Accelerating pandas with GPU: Sort the count of rows grouped on columns

[aka76bm]

Accelerating pandas with GPU: Sort the count of rows grouped on columns

Add %load_ext cudf.pandas before importing pandas to speed up operations using GPU

%load_ext cudf.pandas
import pandas as pd
import random

Define the species categories

species_categories = ['setosa', 'versicolor', 'virginica']
flower_color_categories = ['red','yellow','green']

Define the range for each attribute based on typical iris flower measurements

sepal_length_range = (4.0, 8.0)

Create data for 1,000,000 samples

n = 1000000
data = {
'sepal_length': [random.uniform(*sepal_length_range) for _ in range(n)],
'flower_color': [random.choice(flower_color_categories) for _ in range(n)],
'species': [random.choice(species_categories) for _ in range(n)]
}

df = pd.DataFrame(data)

df.groupby(['species','flower_color']).size().sort_values(ascending=False)

Acceleration pandas with GPU: Merging / Joining dataframes-

Add %load_ext cudf.pandas before importing pandas to speed up operations using GPU

%load_ext cudf.pandas
import pandas as pd
import numpy as np

Define the number of rows

num_rows = 1000000

states = ["NY", "NJ", "CA", "TX"]
violations = ["Double Parking", "Expired Meter", "No Parking", "Fire Hydrant",
"Bus Stop"]
vehicle_types = ["SUBN", "SDN"]

Generate random data for Dataset 1

data1 = {
"Registration State": np.random.choice(states, size=num_rows),
"Ticket Number": np.random.randint(1000000000, 9999999999, size=num_rows)
}

Generate random data for Dataset 2

data2 = {
"Ticket Number": np.random.choice(data1['Ticket Number'], size=num_rows), # Reusing ticket numbers to ensure matches
"Violation Description": np.random.choice(violations, size=num_rows)
}

Create DataFrames

df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

Perform an inner join on 'Ticket Number'

merged_df = pd.merge(df1, df2, on="Ticket Number", how="inner")

Display some of the joined data

print(merged_df.head())

Accelerating pandas with GPU: Groupby aggregate on timeseries data

Add %load_ext cudf.pandas before importing pandas to speed up operations using GPU

%load_ext cudf.pandas
import pandas as pd
import numpy as np

import pandas as pd
import numpy as np

Define the number of rows

num_rows = 1000000

Define the possible values

states = ["NY", "NJ", "CA", "TX"]
violations = ["Double Parking", "Expired Meter", "No Parking", "Fire Hydrant", "Bus Stop"]
vehicle_types = ["SUBN", "SDN"]

start_date = "2022-01-01"
end_date = "2022-12-31"

Create a date range

dates = pd.date_range(start=start_date, end=end_date, freq='D')

Generate random data

data = {
"Registration State": np.random.choice(states, size=num_rows),
"Violation Description": np.random.choice(violations, size=num_rows),
"Vehicle Body Type": np.random.choice(vehicle_types, size=num_rows),
"Issue Date": np.random.choice(dates, size=num_rows),
"Ticket Number": np.random.randint(1000000000, 9999999999, size=num_rows)
}

Create a DataFrame

df = pd.DataFrame(data)

Adding issue weekday based on the "Issue Date"

weekday_names = {
0: "Monday",
1: "Tuesday",
2: "Wednesday",
3: "Thursday",
4: "Friday",
5: "Saturday",
6: "Sunday",
}

df["issue_weekday"] = df["Issue Date"].dt.weekday.map(weekday_names)

Grouping by issue_weekday and counting the Summons Number

df.groupby(["Issue Date"])["Ticket Number"
].count().sort_values()

Accelerating pandas with GPU: Count of values and GroupBy

Add %load_ext cudf.pandas before importing pandas to speed up operations using GPU

%load_ext cudf.pandas
import pandas as pd
import numpy as np

Randomly generated dataset of parking violations-

Define the number of rows

num_rows = 1000000

states = ["NY", "NJ", "CA", "TX"]
violations = ["Double Parking", "Expired Meter", "No Parking",
"Fire Hydrant", "Bus Stop"]
vehicle_types = ["SUBN", "SDN"]

Create a date range

start_date = "2022-01-01"
end_date = "2022-12-31"
dates = pd.date_range(start=start_date, end=end_date, freq='D')

Generate random data

data = {
"Registration State": np.random.choice(states, size=num_rows),
"Violation Description": np.random.choice(violations, size=num_rows),
"Vehicle Body Type": np.random.choice(vehicle_types, size=num_rows),
"Issue Date": np.random.choice(dates, size=num_rows),
"Ticket Number": np.random.randint(1000000000, 9999999999, size=num_rows)
}

Create a DataFrame

df = pd.DataFrame(data)

Which parking violation is most commonly committed by vehicles from various U.S states?

(df[["Registration State", "Violation Description"]] # get only these two columns
.value_counts() # get the count of offences per state and per type of offence
.groupby("Registration State") # group by state
.head(1) # get the first row in each group (the type of offence with the largest count)
.sort_index() # sort by state name
.reset_index()
)

Accelerating pandas with GPU: Rolling Window Average

%load_ext cudf.pandas
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Randomly generated dataset of parking violations-

Define the number of rows

num_rows = 1000000

states = ["NY", "NJ", "CA", "TX"]
violations = ["Double Parking", "Expired Meter", "No Parking",
"Fire Hydrant", "Bus Stop"]
vehicle_types = ["SUBN", "SDN"]

Create a date range

start_date = "2022-01-01"
end_date = "2022-12-31"
dates = pd.date_range(start=start_date, end=end_date, freq='D')

Generate random data

data = {
"Registration State": np.random.choice(states, size=num_rows),
"Violation Description": np.random.choice(violations, size=num_rows),
"Vehicle Body Type": np.random.choice(vehicle_types, size=num_rows),
"Issue Date": np.random.choice(dates, size=num_rows),
"Ticket Number": np.random.randint(1000000000, 9999999999, size=num_rows)
}

Create a DataFrame

df = pd.DataFrame(data)

How does the parking violations change from day to day segmented by vehicle type

Averaged using a 7-day rolling mean

daily_counts = df.groupby(['Issue Date', 'Vehicle Body Type']
).size().unstack(fill_value=0)

Calculate a 7-day rolling mean of daily violations for each vehicle type

rolling_means = daily_counts.rolling(window=7).mean()

Display the rolling means for each vehicle type over time

rolling_means.tail(100).plot(figsize=(14, 7),
title="7-Day Rolling Average of Parking Violations by Vehicle Type")
plt.ylabel("Average Number of Violations")
plt.xlabel("Date")
plt.show()

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