Pandas/Categorical-Datatype.ipynb

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   "id": "d2fdd151",
   "metadata": {},
   "source": [
    "# Daily-Dose-of-Data-Science\n",
    "\n",
    "[Daily Dose of Data Science](https://avichawla.substack.com) is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless. \n",
    "\n",
    "Author: Avi Chawla\n",
    "\n",
    "[Medium](https://medium.com/@avi_chawla) | [LinkedIn](https://www.linkedin.com/in/avi-chawla/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30024a0c",
   "metadata": {},
   "source": [
    "# Define the Correct DataType for Categorical Columns\n",
    "\n",
    "Post Link: [Substack](https://avichawla.substack.com/p/define-the-correct-datatype-for-categorical)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/posts/avi-chawla_datascience-python-pandas-activity-6984052112275959809-QfyU?utm_source=share&utm_medium=member_desktop)"
   ]
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   "execution_count": 1,
   "id": "8b7de67f",
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     "end_time": "2022-12-01T18:54:58.427271Z",
     "start_time": "2022-12-01T18:54:57.544271Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "659d3182",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-01T18:54:58.442084Z",
     "start_time": "2022-12-01T18:54:58.428451Z"
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   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
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       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Gender   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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   ],
   "source": [
    "df = pd.read_csv(\"../_Datasets/titanic_train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f07ea938",
   "metadata": {
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     "end_time": "2022-12-01T18:55:03.402973Z",
     "start_time": "2022-12-01T18:55:03.393336Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9f032d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-01T18:55:10.371713Z",
     "start_time": "2022-12-01T18:55:10.354444Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 1500 entries, 0 to 1499\n",
      "Series name: Gender\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "1500 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 90.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.Gender.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01b9bdd3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-01T18:55:18.940951Z",
     "start_time": "2022-12-01T18:55:18.929446Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"Gender\"] = df.Gender.astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc76b10d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-01T18:55:25.151716Z",
     "start_time": "2022-12-01T18:55:25.137139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 1500 entries, 0 to 1499\n",
      "Series name: Gender\n",
      "Non-Null Count  Dtype   \n",
      "--------------  -----   \n",
      "1500 non-null   category\n",
      "dtypes: category(1)\n",
      "memory usage: 1.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.Gender.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc87785f",
   "metadata": {},
   "source": [
    "## Read my full Medium article on Memory Optimization: https://towardsdatascience.com/seven-killer-memory-optimization-techniques-every-pandas-user-should-know-64707348ab20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1468c48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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