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whylogs profile visualizer example
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1599 entries, 0 to 1598\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 fixed acidity 1599 non-null float64\n",
" 1 volatile acidity 1599 non-null float64\n",
" 2 citric acid 1599 non-null float64\n",
" 3 residual sugar 1599 non-null float64\n",
" 4 chlorides 1599 non-null float64\n",
" 5 free sulfur dioxide 1599 non-null float64\n",
" 6 total sulfur dioxide 1599 non-null float64\n",
" 7 density 1599 non-null float64\n",
" 8 pH 1599 non-null float64\n",
" 9 sulphates 1599 non-null float64\n",
" 10 alcohol 1599 non-null float64\n",
" 11 quality 1599 non-null int64 \n",
"dtypes: float64(11), int64(1)\n",
"memory usage: 150.0 KB\n"
]
}
],
"source": [
"import pandas as pd\n",
"pd.options.mode.chained_assignment = None # Disabling false positive warning\n",
"\n",
"url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
"wine = pd.read_csv(url,sep=\";\")\n",
"wine.info()"
]
}
],
"metadata": {
"interpreter": {
"hash": "498e653baf04937463e348bd994b2947df7aeb22ca9551fbd507161022670ceb"
},
"kernelspec": {
"display_name": "whylabs_env",
"language": "python",
"name": "whylabs_env"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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