Lecture2.ipynb

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  "metadata": {
    "colab": {
      "name": "Lecture2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMmuAap9xa81YXgmNB2+t3G",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Santigs/DS-Course/blob/main/Lecture2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbPnNgCfiVQh"
      },
      "source": [
        "#import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns; sns.set()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TAH8zhYwSZ-M"
      },
      "source": [
        "https://numpy.org/ \\\n",
        "https://pandas.pydata.org/ \\\n",
        "https://matplotlib.org/ \\\n",
        "https://github.com/Santigs/DS-Course\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11Jrn0MYaAH-"
      },
      "source": [
        "# Basic operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6i0oUDYsaGug"
      },
      "source": [
        "data = {\n",
        "    'apples': [3, 2, 0, 1], \n",
        "    'oranges': [0, 3, 7, 2]\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_zoWdSQaKir",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "outputId": "8a7660ad-2875-4d45-be01-d71cf2702923"
      },
      "source": [
        "purchases = pd.DataFrame(data)\n",
        "purchases"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apples</th>\n",
              "      <th>oranges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   apples  oranges\n",
              "0       3        0\n",
              "1       2        3\n",
              "2       0        7\n",
              "3       1        2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlAqPBc7aN9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "outputId": "5899330f-6dab-404c-c1eb-6bbf10cdd6fe"
      },
      "source": [
        "purchases = pd.DataFrame(data, index=['June', 'Robert', 'Lily', 'David'])\n",
        "purchases"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apples</th>\n",
              "      <th>oranges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>June</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Robert</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lily</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>David</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        apples  oranges\n",
              "June         3        0\n",
              "Robert       2        3\n",
              "Lily         0        7\n",
              "David        1        2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Giy0EC7uaQms",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ca07474-0fbe-4207-fa4b-ad4cc10fe911"
      },
      "source": [
        "purchases.loc['Lily']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "apples     0\n",
              "oranges    7\n",
              "Name: Lily, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DV7V3NfsaT04",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c732a485-3f47-48c2-95e4-2e6a31c0a5a9"
      },
      "source": [
        "purchases.iloc[2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "apples     0\n",
              "oranges    7\n",
              "Name: Lily, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "It40-e870K_q"
      },
      "source": [
        "# Importing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vv2hhCTYiZEV"
      },
      "source": [
        "#create dataframe with data from csv local file\n",
        "df = pd.read_csv(\"Titanic.csv\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZlr0GDIoHT2"
      },
      "source": [
        "#create dataframe with data from csv raw github file\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/Santigs/DS-Course/main/Titanic.csv')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIiOh4Nf58ka"
      },
      "source": [
        "Dataset description\n",
        "https://www.kaggle.com/c/titanic/data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0ANWXk_0fNt"
      },
      "source": [
        "# Basic dataframe information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nx62Iwbeoev5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a393b92d-0816-403c-9179-6e5774047286"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "quxOXOqso0OP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c26ad2e-8956-4c6d-86c2-7763cbcb16ec"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3tV6iVSpgks",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a00bf31-7434-4f87-cf54-f5b505ddf388"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIMg9Cofp81t"
      },
      "source": [
        "df.tail(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exJ3ptOf0zb9"
      },
      "source": [
        "# Basic numerical information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YnTqriup8wz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "dc05a94e-407b-41ca-8967-7676346521aa"
      },
      "source": [
        "#shows only numerical variables\n",
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\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>668.500000</td>\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>891.000000</td>\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>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVOOCs0_qxk1"
      },
      "source": [
        "#shows non numerical variables\n",
        "df.describe(include=[bool,object])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOxDWi91reVT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12918031-4a6e-44ce-98d5-0fb90ca6084a"
      },
      "source": [
        "df.Fare.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32.2042079685746"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtErVYP6rgHc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9e2d6aa-12f4-498e-d837-cea0a3925578"
      },
      "source": [
        "df[df['Survived']==1].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    444.368421\n",
              "Survived         1.000000\n",
              "Pclass           1.950292\n",
              "Age             28.343690\n",
              "SibSp            0.473684\n",
              "Parch            0.464912\n",
              "Fare            48.395408\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxpcYcQyzjQg"
      },
      "source": [
        "# Filtering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hja9FGLfyBbf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "798e60dc-825a-4e3b-9f0d-bc75b7f7b8fd"
      },
      "source": [
        "df.Age"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      22.0\n",
              "1      38.0\n",
              "2      26.0\n",
              "3      35.0\n",
              "4      35.0\n",
              "       ... \n",
              "886    27.0\n",
              "887    19.0\n",
              "888     NaN\n",
              "889    26.0\n",
              "890    32.0\n",
              "Name: Age, Length: 891, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ElpqSj_CyKQb"
      },
      "source": [
        "df[['Pclass','Sex','Age']]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnx6YWKezOR-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "c7e3d8c5-fddb-47c1-96a3-6a2aff13d159"
      },
      "source": [
        "df.iloc[:,3:6]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  Name     Sex   Age\n",
              "0                              Braund, Mr. Owen Harris    male  22.0\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0\n",
              "2                               Heikkinen, Miss. Laina  female  26.0\n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0\n",
              "4                             Allen, Mr. William Henry    male  35.0\n",
              "..                                                 ...     ...   ...\n",
              "886                              Montvila, Rev. Juozas    male  27.0\n",
              "887                       Graham, Miss. Margaret Edith  female  19.0\n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN\n",
              "889                              Behr, Mr. Karl Howell    male  26.0\n",
              "890                                Dooley, Mr. Patrick    male  32.0\n",
              "\n",
              "[891 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ho9qpnEtrn7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a53db9d7-7e52-44c7-fa10-5b1e553268d9"
      },
      "source": [
        "df[(df['Survived'] == 1) & (df['Pclass'] == 1)]['Age'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8JOjfJUsLqU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c47951e-abcd-4d26-b6b5-08cee4e18e24"
      },
      "source": [
        "df[(df['Survived'] == 0) & (df['Pclass'] == 1)]['Age'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "71.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYlxPy7jHpNC"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwXy78grHpF7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "geJdTQHdw2IA"
      },
      "source": [
        "classes_name = {1 : 'Class I', 2 : 'Class II', 3:'Class III'}\n",
        "df_new=df.replace({'Pclass': classes_name})\n",
        "df_new.head(20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OoqOFNMG6R1e"
      },
      "source": [
        "# Grouping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXHqLYwC6VGV"
      },
      "source": [
        "survived_count = df.groupby('Survived')['Survived'].count()\n",
        "survived_count"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVEVJtDq6ctl"
      },
      "source": [
        "type(survived_count)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wshMqLaD624U"
      },
      "source": [
        "var_of_interest = ['Fare', 'Age']\n",
        "\n",
        "df.groupby(['Survived'])[var_of_interest].agg([np.mean, np.std, np.min, np.max])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15FWUVPslcK4"
      },
      "source": [
        "#Contingency table\n",
        "pd.crosstab(df['Survived'], df['Pclass'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQa16iNK9ZvO"
      },
      "source": [
        "# Basic Charts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "27sWmg0E7ajT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "27d96670-e7f5-4452-8c62-ed1450f9326f"
      },
      "source": [
        "plt.figure(figsize=(6,5))\n",
        "plt.bar(survived_count.index, survived_count.values)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "#plt.text(1,0,'hello', horizontalalignment='center', verticalalignment='center')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvrzcDP6pZku",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "bc72b7c9-a501-4af2-d5d2-7554f6d59811"
      },
      "source": [
        "plt.figure(figsize=(4,5))\n",
        "plt.bar(survived_count.index, survived_count.values)\n",
        "plt.title('Grouped by survival')\n",
        "plt.xticks([0,1],['Not survived', 'Survived'])\n",
        "for i, value in enumerate(survived_count.values):\n",
        "    plt.text(i, value-40, str(value), fontsize=18, color='white',\n",
        "             horizontalalignment='center', verticalalignment='center')\n",
        "    \n",
        "    \n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_WVh9Pd-axR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21358c05-f597-4a1f-c2da-0c53210c80bf"
      },
      "source": [
        "survived_sex = df.groupby('Sex')['Survived'].sum()\n",
        "survived_sex"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "female    233\n",
              "male      109\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqIDqlOxEEkc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "2c35e0d9-5ee5-4d01-cf46-1f9c9394d296"
      },
      "source": [
        "plt.figure(figsize=(4,5))\n",
        "plt.bar(survived_sex.index, survived_sex.values)\n",
        "plt.title('Survived female and male')\n",
        "for i, value in enumerate(survived_sex.values):\n",
        "    plt.text(i, value-20, str(value), fontsize=12, color='white',\n",
        "             horizontalalignment='center', verticalalignment='center')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZdmgIbzJ4hn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "135b02b4-5810-47b8-a3a4-a47627cbc29d"
      },
      "source": [
        "pclass_count = df.groupby('Pclass')['Pclass'].count()\n",
        "\n",
        "plt.figure(figsize=(7,7))\n",
        "plt.title('Grouped by pclass')\n",
        "plt.pie(pclass_count.values, labels=['Class 1', 'Class 2', 'Class 3'], autopct='%1.1f%%', textprops={'fontsize':13})\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBJ8zhw4L4Vk"
      },
      "source": [
        "# Create a pie chart with the embarking city distribution\n",
        "Make the chart bigger\\\n",
        "Show the city names (hint, you can find them in kaggle)\\\n",
        "Bonus. Explode Cherbourg wedge and change font color"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMO2-JJZL-1Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "a65155d4-3483-4569-f8ff-f8259e90bb0e"
      },
      "source": [
        "pcity_count = df.groupby('Embarked')['Embarked'].count()\n",
        "\n",
        "plt.figure(figsize=(7,7))\n",
        "plt.title('Grouped by City')\n",
        "\n",
        "myexplode = [0.2, 0, 0]\n",
        "plt.pie(pcity_count.values, labels=['Cherbourg', 'Queenstown', 'Southampton'], autopct='%1.1f%%', textprops={'fontsize':15, 'color':'blue'}, explode = myexplode)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tsV9ZgtSoR3"
      },
      "source": [
        "### Age distribution chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rz7fYGHZSr4O"
      },
      "source": [
        "ages = df[df['Age'].notnull()]['Age'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SIgOgGrTPCS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99f01cde-1678-47a1-8abd-5408df6b76fa"
      },
      "source": [
        "ages_hist = np.histogram(ages, bins=[0,10,20,30,40,50,60,70,80,90])\n",
        "ages_hist"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 62, 102, 220, 167,  89,  48,  19,   6,   1]),\n",
              " array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GP5DE5Q7TPyl"
      },
      "source": [
        "ages_hist_labels = ['0–10', '11–20', '21–30', '31–40', '41–50', '51–60', '61–70', '71–80', '81–90']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3v7net5uTTI1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "outputId": "643a70d0-c5dd-4a7a-e555-c7ef3d56013c"
      },
      "source": [
        "plt.figure(figsize=(7,7))\n",
        "plt.title('Age distribution')\n",
        "plt.bar(ages_hist_labels, ages_hist[0])\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('No of passenger')\n",
        "for i, bin in zip(ages_hist[0], range(9)):\n",
        "    plt.text(bin, i+5, str(int(i)), fontsize=12,\n",
        "             horizontalalignment='center', verticalalignment='center')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUknQwbEe6bJ"
      },
      "source": [
        "Create a histogram with fare values"
      ]
    }
  ]
}