Pandas_Weather_Stations.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Modules"
      ],
      "metadata": {
        "id": "bYBIymv8X98d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "from matplotlib.colors import ListedColormap\n",
        "import csv\n",
        "import seaborn as sn\n"
      ],
      "metadata": {
        "id": "2Vs0H_bIYCAY"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# For load file \"weather.csv\" into Jupyter Notebook Colab  \n",
        "(not needed if not using Colab)"
      ],
      "metadata": {
        "id": "niHGNJDH8uKM"
      }
    },
    {
      "cell_type": "code",
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        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "! ls"
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          "base_uri": "https://localhost:8080/",
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              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving weather.csv to weather.csv\n",
            "sample_data  weather.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load the data \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "zcymN_nK-p5b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"weather.csv\") "
      ],
      "metadata": {
        "id": "2mD49T3H9tV2"
      },
      "execution_count": 3,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df.head()"
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              "   YEAR  MONTH  DAY  WS_1_MAX_T  WS_1_MIN_T  WS_1_MEAN_T  WS_1_MAX_H  \\\n",
              "0  2022      1    1        13.3         2.7          7.4        91.0   \n",
              "1  2022      1    2        10.6         1.3          5.4        94.0   \n",
              "2  2022      1    3         8.9         1.9          5.9        92.0   \n",
              "3  2022      1    4         7.2         4.6          6.3        94.0   \n",
              "4  2022      1    5        10.0         3.8          7.3        95.0   \n",
              "\n",
              "   WS_1_MIN_H  WS_1_MEAN_H  WS_1_MAX_P  ...  WS_124_MAX_T  WS_124_MIN_T  \\\n",
              "0        57.0         80.5      1027.3  ...          14.2          11.6   \n",
              "1        67.0         85.8      1027.4  ...          13.3          11.4   \n",
              "2        78.0         84.8      1023.1  ...          15.8          10.2   \n",
              "3        88.0         92.6      1023.1  ...          14.4           9.7   \n",
              "4        52.0         87.3      1006.9  ...          14.4          10.8   \n",
              "\n",
              "   WS_124_MEAN_T  WS_124_MAX_H  WS_124_MIN_H  WS_124_MEAN_H  WS_124_MAX_P  \\\n",
              "0           12.5          91.0          81.0           87.4        1028.0   \n",
              "1           12.2          84.0          74.0           81.1        1028.0   \n",
              "2           12.6          90.0          66.0           79.3        1023.5   \n",
              "3           12.2          91.0          74.0           86.0        1016.0   \n",
              "4           13.0          94.0          22.0           78.3        1006.3   \n",
              "\n",
              "   WS_124_MIN_P  WS_124_MEAN_P  WS_124_RAIN  \n",
              "0        1025.9         1026.9          0.0  \n",
              "1        1023.5         1026.0          0.0  \n",
              "2        1016.0         1019.3          0.0  \n",
              "3        1006.3         1010.8          0.4  \n",
              "4         997.1         1000.4          3.8  \n",
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              "      <th>0</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
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              "      <td>13.3</td>\n",
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              "      <td>1027.3</td>\n",
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              "      <td>91.0</td>\n",
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              "      <td>1025.9</td>\n",
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              "      <th>1</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>10.6</td>\n",
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              "      <td>94.0</td>\n",
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              "      <td>1027.4</td>\n",
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              "      <td>13.3</td>\n",
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              "      <th>2</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8.9</td>\n",
              "      <td>1.9</td>\n",
              "      <td>5.9</td>\n",
              "      <td>92.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>84.8</td>\n",
              "      <td>1023.1</td>\n",
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              "      <td>15.8</td>\n",
              "      <td>10.2</td>\n",
              "      <td>12.6</td>\n",
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              "      <td>66.0</td>\n",
              "      <td>79.3</td>\n",
              "      <td>1023.5</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>1019.3</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>2022</td>\n",
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              "      <td>7.2</td>\n",
              "      <td>4.6</td>\n",
              "      <td>6.3</td>\n",
              "      <td>94.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>92.6</td>\n",
              "      <td>1023.1</td>\n",
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              "      <td>14.4</td>\n",
              "      <td>9.7</td>\n",
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              "      <td>74.0</td>\n",
              "      <td>86.0</td>\n",
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              "      <td>1006.3</td>\n",
              "      <td>1010.8</td>\n",
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              "      <td>2022</td>\n",
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              "      <td>10.0</td>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d3064a59-a56a-435b-bb02-cf70822e27cb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d3064a59-a56a-435b-bb02-cf70822e27cb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first three columns corresponds to the year, month and day in which the observations took place.\n",
        "\n",
        "The remaining columns are organized in (49) groups of 10 columns each:\n",
        "\n",
        "Each group represent a set of atmospheric observations collected at a given weather station in a given day. The following observations are reported (in order):\n",
        "- Maximum Temperature (°C)\n",
        "- Minimum Temperature (°C)\n",
        "- Mean Temperature (°C)\n",
        "- Maximum Relative Humidity (%)\n",
        "- Minimum Relative Humidity (%)\n",
        "- Mean Relative Humidity (%)\n",
        "- Maximum Pressure (hPa)\n",
        "- Minimum Pressure (hPa)\n",
        "- Mean Pressure (hPa)\n",
        "- Rainfall (mm)"
      ],
      "metadata": {
        "id": "EpeigKoAiF-d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Perform 1\n",
        "\n",
        "Calculate the minimum, maximum and average rainfall and standard deviation of the rainfall over all the stations and all the days (a single set of numbers for the entire dataset)."
      ],
      "metadata": {
        "id": "k4ctMt9QAICn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Prepare the dataset\n",
        "\n",
        "d = (len(df.columns))\n",
        "print(d)\n",
        "print(df.iloc[:,12:d:10])  # jump the first 3 + 9 colunm, and step of 10 up to end  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h34H-Hozvy-O",
        "outputId": "8b2234c5-1a06-4a9a-a128-8d430b389df8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "493\n",
            "     WS_1_RAIN  WS_130_RAIN  WS_5_RAIN  WS_10_RAIN  WS_140_RAIN  WS_142_RAIN  \\\n",
            "0          0.2          0.0        0.0         0.0          0.0          0.2   \n",
            "1          0.4          0.0        0.0         0.0          0.0          0.2   \n",
            "2          0.0          0.0        0.0         0.0          0.0          0.2   \n",
            "3          1.0          0.4        0.0         1.2          0.0          3.3   \n",
            "4          0.8          0.0        0.4         0.6          0.0          1.3   \n",
            "..         ...          ...        ...         ...          ...          ...   \n",
            "146        0.0          0.2        0.0         8.0          1.0          0.0   \n",
            "147        0.2          0.0        0.4         0.2         10.8         14.0   \n",
            "148        0.0          0.0        0.0         0.0          5.0          0.2   \n",
            "149        0.0          0.0        0.0         0.0          0.2          0.0   \n",
            "150        0.0          0.0        0.0         0.0          0.2          0.0   \n",
            "\n",
            "     WS_143_RAIN  WS_145_RAIN  WS_21_RAIN  WS_22_RAIN  ...  WS_72_RAIN  \\\n",
            "0            0.4          0.0         0.0         0.0  ...         0.0   \n",
            "1            0.4          0.0         0.0         0.0  ...         0.0   \n",
            "2            0.0          0.0         0.0         0.0  ...         0.0   \n",
            "3            3.6          0.0         0.0         0.0  ...         0.0   \n",
            "4            1.2          0.0         0.6         0.0  ...         0.2   \n",
            "..           ...          ...         ...         ...  ...         ...   \n",
            "146          0.0          0.2         0.2         0.0  ...         3.4   \n",
            "147         14.6          0.0         0.0         0.0  ...         0.8   \n",
            "148          1.0          0.0         0.0         0.0  ...         1.8   \n",
            "149          0.0          0.0         0.0         0.0  ...         0.0   \n",
            "150          0.0          0.0         0.0         0.0  ...         0.0   \n",
            "\n",
            "     WS_202_RAIN  WS_204_RAIN  WS_205_RAIN  WS_85_RAIN  WS_86_RAIN  \\\n",
            "0            0.2          0.0          0.0         0.0         0.0   \n",
            "1            0.3          0.0          0.0         0.0         0.0   \n",
            "2            0.2          0.0          0.0         0.0         0.0   \n",
            "3            1.0          0.0          0.0         0.3         0.0   \n",
            "4            4.4          0.0          0.0         4.3         3.8   \n",
            "..           ...          ...          ...         ...         ...   \n",
            "146          0.0          0.0          0.0         0.0         0.0   \n",
            "147          3.3          0.0          0.0        18.0        22.9   \n",
            "148         24.7          2.6          0.0        18.8         6.6   \n",
            "149          0.0          0.0          0.0         0.0         0.0   \n",
            "150          0.0          0.0          0.0         0.0         0.0   \n",
            "\n",
            "     WS_92_RAIN  WS_108_RAIN  WS_111_RAIN  WS_124_RAIN  \n",
            "0           0.2          0.0          0.2          0.0  \n",
            "1           0.0          0.0          0.2          0.0  \n",
            "2           0.4          0.0          0.2          0.0  \n",
            "3           0.4          0.0          2.2          0.4  \n",
            "4           4.8          0.2          0.8          3.8  \n",
            "..          ...          ...          ...          ...  \n",
            "146         0.0          0.0          0.2          0.0  \n",
            "147         4.8          0.0          1.4          0.0  \n",
            "148         0.4          0.0          0.0          0.0  \n",
            "149         0.0          0.0          0.0          0.0  \n",
            "150         0.0          0.0          0.0          0.0  \n",
            "\n",
            "[151 rows x 49 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f = df.iloc[:,12:d:10]\n",
        "\n",
        "mn = f.min(axis=1)\n",
        "print(\"Total min : \",mn.min().round(2) )\n",
        "\n",
        "mx = f.max(axis=1)\n",
        "print(\"Total max : \",mx.max().round(2) )\n",
        "\n",
        "m1 = f.mean(axis=1)\n",
        "print(\"Total mean : \",m1.mean().round(2) )\n",
        "\n",
        "s1 = f.std(axis=1)\n",
        "print(\"Standard deviation : \",s1.mean().round(2) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-M0GF40d2nyX",
        "outputId": "3257641e-a0e7-467f-efbb-8cb0244ec8b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total min :  0.0\n",
            "Total max :  110.8\n",
            "Total mean :  1.23\n",
            "Standard deviation :  1.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Perform 2\n",
        "\n",
        "Calculate the minimum, maximum and average rainfall and standard deviation of the rainfall at each station over all the days (a different set of numbers for each station)."
      ],
      "metadata": {
        "id": "fR6nEdcT-chd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f = df.iloc[:,12:d:10]\n",
        "\n",
        "print(f'{f.describe()}') # stat for each column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j5OM785-zcJQ",
        "outputId": "6164a4d3-72a7-4a1b-9142-d2b6977ecd69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        WS_1_RAIN  WS_130_RAIN   WS_5_RAIN  WS_10_RAIN  WS_140_RAIN  \\\n",
            "count  151.000000   151.000000  151.000000  151.000000   151.000000   \n",
            "mean     0.634437     0.431788    1.364238    0.887417     2.141722   \n",
            "std      2.031454     1.378713    4.818193    2.827727     7.775709   \n",
            "min      0.000000     0.000000    0.000000    0.000000     0.000000   \n",
            "25%      0.000000     0.000000    0.000000    0.000000     0.000000   \n",
            "50%      0.000000     0.000000    0.000000    0.000000     0.000000   \n",
            "75%      0.000000     0.000000    0.000000    0.000000     0.200000   \n",
            "max     13.400000     9.000000   28.800000   19.000000    79.200000   \n",
            "\n",
            "       WS_142_RAIN  WS_143_RAIN  WS_145_RAIN  WS_21_RAIN  WS_22_RAIN  ...  \\\n",
            "count   151.000000   151.000000   151.000000  151.000000  151.000000  ...   \n",
            "mean      1.029139     0.776159     0.601325    1.280795    0.530464  ...   \n",
            "std       3.497496     2.805797     2.625871    3.831322    2.647137  ...   \n",
            "min       0.000000     0.000000     0.000000    0.000000    0.000000  ...   \n",
            "25%       0.000000     0.000000     0.000000    0.000000    0.000000  ...   \n",
            "50%       0.000000     0.000000     0.000000    0.000000    0.000000  ...   \n",
            "75%       0.200000     0.200000     0.000000    0.000000    0.000000  ...   \n",
            "max      28.200000    26.000000    27.000000   29.800000   30.000000  ...   \n",
            "\n",
            "       WS_72_RAIN  WS_202_RAIN  WS_204_RAIN  WS_205_RAIN  WS_85_RAIN  \\\n",
            "count  151.000000   151.000000   151.000000   151.000000  151.000000   \n",
            "mean     2.266225     1.326490     2.503311     0.860927    1.372185   \n",
            "std      9.988519     5.513277    12.064534     4.853864    5.389795   \n",
            "min      0.000000     0.000000     0.000000     0.000000    0.000000   \n",
            "25%      0.000000     0.000000     0.000000     0.000000    0.000000   \n",
            "50%      0.000000     0.000000     0.000000     0.000000    0.000000   \n",
            "75%      0.200000     0.000000     0.200000     0.000000    0.000000   \n",
            "max    110.800000    48.200000   102.000000    57.000000   54.400000   \n",
            "\n",
            "       WS_86_RAIN  WS_92_RAIN  WS_108_RAIN  WS_111_RAIN  WS_124_RAIN  \n",
            "count  151.000000  151.000000   151.000000   151.000000   151.000000  \n",
            "mean     1.278808    1.029139     1.141722     0.876821     0.708609  \n",
            "std      6.056970    3.690376     6.465069     2.650772     3.677009  \n",
            "min      0.000000    0.000000     0.000000     0.000000     0.000000  \n",
            "25%      0.000000    0.000000     0.000000     0.000000     0.000000  \n",
            "50%      0.000000    0.000000     0.000000     0.000000     0.000000  \n",
            "75%      0.000000    0.000000     0.000000     0.200000     0.000000  \n",
            "max     66.300000   28.400000    76.800000    14.000000    36.200000  \n",
            "\n",
            "[8 rows x 49 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f = df.iloc[:,12:d:10]\n",
        "\n",
        "# similar perform \n",
        "print(f'{f.min() = }')\n",
        "print(f'{f.max() = }')\n",
        "print(f'{f.mean() = }')\n",
        "print(f'{f.std() = }')"
      ],
      "metadata": {
        "id": "87sgQ8_GzPZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Perform 3\n",
        "\n",
        "Calculate the minimum, maximum and average rainfall and standard deviation of the rainfall across all stations at each day (a different set of numbers for each day)."
      ],
      "metadata": {
        "id": "SihzQtmhFZtF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f = df.iloc[:,12:d:10]\n",
        "\n",
        "f.apply(pd.DataFrame.describe, axis=1)  # stat for each day (row)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "e27ae1ce-ca79-4f44-b4a4-0e71cde76d15",
        "id": "cOPwbJ6k5Cr5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     count      mean       std  min  25%  50%  75%   max\n",
              "0     49.0  0.071429  0.168325  0.0  0.0  0.0  0.0   1.0\n",
              "1     49.0  0.069388  0.154386  0.0  0.0  0.0  0.0   0.8\n",
              "2     49.0  0.057143  0.106066  0.0  0.0  0.0  0.0   0.4\n",
              "3     49.0  0.934694  2.157019  0.0  0.0  0.0  0.8  12.8\n",
              "4     49.0  1.883673  4.117865  0.0  0.2  0.6  2.2  26.8\n",
              "..     ...       ...       ...  ...  ...  ...  ...   ...\n",
              "146   49.0  0.322449  1.263472  0.0  0.0  0.0  0.0   8.0\n",
              "147   49.0  3.473469  5.925678  0.0  0.0  0.2  3.3  22.9\n",
              "148   49.0  3.010204  5.919714  0.0  0.0  0.2  2.7  25.8\n",
              "149   49.0  0.151020  0.553068  0.0  0.0  0.0  0.0   3.2\n",
              "150   49.0  0.151020  0.553068  0.0  0.0  0.0  0.0   3.2\n",
              "\n",
              "[151 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-48b320bd-3e78-4660-8b05-dccb61923d39\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.168325</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.069388</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.057143</td>\n",
              "      <td>0.106066</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.934694</td>\n",
              "      <td>2.157019</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49.0</td>\n",
              "      <td>1.883673</td>\n",
              "      <td>4.117865</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>26.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.322449</td>\n",
              "      <td>1.263472</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>49.0</td>\n",
              "      <td>3.473469</td>\n",
              "      <td>5.925678</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>49.0</td>\n",
              "      <td>3.010204</td>\n",
              "      <td>5.919714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2.7</td>\n",
              "      <td>25.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.151020</td>\n",
              "      <td>0.553068</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.151020</td>\n",
              "      <td>0.553068</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>151 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48b320bd-3e78-4660-8b05-dccb61923d39')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-48b320bd-3e78-4660-8b05-dccb61923d39');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Perform 4\n",
        "\n",
        "Find the station that registered the highest temperature on each day (a station id for each day)."
      ],
      "metadata": {
        "id": "YJgszgkDL1rh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Prepare the dataset\n",
        "d = (len(df.columns))\n",
        "print(df.iloc[:,3:d:10]) \n"
      ],
      "metadata": {
        "id": "Doi1f4Fr7dNd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ft = df.iloc[:,3:d:10]\n",
        "\n",
        "print(ft.max(axis=1))  #  Max value for each day (row)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDurA5Nw9zBN",
        "outputId": "189a8901-9d8d-4fcd-9a7d-660c5c1a3d17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      20.0\n",
            "1      19.8\n",
            "2      16.2\n",
            "3      14.4\n",
            "4      14.8\n",
            "       ... \n",
            "146    34.0\n",
            "147    33.3\n",
            "148    32.9\n",
            "149    23.3\n",
            "150    23.3\n",
            "Length: 151, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maxValueIndex = ft.idxmax(axis=1)\n",
        "print(\"Row    Max is in column\")\n",
        "print(maxValueIndex)           # Max value of row ... is at columns ..."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6R98uAVD8EJk",
        "outputId": "50dc72d2-b972-4bfb-ece3-a030eec2e139"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row    Max is in column\n",
            "0      WS_156_MAX_T\n",
            "1      WS_163_MAX_T\n",
            "2      WS_205_MAX_T\n",
            "3      WS_124_MAX_T\n",
            "4       WS_92_MAX_T\n",
            "           ...     \n",
            "146     WS_92_MAX_T\n",
            "147    WS_111_MAX_T\n",
            "148     WS_28_MAX_T\n",
            "149    WS_124_MAX_T\n",
            "150    WS_124_MAX_T\n",
            "Length: 151, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Perform 5\n",
        "\n",
        "Find the day in which the lowest temperature has been recorded."
      ],
      "metadata": {
        "id": "sB66qQI-Alnh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Prepare the dataset\n",
        "d = (len(df.columns))\n",
        "print(df.iloc[:,4:d:10]) "
      ],
      "metadata": {
        "id": "u5yKduVpACNv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fmt = df.iloc[:,4:d:10] \n",
        "print(\"Value of lowest temperature has been recorded: \", fmt.min().min())\n",
        "mc = fmt.min().min()\n",
        "\n",
        "reID = [col for col in fmt if (fmt[col] == mc).any()]\n",
        "print(\"ID station : \", reID[0])\n",
        "\n",
        "r = fmt.index[fmt[reID[0]]==mc].tolist()\n",
        "print(\"Row :   \", r[0])\n",
        "print( \"  Equivalent Day : \", df.iloc[r[0],2],  \", Month : \", df.iloc[r[0],1])\n",
        "\n",
        "#More:\n",
        "#minv = fmt.idxmin()\n",
        "#print(\"Min values of columns are at row index position :\")\n",
        "#print(minv)\n",
        "\n",
        "#print(\"lowest temperature for columns:\")\n",
        "#print(fmt.min())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RM6znK_1-omT",
        "outputId": "0f4a09d1-7fa8-402f-9dda-bbfef35d12f1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Value of lowest temperature has been recorded:  -20.7\n",
            "ID station :  WS_154_MIN_T\n",
            "Row :    10\n",
            "  Equivalent Day :  11 , Month :  1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Perform 6\n",
        "Find the average relative humidity recorded in February for each station."
      ],
      "metadata": {
        "id": "J33fgzedDPaj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Prepare dataset\n",
        "\n",
        "February = df[\"MONTH\"] == 2\n",
        "feb = df.loc[February]\n",
        "feb.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "QYLmMX03LaPg",
        "outputId": "90f6560e-09c3-40d9-e160-8b4933de9db9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    YEAR  MONTH  DAY  WS_1_MAX_T  WS_1_MIN_T  WS_1_MEAN_T  WS_1_MAX_H  \\\n",
              "31  2022      2    1        12.2         1.7          7.7        58.0   \n",
              "32  2022      2    2        17.2         2.9          9.5        61.0   \n",
              "33  2022      2    3        12.8         5.1          8.8        80.0   \n",
              "34  2022      2    4        12.2         3.7          7.7        86.0   \n",
              "35  2022      2    5        12.2         2.4          7.2        90.0   \n",
              "\n",
              "    WS_1_MIN_H  WS_1_MEAN_H  WS_1_MAX_P  ...  WS_124_MAX_T  WS_124_MIN_T  \\\n",
              "31        23.0         38.7      1017.9  ...          15.5           6.2   \n",
              "32        31.0         48.6      1018.7  ...          18.6           5.7   \n",
              "33        54.0         65.7      1022.2  ...          15.9           8.5   \n",
              "34        52.0         74.1      1021.0  ...          12.5          10.3   \n",
              "35        60.0         76.0      1024.7  ...          16.8           8.2   \n",
              "\n",
              "    WS_124_MEAN_T  WS_124_MAX_H  WS_124_MIN_H  WS_124_MEAN_H  WS_124_MAX_P  \\\n",
              "31           10.3          73.0          21.0           45.5        1015.1   \n",
              "32           11.0          73.0          34.0           59.6        1017.5   \n",
              "33           11.8          89.0          56.0           72.5        1022.3   \n",
              "34           11.4          94.0          80.0           87.7        1020.1   \n",
              "35           11.3          88.0          57.0           78.5        1023.2   \n",
              "\n",
              "    WS_124_MIN_P  WS_124_MEAN_P  WS_124_RAIN  \n",
              "31        1010.1         1013.6          0.0  \n",
              "32        1011.2         1013.3          0.0  \n",
              "33        1017.3         1020.2          0.0  \n",
              "34        1015.8         1017.3          1.2  \n",
              "35        1016.1         1018.7          0.0  \n",
              "\n",
              "[5 rows x 493 columns]"
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              "      <th>WS_1_MIN_T</th>\n",
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              "      <th>WS_1_MIN_H</th>\n",
              "      <th>WS_1_MEAN_H</th>\n",
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              "      <th>WS_124_MIN_T</th>\n",
              "      <th>WS_124_MEAN_T</th>\n",
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              "      <th>WS_124_MIN_H</th>\n",
              "      <th>WS_124_MEAN_H</th>\n",
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              "      <th>31</th>\n",
              "      <td>2022</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2</td>\n",
              "      <td>1.7</td>\n",
              "      <td>7.7</td>\n",
              "      <td>58.0</td>\n",
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              "      <td>15.5</td>\n",
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              "      <td>10.3</td>\n",
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              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2022</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>17.2</td>\n",
              "      <td>2.9</td>\n",
              "      <td>9.5</td>\n",
              "      <td>61.0</td>\n",
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              "      <td>48.6</td>\n",
              "      <td>1018.7</td>\n",
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              "      <td>18.6</td>\n",
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              "      <td>11.0</td>\n",
              "      <td>73.0</td>\n",
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              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2022</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>12.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>8.8</td>\n",
              "      <td>80.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>65.7</td>\n",
              "      <td>1022.2</td>\n",
              "      <td>...</td>\n",
              "      <td>15.9</td>\n",
              "      <td>8.5</td>\n",
              "      <td>11.8</td>\n",
              "      <td>89.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>72.5</td>\n",
              "      <td>1022.3</td>\n",
              "      <td>1017.3</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2022</td>\n",
              "      <td>2</td>\n",
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              "      <td>12.2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>7.7</td>\n",
              "      <td>86.0</td>\n",
              "      <td>52.0</td>\n",
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              "      <td>1021.0</td>\n",
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              "      <td>12.5</td>\n",
              "      <td>10.3</td>\n",
              "      <td>11.4</td>\n",
              "      <td>94.0</td>\n",
              "      <td>80.0</td>\n",
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              "      <td>1.2</td>\n",
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              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2022</td>\n",
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              "          const element = document.querySelector('#df-6b45ded9-37b8-4105-8fa5-5b8cdf75321a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hum = (feb.iloc[:,8:d:10]) \n",
        "#print(feb.iloc[:,8:d:10]) \n",
        "\n",
        "print(\"Humidity recorded in February for each station:\\n\")\n",
        "print(hum.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIqm72OuM7ya",
        "outputId": "14c9c90d-1945-4a63-b0fc-9b40e32624b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Humidity recorded in February for each station:\n",
            "\n",
            "WS_1_MEAN_H      61.482143\n",
            "WS_130_MEAN_H    74.271429\n",
            "WS_5_MEAN_H      57.150000\n",
            "WS_10_MEAN_H     59.760714\n",
            "WS_140_MEAN_H    60.271429\n",
            "WS_142_MEAN_H    72.521429\n",
            "WS_143_MEAN_H    75.457143\n",
            "WS_145_MEAN_H    47.664286\n",
            "WS_21_MEAN_H     60.010714\n",
            "WS_22_MEAN_H     28.582143\n",
            "WS_151_MEAN_H    75.735714\n",
            "WS_24_MEAN_H     56.771429\n",
            "WS_152_MEAN_H    61.307143\n",
            "WS_154_MEAN_H    73.164286\n",
            "WS_28_MEAN_H     70.167857\n",
            "WS_29_MEAN_H     57.039286\n",
            "WS_156_MEAN_H    48.500000\n",
            "WS_158_MEAN_H    65.903571\n",
            "WS_159_MEAN_H    48.017857\n",
            "WS_161_MEAN_H    52.607143\n",
            "WS_163_MEAN_H    53.853571\n",
            "WS_37_MEAN_H     55.164286\n",
            "WS_42_MEAN_H     68.425000\n",
            "WS_43_MEAN_H     52.314286\n",
            "WS_170_MEAN_H    62.117857\n",
            "WS_172_MEAN_H    70.350000\n",
            "WS_174_MEAN_H    75.428571\n",
            "WS_176_MEAN_H    53.632143\n",
            "WS_179_MEAN_H    62.092857\n",
            "WS_187_MEAN_H    57.775000\n",
            "WS_191_MEAN_H    64.642857\n",
            "WS_192_MEAN_H    47.400000\n",
            "WS_65_MEAN_H     72.907143\n",
            "WS_193_MEAN_H    56.707143\n",
            "WS_67_MEAN_H     69.571429\n",
            "WS_194_MEAN_H    55.864286\n",
            "WS_195_MEAN_H    65.114286\n",
            "WS_198_MEAN_H    55.557143\n",
            "WS_199_MEAN_H    58.042857\n",
            "WS_72_MEAN_H     62.071429\n",
            "WS_202_MEAN_H    64.371429\n",
            "WS_204_MEAN_H    55.367857\n",
            "WS_205_MEAN_H    44.353571\n",
            "WS_85_MEAN_H     66.796429\n",
            "WS_86_MEAN_H     59.296429\n",
            "WS_92_MEAN_H     70.260714\n",
            "WS_108_MEAN_H    57.357143\n",
            "WS_111_MEAN_H    53.625000\n",
            "WS_124_MEAN_H    66.542857\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Perform 7\n",
        "Calculate the average relative humidity for each month in the dataset."
      ],
      "metadata": {
        "id": "UsadFBGpDW8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Prepare dataset\n",
        "grouped = df.groupby(df.MONTH)\n",
        "\n",
        "df_gen = grouped.get_group(1)\n",
        "df_feb = grouped.get_group(2)\n",
        "df_mar = grouped.get_group(3)\n",
        "df_apr = grouped.get_group(4)\n",
        "df_mag = grouped.get_group(5)\n",
        "\n",
        "df_mag.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "waCvZSEcOD7u",
        "outputId": "845773f2-9099-4355-8cf1-43b15f4ab1ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     YEAR  MONTH  DAY  WS_1_MAX_T  WS_1_MIN_T  WS_1_MEAN_T  WS_1_MAX_H  \\\n",
              "146  2022      5   27        31.1        18.1         24.8        84.0   \n",
              "147  2022      5   28        30.6        17.4         24.9        86.0   \n",
              "148  2022      5   29        18.9        13.2         16.7        86.0   \n",
              "149  2022      5   30        19.4        14.2         17.1        79.0   \n",
              "150  2022      5   31        19.4        14.2         17.1        79.0   \n",
              "\n",
              "     WS_1_MIN_H  WS_1_MEAN_H  WS_1_MAX_P  ...  WS_124_MAX_T  WS_124_MIN_T  \\\n",
              "146        36.0         63.3      1020.2  ...          33.1          23.8   \n",
              "147        35.0         55.9      1011.7  ...          30.2          20.3   \n",
              "148        59.0         70.3      1008.8  ...          24.7          18.8   \n",
              "149        56.0         69.0      1013.4  ...          23.3          16.8   \n",
              "150        56.0         69.0      1013.4  ...          23.3          16.8   \n",
              "\n",
              "     WS_124_MEAN_T  WS_124_MAX_H  WS_124_MIN_H  WS_124_MEAN_H  WS_124_MAX_P  \\\n",
              "146           28.3          55.0          27.0           41.9        1017.4   \n",
              "147           24.8          75.0          37.0           55.8        1010.8   \n",
              "148           21.5          81.0          53.0           68.9        1005.5   \n",
              "149           20.1          81.0          58.0           68.3        1012.0   \n",
              "150           20.1          81.0          58.0           68.3        1012.0   \n",
              "\n",
              "     WS_124_MIN_P  WS_124_MEAN_P  WS_124_RAIN  \n",
              "146        1010.7         1014.2          0.0  \n",
              "147        1001.4         1005.7          0.0  \n",
              "148        1002.0         1003.1          0.0  \n",
              "149        1005.1         1007.9          0.0  \n",
              "150        1005.1         1007.9          0.0  \n",
              "\n",
              "[5 rows x 493 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>YEAR</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>DAY</th>\n",
              "      <th>WS_1_MAX_T</th>\n",
              "      <th>WS_1_MIN_T</th>\n",
              "      <th>WS_1_MEAN_T</th>\n",
              "      <th>WS_1_MAX_H</th>\n",
              "      <th>WS_1_MIN_H</th>\n",
              "      <th>WS_1_MEAN_H</th>\n",
              "      <th>WS_1_MAX_P</th>\n",
              "      <th>...</th>\n",
              "      <th>WS_124_MAX_T</th>\n",
              "      <th>WS_124_MIN_T</th>\n",
              "      <th>WS_124_MEAN_T</th>\n",
              "      <th>WS_124_MAX_H</th>\n",
              "      <th>WS_124_MIN_H</th>\n",
              "      <th>WS_124_MEAN_H</th>\n",
              "      <th>WS_124_MAX_P</th>\n",
              "      <th>WS_124_MIN_P</th>\n",
              "      <th>WS_124_MEAN_P</th>\n",
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              "      <th>146</th>\n",
              "      <td>2022</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>31.1</td>\n",
              "      <td>18.1</td>\n",
              "      <td>24.8</td>\n",
              "      <td>84.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>63.3</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>...</td>\n",
              "      <td>33.1</td>\n",
              "      <td>23.8</td>\n",
              "      <td>28.3</td>\n",
              "      <td>55.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>41.9</td>\n",
              "      <td>1017.4</td>\n",
              "      <td>1010.7</td>\n",
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              "      <th>147</th>\n",
              "      <td>2022</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>30.6</td>\n",
              "      <td>17.4</td>\n",
              "      <td>24.9</td>\n",
              "      <td>86.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>55.9</td>\n",
              "      <td>1011.7</td>\n",
              "      <td>...</td>\n",
              "      <td>30.2</td>\n",
              "      <td>20.3</td>\n",
              "      <td>24.8</td>\n",
              "      <td>75.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>55.8</td>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>2022</td>\n",
              "      <td>5</td>\n",
              "      <td>29</td>\n",
              "      <td>18.9</td>\n",
              "      <td>13.2</td>\n",
              "      <td>16.7</td>\n",
              "      <td>86.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>70.3</td>\n",
              "      <td>1008.8</td>\n",
              "      <td>...</td>\n",
              "      <td>24.7</td>\n",
              "      <td>18.8</td>\n",
              "      <td>21.5</td>\n",
              "      <td>81.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>68.9</td>\n",
              "      <td>1005.5</td>\n",
              "      <td>1002.0</td>\n",
              "      <td>1003.1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>2022</td>\n",
              "      <td>5</td>\n",
              "      <td>30</td>\n",
              "      <td>19.4</td>\n",
              "      <td>14.2</td>\n",
              "      <td>17.1</td>\n",
              "      <td>79.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>1013.4</td>\n",
              "      <td>...</td>\n",
              "      <td>23.3</td>\n",
              "      <td>16.8</td>\n",
              "      <td>20.1</td>\n",
              "      <td>81.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>68.3</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>1005.1</td>\n",
              "      <td>1007.9</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2022</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>19.4</td>\n",
              "      <td>14.2</td>\n",
              "      <td>17.1</td>\n",
              "      <td>79.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>1013.4</td>\n",
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              "      <td>23.3</td>\n",
              "      <td>16.8</td>\n",
              "      <td>20.1</td>\n",
              "      <td>81.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>68.3</td>\n",
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              "      <td>1005.1</td>\n",
              "      <td>1007.9</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 493 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_gen = df_gen.iloc[:,8:d:10]   #only humudity column\n",
        "df_feb = df_feb.iloc[:,8:d:10]   \n",
        "df_mar = df_mar.iloc[:,8:d:10]   \n",
        "df_apr = df_apr.iloc[:,8:d:10]   \n",
        "df_mag = df_mag.iloc[:,8:d:10]   "
      ],
      "metadata": {
        "id": "XG1tcd8ya_jh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Average humidity for Jan   month : \", df_gen.mean().mean().round(3))\n",
        "print(\"Average humidity for Feb month   : \", df_feb.mean().mean().round(3))\n",
        "print(\"Average humidity for March month : \", df_mar.mean().mean().round(3))\n",
        "print(\"Average humidity for April month : \", df_apr.mean().mean().round(3))\n",
        "print(\"Average humidity for May month   : \", df_mag.mean().mean().round(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpwDzL_rcaZz",
        "outputId": "1b60225f-2ca3-4318-e28e-58585d0a8992"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average humidity for Jan   month :  63.954\n",
            "Average humidity for Feb month   :  60.477\n",
            "Average humidity for March month :  58.274\n",
            "Average humidity for April month :  64.35\n",
            "Average humidity for May month   :  70.956\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Perform 8\n",
        "Compute the monthly average of daily temperature ranges across all stations. Visualize them as a bar plot.\n"
      ],
      "metadata": {
        "id": "9hpDTyF0DdqD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Prepare dataset\n",
        "grouped = df.groupby(df.MONTH)\n",
        "\n",
        "df_gen = grouped.get_group(1)\n",
        "df_feb = grouped.get_group(2)\n",
        "df_mar = grouped.get_group(3)\n",
        "df_apr = grouped.get_group(4)\n",
        "df_mag = grouped.get_group(5)\n",
        "\n",
        "\n",
        "df_gen = df_gen.iloc[:,5:d:10]   #only Temp mean column\n",
        "df_feb = df_feb.iloc[:,5:d:10]   \n",
        "df_mar = df_mar.iloc[:,5:d:10]   \n",
        "df_apr = df_apr.iloc[:,5:d:10]   \n",
        "df_mag = df_mag.iloc[:,5:d:10] \n",
        "df_mag.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "HiRXWCtkeCzK",
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      "execution_count": null,
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              "     WS_1_MEAN_T  WS_130_MEAN_T  WS_5_MEAN_T  WS_10_MEAN_T  WS_140_MEAN_T  \\\n",
              "146         24.8            8.7         24.3          23.9           16.2   \n",
              "147         24.9            7.0         24.3          25.5           16.0   \n",
              "148         16.7            3.1         15.6          15.0            6.9   \n",
              "149         17.1            3.7         15.8          15.6            6.4   \n",
              "150         17.1            3.7         15.8          15.6            6.4   \n",
              "\n",
              "     WS_142_MEAN_T  WS_143_MEAN_T  WS_145_MEAN_T  WS_21_MEAN_T  WS_22_MEAN_T  \\\n",
              "146           24.6           25.3           14.2          19.8          24.6   \n",
              "147           24.0           24.9           13.7          18.9          26.9   \n",
              "148           15.5           16.3            9.4          16.4          16.5   \n",
              "149           16.0           17.3            7.3          15.7          15.0   \n",
              "150           16.0           17.3            7.3          15.7          15.0   \n",
              "\n",
              "     ...  WS_72_MEAN_T  WS_202_MEAN_T  WS_204_MEAN_T  WS_205_MEAN_T  \\\n",
              "146  ...          19.2           18.1           18.9           23.4   \n",
              "147  ...          18.8           18.9           19.3           25.5   \n",
              "148  ...          12.5           15.5           10.5           15.5   \n",
              "149  ...          12.0           16.2           10.7           14.2   \n",
              "150  ...          12.0           16.2           10.7           14.2   \n",
              "\n",
              "     WS_85_MEAN_T  WS_86_MEAN_T  WS_92_MEAN_T  WS_108_MEAN_T  WS_111_MEAN_T  \\\n",
              "146          23.3          22.9          27.6           23.0           23.0   \n",
              "147          22.9          22.6          24.2           24.9           23.6   \n",
              "148          15.7          14.6          18.4           16.6           15.6   \n",
              "149          14.3          14.7          19.0           15.2           16.5   \n",
              "150          14.3          14.7          19.0           15.2           16.5   \n",
              "\n",
              "     WS_124_MEAN_T  \n",
              "146           28.3  \n",
              "147           24.8  \n",
              "148           21.5  \n",
              "149           20.1  \n",
              "150           20.1  \n",
              "\n",
              "[5 rows x 49 columns]"
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              "      <th>WS_1_MEAN_T</th>\n",
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              "      <th>146</th>\n",
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              "      <th>147</th>\n",
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              "      <td>18.9</td>\n",
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              "      <td>23.6</td>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>16.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>15.6</td>\n",
              "      <td>15.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>15.5</td>\n",
              "      <td>16.3</td>\n",
              "      <td>9.4</td>\n",
              "      <td>16.4</td>\n",
              "      <td>16.5</td>\n",
              "      <td>...</td>\n",
              "      <td>12.5</td>\n",
              "      <td>15.5</td>\n",
              "      <td>10.5</td>\n",
              "      <td>15.5</td>\n",
              "      <td>15.7</td>\n",
              "      <td>14.6</td>\n",
              "      <td>18.4</td>\n",
              "      <td>16.6</td>\n",
              "      <td>15.6</td>\n",
              "      <td>21.5</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>17.1</td>\n",
              "      <td>3.7</td>\n",
              "      <td>15.8</td>\n",
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              "      <td>6.4</td>\n",
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              "      <td>17.3</td>\n",
              "      <td>7.3</td>\n",
              "      <td>15.7</td>\n",
              "      <td>15.0</td>\n",
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              "      <td>12.0</td>\n",
              "      <td>16.2</td>\n",
              "      <td>10.7</td>\n",
              "      <td>14.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>14.7</td>\n",
              "      <td>19.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>16.5</td>\n",
              "      <td>20.1</td>\n",
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              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>17.1</td>\n",
              "      <td>3.7</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Monthly average of daily temperature ranges across all stations.\")\n",
        "print(\"\\nTemp for Jan:\\n\",df_gen.mean(axis=1).round(2))\n",
        "print(\"\\nTemp for Feb:\\n\",df_feb.mean(axis=1).round(2))\n",
        "print(\"\\nTemp for March:\\n\",df_mar.mean(axis=1).round(2))\n",
        "print(\"\\nTemp for April:\\n \",df_apr.mean(axis=1).round(2))\n",
        "print(\"\\nTemp for May:\\n\", df_mag.mean(axis=1).round(2))"
      ],
      "metadata": {
        "id": "U0e8-hQIeXAH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df_gen.mean(axis=1).round(2).plot.bar(x='', y='', rot=0, title= \"\\n January \\n\")\n",
        "ax.set_xlabel(\"Day\")\n",
        "ax.set_ylabel(\"Average Temperature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "EyMibu0-jt4B",
        "outputId": "8f5d2e18-1490-41d1-ff0a-0ae581e6607f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Average Temperature')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df_mag.mean(axis=1).round(2).plot.bar(x='', y='', rot=0, title= \"\\n May \\n\")\n",
        "ax.set_xlabel(\"Day\")\n",
        "ax.set_ylabel(\"Average Temperature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "Fl_YzK3ymxMk",
        "outputId": "558d85af-2e01-442c-efd2-07bb21838fc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Average Temperature')"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize = (8, 4))\n",
        "mon = [\"January\", \"February\", \"March\", \"April\", \"May\"]\n",
        "val = [df_gen.mean(axis=1).round(2).mean(),\n",
        "       df_feb.mean(axis=1).round(2).mean(),\n",
        "       df_mar.mean(axis=1).round(2).mean(),\n",
        "       df_apr.mean(axis=1).round(2).mean(),\n",
        "       df_mag.mean(axis=1).round(2).mean()]\n",
        "\n",
        "# creating the bar plot\n",
        "plt.bar(mon, val, color ='green', width = 0.5)\n",
        " \n",
        "plt.xlabel(\"Month\",color ='green')\n",
        "plt.ylabel(\"Temperature\",color ='green')\n",
        "plt.title(\"Monthly average of daily temperature ranges across all stations\",color ='green')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "MDhZd8tgsAK2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "207cde52-8075-42ef-83ad-3dadb29735ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}