RM_Assignment.ipynb

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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
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      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/HESCOM_Category_wise_installations_and_Consumption_for_July_2022.csv'"
      ],
      "metadata": {
        "id": "LsPosu5rcrxl"
      },
      "execution_count": 6,
      "outputs": []
    },
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      "source": [
        "df =pd.read_csv(path)"
      ],
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      "execution_count": 7,
      "outputs": []
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        "df.head()"
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              "   Sl No                        Category  \\\n",
              "0       1  Bhagya Jyothi /Kuteer Jyothi    \n",
              "1       2                       Domestic   \n",
              "2       3                     Commercial   \n",
              "3       4                     Industrial   \n",
              "4       5                    Agriculture   \n",
              "\n",
              "   Low Tension Installations (in Numbers)  \\\n",
              "0                                  757831   \n",
              "1                                 3111263   \n",
              "2                                  442721   \n",
              "3                                  134023   \n",
              "4                                 1005222   \n",
              "\n",
              "   High Tension  Installations (in Numbers)  \\\n",
              "0                                         0   \n",
              "1                                        33   \n",
              "2                                       757   \n",
              "3                                      2562   \n",
              "4                                       370   \n",
              "\n",
              "   Low Tension Consumption (in Million Units)  \\\n",
              "0                                       17.04   \n",
              "1                                      144.08   \n",
              "2                                       47.46   \n",
              "3                                       28.28   \n",
              "4                                      558.08   \n",
              "\n",
              "   High Tension Consumption (in Million Units)  \n",
              "0                                         0.00  \n",
              "1                                         1.39  \n",
              "2                                        11.35  \n",
              "3                                        95.97  \n",
              "4                                        16.15  "
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              "      <th>Sl No</th>\n",
              "      <th>Category</th>\n",
              "      <th>Low Tension Installations (in Numbers)</th>\n",
              "      <th>High Tension  Installations (in Numbers)</th>\n",
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              "      <td>Bhagya Jyothi /Kuteer Jyothi</td>\n",
              "      <td>757831</td>\n",
              "      <td>0</td>\n",
              "      <td>17.04</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Domestic</td>\n",
              "      <td>3111263</td>\n",
              "      <td>33</td>\n",
              "      <td>144.08</td>\n",
              "      <td>1.39</td>\n",
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              "      <td>Commercial</td>\n",
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              "      <td>47.46</td>\n",
              "      <td>11.35</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <td>2562</td>\n",
              "      <td>28.28</td>\n",
              "      <td>95.97</td>\n",
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              "      <td>5</td>\n",
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              "      <td>370</td>\n",
              "      <td>558.08</td>\n",
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              "(8, 6)"
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        "plt.scatter(df['Low Tension Installations (in Numbers)'],df['High Tension  Installations (in Numbers)'])"
      ],
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              "<matplotlib.collections.PathCollection at 0x7fc7740365d0>"
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(df['Sl No '],df['High Tension  Installations (in Numbers)'])\n",
        "plt.xlabel('Index')\n",
        "plt.ylabel('High tension installations (in Numbers)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "VmMYy7nSd0fG",
        "outputId": "6d61e977-42e6-43a4-84f7-cd8af5ef9a18"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = ['High Tension  Installations (in Numbers)','Low Tension Installations (in Numbers)']\n",
        "plt.boxplot(df[data])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "Dd6Iz5WCgexj",
        "outputId": "fa153465-6c03-4911-cd42-4e3a81f57d81"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}