Final_DSBDA9.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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        "df=sns.load_dataset('titanic')\n",
        "df"
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              "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
              "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
              "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
              "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
              "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
              "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
              "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
              "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
              "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
              "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
              "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
              "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
              "\n",
              "       who  adult_male deck  embark_town alive  alone  \n",
              "0      man        True  NaN  Southampton    no  False  \n",
              "1    woman       False    C    Cherbourg   yes  False  \n",
              "2    woman       False  NaN  Southampton   yes   True  \n",
              "3    woman       False    C  Southampton   yes  False  \n",
              "4      man        True  NaN  Southampton    no   True  \n",
              "..     ...         ...  ...          ...   ...    ...  \n",
              "886    man        True  NaN  Southampton    no   True  \n",
              "887  woman       False    B  Southampton   yes   True  \n",
              "888  woman       False  NaN  Southampton    no  False  \n",
              "889    man        True    C    Cherbourg   yes   True  \n",
              "890    man        True  NaN   Queenstown    no   True  \n",
              "\n",
              "[891 rows x 15 columns]"
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              "<p>891 rows × 15 columns</p>\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 15 columns):\n",
            " #   Column       Non-Null Count  Dtype   \n",
            "---  ------       --------------  -----   \n",
            " 0   survived     891 non-null    int64   \n",
            " 1   pclass       891 non-null    int64   \n",
            " 2   sex          891 non-null    object  \n",
            " 3   age          714 non-null    float64 \n",
            " 4   sibsp        891 non-null    int64   \n",
            " 5   parch        891 non-null    int64   \n",
            " 6   fare         891 non-null    float64 \n",
            " 7   embarked     889 non-null    object  \n",
            " 8   class        891 non-null    category\n",
            " 9   who          891 non-null    object  \n",
            " 10  adult_male   891 non-null    bool    \n",
            " 11  deck         203 non-null    category\n",
            " 12  embark_town  889 non-null    object  \n",
            " 13  alive        891 non-null    object  \n",
            " 14  alone        891 non-null    bool    \n",
            "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
            "memory usage: 80.7+ KB\n"
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      "source": [
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              "         survived      pclass         age       sibsp       parch        fare\n",
              "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
              "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
              "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
              "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
              "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
              "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
              "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
              "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
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              "      <th>mean</th>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
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              "      <td>0.523008</td>\n",
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              "      <td>1.102743</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48b82c06-36c7-4d93-ab8f-9bc03a1ae3b4')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      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-48b82c06-36c7-4d93-ab8f-9bc03a1ae3b4 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-48b82c06-36c7-4d93-ab8f-9bc03a1ae3b4');\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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MH6bEKYMQab_",
        "outputId": "8d37ebbb-026b-4afe-a1ba-df5c27dd2086"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived         0\n",
              "pclass           0\n",
              "sex              0\n",
              "age            177\n",
              "sibsp            0\n",
              "parch            0\n",
              "fare             0\n",
              "embarked         2\n",
              "class            0\n",
              "who              0\n",
              "adult_male       0\n",
              "deck           688\n",
              "embark_town      2\n",
              "alive            0\n",
              "alone            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='sex',y='age',data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "oEc2vmkZQczg",
        "outputId": "d338d408-aabc-4472-9a25-200df0f7ff9f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sex', ylabel='age'>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='sex',y='age',data=df,hue='survived')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "2BBY46J_RZ6r",
        "outputId": "356bc77f-942c-445b-fe3b-12422ba2c4cd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sex', ylabel='age'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusion\n",
        "\n",
        "\n",
        "1.   From our box plot regarding age, we've seen some outlier who fail to fit in our dataset.\n",
        "2.   When we look at the survival rate of each gender, we can say that females had the most survival rate as compared to men.\n"
      ],
      "metadata": {
        "id": "tz3QMzUlRmK8"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e9w_Xi43R5f7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}