pandas_cat_4_sklearn.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# Import required packages\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Create DataFrame\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {'patient': [1, 1, 1, 2, 2],\n",
    "        'obs': [1, 2, 3, 1, 2],\n",
    "        'treatment': [0, 1, 0, 1, 0],\n",
    "        'score': ['strong', 'weak', 'normal', 'weak', 'strong']}\n",
    "df = pd.DataFrame(raw_data, columns = ['patient', 'obs', 'treatment', 'score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Fitting The Label Encoder\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a label (category) encoder object\n",
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the encoder to the pandas column\n",
    "le.fit(df['score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>View The Labels\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['normal', 'strong', 'weak']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the labels\n",
    "list(le.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Transform Categories Into Integers\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 2, 1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the fitted encoder to the pandas column\n",
    "le.transform(df['score']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Transform Integers Into Categories\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['weak', 'weak', 'strong']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert some integers into their category names\n",
    "list(le.inverse_transform([2, 2, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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