.tqdm(df.groupby("unit_number"), desc="RUL"): samples.append(run_to_failure_aux(timeseries, timeseries.shape[0], unit_id)) @@ -227,7 +250,6 @@ def leave_one_out( input_fn=None, output_fn=None, ): - if input_fn is not None: subsets = pd.read_csv(input_fn) diff --git a/datasets/gdd/__init__.py b/datasets/gdd/__init__.py index a1f8237..9cec029 100644 --- a/datasets/gdd/__init__.py +++ b/datasets/gdd/__init__.py @@ -7,7 +7,6 @@ def load_data(index="state"): - assert index in ["state", "anomaly", "normal", "linear", "pressure"] fp = os.path.dirname(__file__) @@ -163,7 +162,6 @@ def plot_genesis_nonlabels(df, figsize=(15, 20), cmap="tab10"): def gen_summary(outdir=None): - if outdir is None: outdir = os.path.dirname(__file__) @@ -171,7 +169,6 @@ def gen_summary(outdir=None): sns.set(font_scale=1.1, style="whitegrid") with PdfPages(outdir + "/gdd_summary.pdf") as pp: - print("Plotting Genesis_StateMachineLabel...") df = load_data(index="state") fig, _ = plot_genesis_labels(df) diff --git a/datasets/gfd/__init__.py b/datasets/gfd/__init__.py index cb23013..259ce4f 100644 --- a/datasets/gfd/__init__.py +++ b/datasets/gfd/__init__.py @@ -44,7 +44,6 @@ def plot_sequence(df, st=0, ed=None, ax=None, figsize=(10, 3), individual=True): def gen_summary(outdir=None, st=0, ed=500, wd=20, hg=8): - if outdir is None: outdir = os.path.dirname(__file__) diff --git a/datasets/hydsys/__init__.py b/datasets/hydsys/__init__.py index 2d9458a..bcc3b98 100644 --- a/datasets/hydsys/__init__.py +++ b/datasets/hydsys/__init__.py @@ -5,7 +5,6 @@ def load_data(sensor=None, rw=0): - if sensor is None: # load full data # rw is ignored to concatenate all sensor data @@ -21,7 +20,6 @@ def load_data(sensor=None, rw=0): def load_sensor_data(sensor, rw=0): - data = [] sensor_list = get_sensor_list(sensor) fp = os.path.dirname(__file__) diff --git a/datasets/mapm/__init__.py b/datasets/mapm/__init__.py index 164c025..cb510e0 100644 --- a/datasets/mapm/__init__.py +++ b/datasets/mapm/__init__.py @@ -9,8 +9,8 @@ from sklearn.model_selection import KFold +sensors = ["volt", "rotate", "pressure", "vibration"] -sensors=["volt","rotate","pressure","vibration"] def load_data(): fp = os.path.dirname(__file__) @@ -37,7 +37,6 @@ def load_data(): def cleaning(df): - # NaN values are encoded to -1 df = df.sort_values("errorID") df.errorID = df.errorID.factorize()[0] @@ -69,7 +68,6 @@ def generate_run_to_failure( seed=123, outfn=None, ): - run_to_failure = [] error_ids = raw_data.errorID.dropna().sort_values().unique().tolist() @@ -137,14 +135,12 @@ def generate_run_to_failure( def censoring_augmentation( raw_data, n_samples=10, max_lifetime=150, min_lifetime=2, seed=123 ): - error_ids = raw_data.errorID.dropna().sort_values().unique().tolist() np.random.seed(seed) samples = [] pbar = tqdm.tqdm(total=n_samples, desc="augmentation") while len(samples) < n_samples: - censor_timing = np.random.randint(min_lifetime, max_lifetime) machine_id = np.random.randint(100) + 1 tmp = raw_data[raw_data.machineID == machine_id] @@ -192,7 +188,6 @@ def censoring_augmentation( def generate_validation_sets(method="kfold", n_splits=5, seed=123, outdir=None): - validation_sets = [] if method == "kfold": @@ -206,7 +201,6 @@ def generate_validation_sets(method="kfold", n_splits=5, seed=123, outdir=None): n_splits=n_splits, shuffle=True, random_state=seed ) for i, (train_index, test_index) in enumerate(kfold.split(np.arange(100))): - print("K-fold {}/{}".format(i + 1, n_splits)) # train/test split by machine ID train_machines = raw_data[raw_data.machineID.isin(train_index)] @@ -248,7 +242,6 @@ def load_validation_sets(filepath, n_splits=5): def plot_sequence_and_events(data, machine_id=1): - data = data[data.machineID == machine_id] fig, ax = plt.subplots(4 + 2, figsize=(8, 8)) @@ -274,7 +267,6 @@ def plot_sequence_and_events(data, machine_id=1): def gen_summary(outdir=None): - if outdir is None: outdir = os.path.dirname(__file__) @@ -289,7 +281,7 @@ def gen_summary(outdir=None): plt.close() -def load_failure_sequences_list(dim=sensors): +def load_failure_sequences_list(dim=sensors,once_per_machine=False,len_thre=0): """ Returns @@ -298,46 +290,66 @@ def load_failure_sequences_list(dim=sensors): failure labels [# of seq] """ - clean_df =load_clean_data() + clean_df = load_clean_data() sequence_df_list = [] failure_list = [] - + # source_df = pd.DataFrame(colums=["seq_id","machine_id"]) clean_df["seq_id"] = 0 - for machine_id, m_df in tqdm(clean_df.groupby("machineID"),desc="Segment each machine data"): - # sort + for machine_id, m_df in tqdm( + clean_df.groupby("machineID"), desc="Segment each machine data" + ): + # sort m_df = m_df.sort_values("datetime") # segment & set seq_id - failures_index = m_df["failure"][m_df["failure"]>-1].index - failures_values = m_df["failure"][m_df["failure"]>-1].values + failures_index = m_df["failure"][m_df["failure"] > -1].index + failures_values = m_df["failure"][m_df["failure"] > -1].values for ind in failures_index: - m_df.loc[ind:,"seq_id"] +=1 - - for (seq_id, seq_df), f_val in zip(m_df.groupby("seq_id"),failures_values): - sequence_df_list.append(seq_df.sort_values("datetime").reset_index(drop=True).loc[:,dim]) - failure_list.append(f_val) - + m_df.loc[ind:, "seq_id"] += 1 + + for (seq_id, seq_df), f_val in zip(m_df.groupby("seq_id"), failures_values): + if once_per_machine: + if len(seq_df) > len_thre: + sequence_df_list.append( + seq_df.sort_values("datetime").reset_index(drop=True).loc[:, ["machineID"]+dim] + ) + failure_list.append(f_val) + break + else: + continue + + else: + sequence_df_list.append( + seq_df.sort_values("datetime").reset_index(drop=True).loc[:, ["machineID"]+dim] + ) + failure_list.append(f_val) + return sequence_df_list, failure_list + def load_clean_data_rul_k_folds( - split_ind, - k=5, - random_state=0, + split_ind, + k=5, + random_state=0, + once_per_machine_min=0, ): - - df_list = add_rul(*refine_data(*load_failure_sequences_list())) + if once_per_machine_min==0: + df_list = add_rul(*refine_data(*load_failure_sequences_list())) + else: + df_list = add_rul(*refine_data(*load_failure_sequences_list(once_per_machine=True,len_thre=once_per_machine_min))) data_index = range(len(df_list)) - + kf = KFold( n_splits=k, random_state=random_state, - shuffle=True,) + shuffle=True, + ) - train_idx,test_idx = list(kf.split(data_index))[split_ind] + train_idx, test_idx = list(kf.split(data_index))[split_ind] train_df_list = [df_list[i] for i in train_idx] test_df_list = [df_list[i] for i in test_idx] @@ -345,60 +357,61 @@ def load_clean_data_rul_k_folds( return train_df_list, test_df_list -def refine_data(sequence_df_list, failure_list, event_type="only",min_len=100): +def refine_data(sequence_df_list, failure_list, event_type="only", min_len=100): """ - refine_event: This data contain some sequences with complex/mulitple failue + refine_event: This data contain some sequences with complex/mulitple failue if "only", use sequences with only a failue, remove sequences with mulitple failue elif "all" - use all sequences, and regard complex failues as a new types of failure + use all sequences, and regard complex failues as a new types of failure """ num_seq = len(failure_list) length_arr = np.array([len(ss) for ss in sequence_df_list]) - complex_ind = np.arange(num_seq)[length_arr<=1] + complex_ind = np.arange(num_seq)[length_arr <= 1] # whether all complex failues contains two types of failues or not - assert not np.sum(np.diff(complex_ind)<2), "complex failue events contain three or more failues" - - # remove complex_ind - apply_arr = length_arr>1 + assert not np.sum( + np.diff(complex_ind) < 2 + ), "complex failue events contain three or more failues" + + # remove complex_ind + apply_arr = length_arr > 1 - if event_type=="only": - # remove complex_ind-1 + if event_type == "only": + # remove complex_ind-1 for c_id in complex_ind: - apply_arr[c_id-1] = False - - elif event_type=="all": + apply_arr[c_id - 1] = False + + elif event_type == "all": NotImplementedError # check combination and define new type failures # complex_val = np.array(failure_list)[complex_ind] # # set new failure # for c_id,n_f in zip(complex_ind,new_failures): - # failure_list[c_id-1] = n_f + # failure_list[c_id-1] = n_f else: NotImplementedError - + # remove sequences that not contain min_len values - apply_length_arr = length_arr>min_len - apply_arr &= apply_length_arr - - refined_sequence_df_list=[] - refined_failure_list=[] - for a, seq, failure in zip(apply_arr,sequence_df_list, failure_list): + apply_length_arr = length_arr > min_len + apply_arr &= apply_length_arr + + refined_sequence_df_list = [] + refined_failure_list = [] + for a, seq, failure in zip(apply_arr, sequence_df_list, failure_list): if a: refined_sequence_df_list.append(seq) refined_failure_list.append(failure) - return refined_sequence_df_list,refined_failure_list + return refined_sequence_df_list, refined_failure_list def add_rul(sequence_df_list, failure_list): - label_name = "RUL" - class_label_name="Class" - # + class_label_name = "Class" + # df_list = [] for seq_df, class_label in zip(sequence_df_list, failure_list): seq_df[class_label_name] = class_label @@ -406,4 +419,4 @@ def add_rul(sequence_df_list, failure_list): seq_df[label_name] = rul_array df_list.append(seq_df) - return df_list \ No newline at end of file + return df_list diff --git a/datasets/mapm/view_data.ipynb b/datasets/mapm/view_data.ipynb index 24dbff5..a5ecbf6 100644 --- a/datasets/mapm/view_data.ipynb +++ b/datasets/mapm/view_data.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from collections import Counter\n", "from sklearn import model_selection\n", - "from pysurvival.utils.display import correlation_matrix\n", + "# from pysurvival.utils.display import correlation_matrix\n", "import tqdm\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -44,9 +44,186 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimemachineIDvoltrotatepressurevibrationerrorIDfailure
02015-01-01 06:00:001176.217853418.504078113.07793545.087686NaNNaN
12015-01-01 07:00:001162.879223402.74749095.46052543.413973NaNNaN
22015-01-01 08:00:001170.989902527.34982575.23790534.178847NaNNaN
32015-01-01 09:00:001162.462833346.149335109.24856141.122144NaNNaN
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72015-01-01 13:00:001172.522781409.624717101.00108335.482009NaNNaN
82015-01-01 14:00:001175.324524398.648781110.62436145.482287NaNNaN
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\n", + "
" + ], + "text/plain": [ + " datetime machineID volt rotate pressure \\\n", + "0 2015-01-01 06:00:00 1 176.217853 418.504078 113.077935 \n", + "1 2015-01-01 07:00:00 1 162.879223 402.747490 95.460525 \n", + "2 2015-01-01 08:00:00 1 170.989902 527.349825 75.237905 \n", + "3 2015-01-01 09:00:00 1 162.462833 346.149335 109.248561 \n", + "4 2015-01-01 10:00:00 1 157.610021 435.376873 111.886648 \n", + "5 2015-01-01 11:00:00 1 172.504839 430.323362 95.927042 \n", + "6 2015-01-01 12:00:00 1 156.556031 499.071623 111.755684 \n", + "7 2015-01-01 13:00:00 1 172.522781 409.624717 101.001083 \n", + "8 2015-01-01 14:00:00 1 175.324524 398.648781 110.624361 \n", + "9 2015-01-01 15:00:00 1 169.218423 460.850670 104.848230 \n", + "\n", + " vibration errorID failure \n", + "0 45.087686 NaN NaN \n", + "1 43.413973 NaN NaN \n", + "2 34.178847 NaN NaN \n", + "3 41.122144 NaN NaN \n", + "4 25.990511 NaN NaN \n", + "5 35.655017 NaN NaN \n", + "6 42.753920 NaN NaN \n", + "7 35.482009 NaN NaN \n", + "8 45.482287 NaN NaN \n", + "9 39.901735 NaN NaN " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data = load_data()\n", "raw_data.head(10)" @@ -54,16 +231,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "datetime 8761\n", + "machineID 100\n", + "volt 876100\n", + "rotate 876100\n", + "pressure 876100\n", + "vibration 876100\n", + "errorID 5\n", + "failure 4\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data.nunique()" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -115,180 +311,2204 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "augmentation: 100%|██████████| 1000/1000 [00:14<00:00, 69.19it/s]\n" + "augmentation: 0%| | 0/1000 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "name": "stderr", + "output_type": "stream", + "text": [ + "augmentation: 3%|████▍ | 30/1000 [00:00<00:15, 62.91it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 4%|█████▍ | 37/1000 [00:00<00:14, 65.10it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 4%|██████▍ | 44/1000 [00:00<00:14, 65.27it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 5%|███████▍ | 51/1000 [00:00<00:14, 65.85it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 6%|████████▍ | 58/1000 [00:00<00:14, 66.89it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 6%|█████████▍ | 65/1000 [00:01<00:13, 67.80it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 7%|██████████▌ | 72/1000 [00:01<00:13, 68.37it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 8%|███████████▌ | 79/1000 [00:01<00:13, 68.75it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 9%|████████████▌ | 86/1000 [00:01<00:13, 67.85it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 9%|█████████████▋ | 94/1000 [00:01<00:13, 68.81it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 10%|██████████████▊ | 102/1000 [00:01<00:12, 69.42it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 11%|███████████████▊ | 109/1000 [00:01<00:12, 68.81it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 12%|████████████████▊ | 116/1000 [00:01<00:13, 66.00it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 12%|█████████████████▊ | 123/1000 [00:01<00:13, 66.92it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 13%|██████████████████▊ | 130/1000 [00:02<00:15, 56.93it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 14%|███████████████████▋ | 136/1000 [00:02<00:15, 54.48it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 14%|████████████████████▋ | 143/1000 [00:02<00:14, 57.20it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 15%|█████████████████████▊ | 150/1000 [00:02<00:14, 59.19it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 16%|██████████████████████▊ | 157/1000 [00:02<00:13, 61.30it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 16%|███████████████████████▊ | 164/1000 [00:02<00:13, 61.70it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 17%|████████████████████████▊ | 171/1000 [00:02<00:13, 62.66it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 18%|█████████████████████████▊ | 178/1000 [00:02<00:12, 63.31it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 18%|██████████████████████████▊ | 185/1000 [00:02<00:12, 63.95it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 19%|███████████████████████████▊ | 192/1000 [00:03<00:12, 64.22it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 20%|████████████████████████████▊ | 199/1000 [00:03<00:12, 64.73it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 21%|█████████████████████████████▊ | 206/1000 [00:03<00:12, 64.64it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 21%|██████████████████████████████▉ | 213/1000 [00:03<00:12, 65.54it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 22%|███████████████████████████████▉ | 220/1000 [00:03<00:11, 65.55it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 23%|████████████████████████████████▉ | 227/1000 [00:03<00:11, 64.88it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 23%|█████████████████████████████████▉ | 234/1000 [00:03<00:11, 65.28it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 24%|██████████████████████████████████▉ | 241/1000 [00:03<00:11, 66.00it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 25%|███████████████████████████████████▉ | 248/1000 [00:03<00:11, 66.01it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "augmentation: 26%|████████████████████████████████████▉ | 255/1000 [00:03<00:11, 66.30it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 26%|█████████████████████████████████████▉ | 262/1000 [00:04<00:11, 65.15it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 27%|███████████████████████████████████████ | 269/1000 [00:04<00:11, 65.78it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 28%|████████████████████████████████████████ | 276/1000 [00:04<00:10, 66.05it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 28%|█████████████████████████████████████████ | 283/1000 [00:04<00:12, 55.75it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 29%|█████████████████████████████████████████▉ | 289/1000 [00:04<00:16, 44.03it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 30%|██████████████████████████████████████████▊ | 295/1000 [00:04<00:14, 47.44it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 30%|███████████████████████████████████████████▋ | 301/1000 [00:04<00:14, 49.93it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 31%|████████████████████████████████████████████▌ | 307/1000 [00:05<00:13, 51.46it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 31%|█████████████████████████████████████████████▌ | 314/1000 [00:05<00:12, 54.46it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 32%|██████████████████████████████████████████████▌ | 321/1000 [00:05<00:12, 56.25it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 33%|███████████████████████████████████████████████▍ | 327/1000 [00:05<00:11, 56.62it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 33%|████████████████████████████████████████████████▎ | 333/1000 [00:05<00:11, 56.94it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "augmentation: 34%|█████████████████████████████████████████████████▏ | 339/1000 [00:05<00:11, 56.81it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 34%|██████████████████████████████████████████████████ | 345/1000 [00:05<00:11, 55.27it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 35%|██████████████████████████████████████████████████▉ | 351/1000 [00:05<00:11, 54.43it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 36%|███████████████████████████████████████████████████▉ | 358/1000 [00:05<00:11, 56.11it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 36%|████████████████████████████████████████████████████▉ | 365/1000 [00:06<00:11, 56.01it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "augmentation: 37%|█████████████████████████████████████████████████████▊ | 371/1000 [00:06<00:11, 56.93it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 38%|██████████████████████████████████████████████████████▋ | 377/1000 [00:06<00:11, 56.26it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 38%|███████████████████████████████████████████████████████▌ | 383/1000 [00:06<00:10, 56.29it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 39%|████████████████████████████████████████████████████████▍ | 389/1000 [00:06<00:11, 53.07it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 40%|█████████████████████████████████████████████████████████▎ | 395/1000 [00:06<00:12, 48.51it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 40%|██████████████████████████████████████████████████████████▏ | 401/1000 [00:06<00:11, 51.38it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 41%|███████████████████████████████████████████████████████████▏ | 408/1000 [00:06<00:11, 53.78it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 41%|████████████████████████████████████████████████████████████ | 414/1000 [00:07<00:13, 41.93it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 42%|████████████████████████████████████████████████████████████▊ | 419/1000 [00:07<00:13, 41.96it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 42%|█████████████████████████████████████████████████████████████▍ | 424/1000 [00:07<00:13, 42.45it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 43%|██████████████████████████████████████████████████████████████▍ | 431/1000 [00:07<00:12, 46.16it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 44%|███████████████████████████████████████████████████████████████▏ | 436/1000 [00:07<00:14, 38.20it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 44%|████████████████████████████████████████████████████████████████ | 442/1000 [00:07<00:13, 41.94it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 45%|████████████████████████████████████████████████████████████████▊ | 447/1000 [00:07<00:12, 43.23it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 45%|█████████████████████████████████████████████████████████████████▌ | 452/1000 [00:08<00:15, 35.24it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "augmentation: 46%|██████████████████████████████████████████████████████████████████ | 456/1000 [00:08<00:16, 32.95it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 46%|██████████████████████████████████████████████████████████████████▋ | 460/1000 [00:08<00:18, 28.60it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 46%|███████████████████████████████████████████████████████████████████▎ | 464/1000 [00:08<00:18, 29.57it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 47%|███████████████████████████████████████████████████████████████████▊ | 468/1000 [00:08<00:18, 28.88it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 47%|████████████████████████████████████████████████████████████████████▍ | 472/1000 [00:08<00:18, 27.91it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 48%|█████████████████████████████████████████████████████████████████████ | 476/1000 [00:08<00:18, 28.54it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "augmentation: 48%|█████████████████████████████████████████████████████████████████████▋ | 481/1000 [00:09<00:16, 31.35it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 48%|██████████████████████████████████████████████████████████████████████▎ | 485/1000 [00:09<00:16, 31.82it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 49%|██████████████████████████████████████████████████████████████████████▉ | 489/1000 [00:09<00:15, 32.97it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 49%|███████████████████████████████████████████████████████████████████████▍ | 493/1000 [00:09<00:14, 34.01it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 50%|████████████████████████████████████████████████████████████████████████ | 497/1000 [00:09<00:15, 31.81it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 50%|████████████████████████████████████████████████████████████████████████▋ | 501/1000 [00:09<00:15, 31.68it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 50%|█████████████████████████████████████████████████████████████████████████▏ | 505/1000 [00:09<00:14, 33.42it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 51%|█████████████████████████████████████████████████████████████████████████▊ | 509/1000 [00:09<00:16, 29.65it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 51%|██████████████████████████████████████████████████████████████████████████▍ | 513/1000 [00:10<00:20, 23.69it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 52%|██████████████████████████████████████████████████████████████████████████▉ | 517/1000 [00:10<00:18, 25.63it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 52%|███████████████████████████████████████████████████████████████████████████▌ | 521/1000 [00:10<00:17, 27.41it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 52%|████████████████████████████████████████████████████████████████████████████▏ | 525/1000 [00:10<00:15, 30.06it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 53%|████████████████████████████████████████████████████████████████████████████▋ | 529/1000 [00:10<00:16, 29.09it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 53%|█████████████████████████████████████████████████████████████████████████████▎ | 533/1000 [00:10<00:16, 28.11it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 54%|█████████████████████████████████████████████████████████████████████████████▊ | 537/1000 [00:10<00:15, 30.59it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 54%|██████████████████████████████████████████████████████████████████████████████▋ | 543/1000 [00:11<00:12, 37.26it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 55%|███████████████████████████████████████████████████████████████████████████████▌ | 549/1000 [00:11<00:10, 41.29it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 55%|████████████████████████████████████████████████████████████████████████████████▎ | 554/1000 [00:11<00:10, 42.30it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 56%|█████████████████████████████████████████████████████████████████████████████████▍ | 562/1000 [00:11<00:08, 50.53it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 57%|██████████████████████████████████████████████████████████████████████████████████▌ | 569/1000 [00:11<00:07, 55.48it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 58%|███████████████████████████████████████████████████████████████████████████████████▌ | 576/1000 [00:11<00:07, 58.66it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "augmentation: 58%|████████████████████████████████████████████████████████████████████████████████████▋ | 584/1000 [00:11<00:06, 62.69it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 59%|█████████████████████████████████████████████████████████████████████████████████████▊ | 592/1000 [00:11<00:06, 64.97it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 60%|██████████████████████████████████████████████████████████████████████████████████████▊ | 599/1000 [00:11<00:06, 65.85it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 61%|███████████████████████████████████████████████████████████████████████████████████████▊ | 606/1000 [00:12<00:05, 66.48it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "augmentation: 61%|████████████████████████████████████████████████████████████████████████████████████████▉ | 613/1000 [00:12<00:05, 66.33it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 62%|█████████████████████████████████████████████████████████████████████████████████████████▉ | 620/1000 [00:12<00:05, 65.82it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 63%|███████████████████████████████████████████████████████████████████████████████████████████ | 628/1000 [00:12<00:05, 67.54it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 64%|████████████████████████████████████████████████████████████████████████████████████████████▏ | 636/1000 [00:12<00:05, 67.62it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 64%|█████████████████████████████████████████████████████████████████████████████████████████████▏ | 643/1000 [00:12<00:06, 58.27it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 65%|██████████████████████████████████████████████████████████████████████████████████████████████▎ | 650/1000 [00:12<00:05, 60.99it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 66%|███████████████████████████████████████████████████████████████████████████████████████████████▎ | 657/1000 [00:12<00:06, 56.07it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 66%|████████████████████████████████████████████████████████████████████████████████████████████████▏ | 663/1000 [00:13<00:06, 53.48it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 67%|█████████████████████████████████████████████████████████████████████████████████████████████████▏ | 670/1000 [00:13<00:05, 56.33it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 68%|██████████████████████████████████████████████████████████████████████████████████████████████████▏ | 677/1000 [00:13<00:05, 59.77it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 68%|███████████████████████████████████████████████████████████████████████████████████████████████████▏ | 684/1000 [00:13<00:05, 62.09it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 69%|████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 692/1000 [00:13<00:04, 64.53it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 70%|█████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 699/1000 [00:13<00:04, 64.76it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 71%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 707/1000 [00:13<00:04, 66.43it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 71%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 714/1000 [00:13<00:04, 66.46it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 72%|████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 721/1000 [00:13<00:04, 66.22it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 73%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 729/1000 [00:14<00:04, 67.55it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 736/1000 [00:14<00:03, 67.56it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 74%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 743/1000 [00:14<00:05, 49.45it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 75%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 749/1000 [00:14<00:06, 40.94it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 754/1000 [00:14<00:06, 35.66it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 759/1000 [00:14<00:07, 32.92it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 763/1000 [00:15<00:07, 31.11it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 767/1000 [00:15<00:07, 30.36it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 771/1000 [00:15<00:07, 29.10it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 78%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 777/1000 [00:15<00:06, 35.10it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 781/1000 [00:15<00:06, 35.09it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 79%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 788/1000 [00:15<00:04, 42.93it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 795/1000 [00:15<00:04, 49.78it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 803/1000 [00:15<00:03, 55.96it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 81%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 811/1000 [00:16<00:03, 60.74it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 819/1000 [00:16<00:02, 63.94it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 826/1000 [00:16<00:02, 65.57it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 834/1000 [00:16<00:02, 67.38it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 842/1000 [00:16<00:02, 68.53it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 850/1000 [00:16<00:02, 69.45it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 858/1000 [00:16<00:02, 69.77it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 866/1000 [00:16<00:01, 70.48it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 874/1000 [00:16<00:01, 70.93it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 882/1000 [00:17<00:01, 69.46it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 890/1000 [00:17<00:01, 69.77it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 898/1000 [00:17<00:01, 70.42it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 906/1000 [00:17<00:01, 71.00it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 914/1000 [00:17<00:01, 70.21it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 922/1000 [00:17<00:01, 70.05it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 930/1000 [00:17<00:00, 70.26it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 938/1000 [00:18<00:01, 46.37it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 944/1000 [00:18<00:01, 43.01it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 950/1000 [00:18<00:01, 44.19it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 957/1000 [00:18<00:00, 48.97it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 963/1000 [00:18<00:00, 45.54it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 968/1000 [00:18<00:00, 42.43it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 973/1000 [00:18<00:00, 40.76it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 979/1000 [00:18<00:00, 44.22it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 985/1000 [00:19<00:00, 47.12it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 990/1000 [00:19<00:00, 41.15it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍| 996/1000 [00:19<00:00, 45.53it/s]/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['errorID', 'failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "/tmp/ipykernel_123172/2008249001.py:32: FutureWarning: ['failure'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n", + " numerical_features = cycle.agg(['min', 'max', 'mean']).unstack().reset_index()\n", + "augmentation: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:19<00:00, 51.48it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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pressure_maxpressure_meanpressure_minrotate_maxrotate_meanrotate_minvibration_maxvibration_meanvibration_minvolt_maxvolt_meanvolt_minerror1error2error3error4error5machine_idlifetimebroken
0130.706018100.23613472.092543586.941563448.942591338.89119551.42430039.79404128.741474212.028491170.740329136.29101000000671120
1114.678705101.23115576.547574522.074447431.497500307.77843350.79206640.49266131.052696195.527742166.275130135.8884080000084200
2135.959689100.07717873.309133581.808774448.433424345.68634753.09565340.26250429.773657211.858373173.794237134.90593600100581260
3116.47355198.78198879.125328531.943470453.146862300.56237354.15598339.55437821.629032208.756064168.011703132.1770140000048990
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pressure_maxpressure_meanpressure_minrotate_maxrotate_meanrotate_minvibration_maxvibration_meanvibration_minvolt_maxvolt_meanvolt_minerror1error2error3error4error5machine_idlifetimebroken
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1114.678705101.23115576.547574522.074447431.497500307.77843350.79206640.49266131.052696195.527742166.275130135.8884080000084200
2135.959689100.07717873.309133581.808774448.433424345.68634753.09565340.26250429.773657211.858373173.794237134.90593600100581260
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datetimemachineIDcomp
02014-06-01 06:00:001comp2
12014-07-16 06:00:001comp4
22014-07-31 06:00:001comp3
32014-12-13 06:00:001comp1
42015-01-05 06:00:001comp4
............
32812015-10-10 06:00:00100comp3
32822015-10-25 06:00:00100comp4
32832015-11-09 06:00:00100comp4
32842015-12-09 06:00:00100comp2
32852015-12-24 06:00:00100comp2
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3286 rows × 3 columns

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datetimemachineIDvoltrotatepressurevibration
02015-01-01 06:00:001176.217853418.504078113.07793545.087686
12015-01-01 07:00:001162.879223402.74749095.46052543.413973
22015-01-01 08:00:001170.989902527.34982575.23790534.178847
32015-01-01 09:00:001162.462833346.149335109.24856141.122144
42015-01-01 10:00:001157.610021435.376873111.88664825.990511
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8760952016-01-01 02:00:00100179.438162395.222827102.29071550.771941
8760962016-01-01 03:00:00100189.617555446.20797298.18060735.123072
8760972016-01-01 04:00:00100192.483414447.81652494.13283748.314561
8760982016-01-01 05:00:00100165.475310413.771670104.08107344.835259
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datetimemachineIDvoltrotatepressurevibrationerrorIDfailure
02015-01-01 06:00:001176.217853418.504078113.07793545.087686-1-1
12015-01-01 07:00:001162.879223402.74749095.46052543.413973-1-1
22015-01-01 08:00:001170.989902527.34982575.23790534.178847-1-1
32015-01-01 09:00:001162.462833346.149335109.24856141.122144-1-1
42015-01-01 10:00:001157.610021435.376873111.88664825.990511-1-1
...........................
8764402016-01-01 02:00:00100179.438162395.222827102.29071550.771941-1-1
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8764422016-01-01 04:00:00100192.483414447.81652494.13283748.314561-1-1
8764432016-01-01 05:00:00100165.475310413.771670104.08107344.835259-1-1
8764442016-01-01 06:00:00100171.336037496.09687079.09553837.845245-1-1
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datetimemachineIDvoltrotatepressurevibrationerrorIDfailure
87632015-01-01 06:00:002176.558913424.62416276.00533243.767049-1NaN
87642015-01-01 07:00:002158.282044432.372960110.90780637.267114-1NaN
87652015-01-01 08:00:002168.242028454.62963997.87700739.709461-1NaN
87662015-01-01 09:00:002180.280316438.39102284.44043040.490443-1NaN
87672015-01-01 10:00:002169.719531473.055664110.39568341.229578-1NaN
...........................
175242016-01-01 02:00:002184.450454417.06233986.02106840.792375-1NaN
175252016-01-01 03:00:002167.267059479.380172101.74481939.156569-1NaN
175262016-01-01 04:00:002169.047260470.61249685.70994634.862975-1NaN
175272016-01-01 05:00:002178.900281493.76066893.54760651.932874-1NaN
175282016-01-01 06:00:002182.021908392.27018799.94629341.676184-1NaN
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8766 rows × 8 columns

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