Exploratory_data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'PERTUSSIS', 'MUMPS', 'HEPATITIS A', 'MEASLES', 'POLIO', 'RUBELLA'}\n"
     ]
    }
   ],
   "source": [
    "file = pd.read_csv('for_visulisation.csv')\n",
    "file.shape\n",
    "\n",
    "diseases_list = set(list(file.disease))\n",
    "print(diseases_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disease Plot\n",
    "- Hepatitis A\n",
    "- Pertusis\n",
    "- Measles\n",
    "- Mumps\n",
    "- Polio\n",
    "- Rubella\n",
    "##### Plotting it With the no of cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_data(disease_name):\n",
    "    the_ds_data = file.loc[file['disease'] == disease_name]\n",
    "    the_ds_data.head()\n",
    "    tf = the_ds_data.filter(['state_name','state','cases'], axis=1)\n",
    "    tf.cases = tf.cases.astype(int)\n",
    "    the_ds_data_2 = tf.groupby(['state_name','state']).sum().reset_index()\n",
    "    the_ds_data_2['text'] = the_ds_data_2['state_name']+'<br>'+'Total case:'+str(the_ds_data_2['cases'])\n",
    "    return the_ds_data_2\n",
    "\n",
    "def get_data_with_year(disease_name):\n",
    "    the_ds_data = file.loc[file['disease'] == disease_name]\n",
    "    tf = the_ds_data.filter(['Year','cases'], axis=1)\n",
    "    tf.cases = tf.cases.astype(int)\n",
    "    the_ds_data_2 = tf.groupby(['Year']).sum().reset_index()\n",
    "    return the_ds_data_2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~Ubaid01/2.embed\" height=\"525px\" width=\"100%\"></iframe>"
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      "text/plain": [
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hepatitis A\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "a = get_data('HEPATITIS A')\n",
    "trace0 = go.Bar(\n",
    "    x = a['state_name'],\n",
    "    y = a['cases'],\n",
    "    text = a['text'],\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5,\n",
    "        )\n",
    "    ),\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "data = [trace0]\n",
    "layout = go.Layout(\n",
    "    title='Hepatitis A Report ALL YEARS (1960-2011)',\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='text-hover-bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Pertusis\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "a = get_data('PERTUSSIS')\n",
    "trace0 = go.Bar(\n",
    "    x = a['state_name'],\n",
    "    y = a['cases'],\n",
    "    text = a['text'],\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5,\n",
    "        )\n",
    "    ),\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "data = [trace0]\n",
    "layout = go.Layout(\n",
    "    title='PERTUSIS Report ALL YEARS(1960-2011)',\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='text-hover-bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#POLIO\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "a = get_data('POLIO')\n",
    "trace0 = go.Bar(\n",
    "    x = a['state_name'],\n",
    "    y = a['cases'],\n",
    "    text = a['text'],\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5,\n",
    "        )\n",
    "    ),\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "data = [trace0]\n",
    "layout = go.Layout(\n",
    "    title='POLIO Report ALL YEARS(1960-2011)',\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='text-hover-bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<plotly.tools.PlotlyDisplay object>"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MEASLES\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "a = get_data('MEASLES')\n",
    "trace0 = go.Bar(\n",
    "    x = a['state_name'],\n",
    "    y = a['cases'],\n",
    "    text = a['text'],\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5,\n",
    "        )\n",
    "    ),\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "data = [trace0]\n",
    "layout = go.Layout(\n",
    "    title='MEASLES Report ALL YEARS(1960-2011)',\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='text-hover-bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RUBELLA\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "a = get_data('RUBELLA')\n",
    "trace0 = go.Bar(\n",
    "    x = a['state_name'],\n",
    "    y = a['cases'],\n",
    "    text = a['text'],\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5,\n",
    "        )\n",
    "    ),\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "data = [trace0]\n",
    "layout = go.Layout(\n",
    "    title='RUBELLA Report ALL YEARS(1960-2011)',\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='text-hover-bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "title = 'ALL CONTAGIOUS DISEASES YEAR WISE ALL OVER USA'\n",
    "\n",
    "labels = ['HEPATITIS A','PERTUSSIS','MEASLES','MUMPS']\n",
    "\n",
    "colors = ['rgb(67,67,67)', 'rgb(115,115,115)', 'rgb(49,130,189)', 'rgb(189,189,189)']\n",
    "mode_size = [12, 8, 8, 8]\n",
    "\n",
    "line_size = [4, 2, 2, 2]\n",
    "\n",
    "x_data = [list(get_data_with_year(f)['Year']) for f in diseases_list]\n",
    "   \n",
    "y_data = [get_data_with_year(f)['cases'] for f in diseases_list]\n",
    "\n",
    "traces = []\n",
    "\n",
    "for i in range(0, 4):\n",
    "    traces.append(go.Scatter(\n",
    "        x=x_data[i],\n",
    "        y=y_data[i],\n",
    "        mode='lines',\n",
    "        line=dict(color=colors[i], width=line_size[i]),\n",
    "        connectgaps=True,\n",
    "    ))\n",
    "\n",
    "    traces.append(go.Scatter(\n",
    "        x=[x_data[i][0], x_data[i][len(x_data)-1]],\n",
    "        y=[y_data[i][0], y_data[i][len(y_data)-1]],\n",
    "        mode='markers',\n",
    "        marker=dict(color=colors[i], size=mode_size[i])\n",
    "    ))\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        showline=True,\n",
    "        showgrid=False,\n",
    "        showticklabels=True,\n",
    "        linecolor='rgb(204, 204, 204)',\n",
    "        linewidth=2,\n",
    "        ticks='outside',\n",
    "        tickcolor='rgb(204, 204, 204)',\n",
    "        tickwidth=2,\n",
    "        ticklen=5,\n",
    "        tickfont=dict(\n",
    "            family='Arial',\n",
    "            size=10,\n",
    "            color='rgb(82, 82, 82)',\n",
    "        ),\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False,\n",
    "        showticklabels=False,\n",
    "    ),\n",
    "    autosize=False,\n",
    "    margin=dict(\n",
    "        autoexpand=False,\n",
    "        l=100,\n",
    "        r=20,\n",
    "        t=110,\n",
    "    ),\n",
    "    showlegend=False\n",
    ")\n",
    "\n",
    "annotations = []\n",
    "\n",
    "# Adding labels\n",
    "for y_trace, label, color in zip(y_data, labels, colors):\n",
    "    # labeling the left_side of the plot\n",
    "    annotations.append(dict(xref='paper', x=0.05, y=y_trace[0],\n",
    "                                  xanchor='right', yanchor='middle',\n",
    "                                  text=label + '{}'.format(y_trace[0]),\n",
    "                                  font=dict(family='Arial',\n",
    "                                            size=11),\n",
    "                                  showarrow=False))\n",
    "    # labeling the right_side of the plot\n",
    "    annotations.append(dict(xref='paper', x=0.95, y=y_trace[len(y_data)-1],\n",
    "                                  xanchor='left', yanchor='middle',\n",
    "                                  text='{}'.format(y_trace[len(x_data)-1]),\n",
    "                                  font=dict(family='Arial',\n",
    "                                            size=11),\n",
    "                                  showarrow=False))\n",
    "# Title\n",
    "annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n",
    "                              xanchor='left', yanchor='bottom',\n",
    "                              text=title,\n",
    "                              font=dict(family='Arial',\n",
    "                                        size=30,\n",
    "                                        color='rgb(37,37,37)'),\n",
    "                              showarrow=False))\n",
    "# Source\n",
    "annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.1,\n",
    "                              xanchor='center', yanchor='top',\n",
    "                              text='Source: PewResearch Center & ' +\n",
    "                                   'Storytelling with data',\n",
    "                              font=dict(family='Arial',\n",
    "                                        size=11,\n",
    "                                        color='rgb(150,150,150)'),\n",
    "                              showarrow=False))\n",
    "\n",
    "layout['annotations'] = annotations\n",
    "\n",
    "fig = go.Figure(data=traces, layout=layout)\n",
    "py.iplot(fig, filename='news-source')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No of Cases of each disease VS Population Over the years\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_data_with_year(disease_name):\n",
    "    pd.options.display.float_format = '{:,.4f}'.format\n",
    "    pop = pd.read_csv('Population_statistics.csv')\n",
    "    the_ds_data = file.loc[file['disease'] == disease_name]\n",
    "    tf = the_ds_data.filter(['Year','cases'], axis=1)\n",
    "    tf.cases = tf.cases.astype('int32')\n",
    "    the_ds_data_2 = tf.groupby(['Year']).sum().reset_index()\n",
    "    pop.columns=['Year','population']\n",
    "    result = pd.merge(the_ds_data_2,pop,on = ['Year'])\n",
    "    result['cases_per_pop'] = (result['cases']/result['population'])*100\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~Ubaid01/4.embed\" height=\"525px\" width=\"100%\"></iframe>"
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      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "title = 'CASES PER POPULATION EACH YEAR '\n",
    "\n",
    "labels = diseases_list\n",
    "colors = ['rgb(67,67,67)', 'rgb(115,115,115)', 'rgb(49,130,189)', 'rgb(189,189,189)', 'rgb(24,67,178)', 'rgb(34,67,89)']\n",
    "mode_size = [12, 8, 8, 8, 8, 8]\n",
    "\n",
    "line_size = [4, 2, 2, 2, 2, 2]\n",
    "\n",
    "x_data = [list(get_data_with_year(f)['Year']) for f in diseases_list]\n",
    "   \n",
    "y_data = [list(np.around(get_data_with_year(f)['cases_per_pop'],4)) for f in diseases_list]\n",
    "\n",
    "traces = []\n",
    "\n",
    "for i in range(0, 6):\n",
    "    traces.append(go.Scatter(\n",
    "        x=x_data[i],\n",
    "        y=y_data[i],\n",
    "        mode='lines',\n",
    "        line=dict(color=colors[i], width=line_size[i]),\n",
    "        connectgaps=True,\n",
    "    ))\n",
    "\n",
    "    traces.append(go.Scatter(\n",
    "        x=[x_data[i][0], x_data[i][len(x_data)-1]],\n",
    "        y=[y_data[i][0], y_data[i][len(y_data)-1]],\n",
    "        mode='markers',\n",
    "        marker=dict(color=colors[i], size=mode_size[i])\n",
    "    ))\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        showline=True,\n",
    "        showgrid=False,\n",
    "        showticklabels=True,\n",
    "        linecolor='rgb(204, 204, 204)',\n",
    "        linewidth=2,\n",
    "        ticks='outside',\n",
    "        tickcolor='rgb(204, 204, 204)',\n",
    "        tickwidth=2,\n",
    "        ticklen=5,\n",
    "        tickfont=dict(\n",
    "            family='Arial',\n",
    "            size=10,\n",
    "            color='rgb(82, 82, 82)',\n",
    "        ),\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False,\n",
    "        showticklabels=False,\n",
    "    ),\n",
    "    autosize=False,\n",
    "    margin=dict(\n",
    "        autoexpand=False,\n",
    "        l=100,\n",
    "        r=20,\n",
    "        t=110,\n",
    "    ),\n",
    "    showlegend=False\n",
    ")\n",
    "\n",
    "annotations = []\n",
    "\n",
    "# Adding labels\n",
    "for (y_trace, label, color) in (zip(y_data, labels, colors)):\n",
    "    # labeling the left_side of the plot\n",
    "    annotations.append(dict(xref='paper', x=0.05, y=y_trace[0],\n",
    "                                  xanchor='right', yanchor='middle',\n",
    "                                  text=label + '{}%'.format(y_trace[0]),\n",
    "                                  font=dict(family='Arial',\n",
    "                                            size=11),\n",
    "                                  showarrow=False))\n",
    "    # labeling the right_side of the plot\n",
    "    annotations.append(dict(xref='paper', x=0.95, y=y_trace[len(y_data)-1],\n",
    "                                  xanchor='left', yanchor='middle',\n",
    "                                  text='{}%'.format(y_trace[len(x_data)-1]),\n",
    "                                  font=dict(family='Arial',\n",
    "                                            size=11),\n",
    "                                  showarrow=False))\n",
    "# Title\n",
    "annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n",
    "                              xanchor='left', yanchor='bottom',\n",
    "                              text=title,\n",
    "                              font=dict(family='Arial',\n",
    "                                        size=30,\n",
    "                                        color='rgb(37,37,37)'),\n",
    "                              showarrow=False))\n",
    "# Source\n",
    "annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.1,\n",
    "                              xanchor='center', yanchor='top',\n",
    "                              text='Source: PewResearch Center & ' +\n",
    "                                   'Storytelling with data',\n",
    "                              font=dict(family='Arial',\n",
    "                                        size=11,\n",
    "                                        color='rgb(150,150,150)'),\n",
    "                              showarrow=False))\n",
    "\n",
    "layout['annotations'] = annotations\n",
    "\n",
    "fig = go.Figure(data=traces, layout=layout)\n",
    "py.iplot(fig, filename='news-source')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disease varying with temperature\n",
    "- Plotting avg temp with cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_modified_file = pd.read_csv('for_temp_visualization.csv')\n",
    "temp_file = pd.read_excel('TemperaturesByState.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>AverageTemperatureUncertainty</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01</td>\n",
       "      <td>43.3148</td>\n",
       "      <td>0.5370</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-02</td>\n",
       "      <td>43.7414</td>\n",
       "      <td>0.5200</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-03</td>\n",
       "      <td>52.1834</td>\n",
       "      <td>0.3720</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-04</td>\n",
       "      <td>62.7854</td>\n",
       "      <td>0.3130</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-05</td>\n",
       "      <td>70.3598</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index       Date  AverageTemperature  AverageTemperatureUncertainty  \\\n",
       "0      0 1900-01-01             43.3148                         0.5370   \n",
       "1      0 1900-01-02             43.7414                         0.5200   \n",
       "2      0 1900-01-03             52.1834                         0.3720   \n",
       "3      0 1900-01-04             62.7854                         0.3130   \n",
       "4      0 1900-01-05             70.3598                         0.3500   \n",
       "\n",
       "     State        Country  \n",
       "0  Alabama  United States  \n",
       "1  Alabama  United States  \n",
       "2  Alabama  United States  \n",
       "3  Alabama  United States  \n",
       "4  Alabama  United States  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_file.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>week</th>\n",
       "      <th>state</th>\n",
       "      <th>state_name</th>\n",
       "      <th>disease</th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "      <th>MonthName</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.2350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>RUBELLA</td>\n",
       "      <td>7</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.2350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>11</td>\n",
       "      <td>0.5800</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>196601</td>\n",
       "      <td>AZ</td>\n",
       "      <td>arizona</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>37.9076</td>\n",
       "      <td>0.2850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    week state state_name      disease  cases  \\\n",
       "0           0  196601    AL    alabama  HEPATITIS A      5   \n",
       "1           1  196601    AL    alabama      RUBELLA      7   \n",
       "2           2  196601    AR   arkansas  HEPATITIS A     11   \n",
       "3           3  196601    AR   arkansas      MEASLES      2   \n",
       "4           4  196601    AZ    arizona  HEPATITIS A      6   \n",
       "\n",
       "   incidence_per_capita LastDayWeek  MonthMax  Year MonthName   t_avg  \\\n",
       "0                0.1400  1966-01-01         1  1966   January 40.4798   \n",
       "1                0.2000  1966-01-01         1  1966   January 40.4798   \n",
       "2                0.5800  1966-01-01         1  1966   January 35.2310   \n",
       "3                0.1100  1966-01-01         1  1966   January 35.2310   \n",
       "4                0.3700  1966-01-01         1  1966   January 37.9076   \n",
       "\n",
       "   tavg_anomaly  \n",
       "0        0.2350  \n",
       "1        0.2350  \n",
       "2        0.2250  \n",
       "3        0.2250  \n",
       "4        0.2850  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_modified_file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def state_temp_vary(state_name):\n",
    "    the_temp_data = temp_file.loc[temp_file['State'] == state_name]\n",
    "    the_temp_data.head()\n",
    "    \n",
    "    tf = the_temp_data.filter(['State','AverageTemperature','Date'], axis=1)\n",
    "    tf['Year'] = pd.DatetimeIndex(tf['Date']).year()\n",
    "    tf.AverageTemperature = tf.AverageTemperature.astype('float32')\n",
    "    tf = tf.groupby(['Year']).mean().reset_index()\n",
    "    return tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'Int64Index' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-fa5e62cc9b11>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#view temperature graph variation of each state year wise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mstate_temp_vary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Alabama'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-27-25a3ef434576>\u001b[0m in \u001b[0;36mstate_temp_vary\u001b[1;34m(state_name)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mtf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mthe_temp_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'State'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'AverageTemperature'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mtf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Year'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDatetimeIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAverageTemperature\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAverageTemperature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'float32'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mtf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Year'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'Int64Index' object is not callable"
     ]
    }
   ],
   "source": [
    "#view temperature graph variation of each state year wise\n",
    "state_temp_vary('Alabama')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>week</th>\n",
       "      <th>state</th>\n",
       "      <th>state_name</th>\n",
       "      <th>disease</th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MonthMax</th>\n",
       "      <th>Year</th>\n",
       "      <th>MonthName</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.2350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>196601</td>\n",
       "      <td>AL</td>\n",
       "      <td>alabama</td>\n",
       "      <td>RUBELLA</td>\n",
       "      <td>7</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.2350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>11</td>\n",
       "      <td>0.5800</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>196601</td>\n",
       "      <td>AR</td>\n",
       "      <td>arkansas</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>196601</td>\n",
       "      <td>AZ</td>\n",
       "      <td>arizona</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>January</td>\n",
       "      <td>37.9076</td>\n",
       "      <td>0.2850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    week state state_name      disease  cases  \\\n",
       "0           0  196601    AL    alabama  HEPATITIS A      5   \n",
       "1           1  196601    AL    alabama      RUBELLA      7   \n",
       "2           2  196601    AR   arkansas  HEPATITIS A     11   \n",
       "3           3  196601    AR   arkansas      MEASLES      2   \n",
       "4           4  196601    AZ    arizona  HEPATITIS A      6   \n",
       "\n",
       "   incidence_per_capita LastDayWeek  MonthMax  Year MonthName   t_avg  \\\n",
       "0                0.1400  1966-01-01         1  1966   January 40.4798   \n",
       "1                0.2000  1966-01-01         1  1966   January 40.4798   \n",
       "2                0.5800  1966-01-01         1  1966   January 35.2310   \n",
       "3                0.1100  1966-01-01         1  1966   January 35.2310   \n",
       "4                0.3700  1966-01-01         1  1966   January 37.9076   \n",
       "\n",
       "   tavg_anomaly  \n",
       "0        0.2350  \n",
       "1        0.2350  \n",
       "2        0.2250  \n",
       "3        0.2250  \n",
       "4        0.2850  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_file = pd.read_csv('for_temp_visualization.csv')\n",
    "correlation_file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "      <th>Year</th>\n",
       "      <th>disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>40.4798</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>1966</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>0.5800</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.2250</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>35.2310</td>\n",
       "      <td>0.2250</td>\n",
       "      <td>1966</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>37.9076</td>\n",
       "      <td>0.2850</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cases  incidence_per_capita   t_avg  tavg_anomaly  Year  disease\n",
       "0      5                0.1400 40.4798        0.2350  1966        0\n",
       "1      7                0.2000 40.4798        0.2350  1966        5\n",
       "2     11                0.5800 35.2310        0.2250  1966        0\n",
       "3      2                0.1100 35.2310        0.2250  1966        1\n",
       "4      6                0.3700 37.9076        0.2850  1966        0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "filtered_table = correlation_file.filter(['cases','incidence_per_capita','t_avg','tavg_anomaly','Year','disease'])\n",
    "#clases = {'HEPATITIS A':1,'RUBELLA':2,'MEASLES':3,'MUMPS':4,'PERTUSSIS':5,'POLIO':6}\n",
    "#filtered_table['disease'] = filtered_table['disease'].astype('str')\n",
    "#filtered_table['disease'] = filtered_table['disease'].map(clases) \n",
    "le = LabelEncoder()\n",
    "filtered_table['disease'] = le.fit_transform(filtered_table['disease'])\n",
    "filtered_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "      <th>Year</th>\n",
       "      <th>disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cases</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.5751</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>-0.2592</td>\n",
       "      <td>-0.0776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <td>0.5751</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.0425</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>-0.2568</td>\n",
       "      <td>-0.0660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_avg</th>\n",
       "      <td>0.0163</td>\n",
       "      <td>-0.0425</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>-0.0135</td>\n",
       "      <td>0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tavg_anomaly</th>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.2185</td>\n",
       "      <td>-0.0112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>-0.2592</td>\n",
       "      <td>-0.2568</td>\n",
       "      <td>-0.0135</td>\n",
       "      <td>-0.2185</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.0291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disease</th>\n",
       "      <td>-0.0776</td>\n",
       "      <td>-0.0660</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>-0.0112</td>\n",
       "      <td>-0.0291</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       cases  incidence_per_capita   t_avg  tavg_anomaly  \\\n",
       "cases                 1.0000                0.5751  0.0163        0.0213   \n",
       "incidence_per_capita  0.5751                1.0000 -0.0425        0.0599   \n",
       "t_avg                 0.0163               -0.0425  1.0000        0.1140   \n",
       "tavg_anomaly          0.0213                0.0599  0.1140        1.0000   \n",
       "Year                 -0.2592               -0.2568 -0.0135       -0.2185   \n",
       "disease              -0.0776               -0.0660  0.0050       -0.0112   \n",
       "\n",
       "                        Year  disease  \n",
       "cases                -0.2592  -0.0776  \n",
       "incidence_per_capita -0.2568  -0.0660  \n",
       "t_avg                -0.0135   0.0050  \n",
       "tavg_anomaly         -0.2185  -0.0112  \n",
       "Year                  1.0000  -0.0291  \n",
       "disease              -0.0291   1.0000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_table.corr(method='pearson')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases</th>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <th>t_avg</th>\n",
       "      <th>tavg_anomaly</th>\n",
       "      <th>Year</th>\n",
       "      <th>disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cases</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7905</td>\n",
       "      <td>-0.0035</td>\n",
       "      <td>0.0704</td>\n",
       "      <td>-0.4531</td>\n",
       "      <td>-0.1201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incidence_per_capita</th>\n",
       "      <td>0.7905</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.0820</td>\n",
       "      <td>0.1388</td>\n",
       "      <td>-0.5491</td>\n",
       "      <td>-0.1601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_avg</th>\n",
       "      <td>-0.0035</td>\n",
       "      <td>-0.0820</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.1310</td>\n",
       "      <td>-0.0158</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tavg_anomaly</th>\n",
       "      <td>0.0704</td>\n",
       "      <td>0.1388</td>\n",
       "      <td>0.1310</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.1948</td>\n",
       "      <td>-0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>-0.4531</td>\n",
       "      <td>-0.5491</td>\n",
       "      <td>-0.0158</td>\n",
       "      <td>-0.1948</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.0176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disease</th>\n",
       "      <td>-0.1201</td>\n",
       "      <td>-0.1601</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>-0.0225</td>\n",
       "      <td>-0.0176</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       cases  incidence_per_capita   t_avg  tavg_anomaly  \\\n",
       "cases                 1.0000                0.7905 -0.0035        0.0704   \n",
       "incidence_per_capita  0.7905                1.0000 -0.0820        0.1388   \n",
       "t_avg                -0.0035               -0.0820  1.0000        0.1310   \n",
       "tavg_anomaly          0.0704                0.1388  0.1310        1.0000   \n",
       "Year                 -0.4531               -0.5491 -0.0158       -0.1948   \n",
       "disease              -0.1201               -0.1601  0.0028       -0.0225   \n",
       "\n",
       "                        Year  disease  \n",
       "cases                -0.4531  -0.1201  \n",
       "incidence_per_capita -0.5491  -0.1601  \n",
       "t_avg                -0.0158   0.0028  \n",
       "tavg_anomaly         -0.1948  -0.0225  \n",
       "Year                  1.0000  -0.0176  \n",
       "disease              -0.0176   1.0000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_table.corr(method='spearman')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d2a60822e8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ki4tVvXr1cu2HMOqhirNP2C7Bo/nUvUnp2bm2y/BoIXXr2C4BAPA/KvoKTPn5+XI6naX3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\nCgAAAGsIowAAALDm/wGRqegX0vqOFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d2a58b0438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "corr = filtered_table.corr()\n",
    "\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>cases</th>\n",
       "      <th>population</th>\n",
       "      <th>cases_per_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1966</td>\n",
       "      <td>32416</td>\n",
       "      <td>196560338</td>\n",
       "      <td>0.0165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1967</td>\n",
       "      <td>38280</td>\n",
       "      <td>198712056</td>\n",
       "      <td>0.0193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1968</td>\n",
       "      <td>45204</td>\n",
       "      <td>200706052</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
       "      <td>46012</td>\n",
       "      <td>202676946</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970</td>\n",
       "      <td>55188</td>\n",
       "      <td>205052174</td>\n",
       "      <td>0.0269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  cases  population  cases_per_pop\n",
       "0  1966  32416   196560338         0.0165\n",
       "1  1967  38280   198712056         0.0193\n",
       "2  1968  45204   200706052         0.0225\n",
       "3  1969  46012   202676946         0.0227\n",
       "4  1970  55188   205052174         0.0269"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_population = get_data_with_year('HEPATITIS A')\n",
    "correlation_population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>cases</th>\n",
       "      <th>population</th>\n",
       "      <th>cases_per_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.8989</td>\n",
       "      <td>0.9971</td>\n",
       "      <td>-0.9027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cases</th>\n",
       "      <td>-0.8989</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.8841</td>\n",
       "      <td>0.9956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.9971</td>\n",
       "      <td>-0.8841</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.8830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cases_per_pop</th>\n",
       "      <td>-0.9027</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>-0.8830</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Year   cases  population  cases_per_pop\n",
       "Year           1.0000 -0.8989      0.9971        -0.9027\n",
       "cases         -0.8989  1.0000     -0.8841         0.9956\n",
       "population     0.9971 -0.8841      1.0000        -0.8830\n",
       "cases_per_pop -0.9027  0.9956     -0.8830         1.0000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_population.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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