Untitled0.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mohitagarwal1/datavisualization/blob/master/Untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWskzcYDkMvt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "496fszuFkgZT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#now loading data from url\n",
        "data=pd.read_csv('http://13.234.66.67/summer19/datasets/customer.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGqGaNUyleFL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "273009fd-6e97-49f9-a561-46938d61f580"
      },
      "source": [
        "#checking rows and cloumns \n",
        "data.info()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            "CustomerID                200 non-null int64\n",
            "Genre                     200 non-null object\n",
            "Age                       200 non-null int64\n",
            "Annual Income (k$)        200 non-null int64\n",
            "Spending Score (1-100)    200 non-null int64\n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKfe6DEYlrC8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "1bc52366-e013-4d1c-bb67-4e6825e0a421"
      },
      "source": [
        "# printing top 5 rows only\n",
        "data.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SdllK9zmC3r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5b4WeaamkRo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "2f088efb-01eb-4dc1-e28a-cfa42c624fc2"
      },
      "source": [
        "# any particular column\n",
        "data['Age'].head()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    19\n",
              "1    21\n",
              "2    20\n",
              "3    23\n",
              "4    31\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXBaUSqHm6FG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "bcb6740f-09cf-4bb7-e1fc-8f5d4d1ad206"
      },
      "source": [
        "#Range of columns\n",
        "#data.iloc[rows,columns]\n",
        "data.iloc[0:4,0:5]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZhZ5U0_oqc-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f195a59c-6004-4034-a548-64c3cc33b0cc"
      },
      "source": [
        "data.iloc[0:,[1,3]]"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Genre</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Female</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Female</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Female</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Female</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Female</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Male</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Male</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Female</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Male</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Male</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Male</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Female</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Male</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Female</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Male</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Female</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Male</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Male</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Male</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Male</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Male</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Female</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Female</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Male</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Male</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Male</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>Male</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>Female</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>Female</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>Male</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>Female</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>Female</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>Male</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>Female</td>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>Male</td>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>Female</td>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>Female</td>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Female</td>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>Female</td>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>Male</td>\n",
              "      <td>113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>Female</td>\n",
              "      <td>113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>Female</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>Female</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>Female</td>\n",
              "      <td>126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>Male</td>\n",
              "      <td>126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>Male</td>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Male</td>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Genre  Annual Income (k$)\n",
              "0      Male                  15\n",
              "1      Male                  15\n",
              "2    Female                  16\n",
              "3    Female                  16\n",
              "4    Female                  17\n",
              "5    Female                  17\n",
              "6    Female                  18\n",
              "7    Female                  18\n",
              "8      Male                  19\n",
              "9    Female                  19\n",
              "10     Male                  19\n",
              "11   Female                  19\n",
              "12   Female                  20\n",
              "13   Female                  20\n",
              "14     Male                  20\n",
              "15     Male                  20\n",
              "16   Female                  21\n",
              "17     Male                  21\n",
              "18     Male                  23\n",
              "19   Female                  23\n",
              "20     Male                  24\n",
              "21     Male                  24\n",
              "22   Female                  25\n",
              "23     Male                  25\n",
              "24   Female                  28\n",
              "25     Male                  28\n",
              "26   Female                  28\n",
              "27     Male                  28\n",
              "28   Female                  29\n",
              "29   Female                  29\n",
              "..      ...                 ...\n",
              "170    Male                  87\n",
              "171    Male                  87\n",
              "172    Male                  87\n",
              "173    Male                  87\n",
              "174  Female                  88\n",
              "175  Female                  88\n",
              "176    Male                  88\n",
              "177    Male                  88\n",
              "178    Male                  93\n",
              "179    Male                  93\n",
              "180  Female                  97\n",
              "181  Female                  97\n",
              "182    Male                  98\n",
              "183  Female                  98\n",
              "184  Female                  99\n",
              "185    Male                  99\n",
              "186  Female                 101\n",
              "187    Male                 101\n",
              "188  Female                 103\n",
              "189  Female                 103\n",
              "190  Female                 103\n",
              "191  Female                 103\n",
              "192    Male                 113\n",
              "193  Female                 113\n",
              "194  Female                 120\n",
              "195  Female                 120\n",
              "196  Female                 126\n",
              "197    Male                 126\n",
              "198    Male                 137\n",
              "199    Male                 137\n",
              "\n",
              "[200 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zwj3P88xpcEo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "97959617-194f-4d58-a80c-9b86d61c532a"
      },
      "source": [
        "# all description \n",
        "data.describe()"
      ],
      "execution_count": 41,
      "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>CustomerID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
              "count  200.000000  200.000000          200.000000              200.000000\n",
              "mean   100.500000   38.850000           60.560000               50.200000\n",
              "std     57.879185   13.969007           26.264721               25.823522\n",
              "min      1.000000   18.000000           15.000000                1.000000\n",
              "25%     50.750000   28.750000           41.500000               34.750000\n",
              "50%    100.500000   36.000000           61.500000               50.000000\n",
              "75%    150.250000   49.000000           78.000000               73.000000\n",
              "max    200.000000   70.000000          137.000000               99.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDQIdGLMp6LL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# now importing data visulaization library\n",
        "import seaborn as sb\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yInFB3q0sC3k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "34882684-7671-4f20-9489-390f73ba45ba"
      },
      "source": [
        "# count and plot age column \n",
        "sb.countplot(data['Age'])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d5fd6ba20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7tIrJ+m/U5vVUbXrX12KWA3Nq/XBP1i3Ntlcx/1Qrfx74Tpa/BfhLTmshcD/wzaw7Ocs9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\niBROiUBEpHBKBCIihfv/REW3tH1aKQ0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_DXhNdARsee6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ap34L6jtglL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "distance=[5,12,20] #this is in kms\n",
        "time=[2,5,8] #this is time taken to cover distance(in hr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9rtt810t6e_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "e30c4689-ba5e-4aed-b9c7-4e15c6b199a2"
      },
      "source": [
        "# Now designing x-axis\n",
        "plt.xlabel(\"Distance\")\n",
        "plt.ylabel(\"Time\")\n",
        "plt.scatter(distance,time,label=\"Prakhar performance\")     #to show dots and label for name\n",
        "plt.plot(distance,time)      #to show lines\n",
        "plt.grid(color='blue')\n",
        "plt.legend()    #to show label\n",
        "plt.show()   #showing graph"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hK0vtOpTLpAWb2Lg3m+GxbZk5ojuN6ulBVEp5gha+soSI8M7qPfz10xQC/Q0v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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oAzCaSiiuqza",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "21612eeb-023d-487b-92d2-ad53bb41f165"
      },
      "source": [
        "plt.bar(distance,time)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 3 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LwL0M37zHTDvTsmwfAB4E7gP+GfjlKWa5geFc+08YFs47gFcw/Cb9YeDfgWNnJNcjDH9+\n+O7R6R9n5TVbcftu4LhZyMWwqK8bvdd2AufMymsGnA3sAO5hOK/8u1PIdTbDaZB7l72v3jYL64B7\nTkpSY9xzUpIaY3FLUmMsbklqjMUtSY2xuCWpMRa3JDXG4pakxljcktSY/wOaNQ2R3dNw0QAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-stHlcCyB4y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "8740a38b-8918-46cd-e9b4-bd72d89c1f6b"
      },
      "source": [
        "plt.xlabel('gender')\n",
        "plt.ylabel('Age')\n",
        "plt.grid(color='green')\n",
        "plt.bar(data['Genre'],data['Age'])\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5ErgGuDjJbuDNXVuSNEKr+tpwVV0xx1sX9bVPSdLh88piSWqcQSBJjTMIJKlxBoEkNc4g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\n+OsFClndAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dw-6MuBRzsrC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}