ML_AI_1_Transform.ipynb

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   "execution_count": 14,
   "id": "c7704ed3-6fa1-469d-bca8-4324e6932876",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e47d0def-bfb4-40af-8061-e80de4d5b82b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d5d50408-500a-4c48-9f34-8ec3e72be0d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int = df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4ef583-c262-45ef-9d2e-91de1d890674",
   "metadata": {},
   "source": [
    "جایگذاری با مقدار صحیح"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e9b898fb-52a2-4fcc-8418-dcfe9a6fb5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int[\"Sex\"].replace('female',0, inplace=True)\n",
    "df_int[\"Sex\"].replace('male',1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "062a027c-3ef8-4089-964c-2156ff7c2649",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings/Spouses Aboard</th>\n",
       "      <th>Parents/Children Aboard</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. Owen Harris Braund</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Laina Heikkinen</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs. Jacques Heath (Lily May Peel) Futrelle</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <td>3</td>\n",
       "      <td>Mr. William Henry Allen</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Rev. Juozas Montvila</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
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       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss. Margaret Edith Graham</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Catherine Helen Johnston</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
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       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr. Karl Howell Behr</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. Patrick Dooley</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
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      "text/plain": [
       "     Survived  Pclass                                               Name  Sex  \\\n",
       "0           0       3                             Mr. Owen Harris Braund    1   \n",
       "1           1       1  Mrs. John Bradley (Florence Briggs Thayer) Cum...    0   \n",
       "2           1       3                              Miss. Laina Heikkinen    0   \n",
       "3           1       1        Mrs. Jacques Heath (Lily May Peel) Futrelle    0   \n",
       "4           0       3                            Mr. William Henry Allen    1   \n",
       "..        ...     ...                                                ...  ...   \n",
       "882         0       2                               Rev. Juozas Montvila    1   \n",
       "883         1       1                        Miss. Margaret Edith Graham    0   \n",
       "884         0       3                     Miss. Catherine Helen Johnston    0   \n",
       "885         1       1                               Mr. Karl Howell Behr    1   \n",
       "886         0       3                                 Mr. Patrick Dooley    1   \n",
       "\n",
       "      Age  Siblings/Spouses Aboard  Parents/Children Aboard     Fare  \n",
       "0    22.0                        1                        0   7.2500  \n",
       "1    38.0                        1                        0  71.2833  \n",
       "2    26.0                        0                        0   7.9250  \n",
       "3    35.0                        1                        0  53.1000  \n",
       "4    35.0                        0                        0   8.0500  \n",
       "..    ...                      ...                      ...      ...  \n",
       "882  27.0                        0                        0  13.0000  \n",
       "883  19.0                        0                        0  30.0000  \n",
       "884   7.0                        1                        2  23.4500  \n",
       "885  26.0                        0                        0  30.0000  \n",
       "886  32.0                        0                        0   7.7500  \n",
       "\n",
       "[887 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "779242ae-623e-4a6e-93bb-120163a88a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887, 8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_int.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921619c2-c7fb-4a60-8aa9-53a437848d08",
   "metadata": {},
   "source": [
    "One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d00a4d8-e7b2-494e-aa91-738647c9196a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/parham/anaconda3/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(887, 9)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_features_string = ['Sex']\n",
    "\n",
    "transformer = make_column_transformer(\n",
    "    (OneHotEncoder(), list_of_features_string),\n",
    "    remainder='passthrough')\n",
    "\n",
    "transformer_one_hot = transformer.fit_transform(df)\n",
    "transformer_one_hot_df = pd.DataFrame(transformer_one_hot, columns=transformer.get_feature_names())\n",
    "transformer_one_hot_df.shape"
   ]
  }
 ],
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