Copy_of_Untitled2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM3hMslsXgGyL669i5nJDcz",
      "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/pallamadhavi/FP/blob/main/Copy_of_Untitled2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrA8RScZoja0",
        "outputId": "915a96c5-0903-412f-a671-38a1dedfabd1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-10-05 11:54:28--  https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.18, 2620:100:601d:18::a27d:512\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv [following]\n",
            "--2023-10-05 11:54:28--  https://www.dropbox.com/s/raw/wa8d1sujzlx56hh/ETL_DATA_new.csv\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://ucc01be6c3cb129ca20326844a89.dl.dropboxusercontent.com/cd/0/inline/CFA16EjNdntnyFWMrQ1XN5v3D_TgeEzggI677TgvRLgaSPAR05ZCSVigIZ0Mz0ua4N_25pQ6MnD3-iE1J_KOaXihl1HePdQStC-Z49K0anwIyfWPuFY_tZaR_fpows4Diw6q4ZFot01eLvnzT_pYMJwG/file# [following]\n",
            "--2023-10-05 11:54:28--  https://ucc01be6c3cb129ca20326844a89.dl.dropboxusercontent.com/cd/0/inline/CFA16EjNdntnyFWMrQ1XN5v3D_TgeEzggI677TgvRLgaSPAR05ZCSVigIZ0Mz0ua4N_25pQ6MnD3-iE1J_KOaXihl1HePdQStC-Z49K0anwIyfWPuFY_tZaR_fpows4Diw6q4ZFot01eLvnzT_pYMJwG/file\n",
            "Resolving ucc01be6c3cb129ca20326844a89.dl.dropboxusercontent.com (ucc01be6c3cb129ca20326844a89.dl.dropboxusercontent.com)... 162.125.5.15, 2620:100:601d:15::a27d:50f\n",
            "Connecting to ucc01be6c3cb129ca20326844a89.dl.dropboxusercontent.com (ucc01be6c3cb129ca20326844a89.dl.dropboxusercontent.com)|162.125.5.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 262918270 (251M) [text/plain]\n",
            "Saving to: ‘ETL_DATA_new.csv’\n",
            "\n",
            "ETL_DATA_new.csv    100%[===================>] 250.74M  54.2MB/s    in 4.8s    \n",
            "\n",
            "2023-10-05 11:54:34 (52.6 MB/s) - ‘ETL_DATA_new.csv’ saved [262918270/262918270]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "\n",
        "df = pd.read_csv(\"stationInfo.csv\")\n",
        "s = pd.Series(['stationId','Location'])\n",
        "s.str.strip(\"Point()\")\n",
        "df[['Longitude', 'Latitude']] = df[\"Location\"].apply(lambda x: pd.Series(str(x).strip(\"Point()\").split(\" \")))\n",
        "#df.plot(x = 'Longitude', y = 'Latitude',kind = \"scatter\",colormap = \"blue\")\n",
        "new_df = df.drop([\"rowNumber\",\"Address\"], axis = 1)\n",
        "#df\n",
        "fig = px.scatter_geo(df,\n",
        "                    lat=df.Latitude,\n",
        "                    lon=df.Longitude)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "Vi2swY6Z4D3S",
        "outputId": "f292c5be-a2fa-45a2-f78a-fbd3d785c523"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-72968200f57d>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpress\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"stationInfo.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'stationId'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Location'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Point()\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'stationInfo.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "dataset = pd.read_csv('ETL_DATA_new.csv', index_col=0)\n",
        "\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "id": "BEVLUuwwklxW",
        "outputId": "33e818bf-b51b-45f1-ed06-d6f2b082f329"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 TimeStamp  Point(139.0794379 36.3727776)  \\\n",
              "                                                            \n",
              "0      2018-01-01 01:00:00                            NaN   \n",
              "1      2018-01-01 02:00:00                            NaN   \n",
              "2      2018-01-01 03:00:00                            NaN   \n",
              "3      2018-01-01 04:00:00                            NaN   \n",
              "4      2018-01-01 05:00:00                            NaN   \n",
              "...                    ...                            ...   \n",
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              "        document.querySelector('#df-1e7878a2-5974-4a45-9be0-7135df307841 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1e7878a2-5974-4a45-9be0-7135df307841');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "            title=\"Suggest charts.\"\n",
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              "    </g>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-66b7b78d-106b-4294-8085-f8116dec56e3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop('TimeStamp', inplace=True, axis=1)\n",
        "n_impacts = len(dataset)\n",
        "print(n_impacts, \"impacts\")\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "Pp3dqtbRo4Xw",
        "outputId": "ee018e1b-be01-43a5-bd87-4edff3c66df5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46005 impacts\n"
          ]
        },
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              "      <th>46001</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>14.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>46005 rows × 1831 columns</p>\n",
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              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-f9944b10-f637-48fe-800b-7348771891d1');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ea99f4be-de69-40b3-9381-6d20c8178ffd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sensorsWithPointInformation = [col for col in dataset if 'Unnamed' in col]\n",
        "dataset.drop(columns=sensorsWithPointInformation, inplace=True, axis=1)\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "GyLQSbm5o9nz",
        "outputId": "c9440093-3252-44ba-c82b-45cbae7447cb"
      },
      "execution_count": null,
      "outputs": [
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              "      <td>13.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1764 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b1d2c279-86a8-4fec-b369-a24844b81cca button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b1d2c279-86a8-4fec-b369-a24844b81cca');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-73873ef6-8cb5-4c48-92a0-fdbf5e39edfd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-73873ef6-8cb5-4c48-92a0-fdbf5e39edfd')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-73873ef6-8cb5-4c48-92a0-fdbf5e39edfd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.fillna(0)\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "xGN_nXjtpDe7",
        "outputId": "cf7c3f2d-f065-4f8c-dbaf-31d91c778286"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1764 columns</p>\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-40f58797-97e5-41d0-9752-a0a08366ea32 button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "x = dataset.iloc[:,:-1].values.reshape(-1,1)\n",
        "y = dataset.iloc[:,1].values.reshape(-1,1)\n",
        "\n",
        "plt.scatter(x=x,y=y)\n",
        "plt.xlabel(\"Air Pollution\")\n",
        "plt.ylabel(\"Population\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "w9G2WpPBmmbB",
        "outputId": "8db1c481-a0a1-4fd0-efad-ec604c3e5108"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-1f3499af80d2>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Air Pollution\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Population\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m   2860\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2861\u001b[0m         edgecolors=None, plotnonfinite=False, data=None, **kwargs):\n\u001b[0;32m-> 2862\u001b[0;31m     __ret = gca().scatter(\n\u001b[0m\u001b[1;32m   2863\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2864\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4582\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4583\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4584\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4586\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: x and y must be the same size"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maxValueInEachColumn = dataset.max() #Gathering the maximum PM2.5 value recorded by each sensor\n",
        "maxValueInEachColumn      #Printing the maxValue of each sensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmTM0LpYpHbn",
        "outputId": "da8168ec-f6f6-4147-e219-009456183e60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Point(139.0794379 36.3727776)             0.0\n",
              "Point(139.1051411 36.3963822)             0.0\n",
              "Point(139.0960211 36.4047323)           110.0\n",
              "Point(139.0428727 36.3816035)            77.0\n",
              "Point(138.9955116 36.33801589999999)    200.0\n",
              "                                        ...  \n",
              "Point(139.9074816 36.4445767)             0.0\n",
              "Point(140.0934838 36.4673588)           137.0\n",
              "Point(139.7422865 36.2305774)             0.0\n",
              "Point(139.7151723 36.822353)              0.0\n",
              "Point(140.1510903 36.6598314)           165.0\n",
              "Length: 1764, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maxValueInEachColumn.plot()  #point the maximum values recorded by each sensor."
      ],
      "metadata": {
        "id": "oRt6_sBCpMq6",
        "outputId": "591b8aaf-e24d-4d21-f263-a69a4aa1a63d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.where(dataset <= 250, 0, inplace=True)\n",
        "dataset.max().plot()\n"
      ],
      "metadata": {
        "id": "CSvErP2QpRDY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "a699ff75-1294-4cfa-a6e1-3ea583e756a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "minValueInEachColumn = dataset.min() #Reading the minimum PM2.5 value recorded by each sensor\n",
        "minValueInEachColumn.plot()  #ploting them\n",
        "\n",
        "#dataset.min().plot()      #memory efficient approach\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "jeYdetiFrdED",
        "outputId": "5c559b9a-393b-41a8-9300-87e203ebb36c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.where(dataset > 0, 0, inplace=True)\n",
        "dataset.min().plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "KZTnwRFmrigY",
        "outputId": "089dd4af-4123-4db3-941d-7376a908c8d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thresholdValue = 15\n",
        "pm25 = pd.DataFrame(columns=[\"long\", \"lat\", \"pm25\"])\n",
        "for col in dataset[1:]:\n",
        "    res = [i for i in dataset[col].values if i >= thresholdValue]\n",
        "    if len(res) == 0 or col == \"Unnamed\":\n",
        "        dataset = dataset.drop([col], axis = 1)\n",
        "    else:\n",
        "        if \"Poi\" in col:\n",
        "            #print(\"Hey\")\n",
        "            col = col.strip(\"Point()\")\n",
        "            col = col.rstrip(\").1\")\n",
        "            long, lat = col.split()\n",
        "            pm25 = pm25._append({'long': float(long), 'lat': float(lat), 'pm25': len(res)}, ignore_index=True)\n",
        "pm25.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pG4buEvsrlgv",
        "outputId": "c699ee3b-1b22-401f-9fc7-2fc3d52538ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         long        lat     pm25\n",
              "0  139.096021  36.404732   8204.0\n",
              "1  139.042873  36.381603   8999.0\n",
              "2  138.995512  36.338016  13929.0\n",
              "3  139.342672  36.410566  12667.0\n",
              "4  139.381732  36.290913  10391.0"
            ],
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              "      const buttonEl =\n",
              "        document.querySelector('#df-2e9b49a6-6949-46bc-847f-e24d8a10daf6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2e9b49a6-6949-46bc-847f-e24d8a10daf6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0a9a6d98-3dfb-4ccb-b8f5-77401d7d8b0a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0a9a6d98-3dfb-4ccb-b8f5-77401d7d8b0a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0a9a6d98-3dfb-4ccb-b8f5-77401d7d8b0a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pm25.head()\n",
        "pm25_list = df[\"pm25\"].tolist()\n",
        "pm25_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k80475TlyatM",
        "outputId": "68b082ca-98f6-4fc0-837d-b6ac16f76611"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[8204, 8999, 13929, 12667, 10391]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "Ji1V_tC25JYk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "framerate = 44100\n",
        "t = np.linspace(0, 5, framerate * 5)\n",
        "chunk = np.sin(2 * np.pi * 440 * t)\n",
        "data = np.array(0)\n",
        "for i in range((44100 * 5)//870 + 1):\n",
        "    data = np.append((pm25_list), chunk[:870])"
      ],
      "metadata": {
        "id": "g9qHvg3g48UI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.plot(pm25_list)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "ZnuD4Z7i5ZwP",
        "outputId": "87b443a1-8465-4968-acea-c3119f00265c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7db4d324f610>]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "const [audio] = useState(typeof Audio !== \"undefined\" && new Audio(/content/(song.com).mp3));\n",
        "audio.play(pm25_list, rate=44100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "eJO_8haP55M1",
        "outputId": "31c0170f-1cb9-4bbd-d3fe-a5bd23619c8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mSyntaxError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/compilerop.py\u001b[0m in \u001b[0;36mast_parse\u001b[0;34m(self, source, filename, symbol)\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0mArguments\u001b[0m \u001b[0mare\u001b[0m \u001b[0mexactly\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msame\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mstandard\u001b[0m \u001b[0mlibrary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m         and are passed to the built-in compile function.\"\"\"\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymbol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mPyCF_ONLY_AST\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreset_compiler_flags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mSyntaxError\u001b[0m: invalid syntax. Perhaps you forgot a comma? (<ipython-input-59-dbe558676644>, line 1)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install numpy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3pBxNWl5Ag2",
        "outputId": "0706e47a-5221-4adf-9b48-799354b7c6ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.23.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "IFrame(\"https://en.wikipedia.org/wiki/Perlin_noise\", width=\"100%\", height=\"400\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "JTtipkRR4ZXm",
        "outputId": "e70f9072-14aa-4d75-fc02-5a4f4acb6abc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-50-21b72988d303>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mIFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"https://en.wikipedia.org/wiki/Perlin_noise\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"100%\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"400\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'IFrame' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://raw.githubusercontent.com/caseman/noise/master/perlin.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AADLV9gz4KkD",
        "outputId": "f2b23e50-b0c3-4237-cce6-8fb87cadbe98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-10-05 12:47:11--  https://raw.githubusercontent.com/caseman/noise/master/perlin.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 12249 (12K) [text/plain]\n",
            "Saving to: ‘perlin.py’\n",
            "\n",
            "perlin.py           100%[===================>]  11.96K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-10-05 12:47:11 (73.5 MB/s) - ‘perlin.py’ saved [12249/12249]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from perlin import *"
      ],
      "metadata": {
        "id": "xjChpZR54Nvi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Audio(pm25_list, rate=44100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "O6m7TcYF39yO",
        "outputId": "af940ba0-5c94-4374-ee62-c90d651fac80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-482d3830a2a8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mAudio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm25_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m44100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'Audio' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "\n",
        "fig = px.density_mapbox(pm25, lat = 'lat', lon = 'long', z = 'pm25',\n",
        "                        radius = 8,\n",
        "                        zoom = 6,\n",
        "                        mapbox_style = 'open-street-map')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "htnRnrCGrpRy",
        "outputId": "4e185501-a04a-42f6-8f95-509ad86a2bbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def noise50(t):\n",
        "    return 0.5 * random.uniform(-1, 1)\n",
        "\n",
        "def noise75(t):\n",
        "    return 0.75 * random.uniform(-1, 1)"
      ],
      "metadata": {
        "id": "hmSADOqI3itg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pylab as plt\n",
        "pm25 = pd.DataFrame(columns=[\"long\", \"lat\", \"pm25\"])\n",
        "x = pm25[\"long\"].values\n",
        "y = pm25[\"lat\"].values\n",
        "s = pm25[\"pm25\"].values\n",
        "plt.scatter(x, y, s)\n",
        "plt.xlabel(\"x values\")\n",
        "plt.ylabel(\"y values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "Z-VOzmwIvc6P",
        "outputId": "317aa4ec-414a-4209-a467-5f2d34d48533"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-478064834e95>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm25\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"lat\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm25\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pm25\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m   2860\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2861\u001b[0m         edgecolors=None, plotnonfinite=False, data=None, **kwargs):\n\u001b[0;32m-> 2862\u001b[0;31m     __ret = gca().scatter(\n\u001b[0m\u001b[1;32m   2863\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2864\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4591\u001b[0m                 (not np.issubdtype(s.dtype, np.floating) and\n\u001b[1;32m   4592\u001b[0m                  not np.issubdtype(s.dtype, np.integer))):\n\u001b[0;32m-> 4593\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m   4594\u001b[0m                 \u001b[0;34m\"s must be a scalar, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4595\u001b[0m                 \"or float array-like with the same size as x and y\")\n",
            "\u001b[0;31mValueError\u001b[0m: s must be a scalar, or float array-like with the same size as x and y"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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AAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maxPM25 = pd.DataFrame(columns=[\"long\", \"lat\", \"maxPM25\"])\n",
        "for col in dataset[1:]:\n",
        "    res = [i for i in dataset[col].values if i >= 15]\n",
        "    if len(res) == 0 or col == \"Unnamed\":\n",
        "        dataset = dataset.drop([col], axis = 1)\n",
        "    else:\n",
        "        if \"Poi\" in col:\n",
        "            col = col.strip(\"Point()\")\n",
        "            col = col.rstrip(\").1\")\n",
        "            long, lat = col.split()\n",
        "            maxPM25 = maxPM25._append({'long': float(long), 'lat': float(lat), 'maxPM25': max(res)}, ignore_index=True)\n",
        "maxPM25.head()\n",
        "\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "\n",
        "fig = px.density_mapbox(maxPM25, lat = 'lat', lon = 'long', z = 'maxPM25',\n",
        "                        radius = 8,\n",
        "                        zoom = 6,\n",
        "                        mapbox_style = 'open-street-map')\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "pLYG_ltiscs8",
        "outputId": "4645fde0-bc0f-419b-f6a9-0c40aeecbf98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "maxPM25 = pd.DataFrame(columns=[\"long\", \"lat\", \"maxPM25\"])\n",
        "for col in dataset[1:]:\n",
        "    res = [i for i in dataset[col].values if i >= 15]\n",
        "    if len(res) == 0 or col == \"Unnamed\":\n",
        "        dataset = dataset.drop([col], axis = 1)\n",
        "    else:\n",
        "        if \"Poi\" in col:\n",
        "            col = col.strip(\"Point()\")\n",
        "            col = col.rstrip(\").1\")\n",
        "            long, lat = col.split()\n",
        "            maxPM25 = maxPM25._append({'long': float(long), 'lat': float(lat), 'maxPM25': max(res)}, ignore_index=True)\n",
        "maxPM25.head()\n",
        "\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "\n",
        "fig = px.density_mapbox(maxPM25, lat = 'lat', lon = 'long', z = 'maxPM25',\n",
        "                        radius = 8,\n",
        "                        zoom = 6,\n",
        "                        mapbox_style = 'open-street-map')\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "bhNhluaisoP6",
        "outputId": "4f34b194-ab4c-4d65-cc5e-2a740a64c5bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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