4_b_convert_categorical_columns_to_label_encoded_columns_or_integer_column.ipynb

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  {
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   "source": [
    "get columns from dataframe which are categorical and convert them using label encoding"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
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   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "df = pd.DataFrame({'col_1': [1, 0, 1, None], \n",
    "                   'col_2': [1.2, 3.1, 4.4, 5.5], \n",
    "                   'col_3': [1, 2, 3, 4], \n",
    "                   'col_4': ['a', 'b', 'c', 'd']})"
   ]
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   "execution_count": 3,
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       "   col_1  col_2  col_3 col_4\n",
       "0    1.0    1.2      1     a\n",
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       "2    1.0    4.4      3     c\n",
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     "execution_count": 3,
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      "col_1    3 non-null float64\n",
      "col_2    4 non-null float64\n",
      "col_3    4 non-null int64\n",
      "col_4    4 non-null object\n",
      "dtypes: float64(2), int64(1), object(1)\n",
      "memory usage: 208.0+ bytes\n"
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   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bool_cols = [col for col in df if len(df[[col]].dropna()[col].unique()) == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for col in bool_cols:\n",
    "    label = LabelEncoder()\n",
    "    label.fit(list(df[col].values.astype(\"str\")))\n",
    "    df[col] = label.transform(list(df[col].values.astype(\"str\")))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      "col_1    4 non-null int64\n",
      "col_2    4 non-null float64\n",
      "col_3    4 non-null int64\n",
      "col_4    4 non-null object\n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 208.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "   col_1  col_2  col_3 col_4\n",
       "0      1    1.2      1     a\n",
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "df"
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  {
   "cell_type": "code",
   "execution_count": null,
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