Explore_DataFrame.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **[YBI Foundation](https://www.ybifoundation.org/)**"
      ],
      "metadata": {
        "id": "6g0nl2E54Yha"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**[Join Telegram to Get Updates of all Future FREE Bootcamps and Courses](https://telegram.me/ybif_ybifoundation)**"
      ],
      "metadata": {
        "id": "1TUiygq04Yhb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Explore DataFrame**"
      ],
      "metadata": {
        "id": "sc8j2Wyd4Yhc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**[Watch Video Tutorial](https://www.youtube.com/c/YBIFoundation?sub_confirmation=1)**"
      ],
      "metadata": {
        "id": "5hgeR8Zd4Yhc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import library\n",
        "import pandas as pd"
      ],
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        "id": "iJ1MKH2X4Yhh"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# read data\n",
        "titanic = pd.read_csv('https://github.com/YBIFoundation/Dataset/raw/main/Titanic.csv')"
      ],
      "metadata": {
        "id": "FlmuZ-eY4Yhi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# display first 5 rows\n",
        "titanic.head()"
      ],
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              "   pclass  survived                                             name     sex  \\\n",
              "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
              "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
              "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
              "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
              "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
              "\n",
              "     age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
              "0  29.00      0      0   24160  211.3375       B5        S    2    NaN   \n",
              "1   0.92      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
              "2   2.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
              "3  30.00      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
              "4  25.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
              "\n",
              "                         home.dest  \n",
              "0                     St Louis, MO  \n",
              "1  Montreal, PQ / Chesterville, ON  \n",
              "2  Montreal, PQ / Chesterville, ON  \n",
              "3  Montreal, PQ / Chesterville, ON  \n",
              "4  Montreal, PQ / Chesterville, ON  "
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              "      <td>Allison, Miss. Helen Loraine</td>\n",
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              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
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              "      <td>135.0</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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              "      <th>4</th>\n",
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      "cell_type": "code",
      "source": [
        "# display last 5 rows\n",
        "titanic.tail()"
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              "      pclass  survived                       name     sex   age  sibsp  parch  \\\n",
              "1304       3         0       Zabour, Miss. Hileni  female  14.5      1      0   \n",
              "1305       3         0      Zabour, Miss. Thamine  female   NaN      1      0   \n",
              "1306       3         0  Zakarian, Mr. Mapriededer    male  26.5      0      0   \n",
              "1307       3         0        Zakarian, Mr. Ortin    male  27.0      0      0   \n",
              "1308       3         0         Zimmerman, Mr. Leo    male  29.0      0      0   \n",
              "\n",
              "      ticket     fare cabin embarked boat   body home.dest  \n",
              "1304    2665  14.4542   NaN        C  NaN  328.0       NaN  \n",
              "1305    2665  14.4542   NaN        C  NaN    NaN       NaN  \n",
              "1306    2656   7.2250   NaN        C  NaN  304.0       NaN  \n",
              "1307    2670   7.2250   NaN        C  NaN    NaN       NaN  \n",
              "1308  315082   7.8750   NaN        S  NaN    NaN       NaN  "
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5907eaee-4f09-4714-8094-0d6ce1399945');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display info\n",
        "titanic.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uzZRAY5N4oIZ",
        "outputId": "b3a183c7-3403-48f3-d154-5ca7f76693e7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1309 entries, 0 to 1308\n",
            "Data columns (total 14 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   pclass     1309 non-null   int64  \n",
            " 1   survived   1309 non-null   int64  \n",
            " 2   name       1309 non-null   object \n",
            " 3   sex        1309 non-null   object \n",
            " 4   age        1046 non-null   float64\n",
            " 5   sibsp      1309 non-null   int64  \n",
            " 6   parch      1309 non-null   int64  \n",
            " 7   ticket     1309 non-null   object \n",
            " 8   fare       1308 non-null   float64\n",
            " 9   cabin      295 non-null    object \n",
            " 10  embarked   1307 non-null   object \n",
            " 11  boat       486 non-null    object \n",
            " 12  body       121 non-null    float64\n",
            " 13  home.dest  745 non-null    object \n",
            "dtypes: float64(3), int64(4), object(7)\n",
            "memory usage: 143.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "info() function prints information about a DataFrame including the index dtype and columns, non-null values and memory usage."
      ],
      "metadata": {
        "id": "wFTbVDah42YQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# display summary statistics of numerical columns\n",
        "titanic.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "JQ_zYmDJ4oLu",
        "outputId": "0f052ca1-139f-4896-fb06-720ac1e61cba"
      },
      "execution_count": 6,
      "outputs": [
        {
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              "            pclass     survived          age        sibsp        parch  \\\n",
              "count  1309.000000  1309.000000  1046.000000  1309.000000  1309.000000   \n",
              "mean      2.294882     0.381971    29.881138     0.498854     0.385027   \n",
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              "\n",
              "              fare        body  \n",
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              "std      51.758668   97.696922  \n",
              "min       0.000000    1.000000  \n",
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              "      <th>mean</th>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "    <tr>\n",
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              "      <td>1.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
          "execution_count": 6
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    {
      "cell_type": "code",
      "source": [
        "# display summary statistics of all columns\n",
        "titanic.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "6fdaGpCi4oOy",
        "outputId": "1132c299-b005-45c9-811d-31d0f011ee93"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             pclass     survived                  name   sex          age  \\\n",
              "count   1309.000000  1309.000000                  1309  1309  1046.000000   \n",
              "unique          NaN          NaN                  1307     2          NaN   \n",
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              "freq            NaN          NaN                     2   843          NaN   \n",
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              "std        0.837836     0.486055                   NaN   NaN    14.413493   \n",
              "min        1.000000     0.000000                   NaN   NaN     0.170000   \n",
              "25%        2.000000     0.000000                   NaN   NaN    21.000000   \n",
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              "75%        3.000000     1.000000                   NaN   NaN    39.000000   \n",
              "max        3.000000     1.000000                   NaN   NaN    80.000000   \n",
              "\n",
              "              sibsp        parch    ticket         fare        cabin embarked  \\\n",
              "count   1309.000000  1309.000000      1309  1308.000000          295     1307   \n",
              "unique          NaN          NaN       929          NaN          186        3   \n",
              "top             NaN          NaN  CA. 2343          NaN  C23 C25 C27        S   \n",
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              "\n",
              "       boat        body     home.dest  \n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.895800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.275000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>328.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ce70871-fb23-48ac-a7cf-ca90706f2598')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5ce70871-fb23-48ac-a7cf-ca90706f2598 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5ce70871-fb23-48ac-a7cf-ca90706f2598');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display shape\n",
        "titanic.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0j079MGC4oR3",
        "outputId": "518f9cca-106b-4081-d002-a70c200d4c0e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1309, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display column labels\n",
        "titanic.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MPV3lEov4oVF",
        "outputId": "1392fe6a-44f6-484c-ca5a-e0fe349bb111"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n",
              "       'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# select a column as a series\n",
        "titanic['sex']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aiCkW9-m5b2T",
        "outputId": "daa0e77a-d5ce-49c3-a992-81a77116df66"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       female\n",
              "1         male\n",
              "2       female\n",
              "3         male\n",
              "4       female\n",
              "         ...  \n",
              "1304    female\n",
              "1305    female\n",
              "1306      male\n",
              "1307      male\n",
              "1308      male\n",
              "Name: sex, Length: 1309, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic['sex'].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TR_o_65v6POd",
        "outputId": "e561c5fd-2f7b-48d3-f2c8-08c5a3e63b1d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1309,)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# select a column as a dataframe\n",
        "titanic[['sex']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "JsRmzjlS6LcQ",
        "outputId": "04618adf-f8c2-4082-e9d4-e90f769c630e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         sex\n",
              "0     female\n",
              "1       male\n",
              "2     female\n",
              "3       male\n",
              "4     female\n",
              "...      ...\n",
              "1304  female\n",
              "1305  female\n",
              "1306    male\n",
              "1307    male\n",
              "1308    male\n",
              "\n",
              "[1309 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4c1c503-baca-475b-9813-88d7a31aa511\">\n",
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              "<style scoped>\n",
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              "      <th>sex</th>\n",
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              "      <th>4</th>\n",
              "      <td>female</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>1304</th>\n",
              "      <td>female</td>\n",
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              "      <th>1305</th>\n",
              "      <td>female</td>\n",
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              "      <th>1306</th>\n",
              "      <td>male</td>\n",
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              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>male</td>\n",
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              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>male</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1309 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4c1c503-baca-475b-9813-88d7a31aa511')\"\n",
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              "  </svg>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a4c1c503-baca-475b-9813-88d7a31aa511');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic[['sex']].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHUla3s86Sv5",
        "outputId": "397ed470-c69b-46e7-e4f1-1d582d0f5d6c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1309, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# unique categories in a column\n",
        "titanic['sex'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HES3mWV25q1T",
        "outputId": "0052bb58-5a25-4f05-bd3a-f17c65d0bbf7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['female', 'male'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# number of unique categories in a column\n",
        "titanic['sex'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MgBihKy45Ura",
        "outputId": "d396747d-7b11-46b9-acb0-be90d171c561"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# categories wise number of observations in a column\n",
        "titanic['sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72690XDP5UuS",
        "outputId": "26b4c021-b1c1-44e7-883f-5213e008325b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      843\n",
              "female    466\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# count of missing values\n",
        "titanic.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzHaSkbs5Uxc",
        "outputId": "7fe51e8b-350d-497f-a838-25e5d882c01f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass          0\n",
              "survived        0\n",
              "name            0\n",
              "sex             0\n",
              "age           263\n",
              "sibsp           0\n",
              "parch           0\n",
              "ticket          0\n",
              "fare            1\n",
              "cabin        1014\n",
              "embarked        2\n",
              "boat          823\n",
              "body         1188\n",
              "home.dest     564\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZNjN9Jg5U0c",
        "outputId": "e762ae8e-fd75-47a7-9ced-4291a1f49b26"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass          0\n",
              "survived        0\n",
              "name            0\n",
              "sex             0\n",
              "age           263\n",
              "sibsp           0\n",
              "parch           0\n",
              "ticket          0\n",
              "fare            1\n",
              "cabin        1014\n",
              "embarked        2\n",
              "boat          823\n",
              "body         1188\n",
              "home.dest     564\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Don't Forget to Star and Watch on GitHub to Receive Updates**\n",
        "**Action 1: ⭐Star Repository as it make easy for you to find it again. You can see all the repositories and topics you have starred by going to your stars page.**\n",
        "\n",
        "**Action 2: 👁 Watch Repository and get notified of all future updates and activities in this repository.**\n",
        "\n",
        "**[Click Here to Visit Fundamental Repository on GitHub](https://github.com/YBIFoundation/Fundamentals)**"
      ],
      "metadata": {
        "id": "r8aulDQM4Yhx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "3A_cKjSY4Yhx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Don't Forget to Upvote NoteBook on Kaggle and Receive Updates** \n",
        "**[Click Here to Visit Kaggle](https://www.kaggle.com/ybifoundation)**"
      ],
      "metadata": {
        "id": "Kd4k3sk54Yhz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "_EZocs8J4Yhz"
      }
    }
  ]
}