NuclearMissle.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPX8tKRhEsA5J9r3Xk8FDUY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AndrewJGroves/PlottingInformation/blob/main/NuclearMissle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8yKFcjtgdykV"
      },
      "source": [
        "# Plotting Nuclear Missle Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEAHQnEMd5pY"
      },
      "source": [
        "Code to take data on nuclear missiles by country and plot them on a graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yg1_IcHNQRR5",
        "outputId": "7f95f91a-6629-48c2-a23a-1f7983d9cb85"
      },
      "source": [
        "pip install wikipedia"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wikipedia in /usr/local/lib/python3.7/dist-packages (1.4.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wikipedia) (2.23.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from wikipedia) (4.6.3)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2020.12.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMdLUNaUeA15"
      },
      "source": [
        "Start my taking the information from wikipedia and cleaning it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "1o-ipYuQQXwQ",
        "outputId": "68bfec66-424e-4a8a-ce55-74a7a4c0123c"
      },
      "source": [
        "import pandas as pd\n",
        "import wikipedia as wp\n",
        "import numpy as np\n",
        "\n",
        "html = wp.page(\"Historical nuclear weapons stockpiles and nuclear tests by country\").html().encode(\"UTF-8\")\n",
        "df = pd.read_html(html)[0] \n",
        "\n",
        "#Finding the countires and putting them into the index of a new data frame\n",
        "countries=list(df.columns[0]) \n",
        "countries=countries[1:-1]\n",
        "new_df=pd.DataFrame(columns=countries)\n",
        "\n",
        "for i in range(1,len(df.columns)-1):\n",
        "  li=list(df.columns[i])\n",
        "  year=li[0]\n",
        "  li=li[1:-1]\n",
        "  new_li=[]\n",
        "  for i in li: #Cleaning the data and making the numbers useable\n",
        "    if i == 'â\\x80\\x94':\n",
        "      new_li.append(0)\n",
        "    else:\n",
        "      x=i.split('-')\n",
        "      if len(x) > 1:\n",
        "        x=x[1]\n",
        "      else:\n",
        "        x=x[0]\n",
        "      x=x.split('[',1)\n",
        "      x=x[0]\n",
        "      x=x.split()\n",
        "      x=x[0]\n",
        "      x=x.replace(',','')\n",
        "      try:\n",
        "        x=int(x)\n",
        "      except:\n",
        "        x=np.nan\n",
        "      new_li.append(x)\n",
        "  new_df.loc[year]=new_li\n",
        "new_df.fillna(81,inplace=True) #Single value was hard to deal with and fast to input by hand due to the size of the dataframe\n",
        "\n",
        "df = new_df.rename(columns = {'United States': 'US', 'Soviet Union Russia': 'USSR/Russia','United Kingdom':'UK'}, inplace = False)\n",
        "df.index = pd.to_datetime(df.index)\n",
        "df.index.name = 'date'\n",
        "df=df.replace([0],np.nan)\n",
        "del df['South Africa']\n",
        "del df['Kazakhstan']\n",
        "del df['Ukraine']\n",
        "del df['Belarus']\n",
        "del df['Lithuania']\n",
        "\n",
        "df.head(20)\n"
      ],
      "execution_count": 79,
      "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>US</th>\n",
              "      <th>USSR/Russia</th>\n",
              "      <th>China</th>\n",
              "      <th>France</th>\n",
              "      <th>UK</th>\n",
              "      <th>India</th>\n",
              "      <th>Pakistan</th>\n",
              "      <th>Israel</th>\n",
              "      <th>North Korea</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</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>1945-01-01</th>\n",
              "      <td>2.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1950-01-01</th>\n",
              "      <td>299.0</td>\n",
              "      <td>5.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1955-01-01</th>\n",
              "      <td>2422.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-01-01</th>\n",
              "      <td>18638.0</td>\n",
              "      <td>1605.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1965-01-01</th>\n",
              "      <td>31149.0</td>\n",
              "      <td>6129.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>436.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1970-01-01</th>\n",
              "      <td>26008.0</td>\n",
              "      <td>11643.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-01-01</th>\n",
              "      <td>27519.0</td>\n",
              "      <td>19055.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>492.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1980-01-01</th>\n",
              "      <td>23368.0</td>\n",
              "      <td>30062.0</td>\n",
              "      <td>205.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>492.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1985-01-01</th>\n",
              "      <td>21392.0</td>\n",
              "      <td>39197.0</td>\n",
              "      <td>243.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>422.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990-01-01</th>\n",
              "      <td>10904.0</td>\n",
              "      <td>37000.0</td>\n",
              "      <td>232.0</td>\n",
              "      <td>505.0</td>\n",
              "      <td>422.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-01</th>\n",
              "      <td>10577.0</td>\n",
              "      <td>27000.0</td>\n",
              "      <td>234.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>422.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-01</th>\n",
              "      <td>8360.0</td>\n",
              "      <td>21500.0</td>\n",
              "      <td>232.0</td>\n",
              "      <td>470.0</td>\n",
              "      <td>281.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-01</th>\n",
              "      <td>7700.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>281.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>7260.0</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>5800.0</td>\n",
              "      <td>6375.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>290.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 US  USSR/Russia  China  ...  Pakistan  Israel  North Korea\n",
              "date                                     ...                               \n",
              "1945-01-01      2.0          NaN    NaN  ...       NaN     NaN          NaN\n",
              "1950-01-01    299.0          5.0    NaN  ...       NaN     NaN          NaN\n",
              "1955-01-01   2422.0        200.0    NaN  ...       NaN     NaN          NaN\n",
              "1960-01-01  18638.0       1605.0    NaN  ...       NaN     NaN          NaN\n",
              "1965-01-01  31149.0       6129.0    5.0  ...       NaN     NaN          NaN\n",
              "1970-01-01  26008.0      11643.0   75.0  ...       NaN     8.0          NaN\n",
              "1975-01-01  27519.0      19055.0  180.0  ...       NaN    20.0          NaN\n",
              "1980-01-01  23368.0      30062.0  205.0  ...       NaN    31.0          NaN\n",
              "1985-01-01  21392.0      39197.0  243.0  ...       NaN    42.0          NaN\n",
              "1990-01-01  10904.0      37000.0  232.0  ...       4.0    53.0          1.0\n",
              "1995-01-01  10577.0      27000.0  234.0  ...      13.0    63.0          2.0\n",
              "2000-01-01   8360.0      21500.0  232.0  ...      28.0    72.0          2.0\n",
              "2005-01-01   7700.0      17000.0  235.0  ...      38.0    80.0          8.0\n",
              "2014-01-01   7260.0       7500.0  260.0  ...     120.0    80.0          8.0\n",
              "2020-01-01   5800.0       6375.0  320.0  ...     160.0    90.0         40.0\n",
              "\n",
              "[15 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwNNBJEdeZNp"
      },
      "source": [
        "Now we have a dataframe to work with we can start to do plotting, first I will do it with matplotlib before trying with seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbCoTa7XaQkw",
        "outputId": "c7c993c5-ecf2-4657-8180-d90db0b3140d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.figure()\n",
        "ax=plt.subplot()\n",
        "plt.plot(df.index, df['US'],label='US')\n",
        "plt.plot(df.index, df['USSR/Russia'],label='USSR/Russia')\n",
        "plt.plot(df.index, df['France'],label='France')\n",
        "plt.plot(df.index, df['China'],label='China')\n",
        "plt.plot(df.index, df['UK'],label='UK')\n",
        "plt.plot(df.index, df['India'],label='India')\n",
        "plt.plot(df.index, df['Pakistan'],label='Pakistan')\n",
        "plt.plot(df.index, df['Israel'],label='Israel')\n",
        "plt.plot(df.index, df['North Korea'],label='North Korea')\n",
        "ax.set_yscale('log')\n",
        "plt.xticks(rotation=50)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6W7_o5AZ2rT",
        "outputId": "64b11916-c410-4bcd-9b9d-8d62305f3c71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "def nice_axes(ax):\n",
        "    ax.set_facecolor('.8')\n",
        "    ax.tick_params(labelsize=8, length=0)\n",
        "    ax.grid(True, axis='x', color='white')\n",
        "    ax.set_axisbelow(True)\n",
        "    [spine.set_visible(False) for spine in ax.spines.values()]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(4, 2.5), dpi=144)\n",
        "colors = plt.cm.Dark2(range(6))\n",
        "s = df.loc['2020-01-01']\n",
        "y = s.index\n",
        "width = s.values\n",
        "ax.barh(y=y, width=width, color=colors);    \n",
        "nice_axes(ax)\n"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_U8fMH8Chz8w"
      },
      "source": [
        "def prepare_data(df, steps=5):\n",
        "    df = df.reset_index()\n",
        "    df.index = df.index * steps\n",
        "    last_idx = df.index[-1] + 1\n",
        "    df_expanded = df.reindex(range(last_idx))\n",
        "    df_expanded['date'] = df_expanded['date'].fillna(method='ffill')\n",
        "    df_expanded = df_expanded.set_index('date')\n",
        "    df_rank_expanded = df_expanded.rank(axis=1, method='first')\n",
        "    df_expanded = df_expanded.interpolate()\n",
        "    df_rank_expanded = df_rank_expanded.interpolate()\n",
        "    return df_expanded, df_rank_expanded\n",
        "\n",
        "df_expanded, df_rank_expanded = prepare_data(df)\n",
        "df_expanded.head()\n",
        "labels = df_expanded.columns\n"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9MI83Qnixv7"
      },
      "source": [
        "from matplotlib.animation import FuncAnimation\n",
        "\n",
        "def init():\n",
        "    ax.clear()\n",
        "    nice_axes(ax)\n",
        "    ax.set_ylim(.2, 6.8)\n",
        "\n",
        "def update(i):\n",
        "    for bar in ax.containers:\n",
        "        bar.remove()\n",
        "    y = df_rank_expanded.iloc[i]\n",
        "    width = df_expanded.iloc[i]\n",
        "    ax.barh(y=y, width=width, color=colors, tick_label=labels)\n",
        "    date_str = df_expanded.index[i].strftime('%B %-d, %Y')\n",
        "    ax.set_title(f'Nuclear Warheads by Country - {date_str}', fontsize='smaller')\n",
        "    \n",
        "fig = plt.Figure(figsize=(4, 2.5), dpi=144)\n",
        "ax = fig.add_subplot()\n",
        "anim = FuncAnimation(fig=fig, func=update, init_func=init, frames=len(df_expanded), \n",
        "                     interval=100, repeat=False)"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lL-ysbVXi0Rs"
      },
      "source": [
        "from IPython.display import HTML\n",
        "html = anim.to_html5_video()\n",
        "HTML(html)\n",
        "anim.save('Nuclear.mp4')\n"
      ],
      "execution_count": 84,
      "outputs": []
    }
  ]
}