diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 68b3762..474a663 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -12,11 +12,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "#Import your libraries\n" + "#Import your libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" ] }, { @@ -38,11 +43,401 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempAvgFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercent...SeaLevelPressureAvgInchesSeaLevelPressureLowInchesVisibilityHighMilesVisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHPrecipitationSumInchesEvents
02013-12-21746045674943937557...29.6829.591072204310.46Rain , Thunderstorm
12013-12-22564839433628936843...30.1329.8710105166250
22013-12-23584532312723765227...30.4930.4110101083120
32013-12-24614631362821895622...30.4530.310107124200
42013-12-25585041444036867156...30.3330.271010710216T
..................................................................
13142017-07-271038975716761825425...29.9729.88101010125210
13152017-07-281059176716455875420...29.929.81101010145200
13162017-07-291079277726455825119...29.8629.79101010124170
13172017-07-301069379706863694827...29.9129.87101010134200
13182017-07-31998877666154644322...29.9729.91101010124200
\n", + "

1319 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempAvgF TempLowF DewPointHighF DewPointAvgF \\\n", + "0 2013-12-21 74 60 45 67 49 \n", + "1 2013-12-22 56 48 39 43 36 \n", + "2 2013-12-23 58 45 32 31 27 \n", + "3 2013-12-24 61 46 31 36 28 \n", + "4 2013-12-25 58 50 41 44 40 \n", + "... ... ... ... ... ... ... \n", + "1314 2017-07-27 103 89 75 71 67 \n", + "1315 2017-07-28 105 91 76 71 64 \n", + "1316 2017-07-29 107 92 77 72 64 \n", + "1317 2017-07-30 106 93 79 70 68 \n", + "1318 2017-07-31 99 88 77 66 61 \n", + "\n", + " DewPointLowF HumidityHighPercent HumidityAvgPercent HumidityLowPercent \\\n", + "0 43 93 75 57 \n", + "1 28 93 68 43 \n", + "2 23 76 52 27 \n", + "3 21 89 56 22 \n", + "4 36 86 71 56 \n", + "... ... ... ... ... \n", + "1314 61 82 54 25 \n", + "1315 55 87 54 20 \n", + "1316 55 82 51 19 \n", + "1317 63 69 48 27 \n", + "1318 54 64 43 22 \n", + "\n", + " ... SeaLevelPressureAvgInches SeaLevelPressureLowInches \\\n", + "0 ... 29.68 29.59 \n", + "1 ... 30.13 29.87 \n", + "2 ... 30.49 30.41 \n", + "3 ... 30.45 30.3 \n", + "4 ... 30.33 30.27 \n", + "... ... ... ... \n", + "1314 ... 29.97 29.88 \n", + "1315 ... 29.9 29.81 \n", + "1316 ... 29.86 29.79 \n", + "1317 ... 29.91 29.87 \n", + "1318 ... 29.97 29.91 \n", + "\n", + " VisibilityHighMiles VisibilityAvgMiles VisibilityLowMiles WindHighMPH \\\n", + "0 10 7 2 20 \n", + "1 10 10 5 16 \n", + "2 10 10 10 8 \n", + "3 10 10 7 12 \n", + "4 10 10 7 10 \n", + "... ... ... ... ... \n", + "1314 10 10 10 12 \n", + "1315 10 10 10 14 \n", + "1316 10 10 10 12 \n", + "1317 10 10 10 13 \n", + "1318 10 10 10 12 \n", + "\n", + " WindAvgMPH WindGustMPH PrecipitationSumInches Events \n", + "0 4 31 0.46 Rain , Thunderstorm \n", + "1 6 25 0 \n", + "2 3 12 0 \n", + "3 4 20 0 \n", + "4 2 16 T \n", + "... ... ... ... ... \n", + "1314 5 21 0 \n", + "1315 5 20 0 \n", + "1316 4 17 0 \n", + "1317 4 20 0 \n", + "1318 4 20 0 \n", + "\n", + "[1319 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin = pd.read_csv(\"C:/Users/gaelm/Desktop/lab/lab-intro-to-ml/your-code/austin_weather.csv\")\n", + "austin" ] }, { @@ -57,29 +452,395 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1319 entries, 0 to 1318\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 1319 non-null object\n", + " 1 TempHighF 1319 non-null int64 \n", + " 2 TempAvgF 1319 non-null int64 \n", + " 3 TempLowF 1319 non-null int64 \n", + " 4 DewPointHighF 1319 non-null object\n", + " 5 DewPointAvgF 1319 non-null object\n", + " 6 DewPointLowF 1319 non-null object\n", + " 7 HumidityHighPercent 1319 non-null object\n", + " 8 HumidityAvgPercent 1319 non-null object\n", + " 9 HumidityLowPercent 1319 non-null object\n", + " 10 SeaLevelPressureHighInches 1319 non-null object\n", + " 11 SeaLevelPressureAvgInches 1319 non-null object\n", + " 12 SeaLevelPressureLowInches 1319 non-null object\n", + " 13 VisibilityHighMiles 1319 non-null object\n", + " 14 VisibilityAvgMiles 1319 non-null object\n", + " 15 VisibilityLowMiles 1319 non-null object\n", + " 16 WindHighMPH 1319 non-null object\n", + " 17 WindAvgMPH 1319 non-null object\n", + " 18 WindGustMPH 1319 non-null object\n", + " 19 PrecipitationSumInches 1319 non-null object\n", + " 20 Events 1319 non-null object\n", + "dtypes: int64(3), object(18)\n", + "memory usage: 216.5+ KB\n" + ] + } + ], "source": [ - "# Your code here\n" + "austin.info()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + "
TempHighFTempAvgFTempLowF
count1319.0000001319.0000001319.000000
mean80.86277570.64291159.902957
std14.76652314.04590414.190648
min32.00000029.00000019.000000
25%72.00000062.00000049.000000
50%83.00000073.00000063.000000
75%92.00000083.00000073.000000
max107.00000093.00000081.000000
\n", + "
" + ], + "text/plain": [ + " TempHighF TempAvgF TempLowF\n", + "count 1319.000000 1319.000000 1319.000000\n", + "mean 80.862775 70.642911 59.902957\n", + "std 14.766523 14.045904 14.190648\n", + "min 32.000000 29.000000 19.000000\n", + "25% 72.000000 62.000000 49.000000\n", + "50% 83.000000 73.000000 63.000000\n", + "75% 92.000000 83.000000 73.000000\n", + "max 107.000000 93.000000 81.000000" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "austin.describe()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(1319, 21)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "austin.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + "
DateTempHighFTempAvgFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercent...SeaLevelPressureAvgInchesSeaLevelPressureLowInchesVisibilityHighMilesVisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHPrecipitationSumInchesEvents
02013-12-21746045674943937557...29.6829.591072204310.46Rain , Thunderstorm
12013-12-22564839433628936843...30.1329.8710105166250
22013-12-23584532312723765227...30.4930.4110101083120
32013-12-24614631362821895622...30.4530.310107124200
42013-12-25585041444036867156...30.3330.271010710216T
\n", + "

5 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempAvgF TempLowF DewPointHighF DewPointAvgF \\\n", + "0 2013-12-21 74 60 45 67 49 \n", + "1 2013-12-22 56 48 39 43 36 \n", + "2 2013-12-23 58 45 32 31 27 \n", + "3 2013-12-24 61 46 31 36 28 \n", + "4 2013-12-25 58 50 41 44 40 \n", + "\n", + " DewPointLowF HumidityHighPercent HumidityAvgPercent HumidityLowPercent ... \\\n", + "0 43 93 75 57 ... \n", + "1 28 93 68 43 ... \n", + "2 23 76 52 27 ... \n", + "3 21 89 56 22 ... \n", + "4 36 86 71 56 ... \n", + "\n", + " SeaLevelPressureAvgInches SeaLevelPressureLowInches VisibilityHighMiles \\\n", + "0 29.68 29.59 10 \n", + "1 30.13 29.87 10 \n", + "2 30.49 30.41 10 \n", + "3 30.45 30.3 10 \n", + "4 30.33 30.27 10 \n", + "\n", + " VisibilityAvgMiles VisibilityLowMiles WindHighMPH WindAvgMPH WindGustMPH \\\n", + "0 7 2 20 4 31 \n", + "1 10 5 16 6 25 \n", + "2 10 10 8 3 12 \n", + "3 10 7 12 4 20 \n", + "4 10 7 10 2 16 \n", + "\n", + " PrecipitationSumInches Events \n", + "0 0.46 Rain , Thunderstorm \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 T \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin.head()" ] }, { @@ -113,20 +874,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array(['49', '36', '27', '28', '40', '39', '41', '26', '42', '22', '48',\n", + " '32', '8', '11', '45', '55', '61', '37', '47', '25', '23', '20',\n", + " '33', '30', '29', '17', '14', '13', '54', '59', '15', '24', '34',\n", + " '35', '57', '50', '53', '60', '46', '56', '51', '31', '38', '62',\n", + " '43', '63', '64', '67', '66', '58', '70', '68', '65', '69', '71',\n", + " '72', '-', '73', '74', '21', '44', '52', '12', '75', '76', '18'],\n", + " dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin[\"DewPointAvgF\"].unique()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "# Your observation here\n" + "# Your observation here\n", + "# There's a dash in the column so i think thats why te columns is n object column because there's that dash." ] }, { @@ -140,7 +920,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -153,11 +933,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin[wrong_type_columns] = austin[wrong_type_columns].apply(pd.to_numeric, errors='coerce')" ] }, { @@ -169,11 +950,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1319 entries, 0 to 1318\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 1319 non-null object \n", + " 1 TempHighF 1319 non-null int64 \n", + " 2 TempAvgF 1319 non-null int64 \n", + " 3 TempLowF 1319 non-null int64 \n", + " 4 DewPointHighF 1312 non-null float64\n", + " 5 DewPointAvgF 1312 non-null float64\n", + " 6 DewPointLowF 1312 non-null float64\n", + " 7 HumidityHighPercent 1317 non-null float64\n", + " 8 HumidityAvgPercent 1317 non-null float64\n", + " 9 HumidityLowPercent 1317 non-null float64\n", + " 10 SeaLevelPressureHighInches 1316 non-null float64\n", + " 11 SeaLevelPressureAvgInches 1316 non-null float64\n", + " 12 SeaLevelPressureLowInches 1316 non-null float64\n", + " 13 VisibilityHighMiles 1307 non-null float64\n", + " 14 VisibilityAvgMiles 1307 non-null float64\n", + " 15 VisibilityLowMiles 1307 non-null float64\n", + " 16 WindHighMPH 1317 non-null float64\n", + " 17 WindAvgMPH 1317 non-null float64\n", + " 18 WindGustMPH 1315 non-null float64\n", + " 19 PrecipitationSumInches 1195 non-null float64\n", + " 20 Events 1319 non-null object \n", + "dtypes: float64(16), int64(3), object(2)\n", + "memory usage: 216.5+ KB\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin.info()" ] }, { @@ -200,11 +1017,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Date 0\n", + "TempHighF 0\n", + "TempAvgF 0\n", + "TempLowF 0\n", + "DewPointHighF 7\n", + "DewPointAvgF 7\n", + "DewPointLowF 7\n", + "HumidityHighPercent 2\n", + "HumidityAvgPercent 2\n", + "HumidityLowPercent 2\n", + "SeaLevelPressureHighInches 3\n", + "SeaLevelPressureAvgInches 3\n", + "SeaLevelPressureLowInches 3\n", + "VisibilityHighMiles 12\n", + "VisibilityAvgMiles 12\n", + "VisibilityLowMiles 12\n", + "WindHighMPH 2\n", + "WindAvgMPH 2\n", + "WindGustMPH 4\n", + "PrecipitationSumInches 124\n", + "Events 0\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "austin.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 True\n", + " ... \n", + "1314 False\n", + "1315 False\n", + "1316 False\n", + "1317 False\n", + "1318 False\n", + "Length: 1319, dtype: bool" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "missing_values = austin.isnull().any(axis=1)\n", + "missing_values" ] }, { @@ -233,11 +1115,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "136" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "missing_values.sum()" ] }, { @@ -249,11 +1143,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "10.310841546626232" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "missing_values.sum() / len(austin)*100" ] }, { @@ -267,11 +1173,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Date 0\n", + "TempHighF 0\n", + "TempAvgF 0\n", + "TempLowF 0\n", + "DewPointHighF 7\n", + "DewPointAvgF 7\n", + "DewPointLowF 7\n", + "HumidityHighPercent 2\n", + "HumidityAvgPercent 2\n", + "HumidityLowPercent 2\n", + "SeaLevelPressureHighInches 3\n", + "SeaLevelPressureAvgInches 3\n", + "SeaLevelPressureLowInches 3\n", + "VisibilityHighMiles 12\n", + "VisibilityAvgMiles 12\n", + "VisibilityLowMiles 12\n", + "WindHighMPH 2\n", + "WindAvgMPH 2\n", + "WindGustMPH 4\n", + "PrecipitationSumInches 124\n", + "Events 0\n", + "dtype: int64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin.isna().sum()" ] }, { @@ -283,11 +1222,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "124" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "nulls_of_precipitation = austin[\"PrecipitationSumInches\"].isnull().sum()\n", + "nulls_of_precipitation" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1195" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "len(austin) - nulls_of_precipitation" ] }, { @@ -309,12 +1281,401 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempAvgFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercentSeaLevelPressureHighInchesSeaLevelPressureAvgInchesSeaLevelPressureLowInchesVisibilityHighMilesVisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHEvents
02013-12-2174604567.049.043.093.075.057.029.8629.6829.5910.07.02.020.04.031.0Rain , Thunderstorm
12013-12-2256483943.036.028.093.068.043.030.4130.1329.8710.010.05.016.06.025.0
22013-12-2358453231.027.023.076.052.027.030.5630.4930.4110.010.010.08.03.012.0
32013-12-2461463136.028.021.089.056.022.030.5630.4530.3010.010.07.012.04.020.0
42013-12-2558504144.040.036.086.071.056.030.4130.3330.2710.010.07.010.02.016.0
...............................................................
13142017-07-27103897571.067.061.082.054.025.030.0429.9729.8810.010.010.012.05.021.0
13152017-07-28105917671.064.055.087.054.020.029.9729.9029.8110.010.010.014.05.020.0
13162017-07-29107927772.064.055.082.051.019.029.9129.8629.7910.010.010.012.04.017.0
13172017-07-30106937970.068.063.069.048.027.029.9629.9129.8710.010.010.013.04.020.0
13182017-07-3199887766.061.054.064.043.022.030.0429.9729.9110.010.010.012.04.020.0
\n", + "

1319 rows × 20 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempAvgF TempLowF DewPointHighF DewPointAvgF \\\n", + "0 2013-12-21 74 60 45 67.0 49.0 \n", + "1 2013-12-22 56 48 39 43.0 36.0 \n", + "2 2013-12-23 58 45 32 31.0 27.0 \n", + "3 2013-12-24 61 46 31 36.0 28.0 \n", + "4 2013-12-25 58 50 41 44.0 40.0 \n", + "... ... ... ... ... ... ... \n", + "1314 2017-07-27 103 89 75 71.0 67.0 \n", + "1315 2017-07-28 105 91 76 71.0 64.0 \n", + "1316 2017-07-29 107 92 77 72.0 64.0 \n", + "1317 2017-07-30 106 93 79 70.0 68.0 \n", + "1318 2017-07-31 99 88 77 66.0 61.0 \n", + "\n", + " DewPointLowF HumidityHighPercent HumidityAvgPercent \\\n", + "0 43.0 93.0 75.0 \n", + "1 28.0 93.0 68.0 \n", + "2 23.0 76.0 52.0 \n", + "3 21.0 89.0 56.0 \n", + "4 36.0 86.0 71.0 \n", + "... ... ... ... \n", + "1314 61.0 82.0 54.0 \n", + "1315 55.0 87.0 54.0 \n", + "1316 55.0 82.0 51.0 \n", + "1317 63.0 69.0 48.0 \n", + "1318 54.0 64.0 43.0 \n", + "\n", + " HumidityLowPercent SeaLevelPressureHighInches \\\n", + "0 57.0 29.86 \n", + "1 43.0 30.41 \n", + "2 27.0 30.56 \n", + "3 22.0 30.56 \n", + "4 56.0 30.41 \n", + "... ... ... \n", + "1314 25.0 30.04 \n", + "1315 20.0 29.97 \n", + "1316 19.0 29.91 \n", + "1317 27.0 29.96 \n", + "1318 22.0 30.04 \n", + "\n", + " SeaLevelPressureAvgInches SeaLevelPressureLowInches \\\n", + "0 29.68 29.59 \n", + "1 30.13 29.87 \n", + "2 30.49 30.41 \n", + "3 30.45 30.30 \n", + "4 30.33 30.27 \n", + "... ... ... \n", + "1314 29.97 29.88 \n", + "1315 29.90 29.81 \n", + "1316 29.86 29.79 \n", + "1317 29.91 29.87 \n", + "1318 29.97 29.91 \n", + "\n", + " VisibilityHighMiles VisibilityAvgMiles VisibilityLowMiles \\\n", + "0 10.0 7.0 2.0 \n", + "1 10.0 10.0 5.0 \n", + "2 10.0 10.0 10.0 \n", + "3 10.0 10.0 7.0 \n", + "4 10.0 10.0 7.0 \n", + "... ... ... ... \n", + "1314 10.0 10.0 10.0 \n", + "1315 10.0 10.0 10.0 \n", + "1316 10.0 10.0 10.0 \n", + "1317 10.0 10.0 10.0 \n", + "1318 10.0 10.0 10.0 \n", + "\n", + " WindHighMPH WindAvgMPH WindGustMPH Events \n", + "0 20.0 4.0 31.0 Rain , Thunderstorm \n", + "1 16.0 6.0 25.0 \n", + "2 8.0 3.0 12.0 \n", + "3 12.0 4.0 20.0 \n", + "4 10.0 2.0 16.0 \n", + "... ... ... ... ... \n", + "1314 12.0 5.0 21.0 \n", + "1315 14.0 5.0 20.0 \n", + "1316 12.0 4.0 17.0 \n", + "1317 13.0 4.0 20.0 \n", + "1318 12.0 4.0 20.0 \n", + "\n", + "[1319 rows x 20 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here \n", - "\n", + "austin.drop(columns = \"PrecipitationSumInches\",inplace=True, axis = 1)\n", "\n", "# Print `austin` to confirm the column is indeed removed\n", "\n", @@ -336,11 +1697,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\gaelm\\AppData\\Local\\Temp\\ipykernel_24948\\2951143596.py:2: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n", + " austin_fixed = austin.interpolate(inplace=False)\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin_fixed = austin.interpolate(inplace=False)" ] }, { @@ -352,13 +1723,497 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Date 0\n", + "TempHighF 0\n", + "TempAvgF 0\n", + "TempLowF 0\n", + "DewPointHighF 0\n", + "DewPointAvgF 0\n", + "DewPointLowF 0\n", + "HumidityHighPercent 0\n", + "HumidityAvgPercent 0\n", + "HumidityLowPercent 0\n", + "SeaLevelPressureHighInches 0\n", + "SeaLevelPressureAvgInches 0\n", + "SeaLevelPressureLowInches 0\n", + "VisibilityHighMiles 0\n", + "VisibilityAvgMiles 0\n", + "VisibilityLowMiles 0\n", + "WindHighMPH 0\n", + "WindAvgMPH 0\n", + "WindGustMPH 0\n", + "Events 0\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin_fixed.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempAvgFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercentSeaLevelPressureHighInchesSeaLevelPressureAvgInchesSeaLevelPressureLowInchesVisibilityHighMilesVisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHEvents
02013-12-2174604567.049.043.093.075.057.029.8629.6829.5910.07.02.020.04.031.0Rain , Thunderstorm
12013-12-2256483943.036.028.093.068.043.030.4130.1329.8710.010.05.016.06.025.0
22013-12-2358453231.027.023.076.052.027.030.5630.4930.4110.010.010.08.03.012.0
32013-12-2461463136.028.021.089.056.022.030.5630.4530.3010.010.07.012.04.020.0
42013-12-2558504144.040.036.086.071.056.030.4130.3330.2710.010.07.010.02.016.0
...............................................................
13142017-07-27103897571.067.061.082.054.025.030.0429.9729.8810.010.010.012.05.021.0
13152017-07-28105917671.064.055.087.054.020.029.9729.9029.8110.010.010.014.05.020.0
13162017-07-29107927772.064.055.082.051.019.029.9129.8629.7910.010.010.012.04.017.0
13172017-07-30106937970.068.063.069.048.027.029.9629.9129.8710.010.010.013.04.020.0
13182017-07-3199887766.061.054.064.043.022.030.0429.9729.9110.010.010.012.04.020.0
\n", + "

1319 rows × 20 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempAvgF TempLowF DewPointHighF DewPointAvgF \\\n", + "0 2013-12-21 74 60 45 67.0 49.0 \n", + "1 2013-12-22 56 48 39 43.0 36.0 \n", + "2 2013-12-23 58 45 32 31.0 27.0 \n", + "3 2013-12-24 61 46 31 36.0 28.0 \n", + "4 2013-12-25 58 50 41 44.0 40.0 \n", + "... ... ... ... ... ... ... \n", + "1314 2017-07-27 103 89 75 71.0 67.0 \n", + "1315 2017-07-28 105 91 76 71.0 64.0 \n", + "1316 2017-07-29 107 92 77 72.0 64.0 \n", + "1317 2017-07-30 106 93 79 70.0 68.0 \n", + "1318 2017-07-31 99 88 77 66.0 61.0 \n", + "\n", + " DewPointLowF HumidityHighPercent HumidityAvgPercent \\\n", + "0 43.0 93.0 75.0 \n", + "1 28.0 93.0 68.0 \n", + "2 23.0 76.0 52.0 \n", + "3 21.0 89.0 56.0 \n", + "4 36.0 86.0 71.0 \n", + "... ... ... ... \n", + "1314 61.0 82.0 54.0 \n", + "1315 55.0 87.0 54.0 \n", + "1316 55.0 82.0 51.0 \n", + "1317 63.0 69.0 48.0 \n", + "1318 54.0 64.0 43.0 \n", + "\n", + " HumidityLowPercent SeaLevelPressureHighInches \\\n", + "0 57.0 29.86 \n", + "1 43.0 30.41 \n", + "2 27.0 30.56 \n", + "3 22.0 30.56 \n", + "4 56.0 30.41 \n", + "... ... ... \n", + "1314 25.0 30.04 \n", + "1315 20.0 29.97 \n", + "1316 19.0 29.91 \n", + "1317 27.0 29.96 \n", + "1318 22.0 30.04 \n", + "\n", + " SeaLevelPressureAvgInches SeaLevelPressureLowInches \\\n", + "0 29.68 29.59 \n", + "1 30.13 29.87 \n", + "2 30.49 30.41 \n", + "3 30.45 30.30 \n", + "4 30.33 30.27 \n", + "... ... ... \n", + "1314 29.97 29.88 \n", + "1315 29.90 29.81 \n", + "1316 29.86 29.79 \n", + "1317 29.91 29.87 \n", + "1318 29.97 29.91 \n", + "\n", + " VisibilityHighMiles VisibilityAvgMiles VisibilityLowMiles \\\n", + "0 10.0 7.0 2.0 \n", + "1 10.0 10.0 5.0 \n", + "2 10.0 10.0 10.0 \n", + "3 10.0 10.0 7.0 \n", + "4 10.0 10.0 7.0 \n", + "... ... ... ... \n", + "1314 10.0 10.0 10.0 \n", + "1315 10.0 10.0 10.0 \n", + "1316 10.0 10.0 10.0 \n", + "1317 10.0 10.0 10.0 \n", + "1318 10.0 10.0 10.0 \n", + "\n", + " WindHighMPH WindAvgMPH WindGustMPH Events \n", + "0 20.0 4.0 31.0 Rain , Thunderstorm \n", + "1 16.0 6.0 25.0 \n", + "2 8.0 3.0 12.0 \n", + "3 12.0 4.0 20.0 \n", + "4 10.0 2.0 16.0 \n", + "... ... ... ... ... \n", + "1314 12.0 5.0 21.0 \n", + "1315 14.0 5.0 20.0 \n", + "1316 12.0 4.0 17.0 \n", + "1317 13.0 4.0 20.0 \n", + "1318 12.0 4.0 20.0 \n", + "\n", + "[1319 rows x 20 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "austin" ] }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Date 0\n", + "TempHighF 0\n", + "TempAvgF 0\n", + "TempLowF 0\n", + "DewPointHighF 7\n", + "DewPointAvgF 7\n", + "DewPointLowF 7\n", + "HumidityHighPercent 2\n", + "HumidityAvgPercent 2\n", + "HumidityLowPercent 2\n", + "SeaLevelPressureHighInches 3\n", + "SeaLevelPressureAvgInches 3\n", + "SeaLevelPressureLowInches 3\n", + "VisibilityHighMiles 12\n", + "VisibilityAvgMiles 12\n", + "VisibilityLowMiles 12\n", + "WindHighMPH 2\n", + "WindAvgMPH 2\n", + "WindGustMPH 4\n", + "Events 0\n", + "dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "austin.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -377,11 +2232,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Events\n", + " 903\n", + "Rain 192\n", + "Rain , Thunderstorm 137\n", + "Fog , Rain , Thunderstorm 33\n", + "Fog 21\n", + "Thunderstorm 17\n", + "Fog , Rain 14\n", + "Rain , Snow 1\n", + "Fog , Thunderstorm 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "austin[\"Events\"].value_counts()" ] }, { @@ -395,11 +2272,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "# Your answer:\n" + "# Your answer:\n", + "# El mayor número de eventos que ha ocurrido en un mismo día es 3" ] }, { @@ -415,14 +2293,403 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempAvgFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercent...VisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHEventsSnowFogRainThunderstorm
02013-12-2174604567.049.043.093.075.057.0...7.02.020.04.031.0Rain , Thunderstorm0000
12013-12-2256483943.036.028.093.068.043.0...10.05.016.06.025.00000
22013-12-2358453231.027.023.076.052.027.0...10.010.08.03.012.00000
32013-12-2461463136.028.021.089.056.022.0...10.07.012.04.020.00000
42013-12-2558504144.040.036.086.071.056.0...10.07.010.02.016.00000
..................................................................
13142017-07-27103897571.067.061.082.054.025.0...10.010.012.05.021.00000
13152017-07-28105917671.064.055.087.054.020.0...10.010.014.05.020.00000
13162017-07-29107927772.064.055.082.051.019.0...10.010.012.04.017.00000
13172017-07-30106937970.068.063.069.048.027.0...10.010.013.04.020.00000
13182017-07-3199887766.061.054.064.043.022.0...10.010.012.04.020.00000
\n", + "

1319 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempAvgF TempLowF DewPointHighF DewPointAvgF \\\n", + "0 2013-12-21 74 60 45 67.0 49.0 \n", + "1 2013-12-22 56 48 39 43.0 36.0 \n", + "2 2013-12-23 58 45 32 31.0 27.0 \n", + "3 2013-12-24 61 46 31 36.0 28.0 \n", + "4 2013-12-25 58 50 41 44.0 40.0 \n", + "... ... ... ... ... ... ... \n", + "1314 2017-07-27 103 89 75 71.0 67.0 \n", + "1315 2017-07-28 105 91 76 71.0 64.0 \n", + "1316 2017-07-29 107 92 77 72.0 64.0 \n", + "1317 2017-07-30 106 93 79 70.0 68.0 \n", + "1318 2017-07-31 99 88 77 66.0 61.0 \n", + "\n", + " DewPointLowF HumidityHighPercent HumidityAvgPercent \\\n", + "0 43.0 93.0 75.0 \n", + "1 28.0 93.0 68.0 \n", + "2 23.0 76.0 52.0 \n", + "3 21.0 89.0 56.0 \n", + "4 36.0 86.0 71.0 \n", + "... ... ... ... \n", + "1314 61.0 82.0 54.0 \n", + "1315 55.0 87.0 54.0 \n", + "1316 55.0 82.0 51.0 \n", + "1317 63.0 69.0 48.0 \n", + "1318 54.0 64.0 43.0 \n", + "\n", + " HumidityLowPercent ... VisibilityAvgMiles VisibilityLowMiles \\\n", + "0 57.0 ... 7.0 2.0 \n", + "1 43.0 ... 10.0 5.0 \n", + "2 27.0 ... 10.0 10.0 \n", + "3 22.0 ... 10.0 7.0 \n", + "4 56.0 ... 10.0 7.0 \n", + "... ... ... ... ... \n", + "1314 25.0 ... 10.0 10.0 \n", + "1315 20.0 ... 10.0 10.0 \n", + "1316 19.0 ... 10.0 10.0 \n", + "1317 27.0 ... 10.0 10.0 \n", + "1318 22.0 ... 10.0 10.0 \n", + "\n", + " WindHighMPH WindAvgMPH WindGustMPH Events Snow Fog \\\n", + "0 20.0 4.0 31.0 Rain , Thunderstorm 0 0 \n", + "1 16.0 6.0 25.0 0 0 \n", + "2 8.0 3.0 12.0 0 0 \n", + "3 12.0 4.0 20.0 0 0 \n", + "4 10.0 2.0 16.0 0 0 \n", + "... ... ... ... ... ... ... \n", + "1314 12.0 5.0 21.0 0 0 \n", + "1315 14.0 5.0 20.0 0 0 \n", + "1316 12.0 4.0 17.0 0 0 \n", + "1317 13.0 4.0 20.0 0 0 \n", + "1318 12.0 4.0 20.0 0 0 \n", + "\n", + " Rain Thunderstorm \n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "... ... ... \n", + "1314 0 0 \n", + "1315 0 0 \n", + "1316 0 0 \n", + "1317 0 0 \n", + "1318 0 0 \n", + "\n", + "[1319 rows x 24 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "event_list = ['Snow', 'Fog', 'Rain', 'Thunderstorm']\n", "\n", "# Your code here\n", - "\n", + "for event in event_list:\n", + " austin_fixed[event] = 0\n", "\n", "# Print your new dataframe to check whether new columns have been created:\n", "\n", @@ -446,11 +2713,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "# Your code here\n", + "for evento in event_list:\n", + " austin_fixed[evento] = austin_fixed[\"Events\"].str.contains(evento).astype(int)" ] }, { @@ -462,11 +2731,400 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempAvgFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercent...VisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHEventsSnowFogRainThunderstorm
02013-12-2174604567.049.043.093.075.057.0...7.02.020.04.031.0Rain , Thunderstorm0011
12013-12-2256483943.036.028.093.068.043.0...10.05.016.06.025.00000
22013-12-2358453231.027.023.076.052.027.0...10.010.08.03.012.00000
32013-12-2461463136.028.021.089.056.022.0...10.07.012.04.020.00000
42013-12-2558504144.040.036.086.071.056.0...10.07.010.02.016.00000
..................................................................
13142017-07-27103897571.067.061.082.054.025.0...10.010.012.05.021.00000
13152017-07-28105917671.064.055.087.054.020.0...10.010.014.05.020.00000
13162017-07-29107927772.064.055.082.051.019.0...10.010.012.04.017.00000
13172017-07-30106937970.068.063.069.048.027.0...10.010.013.04.020.00000
13182017-07-3199887766.061.054.064.043.022.0...10.010.012.04.020.00000
\n", + "

1319 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempAvgF TempLowF DewPointHighF DewPointAvgF \\\n", + "0 2013-12-21 74 60 45 67.0 49.0 \n", + "1 2013-12-22 56 48 39 43.0 36.0 \n", + "2 2013-12-23 58 45 32 31.0 27.0 \n", + "3 2013-12-24 61 46 31 36.0 28.0 \n", + "4 2013-12-25 58 50 41 44.0 40.0 \n", + "... ... ... ... ... ... ... \n", + "1314 2017-07-27 103 89 75 71.0 67.0 \n", + "1315 2017-07-28 105 91 76 71.0 64.0 \n", + "1316 2017-07-29 107 92 77 72.0 64.0 \n", + "1317 2017-07-30 106 93 79 70.0 68.0 \n", + "1318 2017-07-31 99 88 77 66.0 61.0 \n", + "\n", + " DewPointLowF HumidityHighPercent HumidityAvgPercent \\\n", + "0 43.0 93.0 75.0 \n", + "1 28.0 93.0 68.0 \n", + "2 23.0 76.0 52.0 \n", + "3 21.0 89.0 56.0 \n", + "4 36.0 86.0 71.0 \n", + "... ... ... ... \n", + "1314 61.0 82.0 54.0 \n", + "1315 55.0 87.0 54.0 \n", + "1316 55.0 82.0 51.0 \n", + "1317 63.0 69.0 48.0 \n", + "1318 54.0 64.0 43.0 \n", + "\n", + " HumidityLowPercent ... VisibilityAvgMiles VisibilityLowMiles \\\n", + "0 57.0 ... 7.0 2.0 \n", + "1 43.0 ... 10.0 5.0 \n", + "2 27.0 ... 10.0 10.0 \n", + "3 22.0 ... 10.0 7.0 \n", + "4 56.0 ... 10.0 7.0 \n", + "... ... ... ... ... \n", + "1314 25.0 ... 10.0 10.0 \n", + "1315 20.0 ... 10.0 10.0 \n", + "1316 19.0 ... 10.0 10.0 \n", + "1317 27.0 ... 10.0 10.0 \n", + "1318 22.0 ... 10.0 10.0 \n", + "\n", + " WindHighMPH WindAvgMPH WindGustMPH Events Snow Fog \\\n", + "0 20.0 4.0 31.0 Rain , Thunderstorm 0 0 \n", + "1 16.0 6.0 25.0 0 0 \n", + "2 8.0 3.0 12.0 0 0 \n", + "3 12.0 4.0 20.0 0 0 \n", + "4 10.0 2.0 16.0 0 0 \n", + "... ... ... ... ... ... ... \n", + "1314 12.0 5.0 21.0 0 0 \n", + "1315 14.0 5.0 20.0 0 0 \n", + "1316 12.0 4.0 17.0 0 0 \n", + "1317 13.0 4.0 20.0 0 0 \n", + "1318 12.0 4.0 20.0 0 0 \n", + "\n", + " Rain Thunderstorm \n", + "0 1 1 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "... ... ... \n", + "1314 0 0 \n", + "1315 0 0 \n", + "1316 0 0 \n", + "1317 0 0 \n", + "1318 0 0 \n", + "\n", + "[1319 rows x 24 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin_fixed" ] }, { @@ -478,11 +3136,401 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempAvgFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercent...VisibilityHighMilesVisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHSnowFogRainThunderstorm
02013-12-2174604567.049.043.093.075.057.0...10.07.02.020.04.031.00011
12013-12-2256483943.036.028.093.068.043.0...10.010.05.016.06.025.00000
22013-12-2358453231.027.023.076.052.027.0...10.010.010.08.03.012.00000
32013-12-2461463136.028.021.089.056.022.0...10.010.07.012.04.020.00000
42013-12-2558504144.040.036.086.071.056.0...10.010.07.010.02.016.00000
..................................................................
13142017-07-27103897571.067.061.082.054.025.0...10.010.010.012.05.021.00000
13152017-07-28105917671.064.055.087.054.020.0...10.010.010.014.05.020.00000
13162017-07-29107927772.064.055.082.051.019.0...10.010.010.012.04.017.00000
13172017-07-30106937970.068.063.069.048.027.0...10.010.010.013.04.020.00000
13182017-07-3199887766.061.054.064.043.022.0...10.010.010.012.04.020.00000
\n", + "

1319 rows × 23 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempAvgF TempLowF DewPointHighF DewPointAvgF \\\n", + "0 2013-12-21 74 60 45 67.0 49.0 \n", + "1 2013-12-22 56 48 39 43.0 36.0 \n", + "2 2013-12-23 58 45 32 31.0 27.0 \n", + "3 2013-12-24 61 46 31 36.0 28.0 \n", + "4 2013-12-25 58 50 41 44.0 40.0 \n", + "... ... ... ... ... ... ... \n", + "1314 2017-07-27 103 89 75 71.0 67.0 \n", + "1315 2017-07-28 105 91 76 71.0 64.0 \n", + "1316 2017-07-29 107 92 77 72.0 64.0 \n", + "1317 2017-07-30 106 93 79 70.0 68.0 \n", + "1318 2017-07-31 99 88 77 66.0 61.0 \n", + "\n", + " DewPointLowF HumidityHighPercent HumidityAvgPercent \\\n", + "0 43.0 93.0 75.0 \n", + "1 28.0 93.0 68.0 \n", + "2 23.0 76.0 52.0 \n", + "3 21.0 89.0 56.0 \n", + "4 36.0 86.0 71.0 \n", + "... ... ... ... \n", + "1314 61.0 82.0 54.0 \n", + "1315 55.0 87.0 54.0 \n", + "1316 55.0 82.0 51.0 \n", + "1317 63.0 69.0 48.0 \n", + "1318 54.0 64.0 43.0 \n", + "\n", + " HumidityLowPercent ... VisibilityHighMiles VisibilityAvgMiles \\\n", + "0 57.0 ... 10.0 7.0 \n", + "1 43.0 ... 10.0 10.0 \n", + "2 27.0 ... 10.0 10.0 \n", + "3 22.0 ... 10.0 10.0 \n", + "4 56.0 ... 10.0 10.0 \n", + "... ... ... ... ... \n", + "1314 25.0 ... 10.0 10.0 \n", + "1315 20.0 ... 10.0 10.0 \n", + "1316 19.0 ... 10.0 10.0 \n", + "1317 27.0 ... 10.0 10.0 \n", + "1318 22.0 ... 10.0 10.0 \n", + "\n", + " VisibilityLowMiles WindHighMPH WindAvgMPH WindGustMPH Snow Fog \\\n", + "0 2.0 20.0 4.0 31.0 0 0 \n", + "1 5.0 16.0 6.0 25.0 0 0 \n", + "2 10.0 8.0 3.0 12.0 0 0 \n", + "3 7.0 12.0 4.0 20.0 0 0 \n", + "4 7.0 10.0 2.0 16.0 0 0 \n", + "... ... ... ... ... ... ... \n", + "1314 10.0 12.0 5.0 21.0 0 0 \n", + "1315 10.0 14.0 5.0 20.0 0 0 \n", + "1316 10.0 12.0 4.0 17.0 0 0 \n", + "1317 10.0 13.0 4.0 20.0 0 0 \n", + "1318 10.0 12.0 4.0 20.0 0 0 \n", + "\n", + " Rain Thunderstorm \n", + "0 1 1 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "... ... ... \n", + "1314 0 0 \n", + "1315 0 0 \n", + "1316 0 0 \n", + "1317 0 0 \n", + "1318 0 0 \n", + "\n", + "[1319 rows x 23 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin_fixed.drop(columns=[\"Events\"], inplace=True, axis=1)\n", + "austin_fixed" ] }, { @@ -500,18 +3548,845 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempAvgFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercent...VisibilityHighMilesVisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHSnowFogRainThunderstorm
02013-12-2174604567.049.043.093.075.057.0...10.07.02.020.04.031.00011
12013-12-2256483943.036.028.093.068.043.0...10.010.05.016.06.025.00000
22013-12-2358453231.027.023.076.052.027.0...10.010.010.08.03.012.00000
32013-12-2461463136.028.021.089.056.022.0...10.010.07.012.04.020.00000
42013-12-2558504144.040.036.086.071.056.0...10.010.07.010.02.016.00000
..................................................................
13142017-07-27103897571.067.061.082.054.025.0...10.010.010.012.05.021.00000
13152017-07-28105917671.064.055.087.054.020.0...10.010.010.014.05.020.00000
13162017-07-29107927772.064.055.082.051.019.0...10.010.010.012.04.017.00000
13172017-07-30106937970.068.063.069.048.027.0...10.010.010.013.04.020.00000
13182017-07-3199887766.061.054.064.043.022.0...10.010.010.012.04.020.00000
\n", + "

1319 rows × 23 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempAvgF TempLowF DewPointHighF DewPointAvgF \\\n", + "0 2013-12-21 74 60 45 67.0 49.0 \n", + "1 2013-12-22 56 48 39 43.0 36.0 \n", + "2 2013-12-23 58 45 32 31.0 27.0 \n", + "3 2013-12-24 61 46 31 36.0 28.0 \n", + "4 2013-12-25 58 50 41 44.0 40.0 \n", + "... ... ... ... ... ... ... \n", + "1314 2017-07-27 103 89 75 71.0 67.0 \n", + "1315 2017-07-28 105 91 76 71.0 64.0 \n", + "1316 2017-07-29 107 92 77 72.0 64.0 \n", + "1317 2017-07-30 106 93 79 70.0 68.0 \n", + "1318 2017-07-31 99 88 77 66.0 61.0 \n", + "\n", + " DewPointLowF HumidityHighPercent HumidityAvgPercent \\\n", + "0 43.0 93.0 75.0 \n", + "1 28.0 93.0 68.0 \n", + "2 23.0 76.0 52.0 \n", + "3 21.0 89.0 56.0 \n", + "4 36.0 86.0 71.0 \n", + "... ... ... ... \n", + "1314 61.0 82.0 54.0 \n", + "1315 55.0 87.0 54.0 \n", + "1316 55.0 82.0 51.0 \n", + "1317 63.0 69.0 48.0 \n", + "1318 54.0 64.0 43.0 \n", + "\n", + " HumidityLowPercent ... VisibilityHighMiles VisibilityAvgMiles \\\n", + "0 57.0 ... 10.0 7.0 \n", + "1 43.0 ... 10.0 10.0 \n", + "2 27.0 ... 10.0 10.0 \n", + "3 22.0 ... 10.0 10.0 \n", + "4 56.0 ... 10.0 10.0 \n", + "... ... ... ... ... \n", + "1314 25.0 ... 10.0 10.0 \n", + "1315 20.0 ... 10.0 10.0 \n", + "1316 19.0 ... 10.0 10.0 \n", + "1317 27.0 ... 10.0 10.0 \n", + "1318 22.0 ... 10.0 10.0 \n", + "\n", + " VisibilityLowMiles WindHighMPH WindAvgMPH WindGustMPH Snow Fog \\\n", + "0 2.0 20.0 4.0 31.0 0 0 \n", + "1 5.0 16.0 6.0 25.0 0 0 \n", + "2 10.0 8.0 3.0 12.0 0 0 \n", + "3 7.0 12.0 4.0 20.0 0 0 \n", + "4 7.0 10.0 2.0 16.0 0 0 \n", + "... ... ... ... ... ... ... \n", + "1314 10.0 12.0 5.0 21.0 0 0 \n", + "1315 10.0 14.0 5.0 20.0 0 0 \n", + "1316 10.0 12.0 4.0 17.0 0 0 \n", + "1317 10.0 13.0 4.0 20.0 0 0 \n", + "1318 10.0 12.0 4.0 20.0 0 0 \n", + "\n", + " Rain Thunderstorm \n", + "0 1 1 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "... ... ... \n", + "1314 0 0 \n", + "1315 0 0 \n", + "1316 0 0 \n", + "1317 0 0 \n", + "1318 0 0 \n", + "\n", + "[1319 rows x 23 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "austin_fixed[\"Date\"] = pd.to_datetime(austin_fixed[\"Date\"])\n", + "austin_fixed" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 32, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempAvgFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercent...VisibilityHighMilesVisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHSnowFogRainThunderstorm
073522374604567.049.043.093.075.057.0...10.07.02.020.04.031.00011
173522456483943.036.028.093.068.043.0...10.010.05.016.06.025.00000
273522558453231.027.023.076.052.027.0...10.010.010.08.03.012.00000
373522661463136.028.021.089.056.022.0...10.010.07.012.04.020.00000
473522758504144.040.036.086.071.056.0...10.010.07.010.02.016.00000
..................................................................
1314736537103897571.067.061.082.054.025.0...10.010.010.012.05.021.00000
1315736538105917671.064.055.087.054.020.0...10.010.010.014.05.020.00000
1316736539107927772.064.055.082.051.019.0...10.010.010.012.04.017.00000
1317736540106937970.068.063.069.048.027.0...10.010.010.013.04.020.00000
131873654199887766.061.054.064.043.022.0...10.010.010.012.04.020.00000
\n", + "

1319 rows × 23 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempAvgF TempLowF DewPointHighF DewPointAvgF \\\n", + "0 735223 74 60 45 67.0 49.0 \n", + "1 735224 56 48 39 43.0 36.0 \n", + "2 735225 58 45 32 31.0 27.0 \n", + "3 735226 61 46 31 36.0 28.0 \n", + "4 735227 58 50 41 44.0 40.0 \n", + "... ... ... ... ... ... ... \n", + "1314 736537 103 89 75 71.0 67.0 \n", + "1315 736538 105 91 76 71.0 64.0 \n", + "1316 736539 107 92 77 72.0 64.0 \n", + "1317 736540 106 93 79 70.0 68.0 \n", + "1318 736541 99 88 77 66.0 61.0 \n", + "\n", + " DewPointLowF HumidityHighPercent HumidityAvgPercent \\\n", + "0 43.0 93.0 75.0 \n", + "1 28.0 93.0 68.0 \n", + "2 23.0 76.0 52.0 \n", + "3 21.0 89.0 56.0 \n", + "4 36.0 86.0 71.0 \n", + "... ... ... ... \n", + "1314 61.0 82.0 54.0 \n", + "1315 55.0 87.0 54.0 \n", + "1316 55.0 82.0 51.0 \n", + "1317 63.0 69.0 48.0 \n", + "1318 54.0 64.0 43.0 \n", + "\n", + " HumidityLowPercent ... VisibilityHighMiles VisibilityAvgMiles \\\n", + "0 57.0 ... 10.0 7.0 \n", + "1 43.0 ... 10.0 10.0 \n", + "2 27.0 ... 10.0 10.0 \n", + "3 22.0 ... 10.0 10.0 \n", + "4 56.0 ... 10.0 10.0 \n", + "... ... ... ... ... \n", + "1314 25.0 ... 10.0 10.0 \n", + "1315 20.0 ... 10.0 10.0 \n", + "1316 19.0 ... 10.0 10.0 \n", + "1317 27.0 ... 10.0 10.0 \n", + "1318 22.0 ... 10.0 10.0 \n", + "\n", + " VisibilityLowMiles WindHighMPH WindAvgMPH WindGustMPH Snow Fog \\\n", + "0 2.0 20.0 4.0 31.0 0 0 \n", + "1 5.0 16.0 6.0 25.0 0 0 \n", + "2 10.0 8.0 3.0 12.0 0 0 \n", + "3 7.0 12.0 4.0 20.0 0 0 \n", + "4 7.0 10.0 2.0 16.0 0 0 \n", + "... ... ... ... ... ... ... \n", + "1314 10.0 12.0 5.0 21.0 0 0 \n", + "1315 10.0 14.0 5.0 20.0 0 0 \n", + "1316 10.0 12.0 4.0 17.0 0 0 \n", + "1317 10.0 13.0 4.0 20.0 0 0 \n", + "1318 10.0 12.0 4.0 20.0 0 0 \n", + "\n", + " Rain Thunderstorm \n", + "0 1 1 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "... ... ... \n", + "1314 0 0 \n", + "1315 0 0 \n", + "1316 0 0 \n", + "1317 0 0 \n", + "1318 0 0 \n", + "\n", + "[1319 rows x 23 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### Print `austin_fixed` to check your `Date` column." + "austin_fixed[\"Date\"] = austin_fixed[\"Date\"].apply(lambda x: x.toordinal())\n", + "austin_fixed" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1319 entries, 0 to 1318\n", + "Data columns (total 23 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 1319 non-null int64 \n", + " 1 TempHighF 1319 non-null int64 \n", + " 2 TempAvgF 1319 non-null int64 \n", + " 3 TempLowF 1319 non-null int64 \n", + " 4 DewPointHighF 1319 non-null float64\n", + " 5 DewPointAvgF 1319 non-null float64\n", + " 6 DewPointLowF 1319 non-null float64\n", + " 7 HumidityHighPercent 1319 non-null float64\n", + " 8 HumidityAvgPercent 1319 non-null float64\n", + " 9 HumidityLowPercent 1319 non-null float64\n", + " 10 SeaLevelPressureHighInches 1319 non-null float64\n", + " 11 SeaLevelPressureAvgInches 1319 non-null float64\n", + " 12 SeaLevelPressureLowInches 1319 non-null float64\n", + " 13 VisibilityHighMiles 1319 non-null float64\n", + " 14 VisibilityAvgMiles 1319 non-null float64\n", + " 15 VisibilityLowMiles 1319 non-null float64\n", + " 16 WindHighMPH 1319 non-null float64\n", + " 17 WindAvgMPH 1319 non-null float64\n", + " 18 WindGustMPH 1319 non-null float64\n", + " 19 Snow 1319 non-null int32 \n", + " 20 Fog 1319 non-null int32 \n", + " 21 Rain 1319 non-null int32 \n", + " 22 Thunderstorm 1319 non-null int32 \n", + "dtypes: float64(15), int32(4), int64(4)\n", + "memory usage: 216.5 KB\n" + ] + } + ], + "source": [ + "austin_fixed.info()" ] }, { @@ -519,8 +4394,66 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print `austin_fixed` to check your `Date` column." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1319 entries, 0 to 1318\n", + "Data columns (total 23 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 1319 non-null int64 \n", + " 1 TempHighF 1319 non-null int64 \n", + " 2 TempAvgF 1319 non-null int64 \n", + " 3 TempLowF 1319 non-null int64 \n", + " 4 DewPointHighF 1319 non-null float64\n", + " 5 DewPointAvgF 1319 non-null float64\n", + " 6 DewPointLowF 1319 non-null float64\n", + " 7 HumidityHighPercent 1319 non-null float64\n", + " 8 HumidityAvgPercent 1319 non-null float64\n", + " 9 HumidityLowPercent 1319 non-null float64\n", + " 10 SeaLevelPressureHighInches 1319 non-null float64\n", + " 11 SeaLevelPressureAvgInches 1319 non-null float64\n", + " 12 SeaLevelPressureLowInches 1319 non-null float64\n", + " 13 VisibilityHighMiles 1319 non-null float64\n", + " 14 VisibilityAvgMiles 1319 non-null float64\n", + " 15 VisibilityLowMiles 1319 non-null float64\n", + " 16 WindHighMPH 1319 non-null float64\n", + " 17 WindAvgMPH 1319 non-null float64\n", + " 18 WindGustMPH 1319 non-null float64\n", + " 19 Snow 1319 non-null int32 \n", + " 20 Fog 1319 non-null int32 \n", + " 21 Rain 1319 non-null int32 \n", + " 22 Thunderstorm 1319 non-null int32 \n", + "dtypes: float64(15), int32(4), int64(4)\n", + "memory usage: 216.5 KB\n" + ] + } + ], "source": [ - "austin_fixed.head(5)" + "austin_fixed.info()" ] }, { @@ -577,11 +4510,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "y = austin_fixed[\"TempAvgF\"]\n", + "X = austin_fixed.drop(columns=\"TempAvgF\")" ] }, { @@ -593,11 +4528,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "#Your code here:\n" + "#Your code here:\n", + "from sklearn.model_selection import train_test_split" ] }, { @@ -612,13 +4548,88 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "#Your code here:\n" + "#Your code here:\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1055" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1055" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(y_train)" ] }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7998483699772555" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1055/len(austin_fixed)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -641,11 +4652,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1056" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "import math\n", + "\n", + "ts_rows = math.ceil(len(austin_fixed)*0.8) #math.ceil redondea al próximo entero superior\n", + "ts_rows" ] }, { @@ -657,11 +4683,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "X_ts_train, X_ts_test, y_ts_train, y_ts_test = train_test_split(X, y, test_size=0.2, shuffle=False)" ] }, { @@ -671,19 +4698,4391 @@ "Assign the first `ts_rows` rows of `y` to `y_ts_train` and the remaining rows to `y_ts_test`." ] }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercentSeaLevelPressureHighInches...VisibilityHighMilesVisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHSnowFogRainThunderstorm
0735223744567.049.043.093.075.057.029.86...10.07.02.020.04.031.00011
1735224563943.036.028.093.068.043.030.41...10.010.05.016.06.025.00000
2735225583231.027.023.076.052.027.030.56...10.010.010.08.03.012.00000
3735226613136.028.021.089.056.022.030.56...10.010.07.012.04.020.00000
4735227584144.040.036.086.071.056.030.41...10.010.07.010.02.016.00000
..................................................................
1051736274765967.060.057.093.085.076.030.19...10.010.05.012.02.020.00010
1052736275716169.065.061.0100.094.087.030.14...10.05.01.07.02.012.00010
1053736276696266.064.062.0100.092.084.030.21...10.08.02.010.04.016.00011
1054736277656062.061.058.0100.094.087.030.29...10.06.01.010.06.017.00010
1055736278715658.055.053.090.072.053.030.34...10.010.08.08.03.012.00000
\n", + "

1056 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempLowF DewPointHighF DewPointAvgF DewPointLowF \\\n", + "0 735223 74 45 67.0 49.0 43.0 \n", + "1 735224 56 39 43.0 36.0 28.0 \n", + "2 735225 58 32 31.0 27.0 23.0 \n", + "3 735226 61 31 36.0 28.0 21.0 \n", + "4 735227 58 41 44.0 40.0 36.0 \n", + "... ... ... ... ... ... ... \n", + "1051 736274 76 59 67.0 60.0 57.0 \n", + "1052 736275 71 61 69.0 65.0 61.0 \n", + "1053 736276 69 62 66.0 64.0 62.0 \n", + "1054 736277 65 60 62.0 61.0 58.0 \n", + "1055 736278 71 56 58.0 55.0 53.0 \n", + "\n", + " HumidityHighPercent HumidityAvgPercent HumidityLowPercent \\\n", + "0 93.0 75.0 57.0 \n", + "1 93.0 68.0 43.0 \n", + "2 76.0 52.0 27.0 \n", + "3 89.0 56.0 22.0 \n", + "4 86.0 71.0 56.0 \n", + "... ... ... ... \n", + "1051 93.0 85.0 76.0 \n", + "1052 100.0 94.0 87.0 \n", + "1053 100.0 92.0 84.0 \n", + "1054 100.0 94.0 87.0 \n", + "1055 90.0 72.0 53.0 \n", + "\n", + " SeaLevelPressureHighInches ... VisibilityHighMiles \\\n", + "0 29.86 ... 10.0 \n", + "1 30.41 ... 10.0 \n", + "2 30.56 ... 10.0 \n", + "3 30.56 ... 10.0 \n", + "4 30.41 ... 10.0 \n", + "... ... ... ... \n", + "1051 30.19 ... 10.0 \n", + "1052 30.14 ... 10.0 \n", + "1053 30.21 ... 10.0 \n", + "1054 30.29 ... 10.0 \n", + "1055 30.34 ... 10.0 \n", + "\n", + " VisibilityAvgMiles VisibilityLowMiles WindHighMPH WindAvgMPH \\\n", + "0 7.0 2.0 20.0 4.0 \n", + "1 10.0 5.0 16.0 6.0 \n", + "2 10.0 10.0 8.0 3.0 \n", + "3 10.0 7.0 12.0 4.0 \n", + "4 10.0 7.0 10.0 2.0 \n", + "... ... ... ... ... \n", + "1051 10.0 5.0 12.0 2.0 \n", + "1052 5.0 1.0 7.0 2.0 \n", + "1053 8.0 2.0 10.0 4.0 \n", + "1054 6.0 1.0 10.0 6.0 \n", + "1055 10.0 8.0 8.0 3.0 \n", + "\n", + " WindGustMPH Snow Fog Rain Thunderstorm \n", + "0 31.0 0 0 1 1 \n", + "1 25.0 0 0 0 0 \n", + "2 12.0 0 0 0 0 \n", + "3 20.0 0 0 0 0 \n", + "4 16.0 0 0 0 0 \n", + "... ... ... ... ... ... \n", + "1051 20.0 0 0 1 0 \n", + "1052 12.0 0 0 1 0 \n", + "1053 16.0 0 0 1 1 \n", + "1054 17.0 0 0 1 0 \n", + "1055 12.0 0 0 0 0 \n", + "\n", + "[1056 rows x 22 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_ts_train_2 = X.iloc[0: ts_rows]# Your code here:\n", + "X_ts_train_2" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 60\n", + "1 48\n", + "2 45\n", + "3 46\n", + "4 50\n", + " ..\n", + "1051 68\n", + "1052 66\n", + "1053 66\n", + "1054 63\n", + "1055 64\n", + "Name: TempAvgF, Length: 1056, dtype: int64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_ts_train_2 = y.iloc[0: ts_rows]\n", + "y_ts_train_2" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
DateTempHighFTempLowFDewPointHighFDewPointAvgFDewPointLowFHumidityHighPercentHumidityAvgPercentHumidityLowPercentSeaLevelPressureHighInches...VisibilityHighMilesVisibilityAvgMilesVisibilityLowMilesWindHighMPHWindAvgMPHWindGustMPHSnowFogRainThunderstorm
1056736279745560.057.054.0100.079.057.030.31...10.08.02.08.02.012.00010
1057736280775758.057.054.093.072.050.030.28...10.08.01.09.04.013.00000
1058736281765458.055.052.093.075.057.030.27...10.010.09.07.02.012.00000
1059736282825662.058.055.0100.074.048.030.15...10.06.00.09.02.016.00100
1060736283865958.055.051.087.060.032.030.11...10.010.010.08.02.014.00000
..................................................................
13147365371037571.067.061.082.054.025.030.04...10.010.010.012.05.021.00000
13157365381057671.064.055.087.054.020.029.97...10.010.010.014.05.020.00000
13167365391077772.064.055.082.051.019.029.91...10.010.010.012.04.017.00000
13177365401067970.068.063.069.048.027.029.96...10.010.010.013.04.020.00000
1318736541997766.061.054.064.043.022.030.04...10.010.010.012.04.020.00000
\n", + "

263 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " Date TempHighF TempLowF DewPointHighF DewPointAvgF DewPointLowF \\\n", + "1056 736279 74 55 60.0 57.0 54.0 \n", + "1057 736280 77 57 58.0 57.0 54.0 \n", + "1058 736281 76 54 58.0 55.0 52.0 \n", + "1059 736282 82 56 62.0 58.0 55.0 \n", + "1060 736283 86 59 58.0 55.0 51.0 \n", + "... ... ... ... ... ... ... \n", + "1314 736537 103 75 71.0 67.0 61.0 \n", + "1315 736538 105 76 71.0 64.0 55.0 \n", + "1316 736539 107 77 72.0 64.0 55.0 \n", + "1317 736540 106 79 70.0 68.0 63.0 \n", + "1318 736541 99 77 66.0 61.0 54.0 \n", + "\n", + " HumidityHighPercent HumidityAvgPercent HumidityLowPercent \\\n", + "1056 100.0 79.0 57.0 \n", + "1057 93.0 72.0 50.0 \n", + "1058 93.0 75.0 57.0 \n", + "1059 100.0 74.0 48.0 \n", + "1060 87.0 60.0 32.0 \n", + "... ... ... ... \n", + "1314 82.0 54.0 25.0 \n", + "1315 87.0 54.0 20.0 \n", + "1316 82.0 51.0 19.0 \n", + "1317 69.0 48.0 27.0 \n", + "1318 64.0 43.0 22.0 \n", + "\n", + " SeaLevelPressureHighInches ... VisibilityHighMiles \\\n", + "1056 30.31 ... 10.0 \n", + "1057 30.28 ... 10.0 \n", + "1058 30.27 ... 10.0 \n", + "1059 30.15 ... 10.0 \n", + "1060 30.11 ... 10.0 \n", + "... ... ... ... \n", + "1314 30.04 ... 10.0 \n", + "1315 29.97 ... 10.0 \n", + "1316 29.91 ... 10.0 \n", + "1317 29.96 ... 10.0 \n", + "1318 30.04 ... 10.0 \n", + "\n", + " VisibilityAvgMiles VisibilityLowMiles WindHighMPH WindAvgMPH \\\n", + "1056 8.0 2.0 8.0 2.0 \n", + "1057 8.0 1.0 9.0 4.0 \n", + "1058 10.0 9.0 7.0 2.0 \n", + "1059 6.0 0.0 9.0 2.0 \n", + "1060 10.0 10.0 8.0 2.0 \n", + "... ... ... ... ... \n", + "1314 10.0 10.0 12.0 5.0 \n", + "1315 10.0 10.0 14.0 5.0 \n", + "1316 10.0 10.0 12.0 4.0 \n", + "1317 10.0 10.0 13.0 4.0 \n", + "1318 10.0 10.0 12.0 4.0 \n", + "\n", + " WindGustMPH Snow Fog Rain Thunderstorm \n", + "1056 12.0 0 0 1 0 \n", + "1057 13.0 0 0 0 0 \n", + "1058 12.0 0 0 0 0 \n", + "1059 16.0 0 1 0 0 \n", + "1060 14.0 0 0 0 0 \n", + "... ... ... ... ... ... \n", + "1314 21.0 0 0 0 0 \n", + "1315 20.0 0 0 0 0 \n", + "1316 17.0 0 0 0 0 \n", + "1317 20.0 0 0 0 0 \n", + "1318 20.0 0 0 0 0 \n", + "\n", + "[263 rows x 22 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_ts_train_2 = X.iloc[ts_rows: len(austin_fixed)]\n", + "y_ts_train_2" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1056 65\n", + "1057 67\n", + "1058 65\n", + "1059 69\n", + "1060 73\n", + " ..\n", + "1314 89\n", + "1315 91\n", + "1316 92\n", + "1317 93\n", + "1318 88\n", + "Name: TempAvgF, Length: 263, dtype: int64" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_ts_test_2 = y.iloc[ts_rows: len(austin_fixed)]\n", + "y_ts_test_2" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "num_corr = round(austin_fixed.corr(), 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mask = np.zeros_like(num_corr)\n", + "mask[np.triu_indices_from(mask)] = True # optional, to hide repeat half of the matrix\n", + "\n", + "f, ax = plt.subplots(figsize=(25, 15))\n", + "sns.set(font_scale=1.5) # increase font size\n", + "\n", + "ax = sns.heatmap(num_corr, mask=mask, annot=True, annot_kws={\"size\": 12}, linewidths=.5, cmap=\"coolwarm\", fmt=\".2f\", ax=ax) # round to 2 decimal places\n", + "ax.set_title(\"Dealing with Multicollinearity\", fontsize=20) # add title\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1319 entries, 0 to 1318\n", + "Data columns (total 23 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 1319 non-null int64 \n", + " 1 TempHighF 1319 non-null int64 \n", + " 2 TempAvgF 1319 non-null int64 \n", + " 3 TempLowF 1319 non-null int64 \n", + " 4 DewPointHighF 1319 non-null float64\n", + " 5 DewPointAvgF 1319 non-null float64\n", + " 6 DewPointLowF 1319 non-null float64\n", + " 7 HumidityHighPercent 1319 non-null float64\n", + " 8 HumidityAvgPercent 1319 non-null float64\n", + " 9 HumidityLowPercent 1319 non-null float64\n", + " 10 SeaLevelPressureHighInches 1319 non-null float64\n", + " 11 SeaLevelPressureAvgInches 1319 non-null float64\n", + " 12 SeaLevelPressureLowInches 1319 non-null float64\n", + " 13 VisibilityHighMiles 1319 non-null float64\n", + " 14 VisibilityAvgMiles 1319 non-null float64\n", + " 15 VisibilityLowMiles 1319 non-null float64\n", + " 16 WindHighMPH 1319 non-null float64\n", + " 17 WindAvgMPH 1319 non-null float64\n", + " 18 WindGustMPH 1319 non-null float64\n", + " 19 Snow 1319 non-null int32 \n", + " 20 Fog 1319 non-null int32 \n", + " 21 Rain 1319 non-null int32 \n", + " 22 Thunderstorm 1319 non-null int32 \n", + "dtypes: float64(15), int32(4), int64(4)\n", + "memory usage: 216.5 KB\n" + ] + } + ], + "source": [ + "austin_fixed.info()" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "austin_fixed.drop(columns = [\"TempHighF\",\"TempLowF\",\"DewPointHighF\",\"DewPointLowF\",\"HumidityHighPercent\",\"HumidityAvgPercent\",\"HumidityLowPercent\",\"SeaLevelPressureHighInches\",\"SeaLevelPressureLowInches\",\"VisibilityHighMiles\",\"VisibilityLowMiles\",\"WindHighMPH\",\"WindGustMPH\"],inplace=True, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DateTempAvgFDewPointAvgFSeaLevelPressureAvgInchesVisibilityAvgMilesWindAvgMPHSnowFogRainThunderstorm
07352236049.029.687.04.00011
17352244836.030.1310.06.00000
27352254527.030.4910.03.00000
37352264628.030.4510.04.00000
47352275040.030.3310.02.00000
.................................
13147365378967.029.9710.05.00000
13157365389164.029.9010.05.00000
13167365399264.029.8610.04.00000
13177365409368.029.9110.04.00000
13187365418861.029.9710.04.00000
\n", + "

1319 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " Date TempAvgF DewPointAvgF SeaLevelPressureAvgInches \\\n", + "0 735223 60 49.0 29.68 \n", + "1 735224 48 36.0 30.13 \n", + "2 735225 45 27.0 30.49 \n", + "3 735226 46 28.0 30.45 \n", + "4 735227 50 40.0 30.33 \n", + "... ... ... ... ... \n", + "1314 736537 89 67.0 29.97 \n", + "1315 736538 91 64.0 29.90 \n", + "1316 736539 92 64.0 29.86 \n", + "1317 736540 93 68.0 29.91 \n", + "1318 736541 88 61.0 29.97 \n", + "\n", + " VisibilityAvgMiles WindAvgMPH Snow Fog Rain Thunderstorm \n", + "0 7.0 4.0 0 0 1 1 \n", + "1 10.0 6.0 0 0 0 0 \n", + "2 10.0 3.0 0 0 0 0 \n", + "3 10.0 4.0 0 0 0 0 \n", + "4 10.0 2.0 0 0 0 0 \n", + "... ... ... ... ... ... ... \n", + "1314 10.0 5.0 0 0 0 0 \n", + "1315 10.0 5.0 0 0 0 0 \n", + "1316 10.0 4.0 0 0 0 0 \n", + "1317 10.0 4.0 0 0 0 0 \n", + "1318 10.0 4.0 0 0 0 0 \n", + "\n", + "[1319 rows x 10 columns]" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "austin_fixed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Selecting the Model: Linear Regression

" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "X = austin_fixed.drop(\"TempAvgF\", axis=1)\n", + "y = austin_fixed[\"TempAvgF\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100% of our data: 1319.\n", + "70% for training data: 923.\n", + "30% for test data: 396.\n" + ] + } + ], + "source": [ + "print(f'100% of our data: {len(austin_fixed)}.')\n", + "print(f'70% for training data: {len(X_train)}.')\n", + "print(f'30% for test data: {len(X_test)}.')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LinearRegression()\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30% for test prediction data: 396.\n" + ] + } + ], + "source": [ + "print(f'30% for test prediction data: {len(predictions)}.')" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\gaelm\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "r2 = r2_score(y_test, predictions)\n", + "RMSE = mean_squared_error(y_test, predictions, squared=False)\n", + "MSE = mean_squared_error(y_test, predictions)\n", + "MAE = mean_absolute_error(y_test, predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 = 0.8902\n", + "RMSE = 4.6883\n", + "The value of the metric MSE is 21.9806\n", + "MAE = 3.6567\n" + ] + } + ], + "source": [ + "print(\"R2 = \", round(r2, 4))\n", + "print(\"RMSE = \", round(RMSE, 4))\n", + "print(\"The value of the metric MSE is \", round(MSE, 4))\n", + "print(\"MAE = \", round(MAE, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Selecting the Model: Ridge Regression

" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import Ridge" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Ridge()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Ridge()" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ridge = Ridge()\n", + "ridge.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "predictions_ridge = ridge.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\gaelm\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "r2_ridge = r2_score(y_test, predictions_ridge)\n", + "RMSE_ridge = mean_squared_error(y_test, predictions_ridge, squared=False)\n", + "MSE_ridge = mean_squared_error(y_test, predictions_ridge)\n", + "MAE_ridge = mean_absolute_error(y_test, predictions_ridge)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 = 0.8901\n", + "RMSE = 4.6915\n", + "The value of the metric MSE is 22.0101\n", + "MAE = 3.6573\n" + ] + } + ], + "source": [ + "print(\"R2 = \", round(r2_ridge, 4))\n", + "print(\"RMSE = \", round(RMSE_ridge, 4))\n", + "print(\"The value of the metric MSE is \", round(MSE_ridge, 4))\n", + "print(\"MAE = \", round(MAE_ridge, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Selecting the Model: Lasso Regression

" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import Lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Lasso()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Lasso()" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lasso = Lasso()\n", + "lasso.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "predictions_lasso = lasso.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\gaelm\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "r2_lasso = r2_score(y_test, predictions_lasso)\n", + "RMSE_lasso = mean_squared_error(y_test, predictions_lasso, squared=False)\n", + "MSE_lasso = mean_squared_error(y_test, predictions_lasso)\n", + "MAE_lasso = mean_absolute_error(y_test, predictions_lasso)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 = 0.8665\n", + "RMSE = 5.1711\n", + "The value of the metric MSE is 26.7405\n", + "MAE = 4.0819\n" + ] + } + ], + "source": [ + "print(\"R2 = \", round(r2_lasso, 4))\n", + "print(\"RMSE = \", round(RMSE_lasso, 4))\n", + "print(\"The value of the metric MSE is \", round(MSE_lasso, 4))\n", + "print(\"MAE = \", round(MAE_lasso, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "# Your code here:\n" + "

Selecting the Model: Decision Tree Regression

" ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
DecisionTreeRegressor()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DecisionTreeRegressor()" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeRegressor()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "predictions_tree = tree.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\gaelm\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "r2_tree = r2_score(y_test, predictions_tree)\n", + "RMSE_tree = mean_squared_error(y_test, predictions_tree, squared=False)\n", + "MSE_tree = mean_squared_error(y_test, predictions_tree)\n", + "MAE_tree = mean_absolute_error(y_test, predictions_tree)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 = 0.8292\n", + "RMSE = 5.8487\n", + "The value of the metric MSE is 34.2071\n", + "MAE = 4.2071\n" + ] + } + ], + "source": [ + "print(\"R2 = \", round(r2_tree, 4))\n", + "print(\"RMSE = \", round(RMSE_tree, 4))\n", + "print(\"The value of the metric MSE is \", round(MSE_tree, 4))\n", + "print(\"MAE = \", round(MAE_tree, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Selecting the Model: KNN Regression

" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
KNeighborsRegressor()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "KNeighborsRegressor()" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn = KNeighborsRegressor()\n", + "knn.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "predictions_knn = knn.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\gaelm\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "r2_knn = r2_score(y_test, predictions_knn)\n", + "RMSE_knn = mean_squared_error(y_test, predictions_knn, squared=False)\n", + "MSE_knn = mean_squared_error(y_test, predictions_knn)\n", + "MAE_knn = mean_absolute_error(y_test, predictions_knn)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 = 0.8908\n", + "RMSE = 4.6753\n", + "The value of the metric MSE is 21.858\n", + "MAE = 3.3333\n" + ] + } + ], + "source": [ + "print(\"R2 = \", round(r2_knn, 4))\n", + "print(\"RMSE = \", round(RMSE_knn, 4))\n", + "print(\"The value of the metric MSE is \", round(MSE_knn, 4))\n", + "print(\"MAE = \", round(MAE_knn, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Selecting the Model: XGBoost Regression

" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
XGBRFRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "               colsample_bylevel=None, colsample_bytree=None, device=None,\n",
+       "               early_stopping_rounds=None, enable_categorical=False,\n",
+       "               eval_metric=None, feature_types=None, gamma=None,\n",
+       "               grow_policy=None, importance_type=None,\n",
+       "               interaction_constraints=None, max_bin=None,\n",
+       "               max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "               max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "               multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "               num_parallel_tree=None, objective='reg:squarederror',\n",
+       "               random_state=None, reg_alpha=None, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "XGBRFRegressor(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bytree=None, device=None,\n", + " early_stopping_rounds=None, enable_categorical=False,\n", + " eval_metric=None, feature_types=None, gamma=None,\n", + " grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=None, n_jobs=None,\n", + " num_parallel_tree=None, objective='reg:squarederror',\n", + " random_state=None, reg_alpha=None, ...)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xgbr = xgb.XGBRFRegressor()\n", + "xgbr.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "predictions_xgb = xgbr.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\gaelm\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "r2_xg = r2_score(y_test, predictions_xgb)\n", + "RMSE_xg = mean_squared_error(y_test, predictions_xgb, squared=False)\n", + "MSE_xg = mean_squared_error(y_test, predictions_xgb)\n", + "MAE_xg = mean_absolute_error(y_test, predictions_xgb)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2 = 0.9024\n", + "RMSE = 4.4207\n", + "The value of the metric MSE is 19.5429\n", + "MAE = 3.2804\n" + ] + } + ], + "source": [ + "print(\"R2 = \", round(r2_xg, 4))\n", + "print(\"RMSE = \", round(RMSE_xg, 4))\n", + "print(\"The value of the metric MSE is \", round(MSE_xg, 4))\n", + "print(\"MAE = \", round(MAE_xg, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Final Comparision?

" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "metrics = {\n", + " 'Model': ['Linear Regression', 'Ridge', 'Lasso', 'Decision Tree', 'KNN', 'XGBoost'],\n", + " 'R²': [r2, r2_ridge, r2_lasso, r2_tree, r2_knn, r2_xg],\n", + " 'RMSE': [RMSE, RMSE_ridge, RMSE_lasso, RMSE_tree, RMSE_knn, RMSE_xg],\n", + " 'MSE': [MSE, MSE_ridge, MSE_lasso, MSE_tree, MSE_knn, MSE_xg],\n", + " 'MAE': [MAE, MAE_ridge, MAE_lasso, MAE_tree, MAE_knn, MAE_xg]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + "
ModelLinear RegressionRidgeLassoDecision TreeKNNXGBoost
R²0.890.890.870.830.890.90
RMSE4.694.695.175.854.684.42
MSE21.9822.0126.7434.2121.8619.54
MAE3.663.664.084.213.333.28
\n", + "
" + ], + "text/plain": [ + "Model Linear Regression Ridge Lasso Decision Tree KNN XGBoost\n", + "R² 0.89 0.89 0.87 0.83 0.89 0.90\n", + "RMSE 4.69 4.69 5.17 5.85 4.68 4.42\n", + "MSE 21.98 22.01 26.74 34.21 21.86 19.54\n", + "MAE 3.66 3.66 4.08 4.21 3.33 3.28" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_metrics = round(pd.DataFrame(metrics),2)\n", + "df_metrics.set_index(\"Model\").T" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -697,9 +9096,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.12.4" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 }