Jovana_Kalamkovic_BI3_2018.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNx9jbGlwE1lI8LfkGXPY+q",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jovanakalamkovic99/pattern-rec-hw-2/blob/main/Jovana_Kalamkovic_BI3_2018.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1B8YiP5WAgD7"
      },
      "source": [
        "# imports\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sb\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "from sklearn import datasets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oaI_Hn8qr7kF"
      },
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2deeeFMlAg7e"
      },
      "source": [
        "df= pd.read_csv(\"ShanghaiPM20100101_20151231.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuDQ3FaiAqpS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "outputId": "2a02cf9e-1564-432f-b6c0-744442db7acf"
      },
      "source": [
        "print(\"shape: \\n\", df.shape)\n",
        "print(\"\\n types:\\n\", df.dtypes, \"\\n\")\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: \n",
            " (52584, 17)\n",
            "\n",
            " types:\n",
            " No                 int64\n",
            "year               int64\n",
            "month              int64\n",
            "day                int64\n",
            "hour               int64\n",
            "season             int64\n",
            "PM_Jingan        float64\n",
            "PM_US Post       float64\n",
            "PM_Xuhui         float64\n",
            "DEWP             float64\n",
            "HUMI             float64\n",
            "PRES             float64\n",
            "TEMP             float64\n",
            "cbwd              object\n",
            "Iws              float64\n",
            "precipitation    float64\n",
            "Iprec            float64\n",
            "dtype: object \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>No</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>season</th>\n",
              "      <th>PM_Jingan</th>\n",
              "      <th>PM_US Post</th>\n",
              "      <th>PM_Xuhui</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>HUMI</th>\n",
              "      <th>PRES</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>cbwd</th>\n",
              "      <th>Iws</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Iprec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>59.48</td>\n",
              "      <td>1026.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>cv</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>59.48</td>\n",
              "      <td>1025.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>59.21</td>\n",
              "      <td>1025.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>63.94</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>63.94</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   No  year  month  day  hour  ...  TEMP  cbwd  Iws  precipitation  Iprec\n",
              "0   1  2010      1    1     0  ...   1.0    cv  1.0            0.0    0.0\n",
              "1   2  2010      1    1     1  ...   1.0    SE  2.0            0.0    0.0\n",
              "2   3  2010      1    1     2  ...   0.0    SE  4.0            0.0    0.0\n",
              "3   4  2010      1    1     3  ...   0.0    SE  5.0            0.0    0.0\n",
              "4   5  2010      1    1     4  ...   0.0    SE  8.0            0.0    0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMfdFeJ9A_6z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a93dce18-abee-48c5-e29b-f44bdeb27d5e"
      },
      "source": [
        "print(df.info())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 52584 entries, 0 to 52583\n",
            "Data columns (total 17 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   No             52584 non-null  int64  \n",
            " 1   year           52584 non-null  int64  \n",
            " 2   month          52584 non-null  int64  \n",
            " 3   day            52584 non-null  int64  \n",
            " 4   hour           52584 non-null  int64  \n",
            " 5   season         52584 non-null  int64  \n",
            " 6   PM_Jingan      24700 non-null  float64\n",
            " 7   PM_US Post     34039 non-null  float64\n",
            " 8   PM_Xuhui       25189 non-null  float64\n",
            " 9   DEWP           52571 non-null  float64\n",
            " 10  HUMI           52571 non-null  float64\n",
            " 11  PRES           52556 non-null  float64\n",
            " 12  TEMP           52571 non-null  float64\n",
            " 13  cbwd           52572 non-null  object \n",
            " 14  Iws            52572 non-null  float64\n",
            " 15  precipitation  48575 non-null  float64\n",
            " 16  Iprec          48575 non-null  float64\n",
            "dtypes: float64(10), int64(6), object(1)\n",
            "memory usage: 6.8+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z25OeokSIii2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0453e87-e79e-487e-c98c-57e74be2bfcc"
      },
      "source": [
        "df.loc[df['DEWP'].isnull()].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([23, 24, 340, 341, 342, 3602, 10924, 12432, 27926, 28004, 31927,\n",
              "            41426, 43961],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hss3b-aPvQ3t",
        "outputId": "91437105-1b16-45b1-a73c-f18f8c2ad45b"
      },
      "source": [
        "df.loc[df['PRES'].isnull()].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([   23,    24,   340,   341,   342,  3602, 10924, 12432, 27926,\n",
              "            28004, 31927, 41426, 48423, 48424, 48425, 48426, 48427, 48428,\n",
              "            48429, 48430, 48431, 48432, 48433, 48434, 48435, 48436, 48437,\n",
              "            48438],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftM4sJrZvHbx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b74930c-7a58-4f61-be63-6c1e015f9853"
      },
      "source": [
        "df.loc[df['precipitation'].isnull()].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([  230,   584,   812,   813,   875,   995,  1384,  1900,  2095,\n",
              "             2159,\n",
              "            ...\n",
              "            52540, 52541, 52542, 52543, 52544, 52554, 52555, 52557, 52558,\n",
              "            52559],\n",
              "           dtype='int64', length=4009)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Gzg6WOOvNBP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab355b75-b1ca-4c14-ebc4-3cd39717f378"
      },
      "source": [
        "df.loc[df['Iprec'].isnull()].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([  230,   584,   812,   813,   875,   995,  1384,  1900,  2095,\n",
              "             2159,\n",
              "            ...\n",
              "            52540, 52541, 52542, 52543, 52544, 52554, 52555, 52557, 52558,\n",
              "            52559],\n",
              "           dtype='int64', length=4009)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNqo4Ms4vPG6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0732dbbd-da41-4aff-e1fc-dbbc7d4baa41"
      },
      "source": [
        "df.loc[df['HUMI'].isnull()].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([23, 24, 340, 341, 342, 3602, 10924, 12432, 27926, 28004, 31927,\n",
              "            41426, 43961],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVuWE9U2vShp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b0f4313-8c04-42cb-efe3-46b2092b4b0c"
      },
      "source": [
        "df.loc[df['TEMP'].isnull()].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([23, 24, 340, 341, 342, 3602, 10924, 12432, 27926, 28004, 31927,\n",
              "            41426, 43961],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBaRN-khvUcC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7857ed98-dc16-4601-c02e-0474e08bc426"
      },
      "source": [
        "df.loc[df['cbwd'].isnull()].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([23, 24, 340, 341, 342, 3602, 10924, 12432, 27926, 28004, 31927,\n",
              "            41426],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-6kmMcwvWa4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "604ac32b-229a-429d-96c4-c245558548ed"
      },
      "source": [
        "df.loc[df['Iws'].isnull()].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([23, 24, 340, 341, 342, 3602, 10924, 12432, 27926, 28004, 31927,\n",
              "            41426],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3-AOCinI8Rt"
      },
      "source": [
        "df.drop([ 23,    24,   340,   341,   342,  3602, 10924, 12432, 27926,\n",
        "            28004, 31927, 41426, 48423, 48424, 48425, 48426, 48427, 48428,\n",
        "            48429, 48430, 48431, 48432, 48433, 48434, 48435, 48436, 48437,\n",
        "            48438, 43961], inplace= True, axis = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "MnqY2oFivoho",
        "outputId": "d5c46074-dbea-4006-b9c7-4bfed8bd0ad6"
      },
      "source": [
        "total = df.isnull().sum().sort_values(ascending=False)\n",
        "percent = (df.isnull().sum()/len(df)).sort_values(ascending=False)*100\n",
        "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "missing_data.head(20)"
      ],
      "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",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total</th>\n",
              "      <th>Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PM_Jingan</th>\n",
              "      <td>27876</td>\n",
              "      <td>53.041575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PM_Xuhui</th>\n",
              "      <td>27384</td>\n",
              "      <td>52.105413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PM_US Post</th>\n",
              "      <td>18536</td>\n",
              "      <td>35.269717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>precipitation</th>\n",
              "      <td>4006</td>\n",
              "      <td>7.622491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iprec</th>\n",
              "      <td>4006</td>\n",
              "      <td>7.622491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PRES</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HUMI</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEWP</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEMP</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cbwd</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iws</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hour</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Total    Percent\n",
              "PM_Jingan      27876  53.041575\n",
              "PM_Xuhui       27384  52.105413\n",
              "PM_US Post     18536  35.269717\n",
              "precipitation   4006   7.622491\n",
              "Iprec           4006   7.622491\n",
              "PRES               0   0.000000\n",
              "HUMI               0   0.000000\n",
              "DEWP               0   0.000000\n",
              "TEMP               0   0.000000\n",
              "cbwd               0   0.000000\n",
              "Iws                0   0.000000\n",
              "season             0   0.000000\n",
              "hour               0   0.000000\n",
              "day                0   0.000000\n",
              "month              0   0.000000\n",
              "year               0   0.000000\n",
              "No                 0   0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ww-T10w4xK92"
      },
      "source": [
        "df['precipitation'].fillna(df['precipitation'].median(), inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSLrsb1NxQkT"
      },
      "source": [
        "df['Iprec'].fillna(df['Iprec'].median(), inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awhZGEeUJwBe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "7a9d6141-7965-4139-b99c-ba36368192a0"
      },
      "source": [
        "sb.boxplot(y='year',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b2e9ae50>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSXm4YEHK6sJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "c7ac6b8d-55cc-433e-d046-5420aa864406"
      },
      "source": [
        "sb.boxplot(y='month',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b25c6dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKjNXT5aK6ox",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "ff2af0ba-1c18-4afa-d1a1-199e8dceee53"
      },
      "source": [
        "sb.boxplot(y='day',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b2103450>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiiHD3UfK6kA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a160681c-cb92-4385-b91c-e492a1874010"
      },
      "source": [
        "sb.boxplot(y='hour',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b208d210>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tZ8DhlyK6d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "fbe1eae9-d6ac-42b0-e3e3-a4af5539b506"
      },
      "source": [
        "sb.boxplot(y='season',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1ff5d50>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BdgsjhKfK6Xi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "b89679dd-1b26-4210-8d2d-6f8ee17b7988"
      },
      "source": [
        "sb.boxplot(y='PM_US Post',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1fdfc50>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_We1mzGYHJZ",
        "outputId": "f2bed511-5c19-43bd-85e3-a2eba37738db"
      },
      "source": [
        "df.loc[df['PM_US Post']>200].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([17511, 17582, 17765, 17766, 17767, 17768, 17769, 17770, 17771,\n",
              "            18049,\n",
              "            ...\n",
              "            52418, 52419, 52420, 52424, 52425, 52426, 52562, 52563, 52564,\n",
              "            52565],\n",
              "           dtype='int64', length=401)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSN2JBR7V91f"
      },
      "source": [
        "prevelik_PM = df.loc[df['PM_US Post']>200].index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pP93jdVUQsgk"
      },
      "source": [
        "df.drop(prevelik_PM, inplace= True, axis = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0k5V-jJK6Qi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "outputId": "a4c9878f-080c-4ffc-8e17-aba5f004e77d"
      },
      "source": [
        "sb.boxplot(y='DEWP',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1f49590>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hV0LQSJGK6Jt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a1333971-b461-49f1-8304-0ff61bbe3761"
      },
      "source": [
        "sb.boxplot(y='HUMI',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1eb5b10>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tX4dhFKCK6BI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "2fc5f232-c009-4dab-c497-6d4fb4770c4e"
      },
      "source": [
        "sb.boxplot(y='PRES',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1e32410>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0Tvm3liK5y6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "4782f664-c601-4de1-c634-8a2081d50e4d"
      },
      "source": [
        "sb.boxplot(y='TEMP',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1e1c3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zp2l9_8JLGkt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "fa87dc6f-3e90-46fe-d066-acac59190e4c"
      },
      "source": [
        "sb.boxplot(y='Iws',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1d93c90>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpnRgs7jWlyc"
      },
      "source": [
        "Sređivanje svih neverovatnih vrednosti za vetar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3FyJPNqnZarb",
        "outputId": "c8459bb8-0bf7-4f6f-baa4-b5e7875eb709"
      },
      "source": [
        "df.loc[df['Iws']>15].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([    7,     8,     9,    10,    14,    19,    20,    21,    37,\n",
              "               38,\n",
              "            ...\n",
              "            52555, 52556, 52557, 52558, 52569, 52570, 52580, 52581, 52582,\n",
              "            52583],\n",
              "           dtype='int64', length=28777)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96YZJv6DZ5Cp",
        "outputId": "ec6dd5cc-2387-4818-8910-ab3510f112a2"
      },
      "source": [
        "vr_prevelikog_vetra = df.loc[df['Iws']>15].index\n",
        "percent = (6645/len(df['Iws']))*100\n",
        "print(percent)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12.741112858074166\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_mwi1JNg9XV"
      },
      "source": [
        "df.loc[df['Iws'] > 15,'Iws'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXu-Z0OmihDI"
      },
      "source": [
        "df['Iws'].fillna(df['Iws'].mean(), inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3966FyrzLGdT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a5b4194f-b2b2-4b28-cbec-75ecac7a0030"
      },
      "source": [
        "sb.boxplot(y='precipitation',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1d08a50>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VXGteP_aH5A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "bf726c70-b064-45ed-e94d-4f71c37a45fd"
      },
      "source": [
        "vr_padavina = df.loc[df['precipitation']>0].index\n",
        "sb.boxplot(y=vr_padavina,data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1c62d10>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YAFTVXmDvoPuQpHPRsJwmkiQNkGEgSTIMJEmQqhp0D6clySzw9KD7kHq4BPjxoJuQevjtqur5Ja03bBhIwyrJdFVNDroPaSk8TSRJMgwkSYaBdDbsGHQD0lJ5zUCS5JGBJMkwkCRhGEiSMAwkSRgGkiTgfwE6jgNtSRM4DQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyV71NbXLGP0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "f483769f-586c-4515-b81a-5304a78a910d"
      },
      "source": [
        "sb.boxplot(y='Iprec',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b1c546d0>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mE3gxVdKS9Qx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "f2aedaa6-b787-497c-ad07-e05bcc870812"
      },
      "source": [
        "vr_padavina_I = df.loc[df['Iprec']>0].index\n",
        "sb.boxplot(y=vr_padavina_I,data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b2601a10>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fidmiA2Fc7r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "dffc22f1-a06e-48e6-e2a7-1de313215203"
      },
      "source": [
        "total = df.isnull().sum().sort_values(ascending=False)\n",
        "percent = (df.isnull().sum()/len(df)*100).sort_values(ascending=False)\n",
        "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "missing_data.head(20)"
      ],
      "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>Total</th>\n",
              "      <th>Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PM_Jingan</th>\n",
              "      <td>27802</td>\n",
              "      <td>53.307512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PM_Xuhui</th>\n",
              "      <td>27312</td>\n",
              "      <td>52.367987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PM_US Post</th>\n",
              "      <td>18536</td>\n",
              "      <td>35.540898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hour</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iprec</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>precipitation</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEWP</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HUMI</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PRES</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEMP</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cbwd</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iws</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Total    Percent\n",
              "PM_Jingan      27802  53.307512\n",
              "PM_Xuhui       27312  52.367987\n",
              "PM_US Post     18536  35.540898\n",
              "year               0   0.000000\n",
              "month              0   0.000000\n",
              "day                0   0.000000\n",
              "hour               0   0.000000\n",
              "season             0   0.000000\n",
              "Iprec              0   0.000000\n",
              "precipitation      0   0.000000\n",
              "DEWP               0   0.000000\n",
              "HUMI               0   0.000000\n",
              "PRES               0   0.000000\n",
              "TEMP               0   0.000000\n",
              "cbwd               0   0.000000\n",
              "Iws                0   0.000000\n",
              "No                 0   0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LhFFlHspGCbS"
      },
      "source": [
        "df.drop(['PM_Jingan', 'PM_Xuhui'], inplace= True, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "krZWt9_PJGUf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b89e1d1-65b1-4fde-dc83-ccb37788bdb4"
      },
      "source": [
        "print(df['DEWP'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean        11.352399\n",
            "std          9.880437\n",
            "min        -21.000000\n",
            "25%          3.000000\n",
            "50%         12.000000\n",
            "75%         20.000000\n",
            "max         28.000000\n",
            "Name: DEWP, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4QjeR1wNy32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acc10adc-e727-42f8-a92b-73438ebacdf4"
      },
      "source": [
        "print(df['TEMP'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean        17.537121\n",
            "std          9.309866\n",
            "min         -5.000000\n",
            "25%         10.000000\n",
            "50%         18.000000\n",
            "75%         25.000000\n",
            "max         41.000000\n",
            "Name: TEMP, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTYjvaKyN577",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfe6b0fd-322f-48fc-d2f0-40b0b5b217d6"
      },
      "source": [
        "print(df['HUMI'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean        69.523489\n",
            "std         17.673930\n",
            "min         11.320000\n",
            "25%         57.930000\n",
            "50%         72.420000\n",
            "75%         83.370000\n",
            "max        100.000000\n",
            "Name: HUMI, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "muDu4YoDON_v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "079e713c-4de5-4e31-a687-10476caf93f2"
      },
      "source": [
        "print(df['PRES'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean      1015.962195\n",
            "std          9.018361\n",
            "min        990.000000\n",
            "25%       1008.100000\n",
            "50%       1015.900000\n",
            "75%       1023.000000\n",
            "max       1040.000000\n",
            "Name: PRES, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQ5xwMIlOSLv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "786eeb29-4e60-4a38-fae0-a27ee312fb6b"
      },
      "source": [
        "print(df['Iws'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean         5.936806\n",
            "std          2.781985\n",
            "min          0.000000\n",
            "25%          5.936806\n",
            "50%          5.936806\n",
            "75%          5.936806\n",
            "max         15.000000\n",
            "Name: Iws, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOSvNP0xOWc-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f3df2fb-d9a3-47c3-9e30-601681773b03"
      },
      "source": [
        "print(df['precipitation'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean         0.131401\n",
            "std          1.026962\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          0.000000\n",
            "max         61.600000\n",
            "Name: precipitation, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUbZ8DMiOcdw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b2446cc-627e-45a6-808c-29746c3889ae"
      },
      "source": [
        "print(df['Iprec'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean         0.855244\n",
            "std          6.276498\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          0.000000\n",
            "max        226.400000\n",
            "Name: Iprec, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4n0AuzaWOutA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b06a89c-7ff9-48ae-e276-792ca687a475"
      },
      "source": [
        "print(df['PM_US Post'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    33618.000000\n",
            "mean        50.545958\n",
            "std         35.938383\n",
            "min          1.000000\n",
            "25%         26.000000\n",
            "50%         41.000000\n",
            "75%         65.000000\n",
            "max        200.000000\n",
            "Name: PM_US Post, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usbVFdK9PFg1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c60659d-34ac-4c12-8f35-47b2f17eac3a"
      },
      "source": [
        "print(df['year'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean      2012.491985\n",
            "std          1.709330\n",
            "min       2010.000000\n",
            "25%       2011.000000\n",
            "50%       2012.000000\n",
            "75%       2014.000000\n",
            "max       2015.000000\n",
            "Name: year, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cBPqGCiPKaF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b43eaa1-208c-463d-a889-b1695589ce1d"
      },
      "source": [
        "print(df['month'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean         6.527150\n",
            "std          3.432881\n",
            "min          1.000000\n",
            "25%          4.000000\n",
            "50%          7.000000\n",
            "75%         10.000000\n",
            "max         12.000000\n",
            "Name: month, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwEoMjaoPM1J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3dec0736-0e4b-4ec0-ce15-7b0a30777b71"
      },
      "source": [
        "print(df['day'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean        15.737834\n",
            "std          8.796480\n",
            "min          1.000000\n",
            "25%          8.000000\n",
            "50%         16.000000\n",
            "75%         23.000000\n",
            "max         31.000000\n",
            "Name: day, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TuBg5oFPPak",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "439e731d-ff67-48aa-b2df-11b05d612aa5"
      },
      "source": [
        "print(df['hour'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean        11.506404\n",
            "std          6.920453\n",
            "min          0.000000\n",
            "25%          6.000000\n",
            "50%         12.000000\n",
            "75%         18.000000\n",
            "max         23.000000\n",
            "Name: hour, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93Wky2KbPVYc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9eeeae8-c338-49c7-e980-7b9dbaec78a0"
      },
      "source": [
        "print(df['season'].describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    52154.000000\n",
            "mean         2.482111\n",
            "std          1.114208\n",
            "min          1.000000\n",
            "25%          1.000000\n",
            "50%          2.000000\n",
            "75%          3.000000\n",
            "max          4.000000\n",
            "Name: season, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65shgb8gPYCU"
      },
      "source": [
        "df['PM_US Post'].fillna(method='ffill', inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xi3WFbKnQXGt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "f46fd0c8-77d5-412a-ac23-7f6fc3db3619"
      },
      "source": [
        "total = df.isnull().sum().sort_values(ascending=False)\n",
        "percent = (df.isnull().sum()/len(df)).sort_values(ascending=False)\n",
        "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "missing_data.head(20)"
      ],
      "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>Total</th>\n",
              "      <th>Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PM_US Post</th>\n",
              "      <td>17434</td>\n",
              "      <td>0.334279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iprec</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>precipitation</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iws</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cbwd</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEMP</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PRES</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HUMI</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEWP</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hour</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Total   Percent\n",
              "PM_US Post     17434  0.334279\n",
              "Iprec              0  0.000000\n",
              "precipitation      0  0.000000\n",
              "Iws                0  0.000000\n",
              "cbwd               0  0.000000\n",
              "TEMP               0  0.000000\n",
              "PRES               0  0.000000\n",
              "HUMI               0  0.000000\n",
              "DEWP               0  0.000000\n",
              "season             0  0.000000\n",
              "hour               0  0.000000\n",
              "day                0  0.000000\n",
              "month              0  0.000000\n",
              "year               0  0.000000\n",
              "No                 0  0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olRH1pcqYLZ9"
      },
      "source": [
        "vr_nule_pm = df.loc[df['PM_US Post'].isnull()].index\n",
        "df.drop(vr_nule_pm, inplace= True, axis = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jxrw8M75ekzE"
      },
      "source": [
        "#sb.set()\n",
        "#sb.pairplot(df, height = 2.5)\n",
        "#plt.show();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgVKJhYBZRSN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8fba449-06a7-4007-e279-30ca7d08c99a"
      },
      "source": [
        "df['cbwd'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['NE', 'NW', 'cv', 'SE', 'SW'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORSwlSBrM8-k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6354e46a-2e56-498c-da3b-2ba09bb63868"
      },
      "source": [
        "df.loc[df['cbwd']=='cv','cbwd']=1\n",
        "df.loc[df['cbwd']=='NE','cbwd']=2\n",
        "df.loc[df['cbwd']=='NW','cbwd']=3\n",
        "df.loc[df['cbwd']=='SW','cbwd']=4\n",
        "df.loc[df['cbwd']=='SE','cbwd']=5\n",
        "print(df['cbwd'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 3 1 5 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Al5Io1c2Z_dJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "outputId": "6600fcd4-0db4-4e09-dbef-7e01a270f471"
      },
      "source": [
        "corr = df.corr()\n",
        "f = plt.figure(figsize=(12, 9))\n",
        "sb.heatmap(corr, annot=True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCqtYvZWnn0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "5ffaa85d-467f-4255-9ecf-0a7ae98a6e25"
      },
      "source": [
        "plt.scatter(df['DEWP'],df['PM_US Post'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f17a907a750>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gg-yMP03peqc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "ce058ef4-2ab9-4937-aa7e-f9cf82c9585d"
      },
      "source": [
        "plt.scatter(df['TEMP'],df['PM_US Post'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f17a7859bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1ruqQmlpmgC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "ac213ac4-d3be-4182-b858-1f1e259bb236"
      },
      "source": [
        "plt.scatter(df['HUMI'],df['PM_US Post'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f17a782ba10>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiDeJq-FpqOm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "0eea3fc3-caac-43c5-c2ea-482e5137ae89"
      },
      "source": [
        "plt.scatter(df['PRES'],df['PM_US Post'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f17a7731ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3y4lDRzpvFs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "bcc96901-b88f-475c-ffe1-56bc24df40f3"
      },
      "source": [
        "plt.scatter(df['Iws'],df['PM_US Post'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f17a76a1d10>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4bNGPF/rDogzKMMs39FyBOBpzl9LJ0lsqw8qVIU22v3dM6GqemdN7Yz9g0ybjC8+K333qwrPS7T4Vp5WdmG43s71m9mRd7EYzGzKzrdG/j9Xdd62ZPWtmO81scbCWTmhXe3GZKKmePW2dexlMthmlSdPjs0ybn4xCT9xau3l3W/E0Wum53wF8G/h+Q/yb7n5rfcDMTqe2/d584ETgH83sXe4etlyCyXm6GdrUKfGTmKZWeEu3KRa/GXbaP/nYGb2xZxJFWaJ3rHpl9aZdjLrTY8bSc2ZXuqoFij9xayRhJmpSPI1Wttn7qZnNafF4lwB/5+5vAb80s2eBs6ntwSoF04kp0EWTtDZT2jWbbrhoPl++54lxlRQ9U4q1RO9Xl5xR+WReL6+JW2WT5YLqF81sWzRsM7YbQz9Qv+vB7ig2gZktM7NBMxscHk639rUUS1Lvt8InAsDEN5GqFLorj4lbndgWL7S0r8PbgNOABcAe4OvtHsDdV7n7gLsP9PWl27VGiiV0rzgPoRcOW7lhZ+xU96Ls5QvFXys9dF16HhO3ir5nc5xUyd3dX3H3UXc/BHyX2tALwBBQf6XmpCg26UzG5W/L8DeHnrgVerOOpLlFaecclaHHecGZ8RUiSfFm8tiCs4wLh6V6yZhZ/aP+cWCskuZ+4JNm9ntmdgowF/h/2ZpYTtOmxj+0SfEqKOuM1yxCf6BNmxo/UzYp3kwZepyhtyoMva8tlHPP5qYXVM1sNfBB4Hgz2w3cAHzQzBZQe9++AHwOwN13mNndwFPAQeALeVTKlMFbCTMAk+JSTqE/0EKvz5NHj7N3CsRdc097dhG6jaE/LADmvD1+Vcg5b0+X3Dsx/6CVapmlMeHvHeH7vwZ8LUujRCSMYxIm3xyTYSZkb88UDhyamN3TLuEwM6GcNO16Onl8oD36/G/aijdzat+M2GWhT+2bkep4cao7RiAi7D8Y3+NPircidAlt6DkreQyhhJ789uxw/Hr/SfE0lNyldKo8ySq0MsxleC1h3ZykeDPLF8+LXTgsy2qiScMlaYdROjEJU8ldSif0LvHSXb+fMFifFG9J3O7TGZx76rFtxYtAyV1Euup3CWcRSfFm8ph70InNNUJTchcpkNCllZ1YWjaros89AK3nLiIZhU50SUvIhlxaVtrXiQl/Su4iFZZHzXdoeWwFGFromcOdmPBXnEdPRILLY4gidDK++dIz24p3w8GErJsUbyZ09U0cJXcRaUsea5H3NJS3Nt7uttCli53YNEbJXUS66qYHdoxbDx9g9JBn2is3tNDLWXdieWwldxHpqjJUovxewoJ/SfFmOrE8tpK7iEgToWvxO0HJXaTC+hPWU0mKV0HozT+gnEv+KrmLVFgea5sX3eknHN1WvBXLF8+jt2FAvHdKtvVq8qbkLlJhP3piT1vxKvi/z7/aVrxVjQMwxR2QqVFyF6mwuLXcjxSvgjxWXCxDRU+jpsndzG43s71m9mRdbKWZ/cLMtpnZfWY2M4rPMbMRM9sa/ftOno0XEemEMlT0NGql534HcH5DbCPwHnc/E/gX4Nq6+55z9wXRv8+HaaaIVFUZNlYvo6bJ3d1/CrzaEPuxux+Mbj4KnJRD20QmncmY6CbjxuqdWK0zxJj7XwB/X3f7FDPbYmb/28z+MOmHzGyZmQ2a2eDwcHEWMRLppk7MXJTuu/Hi+bHVNzdePD/Y78iU3M3sK8BB4K4otAc42d0XAtcAPzCzfxf3s+6+yt0H3H2gr6+6ZVki7RhN6K4mxbshaW2rgGteVd6Shf1cfvbswwuF9Zhx+dmzWbKwP9jvSJ3czewzwIXAFe6169Du/pa7/zr6ejPwHPCuAO0UCeKoaT1txWWipKRR5dK70JPB1m8ZYu3mocMLhY26s3bzEOu3DKVuY6NUz4eZnQ/8JXCxu79ZF+8zs57o61OBucDzIRoqEsL+g6Ntxctu0WnHtRVvRdHPLvK4bhF6MtjKDTsZOTD+NTdyYDTTVoCNWimFXA38HJhnZrvN7Crg28DRwMaGkscPANvMbCtwL/B5d882c0AkoKSlQAq8REgmp/S9ra14FeRxgXbd5t1txZvJY539RlObfYO7L40Jfy/he9cCa7M2SqQspvUY+2O6rNN6ijEA/YNNv0qMf3XJGR1uTXm9mfDpnxRvpscsdu12bdYhUhAHEsYikuLNhK6W6cTSstI+bdYhUnChk3HodcNl8tIrRiSD0BcXy7huuBSTkrtIgRyTMEMxKS6SRMldpEDe3H+wrXgzM3rj3+JJcakOPcMiBRJXeXOkeDOTcd2WMkj6bA35mavkLlJhIwlj9Ulx6YxOzLdQchcRqSAldxGRClJyFxGpICV3EZEKUnIXEakgJXcRkQpSchcRqSAldxGRCmopuZvZ7Wa218yerIsdZ2YbzeyZ6P9jo7iZ2f8ws2fNbJuZvTevxot0W9Ky7QVZzr0U8tg5SVrvud8BnN8QWwE87O5zgYej2wAfpba93lxgGXBb9maKFFPRt5wrAy2RkI+Wkru7/xRo3C7vEuDO6Os7gSV18e97zaPATDM7IURjRUSkNVnG3Ge5+57o65eBWdHX/cCuuu/bHcVERKRDglxQdXenzbMoM1tmZoNmNjg8PByiGSIiEsmS3F8ZG26J/t8bxYeA2XXfd1IUG8fdV7n7gLsP9PX1ZWiGiIg0ypLc7weujL6+EvhhXfzPoqqZc4HX6oZvRESkA6a28k1mthr4IHC8me0GbgBuAe42s6uAF4HLom9/CPgY8CzwJvDngdssIiJNtJTc3X1pwl3nxXyvA1/I0igREclGM1RFRCpIyV1EpIKU3EVEKkjJXUSkgpTcRUQqSMldRKSClNxFRCpIyV1EpIKU3EVEKkjJXUSkgpTcRUQqSMldRKSClNxFRCpIyV1EpIKU3EVEKkjJXUSkglrarCOOmc0D1tSFTgX+CpgJfBYY2/X6Ond/KHULRUSkbamTu7vvBBYAmFkPtU2w76O2rd433f3WIC0UEZG2hRqWOQ94zt1fDHQ8ERHJIFRy/ySwuu72F81sm5ndbmbHxv2AmS0zs0EzGxweHo77FhERSSlzcjezacDFwD1R6DbgNGpDNnuAr8f9nLuvcvcBdx/o6+vL2gwREakTouf+UeBxd38FwN1fcfdRdz8EfBc4O8DvEBGRNoRI7kupG5IxsxPq7vs48GSA3yEiIm1IXS0DYGZHAR8BPlcX/u9mtgBw4IWG+0REpAMyJXd3fwN4e0Ps05laJCIimWmGqohIBSm5i4hUkJK7iEgFKbmLiFSQkruISAUpuYuIVJCSu4hIBSm5i4hUkJK7iEgFKbmLiFSQkruISAUpuYuIVJCSu4hIBSm5i4hUkJK7iEgFKbmLiFRQps06AMzsBeB1YBQ46O4DZnYcsAaYQ203psvc/TdZf5eIiLQmVM/9Q+6+wN0HotsrgIfdfS7wcHRbREQ6JK9hmUuAO6Ov7wSW5PR7REQkRojk7sCPzWyzmS2LYrPcfU/09cvArMYfMrNlZjZoZoPDw8MBmiEiImMyj7kD/8Hdh8zsHcBGM/tF/Z3u7mbmjT/k7quAVQADAwMT7hcRkfQy99zdfSj6fy9wH3A28IqZnQAQ/b836+8REZHWZUruZnaUmR099jXwx8CTwP3AldG3XQn8MMvvERGR9mQdlpkF3GdmY8f6gbv/g5k9BtxtZlcBLwKXZfw9IiLShkzJ3d2fB86Kif8aOC/LsUVEJD3NUBURqSAldxGRClJyFxGpICV3EZEKUnIXEakgJXcRkQpSchcRqSAldxGRClJyFxGpICV3EZEKUnIXEakgJXcRkQpSchcRqSAldxGRClJyFxGpICV3EZEKSp3czWy2mf3EzJ4ysx1m9qUofqOZDZnZ1ujfx8I1V0REWpFlJ6aDwJfd/fFoH9XNZrYxuu+b7n5r9uaJiEgaqZO7u+8B9kRfv25mTwP9oRomIiLpBRlzN7M5wEJgUxT6opltM7PbzezYhJ9ZZmaDZjY4PDwcohkiIhLJnNzN7G3AWuBqd/8tcBtwGrCAWs/+63E/5+6r3H3A3Qf6+vqyNkNEROpkSu5m1kstsd/l7usA3P0Vdx9190PAd4GzszdTRETakaVaxoDvAU+7+zfq4ifUfdvHgSfTN09ERNLIUi2zCPg0sN3Mtkax64ClZrYAcOAF4HOZWigiIm3LUi3zz4DF3PVQ+uaIiEgImqEqIlJBSu4iIhWk5C4iUkFK7iIiFaTkLiJSQUruIiIVpOQuIlJBSu4iIhWk5C4iUkFK7iIiFaTkLiJSQUrukaQHQg+QiJSRclfkUJtxEZEiU3IXEamg0ib33oSWJ8WbOXZGb1txEZEiK21yP5AwXpIUb+aGi+bTM2X88vQ9U4wbLpqf7oAiIl2UW3I3s/PNbKeZPWtmK0Ifv8fi9glJjrei8cEo7SefiEx6ueQvM+sB/ifwUeB0alvvnR7yd4y6txVvZuWGnRw4NP5nDxxyVm7Ymep4IiLdlFfn9GzgWXd/3t33A38HXBLyF/TPnN5WvJmX9o20FRfJQ9J5Z/rzUZms8kru/cCuutu7o9hhZrbMzAbNbHB4eLjtX7B88Tym9/aMi03v7WH54nkpmgsnJnwoJMU7beqU+Ld3UrwbkpoSuonT0141L4Erzj25rXgzeXxYhH6eQ7cxj+KI0MfsRAFH194l7r7K3QfcfaCvr6/tn1+ysJ+bLz2D/pnTMWo99psvPYMlC/ub/myc0B8Wc99xVFvxZm79j2dNeLFbFE9r1tHT2oo386fnxCegpHhaN196ZuqfDf28fCoh6SbFm/nqkjP41LknH7521GPGp849ma8uOSPV8UJ/WED45zl0G2+4aD69PePfLb092YojQh8zjzY2Mk85Rn3Eg5q9H7jR3RdHt68FcPeb475/YGDABwcHg7ejXeu3DLFyw05e2jfCiTOns3zxvNQfFgAf+cY/8czeNw7fnvuOo9h4zQcL0z6Ac762kVde33/49qyjp7HpKx9Jfbzr129n9aZdjLrTY8bSc2anTkxzVjw4IfbXly/I/DeHfl5C/s15yKN9oY8Z+nh5vFdCHzPE8cxss7sPxN6XU3KfCvwLcB4wBDwG/Km774j7/qIkdxGRMjlScp+axy9094Nm9kVgA9AD3J6U2EVEJLxckjuAuz8EPJTX8UVEJFl1yw5ERCYxJXcRkQpSchcRqaBcqmXaboTZMPBihkMcD/xroObkoejtg+K3sejtg+K3sejtA7WxXe9099iJQoVI7lmZ2WBSOVARFL19UPw2Fr19UPw2Fr19oDaGpGEZEZEKUnIXEamgqiT3Vd1uQBNFbx8Uv41Fbx8Uv41Fbx+ojcFUYsxdRETGq0rPXURE6ii5i4hUUKmTe977tGZlZrPN7Cdm9pSZ7TCzL3W7TXHMrMfMtpjZj7rdljhmNtPM7jWzX5jZ09GS0oVhZv8len6fNLPVZvb7BWjT7Wa218yerIsdZ2YbzeyZ6P9jC9jGldHzvM3M7jOzmUVqX919XzYzN7Pju9G2VpQ2uXdin9YADgJfdvfTgXOBLxSwjQBfAp7udiOO4FvAP7j7u4GzKFBbzawf+M/AgLu/h9oqqJ/sbqsAuAM4vyG2AnjY3ecCD0e3u+kOJrZxI/Aedz+T2rLh13a6UXXuYGL7MLPZwB8Dv+p0g9pR2uROB/Zpzcrd97j749HXr1NLStl2DAjMzE4CLgD+ptttiWNmxwAfAL4H4O773X1fd1s1wVRgerSPwQzgpS63B3f/KfBqQ/gS4M7o6zuBJR1tVIO4Nrr7j939YHTzUeCkjjfs39oS9xgCfBP4S6DQ1ShlTu5N92ktEjObAywENnW3JRP8NbUX6qFuNyTBKcAw8L+ioaO/MbN0e+LlwN2HgFup9eL2AK+5+4+726pEs9x9T/T1y8CsbjamBX8B/H23G1HPzC4Bhtz9iW63pZkyJ/fSMLO3AWuBq939t91uzxgzuxDY6+6bu92WI5gKvBe4zd0XAm/Q/eGEw6Jx60uofQidCBxlZp/qbqua81oNdGF7nmb2FWrDmnd1uy1jzGwGcB3wV91uSyvKnNyHgNl1t0+KYoViZr3UEvtd7r6u2+1psAi42MxeoDas9WEz+/BGWucAAAFgSURBVNvuNmmC3cBudx8747mXWrIvij8Cfunuw+5+AFgH/EGX25TkFTM7ASD6f2+X2xPLzD4DXAhc4cWaiHMatQ/xJ6L3zEnA42b277vaqgRlTu6PAXPN7BQzm0btItb9XW7TOGZm1MaKn3b3b3S7PY3c/Vp3P8nd51B7/B5x90L1Ot39ZWCXmc2LQucBT3WxSY1+BZxrZjOi5/s8CnTBt8H9wJXR11cCP+xiW2KZ2fnUhgkvdvc3u92eeu6+3d3f4e5zovfMbuC90Wu0cEqb3KOLLmP7tD4N3F3AfVoXAZ+m1iPeGv37WLcbVUL/CbjLzLYBC4D/1uX2HBadUdwLPA5sp/ae6vr0dDNbDfwcmGdmu83sKuAW4CNm9gy1M45bCtjGbwNHAxuj98t3Cta+0tDyAyIiFVTanruIiCRTchcRqSAldxGRClJyFxGpICV3EZEKUnIXEakgJXcRkQr6/7RNYuAj+JvyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Urh-cXxZpxWH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a7b41463-2e63-461e-8f9f-36c6c153d0ab"
      },
      "source": [
        "plt.scatter(df['precipitation'],df['PM_US Post'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f17a769b610>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LBeropwP1hxGRVsl8cIdifvzeJ19moqSovdZNOhotTZzpihf9MRHJjoaDu5ktBjZEDr0N+BugH/grYDQ4/mV3f6ThEc6Q0sAeiubNKwXHTi9NVLMxkWxpuFrG3fe6+xJ3XwJcQHFLvR8HD98RPtYNgR2K2+uVE+bNay137FRqNiaSLUmVQl4KvODu+xP6eTOqL9fLygvPrFjnnnRwTKINQT3UbEwkW5IK7p8E7o3c/4KZPWVmd5rZvHIvMLNVZjZsZsOjo6PlnjJj8oUJHt8zyjUXDMbWuScZHNvxKUDNxkSypengbmazgSuBHwWHvg28HVgCHAS+Xu517r7e3YfcfWhgYKDZYTRtZCzPxu0jrF6+mBfXXsETay6ZkotOMji2I0WiZmMi2ZLEzP3DwK/c/RCAux9y9wl3Pw58B3h3Au8xIyoF2CSDYztSJM2swBWR7pNEKeRKIikZM5vv7geDux8DnkngPWZMXIBNshNjrYuektbpFT0ikpymgruZzQU+CHwucvi/mdkSiuuD9pU81vEqBdhKwbGeGvJ6Fj2JiDSiqeDu7q8Df1Jy7NNNjaiNGg2w9daQqx+7iLSaVqgGes2m5aBrnY1XukAaF7A7KUWilasi6aPgHjjuPi2wr75/F4WJ4srVkbE8q+/fBUyfjXdzDblWroqkU6b7uUc5sHDNZpb+7U/ZtGOE2x7aPRnYQ4UJ57aHdk97bTfXkGvlqkg6KbiXODJe4KYf7eLIePm9U8sdL1cmmesxxo8em7EVqI3q5k8dIhJPaZkyJo7HbZddFG7w8dVNT092kzRg7uxexo9OcHJfjtePHpv8Q9CKVEdSefJ2lWWKSGtp5t6AdVv28tVNT3P3tpcmu0k68PrRCT510QLmnjRrWkonLtXRSI+ZJNsXaOWqSDopuDfgt2N57n3y5bKP3fvkyzWnOhoN0knmybVyVSSdlJaJ0d+Xw6x8jr3HLLb/+4Q7gzWmOhopoYTk8+SdVJYpIsnQzD3GR86fzy0fPXdaygLiN/aAYr18ramOuGA8MpavmKbp5uocEZkZCu4xNm4vBtXbrz4vdiOPclZeeGbNqY5KwbhSmqabq3NEZGaYV5iFzpShoSEfHh6u6zUL12xu0WhOGOzv44k1l7BozWbi/iv1BimaXjNWXngmf7/ivJp/fukComrjKH1tWC0TVudEL+L25XqVOxdJOTPb7u5D5R5Tzr2CMD0Sl2MvF3TrUdpjJu4PSLn0TTRPvmztVsbyU68N1Lq5t4ikk9IyVTjlc+zNlguGJZA3btgJwB2fWMJgg7l0LUQSkVKZD+653trz6RZ8lcuh11OvHlcCefHZAw3VnOsCq4iUynxaZt3Hz2fdlr2MjOUn8+dxHNi39orJ+2Hee2QsjwWPQ/UVqXElkA/vOsibcj2Tj/X35bj1ynOrplbUH15ESmU+uA/vf2Va3rzSxdpla7dOBs1oQC39k1Ap5x2XLinNm79x7Hi14QPV+8Orpa9I9jQd3M1sH/AaMAEcc/chMzsF2AAspLgb07XufqTZ92qFe7a9BMDje0Yng9/c2b28frR8BUs4Kz9pVk/VKpe4IB7Xz6VUPRdF4xYiqaWvSDYllXO/2N2XREpy1gCPuftZwGPB/Y7kwN3bXpqS/z567Di9PfG5+HxhYtosu5y4nHe5OvU4zV4UVUtfkWxq1QXVq4C7gtt3ASta9D4tUTju9MQWJtamUs673CKneXNyZZ/b7EVRVdKIZFMSOXcHfmpmDvwvd18PnObuB4PHfwecVvoiM1sFrAJYsGBBAsNIVqG2dPcU4UXVwRry2qVplHILmpK4KKqWviLZlERwf6+7j5jZnwKPmtme6IPu7kHgp+T4emA9FFeoJjCOtuo14+vXnt9wHrtVm2arkkYkm5oO7u4+Enw/bGY/Bt4NHDKz+e5+0MzmA4ebfZ+Z1mNQZc+OKUr3YG1EK7oz1vNHQ1U1IunRVHA3s7lAj7u/Ftz+EPC3wIPAdcDa4PtPmh3oTJo7u5ePvWuQu4NKmqi+XA/5MjmbdqY5qgXlWv5oqKpGJF2anbmfBvzYil0TZwE/cPd/MrNfAveZ2fXAfuDaJt9nRo0fnWDoracATG6jFzYGG3rrKVXTHDM5A04qKDfaW15mVqO/W/pUlj3qChmjUlfFSv9Qyl0YredCa62iq2PLqbepWVznSwNejKzKlfaJu+herftno6+TzqeukA2oNGuttGDopvt2TWthUNqWYHj/K1MWTdUb8GtpFVxvqaOqajpfo5+u9KksmzLfOKySegJkGHAr9aaB4j+qe0oWTdW7uXW5f6yl6g3K2ii78zW6ZkFrHbJJwb2CHrMpQbdS58daAm4org9Nrar9o2wkKJcurJo3J8dJs3q4ccNO7ezUIRrt/qmuodmktEwFE+7cuGEnw/tfmXYhtfTCZbOzoPD1m3aMcOuDuyfbG8ybk+OWj07tDFmpN00zef0w3aTKmc7U6JoFrXXIJs3cq3CKzcVue2h32bzlbQ/tBpqfBZ3e38emHSOs/tGuKX1rjowXWH3/rikz57gUyj98YglPrLmk6QCsfjSdqda9eZN6nXQ3zdxr4BSDbDlHxgvFoFxmdlSrcBa1bsteCmVWThUmfMrFr1YvTGokR6tSu5nR6EK3ViyQk86m4J6AdVv2TpYdRlMqteg1m5xFhVvulVMaWFu5MKneyply7/OlDTu59cHdNW02IiLJU1omAdHAW+sGG6Fo24JKqZ1G0j5x6ZWb7ttV8QJpvZUzcReTx/KFuiuBRCQZCu4JCANvXJDrNauppe/q5YvJlekjn+u1hi5+xaVRJtwrBt16c7SV0jXK1Yu0h9IyTYoG3krB9Mh4Yco+q1B+Njz3pFlT0jrlqmVqVamqptoilnpytNV2llI9tcjM08y9SXNnn/j7WC114hSX88P02XCYt44G9r5cb8OBHarv+JRU0K32PqqnFpl5mrk3aSxf4MYNO/nShp3Mm5Mj12NlK15C0R4z67bs5cYNOzm9v4/xo8cSXyIevq5cSwRILuiG73PbQ7unVRU1Uk+tyhuR5im4JyAMm0fGC/T2GP19OV7NF2I36gurVqLVJXGanV2HQbHVi1iiC6DChma9ZlNy7rV2L9QCKpHmKbgnbOK4M5YvMG9OjrHx8gE+DHq1SGJ2nfQuT6UBfMJ92srYRgO0mlyJJEPBvUXiFj315XprDuxJzq6TWsRSOrMO0z3RAN5MgFaTK8mKVqcfFdxn2JtyPbwp1xMb/EMGXHNB560qrNQgLQzgzQTomWw9rNy+tMtMpB8brpYxszPN7HEze9bMdpvZF4Pjt5rZiJntDL4uT2SkKXFkvMC///FY1ec58Pie0dYPqE61tJdtpgvhTLUeDv9xNdN6WaRRM9G/qZlSyGPATe5+DnARcIOZnRM8doe7Lwm+Hml6lClTqZomqhNTEbW0l20mQCfd5CquTbOao0k7zUT6seG0jLsfBA4Gt18zs+cAfaZNUCfWh1dqkBYG8GYv4Lbq+kD0o69y+9JOM5F+TCTnbmYLgaXAk8Ay4Atm9hlgmOLs/kiZ16wCVgEsWLAgiWF0lbDKJE6n9duO5qdP7stNXjeIq5bphC6ElWbn2lZQ2mkmeuw3vUG2mb0Z+BnwNXd/wMxOA35PMW38d8B8d//LSj+jEzfIbqWwDUFpO4Lw/rw5Odzh1Xyh7Kx3pi8EtnqD5VadT6VNv+/4xBJtGi1tlcTvfcs2yDazHLARuMfdHwBw90ORx78DPNzMe6RNNKCXBp7+OTmueOd8NvzyZQoTJ0oMV9+/C6BtuyS1sva8ledTaXaedO2/SL1a/em2mWoZA74LPOfu34gcnx952seAZxofXvpU+px0ZLzA3dtemgzsocKET+741I4Lga3MT7fyfKpd2F2xdJAn1lzCi2uvSGQHK5FO0szMfRnwaeBpMwt3mfgysNLMllCMY/uAzzU1QgFOLIpqx4XAWvPTM7XrU600O5csa6Za5l840eQwSqWPLdSOC4G1XPyZqV2f6tUJF3ZF2kEtf7tEf19xs48kF/nE1YCXqqX2vNH0ykwtWhLJGrUf6DC5XmNiwolu1pfrMW698lwguVRDvTPtajPgRtMrSp2ItIaCewcJd12CysEuiVTDbQ/tTrQCppn0SpKpE/WLESlScO8g7idmsq2uW49rXNbohcyZWJRRjXrBi5yg4N5BolvstXIGWikPXm2mHTeuTkivpL0XvD6VSD0U3DtMeFGzdAYabuVXusy/EZVm55Vm2tVmxu2uTElzvxh9KpF6qVqmw6zbsrfsDDRc1pREa9q42Xl/X65ioOj0TorNtBrudJ3w377W6irpDAruHWZkLF9xT1Vo/h91XPlhWJETp9Nnxmkuq2z3f3v1v+8+Ssu0kFnxImldr6Fyi4LQyFieRWs2V8y9Jp0fj6uI6Z+TY9narW3PBXdC3r9V2t3FMu3XM9JIwb2FwsB+0qwe3jh2vPKTqT2wT/58yudeN+0Y4baHdk+piKknPx73R6FcRUyu1/j3Px6bfK9254LbnfdvlXZXI7X7k4PUT2mZGVBLYB/s76srsEdF0zThx+dypY75wgS3PbS7Yt600sfvcitV586eNW1nqU7Kw6dF0jtU1SvN1zPSqul+7knIWj/3Uv19OXbe8iGWrd1aNd9eyWB/H+NHj1XdfDuqtId53BgG+/t4Ys0l045X6pn+4torah4HqNSvk7W6p780pmX93CUZrwb17ZW2sKtFI38Ywtl8GFTj/tTHffxOKhesUr/OlubrGWml4N4BTu47cUHyTbnKmbJ5c3L8sXC84T8A5RwZL1Sd7ccF66Rywbpg1/nSej0jrZRzb7Ncj/H60WOTOe58oXJ+/op3zp/MvdbDONFZshEXnz1Q9nhSueC4TwYjY3nVVIs0QMG9zSbcp+28VMnd215i3Za9rF6+uO4Af+uV506rA6/V43tGYx8LK2lO7+/jt2N51m3ZW3cwrpTGUU21SP1aFtzN7DIz22tmz5vZmla9T7c73sD17DDYXXz2ALnecvulTBfuG1o6y651Nl+p5C2JBS7lFiBFqQJHpD4tybmbWS/wP4EPAgeAX5rZg+7+bCver9PVW79ei3xhgsf3jDJ39qwpDcfKKd03NJoyKVcFUU6lmXUS+fLoBbu4C8OqqRapXasuqL4beN7dfwNgZj8ErgIyEdxLywCjJX5JBvlqwc6galVDaRXEyX05Xj96bEqqqNoF0qQWuIR/eOLKMVVTLVK7VgX3QeDlyP0DwIXRJ5jZKmAVwIIFC1o0jOTNm5NjbLxQMUiXBqHobLnWWva+XC/XXDDI43tGY58fvk89denllJvN11PylvTS+HavxhRJg7aVQrr7emA9FBcxtfK9DPjTt8zm0GtHY5/TY5Xz37leY93Hz58Mcl/d9DR3b3tp+s+hctvcuMAVBvJyATVuAUn4PkkHwnpL3pIOxqqpFmleq4L7CHBm5P4ZwbHE7Ft7RdlVqsvefgr3/NV7Yl+3accIX37gKcaDkkMz+NSFCxh66ylTgsnFZw/EBluAv19xHgA/ePKlyT8Kfbkebr/6nRWDUCOBq5bXtDMQtiIYq6ZapDktaT9gZrOAfwMupRjUfwn8J3ffXe75jbQfEBHJuhlvP+Dux8zsC8AWoBe4My6wi4hI8lqWc3f3R4BHWvXzRUQknlaoioikkIK7iEgKKbiLiKRQR2zWYWajwP4mfsSpwO8TGk676Bw6RxrOIw3nAOk4j1aew1vdvWzL1o4I7s0ys+G4cqBuoXPoHGk4jzScA6TjPNp1DkrLiIikkIK7iEgKpSW4r2/3ABKgc+gcaTiPNJwDpOM82nIOqci5i4jIVGmZuYuISISCu4hICnV1cO/WfVrN7E4zO2xmz0SOnWJmj5rZr4Pv89o5xmrM7Ewze9zMnjWz3Wb2xeB415yHmb3JzH5hZruCc7gtOL7IzJ4Mfq82mNnsdo+1GjPrNbMdZvZwcL8bz2GfmT1tZjvNbDg41jW/TwBm1m9m95vZHjN7zsze065z6NrgHtmn9cPAOcBKMzunvaOq2feAy0qOrQEec/ezgMeC+53sGHCTu58DXATcEPz376bzeAO4xN3PB5YAl5nZRcB/Be5w9+OxxG4AAALDSURBVP8IHAGub+MYa/VF4LnI/W48B4CL3X1JpC68m36fAL4J/JO7nw2cT/H/SXvOwd278gt4D7Alcv9m4OZ2j6uO8S8Enonc3wvMD27PB/a2e4x1ns9PKG6I3pXnAcwBfkVxO8jfA7OC41N+zzrxi+JmOI8BlwAPU9x8rKvOIRjnPuDUkmNd8/sEnAy8SFCo0u5z6NqZO+X3ae3mrXtOc/eDwe3fAae1czD1MLOFwFLgSbrsPIJ0xk7gMPAo8AIw5u7Hgqd0w+/VPwB/DRwP7v8J3XcOAA781My2B3ssQ3f9Pi0CRoH/E6TI/reZzaVN59DNwT21vPgnvitqVM3szcBG4Evu/ofoY91wHu4+4e5LKM5+3w2c3eYh1cXMPgIcdvft7R5LAt7r7u+imGq9wczeH32wC36fZgHvAr7t7kuB1ylJwczkOXRzcG/5Pq0z7JCZzQcIvh9u83iqMrMcxcB+j7s/EBzuuvMAcPcx4HGKKYz+YKtI6Pzfq2XAlWa2D/ghxdTMN+mucwDA3UeC74eBH1P8Y9tNv08HgAPu/mRw/36Kwb4t59DNwf2XwFlBVcBs4JPAg20eUzMeBK4Lbl9HMYfdsczMgO8Cz7n7NyIPdc15mNmAmfUHt/soXjN4jmKQ/3jwtI4+B3e/2d3PcPeFFP8NbHX3T9FF5wBgZnPN7C3hbeBDwDN00e+Tu/8OeNnMFgeHLgWepV3n0O6LEE1ewLic4kbcLwBfafd46hj3vcBBoEDxr/31FPOkjwG/Bv4ZOKXd46xyDu+l+PHyKWBn8HV5N50H8E5gR3AOzwB/Exx/G/AL4HngR8BJ7R5rjefzAeDhbjyHYLy7gq/d4b/nbvp9Csa7BBgOfqc2AfPadQ5qPyAikkLdnJYREZEYCu4iIimk4C4ikkIK7iIiKaTgLiKSQgruIiIppOAuIpJC/x/a73vjsoVvRQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "keiBf9F9bKCv"
      },
      "source": [
        "PM US Post nema nikakvu specijalnu korelisanost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lO6XnRucdkv2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "41784b4b-6665-4536-b361-0d71c6763e34"
      },
      "source": [
        "plt.scatter(df['cbwd'], df['precipitation'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f17a760c910>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "soF2YJQOef8B"
      },
      "source": [
        "Kad je bilo malo vetra, manje je i kise, ali pravac vetra ne donosi promene u kolicini kise koja je pala"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-nOnTrOjkDP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "3c6d2457-db5f-4b29-ce90-ee192ac79abe"
      },
      "source": [
        "gb = df.groupby(by=['year', 'month']).mean()\n",
        "T_2012 = gb.loc[2012]['TEMP']\n",
        "T_2015 = gb.loc[2015]['TEMP']\n",
        "plt.plot(np.arange(1, 13, 1), T_2012, 'b', label='2012') \n",
        "plt.plot(np.arange(1, 13, 1), T_2015, 'g', label='2015')\n",
        "plt.ylabel('Prosečna mesečna temperatura (℃)')\n",
        "plt.xlabel('Mesec')\n",
        "plt.legend();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVI-IMbtkywq"
      },
      "source": [
        "LIN REG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uo5uTkiXEQ2"
      },
      "source": [
        "def model_evaluation(y, y_predicted, N, d):\n",
        "    mse = mean_squared_error(y_test, y_predicted)\n",
        "    mae = mean_absolute_error(y_test, y_predicted)\n",
        "    rmse = np.sqrt(mse)\n",
        "    r2 = r2_score(y_test, y_predicted)\n",
        "    r2_adj = 1-(1-r2)*(N-1)/(N-d-1)\n",
        "\n",
        "    print('Mean squared error: ', mse)\n",
        "    print('Mean absolute error: ', mae)\n",
        "    print('Root mean squared error: ', rmse)\n",
        "    print('R2 score: ', r2)\n",
        "    print('R2 adjusted score: ', r2_adj)\n",
        "\n",
        "    res=pd.concat([pd.DataFrame(y.values), pd.DataFrame(y_predicted)], axis=1)\n",
        "    res.columns = ['y', 'y_pred']\n",
        "    print(res.head(20))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMcvMHGKXFxs"
      },
      "source": [
        "y=df['PM_US Post']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wz6jeznYiW4C"
      },
      "source": [
        "x = df.drop('PM_US Post',axis = 1,inplace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zc4Yiy8o4zq0"
      },
      "source": [
        "x = x.drop('No',axis = 1,inplace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IhovT4yXR2r"
      },
      "source": [
        "x = x.drop('cbwd',axis = 1,inplace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ITQsb2ia5P6w",
        "outputId": "d729d470-56d4-4eaa-b216-caefd24ff397"
      },
      "source": [
        "print(x.info())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 34720 entries, 17442 to 52583\n",
            "Data columns (total 12 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   year           34720 non-null  int64  \n",
            " 1   month          34720 non-null  int64  \n",
            " 2   day            34720 non-null  int64  \n",
            " 3   hour           34720 non-null  int64  \n",
            " 4   season         34720 non-null  int64  \n",
            " 5   DEWP           34720 non-null  float64\n",
            " 6   HUMI           34720 non-null  float64\n",
            " 7   PRES           34720 non-null  float64\n",
            " 8   TEMP           34720 non-null  float64\n",
            " 9   Iws            34720 non-null  float64\n",
            " 10  precipitation  34720 non-null  float64\n",
            " 11  Iprec          34720 non-null  float64\n",
            "dtypes: float64(7), int64(5)\n",
            "memory usage: 3.4 MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_FZyu6NYiiW"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odSTQqTkY-x0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "outputId": "0bf4870b-011b-452e-97fe-3b6cfe8bb26d"
      },
      "source": [
        "# Inicijalizacija\n",
        "first_regression_model = LinearRegression(fit_intercept=True)\n",
        "\n",
        "# Obuka\n",
        "first_regression_model.fit(x_train, y_train)\n",
        "\n",
        "# Testiranje\n",
        "y_predicted = first_regression_model.predict(x_test)\n",
        "\n",
        "# Evaluacija\n",
        "model_evaluation(y_test, y_predicted, x_train.shape[0], x_train.shape[1])\n",
        "\n",
        "# Ilustracija koeficijenata\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(range(len(first_regression_model.coef_)),first_regression_model.coef_)\n",
        "print(\"koeficijenti: \", first_regression_model.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error:  1044.0059129312633\n",
            "Mean absolute error:  24.570197285355082\n",
            "Root mean squared error:  32.31108034299168\n",
            "R2 score:  0.1443258281151759\n",
            "R2 adjusted score:  0.1440792992866029\n",
            "        y     y_pred\n",
            "0    11.0  41.201327\n",
            "1    68.0  70.540958\n",
            "2    27.0  39.159925\n",
            "3    32.0  42.401765\n",
            "4     6.0  40.134912\n",
            "5    14.0  50.259251\n",
            "6    66.0  63.382259\n",
            "7   146.0  61.724926\n",
            "8    24.0  44.097877\n",
            "9    78.0  54.972918\n",
            "10  104.0  56.770273\n",
            "11    6.0  39.909209\n",
            "12   80.0  52.159396\n",
            "13   34.0  46.396703\n",
            "14   31.0  64.728921\n",
            "15   40.0  51.140761\n",
            "16   31.0  39.733810\n",
            "17   32.0  37.410261\n",
            "18   21.0  56.722429\n",
            "19   44.0  39.813059\n",
            "koeficijenti:  [ 0.45205515  0.21714404  1.14691322 -0.33864109 -0.98555023 -2.08628825\n",
            " -0.66019717 -1.03536361 -0.39800551]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2eHPeQycK4O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "outputId": "18d1057e-f3aa-4343-d5d2-5b408c8d4c59"
      },
      "source": [
        "# Selekcija obelezja\n",
        "import statsmodels.api as sm\n",
        "X = sm.add_constant(x_train)\n",
        "#np.asarray(x_train)\n",
        "model = sm.OLS(y_train, X).fit()\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>PM_US Post</td>    <th>  R-squared:         </th>  <td>   0.143</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.143</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   578.2</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Mon, 29 Nov 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>17:15:55</td>     <th>  Log-Likelihood:    </th> <td>-1.5389e+05</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td> 31248</td>      <th>  AIC:               </th>  <td>3.078e+05</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td> 31238</td>      <th>  BIC:               </th>  <td>3.079e+05</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>      <td> </td>     \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>         <td> 1108.2807</td> <td>   50.560</td> <td>   21.920</td> <td> 0.000</td> <td> 1009.182</td> <td> 1207.380</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>month</th>         <td>    0.4521</td> <td>    0.064</td> <td>    7.090</td> <td> 0.000</td> <td>    0.327</td> <td>    0.577</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>hour</th>          <td>    0.2171</td> <td>    0.028</td> <td>    7.623</td> <td> 0.000</td> <td>    0.161</td> <td>    0.273</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>season</th>        <td>    1.1469</td> <td>    0.215</td> <td>    5.322</td> <td> 0.000</td> <td>    0.725</td> <td>    1.569</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>HUMI</th>          <td>   -0.3386</td> <td>    0.013</td> <td>  -26.557</td> <td> 0.000</td> <td>   -0.364</td> <td>   -0.314</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>PRES</th>          <td>   -0.9856</td> <td>    0.049</td> <td>  -20.153</td> <td> 0.000</td> <td>   -1.081</td> <td>   -0.890</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>TEMP</th>          <td>   -2.0863</td> <td>    0.047</td> <td>  -44.617</td> <td> 0.000</td> <td>   -2.178</td> <td>   -1.995</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Iws</th>           <td>   -0.6602</td> <td>    0.069</td> <td>   -9.521</td> <td> 0.000</td> <td>   -0.796</td> <td>   -0.524</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>precipitation</th> <td>   -1.0354</td> <td>    0.213</td> <td>   -4.860</td> <td> 0.000</td> <td>   -1.453</td> <td>   -0.618</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Iprec</th>         <td>   -0.3980</td> <td>    0.030</td> <td>  -13.096</td> <td> 0.000</td> <td>   -0.458</td> <td>   -0.338</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>7307.885</td> <th>  Durbin-Watson:     </th> <td>   1.998</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>15679.301</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>           <td> 1.364</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>       <td> 5.144</td>  <th>  Cond. No.          </th> <td>2.73e+05</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.73e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:             PM_US Post   R-squared:                       0.143\n",
              "Model:                            OLS   Adj. R-squared:                  0.143\n",
              "Method:                 Least Squares   F-statistic:                     578.2\n",
              "Date:                Mon, 29 Nov 2021   Prob (F-statistic):               0.00\n",
              "Time:                        17:15:55   Log-Likelihood:            -1.5389e+05\n",
              "No. Observations:               31248   AIC:                         3.078e+05\n",
              "Df Residuals:                   31238   BIC:                         3.079e+05\n",
              "Df Model:                           9                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "=================================================================================\n",
              "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------\n",
              "const          1108.2807     50.560     21.920      0.000    1009.182    1207.380\n",
              "month             0.4521      0.064      7.090      0.000       0.327       0.577\n",
              "hour              0.2171      0.028      7.623      0.000       0.161       0.273\n",
              "season            1.1469      0.215      5.322      0.000       0.725       1.569\n",
              "HUMI             -0.3386      0.013    -26.557      0.000      -0.364      -0.314\n",
              "PRES             -0.9856      0.049    -20.153      0.000      -1.081      -0.890\n",
              "TEMP             -2.0863      0.047    -44.617      0.000      -2.178      -1.995\n",
              "Iws              -0.6602      0.069     -9.521      0.000      -0.796      -0.524\n",
              "precipitation    -1.0354      0.213     -4.860      0.000      -1.453      -0.618\n",
              "Iprec            -0.3980      0.030    -13.096      0.000      -0.458      -0.338\n",
              "==============================================================================\n",
              "Omnibus:                     7307.885   Durbin-Watson:                   1.998\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            15679.301\n",
              "Skew:                           1.364   Prob(JB):                         0.00\n",
              "Kurtosis:                       5.144   Cond. No.                     2.73e+05\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 2.73e+05. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPiJhf_3aCry",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8ddb5d83-3305-4031-fcca-925bdd21b164"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(x_train)\n",
        "x_train_std = scaler.transform(x_train)\n",
        "x_test_std = scaler.transform(x_test)\n",
        "x_train_std = pd.DataFrame(x_train_std)\n",
        "x_test_std = pd.DataFrame(x_test_std)\n",
        "x_train_std.columns = list(x.columns)\n",
        "x_test_std.columns = list(x.columns)\n",
        "x_train_std.head()"
      ],
      "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>month</th>\n",
              "      <th>hour</th>\n",
              "      <th>season</th>\n",
              "      <th>HUMI</th>\n",
              "      <th>PRES</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>Iws</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>Iprec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.448699</td>\n",
              "      <td>0.938457</td>\n",
              "      <td>-1.330744</td>\n",
              "      <td>-1.623054</td>\n",
              "      <td>-0.645981</td>\n",
              "      <td>0.584809</td>\n",
              "      <td>-0.009995</td>\n",
              "      <td>-0.140153</td>\n",
              "      <td>-0.135140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.157766</td>\n",
              "      <td>0.072445</td>\n",
              "      <td>-0.433571</td>\n",
              "      <td>1.040293</td>\n",
              "      <td>-0.424077</td>\n",
              "      <td>0.475702</td>\n",
              "      <td>-0.009995</td>\n",
              "      <td>-0.140153</td>\n",
              "      <td>-0.135140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.587833</td>\n",
              "      <td>1.371463</td>\n",
              "      <td>1.360776</td>\n",
              "      <td>-0.240669</td>\n",
              "      <td>1.473206</td>\n",
              "      <td>-1.160914</td>\n",
              "      <td>-0.351511</td>\n",
              "      <td>-0.140153</td>\n",
              "      <td>-0.135140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.296900</td>\n",
              "      <td>-1.370909</td>\n",
              "      <td>0.463603</td>\n",
              "      <td>1.345124</td>\n",
              "      <td>-0.424077</td>\n",
              "      <td>0.039271</td>\n",
              "      <td>-0.351511</td>\n",
              "      <td>2.677808</td>\n",
              "      <td>1.183489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.448699</td>\n",
              "      <td>0.216780</td>\n",
              "      <td>-1.330744</td>\n",
              "      <td>0.748910</td>\n",
              "      <td>-0.535029</td>\n",
              "      <td>0.584809</td>\n",
              "      <td>-0.009995</td>\n",
              "      <td>-0.140153</td>\n",
              "      <td>-0.135140</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      month      hour    season  ...       Iws  precipitation     Iprec\n",
              "0 -0.448699  0.938457 -1.330744  ... -0.009995      -0.140153 -0.135140\n",
              "1 -0.157766  0.072445 -0.433571  ... -0.009995      -0.140153 -0.135140\n",
              "2  1.587833  1.371463  1.360776  ... -0.351511      -0.140153 -0.135140\n",
              "3  1.296900 -1.370909  0.463603  ... -0.351511       2.677808  1.183489\n",
              "4 -0.448699  0.216780 -1.330744  ... -0.009995      -0.140153 -0.135140\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vd7hU-XOS83S"
      },
      "source": [
        "x = x.drop('DEWP',axis = 1,inplace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LvpPTb7XapW"
      },
      "source": [
        "x = x.drop('day',axis = 1,inplace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDKP6KvYsVBB"
      },
      "source": [
        "x = x.drop('year',axis = 1,inplace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_Xs1v6paHck",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "outputId": "797e6267-7d69-487d-8a17-b92dde4fe205"
      },
      "source": [
        "# Inicijalizacija\n",
        "regression_model_std = LinearRegression()\n",
        "\n",
        "# Obuka modela\n",
        "regression_model_std.fit(x_train_std, y_train)\n",
        "\n",
        "# Testiranje\n",
        "y_predicted = regression_model_std.predict(x_test_std)\n",
        "\n",
        "# Evaluacija\n",
        "model_evaluation(y_test, y_predicted, x_train_std.shape[0], x_train_std.shape[1])\n",
        "\n",
        "\n",
        "# Ilustracija koeficijenata\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(range(len(regression_model_std.coef_)),regression_model_std.coef_)\n",
        "print(\"koeficijenti: \", regression_model_std.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error:  1044.0059129312635\n",
            "Mean absolute error:  24.570197285355132\n",
            "Root mean squared error:  32.31108034299168\n",
            "R2 score:  0.1443258281151758\n",
            "R2 adjusted score:  0.1440792992866028\n",
            "        y     y_pred\n",
            "0    11.0  41.201327\n",
            "1    68.0  70.540958\n",
            "2    27.0  39.159925\n",
            "3    32.0  42.401765\n",
            "4     6.0  40.134912\n",
            "5    14.0  50.259251\n",
            "6    66.0  63.382259\n",
            "7   146.0  61.724926\n",
            "8    24.0  44.097877\n",
            "9    78.0  54.972918\n",
            "10  104.0  56.770273\n",
            "11    6.0  39.909209\n",
            "12   80.0  52.159396\n",
            "13   34.0  46.396703\n",
            "14   31.0  64.728921\n",
            "15   40.0  51.140761\n",
            "16   31.0  39.733810\n",
            "17   32.0  37.410261\n",
            "18   21.0  56.722429\n",
            "19   44.0  39.813059\n",
            "koeficijenti:  [  1.55381141   1.50444104   1.27836298  -6.04337732  -8.88265385\n",
            " -19.12136219  -1.81097175  -1.02876447  -2.77686245]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "C_WhU954Txuy",
        "outputId": "b3bcf300-a305-4a94-f7fc-d275248376f4"
      },
      "source": [
        "poly = PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)\n",
        "x_inter_train = poly.fit_transform(x_train)\n",
        "x_inter_test = poly.transform(x_test)\n",
        "\n",
        "# Linearna regresija sa hipotezom y=b0+b1x1+b2x2+...+bnxn+c1x1x2+c2x1x3+...+d1x1^2+d2x2^2+...+dnxn^2\n",
        "\n",
        "# Inicijalizacija\n",
        "regression_model_degree = LinearRegression()\n",
        "\n",
        "# Obuka modela\n",
        "regression_model_degree.fit(x_inter_train, y_train)\n",
        "\n",
        "# Testiranje\n",
        "y_predicted = regression_model_degree.predict(x_inter_test)\n",
        "\n",
        "# Evaluacija\n",
        "model_evaluation(y_test, y_predicted, x_inter_train.shape[0], x_inter_train.shape[1])\n",
        "\n",
        "# Ilustracija koeficijenata\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(range(len(regression_model_degree.coef_)),regression_model_degree.coef_)\n",
        "print(\"koeficijenti: \", regression_model_degree.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error:  898.9823778468565\n",
            "Mean absolute error:  22.32330658040249\n",
            "Root mean squared error:  29.983034833833223\n",
            "R2 score:  0.26318807951636114\n",
            "R2 adjusted score:  0.261912541937221\n",
            "        y     y_pred\n",
            "0    11.0  26.703773\n",
            "1    68.0  68.741901\n",
            "2    27.0  27.053828\n",
            "3    32.0  39.627976\n",
            "4     6.0  27.693677\n",
            "5    14.0  41.814456\n",
            "6    66.0  59.208005\n",
            "7   146.0  49.433987\n",
            "8    24.0  44.843311\n",
            "9    78.0  48.371381\n",
            "10  104.0  60.339851\n",
            "11    6.0  32.589812\n",
            "12   80.0  49.788161\n",
            "13   34.0  43.774377\n",
            "14   31.0  54.421221\n",
            "15   40.0  59.035527\n",
            "16   31.0  34.114276\n",
            "17   32.0  50.220936\n",
            "18   21.0  61.442055\n",
            "19   44.0  27.566505\n",
            "koeficijenti:  [ 8.38282121e+01 -1.13365269e+01 -2.25516957e+02 -2.40580793e+01\n",
            "  1.94776826e+01 -5.64845880e+01 -1.00863903e+01 -6.06845197e+01\n",
            " -3.18542741e+01  1.98022526e+00  1.07083452e-02  4.40194703e-01\n",
            " -7.42739389e-03 -1.06007844e-01 -3.07756681e-01  3.31333265e-02\n",
            " -3.63197853e-01 -1.45739161e-02 -6.71812146e-03  6.57483364e-02\n",
            " -1.04707279e-02  1.19403586e-02  1.09272673e-02 -1.44028521e-02\n",
            " -2.23622003e-02  9.32649730e-03 -4.39446318e+00 -8.46495847e-02\n",
            "  2.43834230e-01 -1.98777328e-01  3.53225792e-02 -7.59132705e-01\n",
            "  6.13608730e-03 -4.46379559e-03  2.45528461e-02 -7.68684565e-03\n",
            "  3.00243739e-03  7.12448704e-02  9.39513828e-03 -1.14729094e-02\n",
            "  5.74503335e-02  3.88754504e-03  4.75376255e-02  2.79141155e-02\n",
            "  6.96966938e-03 -8.07990299e-04  4.22190963e-01  9.82580130e-02\n",
            "  3.50803022e-01  2.75018722e-02  1.19226945e-02 -1.75033313e-02\n",
            "  2.91448384e-02  2.43722639e-03]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dk+T53f2MJOcl2V1Vz0ryh0ne2t1PTvKNJK+fcY4b2ZuSfG7FtnUf53ndfd6Kj5HYtM81wuv+zk9yuLtv6+7vJLkyyUUzz2nD6u6PJ/n6McMXJXnX9PhdSV42dFKbQHff2d3/Pj3+dpb/Mjo71n6hetl/T5sPm746yfOTvG8at+4LUFXbklyY5J3TdsW6z2nTPtcIr/s7O8mXVmzfMY0xzlndfef0+MtJzppzMhtdVe1I8pNJPhlrv3DTy103JbkryXVJvpDkm91973SI55zF+PMkb07y3Wn7sbHuo3SSj1TVjVW1ZxrbtM81W+eeAJxId3dVeevtglTVDyd5f5Lf7O5vLV8EWGbtF6O770tyXlU9Ksn+JD8x85Q2vKp6SZK7uvvGqnru3PPZhJ7T3Uer6vFJrquqz6/cudmea1zxur+jSc5Zsb1tGmOcr1TVE5Nk+vOumeezIVXVw7IcXe/u7n+ahq39IN39zSQfS/LsJI+qqu/9Q9hzzqn300leWlVHsnz7yPOT/EWs+xDdfXT6864s/2Pj/Gzi5xrhdX83JNk5vdvljCQXJzkw85w2mwNJXjc9fl2Sq2ecy4Y03d/yd0k+191/tmKXtV+gqjpzutKVqvqhJD+X5fvrPpbkldNh1v0U6+5Lu3tbd+/I8nP6v3T3L8S6L1xVPaKqHvm9x0l+PsnN2cTPNT5AdRVV9eIs3w+wJckV3X3ZzFPasKrqH5M8N8u/rf4rSX43yQeSXJVke5Lbk7y6u4+9AZ81qKrnJPm3JJ/N9+95+Z0s3+dl7Rekqp6e5RuJt2T5H75XdfcfVNWTsnwl5jFJPp3kF7v7nvlmunFNLzX+dne/xLov3rTG+6fNrUne092XVdVjs0mfa4QXAMAgXmoEABhEeAEADCK8AAAGEV4AAIMILwCAQYQXAMAgwgsAYBDhBQAwyP8BcgLd1ymTU5gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0IDgsButT1EJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "outputId": "4644b38f-b7d9-4673-d73d-7d47db9ce1fa"
      },
      "source": [
        "poly = PolynomialFeatures(degree=5, interaction_only=False, include_bias=False)\n",
        "x_inter_train = poly.fit_transform(x_train)\n",
        "x_inter_test = poly.transform(x_test)\n",
        "\n",
        "# Linearna regresija sa hipotezom y=b0+b1x1+b2x2+...+bnxn+c1x1x2+c2x1x3+...+d1x1^2+d2x2^2+...+dnxn^2\n",
        "\n",
        "# Inicijalizacija\n",
        "regression_model_degree = LinearRegression()\n",
        "\n",
        "# Obuka modela\n",
        "regression_model_degree.fit(x_inter_train, y_train)\n",
        "\n",
        "# Testiranje\n",
        "y_predicted = regression_model_degree.predict(x_inter_test)\n",
        "\n",
        "# Evaluacija\n",
        "model_evaluation(y_test, y_predicted, x_inter_train.shape[0], x_inter_train.shape[1])\n",
        "\n",
        "# Ilustracija koeficijenata\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(range(len(regression_model_degree.coef_)),regression_model_degree.coef_)\n",
        "print(\"koeficijenti: \", regression_model_degree.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error:  231995635.0534065\n",
            "Mean absolute error:  351.18936198285724\n",
            "Root mean squared error:  15231.402924662143\n",
            "R2 score:  -190144.16148462408\n",
            "R2 adjusted score:  -203153.81983553473\n",
            "        y     y_pred\n",
            "0    11.0  35.932131\n",
            "1    68.0  67.572738\n",
            "2    27.0  11.730235\n",
            "3    32.0  47.609782\n",
            "4     6.0  17.009755\n",
            "5    14.0  32.415812\n",
            "6    66.0 -33.133551\n",
            "7   146.0  50.643877\n",
            "8    24.0  42.046476\n",
            "9    78.0  72.931411\n",
            "10  104.0  47.792796\n",
            "11    6.0  26.217514\n",
            "12   80.0  94.705458\n",
            "13   34.0  42.581031\n",
            "14   31.0  21.732095\n",
            "15   40.0  62.355437\n",
            "16   31.0  27.262162\n",
            "17   32.0  48.269876\n",
            "18   21.0  65.465542\n",
            "19   44.0  37.293767\n",
            "koeficijenti:  [-7.73188948e+00 -4.05868081e-04  5.70839984e-05 ... -1.68531037e-06\n",
            " -5.68885271e-07 -1.60743751e-07]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wgp0VOZkbzF0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a9b04f10-017c-4050-9494-fef5c166e81f"
      },
      "source": [
        "poly = PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)\n",
        "x_inter_train = poly.fit_transform(x_train)\n",
        "x_inter_test = poly.transform(x_test)\n",
        "\n",
        "#Ridge regresija\n",
        "\n",
        "# Inicijalizacija\n",
        "ridge_model = Ridge(alpha=7)\n",
        "\n",
        "# Obuka modela\n",
        "ridge_model.fit(x_inter_train, y_train)\n",
        "\n",
        "# Testiranje\n",
        "y_predicted = ridge_model.predict(x_inter_test)\n",
        "\n",
        "# Evaluacija\n",
        "model_evaluation(y_test, y_predicted, x_inter_train.shape[0], x_inter_train.shape[1])\n",
        "\n",
        "\n",
        "# Ilustracija koeficijenata\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(range(len(ridge_model.coef_)),ridge_model.coef_)\n",
        "print(\"koeficijenti: \", ridge_model.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error:  900.5528792757799\n",
            "Mean absolute error:  22.342472169051433\n",
            "Root mean squared error:  30.00921323986652\n",
            "R2 score:  0.2619008861269435\n",
            "R2 adjusted score:  0.26062312021314415\n",
            "        y     y_pred\n",
            "0    11.0  26.522871\n",
            "1    68.0  70.221190\n",
            "2    27.0  26.929029\n",
            "3    32.0  39.914120\n",
            "4     6.0  28.137480\n",
            "5    14.0  42.016347\n",
            "6    66.0  60.564998\n",
            "7   146.0  47.577226\n",
            "8    24.0  44.720154\n",
            "9    78.0  47.009086\n",
            "10  104.0  61.625728\n",
            "11    6.0  32.975016\n",
            "12   80.0  50.056047\n",
            "13   34.0  43.808751\n",
            "14   31.0  54.602231\n",
            "15   40.0  59.137865\n",
            "16   31.0  34.229803\n",
            "17   32.0  50.546416\n",
            "18   21.0  60.634130\n",
            "19   44.0  27.210012\n",
            "koeficijenti:  [ 1.99217452e+01 -1.05884965e+01 -6.45300859e+00 -2.28493526e+01\n",
            "  2.45999248e+01 -2.74857383e+01 -3.74467299e+00 -4.37697030e+00\n",
            " -2.46812558e+01  1.96771051e+00  9.62461489e-03  4.30608877e-01\n",
            " -2.52178649e-03 -4.41709018e-02 -2.52039057e-01  3.27171157e-02\n",
            " -2.66896686e-01 -8.17366403e-03 -6.44847561e-03  6.70819620e-02\n",
            " -1.03210137e-02  1.11938677e-02  1.06153775e-02 -1.47562600e-02\n",
            " -2.59919998e-02  8.83798097e-03 -4.07266397e+00 -1.11799648e-01\n",
            "  3.12037786e-02 -3.58912099e-01  4.08101090e-02 -7.76840543e-01\n",
            "  1.03619243e-02 -4.47693211e-03  2.34565183e-02 -1.08526189e-02\n",
            "  2.43495427e-03  6.08475704e-02  8.48449622e-03 -1.36289716e-02\n",
            "  2.98882186e-02 -2.24161597e-03 -6.61606853e-03  2.10604969e-02\n",
            " -1.28741848e-02 -5.21102025e-03  3.60275011e-01  8.80737486e-02\n",
            "  3.50535604e-01  3.36627410e-02  7.44878931e-03 -1.33890475e-02\n",
            "  2.39328908e-02  2.50580624e-03]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wua0rPrGb74E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c4bc10f0-cb87-4f2c-e200-d10de5f3879a"
      },
      "source": [
        "#Lasso regresija\n",
        "\n",
        "# Inicijalizacija\n",
        "lasso_model = Lasso(alpha=0.01)\n",
        "\n",
        "# Obuka modela\n",
        "lasso_model.fit(x_inter_train, y_train)\n",
        "\n",
        "# Testiranje\n",
        "y_predicted = lasso_model.predict(x_inter_test)\n",
        "\n",
        "# Evaluacija\n",
        "model_evaluation(y_test, y_predicted, x_inter_train.shape[0], x_inter_train.shape[1])\n",
        "\n",
        "\n",
        "#ilustracija koeficijenata\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(range(len(lasso_model.coef_)),lasso_model.coef_)\n",
        "print(\"koeficijenti: \", lasso_model.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.045e+03\n",
            "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random, positive\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error:  905.5269988601339\n",
            "Mean absolute error:  22.468979450683868\n",
            "Root mean squared error:  30.091975655648366\n",
            "R2 score:  0.25782406471867403\n",
            "R2 adjusted score:  0.2565392411843813\n",
            "        y     y_pred\n",
            "0    11.0  26.464583\n",
            "1    68.0  74.162822\n",
            "2    27.0  25.730076\n",
            "3    32.0  39.373139\n",
            "4     6.0  32.060259\n",
            "5    14.0  42.360204\n",
            "6    66.0  63.968139\n",
            "7   146.0  51.341071\n",
            "8    24.0  45.985781\n",
            "9    78.0  48.648729\n",
            "10  104.0  65.925414\n",
            "11    6.0  35.227571\n",
            "12   80.0  54.187507\n",
            "13   34.0  44.285581\n",
            "14   31.0  53.826704\n",
            "15   40.0  58.590227\n",
            "16   31.0  34.579670\n",
            "17   32.0  49.676808\n",
            "18   21.0  61.308467\n",
            "19   44.0  25.481557\n",
            "koeficijenti:  [ 2.88276275e+00  2.65876834e-01  1.21333311e+01 -6.13851519e-01\n",
            "  2.48539539e+00  1.35424619e+00 -4.73293354e+00 -3.04759269e+00\n",
            " -2.05055773e+00  1.93414898e+00  9.35814558e-03 -1.74033922e-01\n",
            " -6.53761455e-04 -2.48925132e-02 -2.44470850e-01  2.87065348e-02\n",
            " -1.91705833e-01  1.32237401e-02 -3.50508085e-03  7.31752446e-02\n",
            " -1.18441901e-02  6.66731309e-04  3.62928814e-03 -1.57701288e-02\n",
            " -3.43811826e-02  9.21736836e-03 -1.42499532e+00 -7.96739661e-02\n",
            " -1.54209977e-03 -1.90145398e-01  3.37106656e-02 -8.02545273e-01\n",
            "  1.94007321e-02 -6.43421768e-03  2.08848142e-03 -2.96469875e-02\n",
            "  2.20393145e-03  4.01046037e-02  2.60871402e-03 -1.74274443e-03\n",
            "  3.10369355e-03 -1.32361563e-03 -5.70966336e-03 -3.41447966e-04\n",
            " -3.23710634e-02 -2.39529055e-03  3.19805895e-01  5.44863201e-02\n",
            "  3.54752238e-01  4.67806740e-02 -3.51990262e-03  2.42566162e-03\n",
            "  1.76165144e-02  2.78153500e-03]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}