Commit 9b1bfff7 authored by Eva Zangerle's avatar Eva Zangerle
Browse files

added hetrec preprocessing example

parent bbaf0270
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......@@ -12,7 +12,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"id": "792af709-0621-4d64-8166-8c8cc28cc73c",
"metadata": {},
"outputs": [],
......@@ -39,7 +39,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"id": "82672d3c-d574-47f1-b2f1-9a42b414e278",
"metadata": {},
"outputs": [],
......@@ -224,6 +224,117 @@
" print(f\"Row {row}, Col {col}: Empty value {repr(val)}\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "023e9778-16a3-4ab1-bb87-3897786ab742",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10197 entries, 0 to 10196\n",
"Data columns (total 21 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 id 10197 non-null int64 \n",
" 1 title 10197 non-null object\n",
" 2 imdbID 10197 non-null int64 \n",
" 3 spanishTitle 10197 non-null object\n",
" 4 imdbPictureURL 10016 non-null object\n",
" 5 year 10197 non-null int64 \n",
" 6 rtID 9886 non-null object\n",
" 7 rtAllCriticsRating 10197 non-null object\n",
" 8 rtAllCriticsNumReviews 10197 non-null object\n",
" 9 rtAllCriticsNumFresh 10197 non-null object\n",
" 10 rtAllCriticsNumRotten 10197 non-null object\n",
" 11 rtAllCriticsScore 10197 non-null object\n",
" 12 rtTopCriticsRating 10197 non-null object\n",
" 13 rtTopCriticsNumReviews 10197 non-null object\n",
" 14 rtTopCriticsNumFresh 10197 non-null object\n",
" 15 rtTopCriticsNumRotten 10197 non-null object\n",
" 16 rtTopCriticsScore 10197 non-null object\n",
" 17 rtAudienceRating 10197 non-null object\n",
" 18 rtAudienceNumRatings 10197 non-null object\n",
" 19 rtAudienceScore 10197 non-null object\n",
" 20 rtPictureURL 10197 non-null object\n",
"dtypes: int64(3), object(18)\n",
"memory usage: 12.6 MB\n"
]
}
],
"source": [
"# real world example (from later example in exploratory data analysis notebook)\n",
"# read in movie data, rt = rottenTomatoes\n",
"movies = pd.read_csv(os.path.join(data_dir,'hetrec/movies.dat'), delimiter='\\t', encoding='latin1') \n",
"movies.info(memory_usage='deep')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a6af2a26-9aab-42a8-9025-36c757168f82",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
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14.667.55.65.57.286.95.47.707.37.464.16.44.856.56.26.52.87.24.76.7007.77.376.45.56.14.86.58.44.75.13.75.16.27.35.95.66.97.105.3005.663.56.75.47.14.253.68.48.28.24.85.15.25.27.23.93.98.15.706.81.83.26.12.58.13.903.603.4077.1\\N6.106.45.54.15.55.606.906.925.40085.704.47.806.65.27.606.44.654.87.76.57.95.78.597.6\\N2.86.31.7007.55.85.74.203.76.163.56.65.45.962.95.8000\\N6005.73.93.25.106.2000063.45.90\\N0\\N6.45.95.26.308.26.13.906.56.8\\N00767.306.2005.202.97.505.3555.5007.46.7000008.15.97.2805.54.84.36.24.44.86.14.26.907.37.16.8\\N0\\N6.406.8\\N86.36.55.44.86.957.17.96.37.44.608.30\\N000\\N8.27.17.1\\N5.22608.2070006.34.475.66.70 to numeric",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib64/python3.9/site-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1601\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1602\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1603\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: could not convert string to float: 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"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib64/python3.9/site-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1605\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1606\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1607\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: complex() arg is a malformed string",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_24177/813377835.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# compute mean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmovies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rtAllCriticsRating'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;32m~/.local/share/virtualenvs/data-engineering-analytics-notebooks-Qx0adyYX/lib64/python3.9/site-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1607\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1608\u001b[0m \u001b[0;31m# e.g. \"foo\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1609\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Could not convert {x} to numeric\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1610\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Could not convert 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080003.903.75.105.9\\N5.27.96.97.46.54.707.83.907.97.48.307.39.353.77.15.18.58.82.79.19.27.68.77.807.55.88.6904.68.74.27.98.47.607.36.190007.37.98.28.78.37.4007.607.808.56.97.38.207.28.16.408.77.95.45.957.886.897.38.3007.37.2\\N4.87.86.58.59.56.67.67.48.8088.86807.37.57.10006.507.89.38.48.27.76.407.77.107.98.27.4606.107.3707.367.46.65.96.44.198.56.3075.27.35.76.98.78.79.26.26.67.77.58.47.68.77.17.27.27.206.6003.360805.88.47.206.37.46.96.27.76.6006.85.5\\N06.76.987.46.50077.75.37.47.38.33.86.904.65.97.48.15.97.709.16.56.406.47.87.17.15.96\\N06.77.24.97.84.86.207.56.27.3067.368.75.86.350055.43.96.18.37.65.66.97.46.24.66.15.45.18.14.7007.27.37.45.17.876.95.6\\N083.906.78.45.72.23.75.68.36.4464.76.87.76.88.5005.94.63.65.57.25.58.23.76.684.47.37.77.26.98.168.62.407.47.33.805.47.93.376.887.4007.106.74.75.37.605.306\\N3.9007.408.47.46.66.675.8006.35.76.976.95.47.205.402.704.206.805.407.45.36.206.4\\N6.1\\N6.25.75.102.97.37.705.86.36.75.16.87.4006.66.66.37.47.406.374.8\\N7.68.26.16.94.77.307.37.206.75.46.47.16.26.97.65.97.66.56.47.666.656.84.67.1\\N04.87.34.457.57.477.46.73.95.47.36.77.62.806.87.97.87.57.14.64.66.666.94.388.38.36.56.27.57.97.607.36.37.855.27.37.15.26.65.56.74.46.96.83.52.86.8000704.57.67.2\\N\\N5.75.6757.55.97.26.65.27.302.9005.13.76.506.56.26.4005.26.9004.35.506.24.77.78.13.74.96.18.705.37.16.46.87.87.97.705.67.74.85.705.93.31.73.873.43.85.8843.65.56.86.38.58.15.883.22.405.88.48.35.66.45.15.58.26.57.567.26.36.6\\N4.46.15.52.85.87.86.75.76.9003.35.705.94.67.83.37.6\\N6.73.94.8\\N5.4876.29.243.63.907.83.74.676.5505.96.16.905.4006.2\\N0\\N05.1\\N04.870004.4007.76.606.33.584.306.77.305.95.65.8\\N45.44.77.36.27.3\\N5.906.705.57.17.46.206.47.57.37.206.85.65.96.905.807.4705.18.77.75.7\\N5.37.66.25.26.2\\N6.4467.13.72.86.2000006.306.707.508.406.4\\N08.25.38.607.36.3\\N6.47.34.26.86.55.83.56.23.85.34.92.407.2\\N3.85.54.37.14.67.25.2007.407.506.66.55.16.2\\N8.37.25.14.25.76.87.94.45.25.6066.92.97.14.74.86.77.46.7667.864.35.23.606.16.56.86.47.73.27.46.302.96.35.45.26.64.677.46.86.56.72.85.86.9707.83.75.57.24.58.47.906.7605.15.80\\N6.67.36.16.65.47.45.45.96.86.53.8507.47.17.17.46.753.64.95.77.52.85.305.30583.75.8005067.57.26.78.74.17.855.15.13.53.25.77.604.75.76.46.23.146.56.75.5\\N0006.577.703.904.23.35.95.27.34.95.37.25.57.906.28.25.75.603.646.15.96.76.98.48.17.48.107.65.485.2\\N6.85.46.907.94.76.808.24.66.78.16.36.17.34.96.272.76.506.46.30006.44.67.24.87.36.14.65.66.87.46.63.75.66.74.57.13.56.37.45.45.76.8004.45.16.904.67.75.57.35.47.46.47.35.806.5066.15.46.103.33.24.65.76.87.17.55.38.77.27.875.87.54.46.97.75.45.95.26.45.27.46.27.26.606.57.47.55.86.84.43.64.96.65.66.662.28.95.908.86.67.26.87\\N7.172.35.83.74.905.85.53.63.75.487.54.47.67.306.95.47.63.93.16.65.16.106.34.94.15.86.14.67.43.24.33.384.65.46.52.44.54.53.12.83.73.57.75.94.14.66.146.5585.16.807.367.37.45.56.45.34.42.86.58.28.65.85.47.58.37.307.872.433.17.15.67.25.96.93.13.85.406.46.85.87.54.74.76.45.97.154.54.5\\N5.75.66.64.28.24.98.43.567.26.85.44.94.85.954.76.94.66.466.2765.154.87.46.664.36.14.25.12.667.27.707.65.45.54.1004.73.56.94.37.17.15.64.67.76.408.66.75.88.57.87.48.63.205.16.85.66.25.46.55.36.76.85.646.46.64.36.26.466.305.44.48.36.84.44.536.17.67.346.92.556.908.47.5566.45.78.48.55.57.67.106.87.73.246.97.67.966.66.52.77.15.74.606.57.23.65.97.66.64.865.15.66.16.36.76.486.34.95.806.5088.65.25.44.68.23.544.756.45.63.44.446.84.305.887.48.44.17.36.66.487.175.76.674.26.75.3\\N6.506.76.26.13.62.24.807.63.95.85.87.27.86.92.905.62.96.106.15.55.785.76.65.32.73.86.35.64.56.2706.54.98.67.15.806.17.26.86.37.16.47.54.56.46.657.38.2034.12.94.56.24.507.907.37.30\\N03.65.53.807.27.46.97.27.38.27.34.54.33.62.33.73.23.93.26.56.45.68.48.1764.47.205.14.374.863.556.33.86.24.77.703.46.84.24.65.26.6\\N7.603.805.16.14.306.95.86.97.7677.506.43.83.96.35.55.63.257.26.46.57.104.97.36.23.37.43.54.96.35.7004.705.25.64.83.56.23.65.15.66.25.84.36.505.55.35.45.44.103.56.2506.44.47.27.35.97.78.107.76.63.82.605.58.35.46.25.74.85.85.46.35.62.65.46.467.705.16.96.36.64.85.56.46.55.87.56.36.6078.38.17.67.876.706.83.14.94.502.76.83.6\\N5.25.95.86.384.96.82.33.57.52.15.245.84.87.92.96.66.67.66.67.45.86.36.966.54.246.74.255.86.92.34.3003.44.14.16.87.76.93.3064.86.17.56.28.87.37.88.38.15.575.5567.65.47.47.77.97.33.564.342.87.54.66.65.3304.76.26.55.85.17.65.17.15.55.46.55.27.72.77.1475.95.14.68.27.46.16.63.85.16.56.47.7\\N3.707.537.85.176.2575.77.78.28.56.2577.64.877.803.94.84.26.54.94.84.55.94.66.67.207.46.67.305.94.36.94.35.66.34.47.65.677.47.38.15.47.35.37.15.16.405.6006.6007.64.78.473.16.605.20003.855.73.67.97.82.55.76.74.44.46.207.206.73.42.504.14.861.94.32.33.707.2676.17.17.34.36.64.43.6046.66.56.43.9056.64.82.15.34.55.58.35.93.33.26.7575.6\\N7.17.13.46.16.55.66.76.107.25.57.26.66.85.98.476.14.45.34.56.54.75.25.65.86.37.45.87.45.75.474.33.75.17.75.605.15.33.96.14.94.47.76.406.85.36.263.86.252.92.86.3\\N46.35.24.55.86.96.203.84.64.55.46.16.37.47.54.65.67.76.96.47.73.805.25.554.97.707.26.26.467.604.75.86.676.357.546.16.64.68.46.55.45.56.27.404.37.1007.4\\N5.26.77.16.37.704.27.607.14.667.55.65.57.286.95.47.707.37.464.16.44.856.56.26.52.87.24.76.7007.77.376.45.56.14.86.58.44.75.13.75.16.27.35.95.66.97.105.3005.663.56.75.47.14.253.68.48.28.24.85.15.25.27.23.93.98.15.706.81.83.26.12.58.13.903.603.4077.1\\N6.106.45.54.15.55.606.906.925.40085.704.47.806.65.27.606.44.654.87.76.57.95.78.597.6\\N2.86.31.7007.55.85.74.203.76.163.56.65.45.962.95.8000\\N6005.73.93.25.106.2000063.45.90\\N0\\N6.45.95.26.308.26.13.906.56.8\\N00767.306.2005.202.97.505.3555.5007.46.7000008.15.97.2805.54.84.36.24.44.86.14.26.907.37.16.8\\N0\\N6.406.8\\N86.36.55.44.86.957.17.96.37.44.608.30\\N000\\N8.27.17.1\\N5.22608.2070006.34.475.66.70 to numeric"
]
}
],
"source": [
"# compute mean\n",
"movies['rtAllCriticsRating'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "193d55a3-a3d6-4d10-9979-baacd6af5afa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.1392996889736136"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# add \\\\N to missing value fields \n",
"movies = pd.read_csv(os.path.join(data_dir,'hetrec/movies.dat'), delimiter='\\t', encoding='latin1', na_values=['\\\\N']) \n",
"movies['rtAllCriticsRating'].mean()"
]
},
{
"cell_type": "markdown",
"id": "b00ee68e-a37e-442f-b494-1882101e99a1",
......
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