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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Air Quality Data Processing with Python\n",
        "\n",
        "Air Quality Open Data Platform and World Air Quality Index project team provides a [new dedicated dataset](https://aqicn.org/data-platform/covid19/), updated 3 times a day, and covering about 380 major cities in the world, from January 2015 until now. The data for each major city is based on the average (median) of several air quality monitoring stations. The data set provides min, max, median and standard deviation for each of the air pollutant species (PM10, PM2.5, NO2, Ozone, SO2, CO) as well as meteorological data (Temperature, Humidity, Wind).\n",
        "\n",
        "R code below downloads several .csv files, combines thems into a full dataset, selects median from provided statistics and narrows a set of countries to European Union members. From available information only meteorological data on Temperature and Humidity is selected and THI (Temperature Humidity Index) is calculated. Finally, daily THI time series for a selected city is summarized and visualized.\n",
        "\n",
        "THI measures the level of discomfort the average person (or animal) is thought to experience as a result of the combined effects of the temperature and humidity of the air. The heat stress index THI is a simple combination of temperature and humidity (in decimal form) and can be calculated by the following formula:\n",
        "\n",
        "$THI = 0.8 \\times Temperature + Humidity \\times (Temperature - 14.4) + 46.4$\n",
        "\n",
        "Habeeb, A. A., Gad, A. E., & Atta, M. A. (2018). Temperature-humidity indices as indicators to heat stress of climatic conditions with relation to production and reproduction of farm animals. *International Journal of Biotechnology and Recent Advances*, 1(1), 35-50. [https://doi.org/10.18689/ijbr-1000107](https://doi.org/10.18689/ijbr-1000107)"
      ],
      "metadata": {
        "id": "bf6pZocovDcD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Prepare Python environment"
      ],
      "metadata": {
        "id": "gIYDJhUMvGvQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install custom Python packages\n",
        "!pip install pyeucountrycodes --quiet\n",
        "!pip install calplot --quiet"
      ],
      "metadata": {
        "id": "UXMar3EgvJtt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d22c801-99c5-4d88-9f39-f81d04bef9f2"
      },
      "execution_count": 1,
      "outputs": [
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          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/132.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m102.4/132.3 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.3/132.3 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for calplot (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load required Python packages\n",
        "import os, re, requests, logging, calplot\n",
        "import pandas as pd\n",
        "import dask.dataframe as dd\n",
        "import multiprocessing as mp\n",
        "from dask.multiprocessing import get\n",
        "from dask.diagnostics import ProgressBar\n",
        "ProgressBar().register()\n",
        "from tqdm import tqdm\n",
        "from itertools import product\n",
        "from eu_country_codes import COUNTRY_CODES"
      ],
      "metadata": {
        "id": "-i4yvfaZvJ7c"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** These are descriptions of the required Python packages.\n",
        "* `os` module in Python provides a way to interact with the operating system, allowing you to perform various operations related to file and directory management, process execution, and environment variables.\n",
        "* `re` module in Python stands for regular expressions. It provides support for working with regular expressions, which are powerful tools for pattern matching and text manipulation.\n",
        "* `requests` package in Python is a popular library for making HTTP requests. It simplifies the process of sending HTTP requests and receiving HTTP responses from web services and websites. The \"requests\" package is widely used for tasks such as web scraping, interacting with web APIs, and accessing web content.\n",
        "* `logging` package in Python is a built-in module that provides a flexible and powerful framework for emitting log messages from Python programs.\n",
        "* `calplot` creates heatmaps from Pandas time series data.\n",
        "* `dask.dataframe` package is part of the Dask library, which is designed for parallel and distributed computing in Python. Dask provides tools for handling larger-than-memory computations, enabling users to work with larger datasets and perform data manipulations efficiently.\n",
        "* `multiprocessing` package in Python is a built-in module that provides a convenient way to create and manage parallel processes. It's primarily used for parallel and concurrent programming, allowing you to take advantage of multiple CPU cores and perform tasks in parallel.\n",
        "* `dask.multiprocessing` package is part of the Dask library, which is designed for parallel and distributed computing in Python. It allows Dask to utilize multiple CPU cores on a single machine for parallel execution of tasks.\n",
        "* `ProgressBar` class from the `dask.diagnostics` module in Dask is a useful tool for monitoring the progress of Dask computations.\n",
        "* `tqdm` package in Python provides a fast and extensible progress bar for loops and other iterable tasks. It's particularly useful for tracking the progress of lengthy computations or operations and providing real-time feedback to the user. The package's name, \"tqdm\" stands for \"taqaddum\" in Arabic, which means \"progress\".\n",
        "* `itertools.product` is used to compute the Cartesian product of input iterables. In other words, it generates all possible combinations of elements from multiple input sequences.\n",
        "* `eu_country_codes` module is a Python package that provides a dictionary of `COUNTRY_CODES`, which contains ISO 3166-1 alpha-2 country codes as keys and the corresponding country names as values for European Union (EU) member states."
      ],
      "metadata": {
        "id": "9tyT5_THvOil"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Download data files"
      ],
      "metadata": {
        "id": "pqpEL_6kvQiM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to download remote file to the disk\n",
        "def urlDownload(urlLink, showProgress = False):\n",
        "  with requests.get(urlLink, stream=True) as r:\n",
        "    fileSize = int(r.headers.get('Content-Length'))\n",
        "    fileName = r.headers.get('Content-Disposition').split(\"filename=\")[1]\n",
        "    if not os.path.exists(fileName) or os.path.getsize(fileName) != fileSize:\n",
        "      block_size = 1024\n",
        "      if showProgress:\n",
        "        print(f\"Downloading {fileName}\")\n",
        "        progress_bar = tqdm(total=fileSize, unit='iB', unit_scale=True)\n",
        "      with open(fileName, 'wb') as file:\n",
        "        for data in r.iter_content(block_size):\n",
        "          if showProgress:\n",
        "            progress_bar.update(len(data))\n",
        "          file.write(data)\n",
        "      if showProgress:\n",
        "        progress_bar.close()\n",
        "    return fileName"
      ],
      "metadata": {
        "id": "ZxyIgYtEvSlN"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This code defines a function called `urlDownload` that takes two arguments: `urlLink` and `showProgress`. The function uses the `requests` library to download a file from a remote URL and save it to the disk.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `with requests.get(urlLink, stream=True) as r:`: This line uses the `requests` library to make a GET request to the URL specified in `urlLink`. The `stream=True` parameter tells `requests` to stream the response, which allows us to read the data in chunks instead of loading the entire file into memory. The `as r` part assigns the response object to the variable `r`.\n",
        "2. `fileSize = int(r.headers.get('Content-Length'))`: This line retrieves the file size from the response headers. The `Content-Length` header contains the size of the file in bytes.\n",
        "3. `fileName = r.headers.get('Content-Disposition').split(\"filename=\")[1]`: This line retrieves the file name from the response headers. The `Content-Disposition` header contains information about the file, including its name. The `split` function is used to split the header value into a list of substrings, and the `[1]` part selects the second substring, which is the file name.\n",
        "4. `if not os.path.exists(fileName) or os.path.getsize(fileName) != fileSize:`: This line checks if the file already exists on the disk and if its size matches the file size retrieved from the response headers. If the file does not exist or its size does not match, the code inside the `if` block is executed.\n",
        "5. `block_size = 1024`: This line sets the block size for reading the file in chunks. The block size is the number of bytes to read at a time.\n",
        "6. `if showProgress:`: This line checks if the `showProgress` argument is set to `True`. If it is, the code inside the `if` block is executed.\n",
        "7. `print(f\"Downloading {fileName}\")`: This line prints a message to the console indicating that the file is being downloaded.\n",
        "8. `progress_bar = tqdm(total=fileSize, unit='iB', unit_scale=True)`: This line creates a progress bar object using the `tqdm` library. The `total` parameter specifies the total number of bytes to download, the `unit` parameter specifies the unit of measurement (in this case, bytes), and the `unit_scale` parameter specifies whether to display the progress bar in units of bytes or in a more human-readable format.\n",
        "9. `for data in r.iter_content(block_size):`: This line reads the file in chunks of the specified block size using the `iter_content` method of the `requests` response object. The `for` loop iterates over the chunks of data and writes them to the file.\n",
        "10. `if showProgress:`: This line checks if the `showProgress` argument is set to `True`. If it is, the code inside the `if` block is executed.\n",
        "11. `progress_bar.update(len(data))`: This line updates the progress bar with the number of bytes read so far.\n",
        "12. `file.write(data)`: This line writes the data."
      ],
      "metadata": {
        "id": "ih0J1LCpvUal"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the newest data\n",
        "urlLocation = 'https://aqicn.org/data-platform/covid19/report/39374-7694ec07/'\n",
        "csvFile = urlDownload(urlLocation, showProgress=True)\n",
        "csvFile"
      ],
      "metadata": {
        "id": "1-zil8WxvYsj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "99945d6a-dd68-492b-f416-11f0cc433d23"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'waqi-covid19-airqualitydata-2024.csv'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This Python code is downloading a CSV file from a URL using the `urllib` library.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `urlLocation = 'https://aqicn.org/data-platform/covid19/report/39374-7694ec07/'` - This line sets the URL of the CSV file to be downloaded.\n",
        "2. `csvFile = urlDownload(urlLocation, showProgress=True)` - This line downloads the CSV file from the URL specified in `urlLocation`. The `showProgress` parameter is set to `True`, which means that the progress of the download will be displayed.\n",
        "\n",
        "In summary, this code downloads a CSV file from a URL and assigns it to a variable named `csvFile`."
      ],
      "metadata": {
        "id": "KMnTnyYxvb9M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create lists of year and quarter names\n",
        "yNames = [str(i) for i in range(2019, 2024)]\n",
        "qNames = [\"Q\" + str(i) for i in range(1, 5)]\n",
        "\n",
        "# Create a data frame with the url locations and year/quarter combinations\n",
        "DF = pd.DataFrame(list(product(yNames, qNames)),columns=['yNames', 'qNames'])\n",
        "DF.insert(loc=0, column='urlLocation', value=urlLocation)\n",
        "\n",
        "# Combine url location and year/quarter combinations into a single column\n",
        "DF = pd.DataFrame({'urlLocations': DF.agg(''.join, axis=1)})\n",
        "DF"
      ],
      "metadata": {
        "id": "yPsBv4YwvdqJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "8e01aaa6-9abb-42af-972a-c1e0153439a6"
      },
      "execution_count": 6,
      "outputs": [
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              "                                         urlLocations\n",
              "0   https://aqicn.org/data-platform/covid19/report...\n",
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              "6   https://aqicn.org/data-platform/covid19/report...\n",
              "7   https://aqicn.org/data-platform/covid19/report...\n",
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              "9   https://aqicn.org/data-platform/covid19/report...\n",
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              "12  https://aqicn.org/data-platform/covid19/report...\n",
              "13  https://aqicn.org/data-platform/covid19/report...\n",
              "14  https://aqicn.org/data-platform/covid19/report...\n",
              "15  https://aqicn.org/data-platform/covid19/report...\n",
              "16  https://aqicn.org/data-platform/covid19/report...\n",
              "17  https://aqicn.org/data-platform/covid19/report...\n",
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              "          '<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",
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              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\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",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This code is creating a data frame with two columns: 'yNames' and 'qNames'. The 'yNames' column contains the years 2019, 2020, 2021, 2022 and 2023, and the 'qNames' column contains the quarters Q1, Q2, Q3, and Q4.\n",
        "\n",
        "The code then creates a new data frame 'DF' with the url locations and the year/quarter combinations. The 'urlLocation' column is inserted into the data frame at the beginning, and the 'yNames' and 'qNames' columns are combined into a single column called 'urlLocations'.\n",
        "\n",
        "The code is using the 'product' function from the 'itertools' module to create the combinations of year and quarter for the same url location prexif. The 'agg' function is then used to combine the 'yNames' and 'qNames' columns into a single column called 'urlLocations'."
      ],
      "metadata": {
        "id": "Ylme9IrmvgsJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download legacy data (sequentially)\n",
        "csvFiles = DF.apply(lambda x : urlDownload(x[0], showProgress = True), axis=1)"
      ],
      "metadata": {
        "id": "iWmuCEMvvi_6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7517665a-c689-4589-e6b0-585998547481"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2019Q1.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26.5M/26.5M [00:03<00:00, 8.56MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2019Q2.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28.2M/28.2M [00:03<00:00, 8.72MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2019Q3.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 31.6M/31.6M [00:03<00:00, 9.34MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2019Q4.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 31.3M/31.3M [00:03<00:00, 9.57MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2020Q1.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29.1M/29.1M [00:03<00:00, 8.59MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2020Q2.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 32.6M/32.6M [00:03<00:00, 9.68MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2020Q3.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 32.4M/32.4M [00:03<00:00, 9.10MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2020Q4.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 31.7M/31.7M [00:03<00:00, 9.30MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2021Q1.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 32.0M/32.0M [00:03<00:00, 9.42MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2021Q2.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 31.8M/31.8M [00:03<00:00, 9.39MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2021Q3.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 30.8M/30.8M [00:03<00:00, 8.64MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2021Q4.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29.6M/29.6M [00:03<00:00, 8.70MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2022Q1.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 30.8M/30.8M [00:03<00:00, 10.1MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2022Q2.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 31.1M/31.1M [00:03<00:00, 8.81MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2022Q3.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 30.9M/30.9M [00:03<00:00, 8.69MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2022Q4.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "100%|██████████| 347/347 [00:00<00:00, 339kiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2023Q1.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29.9M/29.9M [00:03<00:00, 9.61MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2023Q2.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 30.6M/30.6M [00:03<00:00, 9.45MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2023Q3.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 13.9M/13.9M [00:02<00:00, 6.16MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading waqi-covid19-airqualitydata-2023Q4.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 11.8M/11.8M [00:01<00:00, 6.06MiB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This code is using the `pandas` library in Python to download a series of CSV files from the internet.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `DF`: This is a `pandas` DataFrame object that contains the URLs of the CSV files to be downloaded.\n",
        "2. `apply`: This is a method of the `pandas` DataFrame object that applies a function to each row of the DataFrame. In this case, the function is `lambda x: urlDownload(x[0], showProgress=True)`.\n",
        "3. `lambda x: urlDownload(x[0], showProgress=True)`: This is an anonymous function that takes a single argument `x` and returns the result of calling the `urlDownload` function with the URL in the first column of the DataFrame (`x[0]`) and the `showProgress` argument set to `True`.\n",
        "4. `urlDownload`: This is a function that downloads a CSV file from the internet and returns the contents of the file as a string.\n",
        "5. `axis=1`: This specifies that the `apply` method should be applied to the rows of the DataFrame, rather than the columns.\n",
        "\n",
        "Overall, this code is downloading a series of CSV files from the internet and storing the contents of each file in a list. The `showProgress` argument is set to `True` to display a progress bar while the files are being downloaded."
      ],
      "metadata": {
        "id": "Masv1IaxvnGB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Delete downloaded legacy data files\n",
        "for delFile in csvFiles:\n",
        "  os.unlink(delFile)"
      ],
      "metadata": {
        "id": "12ddFRydvuoJ"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This Python code is deleting downloaded legacy data files.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `csvFiles` is a list of file names that are downloaded.\n",
        "2. `for delFile in csvFiles:` is a loop that iterates over each file name in the `csvFiles` list.\n",
        "3. `os.unlink(delFile)` is a function that deletes the file specified by `delFile`.\n",
        "\n",
        "So, the code is deleting each file in the `csvFiles` list by calling the `os.unlink()` function on each file name."
      ],
      "metadata": {
        "id": "Jkx36RiJvzih"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download legacy data (in parallel)\n",
        "DDF = dd.from_pandas(DF, npartitions=mp.cpu_count())\n",
        "csvFiles = DDF.apply(lambda x : urlDownload(x[0]), axis=1, meta=pd.Series(dtype=\"str\")).compute(scheduler='threads')"
      ],
      "metadata": {
        "id": "XZ6tv04bvzC5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e70a1b2f-c8c7-4a74-81e7-e7d82a3ff417"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[########################################] | 100% Completed | 41.36 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This code is using the `dask` library to download a large number of files in parallel.\n",
        "\n",
        "Here's a breakdown of what each line does:\n",
        "\n",
        "1. `DDF = dd.from_pandas(DF, npartitions=mp.cpu_count())`: This line creates a `dask.dataframe.DataFrame` (DDF) from a pandas dataframe (DF). The `npartitions` argument specifies the number of partitions to use when distributing the data across multiple CPUs. In this case, it's set to the number of CPUs available on the system.\n",
        "2. `csvFiles = DDF.apply(lambda x : urlDownload(x[0]), axis=1, meta=pd.Series(dtype=\"str\"))`: This line applies a function to each row of the DDF. The function takes a single argument `x`, which is a tuple containing the URL and the file name. The `lambda` function downloads the file and returns the file name. The `axis=1` argument specifies that the function should be applied to each row of the DDF. The `meta` argument specifies the metadata for the resulting DDF, which in this case is a pandas Series with a string dtype.\n",
        "3. `csvFiles.compute(scheduler='threads')`: This line computes the DDF and returns a pandas Series containing the file names. The `scheduler` argument specifies the scheduler to use when distributing the computation across multiple CPUs. In this case, it's set to `'threads'`, which means that the computation will be distributed across multiple CPU threads.\n",
        "\n",
        "Overall, this code is using `dask` to download a large number of files in parallel, and then returning a pandas Series containing the file names"
      ],
      "metadata": {
        "id": "tC8AKvWBv30X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Load and prepare data"
      ],
      "metadata": {
        "id": "Pyo0SOmlv-yB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the columns to load\n",
        "meta_cols = ['Date', 'Country', 'City', 'Specie']\n",
        "main_column = 'median' # 'count', 'min', 'max', 'median', 'variance'\n",
        "selected_cols = meta_cols + [main_column]\n",
        "\n",
        "# Read the newest data file and skip the first 4 lines\n",
        "DF = pd.read_csv(csvFile, skiprows=4, usecols=selected_cols)\n",
        "\n",
        "# Leave EU data, rename main column to Value\n",
        "selectEU = DF['Country'].isin(COUNTRY_CODES)\n",
        "newTable = DF[selectEU].rename(columns={main_column: 'Value'})\n",
        "print(newTable)"
      ],
      "metadata": {
        "id": "_0D-kIv3wAvP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d681a921-c758-424b-e50b-f69f547d18d6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              Date Country      City         Specie  Value\n",
            "19346   2024-01-02      HU  Debrecen            dew    2.5\n",
            "19347   2024-01-08      HU  Debrecen            dew   -9.0\n",
            "19348   2024-01-11      HU  Debrecen            dew   -6.0\n",
            "19349   2024-01-17      HU  Debrecen            dew   -3.0\n",
            "19350   2024-01-20      HU  Debrecen            dew   -5.0\n",
            "...            ...     ...       ...            ...    ...\n",
            "127266  2024-01-19      AT      Graz  precipitation   99.4\n",
            "127267  2024-01-01      AT      Graz  precipitation    1.2\n",
            "127268  2024-01-06      AT      Graz  precipitation    0.5\n",
            "127269  2024-01-07      AT      Graz  precipitation    0.3\n",
            "127270  2024-01-08      AT      Graz  precipitation   99.1\n",
            "\n",
            "[36351 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This Python code is using the pandas library to read a CSV file and perform some data manipulation.\n",
        "\n",
        "Here's a breakdown of what the code is doing:\n",
        "\n",
        "1. The first line defines a list of columns to load from the CSV file. The list includes the columns \"Date\", \"Country\", \"City\", and \"Specie\", as well as the main column to load, which is specified as \"median\".\n",
        "2. The second line defines a list of columns to load from the CSV file. The list includes all the columns defined in the first line, as well as the main column to load.\n",
        "3. The third line reads the CSV file using the `pd.read_csv()` function, skipping the first 4 lines of the file and only loading the columns specified in the second line.\n",
        "4. The fourth line creates a new DataFrame called \"DF\" by selecting only the rows where the \"Country\" column is in the list of EU countries (defined as \"COUNTRY_CODES\").\n",
        "5. The fifth line renames the main column to \"Value\".\n",
        "6. The final line prints the new DataFrame to the console.\n",
        "\n",
        "In summary, this code reads a CSV file, selects only the rows where the \"Country\" column is in the list of EU countries, and renames the main column to \"Value\"."
      ],
      "metadata": {
        "id": "290NaxO7wCWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Read legacy data files (sequentially)\n",
        "fileNamesQ = [f for f in os.listdir('.') if re.match(r'^.*Q\\d.csv$', f)]\n",
        "DF = pd.concat((pd.read_csv(f, skiprows=4, usecols=selected_cols) for f in fileNamesQ), ignore_index=True)\n",
        "selectEU = DF['Country'].isin(COUNTRY_CODES)\n",
        "oldTable = DF[selectEU].rename(columns={main_column: 'Value'})\n",
        "print(oldTable)"
      ],
      "metadata": {
        "id": "w9QQDAK1wTC0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3484a0a7-9fd0-4a85-ffd5-08d21f155987"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                Date Country      City    Specie   Value\n",
            "27384     2020-05-30      AT  Salzburg      pm10    10.0\n",
            "27385     2020-06-12      AT  Salzburg      pm10    10.0\n",
            "27386     2020-04-14      AT  Salzburg      pm10    10.0\n",
            "27387     2020-04-17      AT  Salzburg      pm10    19.0\n",
            "27388     2020-04-25      AT  Salzburg      pm10    25.0\n",
            "...              ...     ...       ...       ...     ...\n",
            "10102970  2020-03-10      LT    Kaunas  pressure  1010.0\n",
            "10102971  2020-01-04      LT    Kaunas  pressure  1003.0\n",
            "10102972  2020-03-30      LT    Kaunas  pressure  1019.0\n",
            "10102973  2020-03-31      LT    Kaunas  pressure  1021.5\n",
            "10102974  2020-03-29      LT    Kaunas  pressure  1013.0\n",
            "\n",
            "[2891334 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This is a Python code that reads legacy data files (sequentially) and concatenates them into a single DataFrame.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `fileNamesQ = [f for f in os.listdir('.') if re.match(r'^.*Q\\d.csv$', f)]`: This line of code uses the `os` module to list all files in the current directory that match the pattern `Q\\d.csv`. The `re.match()` function is used to match the file names against a regular expression that requires the file name to start with any character (`.*`) followed by the letter `Q` followed by any digit (`\\d`). The resulting list of file names is stored in the variable `fileNamesQ`.\n",
        "2. `DF = pd.concat((pd.read_csv(f, skiprows=4, usecols=selected_cols) for f in fileNamesQ), ignore_index=True)`: This line of code uses the `pandas` library to read each file in the `fileNamesQ` list using the `pd.read_csv()` function. The `skiprows` parameter is set to 4 to skip the first 4 rows of each file, which are likely to be header rows. The `usecols` parameter is set to `selected_cols` to specify the columns to read from each file. The resulting DataFrames are concatenated into a single DataFrame using the `pd.concat()` function. The `ignore_index=True` parameter is used to reset the index of the resulting DataFrame.\n",
        "3. `selectEU = DF['Country'].isin(COUNTRY_CODES)`: This line of code uses the `isin()` method of the `Series` object to select only the rows where the value in the `Country` column is in the `COUNTRY_CODES` list. The resulting Series is stored in the variable `selectEU`.\n",
        "4. `oldTable = DF[selectEU].rename(columns={main_column: 'Value'})`: This line of code uses the `rename()` method of the DataFrame to rename the `main_column` column to `Value`. The resulting DataFrame is stored in the variable `oldTable`.\n",
        "5. `print(oldTable)`: This line of code simply prints the `oldTable` DataFrame to the console."
      ],
      "metadata": {
        "id": "JUwKiRiPwTsu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Read legacy data files (in parallel)\n",
        "fileNamesQ = [f for f in os.listdir('.') if re.match(r'^.*Q\\d.csv$', f)]\n",
        "DF = dd.compute(dd.read_csv(fileNamesQ, skiprows=4, usecols=selected_cols))[0]\n",
        "selectEU = DF['Country'].isin(COUNTRY_CODES)\n",
        "oldTable = DF[selectEU].rename(columns={main_column: 'Value'})\n",
        "print(oldTable)"
      ],
      "metadata": {
        "id": "c5LRhy15wXNh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a71040bc-b876-468d-adbf-e663a51226b1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[########################################] | 100% Completed | 9.01 s\n",
            "              Date Country      City    Specie   Value\n",
            "27384   2020-05-30      AT  Salzburg      pm10    10.0\n",
            "27385   2020-06-12      AT  Salzburg      pm10    10.0\n",
            "27386   2020-04-14      AT  Salzburg      pm10    10.0\n",
            "27387   2020-04-17      AT  Salzburg      pm10    19.0\n",
            "27388   2020-04-25      AT  Salzburg      pm10    25.0\n",
            "...            ...     ...       ...       ...     ...\n",
            "539568  2020-03-10      LT    Kaunas  pressure  1010.0\n",
            "539569  2020-01-04      LT    Kaunas  pressure  1003.0\n",
            "539570  2020-03-30      LT    Kaunas  pressure  1019.0\n",
            "539571  2020-03-31      LT    Kaunas  pressure  1021.5\n",
            "539572  2020-03-29      LT    Kaunas  pressure  1013.0\n",
            "\n",
            "[2891334 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This Python code is using the `Dask` library to read and process large datasets in parallel.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `fileNamesQ = [f for f in os.listdir('.') if re.match(r'^.*Q\\d.csv$', f)]`: This line uses the `os` module to list all files in the current directory that match the pattern `Q\\d.csv`. The `re.match` function is used to match the file names against a regular expression that requires the file name to start with `Q` followed by a digit. The resulting list of file names is assigned to `fileNamesQ`.\n",
        "2. `DF = dd.compute(dd.read_csv(fileNamesQ, skiprows=4, usecols=selected_cols))[0]`: This line reads the CSV files in `fileNamesQ` using the `dd.read_csv` function, which is a Dask version of the Pandas `read_csv` function. The `skiprows` parameter is set to 4 to skip the first 4 rows of each file, and the `usecols` parameter is set to `selected_cols` to only read the columns specified in the `selected_cols` list. The `compute` function is used to execute the Dask computation in parallel. The resulting DataFrame is assigned to `DF`.\n",
        "3. `selectEU = DF['Country'].isin(COUNTRY_CODES)`: This line selects all rows in `DF` where the value in the `Country` column is in the `COUNTRY_CODES` list. The `isin` function is used to check if the value in the `Country` column is in the `COUNTRY_CODES` list. The resulting boolean Series is assigned to `selectEU`.\n",
        "4. `oldTable = DF[selectEU].rename(columns={main_column: 'Value'})`: This line selects all rows in `DF` where the value in the `selectEU` column is `True` and renames the `main_column` to `Value`. The resulting DataFrame is assigned to `oldTable`.\n",
        "5. `print(oldTable)`: This line prints the `oldTable` DataFrame to the console."
      ],
      "metadata": {
        "id": "R88ss5qjwXqx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Append old (2018-2023) and new (2024) data tables, sort, remove duplicates\n",
        "DF = pd.concat([oldTable, newTable])\n",
        "dataTableEU = DF.sort_values(by=['Country', 'City', 'Date']).drop_duplicates()\n",
        "print(dataTableEU)"
      ],
      "metadata": {
        "id": "Nrok3QguwZ0U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c34d6046-4702-4301-fcf0-ce79e48ad1de"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             Date Country    City       Specie   Value\n",
            "10266  2018-12-31      AT    Graz         pm10    11.0\n",
            "10392  2018-12-31      AT    Graz     pressure  1027.1\n",
            "10505  2018-12-31      AT    Graz   wind-speed     1.5\n",
            "10547  2018-12-31      AT    Graz           co     0.1\n",
            "10673  2018-12-31      AT    Graz  temperature     3.7\n",
            "...           ...     ...     ...          ...     ...\n",
            "77191  2024-01-23      SK  Košice     humidity    94.0\n",
            "77196  2024-01-23      SK  Košice    wind-gust     2.7\n",
            "77209  2024-01-23      SK  Košice          dew    -4.0\n",
            "77245  2024-01-23      SK  Košice  temperature    -3.3\n",
            "77266  2024-01-23      SK  Košice          so2     4.6\n",
            "\n",
            "[2771315 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This Python code is using the `pandas` library to manipulate and analyze data.\n",
        "\n",
        "Here's a breakdown of what each line does:\n",
        "\n",
        "1. `DF = pd.concat([oldTable, newTable])`: This line combines the data from two tables, `oldTable` and `newTable`, into a single DataFrame called `DF`. The `concat` function is used to concatenate the two tables along the rows.\n",
        "2. `dataTableEU = DF.sort_values(by=['Country', 'City', 'Date']).drop_duplicates()`: This line sorts the data in the `DF` DataFrame by the columns `Country`, `City`, and `Date`, and then removes any duplicate rows.\n",
        "3. `print(dataTableEU)`: This line simply prints the sorted and deduplicated data to the console.\n",
        "\n",
        "In summary, this code combines data from two tables, sorts it by country, city, and date, and then removes any duplicate rows."
      ],
      "metadata": {
        "id": "TjZZXeU9wba2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Check and select vars"
      ],
      "metadata": {
        "id": "UC0vuGydwdG5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Weather conditions and pollutants (PM10, PM2.5, NO2, Ozone, SO2, CO)\n",
        "\n",
        "# Calculate the proportion of each Species in the data table\n",
        "all_vars = 100 * pd.value_counts(dataTableEU.Specie) / len(dataTableEU)\n",
        "\n",
        "# Drop the variables that are not needed\n",
        "drop_weat = ['pressure', 'wind-speed', 'wind-gust', 'wind speed', 'wind gust', 'dew', 'precipitation']\n",
        "drop_poll = ['wd', 'aqi', 'uvi', 'pm1', 'neph', 'mepaqi']\n",
        "keep_vars = set(all_vars.index) - set(drop_weat + drop_poll)\n",
        "\n",
        "# Create a new data table with the info on kept variables\n",
        "new_data_table = pd.DataFrame([all_vars[list(keep_vars)].sort_values(ascending=False)])\n",
        "new_data_table.style.hide(axis=\"index\")"
      ],
      "metadata": {
        "id": "VaZFX49twfBh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "d98488de-21c1-48b7-9035-594a4478bc16"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7bcba997ce20>"
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            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_bd5c1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_bd5c1_level0_col0\" class=\"col_heading level0 col0\" >temperature</th>\n",
              "      <th id=\"T_bd5c1_level0_col1\" class=\"col_heading level0 col1\" >humidity</th>\n",
              "      <th id=\"T_bd5c1_level0_col2\" class=\"col_heading level0 col2\" >pm10</th>\n",
              "      <th id=\"T_bd5c1_level0_col3\" class=\"col_heading level0 col3\" >no2</th>\n",
              "      <th id=\"T_bd5c1_level0_col4\" class=\"col_heading level0 col4\" >o3</th>\n",
              "      <th id=\"T_bd5c1_level0_col5\" class=\"col_heading level0 col5\" >pm25</th>\n",
              "      <th id=\"T_bd5c1_level0_col6\" class=\"col_heading level0 col6\" >so2</th>\n",
              "      <th id=\"T_bd5c1_level0_col7\" class=\"col_heading level0 col7\" >co</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_bd5c1_row0_col0\" class=\"data row0 col0\" >9.893751</td>\n",
              "      <td id=\"T_bd5c1_row0_col1\" class=\"data row0 col1\" >9.885307</td>\n",
              "      <td id=\"T_bd5c1_row0_col2\" class=\"data row0 col2\" >9.635065</td>\n",
              "      <td id=\"T_bd5c1_row0_col3\" class=\"data row0 col3\" >9.417840</td>\n",
              "      <td id=\"T_bd5c1_row0_col4\" class=\"data row0 col4\" >8.339904</td>\n",
              "      <td id=\"T_bd5c1_row0_col5\" class=\"data row0 col5\" >7.234833</td>\n",
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              "      <td id=\"T_bd5c1_row0_col7\" class=\"data row0 col7\" >4.524098</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This code is using the `pandas` library in Python to perform some data manipulation and analysis.\n",
        "\n",
        "Here's a breakdown of what each line does:\n",
        "\n",
        "1. `all_vars = 100 * pd.value_counts(dataTableEU.Specie) / len(dataTableEU)`: This line calculates the proportion of each species in the data table by first counting the number of occurrences of each species using `pd.value_counts()`, then dividing the count by the total number of rows in the data table (`len(dataTableEU)`) to get a proportion. The result is stored in a variable called `all_vars`.\n",
        "2. `drop_weat = ['pressure', 'wind-speed', 'wind-gust', 'wind speed', 'wind gust', 'dew', 'precipitation']`: This line creates a list called `drop_weat` that contains the names of the variables that should be dropped from the data table.\n",
        "3. `drop_poll = ['wd', 'aqi', 'uvi', 'pm1', 'neph', 'mepaqi']`: This line creates a list called `drop_poll` that contains the names of the variables that should be dropped from the data table.\n",
        "4. `keep_vars = set(all_vars.index) - set(drop_weat + drop_poll)`: This line creates a set called `keep_vars` that contains the names of the variables that should be kept in the data table. This is done by first getting the index of the `all_vars` DataFrame using `all_vars.index`, then subtracting the names of the variables to be dropped from the set using the `-` operator.\n",
        "5. `new_data_table = pd.DataFrame([all_vars[list(keep_vars)].sort_values(ascending=False)])`: This line creates a new data table called `new_data_table` that contains only the variables that should be kept, sorted in descending order by their proportion in the data table. The `sort_values()` method is used to sort the data table by the values in the `all_vars` DataFrame, and the `ascending=False` argument is used to sort in descending order.\n",
        "6. `new_data_table.style.hide(axis=\"index\")`: This line hides the index of the `new_data_table` DataFrame, which is the default behavior of the `pd.DataFrame()` constructor. The `hide()` method is used to hide the index, and the `axis=\"index\"` argument is used to specify that the index should be hidden."
      ],
      "metadata": {
        "id": "2J58d5Gywh2J"
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    },
    {
      "cell_type": "code",
      "source": [
        "# Descriptive statistics for daily values of selected variables\n",
        "selectedVars = ['temperature', 'humidity']\n",
        "selectedIdx = dataTableEU['Specie'].isin(selectedVars)\n",
        "dataTableEU = dataTableEU[selectedIdx]\n",
        "dataTableEU.groupby('Specie')['Value'].describe()"
      ],
      "metadata": {
        "id": "3He9pLH-wk7Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "3d7cca49-e375-4fef-d063-a799d5be25d6"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                count       mean        std   min   25%   50%   75%    max\n",
              "Specie                                                                    \n",
              "humidity     273953.0  71.272162  16.891712   0.0  60.0  73.6  84.6  100.0\n",
              "temperature  274187.0  12.625512   8.099295 -67.7   6.8  12.6  18.5   81.1"
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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This Python code is using the `pandas` library to obtain descriptive statistics of a dataset.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `selectedVars = ['temperature', 'humidity']`: This line defines a list of variables that the code will be working with. In this case, the list contains two variables: \"temperature\" and \"humidity\".\n",
        "2. `selectedIdx = dataTableEU['Specie'].isin(selectedVars)`: This line uses the `isin` method to check if the values in the \"Specie\" column of the `dataTableEU` dataframe are in the `selectedVars` list. If a value is in the list, it will be included in the resulting dataframe.\n",
        "3. `dataTableEU = dataTableEU[selectedIdx]`: This line selects the rows of the `dataTableEU` dataframe that have values in the \"Specie\" column that are also in the `selectedVars` list.\n",
        "4. `dataTableEU.groupby('Specie')['Value'].describe()`: This line groups the data by the \"Specie\" column and calculates the descriptive statistics for the \"Value\" column. The `describe` method calculates the mean, standard deviation, minimum, maximum, and count for each group.\n",
        "\n",
        "In summary, this code is selecting a subset of the data that only includes rows where the \"Specie\" column has values that are also in the `selectedVars` list, and then calculating the descriptive statistics for the \"Value\" column for each group."
      ],
      "metadata": {
        "id": "Bt2rrZ8WwleM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2021-10-03 Barcelona fix\n",
        "print(dataTableEU)\n",
        "dataTableEU = dataTableEU.groupby(['Date', 'Country', 'City', 'Specie'])[['Value']].mean().reset_index()\n",
        "print(dataTableEU)"
      ],
      "metadata": {
        "id": "EDWitDsewoJ8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a23a61c4-14df-4900-fe6c-b951a3491ea0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             Date Country    City       Specie  Value\n",
            "10673  2018-12-31      AT    Graz  temperature    3.7\n",
            "10751  2018-12-31      AT    Graz     humidity   70.2\n",
            "10637  2019-01-01      AT    Graz  temperature    3.0\n",
            "10771  2019-01-01      AT    Graz     humidity   75.7\n",
            "10710  2019-01-02      AT    Graz  temperature   -0.2\n",
            "...           ...     ...     ...          ...    ...\n",
            "77244  2024-01-21      SK  Košice  temperature   -2.5\n",
            "77176  2024-01-22      SK  Košice     humidity   94.0\n",
            "77249  2024-01-22      SK  Košice  temperature   -3.8\n",
            "77191  2024-01-23      SK  Košice     humidity   94.0\n",
            "77245  2024-01-23      SK  Košice  temperature   -3.3\n",
            "\n",
            "[548140 rows x 5 columns]\n",
            "              Date Country        City       Specie  Value\n",
            "0       2018-12-31      AT        Graz     humidity   70.2\n",
            "1       2018-12-31      AT        Graz  temperature    3.7\n",
            "2       2018-12-31      AT   Innsbruck     humidity   93.0\n",
            "3       2018-12-31      AT   Innsbruck  temperature    0.0\n",
            "4       2018-12-31      AT        Linz     humidity   92.0\n",
            "...            ...     ...         ...          ...    ...\n",
            "548132  2024-01-23      SE     Uppsala  temperature    4.0\n",
            "548133  2024-01-23      SK  Bratislava     humidity   91.0\n",
            "548134  2024-01-23      SK  Bratislava  temperature   -1.8\n",
            "548135  2024-01-23      SK      Košice     humidity   94.0\n",
            "548136  2024-01-23      SK      Košice  temperature   -3.3\n",
            "\n",
            "[548137 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This Python code is using the `pandas` library to perform some data manipulation and analysis.\n",
        "\n",
        "Here's a breakdown of what each line does:\n",
        "\n",
        "1. `print(dataTableEU)`: This line simply prints the contents of the `dataTableEU` variable to the console.\n",
        "2. `dataTableEU = dataTableEU.groupby(['Date', 'Country', 'City', 'Specie'])[['Value']].mean().reset_index()`: This line groups the data in the `dataTableEU` variable by the columns `'Date'`, `'Country'`, `'City'`, and `'Specie'`, and then calculates the mean of the `'Value'` column for each group. The resulting data is then reset to its original index.\n",
        "\n",
        "In summary, this code is grouping the data in `dataTableEU` by the specified columns and calculating the mean of the `'Value'` column for each group. The resulting data is then reset to its original index."
      ],
      "metadata": {
        "id": "C1K8K9g5wpa5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Pivot and calculate THI"
      ],
      "metadata": {
        "id": "CQ8FVe_5w1UN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create pivot table, calculate THI for each row, drop rows with missing THI values\n",
        "dataTableTHI = dataTableEU.pivot_table(index=['Date', 'Country', 'City'], columns='Specie', values='Value').reset_index()\n",
        "dataTableTHI[\"THI\"] = 0.8 * dataTableTHI.temperature + (dataTableTHI.humidity/100)*(dataTableTHI.temperature-14.4) + 46.4\n",
        "dataTableTHI = dataTableTHI.dropna(subset=[\"THI\"])\n",
        "print(dataTableTHI)"
      ],
      "metadata": {
        "id": "wKTMFsREw3jW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a49aa6c0-612e-4c2f-d998-db1e7119c6b8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Specie        Date Country        City  humidity  temperature      THI\n",
            "0       2018-12-31      AT        Graz      70.2          3.7  41.8486\n",
            "1       2018-12-31      AT   Innsbruck      93.0          0.0  33.0080\n",
            "2       2018-12-31      AT        Linz      92.0          3.3  38.8280\n",
            "3       2018-12-31      AT    Salzburg      96.5          1.0  34.2690\n",
            "4       2018-12-31      AT      Vienna      70.0          4.5  43.0700\n",
            "...            ...     ...         ...       ...          ...      ...\n",
            "274188  2024-01-23      SE       Malmö      89.0          4.4  41.0200\n",
            "274189  2024-01-23      SE   Stockholm      75.0          5.0  43.3500\n",
            "274190  2024-01-23      SE     Uppsala      80.0          4.0  41.2800\n",
            "274191  2024-01-23      SK  Bratislava      91.0         -1.8  30.2180\n",
            "274192  2024-01-23      SK      Košice      94.0         -3.3  27.1220\n",
            "\n",
            "[273944 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This Python code creates a pivot table from a data table called `dataTableEU` and calculates the THI (Temperature Humidity Index) for each row. The THI is calculated using the following formula:\n",
        "\n",
        "THI = 0.8 \\* temperature + (humidity/100) \\* (temperature - 14.4) + 46.4\n",
        "\n",
        "The pivot table is then reset to its original index and the rows with missing THI values are dropped using the `dropna()` method. Finally, the resulting pivot table is printed to the console.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `dataTableTHI = dataTableEU.pivot_table(index=['Date', 'Country', 'City'], columns='Specie', values='Value').reset_index()`: This line creates a pivot table from the `dataTableEU` data table, with the `Date`, `Country`, and `City` columns as the index, and the `Specie` column as the columns. The `values` parameter is set to `'Value'` to specify that the values in the `Value` column should be used as the values in the pivot table. Finally, the `reset_index()` method is called to reset the index of the pivot table to its original values.\n",
        "2. `dataTableTHI[\"THI\"] = 0.8 * dataTableTHI.temperature + (dataTableTHI.humidity/100)*(dataTableTHI.temperature-14.4) + 46.4`: This line calculates the THI for each row in the pivot table.\n",
        "3. `dataTableTHI = dataTableTHI.dropna(subset=[\"THI\"])`: This line drops the rows from the pivot table that have missing values in the `THI` column. The `dropna()` method is called with the `subset` parameter set to `[\"THI\"]` to specify that only the rows with missing values in the `THI` column should be dropped.\n",
        "4. `print(dataTableTHI)`: This line prints the resulting pivot table to the console."
      ],
      "metadata": {
        "id": "g7Ntb2lUw4Au"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. THI statistics and plots"
      ],
      "metadata": {
        "id": "hSCUzjmtxRzZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Descriptive statistics for yearly THI\n",
        "myCity = 'Kaunas'\n",
        "myTable = dataTableTHI[(dataTableTHI['City']==myCity) & (dataTableTHI['Date']>='2019-01-01')][[\"Date\", \"THI\"]]\n",
        "myTable.groupby(pd.to_datetime(myTable['Date']).dt.year).describe()"
      ],
      "metadata": {
        "id": "ZMbSDTzgxTcR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "12e11ba9-e1eb-457a-dae3-eb0073356b4e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Specie    THI                                                              \\\n",
              "        count       mean        std     min      25%       50%        75%   \n",
              "Date                                                                        \n",
              "2019    362.0  49.242705  12.738765  13.640  38.9960  49.53540  59.722625   \n",
              "2020    348.0  50.388563  10.801356  28.020  40.6560  50.18475  59.673125   \n",
              "2021    362.0  46.690309  14.882599   2.696  35.8275  47.17400  58.801250   \n",
              "2022    281.0  50.237457  12.161641  21.632  39.6800  50.30800  60.312000   \n",
              "2023    281.0  44.728998  11.624320  17.000  34.8800  44.34750  53.772000   \n",
              "2024     23.0  25.604696   8.608295   3.476  21.2165  25.91000  33.152000   \n",
              "\n",
              "Specie           \n",
              "            max  \n",
              "Date             \n",
              "2019    75.0560  \n",
              "2020    71.4790  \n",
              "2021    74.7400  \n",
              "2022    73.6255  \n",
              "2023    71.9085  \n",
              "2024    37.2840  "
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
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              "      <th>Specie</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>362.0</td>\n",
              "      <td>49.242705</td>\n",
              "      <td>12.738765</td>\n",
              "      <td>13.640</td>\n",
              "      <td>38.9960</td>\n",
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              "      <td>59.722625</td>\n",
              "      <td>75.0560</td>\n",
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              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>348.0</td>\n",
              "      <td>50.388563</td>\n",
              "      <td>10.801356</td>\n",
              "      <td>28.020</td>\n",
              "      <td>40.6560</td>\n",
              "      <td>50.18475</td>\n",
              "      <td>59.673125</td>\n",
              "      <td>71.4790</td>\n",
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              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>362.0</td>\n",
              "      <td>46.690309</td>\n",
              "      <td>14.882599</td>\n",
              "      <td>2.696</td>\n",
              "      <td>35.8275</td>\n",
              "      <td>47.17400</td>\n",
              "      <td>58.801250</td>\n",
              "      <td>74.7400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022</th>\n",
              "      <td>281.0</td>\n",
              "      <td>50.237457</td>\n",
              "      <td>12.161641</td>\n",
              "      <td>21.632</td>\n",
              "      <td>39.6800</td>\n",
              "      <td>50.30800</td>\n",
              "      <td>60.312000</td>\n",
              "      <td>73.6255</td>\n",
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              "      <th>2023</th>\n",
              "      <td>281.0</td>\n",
              "      <td>44.728998</td>\n",
              "      <td>11.624320</td>\n",
              "      <td>17.000</td>\n",
              "      <td>34.8800</td>\n",
              "      <td>44.34750</td>\n",
              "      <td>53.772000</td>\n",
              "      <td>71.9085</td>\n",
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              "      <th>2024</th>\n",
              "      <td>23.0</td>\n",
              "      <td>25.604696</td>\n",
              "      <td>8.608295</td>\n",
              "      <td>3.476</td>\n",
              "      <td>21.2165</td>\n",
              "      <td>25.91000</td>\n",
              "      <td>33.152000</td>\n",
              "      <td>37.2840</td>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d7e9544-86e2-4727-9ffd-9c7b565b7972')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\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",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1d7e9544-86e2-4727-9ffd-9c7b565b7972 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-1d7e9544-86e2-4727-9ffd-9c7b565b7972');\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-abed73ca-7611-4994-88ec-91300796365c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-abed73ca-7611-4994-88ec-91300796365c')\"\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-abed73ca-7611-4994-88ec-91300796365c 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": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This code is using the `pandas` library in Python to obtain descriptive statistics for a dataset containing THI (Temperature Humidity Index) values for a specific city (Kaunas) over a period of time.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `myCity = 'Kaunas'`: This line sets the variable `myCity` to the string value `'Kaunas'`.\n",
        "2. `myTable = dataTableTHI[(dataTableTHI['City']==myCity) & (dataTableTHI['Date']>='2019-01-01')][[\"Date\", \"THI\"]]`: This line selects a subset of the `dataTableTHI` dataframe that contains only the rows where the `City` column is equal to `myCity` and the `Date` column is starting with year 2019. The `[[\"Date\", \"THI\"]]` syntax is used to select only the `Date` and `THI` columns from the resulting dataframe.\n",
        "3. `myTable.groupby(pd.to_datetime(myTable['Date']).dt.year).describe()`: This line groups the `myTable` dataframe by the year component of the `Date` column (using the `dt.year` attribute of the `pd.to_datetime` function) and then applies the `describe` method to each group. The `describe` method calculates various summary statistics for each group, such as the mean, standard deviation, minimum, maximum, and count.\n",
        "\n",
        "In summary, this code is using the `pandas` library to perform descriptive statistics on a subset of the `dataTableTHI` dataframe that contains only the rows for a specific city and a specific time period, and then grouping the results by year to calculate summary statistics for each year."
      ],
      "metadata": {
        "id": "amfMCIYWxU3p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot THI time series calendar heatmap\n",
        "pdTimeSeries = pd.Series(myTable['THI'].values, index=pd.DatetimeIndex(myTable['Date']))\n",
        "logging.getLogger('matplotlib.font_manager').disabled = True\n",
        "cp = calplot.calplot(pdTimeSeries, dropzero=True, cmap='coolwarm', suptitle='THI @ ' + myCity)"
      ],
      "metadata": {
        "id": "xpiUmXMExfMZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "outputId": "f6357436-0e5f-4e60-aa39-a1c49bb7f1d0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1250x1020 with 7 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This code is using the `matplotlib` library to create a calendar heatmap of the THI (Temperature Humidity Index) time series data from a pandas dataframe called `myTable`.\n",
        "\n",
        "Here's a breakdown of the code:\n",
        "\n",
        "1. `pdTimeSeries = pd.Series(myTable['THI'].values, index=pd.DatetimeIndex(myTable['Date']))`: This line creates a pandas Series object called `pdTimeSeries` from the THI column of the `myTable` dataframe. The `values` attribute of the THI column is used to create the values of the Series, and the `index` attribute is used to create the index of the Series, which is a DatetimeIndex object created from the `Date` column of the dataframe.\n",
        "2. `logging.getLogger('matplotlib.font_manager').disabled = True`: This line disables the logging of font warnings in the `matplotlib.font_manager` module. This is necessary because the `calplot` function uses the `matplotlib.font_manager` module to create the calendar heatmap, and it can generate a lot of warnings if the font used in the heatmap is not available on the system.\n",
        "3. `calplot.calplot(pdTimeSeries, dropzero=True, cmap='coolwarm', suptitle='THI @ ' + myCity)`: This line calls the `calplot` function from the `calplot` module, passing in the `pdTimeSeries` object as the first argument. The `dropzero` argument is set to `True` to drop the zero values from the heatmap, and the `cmap` argument is set to `'coolwarm'` to use the `coolwarm` colormap. The `suptitle` argument is set to `'THI @ ' + myCity` to set the title of the heatmap to `'THI @ ' + myCity`.\n",
        "\n",
        "Overall, this code is creating a calendar heatmap of the THI time series data from the `myTable` dataframe, with the `coolwarm` colormap and a title that includes the city name."
      ],
      "metadata": {
        "id": "psDsOt6Lxvq9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot THI time series dynamics\n",
        "ts = pdTimeSeries.plot(title='THI @ ' + myCity, ylabel=\"THI\", xlabel=\"Time\", lw=1, grid=True)"
      ],
      "metadata": {
        "id": "M9BYszJrx8H2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "677680da-c67f-4286-98b8-b6e431bbfefc"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This code is using the `plot()` method of the `pdTimeSeries` object to create a time series plot of the THI (Temperature Humidity Index) values. The `title` parameter is set to \"THI @ \" + myCity, which means that the title of the plot will be \"THI @ [myCity]\". The `lw` parameter is set to 1, which means that the line width of the plot will be 1. The `grid` parameter is set to True, which means that a grid will be displayed on the plot."
      ],
      "metadata": {
        "id": "P2LW0A15yEti"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Export results to .html"
      ],
      "metadata": {
        "id": "Vd3DCx3SyRmh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After running all notebook blocks, save .ipynb and use *nbconvert* to get .html\n",
        "\n",
        "\n",
        "1. Save notebook with results: **File / Save (Ctrl+S)**\n",
        "2. Download notebook: **File / Download / Download .ipynb**\n",
        "3. Upload .ipynb back to Google Colab: **File / Upload notebook**\n",
        "4. Run the following command-line: *jupyter nbconvert --to html*"
      ],
      "metadata": {
        "id": "NYnPBs6IyUVR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!jupyter nbconvert --to html ASSIST_Air_Data_Python.ipynb"
      ],
      "metadata": {
        "id": "K-N0HNF6yU3R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assistant:** This block contains a command-line to convert a Jupyter notebook file named `ASSIST_Air_Data_Python.ipynb` to an HTML file."
      ],
      "metadata": {
        "id": "yhToMrP3yWWp"
      }
    }
  ]
}