deeper_weather.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "deeper_weather.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "f2mLHDbBrC8v",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Weather Prediction with Deep Learning (30 minutes)\n",
        "Denis Akhiyarov\n",
        "    * Senior Consultant at Wood\n",
        "    * Python.NET (Python for .NET) core developer\n",
        "    * Faculty at North American University\n",
        "    \n",
        "https://notebooks.azure.com/denfromufa/libraries/pmlc\n"
      ]
    },
    {
      "metadata": {
        "id": "5RS9Ep6iWfcd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Economics of weather forecasting?\n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "   <td>\n",
        "       <!---->\n",
        "     <ul>\n",
        "        <h3>1. Insurance and reinsurance</h3>\n",
        "        <h3>2. Transportation (air, taxi, boats)</h3>\n",
        "        <h3>3. Agriculture and fishing</h3>\n",
        "        <h3>4. Input for energy industry</h3>\n",
        "        <h3>5. Climate change and extreme events</h3>     \n",
        "     </ul> \n",
        " </td>\n",
        " <td> \n",
        "   <p>\n",
        "     <img src=\"https://rstorage.filemobile.com/storage/23092306/1085\" \n",
        "          alt>\n",
        "     <!-- width=\"600\" height=\"400\" --> \n",
        "   </p>\n",
        "   <p>\n",
        "     <em>https://www.theweathernetwork.com</em>\n",
        "   </p>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>\n"
      ]
    },
    {
      "metadata": {
        "id": "G4BTNcWNQ0Bi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Models of weather predictions?\n",
        "Unified model - UK Met Office\n",
        "![](https://www.metoffice.gov.uk/binaries/content/gallery/mohippo/images/research/graphics/umsystems_13022018_1920.png)"
      ]
    },
    {
      "metadata": {
        "id": "tYgLDUo6F7pF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Yandex Weather\n",
        "\n",
        "https://yandex.com/company/blog/winter-is-coming-yandex-weather-nowcasting-helps-users-plan-for-precipitation"
      ]
    },
    {
      "metadata": {
        "id": "RVKkCI4UClGU",
        "colab_type": "code",
        "outputId": "575c6873-9561-42f6-fab6-4024f8d6b299",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "cell_type": "code",
      "source": [
        "from IPython.display import YouTubeVideo\n",
        "YouTubeVideo('aR5p7_g8qXQ', width=800, height=600)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"800\"\n",
              "            height=\"600\"\n",
              "            src=\"https://www.youtube.com/embed/aR5p7_g8qXQ\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7f48aa524da0>"
            ],
            "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDBoYFhsaGRoeHRsfIi0mIyIiIy8tKigvLioyMC0t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\nQgEIQgEIQgEIQgEIQgEIQgEIQgEIQg//2Q==\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "8gZNsDS_E9PO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale\n",
        "National Energy Research Scientific Computing Center\n",
        "\n",
        "![](https://6lli539m39y3hpkelqsm3c2fg-wpengine.netdna-ssl.com/wp-content/uploads/2018/03/Intel-NERSC-feature-graphic-weather-675x380.png)"
      ]
    },
    {
      "metadata": {
        "id": "CKPX2EbKR9HB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Package imports"
      ]
    },
    {
      "metadata": {
        "id": "rWRy8e9OrC81",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import exp\n",
        "from IPython.display import HTML, Latex, Math, display\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import seaborn as sns\n",
        "from sympy.printing import latex\n",
        "from sympy.core.sympify import sympify\n",
        "%matplotlib inline\n",
        "pd.options.display.max_columns=50\n",
        "pd.options.display.max_colwidth=100\n",
        "matplotlib.rcParams['savefig.dpi'] = 300\n",
        "matplotlib.rcParams['figure.dpi'] = 300\n",
        "matplotlib.rcParams['figure.figsize'] = (3, 2)\n",
        "fontsize=3\n",
        "matplotlib.rcParams['figure.titlesize'] = fontsize\n",
        "matplotlib.rcParams['axes.labelsize'] = fontsize\n",
        "matplotlib.rcParams['font.size'] = fontsize\n",
        "matplotlib.rcParams['axes.titlesize'] = fontsize\n",
        "matplotlib.rcParams['legend.fontsize'] = fontsize\n",
        "matplotlib.rcParams['xtick.labelsize'] = fontsize\n",
        "matplotlib.rcParams['ytick.labelsize'] = fontsize\n",
        "matplotlib.rcParams['lines.markersize'] = 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1fuv6YIMSJi-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## How to get weather data?!\n",
        "\n",
        "Even Google Datasets does not explain well, multiple commercial offerings online\n",
        "\n",
        "\n",
        "https://www.kaggle.com/selfishgene/historical-hourly-weather-data"
      ]
    },
    {
      "metadata": {
        "id": "MWCjFEitrC88",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Historic data of weather in Houston requested and retrieved from Department of Commerce > NOAA > NESDIS > NCEI\n",
        "\n",
        "https://www.ncdc.noaa.gov/\n",
        "\n",
        "https://www7.ncdc.noaa.gov/CDO/cdopoemain.cmd?datasetabbv=DS3505"
      ]
    },
    {
      "metadata": {
        "id": "BeN1rggjDhJU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![alt text](https://raw.githubusercontent.com/denfromufa/deepweather/master/Capture.PNG)"
      ]
    },
    {
      "metadata": {
        "id": "aO6HehcFrC88",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data Exploration and Cleaning\n",
        "\n",
        "Let's read the head of the table and look at the data using pandas"
      ]
    },
    {
      "metadata": {
        "id": "KfKspF_XrC89",
        "colab_type": "code",
        "outputId": "a30f9983-dd3f-42d7-d0a4-e32c863a24cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "cell_type": "code",
      "source": [
        "dftxtown=pd.read_csv(\"https://raw.githubusercontent.com/denfromufa/deepweather/master/data/8004287785567dat.txt\", \n",
        "                    delim_whitespace=True)\n",
        "dftxtown.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USAF</th>\n",
              "      <th>WBAN</th>\n",
              "      <th>YR--MODAHRMN</th>\n",
              "      <th>DIR</th>\n",
              "      <th>SPD</th>\n",
              "      <th>GUS</th>\n",
              "      <th>CLG</th>\n",
              "      <th>SKC</th>\n",
              "      <th>L</th>\n",
              "      <th>M</th>\n",
              "      <th>H</th>\n",
              "      <th>VSB</th>\n",
              "      <th>MW</th>\n",
              "      <th>MW.1</th>\n",
              "      <th>MW.2</th>\n",
              "      <th>MW.3</th>\n",
              "      <th>AW</th>\n",
              "      <th>AW.1</th>\n",
              "      <th>AW.2</th>\n",
              "      <th>AW.3</th>\n",
              "      <th>W</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>SLP</th>\n",
              "      <th>ALT</th>\n",
              "      <th>STP</th>\n",
              "      <th>MAX</th>\n",
              "      <th>MIN</th>\n",
              "      <th>PCP01</th>\n",
              "      <th>PCP06</th>\n",
              "      <th>PCP24</th>\n",
              "      <th>PCPXX</th>\n",
              "      <th>SD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>720648</td>\n",
              "      <td>230</td>\n",
              "      <td>201511010015</td>\n",
              "      <td>***</td>\n",
              "      <td>0</td>\n",
              "      <td>***</td>\n",
              "      <td>722</td>\n",
              "      <td>SCT</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>68</td>\n",
              "      <td>64</td>\n",
              "      <td>******</td>\n",
              "      <td>29.86</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>720648</td>\n",
              "      <td>230</td>\n",
              "      <td>201511010035</td>\n",
              "      <td>010</td>\n",
              "      <td>6</td>\n",
              "      <td>***</td>\n",
              "      <td>50</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>68</td>\n",
              "      <td>66</td>\n",
              "      <td>******</td>\n",
              "      <td>29.87</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>720648</td>\n",
              "      <td>230</td>\n",
              "      <td>201511010055</td>\n",
              "      <td>010</td>\n",
              "      <td>7</td>\n",
              "      <td>***</td>\n",
              "      <td>50</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>68</td>\n",
              "      <td>64</td>\n",
              "      <td>******</td>\n",
              "      <td>29.88</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>720648</td>\n",
              "      <td>230</td>\n",
              "      <td>201511010115</td>\n",
              "      <td>350</td>\n",
              "      <td>10</td>\n",
              "      <td>***</td>\n",
              "      <td>28</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>70</td>\n",
              "      <td>63</td>\n",
              "      <td>******</td>\n",
              "      <td>29.89</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>720648</td>\n",
              "      <td>230</td>\n",
              "      <td>201511010135</td>\n",
              "      <td>340</td>\n",
              "      <td>11</td>\n",
              "      <td>***</td>\n",
              "      <td>60</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>68</td>\n",
              "      <td>63</td>\n",
              "      <td>******</td>\n",
              "      <td>29.91</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     USAF  WBAN  YR--MODAHRMN  DIR SPD  GUS  CLG  SKC  L  M  H   VSB  MW MW.1  \\\n",
              "0  720648   230  201511010015  ***   0  ***  722  SCT  *  *  *  10.0  **   **   \n",
              "1  720648   230  201511010035  010   6  ***   50  ***  *  *  *  10.0  **   **   \n",
              "2  720648   230  201511010055  010   7  ***   50  ***  *  *  *  10.0  **   **   \n",
              "3  720648   230  201511010115  350  10  ***   28  ***  *  *  *  10.0  **   **   \n",
              "4  720648   230  201511010135  340  11  ***   60  ***  *  *  *  10.0  **   **   \n",
              "\n",
              "  MW.2 MW.3  AW AW.1 AW.2 AW.3  W TEMP DEWP     SLP    ALT     STP  MAX  MIN  \\\n",
              "0   **   **  **   **   **   **  *   68   64  ******  29.86  ******  ***  ***   \n",
              "1   **   **  **   **   **   **  *   68   66  ******  29.87  ******  ***  ***   \n",
              "2   **   **  **   **   **   **  *   68   64  ******  29.88  ******  ***  ***   \n",
              "3   **   **  **   **   **   **  *   70   63  ******  29.89  ******  ***  ***   \n",
              "4   **   **  **   **   **   **  *   68   63  ******  29.91  ******  ***  ***   \n",
              "\n",
              "   PCP01  PCP06  PCP24  PCPXX  SD  \n",
              "0  *****  *****  *****  *****  **  \n",
              "1  *****  *****  *****  *****  **  \n",
              "2  *****  *****  *****  *****  **  \n",
              "3  *****  *****  *****  *****  **  \n",
              "4  *****  *****  *****  *****  **  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "LNcXJoJCeTAA",
        "colab_type": "code",
        "outputId": "03816be5-f989-44bd-b4f8-d62e7669d43d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "dftxtown.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(549546, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "K7LRJ_GqcfMz",
        "colab_type": "code",
        "outputId": "78221b2f-4769-4b3a-ba43-7328fadf0a01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "dftxtown.USAF.value_counts()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "999999    316540\n",
              "720648     77469\n",
              "722530     42688\n",
              "722590     41489\n",
              "722510     38613\n",
              "722580     32747\n",
              "Name: USAF, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "OVLQUw6hcqAD",
        "colab_type": "code",
        "outputId": "2993377d-70f4-48db-dd8e-fbc77129f95e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "dftxtown.WBAN.value_counts()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23907    316540\n",
              "230       77469\n",
              "12921     42688\n",
              "3927      41489\n",
              "12924     38613\n",
              "13960     32747\n",
              "Name: WBAN, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "XkanMOL7b6_w",
        "colab_type": "code",
        "outputId": "d3aa706f-a897-4675-9019-f5a4e59132b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown=pd.read_csv(\"https://raw.githubusercontent.com/denfromufa/deepweather/master/data/8115087791588dat.txt\", \n",
        "                    delim_whitespace=True)\n",
        "dfhtown.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (24) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USAF</th>\n",
              "      <th>WBAN</th>\n",
              "      <th>YR--MODAHRMN</th>\n",
              "      <th>DIR</th>\n",
              "      <th>SPD</th>\n",
              "      <th>GUS</th>\n",
              "      <th>CLG</th>\n",
              "      <th>SKC</th>\n",
              "      <th>L</th>\n",
              "      <th>M</th>\n",
              "      <th>H</th>\n",
              "      <th>VSB</th>\n",
              "      <th>MW</th>\n",
              "      <th>MW.1</th>\n",
              "      <th>MW.2</th>\n",
              "      <th>MW.3</th>\n",
              "      <th>AW</th>\n",
              "      <th>AW.1</th>\n",
              "      <th>AW.2</th>\n",
              "      <th>AW.3</th>\n",
              "      <th>W</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>SLP</th>\n",
              "      <th>ALT</th>\n",
              "      <th>STP</th>\n",
              "      <th>MAX</th>\n",
              "      <th>MIN</th>\n",
              "      <th>PCP01</th>\n",
              "      <th>PCP06</th>\n",
              "      <th>PCP24</th>\n",
              "      <th>PCPXX</th>\n",
              "      <th>SD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>720637</td>\n",
              "      <td>223</td>\n",
              "      <td>201511010015</td>\n",
              "      <td>330</td>\n",
              "      <td>6</td>\n",
              "      <td>***</td>\n",
              "      <td>14</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>61</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>70</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.8</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>720637</td>\n",
              "      <td>223</td>\n",
              "      <td>201511010035</td>\n",
              "      <td>010</td>\n",
              "      <td>5</td>\n",
              "      <td>***</td>\n",
              "      <td>14</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>61</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>70</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.82</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>720637</td>\n",
              "      <td>223</td>\n",
              "      <td>201511010055</td>\n",
              "      <td>350</td>\n",
              "      <td>8</td>\n",
              "      <td>***</td>\n",
              "      <td>19</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>61</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.8</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>0.01</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>720637</td>\n",
              "      <td>223</td>\n",
              "      <td>201511010115</td>\n",
              "      <td>340</td>\n",
              "      <td>6</td>\n",
              "      <td>***</td>\n",
              "      <td>34</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.82</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>0.01</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>720637</td>\n",
              "      <td>223</td>\n",
              "      <td>201511010135</td>\n",
              "      <td>340</td>\n",
              "      <td>5</td>\n",
              "      <td>***</td>\n",
              "      <td>19</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.83</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>0.01</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     USAF  WBAN  YR--MODAHRMN  DIR SPD  GUS CLG  SKC  L  M  H   VSB  MW MW.1  \\\n",
              "0  720637   223  201511010015  330   6  ***  14  ***  *  *  *  10.0  **   **   \n",
              "1  720637   223  201511010035  010   5  ***  14  ***  *  *  *  10.0  **   **   \n",
              "2  720637   223  201511010055  350   8  ***  19  ***  *  *  *  10.0  **   **   \n",
              "3  720637   223  201511010115  340   6  ***  34  ***  *  *  *  10.0  **   **   \n",
              "4  720637   223  201511010135  340   5  ***  19  ***  *  *  *  10.0  **   **   \n",
              "\n",
              "  MW.2 MW.3  AW AW.1 AW.2 AW.3  W TEMP DEWP     SLP    ALT     STP  MAX  MIN  \\\n",
              "0   **   **  61   **   **   **  *   70   68  ******   29.8  ******  ***  ***   \n",
              "1   **   **  61   **   **   **  *   70   68  ******  29.82  ******  ***  ***   \n",
              "2   **   **  61   **   **   **  *   69   68  ******   29.8  ******  ***  ***   \n",
              "3   **   **  **   **   **   **  *   69   68  ******  29.82  ******  ***  ***   \n",
              "4   **   **  **   **   **   **  *   69   68  ******  29.83  ******  ***  ***   \n",
              "\n",
              "   PCP01  PCP06  PCP24  PCPXX  SD  \n",
              "0  *****  *****  *****  *****  **  \n",
              "1  *****  *****  *****  *****  **  \n",
              "2   0.01  *****  *****  *****  **  \n",
              "3   0.01  *****  *****  *****  **  \n",
              "4   0.01  *****  *****  *****  **  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "IUdyZPeawgd4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "12dd89fb-2a68-415f-eb58-32a71f63d9cb"
      },
      "cell_type": "code",
      "source": [
        "dfhtown.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(78216, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "wbx8y1_Wv2Uq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f42b41bd-2176-418b-b990-2f9d71490abf"
      },
      "cell_type": "code",
      "source": [
        "dftxtown = pd.concat([dfhtown,dftxtown])\n",
        "\n",
        "dftxtown.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(627762, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "eBH8yhqlcZga",
        "colab_type": "code",
        "outputId": "a5c7ad8a-5e96-47df-cd69-ddf01debc818",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "cell_type": "code",
      "source": [
        "dftxtown['USAFWBAN'] = dftxtown['USAF'].astype(str) + dftxtown['WBAN'].astype(str)\n",
        "del dftxtown['USAF']\n",
        "del dftxtown['WBAN']\n",
        "dftxtown.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>YR--MODAHRMN</th>\n",
              "      <th>DIR</th>\n",
              "      <th>SPD</th>\n",
              "      <th>GUS</th>\n",
              "      <th>CLG</th>\n",
              "      <th>SKC</th>\n",
              "      <th>L</th>\n",
              "      <th>M</th>\n",
              "      <th>H</th>\n",
              "      <th>VSB</th>\n",
              "      <th>MW</th>\n",
              "      <th>MW.1</th>\n",
              "      <th>MW.2</th>\n",
              "      <th>MW.3</th>\n",
              "      <th>AW</th>\n",
              "      <th>AW.1</th>\n",
              "      <th>AW.2</th>\n",
              "      <th>AW.3</th>\n",
              "      <th>W</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>SLP</th>\n",
              "      <th>ALT</th>\n",
              "      <th>STP</th>\n",
              "      <th>MAX</th>\n",
              "      <th>MIN</th>\n",
              "      <th>PCP01</th>\n",
              "      <th>PCP06</th>\n",
              "      <th>PCP24</th>\n",
              "      <th>PCPXX</th>\n",
              "      <th>SD</th>\n",
              "      <th>USAFWBAN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>201511010015</td>\n",
              "      <td>330</td>\n",
              "      <td>6</td>\n",
              "      <td>***</td>\n",
              "      <td>14</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>61</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>70</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.8</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "      <td>720637223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>201511010035</td>\n",
              "      <td>010</td>\n",
              "      <td>5</td>\n",
              "      <td>***</td>\n",
              "      <td>14</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>61</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>70</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.82</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "      <td>720637223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>201511010055</td>\n",
              "      <td>350</td>\n",
              "      <td>8</td>\n",
              "      <td>***</td>\n",
              "      <td>19</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>61</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.8</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>0.01</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "      <td>720637223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>201511010115</td>\n",
              "      <td>340</td>\n",
              "      <td>6</td>\n",
              "      <td>***</td>\n",
              "      <td>34</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.82</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>0.01</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "      <td>720637223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>201511010135</td>\n",
              "      <td>340</td>\n",
              "      <td>5</td>\n",
              "      <td>***</td>\n",
              "      <td>19</td>\n",
              "      <td>***</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>*</td>\n",
              "      <td>10.0</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>*</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>******</td>\n",
              "      <td>29.83</td>\n",
              "      <td>******</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>0.01</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "      <td>720637223</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   YR--MODAHRMN  DIR SPD  GUS CLG  SKC  L  M  H   VSB  MW MW.1 MW.2 MW.3  AW  \\\n",
              "0  201511010015  330   6  ***  14  ***  *  *  *  10.0  **   **   **   **  61   \n",
              "1  201511010035  010   5  ***  14  ***  *  *  *  10.0  **   **   **   **  61   \n",
              "2  201511010055  350   8  ***  19  ***  *  *  *  10.0  **   **   **   **  61   \n",
              "3  201511010115  340   6  ***  34  ***  *  *  *  10.0  **   **   **   **  **   \n",
              "4  201511010135  340   5  ***  19  ***  *  *  *  10.0  **   **   **   **  **   \n",
              "\n",
              "  AW.1 AW.2 AW.3  W TEMP DEWP     SLP    ALT     STP  MAX  MIN  PCP01  PCP06  \\\n",
              "0   **   **   **  *   70   68  ******   29.8  ******  ***  ***  *****  *****   \n",
              "1   **   **   **  *   70   68  ******  29.82  ******  ***  ***  *****  *****   \n",
              "2   **   **   **  *   69   68  ******   29.8  ******  ***  ***   0.01  *****   \n",
              "3   **   **   **  *   69   68  ******  29.82  ******  ***  ***   0.01  *****   \n",
              "4   **   **   **  *   69   68  ******  29.83  ******  ***  ***   0.01  *****   \n",
              "\n",
              "   PCP24  PCPXX  SD   USAFWBAN  \n",
              "0  *****  *****  **  720637223  \n",
              "1  *****  *****  **  720637223  \n",
              "2  *****  *****  **  720637223  \n",
              "3  *****  *****  **  720637223  \n",
              "4  *****  *****  **  720637223  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "EEN2o84TyeMs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "6dddecf2-6b2f-4c9d-d63f-7e1e59e774f8"
      },
      "cell_type": "code",
      "source": [
        "dftxtown.USAFWBAN.value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "99999923907    316540\n",
              "720637223       78216\n",
              "720648230       77469\n",
              "72253012921     42688\n",
              "7225903927      41489\n",
              "72251012924     38613\n",
              "72258013960     32747\n",
              "Name: USAFWBAN, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "7Q3ED3W4egtr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dftxtown = dftxtown[dftxtown['USAFWBAN'].astype(int).isin(\n",
        "    [99999923907, 72251012924, 72253012921, 7225903927, 720637223])]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xRYAEb5sdwYr",
        "colab_type": "code",
        "outputId": "08ea2f52-7342-498f-a84f-841699cda892",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "cell_type": "code",
      "source": [
        "dftxtown = dftxtown.pivot(index='YR--MODAHRMN', columns='USAFWBAN')\n",
        "\n",
        "dftxtown.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"5\" halign=\"left\">DIR</th>\n",
              "      <th colspan=\"5\" halign=\"left\">SPD</th>\n",
              "      <th colspan=\"5\" halign=\"left\">GUS</th>\n",
              "      <th colspan=\"5\" halign=\"left\">CLG</th>\n",
              "      <th colspan=\"5\" halign=\"left\">SKC</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"5\" halign=\"left\">PCP01</th>\n",
              "      <th colspan=\"5\" halign=\"left\">PCP06</th>\n",
              "      <th colspan=\"5\" halign=\"left\">PCP24</th>\n",
              "      <th colspan=\"5\" halign=\"left\">PCPXX</th>\n",
              "      <th colspan=\"5\" halign=\"left\">SD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USAFWBAN</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>...</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YR--MODAHRMN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>201511010000</th>\n",
              "      <td>None</td>\n",
              "      <td>330</td>\n",
              "      <td>070</td>\n",
              "      <td>320</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>OVC</td>\n",
              "      <td>OVC</td>\n",
              "      <td>OVC</td>\n",
              "      <td>***</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>0.00</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>0.00T*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>**</td>\n",
              "      <td>**</td>\n",
              "      <td>NaN</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201511010005</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201511010010</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201511010015</th>\n",
              "      <td>330</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>6</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>14</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>...</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>**</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201511010020</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>***</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>*****</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 150 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   DIR                                                 \\\n",
              "USAFWBAN     720637223 72251012924 72253012921 7225903927 99999923907   \n",
              "YR--MODAHRMN                                                            \n",
              "201511010000      None         330         070        320         ***   \n",
              "201511010005      None        None        None       None         ***   \n",
              "201511010010      None        None        None       None         ***   \n",
              "201511010015       330        None        None       None         ***   \n",
              "201511010020      None        None        None       None         ***   \n",
              "\n",
              "                   SPD                                                 \\\n",
              "USAFWBAN     720637223 72251012924 72253012921 7225903927 99999923907   \n",
              "YR--MODAHRMN                                                            \n",
              "201511010000      None          15           5         10           6   \n",
              "201511010005      None        None        None       None         ***   \n",
              "201511010010      None        None        None       None         ***   \n",
              "201511010015         6        None        None       None         ***   \n",
              "201511010020      None        None        None       None         ***   \n",
              "\n",
              "                   GUS                                                 \\\n",
              "USAFWBAN     720637223 72251012924 72253012921 7225903927 99999923907   \n",
              "YR--MODAHRMN                                                            \n",
              "201511010000      None         ***         ***        ***         ***   \n",
              "201511010005      None        None        None       None         ***   \n",
              "201511010010      None        None        None       None         ***   \n",
              "201511010015       ***        None        None       None         ***   \n",
              "201511010020      None        None        None       None         ***   \n",
              "\n",
              "                   CLG                                                 \\\n",
              "USAFWBAN     720637223 72251012924 72253012921 7225903927 99999923907   \n",
              "YR--MODAHRMN                                                            \n",
              "201511010000      None         ***         ***        ***         ***   \n",
              "201511010005      None        None        None       None         ***   \n",
              "201511010010      None        None        None       None         ***   \n",
              "201511010015        14        None        None       None         ***   \n",
              "201511010020      None        None        None       None         ***   \n",
              "\n",
              "                   SKC                                                 \\\n",
              "USAFWBAN     720637223 72251012924 72253012921 7225903927 99999923907   \n",
              "YR--MODAHRMN                                                            \n",
              "201511010000      None         OVC         OVC        OVC         ***   \n",
              "201511010005      None        None        None       None         ***   \n",
              "201511010010      None        None        None       None         ***   \n",
              "201511010015       ***        None        None       None         ***   \n",
              "201511010020      None        None        None       None         ***   \n",
              "\n",
              "                 ...         PCP01                                     \\\n",
              "USAFWBAN         ...     720637223 72251012924 72253012921 7225903927   \n",
              "YR--MODAHRMN     ...                                                    \n",
              "201511010000     ...          None       *****       *****      *****   \n",
              "201511010005     ...          None        None        None       None   \n",
              "201511010010     ...          None        None        None       None   \n",
              "201511010015     ...         *****        None        None       None   \n",
              "201511010020     ...          None        None        None       None   \n",
              "\n",
              "                             PCP06                                      \\\n",
              "USAFWBAN     99999923907 720637223 72251012924 72253012921  7225903927   \n",
              "YR--MODAHRMN                                                             \n",
              "201511010000        0.00      None       *****       *****  0.00T*****   \n",
              "201511010005       *****      None        None        None        None   \n",
              "201511010010       *****      None        None        None        None   \n",
              "201511010015       *****     *****        None        None        None   \n",
              "201511010020       *****      None        None        None        None   \n",
              "\n",
              "                             PCP24                                     \\\n",
              "USAFWBAN     99999923907 720637223 72251012924 72253012921 7225903927   \n",
              "YR--MODAHRMN                                                            \n",
              "201511010000       *****      None       *****       *****      *****   \n",
              "201511010005       *****      None        None        None       None   \n",
              "201511010010       *****      None        None        None       None   \n",
              "201511010015       *****     *****        None        None       None   \n",
              "201511010020       *****      None        None        None       None   \n",
              "\n",
              "                             PCPXX                                     \\\n",
              "USAFWBAN     99999923907 720637223 72251012924 72253012921 7225903927   \n",
              "YR--MODAHRMN                                                            \n",
              "201511010000       *****      None       *****       *****         **   \n",
              "201511010005       *****      None        None        None       None   \n",
              "201511010010       *****      None        None        None       None   \n",
              "201511010015       *****     *****        None        None       None   \n",
              "201511010020       *****      None        None        None       None   \n",
              "\n",
              "                                SD                                     \\\n",
              "USAFWBAN     99999923907 720637223 72251012924 72253012921 7225903927   \n",
              "YR--MODAHRMN                                                            \n",
              "201511010000       *****      None          **          **        NaN   \n",
              "201511010005       *****      None        None        None       None   \n",
              "201511010010       *****      None        None        None       None   \n",
              "201511010015       *****        **        None        None       None   \n",
              "201511010020       *****      None        None        None       None   \n",
              "\n",
              "                          \n",
              "USAFWBAN     99999923907  \n",
              "YR--MODAHRMN              \n",
              "201511010000          **  \n",
              "201511010005          **  \n",
              "201511010010          **  \n",
              "201511010015          **  \n",
              "201511010020          **  \n",
              "\n",
              "[5 rows x 150 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "dm745AjRfdu4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dftxtown.index = pd.to_datetime(dftxtown.index, format='%Y%m%d%H%M')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Kpv7aZ-drC9H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The data is messy, the names of columns not clear, so let's look at html version. Again using pandas!"
      ]
    },
    {
      "metadata": {
        "id": "qixn7in5zD0m",
        "colab_type": "code",
        "outputId": "739c4666-9424-4459-b50e-735663bbc8fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "!pip install lxml html5lib"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting lxml\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/03/a4/9eea8035fc7c7670e5eab97f34ff2ef0ddd78a491bf96df5accedb0e63f5/lxml-4.2.5-cp36-cp36m-manylinux1_x86_64.whl (5.8MB)\n",
            "\u001b[K    100% |████████████████████████████████| 5.8MB 5.6MB/s \n",
            "\u001b[?25hCollecting html5lib\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a5/62/bbd2be0e7943ec8504b517e62bab011b4946e1258842bc159e5dfde15b96/html5lib-1.0.1-py2.py3-none-any.whl (117kB)\n",
            "\u001b[K    100% |████████████████████████████████| 122kB 29.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.9 in /usr/local/lib/python3.6/dist-packages (from html5lib) (1.11.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from html5lib) (0.5.1)\n",
            "Installing collected packages: lxml, html5lib\n",
            "Successfully installed html5lib-1.0.1 lxml-4.2.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OkVfEVg-WEFh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def pd2html(df, header=False, index=False):\n",
        "    return HTML(df.to_html(index=False, header=False))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g0w6EEU0rC9I",
        "colab_type": "code",
        "outputId": "550c4906-21d8-48ae-bdbe-3fd3664787a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        }
      },
      "cell_type": "code",
      "source": [
        "dfheaders = []\n",
        "for _city in [99999923907, 72251012924, 72253012921, 72259003927]:\n",
        "  dfheaders.append(pd.read_html(\n",
        "      \"https://raw.githubusercontent.com/denfromufa/deepweather/master/data/{}-2018-11_8004287785567dat.html\".format(_city)))\n",
        "  display(pd2html(dfheaders[-1][0]))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>AWS ID 999999</td>\n",
              "      <td>WBAN ID 23907</td>\n",
              "      <td>Name AUSTIN 33 NW</td>\n",
              "      <td>Country UNITED STATES</td>\n",
              "      <td>State TEXAS</td>\n",
              "      <td>Latitude +30.622</td>\n",
              "      <td>Longitude -098.085</td>\n",
              "      <td>Elevation +0414.8 (meters)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>AWS ID 722510</td>\n",
              "      <td>WBAN ID 12924</td>\n",
              "      <td>Name CORPUS CHRISTI INTERNATIONAL AIRPORT</td>\n",
              "      <td>Country UNITED STATES</td>\n",
              "      <td>State TEXAS</td>\n",
              "      <td>Latitude +27.774</td>\n",
              "      <td>Longitude -097.512</td>\n",
              "      <td>Elevation +0013.4 (meters)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>AWS ID 722530</td>\n",
              "      <td>WBAN ID 12921</td>\n",
              "      <td>Name SAN ANTONIO INTERNATIONAL AIRPORT</td>\n",
              "      <td>Country UNITED STATES</td>\n",
              "      <td>State TEXAS</td>\n",
              "      <td>Latitude +29.544</td>\n",
              "      <td>Longitude -098.484</td>\n",
              "      <td>Elevation +0240.5 (meters)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>AWS ID 722590</td>\n",
              "      <td>WBAN ID 03927</td>\n",
              "      <td>Name DALLAS/FT WORTH INTERNATIONAL AP</td>\n",
              "      <td>Country UNITED STATES</td>\n",
              "      <td>State TEXAS</td>\n",
              "      <td>Latitude +32.898</td>\n",
              "      <td>Longitude -097.019</td>\n",
              "      <td>Elevation +0170.7 (meters)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QZouBM2BrC9U",
        "colab_type": "code",
        "outputId": "c12818cf-7ee2-422e-a7a3-ab600bcb714d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "cell_type": "code",
      "source": [
        "for dfheader in dfheaders:\n",
        "  display(pd2html(dfheader[1][:2]))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>USAF</td>\n",
              "      <td>WBAN</td>\n",
              "      <td>YR--MODAHRMN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>SPD</td>\n",
              "      <td>GUS</td>\n",
              "      <td>CLG</td>\n",
              "      <td>SKC</td>\n",
              "      <td>L</td>\n",
              "      <td>M</td>\n",
              "      <td>H</td>\n",
              "      <td>VSB</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>W</td>\n",
              "      <td>TEMP</td>\n",
              "      <td>DEWP</td>\n",
              "      <td>SLP</td>\n",
              "      <td>ALT</td>\n",
              "      <td>STP</td>\n",
              "      <td>MAX</td>\n",
              "      <td>MIN</td>\n",
              "      <td>PCP01</td>\n",
              "      <td>PCP06</td>\n",
              "      <td>PCP24</td>\n",
              "      <td>PCPXX</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GMT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MPH</td>\n",
              "      <td>MPH</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Miles</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>Mb</td>\n",
              "      <td>inches</td>\n",
              "      <td>Mb</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>USAF</td>\n",
              "      <td>WBAN</td>\n",
              "      <td>YR--MODAHRMN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>SPD</td>\n",
              "      <td>GUS</td>\n",
              "      <td>CLG</td>\n",
              "      <td>SKC</td>\n",
              "      <td>L</td>\n",
              "      <td>M</td>\n",
              "      <td>H</td>\n",
              "      <td>VSB</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>W</td>\n",
              "      <td>TEMP</td>\n",
              "      <td>DEWP</td>\n",
              "      <td>SLP</td>\n",
              "      <td>ALT</td>\n",
              "      <td>STP</td>\n",
              "      <td>MAX</td>\n",
              "      <td>MIN</td>\n",
              "      <td>PCP01</td>\n",
              "      <td>PCP06</td>\n",
              "      <td>PCP24</td>\n",
              "      <td>PCPXX</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GMT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MPH</td>\n",
              "      <td>MPH</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Miles</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>Mb</td>\n",
              "      <td>inches</td>\n",
              "      <td>Mb</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>USAF</td>\n",
              "      <td>WBAN</td>\n",
              "      <td>YR--MODAHRMN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>SPD</td>\n",
              "      <td>GUS</td>\n",
              "      <td>CLG</td>\n",
              "      <td>SKC</td>\n",
              "      <td>L</td>\n",
              "      <td>M</td>\n",
              "      <td>H</td>\n",
              "      <td>VSB</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>W</td>\n",
              "      <td>TEMP</td>\n",
              "      <td>DEWP</td>\n",
              "      <td>SLP</td>\n",
              "      <td>ALT</td>\n",
              "      <td>STP</td>\n",
              "      <td>MAX</td>\n",
              "      <td>MIN</td>\n",
              "      <td>PCP01</td>\n",
              "      <td>PCP06</td>\n",
              "      <td>PCP24</td>\n",
              "      <td>PCPXX</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GMT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MPH</td>\n",
              "      <td>MPH</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Miles</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>Mb</td>\n",
              "      <td>inches</td>\n",
              "      <td>Mb</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>USAF</td>\n",
              "      <td>WBAN</td>\n",
              "      <td>YR--MODAHRMN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>SPD</td>\n",
              "      <td>GUS</td>\n",
              "      <td>CLG</td>\n",
              "      <td>SKC</td>\n",
              "      <td>L</td>\n",
              "      <td>M</td>\n",
              "      <td>H</td>\n",
              "      <td>VSB</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>W</td>\n",
              "      <td>TEMP</td>\n",
              "      <td>DEWP</td>\n",
              "      <td>SLP</td>\n",
              "      <td>ALT</td>\n",
              "      <td>STP</td>\n",
              "      <td>MAX</td>\n",
              "      <td>MIN</td>\n",
              "      <td>PCP01</td>\n",
              "      <td>PCP06</td>\n",
              "      <td>PCP24</td>\n",
              "      <td>PCPXX</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GMT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MPH</td>\n",
              "      <td>MPH</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Miles</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>Mb</td>\n",
              "      <td>inches</td>\n",
              "      <td>Mb</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HbGs8zYUrC9b",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's set the index of dataframe (table) to dates"
      ]
    },
    {
      "metadata": {
        "id": "OnFYVrRArC9c",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# dfhtown.set_index(dfhtown.columns[2], inplace=True)\n",
        "# dfhtown.head(3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qEqJ_M4SrC9j",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#dfhtown.index=pd.to_datetime(dfhtown.index, format=\"%Y%m%d%H%M\")\n",
        "#dfhtown.index.name='datetime'\n",
        "#dfhtown.replace(to_replace='\\*+', value=np.nan, regex=True, inplace=True)\n",
        "#dfhtown.dropna(axis=1, inplace=True, how='all')\n",
        "#dfhtown.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lA_PaG8nrC9u",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's look at subset of columns with physical units from HTML version of the table"
      ]
    },
    {
      "metadata": {
        "id": "rjgsvHCErC9w",
        "colab_type": "code",
        "outputId": "72846fb6-20f0-45db-cc73-4894af8e56a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        }
      },
      "cell_type": "code",
      "source": [
        "dfunits=dfheader[1][:2]\n",
        "dfunits"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USAF</td>\n",
              "      <td>WBAN</td>\n",
              "      <td>YR--MODAHRMN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>SPD</td>\n",
              "      <td>GUS</td>\n",
              "      <td>CLG</td>\n",
              "      <td>SKC</td>\n",
              "      <td>L</td>\n",
              "      <td>M</td>\n",
              "      <td>H</td>\n",
              "      <td>VSB</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>MW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>AW</td>\n",
              "      <td>W</td>\n",
              "      <td>TEMP</td>\n",
              "      <td>DEWP</td>\n",
              "      <td>SLP</td>\n",
              "      <td>ALT</td>\n",
              "      <td>STP</td>\n",
              "      <td>MAX</td>\n",
              "      <td>MIN</td>\n",
              "      <td>PCP01</td>\n",
              "      <td>PCP06</td>\n",
              "      <td>PCP24</td>\n",
              "      <td>PCPXX</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GMT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MPH</td>\n",
              "      <td>MPH</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Miles</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>Mb</td>\n",
              "      <td>inches</td>\n",
              "      <td>Mb</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     0     1             2    3    4    5    6    7    8    9    10     11  \\\n",
              "0  USAF  WBAN  YR--MODAHRMN  DIR  SPD  GUS  CLG  SKC    L    M    H    VSB   \n",
              "1   NaN   NaN           GMT  NaN  MPH  MPH  NaN  NaN  NaN  NaN  NaN  Miles   \n",
              "\n",
              "    12   13   14   15   16   17   18   19   20    21    22   23      24   25  \\\n",
              "0   MW   MW   MW   MW   AW   AW   AW   AW    W  TEMP  DEWP  SLP     ALT  STP   \n",
              "1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     F     F   Mb  inches   Mb   \n",
              "\n",
              "    26   27      28      29      30      31      32  \n",
              "0  MAX  MIN   PCP01   PCP06   PCP24   PCPXX      SD  \n",
              "1    F    F  inches  inches  inches  inches  inches  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "bO1fAHcLrC92",
        "colab_type": "code",
        "outputId": "5fc8a214-94b3-403b-8bd2-0e40d5deca7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        }
      },
      "cell_type": "code",
      "source": [
        "dfunits.rename(columns=dfunits.iloc[0]).iloc[1].loc[dfhtown.columns].dropna().to_frame()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: \n",
            "Passing list-likes to .loc or [] with any missing label will raise\n",
            "KeyError in the future, you can use .reindex() as an alternative.\n",
            "\n",
            "See the documentation here:\n",
            "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>YR--MODAHRMN</th>\n",
              "      <td>GMT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SPD</th>\n",
              "      <td>MPH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GUS</th>\n",
              "      <td>MPH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VSB</th>\n",
              "      <td>Miles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEMP</th>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEWP</th>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SLP</th>\n",
              "      <td>Mb</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ALT</th>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STP</th>\n",
              "      <td>Mb</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAX</th>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIN</th>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PCP01</th>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PCP06</th>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PCP24</th>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PCPXX</th>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>inches</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   1\n",
              "YR--MODAHRMN     GMT\n",
              "SPD              MPH\n",
              "GUS              MPH\n",
              "VSB            Miles\n",
              "TEMP               F\n",
              "DEWP               F\n",
              "SLP               Mb\n",
              "ALT           inches\n",
              "STP               Mb\n",
              "MAX                F\n",
              "MIN                F\n",
              "PCP01         inches\n",
              "PCP06         inches\n",
              "PCP24         inches\n",
              "PCPXX         inches\n",
              "SD            inches"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "ox7sRfjIrC97",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* So SPD and GUS are some velocity measurements for wind\n",
        "* VSB??\n",
        "* TEMP - temperature\n",
        "* DEWP - temperature dewpoint (used for humidity)\n",
        "* ALT - perhaps the pressure in inches of liquid, such as mercury"
      ]
    },
    {
      "metadata": {
        "id": "tQC5QeafrC98",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The types of columns are very generic (object), so let's improve this!"
      ]
    },
    {
      "metadata": {
        "id": "wwszOkDhrC9_",
        "colab_type": "code",
        "outputId": "d3717bf1-2428-4d7f-8f19-c76fc5ab975c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown.dtypes"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USAF             int64\n",
              "WBAN             int64\n",
              "YR--MODAHRMN     int64\n",
              "DIR             object\n",
              "SPD             object\n",
              "GUS             object\n",
              "CLG             object\n",
              "SKC             object\n",
              "L               object\n",
              "M               object\n",
              "H               object\n",
              "VSB             object\n",
              "MW              object\n",
              "MW.1            object\n",
              "MW.2            object\n",
              "MW.3            object\n",
              "AW              object\n",
              "AW.1            object\n",
              "AW.2            object\n",
              "AW.3            object\n",
              "W               object\n",
              "TEMP            object\n",
              "DEWP            object\n",
              "SLP             object\n",
              "ALT             object\n",
              "STP             object\n",
              "MAX             object\n",
              "MIN             object\n",
              "PCP01           object\n",
              "PCP06           object\n",
              "PCP24           object\n",
              "PCPXX           object\n",
              "SD              object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "B1Mz005GrC-F",
        "colab_type": "code",
        "outputId": "2fd6d9f2-7f2b-4316-d9ab-8558c8dd713b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown = dfhtown[['DIR','SPD','VSB','TEMP','DEWP','ALT']].astype('float64',raise_on_error=False)\n",
        "dfhtown.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: the raise_on_error=False keyword is deprecated, use errors='ignore' instead\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DIR</th>\n",
              "      <th>SPD</th>\n",
              "      <th>VSB</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>ALT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>330</td>\n",
              "      <td>6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>70</td>\n",
              "      <td>68</td>\n",
              "      <td>29.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>010</td>\n",
              "      <td>5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>70</td>\n",
              "      <td>68</td>\n",
              "      <td>29.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>350</td>\n",
              "      <td>8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>29.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>340</td>\n",
              "      <td>6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>29.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>340</td>\n",
              "      <td>5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>69</td>\n",
              "      <td>68</td>\n",
              "      <td>29.83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   DIR SPD   VSB TEMP DEWP    ALT\n",
              "0  330   6  10.0   70   68   29.8\n",
              "1  010   5  10.0   70   68  29.82\n",
              "2  350   8  10.0   69   68   29.8\n",
              "3  340   6  10.0   69   68  29.82\n",
              "4  340   5  10.0   69   68  29.83"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "JVrehrIjrC-M",
        "colab_type": "code",
        "outputId": "93125fa0-4fb9-443e-b9cd-9d03b4520095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "dfhtown.dtypes"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DIR     object\n",
              "SPD     object\n",
              "VSB     object\n",
              "TEMP    object\n",
              "DEWP    object\n",
              "ALT     object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "hclRub4boPoN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "outputId": "344bc81d-2464-4fbc-e98b-b18352138f2f"
      },
      "cell_type": "code",
      "source": [
        "dftxtown = dftxtown.filter(regex=\"|\".join(\n",
        "    ['DIR','SPD','VSB','TEMP','DEWP','ALT'])).replace(\n",
        "    '^\\*+$', np.nan, regex=True).astype(\n",
        "    float, errors='ignore').dropna(\n",
        "    how='all', axis='columns').dropna(\n",
        "    axis='rows', how='all').resample('60min').median().dropna()\n",
        "\n",
        "dftxtown.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\">DIR</th>\n",
              "      <th colspan=\"5\" halign=\"left\">SPD</th>\n",
              "      <th colspan=\"4\" halign=\"left\">VSB</th>\n",
              "      <th colspan=\"5\" halign=\"left\">TEMP</th>\n",
              "      <th colspan=\"4\" halign=\"left\">DEWP</th>\n",
              "      <th colspan=\"4\" halign=\"left\">ALT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USAFWBAN</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YR--MODAHRMN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-11-01 00:00:00</th>\n",
              "      <td>330.0</td>\n",
              "      <td>335.0</td>\n",
              "      <td>205.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>15.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.5</td>\n",
              "      <td>75.0</td>\n",
              "      <td>61.5</td>\n",
              "      <td>65.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>61.5</td>\n",
              "      <td>55.5</td>\n",
              "      <td>29.80</td>\n",
              "      <td>29.82</td>\n",
              "      <td>29.880</td>\n",
              "      <td>29.870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-01 01:00:00</th>\n",
              "      <td>340.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>335.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>29.83</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.905</td>\n",
              "      <td>29.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-01 02:00:00</th>\n",
              "      <td>340.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>325.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>60.5</td>\n",
              "      <td>63.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.900</td>\n",
              "      <td>29.920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-01 03:00:00</th>\n",
              "      <td>330.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>29.88</td>\n",
              "      <td>29.89</td>\n",
              "      <td>29.930</td>\n",
              "      <td>29.935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-01 04:00:00</th>\n",
              "      <td>320.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>29.89</td>\n",
              "      <td>29.92</td>\n",
              "      <td>29.940</td>\n",
              "      <td>29.950</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          DIR                                          SPD  \\\n",
              "USAFWBAN            720637223 72251012924 72253012921 7225903927 720637223   \n",
              "YR--MODAHRMN                                                                 \n",
              "2015-11-01 00:00:00     330.0       335.0       205.0      330.0       6.0   \n",
              "2015-11-01 01:00:00     340.0       340.0       335.0      330.0       6.0   \n",
              "2015-11-01 02:00:00     340.0        10.0        60.0      325.0       6.0   \n",
              "2015-11-01 03:00:00     330.0       330.0       185.0      330.0       3.0   \n",
              "2015-11-01 04:00:00     320.0       350.0       320.0      340.0       6.0   \n",
              "\n",
              "                                                                         VSB  \\\n",
              "USAFWBAN            72251012924 72253012921 7225903927 99999923907 720637223   \n",
              "YR--MODAHRMN                                                                   \n",
              "2015-11-01 00:00:00        15.5        10.0       12.0         6.0      10.0   \n",
              "2015-11-01 01:00:00        14.0         9.5       10.0         5.0      10.0   \n",
              "2015-11-01 02:00:00         9.0         7.0       10.5         2.0      10.0   \n",
              "2015-11-01 03:00:00        10.0         6.5       10.0         2.0      10.0   \n",
              "2015-11-01 04:00:00         8.0         8.0        6.0         6.0      10.0   \n",
              "\n",
              "                                                            TEMP              \\\n",
              "USAFWBAN            72251012924 72253012921 7225903927 720637223 72251012924   \n",
              "YR--MODAHRMN                                                                   \n",
              "2015-11-01 00:00:00        10.0        10.0       10.0      70.0        73.5   \n",
              "2015-11-01 01:00:00        10.0         4.5       10.0      69.0        72.0   \n",
              "2015-11-01 02:00:00        10.0        10.0       10.0      69.0        69.0   \n",
              "2015-11-01 03:00:00        10.0        10.0       10.0      69.0        69.0   \n",
              "2015-11-01 04:00:00        10.0        10.0       10.0      69.0        68.0   \n",
              "\n",
              "                                                            DEWP              \\\n",
              "USAFWBAN            72253012921 7225903927 99999923907 720637223 72251012924   \n",
              "YR--MODAHRMN                                                                   \n",
              "2015-11-01 00:00:00        75.0       61.5        65.0      68.0        65.0   \n",
              "2015-11-01 01:00:00        69.0       61.0        63.0      68.0        68.0   \n",
              "2015-11-01 02:00:00        67.0       60.5        63.0      67.0        68.0   \n",
              "2015-11-01 03:00:00        67.0       60.0        63.0      67.0        68.0   \n",
              "2015-11-01 04:00:00        66.0       60.0        62.0      66.0        67.0   \n",
              "\n",
              "                                                 ALT                          \\\n",
              "USAFWBAN            72253012921 7225903927 720637223 72251012924 72253012921   \n",
              "YR--MODAHRMN                                                                   \n",
              "2015-11-01 00:00:00        61.5       55.5     29.80       29.82      29.880   \n",
              "2015-11-01 01:00:00        63.0       55.0     29.83       29.85      29.905   \n",
              "2015-11-01 02:00:00        62.0       54.0     29.85       29.85      29.900   \n",
              "2015-11-01 03:00:00        62.0       54.0     29.88       29.89      29.930   \n",
              "2015-11-01 04:00:00        61.0       53.0     29.89       29.92      29.940   \n",
              "\n",
              "                                \n",
              "USAFWBAN            7225903927  \n",
              "YR--MODAHRMN                    \n",
              "2015-11-01 00:00:00     29.870  \n",
              "2015-11-01 01:00:00     29.900  \n",
              "2015-11-01 02:00:00     29.920  \n",
              "2015-11-01 03:00:00     29.935  \n",
              "2015-11-01 04:00:00     29.950  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "QCfK7om9rC-T",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The relative humidity (RH) is not given in table, so let's calculate using measured temperature and dewpoint temperature"
      ]
    },
    {
      "metadata": {
        "id": "G7ROJga4rC-e",
        "colab_type": "code",
        "outputId": "f8d8bab8-7678-4a25-f7e1-ebfe1ba4b27f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "cell_type": "code",
      "source": [
        "dftxtown.describe()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\">DIR</th>\n",
              "      <th colspan=\"5\" halign=\"left\">SPD</th>\n",
              "      <th colspan=\"4\" halign=\"left\">VSB</th>\n",
              "      <th colspan=\"5\" halign=\"left\">TEMP</th>\n",
              "      <th colspan=\"4\" halign=\"left\">DEWP</th>\n",
              "      <th colspan=\"4\" halign=\"left\">ALT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USAFWBAN</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "      <td>17417.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>176.411839</td>\n",
              "      <td>159.598668</td>\n",
              "      <td>188.619165</td>\n",
              "      <td>197.955733</td>\n",
              "      <td>6.364443</td>\n",
              "      <td>13.489436</td>\n",
              "      <td>9.861917</td>\n",
              "      <td>11.759689</td>\n",
              "      <td>4.200149</td>\n",
              "      <td>9.236444</td>\n",
              "      <td>9.423962</td>\n",
              "      <td>9.432451</td>\n",
              "      <td>9.522444</td>\n",
              "      <td>71.564535</td>\n",
              "      <td>74.741833</td>\n",
              "      <td>71.756588</td>\n",
              "      <td>69.538640</td>\n",
              "      <td>68.487053</td>\n",
              "      <td>62.041569</td>\n",
              "      <td>64.474823</td>\n",
              "      <td>56.713441</td>\n",
              "      <td>52.911782</td>\n",
              "      <td>30.023518</td>\n",
              "      <td>29.984744</td>\n",
              "      <td>30.016027</td>\n",
              "      <td>30.004219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>128.549854</td>\n",
              "      <td>130.006679</td>\n",
              "      <td>180.261087</td>\n",
              "      <td>150.182745</td>\n",
              "      <td>3.843356</td>\n",
              "      <td>5.929081</td>\n",
              "      <td>4.549815</td>\n",
              "      <td>5.508809</td>\n",
              "      <td>2.950346</td>\n",
              "      <td>1.876862</td>\n",
              "      <td>1.699020</td>\n",
              "      <td>1.753733</td>\n",
              "      <td>1.651627</td>\n",
              "      <td>12.728135</td>\n",
              "      <td>12.349128</td>\n",
              "      <td>14.300348</td>\n",
              "      <td>16.677465</td>\n",
              "      <td>15.204274</td>\n",
              "      <td>13.568524</td>\n",
              "      <td>13.105968</td>\n",
              "      <td>14.386607</td>\n",
              "      <td>16.269214</td>\n",
              "      <td>0.166065</td>\n",
              "      <td>0.172456</td>\n",
              "      <td>0.174348</td>\n",
              "      <td>0.196544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>29.450000</td>\n",
              "      <td>29.400000</td>\n",
              "      <td>29.420000</td>\n",
              "      <td>29.370000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>105.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>47.500000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>29.920000</td>\n",
              "      <td>29.880000</td>\n",
              "      <td>29.900000</td>\n",
              "      <td>29.880000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>30.010000</td>\n",
              "      <td>29.970000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>29.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>220.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>79.500000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>30.110000</td>\n",
              "      <td>30.080000</td>\n",
              "      <td>30.120000</td>\n",
              "      <td>30.120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>990.000000</td>\n",
              "      <td>990.000000</td>\n",
              "      <td>990.000000</td>\n",
              "      <td>990.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>30.770000</td>\n",
              "      <td>30.790000</td>\n",
              "      <td>30.760000</td>\n",
              "      <td>30.810000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   DIR                                            \\\n",
              "USAFWBAN     720637223   72251012924   72253012921    7225903927   \n",
              "count     17417.000000  17417.000000  17417.000000  17417.000000   \n",
              "mean        176.411839    159.598668    188.619165    197.955733   \n",
              "std         128.549854    130.006679    180.261087    150.182745   \n",
              "min          10.000000     10.000000     10.000000     10.000000   \n",
              "25%         105.000000    100.000000    100.000000    130.000000   \n",
              "50%         150.000000    140.000000    150.000000    170.000000   \n",
              "75%         220.000000    170.000000    200.000000    220.000000   \n",
              "max         990.000000    990.000000    990.000000    990.000000   \n",
              "\n",
              "                   SPD                                            \\\n",
              "USAFWBAN     720637223   72251012924   72253012921    7225903927   \n",
              "count     17417.000000  17417.000000  17417.000000  17417.000000   \n",
              "mean          6.364443     13.489436      9.861917     11.759689   \n",
              "std           3.843356      5.929081      4.549815      5.508809   \n",
              "min           0.000000      0.000000      0.000000      0.000000   \n",
              "25%           3.000000      9.000000      6.500000      8.000000   \n",
              "50%           6.000000     13.000000      9.000000     11.000000   \n",
              "75%           9.000000     17.000000     13.000000     15.000000   \n",
              "max          26.000000     41.000000     31.000000     36.000000   \n",
              "\n",
              "                                 VSB                              \\\n",
              "USAFWBAN   99999923907     720637223   72251012924   72253012921   \n",
              "count     17417.000000  17417.000000  17417.000000  17417.000000   \n",
              "mean          4.200149      9.236444      9.423962      9.432451   \n",
              "std           2.950346      1.876862      1.699020      1.753733   \n",
              "min           0.000000      0.000000      0.000000      0.000000   \n",
              "25%           2.000000     10.000000     10.000000     10.000000   \n",
              "50%           4.000000     10.000000     10.000000     10.000000   \n",
              "75%           6.000000     10.000000     10.000000     10.000000   \n",
              "max          17.000000     10.000000     10.000000     10.000000   \n",
              "\n",
              "                                TEMP                              \\\n",
              "USAFWBAN    7225903927     720637223   72251012924   72253012921   \n",
              "count     17417.000000  17417.000000  17417.000000  17417.000000   \n",
              "mean          9.522444     71.564535     74.741833     71.756588   \n",
              "std           1.651627     12.728135     12.349128     14.300348   \n",
              "min           0.000000     22.000000     26.000000     20.000000   \n",
              "25%          10.000000     64.000000     68.000000     62.500000   \n",
              "50%          10.000000     73.000000     77.000000     73.000000   \n",
              "75%          10.000000     81.000000     84.000000     82.000000   \n",
              "max          10.000000     98.000000    102.000000    104.000000   \n",
              "\n",
              "                                              DEWP                \\\n",
              "USAFWBAN    7225903927   99999923907     720637223   72251012924   \n",
              "count     17417.000000  17417.000000  17417.000000  17417.000000   \n",
              "mean         69.538640     68.487053     62.041569     64.474823   \n",
              "std          16.677465     15.204274     13.568524     13.105968   \n",
              "min          15.000000     15.000000     12.000000      9.000000   \n",
              "25%          58.000000     59.000000     55.000000     58.000000   \n",
              "50%          71.000000     70.000000     67.000000     69.000000   \n",
              "75%          83.000000     79.500000     73.000000     74.000000   \n",
              "max         108.000000    108.000000     80.000000     83.000000   \n",
              "\n",
              "                                               ALT                \\\n",
              "USAFWBAN   72253012921    7225903927     720637223   72251012924   \n",
              "count     17417.000000  17417.000000  17417.000000  17417.000000   \n",
              "mean         56.713441     52.911782     30.023518     29.984744   \n",
              "std          14.386607     16.269214      0.166065      0.172456   \n",
              "min           5.000000      1.500000     29.450000     29.400000   \n",
              "25%          47.500000     41.000000     29.920000     29.880000   \n",
              "50%          62.000000     57.000000     30.010000     29.970000   \n",
              "75%          68.000000     67.000000     30.110000     30.080000   \n",
              "max          76.000000     79.000000     30.770000     30.790000   \n",
              "\n",
              "                                      \n",
              "USAFWBAN   72253012921    7225903927  \n",
              "count     17417.000000  17417.000000  \n",
              "mean         30.016027     30.004219  \n",
              "std           0.174348      0.196544  \n",
              "min          29.420000     29.370000  \n",
              "25%          29.900000     29.880000  \n",
              "50%          30.000000     29.980000  \n",
              "75%          30.120000     30.120000  \n",
              "max          30.760000     30.810000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "fv46Co1JrC-i",
        "colab_type": "code",
        "outputId": "fdc9e030-ff0d-4a73-c9de-adf249d69307",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        }
      },
      "cell_type": "code",
      "source": [
        "dftxtown.rolling(window=72).mean()[['TEMP']].plot();"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAycAAAIvCAYAAABujaRwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/gP7nnA8/l7nygAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f48a7d6e6d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pSHfNi2TrDAi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "The vapor pressure answers will be in units of millibars (mb) or hectoPascals (hPa).\n",
        "To convert the saturated vapor pressure and/or the actual vapor pressure, the\n",
        "temperature values must be converted to degrees Celsius (°C) ."
      ]
    },
    {
      "metadata": {
        "id": "2XvmnoXsJNBH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Deep learning approach\n",
        "\n",
        "This part is based on the deep learning book \"Deep Learning with Python\" by Keras Lead François Chollet"
      ]
    },
    {
      "metadata": {
        "id": "RWj4ltX3wN0H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/6.3-advanced-usage-of-recurrent-neural-networks.ipynb"
      ]
    },
    {
      "metadata": {
        "id": "HSnRL9SM7YK4",
        "colab_type": "code",
        "outputId": "f4a234d6-717a-4f18-f0be-5f9a36bf6585",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(\"tensorflow version = {}\".format(tf.__version__))\n",
        "\n",
        "import keras\n",
        "print(\"keras version = {}\".format(keras.__version__))\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensorflow version = 1.12.0\n",
            "keras version = 2.2.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r2N5W5KQ7-zc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Transforming the data\n",
        "\n",
        "\n",
        "The exact formulation of our problem will be the following: given data going as far back as `lookback` timesteps (a timestep is 10 minutes) \n",
        "and sampled every `steps` timesteps, can we predict the temperature in `delay` timesteps?\n",
        "\n",
        "We will use the following parameter values:\n",
        "\n",
        "* `lookback = 360`, i.e. our observations will go back 5 days.\n",
        "* `steps = 3`, i.e. our observations will be sampled at one data point per hour.\n",
        "* `delay = 72`, i.e. our targets will be 24 hours in the future.\n",
        "\n",
        "To get started, we need to do two things:\n",
        "\n",
        "* Preprocess the data to a format a neural network can ingest. This is easy: the data is already numerical, so we don't need to do any \n",
        "vectorization. However each timeseries in the data is on a different scale (e.g. temperature is typically between -20 and +30, but \n",
        "pressure, measured in mbar, is around 1000). So we will normalize each timeseries independently so that they all take small values on a \n",
        "similar scale.\n",
        "* Write a Python generator that takes our current array of float data and yields batches of data from the recent past, alongside with a \n",
        "target temperature in the future. Since the samples in our dataset are highly redundant (e.g. sample `N` and sample `N + 1` will have most \n",
        "of their timesteps in common), it would be very wasteful to explicitly allocate every sample. Instead, we will generate the samples on the \n",
        "fly using the original data.\n",
        "\n",
        "We preprocess the data by subtracting the mean of each timeseries and dividing by the standard deviation. We plan on using the first \n",
        "200,000 timesteps as training data, so we compute the mean and standard deviation only on this fraction of the data:"
      ]
    },
    {
      "metadata": {
        "id": "eKtzRgoN6TsB",
        "colab_type": "code",
        "outputId": "dac59d8e-0bdf-45e0-9c48-ee8f2fb6d610",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "cell_type": "code",
      "source": [
        "usedcols = 'DIR\tTEMP\tSPD\tVSB\tDEWP\tALT'.split('\\t')\n",
        "float_data = dftxtown[usedcols].dropna()\n",
        "display(float_data.head(2))\n",
        "float_data = float_data.values\n",
        "float_data.shape\n",
        "\n",
        "# 720637223 TEMP at index 4"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\">DIR</th>\n",
              "      <th colspan=\"5\" halign=\"left\">TEMP</th>\n",
              "      <th colspan=\"5\" halign=\"left\">SPD</th>\n",
              "      <th colspan=\"4\" halign=\"left\">VSB</th>\n",
              "      <th colspan=\"4\" halign=\"left\">DEWP</th>\n",
              "      <th colspan=\"4\" halign=\"left\">ALT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USAFWBAN</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>99999923907</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "      <th>720637223</th>\n",
              "      <th>72251012924</th>\n",
              "      <th>72253012921</th>\n",
              "      <th>7225903927</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YR--MODAHRMN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-11-01 00:00:00</th>\n",
              "      <td>330.0</td>\n",
              "      <td>335.0</td>\n",
              "      <td>205.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.5</td>\n",
              "      <td>75.0</td>\n",
              "      <td>61.5</td>\n",
              "      <td>65.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>15.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>61.5</td>\n",
              "      <td>55.5</td>\n",
              "      <td>29.80</td>\n",
              "      <td>29.82</td>\n",
              "      <td>29.880</td>\n",
              "      <td>29.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-01 01:00:00</th>\n",
              "      <td>340.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>335.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>29.83</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.905</td>\n",
              "      <td>29.90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          DIR                                         TEMP  \\\n",
              "USAFWBAN            720637223 72251012924 72253012921 7225903927 720637223   \n",
              "YR--MODAHRMN                                                                 \n",
              "2015-11-01 00:00:00     330.0       335.0       205.0      330.0      70.0   \n",
              "2015-11-01 01:00:00     340.0       340.0       335.0      330.0      69.0   \n",
              "\n",
              "                                                                         SPD  \\\n",
              "USAFWBAN            72251012924 72253012921 7225903927 99999923907 720637223   \n",
              "YR--MODAHRMN                                                                   \n",
              "2015-11-01 00:00:00        73.5        75.0       61.5        65.0       6.0   \n",
              "2015-11-01 01:00:00        72.0        69.0       61.0        63.0       6.0   \n",
              "\n",
              "                                                                         VSB  \\\n",
              "USAFWBAN            72251012924 72253012921 7225903927 99999923907 720637223   \n",
              "YR--MODAHRMN                                                                   \n",
              "2015-11-01 00:00:00        15.5        10.0       12.0         6.0      10.0   \n",
              "2015-11-01 01:00:00        14.0         9.5       10.0         5.0      10.0   \n",
              "\n",
              "                                                            DEWP              \\\n",
              "USAFWBAN            72251012924 72253012921 7225903927 720637223 72251012924   \n",
              "YR--MODAHRMN                                                                   \n",
              "2015-11-01 00:00:00        10.0        10.0       10.0      68.0        65.0   \n",
              "2015-11-01 01:00:00        10.0         4.5       10.0      68.0        68.0   \n",
              "\n",
              "                                                 ALT                          \\\n",
              "USAFWBAN            72253012921 7225903927 720637223 72251012924 72253012921   \n",
              "YR--MODAHRMN                                                                   \n",
              "2015-11-01 00:00:00        61.5       55.5     29.80       29.82      29.880   \n",
              "2015-11-01 01:00:00        63.0       55.0     29.83       29.85      29.905   \n",
              "\n",
              "                                \n",
              "USAFWBAN            7225903927  \n",
              "YR--MODAHRMN                    \n",
              "2015-11-01 00:00:00      29.87  \n",
              "2015-11-01 01:00:00      29.90  "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17417, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "rVZhWVBx2ubT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mean = float_data.mean(axis=0)\n",
        "float_data -= mean\n",
        "std = float_data.std(axis=0)\n",
        "float_data /= std"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-Z0eHEwJx4Sg",
        "colab_type": "code",
        "outputId": "52b86cef-7189-4edd-af97-1458bf0805e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "#plt.matshow(pd.DataFrame(float_data, columns = usedcols).corr())\n",
        "\n",
        "sns.heatmap(pd.DataFrame(float_data #, columns = usedcols\n",
        "                        ).corr(),\n",
        "            #xticklabels=usedcols,\n",
        "            #yticklabels=usedcols\n",
        "           )\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f48a533aac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwIAAAIqCAYAAACXNP1XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f48a7cfceb8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oG00rtx4AY11",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "Now here is the data generator that we will use. It yields a tuple `(samples, targets)` where `samples` is one batch of input data and \n",
        "`targets` is the corresponding array of target temperatures. It takes the following arguments:\n",
        "\n",
        "* `data`: The original array of floating point data, which we just normalized in the code snippet above.\n",
        "* `lookback`: How many timesteps back should our input data go.\n",
        "* `delay`: How many timesteps in the future should our target be.\n",
        "* `min_index` and `max_index`: Indices in the `data` array that delimit which timesteps to draw from. This is useful for keeping a segment \n",
        "of the data for validation and another one for testing.\n",
        "* `shuffle`: Whether to shuffle our samples or draw them in chronological order.\n",
        "* `batch_size`: The number of samples per batch.\n",
        "* `step`: The period, in timesteps, at which we sample data. We will set it 6 in order to draw one data point every hour."
      ]
    },
    {
      "metadata": {
        "id": "VbAi8DrUB1JM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def generator(data, lookback, delay, min_index, max_index,\n",
        "              shuffle=False, batch_size=128, step=6, target_col = 4):\n",
        "    if max_index is None:\n",
        "        max_index = len(data) - delay - 1\n",
        "    i = min_index + lookback\n",
        "    while True:\n",
        "        if shuffle:\n",
        "            rows = np.random.randint(\n",
        "                min_index + lookback, max_index, size=batch_size)\n",
        "        else:\n",
        "            if i + batch_size >= max_index:\n",
        "                i = min_index + lookback\n",
        "            rows = np.arange(i, min(i + batch_size, max_index))\n",
        "            i += len(rows)\n",
        "\n",
        "        samples = np.zeros((len(rows),\n",
        "                           lookback // step,\n",
        "                           data.shape[-1]))\n",
        "        targets = np.zeros((len(rows),))\n",
        "        for j, row in enumerate(rows):\n",
        "            indices = range(rows[j] - lookback, rows[j], step)\n",
        "            samples[j] = data[indices]\n",
        "            targets[j] = data[rows[j] + delay][target_col]\n",
        "        yield samples, targets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pQxUJQp4CMlR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now let's use our abstract generator function to instantiate three generators, one for training, one for validation and one for testing. Each will look at different temporal segments of the original data: the training generator looks at the first 200,000 timesteps, the validation generator looks at the following 100,000, and the test generator looks at the remainder."
      ]
    },
    {
      "metadata": {
        "id": "4h2QqCUHIrs9",
        "colab_type": "code",
        "outputId": "5e76d246-cb40-4102-beda-aa548cb521ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "float_data.shape"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17417, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "8cdWa9V7CSM0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lookback = 120\n",
        "step = 2\n",
        "delay = 24\n",
        "batch_size = 64\n",
        "min_index_train = 0\n",
        "max_index_train = 10000\n",
        "\n",
        "min_index_valid = 10001\n",
        "max_index_valid = 15000\n",
        "\n",
        "def gen_train_val_test():\n",
        "  train_gen = generator(float_data,\n",
        "                        lookback=lookback,\n",
        "                        delay=delay,\n",
        "                        min_index=min_index_train,\n",
        "                        max_index=max_index_train,\n",
        "                        shuffle=True,\n",
        "                        step=step, \n",
        "                        batch_size=batch_size,\n",
        "                        )\n",
        "  val_gen = generator(float_data,\n",
        "                      lookback=lookback,\n",
        "                      delay=delay,\n",
        "                      min_index=min_index_valid,\n",
        "                      max_index=max_index_valid,\n",
        "                      step=step,\n",
        "                      batch_size=batch_size)\n",
        "  test_gen = generator(float_data,\n",
        "                       lookback=lookback,\n",
        "                       delay=delay,\n",
        "                       min_index=max_index_valid,\n",
        "                       max_index=None,\n",
        "                       step=step,\n",
        "                       batch_size=batch_size)\n",
        "  return train_gen, val_gen, test_gen\n",
        "\n",
        "train_gen, val_gen, test_gen = gen_train_val_test()\n",
        "\n",
        "\n",
        "# This is how many steps to draw from `val_gen`\n",
        "# in order to see the whole validation set:\n",
        "val_steps = (max_index_valid - min_index_valid - lookback) // batch_size\n",
        "\n",
        "# This is how many steps to draw from `test_gen`\n",
        "# in order to see the whole test set:\n",
        "test_steps = (len(float_data) - max_index_valid - lookback) // batch_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AfaXvXXZsChu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Using Recurrent Neural Networks"
      ]
    },
    {
      "metadata": {
        "id": "fwUNOW6Ziq72",
        "colab_type": "code",
        "outputId": "1a6dd5c2-26fd-40b5-e1f0-c261dd66d06f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "from keras.optimizers import RMSprop\n",
        "\n",
        "train_gen, val_gen, test_gen = gen_train_val_test()\n",
        "\n",
        "model = Sequential()\n",
        "#model.add(layers.GRU(16, implementation=2, \n",
        "#input_shape=(None, float_data.shape[-1])))\n",
        "model.add(layers.CuDNNGRU(16, input_shape=(None, float_data.shape[-1])))\n",
        "model.add(layers.Dense(1))\n",
        "\n",
        "model.compile(optimizer=RMSprop(), loss='mae')\n",
        "history = model.fit_generator(train_gen,\n",
        "                              steps_per_epoch=80,\n",
        "                              epochs=20,\n",
        "                              validation_data=val_gen,\n",
        "                              validation_steps=val_steps)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "80/80 [==============================] - 2s 26ms/step - loss: 0.5922 - val_loss: 0.6375\n",
            "Epoch 2/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.5104 - val_loss: 0.6275\n",
            "Epoch 3/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.5025 - val_loss: 0.6144\n",
            "Epoch 4/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4821 - val_loss: 0.6178\n",
            "Epoch 5/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4826 - val_loss: 0.6109\n",
            "Epoch 6/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4596 - val_loss: 0.6060\n",
            "Epoch 7/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4594 - val_loss: 0.6049\n",
            "Epoch 8/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4569 - val_loss: 0.6054\n",
            "Epoch 9/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4423 - val_loss: 0.6053\n",
            "Epoch 10/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4455 - val_loss: 0.6020\n",
            "Epoch 11/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4540 - val_loss: 0.6206\n",
            "Epoch 12/20\n",
            "80/80 [==============================] - 2s 19ms/step - loss: 0.4419 - val_loss: 0.6046\n",
            "Epoch 13/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4392 - val_loss: 0.6060\n",
            "Epoch 14/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4265 - val_loss: 0.6053\n",
            "Epoch 15/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4277 - val_loss: 0.6062\n",
            "Epoch 16/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4192 - val_loss: 0.6109\n",
            "Epoch 17/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4100 - val_loss: 0.6205\n",
            "Epoch 18/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4174 - val_loss: 0.5982\n",
            "Epoch 19/20\n",
            "80/80 [==============================] - 2s 19ms/step - loss: 0.4117 - val_loss: 0.6049\n",
            "Epoch 20/20\n",
            "80/80 [==============================] - 2s 20ms/step - loss: 0.4042 - val_loss: 0.6012\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JIYUmkqQi_eG",
        "colab_type": "code",
        "outputId": "6a7ea32d-3ee7-428c-e7fe-46d8db659412",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(loss))\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAysAAAJMCAYAAAD60TkKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f489e94beb8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cXE_RmuKnWhp",
        "colab_type": "code",
        "outputId": "ae846fa4-83bf-4273-ffd9-df329ec5e70d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1404
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "from keras.optimizers import RMSprop\n",
        "\n",
        "train_gen, val_gen, test_gen = gen_train_val_test()\n",
        "\n",
        "model = Sequential()\n",
        "model.add(layers.GRU(64,\n",
        "                     #dropout=0.1,\n",
        "                     recurrent_dropout=0.2,\n",
        "                     input_shape=(None, float_data.shape[-1])))\n",
        "model.add(layers.Dense(1))\n",
        "\n",
        "model.compile(optimizer=RMSprop(), loss='mae')\n",
        "history = model.fit_generator(train_gen,\n",
        "                              steps_per_epoch=20,\n",
        "                              epochs=40,\n",
        "                              validation_data=val_gen,\n",
        "                              validation_steps=val_steps\n",
        "                             )"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "20/20 [==============================] - 6s 284ms/step - loss: 0.6371 - val_loss: 0.6260\n",
            "Epoch 2/40\n",
            "20/20 [==============================] - 5s 250ms/step - loss: 0.5394 - val_loss: 0.6149\n",
            "Epoch 3/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.5198 - val_loss: 0.6113\n",
            "Epoch 4/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4908 - val_loss: 0.6027\n",
            "Epoch 5/40\n",
            "20/20 [==============================] - 5s 252ms/step - loss: 0.4836 - val_loss: 0.5992\n",
            "Epoch 6/40\n",
            "20/20 [==============================] - 5s 253ms/step - loss: 0.4994 - val_loss: 0.5950\n",
            "Epoch 7/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4732 - val_loss: 0.5982\n",
            "Epoch 8/40\n",
            "20/20 [==============================] - 5s 250ms/step - loss: 0.4837 - val_loss: 0.5894\n",
            "Epoch 9/40\n",
            "20/20 [==============================] - 5s 252ms/step - loss: 0.4661 - val_loss: 0.5924\n",
            "Epoch 10/40\n",
            "20/20 [==============================] - 5s 256ms/step - loss: 0.4549 - val_loss: 0.5918\n",
            "Epoch 11/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4544 - val_loss: 0.5959\n",
            "Epoch 12/40\n",
            "20/20 [==============================] - 5s 249ms/step - loss: 0.4512 - val_loss: 0.5914\n",
            "Epoch 13/40\n",
            "20/20 [==============================] - 5s 247ms/step - loss: 0.4606 - val_loss: 0.5981\n",
            "Epoch 14/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4742 - val_loss: 0.6195\n",
            "Epoch 15/40\n",
            "20/20 [==============================] - 5s 253ms/step - loss: 0.4678 - val_loss: 0.5974\n",
            "Epoch 16/40\n",
            "20/20 [==============================] - 5s 250ms/step - loss: 0.4386 - val_loss: 0.5893\n",
            "Epoch 17/40\n",
            "20/20 [==============================] - 5s 252ms/step - loss: 0.4637 - val_loss: 0.5913\n",
            "Epoch 18/40\n",
            "20/20 [==============================] - 5s 250ms/step - loss: 0.4450 - val_loss: 0.5899\n",
            "Epoch 19/40\n",
            "20/20 [==============================] - 5s 252ms/step - loss: 0.4529 - val_loss: 0.6058\n",
            "Epoch 20/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4499 - val_loss: 0.5855\n",
            "Epoch 21/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4301 - val_loss: 0.5837\n",
            "Epoch 22/40\n",
            "20/20 [==============================] - 5s 253ms/step - loss: 0.4418 - val_loss: 0.6022\n",
            "Epoch 23/40\n",
            "20/20 [==============================] - 5s 255ms/step - loss: 0.4544 - val_loss: 0.5834\n",
            "Epoch 24/40\n",
            "20/20 [==============================] - 5s 257ms/step - loss: 0.4294 - val_loss: 0.5842\n",
            "Epoch 25/40\n",
            "20/20 [==============================] - 5s 253ms/step - loss: 0.4338 - val_loss: 0.5848\n",
            "Epoch 26/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4459 - val_loss: 0.6002\n",
            "Epoch 27/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4251 - val_loss: 0.6003\n",
            "Epoch 28/40\n",
            "20/20 [==============================] - 5s 250ms/step - loss: 0.4304 - val_loss: 0.5995\n",
            "Epoch 29/40\n",
            "20/20 [==============================] - 5s 249ms/step - loss: 0.4172 - val_loss: 0.5846\n",
            "Epoch 30/40\n",
            "20/20 [==============================] - 5s 252ms/step - loss: 0.4310 - val_loss: 0.5959\n",
            "Epoch 31/40\n",
            "20/20 [==============================] - 5s 252ms/step - loss: 0.4293 - val_loss: 0.5930\n",
            "Epoch 32/40\n",
            "20/20 [==============================] - 5s 250ms/step - loss: 0.4159 - val_loss: 0.6055\n",
            "Epoch 33/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4166 - val_loss: 0.5795\n",
            "Epoch 34/40\n",
            "20/20 [==============================] - 5s 254ms/step - loss: 0.4202 - val_loss: 0.5873\n",
            "Epoch 35/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4210 - val_loss: 0.5930\n",
            "Epoch 36/40\n",
            "20/20 [==============================] - 5s 250ms/step - loss: 0.4052 - val_loss: 0.6047\n",
            "Epoch 37/40\n",
            "20/20 [==============================] - 5s 255ms/step - loss: 0.4094 - val_loss: 0.5857\n",
            "Epoch 38/40\n",
            "20/20 [==============================] - 5s 252ms/step - loss: 0.4222 - val_loss: 0.6491\n",
            "Epoch 39/40\n",
            "20/20 [==============================] - 5s 249ms/step - loss: 0.4132 - val_loss: 0.5803\n",
            "Epoch 40/40\n",
            "20/20 [==============================] - 5s 251ms/step - loss: 0.4085 - val_loss: 0.6060\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ska6vOVwoGiU",
        "colab_type": "code",
        "outputId": "b12bec32-b0e0-4a23-af73-80e2ae04a9ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(loss))\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAysAAAJMCAYAAAD60TkKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f489e5a9e48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "R7Deh2zlqjcD",
        "colab_type": "code",
        "outputId": "76881e05-af87-4b5a-d448-941d7491173e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "gru_3 (GRU)                  (None, 64)                17472     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 17,537\n",
            "Trainable params: 17,537\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lb-4QibZq7Gv",
        "colab_type": "code",
        "outputId": "8f9297b7-cbba-4897-c77b-dde37e301fe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "cell_type": "code",
      "source": [
        "train_gen, val_gen, test_gen = gen_train_val_test()\n",
        "\n",
        "batch_maes = []\n",
        "predsi = []\n",
        "targetsi = []\n",
        "for step in range(val_steps):\n",
        "    samples, targets = next(val_gen)\n",
        "    preds = model.predict(samples)\n",
        "    mae = np.mean(np.abs(preds - targets))\n",
        "    #print(mae)\n",
        "    batch_maes.append(mae)\n",
        "    predsi.extend(preds)\n",
        "    targetsi.extend(targets)\n",
        "print(np.mean(batch_maes))\n",
        "plt.plot(predsi[:1000], 'b', linewidth=1, label='prediction');\n",
        "plt.plot(targetsi[:1000], 'g', linewidth=1, label='target');\n",
        "plt.legend();"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6463597669239203\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAysAAAIqCAYAAAAkfNVNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+n+2k5GYsoGrmQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f489cbba0f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "C9qNblUlw8l6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}