SimpleImputer.ipynb

{
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       "      <td>7.0</td>\n",
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       "      <td>3.0</td>\n",
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       "     A    B     C\n",
       "0  1.0  5.0   NaN\n",
       "1  NaN  7.0   9.0\n",
       "2  3.0  NaN  10.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer\n",
    "data=pd.DataFrame({'A':(1,np.nan,3),'B':(5,7,np.nan),'C':(np.nan,9,10)})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c5496bf",
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "array([[ 1. ,  5. ,  9.5],\n",
       "       [ 2. ,  7. ,  9. ],\n",
       "       [ 3. ,  6. , 10. ]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "impute=SimpleImputer(strategy='mean')\n",
    "x=impute.fit(data)\n",
    "x=impute.transform(data)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "afd5c715",
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   "outputs": [
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       "      <td>7.0</td>\n",
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       "      <td>3.0</td>\n",
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       "     A    B     C\n",
       "0  1.0  5.0   NaN\n",
       "1  NaN  7.0   9.0\n",
       "2  3.0  NaN  10.0"
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       "      <td>-0.224203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.220634</td>\n",
       "      <td>0.178134</td>\n",
       "      <td>-0.612061</td>\n",
       "      <td>0.795828</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-0.695926</td>\n",
       "      <td>0.347030</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.834437</td>\n",
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       "      <td>-1.305020</td>\n",
       "      <td>-1.632753</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.647919</td>\n",
       "      <td>1.994331</td>\n",
       "      <td>0.839440</td>\n",
       "      <td>2.075411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.261834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.346287</td>\n",
       "      <td>1.348494</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.412926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.301803</td>\n",
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       "      <th>7</th>\n",
       "      <td>-0.785090</td>\n",
       "      <td>0.041763</td>\n",
       "      <td>0.339128</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.635949</td>\n",
       "      <td>0.528157</td>\n",
       "      <td>-0.363242</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.176332</td>\n",
       "      <td>0.528157</td>\n",
       "      <td>1.646661</td>\n",
       "      <td>-1.560768</td>\n",
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       "          A         B         C         D\n",
       "0  0.741641  0.763065  1.255695 -0.224203\n",
       "1 -1.220634  0.178134 -0.612061  0.795828\n",
       "2       NaN -0.349282 -0.695926  0.347030\n",
       "3 -0.834437 -1.251355 -1.305020 -1.632753\n",
       "4 -0.647919  1.994331  0.839440  2.075411\n",
       "5 -1.261834       NaN  0.346287  1.348494\n",
       "6  2.412926       NaN       NaN -0.301803\n",
       "7 -0.785090  0.041763  0.339128       NaN\n",
       "8  0.635949  0.528157 -0.363242       NaN\n",
       "9 -0.176332  0.528157  1.646661 -1.560768"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a radnom datset of 10 rows and 4 columns\n",
    "df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))\n",
    "\n",
    "# Randomly set some values as null\n",
    "df = df.mask(np.random.random((10, 4)) < .15)\n",
    "\n",
    "# Duplicate two cells with same values\n",
    "df['B'][8] = df['B'][9]\n",
    "df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "id": "7e2eca5e",
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   "outputs": [
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       "      <td>0.795828</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.347030</td>\n",
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       "      <td>1.994331</td>\n",
       "      <td>0.839440</td>\n",
       "      <td>2.075411</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "      <td>0.161218</td>\n",
       "      <td>-0.301803</td>\n",
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       "      <th>7</th>\n",
       "      <td>-0.785090</td>\n",
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       "      <td>0.339128</td>\n",
       "      <td>0.105904</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.635949</td>\n",
       "      <td>0.528157</td>\n",
       "      <td>-0.363242</td>\n",
       "      <td>0.105904</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.176332</td>\n",
       "      <td>0.528157</td>\n",
       "      <td>1.646661</td>\n",
       "      <td>-1.560768</td>\n",
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      "text/plain": [
       "          A         B         C         D\n",
       "0  0.741641  0.763065  1.255695 -0.224203\n",
       "1 -1.220634  0.178134 -0.612061  0.795828\n",
       "2 -0.126192 -0.349282 -0.695926  0.347030\n",
       "3 -0.834437 -1.251355 -1.305020 -1.632753\n",
       "4 -0.647919  1.994331  0.839440  2.075411\n",
       "5 -1.261834  0.304121  0.346287  1.348494\n",
       "6  2.412926  0.304121  0.161218 -0.301803\n",
       "7 -0.785090  0.041763  0.339128  0.105904\n",
       "8  0.635949  0.528157 -0.363242  0.105904\n",
       "9 -0.176332  0.528157  1.646661 -1.560768"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_imputer = SimpleImputer(strategy='mean')\n",
    "\n",
    "result_mean_imputer=mean_imputer.fit_transform(df)\n",
    "\n",
    "pd.DataFrame(result_mean_imputer, columns=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98606df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "column_trans = ColumnTransformer(\n",
    "[('A', SimpleImputer(strategy='mean'), [0]),\n",
    " ('B', SimpleImputer(strategy='constant', fill_value=29), [1])],\n",
    "remainder='passthrough')\n",
    "\n",
    "X=pd.DataFrame(column_trans.fit_transform(df),columns=list('ABCD'))\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "228893f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_trans = ColumnTransformer(\n",
    "[('A', SimpleImputer(strategy='mean'), [0]),\n",
    " ('B', SimpleImputer(strategy='constant', fill_value=50), [1]),\n",
    "('C',SimpleImputer(strategy='most_frequent'),[2])],\n",
    "remainder='passthrough')\n",
    "\n",
    "Y=pd.DataFrame(column_trans.fit_transform(df),columns=list('ABCD'))\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7f38a08",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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