Mandy_EDA_Iodized_salt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 4 Analyzing Iodized Salt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams.update({'font.size': 16, 'font.family': 'sans'}) \n",
    "\n",
    "health = pd.read_csv('data/data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>...</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>Unnamed: 60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Caribbean small states</td>\n",
       "      <td>CSS</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>Central Europe and the Baltics</td>\n",
       "      <td>CEB</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>Early-demographic dividend</td>\n",
       "      <td>EAR</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.186181</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>1468</th>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>EAS</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.987626</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>...</th>\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>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87373</th>\n",
       "      <td>Virgin Islands (U.S.)</td>\n",
       "      <td>VIR</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87718</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>WBG</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.600000</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>88063</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88408</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88753</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Consumption of iodized salt (% of households)</td>\n",
       "      <td>SN.ITK.SALT.ZS</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>...</td>\n",
       "      <td>90.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>258 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Country Name Country Code  \\\n",
       "88                         Arab World          ARB   \n",
       "433            Caribbean small states          CSS   \n",
       "778    Central Europe and the Baltics          CEB   \n",
       "1123       Early-demographic dividend          EAR   \n",
       "1468              East Asia & Pacific          EAS   \n",
       "...                               ...          ...   \n",
       "87373           Virgin Islands (U.S.)          VIR   \n",
       "87718              West Bank and Gaza          WBG   \n",
       "88063                     Yemen, Rep.          YEM   \n",
       "88408                          Zambia          ZMB   \n",
       "88753                        Zimbabwe          ZWE   \n",
       "\n",
       "                                      Indicator Name  Indicator Code  1960  \\\n",
       "88     Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "433    Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "778    Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "1123   Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "1468   Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "...                                              ...             ...   ...   \n",
       "87373  Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "87718  Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "88063  Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "88408  Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "88753  Consumption of iodized salt (% of households)  SN.ITK.SALT.ZS   NaN   \n",
       "\n",
       "       1961  1962  1963  1964  1965  ...  2007  2008  2009       2010  2011  \\\n",
       "88      NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN        NaN   NaN   \n",
       "433     NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN        NaN   NaN   \n",
       "778     NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN        NaN   NaN   \n",
       "1123    NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN  67.186181   NaN   \n",
       "1468    NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN  84.987626   NaN   \n",
       "...     ...   ...   ...   ...   ...  ...   ...   ...   ...        ...   ...   \n",
       "87373   NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN        NaN   NaN   \n",
       "87718   NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN  76.600000   NaN   \n",
       "88063   NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN        NaN   NaN   \n",
       "88408   NaN   NaN   NaN   NaN   NaN  ...   NaN   NaN   NaN        NaN   NaN   \n",
       "88753   NaN   NaN   NaN   NaN   NaN  ...  90.9   NaN   NaN        NaN  94.0   \n",
       "\n",
       "       2012  2013  2014  2015  Unnamed: 60  \n",
       "88      NaN   NaN   NaN   NaN          NaN  \n",
       "433     NaN   NaN   NaN   NaN          NaN  \n",
       "778     NaN   NaN   NaN   NaN          NaN  \n",
       "1123    NaN   NaN   NaN   NaN          NaN  \n",
       "1468    NaN   NaN   NaN   NaN          NaN  \n",
       "...     ...   ...   ...   ...          ...  \n",
       "87373   NaN   NaN   NaN   NaN          NaN  \n",
       "87718   NaN   NaN   NaN   NaN          NaN  \n",
       "88063   NaN   NaN   NaN   NaN          NaN  \n",
       "88408   NaN   NaN   NaN   NaN          NaN  \n",
       "88753   NaN   NaN   NaN   NaN          NaN  \n",
       "\n",
       "[258 rows x 61 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What countires have iodized salt historical data\n",
    "iodized_salt_record = health[health['Indicator Name'].str.contains(\"iodized salt\")]\n",
    "iodized_salt_record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20098    78.166667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The iodized salt consumption trend for each year\n",
    "iodized_salt_yearly_mean = iodized_salt_record.mean(axis=1).loc['1993':'2013'] \n",
    "iodized_salt_yearly_mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (21,) and (1,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-723d02b0e850>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Years'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Consumption of iodized salt (% of households)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Yearly Avg Consumption of iodized salt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1741\u001b[0m         \"\"\"\n\u001b[1;32m   1742\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1743\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1744\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    271\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 273\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 399\u001b[0;31m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m    400\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[1;32m    401\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (21,) and (1,)"
     ]
    },
    {
     "data": {
      "image/png": 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gcTS9dp1U1SXATMnk1DEXMWTmblV9H3hS12tJkiRp9CPdnWgWOj47yZuSTO108UqaZHANzczaZ7ZlkiRJWoBmTPiq6jdV9VJgd+ARwLlJHlZVJwN3AvYDng7caWBvW0mSJC0ws67DV1XfBXZrH+8eneRk4ICq+tKkg5MkSdL4Os/SraqP0jzmvQb4QZKXtmP4JEmStICNmqW7WZJ/T3JpkquSHA/crqr2p3nE+0zgO0lWrq1gJUmSNHejeug+AqwADgCeRTNr9vgkqar/Bu7bHvPlJP826UAlSZI0P6MSvr8HXllVx1TVF4FnADvQTNigGu+n2Rbt5jOfRpIkSevSqITvZzSPbqc8GvgTzRZnf1ZVv6yqfScQmyRJknowapbuAcBnkjwH+D1wa5oev9+OaCNJkqQFZsaEr6pOSrID8ACaR7bfaXfJkCRJ0iIych2+qvo1zZ61kiRJWqRGLcty77meLMmGSe46XkiSJEnq06hJG19PclySR8y2wHKSbZO8HlgNPKbXCCVJkjSWUY90dwIOA44FrklyBnA2sAa4Adgc2BH4O+BuNMneK6rqPyYasSRJkuZk1KSNnwPPSfJaYD9gL+DlwEYDh60Gvg68FjihqmqCsUqSJGkeRk7aAKiqXwFva18k2QzYELiiqv4w0egkSZI0tlkTvunambuSJElaJEZOxpAkSdLiZ8InSZK0xJnwSZIkLXEmfJIkSUucCZ8kSdIS1ynhS/KnJH83Q919kvyp37AkSZLUl649fBlRtz7ggsuSJEkL1Mh1+No9dKeSvfWG7Km7EfBI4PIJxCZJkqQezJjwJTkIOLD9WsDpI87zwT6DkiRJUn9G9fCd2r6HJvH7KPCzacfcAHwf+FLvkUmSJKkXMyZ8VXUacBpAkgI+UlU/X1uBSZIkqR+d9tKtqkMmHYgkSZImY9QYvn+fw3mqqp7bQzySJEnq2agevj3pvtxK52VZkmwDvBt4GM34wJOAA6rqko7tdwYOBfYANgEuAT5YVe/tGoMkSdJyMmoM3/Z9XyzJxsDJNJM99qVJFA8HTklyj6r63SztV7TtTwWeB1wN3AXYtO9YJUmSlopOY/h69HxgR2CnqroQIMk5wAXAC4B3zdSwXQPwE8DXqurxA1WnTC5cSZKkxW/OCV+S2wEbTi/v+Eh2b+DMqWSvbbc6yenAPoxI+ICHALsAL5xTwJIkSctc171010vy5iRXAJcCq4e8utgVOHdI+Xk0ydwoD2zfN0xyZpI/JPlVkiOSbNTx+pIkSctO1710DwBeDPwLzUSLN9OMvVsN/JjmUW0XWwBXDSm/Eth8lrZ/074fA5xIM+nj7TRj+f5jpkZJ9k+yKsmqNWvWdAxTkiRp6eia8O1HMzP2be33z1fVQcDOwM+BbedwzWEzejOkbLqpWD9VVQdW1alV9U7gEOBxSYb2EFbVkVW1oqpWbLnllnMIU5IkaWnomvDtCKyqqj8BfwQ2AqiqPwDvAZ7T8TxX0fTyTbc5w3v+Bl3Rvn91WvmJ7fu9OsYgSZK0rHRN+K7mLxM1fgHsNFB3M4YnccOcRzOOb7pdaPbkna0t3LSHcKp38MaOMUiSJC0rXRO+/+EvkypOAA5J8tQkTwTeAnyn43mOA1Ym2XGqIMn2wG5t3ShfoVm/7xHTyvdq31d1jEGSJGlZ6ZrwvQe4tv18EHAZcDTNBIoNgJd0PM+/ARcBxybZJ8newLHAT4EPTx2UZLskf0xy4FRZVV1Bk1y+sJ0x/PdJXgscCHxicKkXSZIk/UWndfiq6qsDny9L8nfAnYCNgR+0Y/m6nOd3Sfak2VrtKJrHsV+j2VrttwOHBlifmyakhwK/Af4ReCXNEjHvAA7rcn1JkqTlaF47bVRVAfPqUWsXaP6HWY65iCEzd9vrvovRCzRLkiRpQNeFl/dJst/A9+2SnJHkN0k+m8S9bCVJkhaormP43ggMLmL3LmBr4Ehgd+DgfsOSJElSX7omfHcCzgFotzF7FPDyqnoF8Hrg8ZMJT5IkSePqmvBtCFzXfn4Azdi/qQWPf8Rftj2TJEnSAtM14bsIeGD7eR/grKq6uv1+O5qFmSVJkrQAdZ2l+2HgnUkeT7OF2YsG6u7P7LtkSJIkaR3pug7fe5NcDqwEjqiqTw5U3xL42CSCkyRJ0vg6r8NXVUfT7K4xvfwFvUYkSZKkXnUdwydJkqRFyoRPkiRpiTPhkyRJWuJM+CRJkpY4Ez5JkqQlzoRPkiRpiZtxWZYkq4HqeqKq2rGXiCRJktSrUevwncZfJ3wPBW4PnA78sv28G3AZ8LVJBShJkqTxzJjwVdWzpz4n2R+4H/CAqvrZQPk2wAnAGROMUZIkSWPoOobvVcBBg8keQFX9FDgYeE3PcUmSJKknXRO+rYHrZ6i7AbhjP+FIkiSpb10Tvu8Dr0qy4WBhko1oev++33dgkiRJ6seoSRuDXg18GbgkyfH8ZdLGo4BbA4+cTHiSJEkaV6eEr6q+luR/AW8EHgTcAbgUOBE4vKp+OLkQJUmSNI6uPXxU1Q+Ap08wFkmSJE3AnHbaSLJekrsleXCSTSYVlCRJkvrTOeFL8mKaRZbPAU4GdmrLv5DknycTniRJksbVKeFL8nzgvcAXgCcBGaj+BvAPvUcmSZKkXnTt4Xs58C9VtT/w+Wl1P6Tt7ZMkSdLC0zXh24FmC7Vhfgds1ks0kiRJ6l3XhO9yYPsZ6nYCft5LNJIkSepd14Tvi8CBSXYcKKsktwVeRjO2T5IkSQtQ14TvjTR75p4LnAQUcATwA+BPwKETiU6SJElj65TwVdUVwArgLcAGwI9pFm1+P3D/qrq66wWTbJPks0muTnJNks8l2XaugSd5XZJK8s25tpUkSVpO5rLTxm+Aw9rXvCTZmGYNvxuAfWl6Cg8HTklyj6r6Xcfz7Ai8AfjVfGORJElaLrquw3dgkifMUHfHJAd2vN7zgR2Bx1XVF6rqWGBvYDvgBR3PAfCvwNE0j5QlSZI0QtcxfAcDxyR5x5C6rYGDOp5nb+DMqrpwqqCqVgOnA/t0OUGSpwH3Bl7X8ZqSJEnL2lz20v0A8JJ2zN1G87zerjQTP6Y7D9hltsZJNgfeDby6qq6cZwySJEnLylwSvk8Bfw/sBnw9yVbzuN4WwFVDyq8ENu/Q/h3A+cDHu14wyf5JViVZtWbNmq7NJEmSloy5JHxU1enA/YCNgG8nucc8rllDyjKk7K8PSB4EPAt4UVUNO8fwi1UdWVUrqmrFlltuOYcwJUmSloY5JXwAVXURcH/ge8A3gcfOoflVNL18023O8J6/QR8GPgr8LMlmSTajmWW8fvv9FnOIQ5IkadmYc8IHf16i5THAx4DXz6HpeTTj+KbbBfj+LG13Bl5IkxhOvXYDVrafXzSHOCRJkpaNruvw7Uez2PKfVdWNwEuTnEGzn24XxwHvTLJjVf0EIMn2NInba2dpu8eQsvcA6wP/BFw4pF6SJGnZyxyGw41/sWQT4GzgOprt2opmIedbAveoqt+2x21Hk2AeWlUzbtuW5FTgZlX1wC7XX7FiRa1atWqsn0GSJGltSHJWVa3o41wz9vAleRbw5aq6ov08SlXVUbNdrKp+l2RPmqVVjqKZrPE14ICpZG/q8jQ9d/N65CxJkqS/mLGHL8mNwMqq+lb7eZSqqvV7j65n9vBJkqTFYq308AE7AJcOfJYkSdIiNGPCV1UXD/ssSZKkxcUxcpIkSUvcqEkbqxm+K8ZQVbVjLxFJkiSpV6PG8J3GXyd8DwVuD5wO/LL9vBtwGc1MW0mSJC1Ao8bwPXvqc5L9afbQfUBV/WygfBvgBOCMCcYoSZKkMXQdw/cq4KDBZA+gqn4KHAy8pue4JEmS1JOuCd/WwPUz1N0A3LGfcCRJktS3rgnf94FXJdlwsDDJRjS9f9/vOzBJkiT1Y9SkjUGvBr4MXJLkeP4yaeNRwK2BR04mPEmSJI2rU8JXVV9L8r+ANwIPAu5AswvHicDhVfXDyYUoSZKkcXTt4aOqfgA8fYKxSJIkaQI6J3wASQLsAmwBXA78sKo6L84sSZKkta/z1mpJnkfzGPcc4FTgXOAXSZ47mdAkSZLUh049fEmeDhxJs6PGp2h219iK5hHvkUmurar/nFiUkiRJmre5zNI9uqqeOa38E0mOoll42YRPkiRpAer6SHcnmp69YT7V1kuSJGkB6prw/YZmt41htm7rJUmStAB1Tfi+Arw5yYMGC5PcHzi8rZckSdICNJcxfCuBU5P8nGa27lY0vXsXtvWSJElagLrutHFZknsBz6HZaWML4CLgNODjVXXtpAKUJEnSeOay08a1wPvblyRJkhaJzgsvS5IkaXGasYcvyU+Ax1fV2UlWA6O2UKuqulPv0UmSJGlsox7pngZcM/DZPXMlSZIWoRkTvqrab+Dzs9dKNJIkSeqdY/gkSZKWOBM+SZKkJc6ET5IkaYkz4ZMkSVriZkz4ktwqSfq+YJJtknw2ydVJrknyuSTbdmi3IsmRSX6Y5NoklyQ5OskOfccoSZK0lIzq4bsKuC9AkpOT3HXciyXZGDgZuCuwL/BM4C7AKUk2maX5U4BdgSOARwKvBe4NrEqyzbixSZIkLVWj1uH7PbBB+/khwK16uN7zgR2BnarqQoAk5wAXAC8A3jWi7duqas1gQZLTgdXteQ/sIT5JkqQlZ1TCdwHw+iSfab8/alQvX1V9ssP19gbOnEr22nar28RtH0YkfNOTvbbs4iRrgDt2uLYkSdKyNCrhewPwKZrHp8XoHrQCuiR8uwLHDik/D3hih/Z/JcnOwO2AH8y1rSRJ0nIxaqeNLybZAtia5rHpE4Czx7zeFjRjA6e7Eth8LidKcjPgQ8Aa4KMjjtsf2B9g221nnRsiSZK05Izq4aOq/gRcnOQQmkexv+jhmsP25J3PbOD3Aw8AHl1Vw5LI5mJVRwJHAqxYscL9gCVJ0rIzMuGbUlWHALTLtOxC01N3BfCDqppLEnVV23a6zRne8zdUkrfQ9NrtW1UnzuH6kiRJy07nhZeTPA+4FDgHOBX4HvCLJM+dw/XOoxnHN90uwPc7xvEGmiVZXlpVR83h2pIkSctSp4QvydNpHot+D3gO8Kj2/XvAkUme2vF6xwErk+w4cO7tgd3autni+GfgcOANVfW+jteUJEla1tLliWySs4FzquqZQ+qOAu5eVffqcJ5NaCZ+XAe8kWY832HALYF7VNVv2+O2A34MHFpVh7ZlTwH+AzgBOGTaqa+pqll7CFesWFGrVq2a7TBJkqR1LslZVbWij3N1faS7E80SLcN8qq2fVVX9DtgTOB84CjiaZgbwnlPJXivA+tPie0Rb/gjgjGmvD3b8OSRJkpadTpM2gN/QLM8yzNZtfSdVdQnwD7MccxHTZu5W1bOBZ3e9jiRJkhpde/i+Arw5yYMGC5Pcn2ZM3Vf6DkySJEn96NrD92pgJXBqkp/TzNbdiqZ378K2XpIkSQtQ13X4LktyL5qZuQ+iWUvvIuA04ONVde2kApQkSdJ4uvbw0SZ1729fkiRJWiQ6L7wsSZKkxcmET5IkaYkz4ZMkSVriTPgkSZKWOBM+SZKkJc6ET5IkaYnrvCxLkn2BpwLbAhtOq66qulOfgUmSJKkfnRK+JG8CDgHOBb4L3DDBmCRJktSjrj18zwXeW1Uvm2QwkiRJ6l/XMXy3Ab44yUAkSZI0GV0TvtOAe04yEEmSJE1G10e6BwCfS3IFcDxw5fQDqurGHuOSJElST7omfOe37x+bob7mcC5JkiStRV2TtENpkjpJkiQtMp0Svqo6eMJxSJIkaULmvNNGkk2TbJNkk0kEJEmSpH51TviS7JVkFfBr4CLg6iTfSvKwCcUmSZKkHnTdaWMv4MvAhcBhwGXAHYAnA8cneVRVfXViUUqSJGneuk7aOBg4EXjM4PIrSQ4FvkSz7ZoJnyRJ0gLU9ZHuPYEPTF9rr/3+QeBePcclSZKknnRN+G4AbjVD3S3bekmSJC1AXRO+U4HDkuwwWJhkW5rHvaf0G5YkSZL60nUM32uA04EfJTkTuBTYClhJM2v3NROJTpIkSWPr1MNXVecD9wCOAG4B3BvYEHgvcK+qumBiEUqSJGksnfe/rapLgVdOMBZJkiRNwJx32pAkSdLiMmMPX5KTgX+sqh+2n0epqnpov6FJkiSpD6N6+DLtuIx4zWWLtm2SfDbJ1UmuSfK5drZvl7YbJnlHkkuTXJfkjCS7d722JEnScjRjD19V7THw+SF9XCzJxsDJNOv27QsUcDhwSpJ7VNXvZjnFR4FHA68CfgK8GDghyf2r6rt9xChJkrTUdOqZS/KsJLeZoW6LJM/qeL3nAzsCj6uqL1TVscDewHbAC2aJ4Z7A04CXVdW/VdXXgCcBlwCHdry+JEnSstP1UezHgDvNULdDW9/F3sCZVXXhVEFVraZZ42+fDm3/ABwz0PaPwKeBvZLcomMMkiRJy0rXhC8j6jYB/tjxPLsC5w4pPw/YpUPb1VV17ZC2Nwfu3DEGSZKkZWXULN170SywPOWxSe427bCNgKcAXRde3gK4akj5lcDmY7Sdqr+JJPsD+7dfb0gyLOHUwndb4PJ1HYTmzfu3eHnvFjfv3+K2U18nGrXw8j7AQe3nAt4ww3FXAM+dwzVrSNmoHsTBY+bctqqOBI4ESLKqqlZ0uJYWGO/d4ub9W7y8d4ub929xS7Kqr3ONSvjeA3ycJqH6CfC/gf+ZdswNwC+ralgiNsxVDO+J25zhvXeDrgSGLd+y+UC9JEmSphm1LMvVwNUASXYALq2q3495vfNoxuJNtwvw/Q5tH59k42nj+HYBfg9cOLyZJEnS8tZp0kZVXVxVv0+yR5LXJ/lA+77H7K3/ynHAyiQ7ThUk2R7Yra2bre0GwBMH2t4MeDJwYlXd0OH6R84xXi0c3rvFzfu3eHnvFjfv3+LW2/1Ll6exSbYAPgPsAdxI8/h1c5rHvacCT6yqWR+pJtkEOBu4DngjzZi8w4BbAveoqt+2x20H/Bg4tKoOHWj/aWAvmoWXVwMvAh4DPKCqvtPpJ5YkSVpmui7LcgRwX+AZwEZVtSXNDN1nASuA93Y5SbuTxp7A+cBRwNE0idueU8leK8D6Q+Lbj2bNv8OBLwPbAI8w2ZMkSZpZ1x6+q4HXV9UHhtT9E3B4Vd16AvFJkiRpTF17+P7EzGvt/aitXyeSbJPks0muTnJNks8lGTabd1jbDZO8I8mlSa5LckaS3ScdsxrzvXdJViQ5MskPk1yb5JIkR7eTi7SWjPNnb9p5XpekknxzEnFquHHvX5Kdk3wmyeXt788fJXnpJGNWY8y/97ZN8on29+a1Sc5Pcng75EprQZKtk7yvzTmubX//bd+x7bzzlq4J37E0kyOGeQrwhY7n6VWSjYGTgbsC+wLPBO4CnNLxf96P0uzveyDNWMBLgRPaRac1QWPeu6fQzPY+Angk8FqaRcJXJdlmYkHrz3r4szd1nh1p1vj81STi1HDj3r8kK4D/Bm4BPA94FPAvNENxNEHj3Lu2/iRgd+BNwKOBjwCvAP59gmHrr90ZeBLNfIhvzLHt/POWqpr1RbMG38U04+aeTfOX7LOB49vyx9GMzduTZjxep/OO+wJeStO7eOeBsh1otnp7+Sxt70kzaWS/gbKb0fRYHre2fobl+hrz3m05pGw7mglFh67rn205vMa5f9POcwLwYZrJX99c1z/XcnmN+edvPZplsj6/rn+O5fga8949vP177+HTyt/att94Xf98y+EFrDfw+XntPdm+Q7ux8pauPXyfpZkg8UiafwV8uX1/RFv+/wNfpfmXw1c7nrMPewNnVtWf1+CrqtXA6TQ7hczW9g/AMQNt/wh8GtgryS36D1cD5n3vqmrNkLKLgTXAHXuOU8ON82cPgCRPo+mZfd1EItQo49y/h9Csf/quiUWnUca5dzdv36+ZVv5rmkS+y65XGlNV3TjPpmPlLaN22hg01/X21pZdaR43T3ceA+v1jWi7uv56Eeeptjen6XI9b+wINZNx7t1NJNkZuB3wgzHjUjdj3b8kmwPvBl5dVVcm/j2zlo1z/x7Yvm+Y5EzgPjSPpj4NvKaqrustSg0zzr07iWY8/tuSvAi4BPg7ml7DD1WzkoYWrrHylk4JX1WdNu/wJmsLhm/JdiV/2XJtPm2n6jU549y7v9IuwP0hmh6+j44fmjoY9/69g2Z5po/3GJO6G+f+/U37fgzwfpoxtCuAQ2me+Dy+pxg13LzvXVVdn+SBNE/lBhODjwAv6S1CTcpYeUvXHr6FbNi6Ml26CzJGW/Wjr//+7wceADy6qmbbk1n9mdf9S/IgmjU8713tIBStE/P98zc1FOhTVXVg+/nUJOsDb02yS1XNtlWmxjPfP3sb0iTqt6OZ7DHVw3cgzRi+F/UYo/o3Vt7SKeFLsh6wP0138TbAhtMOqararsu5enYVwzPazRmeBQ+6Ehg2jX3zgXpNzjj37s+SvIXm/819q+rEnmLT7Ma5fx+m6Yn9WZLN2rKbAeu336+rblslav7GuX9XtO/Tx2ufSDP4/17Mvje65m+ce/dcmjGYd66qH7dlX2/X2j0yyYeq6uzeIlXfxspbuvbwvR14OfA/wLeB33eNbsLOo3mmPd0uzP4L5zzg8Uk2nvY8fBean+/C4c3Uk3HuHQBJ3kDzOOmfq+qoHmPT7Ma5fzu3rxcOqbsKeBnwnnGC06zG/d0JN+1pmOplmO+AdHUzzr27O3DVQLI35Vvt+840259qYRorb+k6S/cZwGFVdZ+qenpV7Tf9Nb/Yx3YcsLJdywuAdvHC3dq62dpuwMAg13Ys2JOBE+1hmLhx7h1J/plmi703VNX7JhWkZjTO/dtjyOts4Nz282cnEK/+2jj37yvADTSrNAzaq31f1VOMGm6ce3cZsHmSO08rv1/7/vO+gtREjJe3dFwz5nLgoet67ZohcW1Ck9F+j2Y6+t40f3H8BNh04LjtaMYnHDit/adpehSeBzyU5i+a62nGFq3zn28pv8a5dzQLL99I8xfPymmvXdb1z7YcXuP+2RtyvlNxHb5Fc/+Ag9ryNwN/T9PTfh3w8XX9sy3115i/O7enWZLlfJpFm/cAXtWWrWJgfThfE7+PT2hf/0rTW/6i9vuDZ7p/bfm885augX0IePu6/g80Q2zb0sw4ugb4Dc2uH9tPO2b79j/owdPKN6JZS+qy9j/YfwMPWdc/03J5zffe0czsrBlep67rn2u5vMb5szfkXCZ8i+j+0Ty+fXmbePyeZgH+Q4EN1vXPtRxeY967XYD/An5Kk6SfD7wT2Hxd/1zL6TXb32GTyFvSnmCkdiuXo2mWvTiBIQNDq+rkWU8kSZKkta5rwncn4HM0Az4HFe004apyD0VJkqQFqOss3Y8Bt6VZjfuHLJxZupIkSZpF1x6+a4FnVZWz5yRJkhaZrsuyXIK9epIkSYtS14TvcOA1STadZDCSJEnqX9cxfHsBWwMXJTmDm87Srarat9fIJEmS1IuuY/hWz3JIVdWOsxwjSetUks8CewI7V9Uvp9U9BDgZeFlVvXftRydJk9Mp4ZOkpSDJ7Wn2ozylqga3J9oIOAf4FfCgqnI/WElLStcxfJK06LW9egcAT0jyuIGqg2mGrTxn0slekg2SZJLXkKTpOiV8Sbad7TXpQCWpD1X1KeBLwAeTbJbk3jTbhB1cVT8CSPL8JGcnuT7J5Uk+mmSLwfMkeUmSM5JcmeTXSc5M8uhpx2yfpJL8Y5K3J/kFcAOwWZKtknwiyS+S3JDk0iRfSnK7tfNfQtJy0nUM3400u2rMyJ02JC0WSe5I82j388C9aDYpX1lVf0ryVuAVwBE0W0nekWalgp8BD6iqP7XneCfwA+AimglwjwVeDDyqqr7SHrM9sBr4BfBt4CPA+sCJwHE0G6QfQrOv6e1pNkN/a1VdNMEfX9Iy1DXhezY3TfhuAzwa2BE4rKr+vffoJGlCkjwP+DfgD8B9qup7bYL2Y+CQqjp04NjdgG8Cj6+qLww513o0T0yOB66rqn3a8u1pEr7/aa9RA21+C7y+qo6YyA8oSQM6LctSVR+foepdSY6iSfokadGoqo8kORT4ZlV9ry1+GE3idnSSwd+P/w1cA+wOfAEgyX1oeufuC2xJs684wI+GXO4LddN/XX8beFU7nu9k4Nwhx0hSL/qYtPEp4Dk9nEeS1rbf89e7CE2Nn7uQpudv8HUrmicbJNkG+BqwBfBPwANoEr//A2w45DqXDil7Ms1j3VfTzBD+eZID295CSepV14WXR7kdw3/BSdJic0X7/nBuusD8YP0jgFsDT6qqn01VJtl4hvPepOeuqn5FM+bvxUl2Aval6TFcA/zrvKKXpBl0SviS7D6k+ObA3YDXAd/oMyhJWke+CtwIbFtVXx1x3FRi94epgiR/C+xGM7ljTtrZwa9P8kKa36uS1KuuPXynctN/oU6NVzkNeFFfAUnSulJVP07yNuD9ba/bacD1wDY04/s+UlWnACfRzOz9ZJJ/Ae5A0zt3CR2GyiS5dXuOo4Ef0iSO+wCb08zglaRedU349hhSdj1wcVVd1mM8krROVdXrk/yA9nErzT92f0ozZu+C9pjzkjwdOJRmHN6PgdfSPOp9SIfLXA98B3g+zdIsN9JM9nh6VR3b588jSeDWapIkSUte1502bjt9N40kL0jyviSPmUxokiRJ6kPXhZePA35WVf/Yfn8TzXiVq4DNgKdV1TETjFOSJEnz1HW9pxU041emvBB4c1XdBvgAzT6UkiRJWoC6JnxbAL8ESHI3YCvgE23dF4Cdeo9MkiRJveia8F0BbN1+3hP4RVVd0H7fYA7nkSRJ0lrWdVmWk4CDk9wWeAXtXpKtuwIX9xyXJEmSetJ10sbtafbMXUmz4feTqurytu5bwFlV5eLLkiRJC9DY6/AluRVwfVX9ftaDJUmStNa58LIkSdIS13UMH0keDDwV2BbYcFp1VdVD+wxMkiRJ/eiU8CV5AfCvNLN1LwBumH5Iz3FJkiSpJ10nbZwPfAt4jmP1JEmSFpeu6+fdEfiYyZ4kSdLi0zXhOwvYcZKBSJIkaTK6Jnz/DByQZPdJBiNJkqT+dR3D91PgVsCmwLXAVdMOqararv/wJEmSNK6uy7J8DXDBPkmSpEXIhZclSZKWuK5j+CRJkrRIdU74ktw9yWeTrEnyxyS/SvJfSe4+yQAlSZI0nq6TNu4LnAZcBxwHXAZsBTwW2AjYvarOmmCckiRJmqeuCd9JNLN0H1pVvxkovyVwEnB1VT18YlFKkiRp3ro+0l0JvGUw2QNov78NuH/fgUmSJKkfXRO+2boBneorSZK0QM3lke6tgT2nPdLdBDgZH+lKkiQtWF0Tvr8DTgWuB74EXEozaePRNJM2HlJV355cmJIkSZqvzgsvJ7kHcCDwIGAL4EqambuHVdX3JhahJEmSxuJOG5IkSUvcjJM2kqyX5LFJ7jbimLsneexkQpMkSVIfRs3SfQbwn8DvRhzzG+A/kzy116gkSZLUm9kSvo9V1eqZDqiqi4CPAvv2HJckSZJ6MirhuzdwYodznASs6CccSZIk9W1UwndL4KoO57iqPVaSJEkL0KiE73Jguw7n2LY9VpIkSQvQqITvm3Qbm/fs9lhJkiQtQKMSvvcAD03y7iQ3n16ZZIMk7wX2BN49ofgkSZI0ppELLyc5APgX4AqaCRwXt1XbAQ8DbgO8oqreO9kwJUmSNF+z7rSRZHfgtcCDafbNBbiOZm/dt1bVNyYZoCRJksYzl7101wNu2369oqr+NLGoJEmS1Bv30pUkSVriRk3akCRJ0hJgwidJkrTEmfBJkiQtcSZ8kiRJS5wJnyRJ0hL3/wCwgK4ipQ3hrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = np.arange(1993,2014)\n",
    "y = iodized_salt_yearly_mean\n",
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "ax.set_xlabel('Years')\n",
    "ax.set_ylabel('Consumption of iodized salt (% of households)')\n",
    "ax.plot(x, y, linewidth=2)\n",
    "ax.set_title('Yearly Avg Consumption of iodized salt');\n",
    "ax.set_xticks(x[::5]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',\n",
      "       '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n",
      "       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n",
      "       '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n",
      "       '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n",
      "       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n",
      "       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
      "       '2014', '2015', 'Unnamed: 60'],\n",
      "      dtype='object')\n",
      "Empty DataFrame\n",
      "Columns: [Country Code, Indicator Name, Indicator Code, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, Unnamed: 60]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 60 columns]\n"
     ]
    }
   ],
   "source": [
    "print(iodized_salt_record.columns)\n",
    "print(iodized_salt_record.groupby('Country Name').max().loc['1993':'2013'])  #idxmax(axis=0))\n",
    "#iodized_salt_yearly_max = iodized_salt_record.max(axis=0).loc['1993':'2013'] \n",
    "#print(iodized_salt_yearly_max.index)\n",
    "#print(iodized_salt_record.max(axis=0).loc['1993':'2013'])\n",
    "\n",
    "#iodized_salt_record.loc['1993':'2013'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = np.arange(1993,2014)\n",
    "fig, ax = plt.subplots(figsize=(20, 8))\n",
    "ax.bar(x, iodized_salt_yearly_max,color='g')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(x)\n",
    "ax.set_ylabel('Iodized Salt Consumption')\n",
    "ax.set_title('Yearly Highest Iodized Salt Consumption 1993-2013 ') \n",
    "length = len(iodized_salt_yearly_max)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# which country has the higest consumption each year?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}