Project 10.3 Data Visualization with Mall data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project 10.3: Data Visualization with Mall data.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljOw7iK7WZug",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "1c89ab92-f411-40d1-9180-d31368057a37"
      },
      "source": [
        "import pandas as pd\n",
        "m=pd.read_csv(\"/content/Mall_Customers.csv\")\n",
        "m"
      ],
      "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>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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 5 columns</p>\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\n",
              "..          ...     ...  ...                 ...                     ...\n",
              "195         196  Female   35                 120                      79\n",
              "196         197  Female   45                 126                      28\n",
              "197         198    Male   32                 126                      74\n",
              "198         199    Male   32                 137                      18\n",
              "199         200    Male   30                 137                      83\n",
              "\n",
              "[200 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuKZTeY6WqXQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "2f3b6e3e-eb28-4d89-a9de-8470c7117e74"
      },
      "source": [
        "m.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CustomerID', 'Genre', 'Age', 'Annual Income (k$)',\n",
              "       'Spending Score (1-100)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-q0bf8oSr_Wv",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "Plotting countplot for the column 'Genre'\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBp7taEgW25j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "e4bc1ae9-0d79-4d6b-a74e-277b4f83f3b7"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.countplot('Genre',data=m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6f94f7a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X14yi5U2sKzB",
        "colab_type": "text"
      },
      "source": [
        "Plotting countplot for the column 'Age' & segregating it further on the basis of 'Genre'\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcHJEtqgXdd9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "outputId": "971f06a5-3e01-4fba-bfd5-466e1023454a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "f,ax=plt.subplots(figsize=(20,10))\n",
        "ax=sns.countplot('Age',hue='Genre',data=m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5s7M2KHsTFe",
        "colab_type": "text"
      },
      "source": [
        "Plotting countplot for the column 'Annual Income (k$)' & segregating it further on the basis of 'Genre'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fPOzZRKXxXD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "outputId": "6e792e49-b6bc-4a3b-95dc-5fc2c98f857b"
      },
      "source": [
        "f,ax=plt.subplots(figsize=(20,10))\n",
        "ax=sns.countplot('Annual Income (k$)',hue='Genre',data=m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eK3fzwLqsaOs",
        "colab_type": "text"
      },
      "source": [
        "Plotting the histogram with 'Annual Income (k$)'\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKUADLzZY832",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "dc5678ef-5baf-4f7a-e498-f405db06fdb4"
      },
      "source": [
        "sns.distplot(m['Annual Income (k$)'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6f841ce48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cD5Xbo1ksgSz",
        "colab_type": "text"
      },
      "source": [
        "Plotting boxplot for 'Genre' and 'Annual Income (k$)'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPAvFmt7aXIo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "b329f7ca-3590-4cf6-859d-0bfdec6a38e0"
      },
      "source": [
        "sns.boxplot('Genre','Annual Income (k$)',data=m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6f8658908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zt8WAbBbsnaN",
        "colab_type": "text"
      },
      "source": [
        "Plotting boxplot for 'Genre' and 'Spending Score (1-100)'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GV6s5cQKb4a_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "9045d43b-276c-4187-c18d-5ff10ded7737"
      },
      "source": [
        "sns.boxplot('Genre','Spending Score (1-100)',data=m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6eed6d048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AVX0deE6XRUmS+jFKKFBVt23TtLWDWiRJPRvlQvNtSX4FqCSPYnDfwg3dliVJ6sMoZwq/B5wKLAP+CfjlZlmSNM+MMvrobuBlY6hFktSzUUYfHZjk00k2JbkryflJDhxHcZKk8Rql++jvgI8B+wK/AJwLnN1lUZKkfoxyoXn3qvrbKcsfTvK6rgoat0X338uSGy/su4ze7fLPPwDgJ7s9pudK+rfo/nvx4YJaqEYJhc8leQNwDoMps48HLkyyF0BV3dthfZ1auXJl3yXMGRMT9wGw8kD/MYR9/NvQgjVKKLy0eX31Nu0nMAiJR+z1hdWrV/ddwpyxZs0aANauXdtzJZL6NMroowPGUYgkqX8zXmhOcmiSn5+y/Ipm5NF7J7uOJEnzy7DRR38FbAFI8hzgncCHGDxwZ133pUmSxm1YKCyachH5eGBdVX2iqv4EmPVVuCRPSvKlJNcnuS7JmqZ9ryQXJ7mpeX38bI8hSZqdoaGQZPKaw9HAF6esG+UC9UweBP5jVT0NOAw4NcnTgDcAl1TVQcAlzbIkaYyG/eN+NvDlJHcDm4H/DZBkJTvwzOaq2ghsbN7fl+QGBvMqHQMc2Wx2FnApcNpsjyNJ2n4zhkJVvT3JJQzuZP5CVVWzahdgp4zlTLICOBi4HNinCQyAO/DuIUkau6HdQFV12TRt394ZB06yJ/AJ4I+r6gdJph6jktQM+50CnAKwfPnynVGKJKkx0kN2drbmuQyfAD5SVec1zXcm2bdZvy9w13T7VtW6qlpVVauWLl06noIlaYEYeyhkcEpwBnBDVf3FlFUXACc1708Czh93bZK00O3IKKLZOgL498A3k1zTtL2JwX0QH0tyMnALP51eQ5I0JmMPhar6ByAzrD56nLVIkh6ql2sKkqS5yVCQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUW912ApLnr9NNPZ2JiotcaJo+/Zs2aXusAWLlyJatXr+67jE4ZCpLmtCVLlvRdwoJiKEia0Xz/Vqyf5TUFSVLLUJAktQwFSVLLUJAktQwFSVJrTo0+SvI8YC2wCPhgVb2z55I6NxfGgcPcGQu+EMaBS3PZnDlTSLII+Evg+cDTgBOTPK3fqhaOJUuWOB5c0pw6U3gWMFFVNwMkOQc4Bri+16o65rdiSXPJnDlTAJYBt01Zvr1pe4gkpyRZn2T9pk2bxlacJC0EcykURlJV66pqVVWtWrp0ad/lSNK8MpdCYQPwpCnL+zVtkqQxmUuh8DXgoCQHJNkVOAG4oOeaJGlBmTMXmqvqwSR/CFzEYEjqmVV1Xc9lSdKCMmdCAaCqLgQu7LsOSVqo5lL3kSSpZ4aCJKmVquq7hllLsgm4pe865pG9gbv7LkKahn+bO9f+VTXtmP5HdCho50qyvqpW9V2HtC3/NsfH7iNJUstQkCS1DAVNta7vAqQZ+Lc5Jl5TkCS1PFOQJLUMBUlSy1CY55JUkg9PWV6cZFOSzzzMfkc+3DbSKJJsTXLNlJ8VHR7rO0n27urzF4I5NfeROvEj4OlJllTVZuDXcEpyjdfmqvrlvovQaDxTWBguBH6jeX8icPbkiiTPSvLVJFcn+T9JnrLtzkn2SHJmkiua7Y4ZU92ap5I8M8mXk1yZ5KIk+zbtlyZ5d/N0xRuSHJrkvCQ3JfmvU/b/VLPvdUlOmeEYL2/+Zq9J8lfNc+D1MAyFheEc4IQkuwHPAC6fsu5G4Fer6mDgPwHvmGb/NwNfrKpnAf8G+LMke3Rcs+aPJVO6jj6Z5FHA6cBxVfVM4Ezg7VO239LcvfwB4HzgVODpwCuTPKHZ5neafVcBfzSlHYAk/xI4HjiiOUvZCrysw99x3rD7aAGoqm80/bgn8rNTkz8WOCvJQUABj5rmI34deFGS1zbLuwHLgRs6KVjzzUO6j5I8ncE/8hcngcHzUzZO2X7y4VrfBK6rqo3NfjczeDrjPQyC4Lea7Z4EHNS0TzoaeCbwteYYS4C7du6vNT8ZCgvHBcCfA0cCU79V/Snwpar6rSY4Lp1m3wC/XVXf6rZELRBh8I/94TOsf6B5/cmU95PLi5McCTwXOLyq7k9yKYMvKtse46yqeuNOq3qBsPto4TgTeFtVfXOb9sfy0wvPr5xh34uA1Wm+ciU5uJMKtVB8C1ia5HCAJI9K8ovbsf9jge82gfBU4LBptrkEOC7JE5tj7JVk/x0tfCEwFBaIqrq9qt47zar/Dvy3JFcz85njnzLoVvpGkuuaZWlWqmoLcBzwriRfB64BfmU7PuLzDM4YbgDeCVw2zTGuB94CfCHJN4CLgX13tPaFwGkuJEktzxQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQXoYSfZJ8ndJbm7m2/nqlLtppXnFUJCGaG7Y+xTwlao6sJlv5wRgv53w2U7QpjnHUJCGO4rBBG0fmGyoqluq6vQki5L8WZKvJflGkldD+yyKS5N8PMmNST4y5W7w7yR5V5KrgJck+fXmzOOqJOcm2bOfX1MaMBSk4X4RuGqGdScD36+qQ4FDgd9NckCz7mDgj4GnAQcCR0zZ756qOgT4ewZ33T63WV4PvGbn/wrS6JwQT9oOSf4SeDawBbgFeEaS45rVj2UwW+cW4Iqqur3Z5xpgBfAPzXYfbV4PYxAa/9icSOwKfLX730KamaEgDXcd8NuTC1V1avO4x/XArcDqqrpo6g7NLJ5TZ/fcykP/X/vR5KbAxVV1Ygd1S7Ni95E03BeB3ZL8/pS23ZvXi4Dfbx4aQ5Inb+fDhy4Djkiystl/jyRP3hlFS7PlmYI0RFVVkmOBdyd5PbCJwTf904BzGXQLXdVcSN4EHLsdn70pySuBs5M8uml+C/DtnfcbSNvHWVIlSS27jyRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJrf8P2XblGQ0dfzIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-5wZnFtksrnq",
        "colab_type": "text"
      },
      "source": [
        "Plotting boxplot for 'Genre' and 'Age'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvKPD5kJcOTz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "bb7c895e-db69-4fdd-ea73-7b0485f4bfe5"
      },
      "source": [
        "sns.boxplot('Genre','Age',data=m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6eece8eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_42Q0z_QsxR0",
        "colab_type": "text"
      },
      "source": [
        "Plotting the heatmap"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkmWtEZ4cm3V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "177fba8f-991d-441a-e54a-eb3cbca17358"
      },
      "source": [
        "a=m.corr()\n",
        "sns.heatmap(a,annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6eb0fdb00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txCsGNapdN6T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "a476413b-20c9-4b05-ff61-1b0cb2b001db"
      },
      "source": [
        "a"
      ],
      "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>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>CustomerID</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.026763</td>\n",
              "      <td>0.977548</td>\n",
              "      <td>0.013835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.026763</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.012398</td>\n",
              "      <td>-0.327227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <td>0.977548</td>\n",
              "      <td>-0.012398</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.009903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "      <td>0.013835</td>\n",
              "      <td>-0.327227</td>\n",
              "      <td>0.009903</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        CustomerID  ...  Spending Score (1-100)\n",
              "CustomerID                1.000000  ...                0.013835\n",
              "Age                      -0.026763  ...               -0.327227\n",
              "Annual Income (k$)        0.977548  ...                0.009903\n",
              "Spending Score (1-100)    0.013835  ...                1.000000\n",
              "\n",
              "[4 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Zt0dsAbs0y0",
        "colab_type": "text"
      },
      "source": [
        "Plotting Linechart for 'CustomerID' vs 'Annual Income (k$)'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMDOcftOetOQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e7a51b89-00dd-48ef-f2ee-38ea354b5db4"
      },
      "source": [
        "plt.plot(m['CustomerID'],m['Annual Income (k$)'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd6ead1de10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utTKxqx7iQ0t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}