healthDataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"PYSPARK_PYTHON\"]=\"/usr/bin/python3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkContext, SparkConf\n",
    "conf = SparkConf().setAppName(\"Health\").setMaster(\"local[4]\")  #local or hive\n",
    "sc = SparkContext.getOrCreate(conf=conf) # if already running then get else get"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession   # if only SparkSession is needed\n",
    "spark = SparkSession.builder.appName('Health').getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = spark.read.csv(\"file:///home/piyush/Downloads/health.csv\", header = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[summary: string, S.No.: string, State/ UT: string, Total Population in Rural Areas: string, Estimated Tribal Population in Rural Areas: string, Sub Centres - Required: string, Sub Centres - In Position: string, Sub Centres - Shortfall: string, Primary Health Centres (PHCs) - Required: string, Primary Health Centres (PHCs) - In Position: string, Primary Health Centres (PHCs) - Shortfall: string, Community Health Centres (CHCs) - Required: string, Community Health Centres (CHCs) - In Position: string, Community Health Centres (CHCs) - Shortfall: string]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()  #df.collect(), df.take(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Renaming Columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming column names a/c to my wishes!\n",
    "oldColumns = df.schema.names\n",
    "newColumns = [\"sn\", \"state\", \"population\", \"tribal_population\", \"sub_centres_req\",\"sub_centres_present\", \\\n",
    "              \"sub_centres_short\", \"phc_req\", \"phc_present\", \"phc_short\", \"chc_req\", \"chc_present\" \\\n",
    "             ,\"chc_short\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sn: string (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- population: string (nullable = true)\n",
      " |-- tribal_population: string (nullable = true)\n",
      " |-- sub_centres_req: string (nullable = true)\n",
      " |-- sub_centres_present: string (nullable = true)\n",
      " |-- sub_centres_short: string (nullable = true)\n",
      " |-- phc_req: string (nullable = true)\n",
      " |-- phc_present: string (nullable = true)\n",
      " |-- phc_short: string (nullable = true)\n",
      " |-- chc_req: string (nullable = true)\n",
      " |-- chc_present: string (nullable = true)\n",
      " |-- chc_short: string (nullable = true)\n",
      "\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "| sn|            state|population|tribal_population|sub_centres_req|sub_centres_present|sub_centres_short|phc_req|phc_present|phc_short|chc_req|chc_present|chc_short|\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "|  1|   Andhra Pradesh|56,311,788|        4,723,312|         11,892|             12,522|          Surplus|  1,955|      1,624|      331|    488|        281|      207|\n",
      "|  2|Arunachal Pradesh| 1,069,165|          744,996|            313|                286|               27|     48|         97|  Surplus|     12|         48|  Surplus|\n",
      "|  3|            Assam|26,780,516|        3,638,841|          5,841|              4,604|            1,237|    953|        938|       15|    238|        108|      130|\n",
      "|  4|            Bihar|92,075,028|          889,200|         18,533|              9,696|            8,837|  3,083|      1,863|    1,220|    770|         70|      700|\n",
      "|  5|     Chhattisgarh|19,603,658|        7,377,058|          4,904|              5,076|          Surplus|    776|        741|       35|    194|        148|       46|\n",
      "|  6|              Goa|   551,414|              155|            110|                175|          Surplus|     18|         19|  Surplus|      4|          5|  Surplus|\n",
      "|  7|          Gujarat|34,670,817|        7,500,509|          7,934|              7,274|              660|  1,280|      1,123|      157|    320|        305|       15|\n",
      "|  8|          Haryana|16,531,493|                0|          3,306|              2,508|              798|    551|        444|      107|    137|        107|       30|\n",
      "|  9| Himachal Pradesh| 6,167,805|          266,701|          1,269|              2,067|          Surplus|    210|        453|  Surplus|     52|         76|  Surplus|\n",
      "| 10|  Jammu & Kashmir| 9,134,820|        1,262,945|          1,995|              1,907|               88|    325|        397|  Surplus|     81|         83|  Surplus|\n",
      "| 11|        Jharkhand|25,036,946|        7,767,269|          6,043|              3,958|            2,085|    964|        330|      634|    241|        188|       53|\n",
      "| 12|        Karnataka|37,552,529|        3,158,558|          7,931|              8,870|          Surplus|  1,304|      2,310|  Surplus|    326|        180|      146|\n",
      "| 13|           Kerala|17,455,506|          259,169|          3,525|              4,575|          Surplus|    586|        809|  Surplus|    146|        224|  Surplus|\n",
      "| 14|   Madhya Pradesh|52,537,899|       13,550,258|         12,314|              8,869|            3,445|  1,977|      1,156|      821|    494|        333|      161|\n",
      "| 15|      Maharashtra|61,545,441|        8,260,697|         13,410|             10,580|            2,830|  2,189|      1,809|      380|    547|        365|      182|\n",
      "| 16|          Manipur| 1,899,624|          842,941|            492|                420|               72|     77|         80|  Surplus|     19|         16|        3|\n",
      "| 17|        Meghalaya| 2,368,971|        2,137,702|            758|                405|              353|    114|        109|        5|     28|         29|  Surplus|\n",
      "| 18|          Mizoram|   529,037|          509,316|            173|                370|          Surplus|     26|         57|  Surplus|      6|          9|  Surplus|\n",
      "| 19|         Nagaland| 1,406,861|        1,318,698|            457|                396|               61|     68|        126|  Surplus|     17|         21|  Surplus|\n",
      "| 20|           Odisha|34,951,234|        8,599,849|          8,136|              6,688|            1,448|  1,308|      1,228|       80|    327|        377|  Surplus|\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Part of Renaming collumns\n",
    "from functools import reduce\n",
    "df = reduce(lambda data, idx: data.withColumnRenamed(oldColumns[idx], newColumns[idx]), range(len(oldColumns)),df)\n",
    "df.printSchema()\n",
    "df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To use regex replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To use regex replace\n",
    "import re\n",
    "from pyspark.sql.functions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# df1 = df after (1) removing ',' (2) Converting Surplus to 0  (3) Typing casting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df\n",
    "from pyspark.sql.types import *\n",
    "colnames = df1.schema.names\n",
    "for i in colnames:\n",
    "    colname = i\n",
    "    df1 = df1.withColumn(colname, regexp_replace(colname, \",\", \"\"))  #Removing spaces from data in columns\n",
    "    df1 = df1.withColumn(colname, regexp_replace(colname, \"Surplus\", \"0\")) # Modyfying unwanted data\n",
    "    if(colname!='state'):\n",
    "        df1 = df1.withColumn(colname, col(colname).cast(IntegerType())) #Type casting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "| sn|            state|population|tribal_population|sub_centres_req|sub_centres_present|sub_centres_short|phc_req|phc_present|phc_short|chc_req|chc_present|chc_short|\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "|  1|   Andhra Pradesh|56,311,788|        4,723,312|         11,892|             12,522|          Surplus|  1,955|      1,624|      331|    488|        281|      207|\n",
      "|  2|Arunachal Pradesh| 1,069,165|          744,996|            313|                286|               27|     48|         97|  Surplus|     12|         48|  Surplus|\n",
      "|  3|            Assam|26,780,516|        3,638,841|          5,841|              4,604|            1,237|    953|        938|       15|    238|        108|      130|\n",
      "|  4|            Bihar|92,075,028|          889,200|         18,533|              9,696|            8,837|  3,083|      1,863|    1,220|    770|         70|      700|\n",
      "|  5|     Chhattisgarh|19,603,658|        7,377,058|          4,904|              5,076|          Surplus|    776|        741|       35|    194|        148|       46|\n",
      "|  6|              Goa|   551,414|              155|            110|                175|          Surplus|     18|         19|  Surplus|      4|          5|  Surplus|\n",
      "|  7|          Gujarat|34,670,817|        7,500,509|          7,934|              7,274|              660|  1,280|      1,123|      157|    320|        305|       15|\n",
      "|  8|          Haryana|16,531,493|                0|          3,306|              2,508|              798|    551|        444|      107|    137|        107|       30|\n",
      "|  9| Himachal Pradesh| 6,167,805|          266,701|          1,269|              2,067|          Surplus|    210|        453|  Surplus|     52|         76|  Surplus|\n",
      "| 10|  Jammu & Kashmir| 9,134,820|        1,262,945|          1,995|              1,907|               88|    325|        397|  Surplus|     81|         83|  Surplus|\n",
      "| 11|        Jharkhand|25,036,946|        7,767,269|          6,043|              3,958|            2,085|    964|        330|      634|    241|        188|       53|\n",
      "| 12|        Karnataka|37,552,529|        3,158,558|          7,931|              8,870|          Surplus|  1,304|      2,310|  Surplus|    326|        180|      146|\n",
      "| 13|           Kerala|17,455,506|          259,169|          3,525|              4,575|          Surplus|    586|        809|  Surplus|    146|        224|  Surplus|\n",
      "| 14|   Madhya Pradesh|52,537,899|       13,550,258|         12,314|              8,869|            3,445|  1,977|      1,156|      821|    494|        333|      161|\n",
      "| 15|      Maharashtra|61,545,441|        8,260,697|         13,410|             10,580|            2,830|  2,189|      1,809|      380|    547|        365|      182|\n",
      "| 16|          Manipur| 1,899,624|          842,941|            492|                420|               72|     77|         80|  Surplus|     19|         16|        3|\n",
      "| 17|        Meghalaya| 2,368,971|        2,137,702|            758|                405|              353|    114|        109|        5|     28|         29|  Surplus|\n",
      "| 18|          Mizoram|   529,037|          509,316|            173|                370|          Surplus|     26|         57|  Surplus|      6|          9|  Surplus|\n",
      "| 19|         Nagaland| 1,406,861|        1,318,698|            457|                396|               61|     68|        126|  Surplus|     17|         21|  Surplus|\n",
      "| 20|           Odisha|34,951,234|        8,599,849|          8,136|              6,688|            1,448|  1,308|      1,228|       80|    327|        377|  Surplus|\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "only showing top 20 rows\n",
      "\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "| sn|            state|population|tribal_population|sub_centres_req|sub_centres_present|sub_centres_short|phc_req|phc_present|phc_short|chc_req|chc_present|chc_short|\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "|  1|   Andhra Pradesh|  56311788|          4723312|          11892|              12522|                0|   1955|       1624|      331|    488|        281|      207|\n",
      "|  2|Arunachal Pradesh|   1069165|           744996|            313|                286|               27|     48|         97|        0|     12|         48|        0|\n",
      "|  3|            Assam|  26780516|          3638841|           5841|               4604|             1237|    953|        938|       15|    238|        108|      130|\n",
      "|  4|            Bihar|  92075028|           889200|          18533|               9696|             8837|   3083|       1863|     1220|    770|         70|      700|\n",
      "|  5|     Chhattisgarh|  19603658|          7377058|           4904|               5076|                0|    776|        741|       35|    194|        148|       46|\n",
      "|  6|              Goa|    551414|              155|            110|                175|                0|     18|         19|        0|      4|          5|        0|\n",
      "|  7|          Gujarat|  34670817|          7500509|           7934|               7274|              660|   1280|       1123|      157|    320|        305|       15|\n",
      "|  8|          Haryana|  16531493|                0|           3306|               2508|              798|    551|        444|      107|    137|        107|       30|\n",
      "|  9| Himachal Pradesh|   6167805|           266701|           1269|               2067|                0|    210|        453|        0|     52|         76|        0|\n",
      "| 10|  Jammu & Kashmir|   9134820|          1262945|           1995|               1907|               88|    325|        397|        0|     81|         83|        0|\n",
      "| 11|        Jharkhand|  25036946|          7767269|           6043|               3958|             2085|    964|        330|      634|    241|        188|       53|\n",
      "| 12|        Karnataka|  37552529|          3158558|           7931|               8870|                0|   1304|       2310|        0|    326|        180|      146|\n",
      "| 13|           Kerala|  17455506|           259169|           3525|               4575|                0|    586|        809|        0|    146|        224|        0|\n",
      "| 14|   Madhya Pradesh|  52537899|         13550258|          12314|               8869|             3445|   1977|       1156|      821|    494|        333|      161|\n",
      "| 15|      Maharashtra|  61545441|          8260697|          13410|              10580|             2830|   2189|       1809|      380|    547|        365|      182|\n",
      "| 16|          Manipur|   1899624|           842941|            492|                420|               72|     77|         80|        0|     19|         16|        3|\n",
      "| 17|        Meghalaya|   2368971|          2137702|            758|                405|              353|    114|        109|        5|     28|         29|        0|\n",
      "| 18|          Mizoram|    529037|           509316|            173|                370|                0|     26|         57|        0|      6|          9|        0|\n",
      "| 19|         Nagaland|   1406861|          1318698|            457|                396|               61|     68|        126|        0|     17|         21|        0|\n",
      "| 20|           Odisha|  34951234|          8599849|           8136|               6688|             1448|   1308|       1228|       80|    327|        377|        0|\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "only showing top 20 rows\n",
      "\n",
      "root\n",
      " |-- sn: integer (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- population: integer (nullable = true)\n",
      " |-- tribal_population: integer (nullable = true)\n",
      " |-- sub_centres_req: integer (nullable = true)\n",
      " |-- sub_centres_present: integer (nullable = true)\n",
      " |-- sub_centres_short: integer (nullable = true)\n",
      " |-- phc_req: integer (nullable = true)\n",
      " |-- phc_present: integer (nullable = true)\n",
      " |-- phc_short: integer (nullable = true)\n",
      " |-- chc_req: integer (nullable = true)\n",
      " |-- chc_present: integer (nullable = true)\n",
      " |-- chc_short: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.show()  # Orignal data frame\n",
    "df1.show() # Data frame after : 1) Type casting 2) filtering Data\n",
    "df1.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating Table from dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.registerTempTable(\"df1_table\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-----------------+---------+---------+\n",
      "|               state|sub_centres_short|phc_short|chc_short|\n",
      "+--------------------+-----------------+---------+---------+\n",
      "|               Assam|             1237|       15|      130|\n",
      "|               Bihar|             8837|     1220|      700|\n",
      "|             Gujarat|              660|      157|       15|\n",
      "|             Haryana|              798|      107|       30|\n",
      "|           Jharkhand|             2085|      634|       53|\n",
      "|      Madhya Pradesh|             3445|      821|      161|\n",
      "|         Maharashtra|             2830|      380|      182|\n",
      "|              Punjab|              513|      131|       15|\n",
      "|             Tripura|               41|       27|       15|\n",
      "|       Uttar Pradesh|            10516|     1480|      778|\n",
      "|         West Bengal|             2680|     1239|      189|\n",
      "|Dadra & Nagar Haveli|                4|        2|        1|\n",
      "|               Delhi|               42|        5|        3|\n",
      "+--------------------+-----------------+---------+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#List of states that have  shortage of sub_centres , phc_centres and chc centres \n",
    "check = spark.sql(\"select state,sub_centres_short,phc_short,chc_short from df1_table where sub_centres_short>0 and phc_short>0 and chc_short>0 \")\n",
    "(check).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query 2,3,4,5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+-----------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "| sn|      state|population|tribal_population|sub_centres_req|sub_centres_present|sub_centres_short|phc_req|phc_present|phc_short|chc_req|chc_present|chc_short|\n",
      "+---+-----------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "| 34|Lakshadweep|     14121|            13503|              4|                 14|                0|      0|          4|        0|      0|          3|        0|\n",
      "+---+-----------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# States with no shortage of sub_centres,phc,chc\n",
    "check2 = spark.sql(\"select state from df1_table where sub_centres_short==0 and phc_short==0 and chc_short==0 \")\n",
    "#Top 5 most populated states\n",
    "check3 = spark.sql(\"select state,population from df1_table order by population desc limit 5\")\n",
    "#State with maximum tribal population\n",
    "check4 = spark.sql(\"select state,tribal_population from df1_table order by tribal_population desc limit 1\")\n",
    "\n",
    "#all details of a Lakshadweep\n",
    "check5 = spark.sql(\"select *  from df1_table where state='Lakshadweep'\")\n",
    "check5.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+---------+---------+\n",
      "|sub_centres_short|phc_short|chc_short|\n",
      "+-----------------+---------+---------+\n",
      "|             1237|       15|      130|\n",
      "|             8837|     1220|      700|\n",
      "|              660|      157|       15|\n",
      "|              798|      107|       30|\n",
      "|             2085|      634|       53|\n",
      "|             3445|      821|      161|\n",
      "|             2830|      380|      182|\n",
      "|              513|      131|       15|\n",
      "|               41|       27|       15|\n",
      "|            10516|     1480|      778|\n",
      "|             2680|     1239|      189|\n",
      "|                4|        2|        1|\n",
      "|               42|        5|        3|\n",
      "+-----------------+---------+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "check = check['sub_centres_short','phc_short','chc_short'] #filtering data\n",
    "check.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Plotting Graph \n",
    "import pandas\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Increasing size of graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current_size: [10.0, 9.0]\n"
     ]
    }
   ],
   "source": [
    "# Get current size\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "print(\"Current_size:\", fig_size)\n",
    "#Set figure width to 10 and height to 6\n",
    "fig_size[0] = 10\n",
    "fig_size[1] = 9\n",
    "plt.rcParams[\"figure.figsize\"]=  fig_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Removing states with small values of (sub_centres_short) (To make graph look better)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "| sn|         state|population|tribal_population|sub_centres_req|sub_centres_present|sub_centres_short|phc_req|phc_present|phc_short|chc_req|chc_present|chc_short|\n",
      "+---+--------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "|  3|         Assam|  26780516|          3638841|           5841|               4604|             1237|    953|        938|       15|    238|        108|      130|\n",
      "|  4|         Bihar|  92075028|           889200|          18533|               9696|             8837|   3083|       1863|     1220|    770|         70|      700|\n",
      "|  8|       Haryana|  16531493|                0|           3306|               2508|              798|    551|        444|      107|    137|        107|       30|\n",
      "| 11|     Jharkhand|  25036946|          7767269|           6043|               3958|             2085|    964|        330|      634|    241|        188|       53|\n",
      "| 14|Madhya Pradesh|  52537899|         13550258|          12314|               8869|             3445|   1977|       1156|      821|    494|        333|      161|\n",
      "| 15|   Maharashtra|  61545441|          8260697|          13410|              10580|             2830|   2189|       1809|      380|    547|        365|      182|\n",
      "| 20|        Odisha|  34951234|          8599849|           8136|               6688|             1448|   1308|       1228|       80|    327|        377|        0|\n",
      "| 27| Uttar Pradesh| 155111022|           112898|          31037|              20521|            10516|   5172|       3692|     1480|   1293|        515|      778|\n",
      "| 28|   West Bengal|  62213676|          4456160|          13036|              10356|             2680|   2148|        909|     1239|    537|        348|      189|\n",
      "+---+--------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df1_mod = df1.filter(\"sub_centres_short>=798\")\n",
    "df1_mod.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting Dataframe (df1) to Pandas for plotting graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Assam',\n",
       " 'Bihar',\n",
       " 'Haryana',\n",
       " 'Jharkhand',\n",
       " 'Madhya Pradesh',\n",
       " 'Maharashtra',\n",
       " 'Odisha',\n",
       " 'Uttar Pradesh',\n",
       " 'West Bengal']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_pd = df1_mod.toPandas()\n",
    "# df2_pd[\"state\"]\n",
    "label = [i for i in df1_pd[\"state\"]]\n",
    "label\n",
    "#OR\n",
    "# label=[]\n",
    "# for i in df2[\"state\"]:\n",
    "#     label.append(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting Bar graph of : df1 vs state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## numpy.arange(index)\n",
    "**1)returns Array of evenly spaced values from 0 to stop or from start to stop\n",
    "2)used here to give x-cordinates to graph i.e 0, 1, 2 till all elements in the list\n",
    "3)So in following line of plt.bar(index,df1_pd[\"sub_centres_short\"] )\n",
    "4)first we give stream of abcissa, then set of ordinate.So we have passed an array of int first then we pass an array of values of \"sub_centres_short \n",
    "5)We have stored array of states in 'label' variable  \n",
    "6)plt.xlabel() : simply for labely x-axis\n",
    "7)plt.ylabel(): for labeling y-axis\n",
    "9)xticks(): For giving specs of bars of bar-graph\n",
    "            index: array storing all s-co-ordinates\n",
    "            label: array of states\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=np.arange(len(label)) \n",
    "plt.bar(index, df1_pd[\"sub_centres_short\"])  # Or change to df2\n",
    "plt.xlabel(\"States\",size=15)\n",
    "plt.ylabel(\"Sub Centres - Shortfall\", size=15)\n",
    "plt.xticks(index, label, fontsize=15, rotation=30)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First analysis of sub_centre values\n",
    "# df2 = df with modified '_short' column i.e contains -ve , +ve values \n",
    "## It contains both surplus, and shortage values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "| sn|            state|population|tribal_population|sub_centres_req|sub_centres_present|sub_centres_short|phc_req|phc_present|phc_short|chc_req|chc_present|chc_short|\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "|  1|   Andhra Pradesh|  56311788|          4723312|          11892|              12522|              630|   1955|       1624|     -331|    488|        281|     -207|\n",
      "|  2|Arunachal Pradesh|   1069165|           744996|            313|                286|              -27|     48|         97|       49|     12|         48|       36|\n",
      "|  3|            Assam|  26780516|          3638841|           5841|               4604|            -1237|    953|        938|      -15|    238|        108|     -130|\n",
      "|  4|            Bihar|  92075028|           889200|          18533|               9696|            -8837|   3083|       1863|    -1220|    770|         70|     -700|\n",
      "|  5|     Chhattisgarh|  19603658|          7377058|           4904|               5076|              172|    776|        741|      -35|    194|        148|      -46|\n",
      "|  6|              Goa|    551414|              155|            110|                175|               65|     18|         19|        1|      4|          5|        1|\n",
      "|  7|          Gujarat|  34670817|          7500509|           7934|               7274|             -660|   1280|       1123|     -157|    320|        305|      -15|\n",
      "|  8|          Haryana|  16531493|                0|           3306|               2508|             -798|    551|        444|     -107|    137|        107|      -30|\n",
      "|  9| Himachal Pradesh|   6167805|           266701|           1269|               2067|              798|    210|        453|      243|     52|         76|       24|\n",
      "| 10|  Jammu & Kashmir|   9134820|          1262945|           1995|               1907|              -88|    325|        397|       72|     81|         83|        2|\n",
      "| 11|        Jharkhand|  25036946|          7767269|           6043|               3958|            -2085|    964|        330|     -634|    241|        188|      -53|\n",
      "| 12|        Karnataka|  37552529|          3158558|           7931|               8870|              939|   1304|       2310|     1006|    326|        180|     -146|\n",
      "| 13|           Kerala|  17455506|           259169|           3525|               4575|             1050|    586|        809|      223|    146|        224|       78|\n",
      "| 14|   Madhya Pradesh|  52537899|         13550258|          12314|               8869|            -3445|   1977|       1156|     -821|    494|        333|     -161|\n",
      "| 15|      Maharashtra|  61545441|          8260697|          13410|              10580|            -2830|   2189|       1809|     -380|    547|        365|     -182|\n",
      "| 16|          Manipur|   1899624|           842941|            492|                420|              -72|     77|         80|        3|     19|         16|       -3|\n",
      "| 17|        Meghalaya|   2368971|          2137702|            758|                405|             -353|    114|        109|       -5|     28|         29|        1|\n",
      "| 18|          Mizoram|    529037|           509316|            173|                370|              197|     26|         57|       31|      6|          9|        3|\n",
      "| 19|         Nagaland|   1406861|          1318698|            457|                396|              -61|     68|        126|       58|     17|         21|        4|\n",
      "| 20|           Odisha|  34951234|          8599849|           8136|               6688|            -1448|   1308|       1228|      -80|    327|        377|       50|\n",
      "+---+-----------------+----------+-----------------+---------------+-------------------+-----------------+-------+-----------+---------+-------+-----------+---------+\n",
      "only showing top 20 rows\n",
      "\n",
      "root\n",
      " |-- sn: integer (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- population: integer (nullable = true)\n",
      " |-- tribal_population: integer (nullable = true)\n",
      " |-- sub_centres_req: integer (nullable = true)\n",
      " |-- sub_centres_present: integer (nullable = true)\n",
      " |-- sub_centres_short: integer (nullable = true)\n",
      " |-- phc_req: integer (nullable = true)\n",
      " |-- phc_present: integer (nullable = true)\n",
      " |-- phc_short: integer (nullable = true)\n",
      " |-- chc_req: integer (nullable = true)\n",
      " |-- chc_present: integer (nullable = true)\n",
      " |-- chc_short: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2 = df1\n",
    "df2 = df2.withColumn('sub_centres_short', col('sub_centres_present')-col('sub_centres_req'))\n",
    "df2 = df2.withColumn('phc_short', col('phc_present')-col('phc_req'))\n",
    "df2 = df2.withColumn('chc_short', col('chc_present')-col('chc_req'))\n",
    "df2.show()\n",
    "df2.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**breaking df2 into two data frames: (1)surplus (2)shortage Also removing negative sign from shortage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sn: integer (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- population: integer (nullable = true)\n",
      " |-- tribal_population: integer (nullable = true)\n",
      " |-- sub_centres_req: integer (nullable = true)\n",
      " |-- sub_centres_present: integer (nullable = true)\n",
      " |-- sub_centres_short: integer (nullable = true)\n",
      " |-- phc_req: integer (nullable = true)\n",
      " |-- phc_present: integer (nullable = true)\n",
      " |-- phc_short: integer (nullable = true)\n",
      " |-- chc_req: integer (nullable = true)\n",
      " |-- chc_present: integer (nullable = true)\n",
      " |-- chc_short: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2_surplus = df2.filter(\"sub_centres_short>=0\")\n",
    "df2_surplus.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The plot showed a problem that the Y axis is not ordered. :\n",
    "    SOLUTION: Since data type was strings , so type cast it to int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sn: integer (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- population: integer (nullable = true)\n",
      " |-- tribal_population: integer (nullable = true)\n",
      " |-- sub_centres_req: integer (nullable = true)\n",
      " |-- sub_centres_present: integer (nullable = true)\n",
      " |-- sub_centres_short: integer (nullable = true)\n",
      " |-- phc_req: integer (nullable = true)\n",
      " |-- phc_present: integer (nullable = true)\n",
      " |-- phc_short: integer (nullable = true)\n",
      " |-- chc_req: integer (nullable = true)\n",
      " |-- chc_present: integer (nullable = true)\n",
      " |-- chc_short: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    " #Now removing negative sign also\n",
    "    \n",
    "df2_shortage= df2.filter(\"sub_centres_short<0\") #Initialising \n",
    "\n",
    "from pyspark.sql.types import *\n",
    "colnames = df2_shortage.schema.names\n",
    "for i in colnames:\n",
    "    colname = i\n",
    "    if(colname=='sub_centres_short'):\n",
    "        df2_shortage = df2_shortage.withColumn(colname, regexp_replace(colname, \"-\", \"\"))  #Removing spaces from data in columns\n",
    "        df2_shortage = df2_shortage.withColumn(colname, col(colname).cast(IntegerType())) #Type casting\n",
    "df2_shortage.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting Dataframe (df2) to Pandas for plotting graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_surplus_pd  = df2_surplus.toPandas()\n",
    "\n",
    "# df2_shortage = df2_shortage[\"sub_centres_short\"]\n",
    "df2_shortage_pd = df2_shortage.toPandas()\n",
    "\n",
    "label1 = [i for i in df2_surplus_pd[\"state\"]]\n",
    "label2 = [i for i in df2_shortage_pd[\"state\"]]\n",
    "\n",
    "# label1\n",
    "# label2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ploting graph of df2_surplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=np.arange(len(label1)) \n",
    "plt.bar(index, df2_surplus_pd[\"sub_centres_short\"])  # Or change to df2\n",
    "plt.xlabel(\"States\",size=15)\n",
    "plt.ylabel(\"Sub Centres - Surplus\", size=15)\n",
    "plt.xticks(index, label1, fontsize=15, rotation=90)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ploting graph of df2_shortage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=np.arange(len(label2)) \n",
    "plt.bar(index, df2_shortage_pd[\"sub_centres_short\"])  # Or change to df2\n",
    "plt.xlabel(\"States\",size=15)\n",
    "plt.ylabel(\"Sub Centres - Shortage\", size=15)\n",
    "plt.xticks(index, label2, fontsize=15, rotation=90)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now analysis of phc values\n",
    "# df2 = df with modified '_short' column i.e contains -ve , +ve values \n",
    "## It contains both surplus, and shortage values\n",
    "\n",
    "# REPEATING ABOVE STEPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**breaking df2 into two data frames: (1)surplus (2)shortage Also removing negative sign from shortage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sn: integer (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- population: integer (nullable = true)\n",
      " |-- tribal_population: integer (nullable = true)\n",
      " |-- sub_centres_req: integer (nullable = true)\n",
      " |-- sub_centres_present: integer (nullable = true)\n",
      " |-- sub_centres_short: integer (nullable = true)\n",
      " |-- phc_req: integer (nullable = true)\n",
      " |-- phc_present: integer (nullable = true)\n",
      " |-- phc_short: integer (nullable = true)\n",
      " |-- chc_req: integer (nullable = true)\n",
      " |-- chc_present: integer (nullable = true)\n",
      " |-- chc_short: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2_surplus = df2.filter(\"phc_short>=0\")\n",
    "df2_surplus.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The plot showed a problem that the Y axis is not ordered. :\n",
    "    SOLUTION: Since data type was strings , so type cast it to int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sn: integer (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- population: integer (nullable = true)\n",
      " |-- tribal_population: integer (nullable = true)\n",
      " |-- sub_centres_req: integer (nullable = true)\n",
      " |-- sub_centres_present: integer (nullable = true)\n",
      " |-- sub_centres_short: integer (nullable = true)\n",
      " |-- phc_req: integer (nullable = true)\n",
      " |-- phc_present: integer (nullable = true)\n",
      " |-- phc_short: integer (nullable = true)\n",
      " |-- chc_req: integer (nullable = true)\n",
      " |-- chc_present: integer (nullable = true)\n",
      " |-- chc_short: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    " #Now removing negative sign also\n",
    "    \n",
    "df2_shortage= df2.filter(\"phc_short<0\") #Initialising \n",
    "\n",
    "from pyspark.sql.types import *\n",
    "colnames = df2_shortage.schema.names\n",
    "for i in colnames:\n",
    "    colname = i\n",
    "    if(colname=='phc_short'):\n",
    "        df2_shortage = df2_shortage.withColumn(colname, regexp_replace(colname, \"-\", \"\"))  #Removing spaces from data in columns\n",
    "        df2_shortage = df2_shortage.withColumn(colname, col(colname).cast(IntegerType())) #Type casting\n",
    "df2_shortage.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting Dataframe (df2) to Pandas for plotting graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_surplus_pd  = df2_surplus.toPandas()\n",
    "\n",
    "# df2_shortage = df2_shortage[\"sub_centres_short\"]\n",
    "df2_shortage_pd = df2_shortage.toPandas()\n",
    "\n",
    "label1 = [i for i in df2_surplus_pd[\"state\"]]\n",
    "label2 = [i for i in df2_shortage_pd[\"state\"]]\n",
    "\n",
    "# label1\n",
    "# label2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ploting graph of df2_surplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=np.arange(len(label1)) \n",
    "plt.bar(index, df2_surplus_pd[\"phc_short\"])  # Or change to df2\n",
    "plt.xlabel(\"States\",size=15)\n",
    "plt.ylabel(\"PHC Centres - Surplus\", size=15)\n",
    "plt.xticks(index, label1, fontsize=15, rotation=90)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ploting graph of df2_shortage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=np.arange(len(label2)) \n",
    "plt.bar(index, df2_shortage_pd[\"phc_short\"])  # Or change to df2\n",
    "plt.xlabel(\"States\",size=15)\n",
    "plt.ylabel(\"PHC - Shortage\", size=15)\n",
    "plt.xticks(index, label2, fontsize=15, rotation=90)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now analysis of chc values\n",
    "# df2 = df with modified '_short' column i.e contains -ve , +ve values \n",
    "## It contains both surplus, and shortage values\n",
    "\n",
    "# REPEATING ABOVE STEPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**breaking df2 into two data frames: (1)surplus (2)shortage Also removing negative sign from shortage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sn: integer (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- population: integer (nullable = true)\n",
      " |-- tribal_population: integer (nullable = true)\n",
      " |-- sub_centres_req: integer (nullable = true)\n",
      " |-- sub_centres_present: integer (nullable = true)\n",
      " |-- sub_centres_short: integer (nullable = true)\n",
      " |-- phc_req: integer (nullable = true)\n",
      " |-- phc_present: integer (nullable = true)\n",
      " |-- phc_short: integer (nullable = true)\n",
      " |-- chc_req: integer (nullable = true)\n",
      " |-- chc_present: integer (nullable = true)\n",
      " |-- chc_short: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2_surplus = df2.filter(\"chc_short>=0\")\n",
    "df2_surplus.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The plot showed a problem that the Y axis is not ordered. :\n",
    "    SOLUTION: Since data type was strings , so type cast it to int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sn: integer (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- population: integer (nullable = true)\n",
      " |-- tribal_population: integer (nullable = true)\n",
      " |-- sub_centres_req: integer (nullable = true)\n",
      " |-- sub_centres_present: integer (nullable = true)\n",
      " |-- sub_centres_short: integer (nullable = true)\n",
      " |-- phc_req: integer (nullable = true)\n",
      " |-- phc_present: integer (nullable = true)\n",
      " |-- phc_short: integer (nullable = true)\n",
      " |-- chc_req: integer (nullable = true)\n",
      " |-- chc_present: integer (nullable = true)\n",
      " |-- chc_short: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    " #Now removing negative sign also\n",
    "    \n",
    "df2_shortage= df2.filter(\"chc_short<0\") #Initialising \n",
    "\n",
    "from pyspark.sql.types import *\n",
    "colnames = df2_shortage.schema.names\n",
    "for i in colnames:\n",
    "    colname = i\n",
    "    if(colname=='chc_short'):\n",
    "        df2_shortage = df2_shortage.withColumn(colname, regexp_replace(colname, \"-\", \"\"))  #Removing spaces from data in columns\n",
    "        df2_shortage = df2_shortage.withColumn(colname, col(colname).cast(IntegerType())) #Type casting\n",
    "df2_shortage.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting Dataframe (df2) to Pandas for plotting graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_surplus_pd  = df2_surplus.toPandas()\n",
    "\n",
    "# df2_shortage = df2_shortage[\"sub_centres_short\"]\n",
    "df2_shortage_pd = df2_shortage.toPandas()\n",
    "\n",
    "label1 = [i for i in df2_surplus_pd[\"state\"]]\n",
    "label2 = [i for i in df2_shortage_pd[\"state\"]]\n",
    "\n",
    "# label1\n",
    "# label2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ploting graph of df2_surplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=np.arange(len(label1)) \n",
    "plt.bar(index, df2_surplus_pd[\"chc_short\"])  # Or change to df2\n",
    "plt.xlabel(\"States\",size=15)\n",
    "plt.ylabel(\"CHC Centres - Surplus\", size=15)\n",
    "plt.xticks(index, label1, fontsize=15, rotation=90)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ploting graph of df2_shortage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eOsy+2cDPu2zHgcDdwK+Aa4DVwEGNnRExnTIeb3HTfRYD+0bENk3bDqasjXdVl+2RJEmadDqd2fqfwPsj4i7gf6ptERF7A+8GFnRaYUR8Gfge8BNKz9zB1c8RmfkgcF9ELATmRcRKNixQvBlwZtOhzgKOAM6PiJOAnYH5wGmuYSdJkqaiToPdScCOwKcoS5FA6VmbBpydmWeMos6bgDdVxwvKlSNel5mfaSqzkBLkjgW2B5YA+2TmXY0CmbmyCpaLKGvcrQJOp4Q7SZKkKaejYJeZCbw9Ik4DXkgJW78HLs/Mn42mwsw8Djiug/pOqH5GKreUcmkySZKkKW9Uiwxn5i3ALX1qiyRJkrrQUbCLiOeNsPtBymSHmzLz/p60SpIkSaPWaY/dlWxYkgTK2LhsKXNfRHwcOLLNVSMkSZLUZ50GuxdSZsZ+nXIFihXATGAusB9wFPBkyti5NZQFjSVJkjRAnQa7w4FPZeb8lu0XR8R84A2ZuX9EbA68AYOdJEnSwHW6QPE/AFcPs+87wAuq378FPKbbRkmSJGn0Og12vwdeOsy+l1b7oVzO6w/dNkqSJEmj1+mp2JOBMyJiFmUx4NYxdu+oyr0AuK63TZQkSVInOl2geFFE/JpyJYiPUq44sR74IfDyzPxKVfREYF0/GipJkqSRdbxAcRXevhIR04AdgLtblzXJzLt73D5JkiR1aFRXngCowtxdmywoSZKkgeo42FXj614DPAnYsnV/Zr6iZ62SJEnSqHV6SbGnUZYyuZUS7H4CbAvMAm4Hft6n9kmSJKlDnS53cgrwRWAPyuXE3pyZOwPPoVxa7OT+NE+SJEmd6jTY7Ql8Hniwur0lQGZeA3wAWNj7pkmSJGk0Og12CazLzAR+C+zUtO824Im9bpgkSZJGp9NgtxTYpfr9WuBdEfHEiNgJeDdwSz8aJ0mSpM51Oiv2HDb00h0HfBNYVt2+Bziwx+2SJEnSKHV65YnPNP1+Y0Q8GXgmsBXw3cz8bZ/aJ0mSpA51dCo2Il4XEds3bmfmmsy8JDO/BvwpIl7XtxZKkiSpI52OsfsvNoyxa/WEar8kSZLGUafBLkbYtz2wugdtkSRJUheGHWMXEXOBuU2b5kXEipZiWwLPBa7rQ9skSZI0CiNNnngU8FdNt3cBHt1SZh1lhuyHetwuSZIkjdKwwS4zPwZ8DCAirgDempnLhisvSZKk8bXJMXYRsSXwOGBW31sjSZKkMdtksMvM+4Dt2HCdWEmSJE1Anc6K/Szwxn42RJIkSd3p9JJitwKviIjrgMXAXUA27c/M/I9eN06SJEmd6zTYnVr9+xjgaW32J2CwkyRJGkedXiu201O2kiRJGicGNkmSpJro9FQsEbEd8M/Ac4BHAr8Hvg2ck5mr+tM8SZIkdaqjHruI2AX4KbAAeARlMsUjqts/qfZLkiRpHHXaY3c6sAp4Rmb+urExIh4HfB04jYdeV1aSJEkD1ukYu78H3tcc6gCq2wuAF/S4XZIkSRqlToNdAtNGOEYOs0+SJEkD0mmwuwL4YETs1Lyxur0AuKzXDZMkSdLodDrG7l+Ay4GbI+IHlCtPPIqyWPFtwJH9aZ4kSZI61VGPXWYuB3YDjgBuAB4GLAUOB55c7ZckSdI46ngdu8xcB5xV/UiSJGmC6TjYNUTENODhrdszc21PWiRJkqQx6XSB4hkRsSgi7gDuB/7Y5keSJEnjqNMeu7OBlwAfp4ytW9e3FkmSJGlMOg12+wLvysyP97MxkiRJGrtO17G7B7i9nw2RJElSdzoNdqcCb4uITstLkiRpwIY9FRsRJ7ds+hvgpoi4AljVsi8z8z2jrTwiHgfcBDwC2CYz11TbAzgWeCuwA3AdcERm/qjl/rsDZwLPrNr0ceADmbl+tG2RJEma7EYaY3dQy+0Hq/L7tCmbwKiDHXAKsIYS7JodA8wDjgaWUa5scWlE7JGZdwJExBBwKWUyx1xgF0rP4mbA8WNoiyRJ0qQ2bLDLzCf0s+KIeB7wIuDDlIDX2L4lJdidmJmLqm3XAsspV7pohLbDgK2AAzJzNXBJRMwA5kfEydU2SZKkKWNcxsxVixyfCSwA7m7Z/SxgBnBeY0Nm3gNcAMxpKjcHuLglwJ1LCXvP70OzJUmSJrRhg11EPDYintlm+54R8eWIuCEiLo+Il42h3sMoV6/4f2327QasB25u2X5jta+53LLmApl5K7C2pZwkSdKUMFKP3QnAac0bIuKJwLeBF1KC1hDwpYjYu9MKI2J74IPAkZn5QJsiQ8CaNhMgVgLTI2KLpnKtkzga5YY6bY8kSVJdjBTsng18rmXbkZSetudm5oGZ+VTKKdJjRlHnCcB3M/Pro2ppD0TEoRGxJCKWrFixYtDVS5Ik9dVIwe6xlBmnzV4KXJuZP2na9gngKZ1UFhFPAd4ELIiI7SJiO2B6tXvbiNiK0uO2dTUOr9kQsDYzG5czWwls26aaoWrfRjLznMycnZmzZ86c2UmTJUmSJo2RljtZS5mIAEBEPAF4DCXINVsJbNdhfU8EHgZc22bf7cB/UnoJpwG7Uta4a2gdU7eMlrF0EbEjJSg+ZOydJEnSVDBSj92PgNc23X41Zb26C1vK7QL8psP6rgZe0PJzUrVvP8qyJ9cAq2laRy8ipgP7A4ubjrUY2DcitmnadjBwL3BVh+2RJEmqjZF67BYAV0TEj4HfAnsBV2Tm/7aUeznQuq2tzLwbuLJ5W0TMqn79dtOVJxYC8yJiJRsWKN6MskRKw1nAEcD5EXESsDMwHzjNNewkSdJUNNICxVdHxAsoS5NsR5n0cEpzmYiYSbkixSd73K6FlCB3LLA9sATYJzPvamrfymo27iLKBI5VwOmUcCdJkjTljNRjR2ZeTTl9Otz+FZTLeY1ZZn6SlmCYmUkJkids4r5LKT2JkiRJU964XHlCkiRJvWewkyRJqgmDnSRJUk0Y7CRJkmrCYCdJklQTow52EbFZRFweEU/sR4MkSZI0NmPpsQvg74FtNlFOkiRJA+SpWEmSpJow2EmSJNXEqINdZq4HXgDc1PvmSJIkaaxGvKTYcDLzql43RJIkSd3xVKwkSVJNGOwkSZJqwmAnSZJUEwY7SZKkmhg22EXElhHxjoh4xghlnlGV2aI/zZMkSVKnRpoV+zbg3cBuI5S5ETifcjWKM3rYLkmSJI3SSKdiDwHOzMxVwxXIzD8Ai4BX97phkiRJGp2Rgt1TgGs7OMZ3q7KSJEkaRyMFuxxYKyRJktS1kYLdz4Bnd3CMZ1dlJUmSNI5GCnafA94VEU8erkC171+A/+51wyRJkjQ6I82KPQOYC3wvIv4DuBi4lXKK9vHAvsBbgR8CZ/a5nZIkSdqEYYNdZq6LiH2AEygB7qim3QHcA5wNHJ+ZD/S1lZIkSdqkkXrsyMz7gKMi4njgacDjql2/BpZU+yVJkjQBjBjsGjLzXuDqPrdFkiRJXRg22EXEfqM5UGZ+vfvmSJIkaaxG6rG7kDJRIpq2td5u3j6th+2SJEnSKI0U7J7QpuzNwP7A9X1rkSRJksZkpFmxv2q+HRGNHrnftO6TJEnS+BtpgWJJkiRNIgY7SZKkmjDYSZIk1cRIy528rWXTZpTZrwdFxDNa9mVm/kevGydJkqTOjTQrdtEw29/TZlsCBjtJkqRxNNKsWE/TSpIkTSKGN0mSpJoYNthFxGMi4ssRse8IZfatyjyqP82TJElSp0bqsftXYGfgmyOU+SblChVH9bJRkiRJGr2Rgt1LgLMyM4crUO07G5jb64ZJkiRpdEYKdjsBSzs4xo3ArJ60RpIkSWM2UrC7F5jRwTG2rspKkiRpHI0U7H4AvLSDY8ytykqSJGkcjRTsPgq8OSJeP1yBiHgd8EaGX8xYkiRJAzLSAsVfjoh/B/4rIg4HvgHcSrnKxOOBfYHZwOmZ+ZVBNFaSJEnDG+mSYmTmURFxJfAvlOVPHl7tuh/4DjA3My/sawslSZLUkRGDHUBmXgBcEBGbA9tXm3+XmX/qa8skSZI0Kh1fUiwz/5SZd1U/Ywp1EXFgRFwTEb+LiPsi4qaIOD4itmgqExFxXETcFhH3RsS3ImLPNsfaPSIui4i1EXFHRCyIiGljaZckSVIdbLLHrse2By4HTgFWAX8HzAceDRxelTkGmAccDSwDjgQujYg9MvNOgIgYAi6lrLM3F9gFOJUSVI8f0GORJEmaUAYa7DLz7JZNV0TEDODtEfEOyhi+Y4ATM3MRQERcCyynBL9GaDsM2Ao4IDNXA5dUx5kfESdX2yRJkqaUjk/F9tHvgMap2GdRFkU+r7EzM+8BLgDmNN1nDnBxS4A7lxL2nt/X1kqSJE1Q4xLsImJaREyPiOcARwD/UV13djdgPXBzy11urPY17EY5TftnmXkrsLalnCRJ0pQx6DF2DfewYemUT1PG0wEMAWsyc31L+ZXA9IjYIjPXVeVWtTnuymqfJEnSlDNep2KfBTwXOIoy+WEgV66IiEMjYklELFmxYsUgqpQkSRqYcemxy8zGtWWvjoi7gU9FxKmUHretI2JaS6/dELC26q2jKrdtm0MPVfuGq/cc4ByA2bNnZ5cPQ5IkaUKZCJMnGiHvCZRxc9OAXVvKtI6pW0bLWLqI2BGY3lJOkiRpypgIwe7Z1b+/BK4BVgMHNXZGxHRgf2Bx030WA/tGxDZN2w4G7gWu6mtrJUmSJqiBnoqNiG9QFha+gTL79dmUcXZfyMxbqjILgXkRsZINCxRvBpzZdKizKLNpz4+Ik4CdKQsdn+YadpIkaaoa9Bi764A3ALOAPwG/AI6lBLWGhZQgdyzlShVLgH0y865GgcxcGRF7UyZdXECZIXs6JdxJkiRNSYO+8sQ8yuXCRiqTwAnVz0jllgJ79a51kiRJk9tEGGMnSZKkHjDYSZIk1YTBTpIkqSYMdpIkSTVhsJMkSaoJg50kSVJNGOwkSZJqwmAnSZJUEwY7SZKkmjDYSZIk1YTBTpIkqSYMdpIkSTVhsJMkSaoJg50kSVJNGOwkSZJqwmAnSZJUEwY7SZKkmjDYSZIk1YTBTpIkqSYMdpIkSTVhsJMkSaoJg50kSVJNGOwkSZJqwmAnSZJUEwY7SZKkmjDYSZIk1YTBTpIkqSYMdpIkSTVhsJMkSaoJg50kSVJNGOwkSZJqwmAnSZJUEwY7SZKkmjDYSZIk1YTBTpIkqSYMdpIkSTVhsJMkSaoJg50kSVJNGOwkSZJqwmAnSZJUEwY7SZKkmjDYSZIk1YTBTpIkqSYMdpIkSTVhsJMkSaoJg50kSVJNGOwkSZJqYqDBLiIOioivRcSvI2JNRHw/Il7ZptxbIuLmiLivKrN3mzKPi4ivRMQfI+LuiFgUEdMH80gkSZImns0HXN+RwC+BdwF3A/sBn4uIHTLzTIAq6J0FzAeuBt4IXBgRT8/M66syDwMuBtYBhwDbAadV/75mkA9oJLOOuajnx1y+8MU9P6YkSaqHQQe7/TPz7qbbl0fEYymB78xq23zgU5n5QYCIuAp4KnAMG0LbgcCTgV0z85dVuQeAcyPiA5l5c98fiSRJ0gQz0FOxLaGu4YfAYwEiYmfgScB5Tfd5EPgiMKfpPnOA6xqhrvI/lB68F/W42ZIkSZPCRJg88UzgZ9Xvu1X/LmspcyPwyIiY2VTuIWUycx1wS9MxJEmSppRxDXbVpIh/BE6tNg1V/65qKbqyZf9QmzKNckNttjfqOzQilkTEkhUrVoyt0ZIkSRPUuAW7iJgFfA74amZ+chB1ZuY5mTk7M2fPnDlz03eQJEmaRMYl2EXEI4HFwK+AVzftavTMbdtyl6GW/SvblGmUW9lmuyRJUu0NPNhVa81dCGwBvCQz1zbtboybax0ntxvw+8xc0VTuIWUiYgtgZzYenydJkjQlDHqB4s0pM1yfCLwoM3/bvD8zf0GZSHFQ0302q24vbiq6GHh6ROzUtO2lwMOBb/Sn9ZIkSRPboNex+yhlUeJ3AttHxPZN+36YmfdT1rH774hYDnwHeD0lCL6qqeyXgPcC50fEPMpp2dOBz7mGnSRJmqoGHez+ofr339vsewKwPDM/HxFbA+8B5gE3UE7ZXt8omJkPRMSLgEWUNe/uB84Fju5n4yVJkiaygQa7zJzVYbkA9iK5AAAgAElEQVSPAR/bRJnbKUulSJKkPur1JTK9PGb/TIQFiiVJktQDBjtJkqSaMNhJkiTVxKAnT0jSuHO8kKS6ssdOkiSpJgx2kiRJNWGwkyRJqgmDnSRJUk0Y7CRJkmrCYCdJklQTBjtJkqSaMNhJkiTVhMFOkiSpJgx2kiRJNWGwkyRJqgmDnSRJUk1sPt4NkKSGWcdc1PNjLl/44p4fU5ImKnvsJEmSasJgJ0mSVBMGO0mSpJow2EmSJNWEwU6SJKkmDHaSJEk1YbCTJEmqCYOdJElSTbhAsaSO9HrxYBcOlqTes8dOkiSpJgx2kiRJNWGwkyRJqgmDnSRJUk0Y7CRJkmrCYCdJklQTBjtJkqSaMNhJkiTVhAsU14ALx0qSJLDHTpIkqTYMdpIkSTVhsJMkSaoJg50kSVJNGOwkSZJqwmAnSZJUEwY7SZKkmjDYSZIk1YTBTpIkqSYMdpIkSTVhsJMkSaoJg50kSVJNDDzYRcSuEXF2RPwkItZHxJVtykREHBcRt0XEvRHxrYjYs0253SPisohYGxF3RMSCiJg2kAciSZI0wYxHj91TgP2Am4CfDVPmGGAecBKwP7AGuDQiHt0oEBFDwKVAAnOBBcBRwAf61nJJkqQJbDyC3QWZuWNmHgTc0LozIrakBLsTM3NRZl4KHEQJcIc3FT0M2Ao4IDMvycyzKKHuyIiY0fdHIUmSNMEMPNhl5oObKPIsYAZwXtN97gEuAOY0lZsDXJyZq5u2nUsJe8/vTWslSZImj4k4eWI3YD1wc8v2G6t9zeWWNRfIzFuBtS3lJEmSpoSJGOyGgDWZub5l+0pgekRs0VRuVZv7r6z2bSQiDo2IJRGxZMWKFT1rsCRJ0kQwEYNd32TmOZk5OzNnz5w5c7ybI0mS1FMTMditBLZus2zJELA2M9c1ldu2zf2Hqn2SJElTykQMdsuAacCuLdtbx9Qto2UsXUTsCExvKSdJkjQlTMRgdw2wmrLECQARMZ2ynt3ipnKLgX0jYpumbQcD9wJXDaCdkiRJE8rmg66wCmn7VTcfB8yIiAOr21/PzLURsRCYFxErKb1vR1JC6JlNhzoLOAI4PyJOAnYG5gOntSyBIkmSNCUMPNgBjwK+2LKtcfsJwHJgISXIHQtsDywB9snMuxp3yMyVEbE3sIiyxt0q4HRKuJMkSZpyBh7sMnM5EJsok8AJ1c9I5ZYCe/WscZIkSZPYRBxjJ0mSpDEYj1OxkqRJZtYxF/X0eMsXvrinx5NU2GMnSZJUEwY7SZKkmjDYSZIk1YTBTpIkqSYMdpIkSTVhsJMkSaoJg50kSVJNGOwkSZJqwmAnSZJUEwY7SZKkmjDYSZIk1YTBTpIkqSYMdpIkSTVhsJMkSaoJg50kSVJNGOwkSZJqwmAnSZJUEwY7SZKkmth8vBsgqTuzjrmop8dbvvDFPT2eJGlw7LGTJEmqCYOdJElSTRjsJEmSasJgJ0mSVBNOntCE0uuJAOBkAEnS1GGPnSRJUk0Y7CRJkmrCYCdJklQTBjtJkqSaMNhJkiTVhMFOkiSpJgx2kiRJNWGwkyRJqgkXKJb6xMWWJUmDZo+dJElSTRjsJEmSasJgJ0mSVBMGO0mSpJow2EmSJNWEwU6SJKkmDHaSJEk1YbCTJEmqCRco1pTU68WDXThY48WFsEfP17/qzB47SZKkmjDYSZIk1YTBTpIkqSYmdbCLiN0j4rKIWBsRd0TEgoiYNt7tkiRJGg+TdvJERAwBlwJLgbnALsCplLB6/Dg2TZIkaVxM2mAHHAZsBRyQmauBSyJiBjA/Ik6utkmSJE0ZkznYzQEubglw5wInAc8HLhiXVkkSLkMy1fn3n7jqvtzNZA52uwGXN2/IzFsjYm21z2AnSZOIYUjqXmTmeLdhTCLiAeDozPxIy/bbgU9n5nFt7nMocGh18y+Bm/re0M7sANxtPROujrrV42OZ2vX4WKZ2PXV6LIOqZ1CPpVM7ZebMTRWazD12o5aZ5wDnjHc7WkXEksycbT0Tq4661eNjmdr1+Fimdj11eiyDqmdQj6XXJvNyJyuBbdtsH6r2SZIkTSmTOdgto4yl+7OI2BGYXu2TJEmaUiZzsFsM7BsR2zRtOxi4F7hqfJo0ZoM6PVyneur0WAZVj49latfjY5na9dTpsQyqngk3dKsTk3nyxBBlceLrKUuc7AycBnwkM12gWJIkTTmTNthBuaQYsAh4JrAK+DgwPzPXj2vDJEmSxsGkDnaSJEnaYDKPsZMkSVKTKbWOnSRJnYiIjTo+MvPB8WiLNBoGuwGLiIcB7wQOAP4PsGVrmcx81KDbJYiITwAfzMxfttm3E/D+zHzT4Fu2aRHxINDxuIrMnNbH5vRU3V4zEfFw4F+BCzPzx+PdHm0QEe8E3gI8kfafj5PpdbMl8Dzav2YyM/9jDMc8eRTFMzPfM9o6qnp2B27JzPur3zdV0dKx1FNXjrEbsIhYBPwzcCFlVu+61jKZ+YEe1fVYYH/gcbR/YY/pRddSx5bA14APZ+aV3R6vg/qexPAf7l/v8tgPAs/IzO+12fc04HsTNRBFxOFsCHYPA44C1gBfBX4L/AUwF3gEcGrrpfgmskG+Zprq3Bx4PO2fZ11/iFTXtJ6TmX1fmikitgP2AB4D/Aa4PjNX9amuacDDW7dn5toeHPsVwMto/35GZv5dl8d/G7AQOB2YB5wCPAi8oiryb2MJQ+MhIp4DfBkY7vJTOZb3sojY6EvvCDIzdx5tHVU9f34v3sSX1mCMj6XO7LEbvIOAYzLz1H5WEhGHAJ+iPPFXsPGHYQJdB7vMvC8ink6fv8lW39rOBZ5CeUwbNaVHbRjuDWQPyv9jT1VvwE+i/QfVRzs9TmYuajrmacD/Agdl0ze3iDgG+CLwhG7a3E5EHEzp6RjusXTTozaQ1wz8uXfwDOD1tAkolV48z/4X+Fv6uOZmFU5PAN5OWbi9YW1EfBR4b2Y+0IN6ZgAfpvSoPor2r8+u/s8iYiHwbuA64Oe0Cfc98Fbg/ZS//zzgC5n5g4iYB3wdeOxYD1wFotH0qI8pEDU5A/gF8A/A0l78nQEys+fvHcN4AeVLXOP3vqkC/Rczc0X1+0jG1NM5aAa7wQvgJwOo5wTKN7bDMnN1n+v6GvCPwGV9rONsygftAQzTazMW1amXd1Y3E/ifiLi/pdiWlB6vT/aizqrev6D8f+1e1dv4MGx+8+842LV4HfDq5lAH5R0pIj4GfI4Nj7lrEfEq4BOU/5+9qt83A15KWYbo091WwWBeMwDvA14CvBn4LCUU3QO8BtgFeEeP6nk38LmIeIASGu6i5YO/B71cpwGHAguA8yk9t48CXg4cT3leH9FlHVBemy+hLDfVs9dmizdRguiJfTh2w87A9zNzffV32RYgM/8UEWdQHue8MR77yzz073sIJWxfwoa/yz6U59q5Y6yj2V8CB0zWU/3NPdkD6NVeBCyhfHFftImyCRjstJGPAa+kvKD7aXvgPwcQ6gAuBk6JiMcw/IdUV6dJgacCh2TmhV0ep9VSyptuAEcCV1BOVzVbR7lM3Xk9rPdU4A/AjsBtwP+l/L+9hhLMXtzFsacBT6b8XVo9hd7Phj8a+CDlNNahwEerno5tKM/zbgPKoF4zUE67zaf8rT9LOf3+feDTEfEpyunsbp/LUHrsoPSs/PswZbrtGXwtcFxmnta07ffACRFxHyXc9SLY7Qu8KzM/3oNjDecB4Pt9PD6U/5tHVL/fDvwN5f0AYAaw9VgPnJn/2vg9Io4DbgFenJn3NG3fmjLcoBfv2T8BHt2D42xSr846jJfM3Kzd75OZwW4AWrp37wReHRFXUD6oWse69Kqr93zg7+lvL1rDf1f/HlD9tOrFadJbaPPG0a3MvIQqMETEH4GPZ+ave11PG8+n9Jo1QmRk5q3Ah6vZeB+lfGCOxWer42xO6U1t9AjMpfTe/Gc3DW/jicB3qp6O9ZQPQTLzjxFxEmXM0r+N5oDj9JqBErR/Vj2W+4Chpn2fpfR2/nMP6nkTozg1N0YPAjcMs+/6HtZ/DyUI9dO/A/8UEZe09kT30LXA0yiXqzwXmF99OVkH/AtwZY/qeTtwaHOoA8jMNRHxb5QvMh/qso63Ap+MiOX96vHq51mHiFjB6E5dT5rJU4NgsBuMdt27j6d8uLfqVVfv4cB/RsTHgcvZ+MOwF71oDYMYd3EUcHJE/CAzf9GPCno9AH8TtgNWZOaDEbGaErwarqG78Y9HUno4FlAut9dwP+V00ru7OHY7q9kwHu3XlN7CK6vbQek9Hq3xeM1ACdrbVb//kjKr8NLq9i49qoPM/GSvjjWCzwD/RPue27ew4QtZt04F3hYR3+zXciCZeXIVepZFxFW0D/fdjhleQJmYBSVY7UD58rUV5TlwWJfHb5hBGdrRzqMZY89gmzD0CODyiFgH/LG1fA/CUD/POvw/+v/FZ1j9nKQ3CAa7ARin7t0nAX9HCV3tlujo1WQDMvNXvThOq4i4joe+uB9HeWNfTvug2tWsuKrOZ1LGVw13aqHrOiq/pMxShNKr8mrKaRgoM5l/P9YDZ+Y64F0R8UHgrygfFncCP83MMR93BNcBf00JEF8D3hcRf6L0dLwP+O5oDziOp0SuBJ4LXEDpOTklInalhOKDgc+PU7s60tLTuRw4MCJuYOOe220YZS/qCB5HOW15U9Wr2vPQFRGvpvSaPUgJPj2dDBYRQQn1twBk5r2Untle9M62uoDyvFoNfC0z10XEFpS/y0nV/rEYdBjq21mHzJzfkxaO0gAn6fWVy53UVET8sPr1OIaZRdbrQNbrJSIi4pOMrjv+jaOto6W+fSjjpy6jzCZbTPm2/mzKqaarerWOXUScCMzMzH+KiDlsWJbkAcr/4Xsys1cfvH0VEc8AdsrML1RLa3yK8m19M0roe1Vm3jKebexURDwa2CEzr69uvws4kPI8uARY0HoKbYz1bPJU01h6VKqlITrVk2UiOlgCY8zLXjTVcRvwLcpksI16n7pVzYZeC8ztd49MRGxLmWg0l/Ic+CMlaAclgL8+M//Qzzb0QjV0Zb/M/HZErAJe0xgDHRF7AV/NzG16WN8QZXWCHYHFmbmyWm5rXa96iiPi25QvP+9m+KWV+tKR0UsGuwGLiOcCj8zMr1a3d6AMoN6dEiiO6dESBPdQZkW1Ow3TU50sETEZ1hmKiGuB71C++T8AzK4mAexE6Y36cGZ2O8NzuLqfTplZvBVwSWYu7vJ4PV+cdJT1Pxx4eC8m7wzwNfMwSi/3LzPzjm6Pt4m65rNxsBsC9qacqvvEgIcGTGgR8QfK+1nfxgxHxC+AIzPzf/pVR0t9uwNPZ0OP+nVj+QI8ivp2A3ajTAjq+vkdET8BPpSZ50XEd4BbM/OV1b7TKX+vnXpQz+aU5XTeTnl/TODp1XvzRcCSzHx/t/VUda2hP5P0Bisz/RngD2WA7nubbn+WMi7hLOB3lPDQi3quAN4yoMf0QcoYi1dSTpW8lTLG4puUUxv7dXn8LSmnwv6xz4/jD5QP1gDWA89t2ncIcNN4P386fBzPqZ5TDw7zs77H9b1kE/vndXn8Qb1mNqueZ3uN498uKGsNHjfez6OJ9ENZQudDfa7j7ZRhA9uN9+PtwWM5Gzir6fbBwJ+q1/9q4Fk9qONEymQzgDmU3q3bKcNM1gP/2qPHchKwEngjMKt6DH9b7TsU+EEP/99+DBw43n+/bn/ssRuwiPg95dTUNyJiOnA38KbMPDci3kx5Q+96kHZEPJXS3X8Kw0+e6Ho1+Kqum4CTq/oeoHyb+n6171PAfZnZ1ViV6lTMW7OP36Qi4i7K6YRLIuJ24PisBrlHxH6URSwfMdIxxlDnwxl+Jf0xfXuPiB9QAsph9HBx0hHqu5cS7jbqTYmIU4DDM3OrLo4/kNdMVdf1lKD4uV4cb4xt2Bf4r8wc84K4Lcf7Pww/ZrRnpx37uexFlAWwF1J6aPsyGSwiPkP5Yrc1JeC1LtuUmfn6bupoqa+fV9H5FXBs43kcET+jPKZ3A2dSesD37qaONnXOplwZpCdnHZqO+xtgfmaeHeXKJs1nU/YGvpyZ2418lI7reiHls+zA7NMkvUFw8sTgbQHcV/3+bMrf4KLq9s/YMKC+W401nz41QplenR4dxBIRZwNHRMTFfQwqP6Ys7HkJ5QPk2Ij4NeWb6ALgp72qKMrl3s6hfNPdaDfdDdId9OKk7wO+GhEvysyrGxsj4mzKpJB/7PL4g3rNALwXOCkifpqZPft7j9LOlMfclWqpjvMo40Wh/XIUvRhj18/Fthsak1beRP8mg+0KNMZPbVP9tNbRtQEN0H8U5SwKEfFEymM7IDPvjIhzgC90efyNZOYSykK/vbYd1aSWNrag+6uaDHySXr8Z7AZvGfAiyuy7VwPX5obBwI+li9mQLQaxTlbDIJaIaFzvcnlEXEb7b9PdLnfwETYs3XIcZXZaY4zi7ZRvo73yccolpY6k96v1D2xxUoDMPCUitgIuqiagLKEstfESymn4b3VZxaBeM1AW7d0e+FEV6tsttt2L2dftLl20BWWpmFdTTsd260TKRJznAldTnr8rKctR7EUZOtEL/Vz2oqHvSypl5jP7XUelL1fRafF7Niyp8kLgzqwmBFHCZC8C/eNH2P0gsDp7s0D+9ZSJJpe22TcH+EGXx7+Bh77Gh1v7cdIw2A3eAuCL1SmkbSlP2IYXAT9se69RysGsk9VwJf1fIuLl1fGo6mrV9bVvm09/ZOavI+JplG+6WwHLsiwj0ivPpoyB7OXVLBr6vjhpq8xcUE3Y+Aalt/ipwAsz87oeHH4gr5nKDZQPkn5rt07f/ZQvEB8FejFxYj9KUG1c5eKO6u/xrYg4lXLFkFcMd+dR6Odi28DkmIk4Cv26ik6zxcCCqjf13Tz0qjl7UJbC6dZyNtF5EBG3Amdk5uld1PMh4MvVl8cvVnXuGREvo5wJemkXxyYz39DN/Scig92AZebXIuLJlBf3TzPzZ027r2Vw18TspfdSFvMkMz9SrQnVWCLiTMoHc1eyzxefrkLJT4AjMvMbVZ0J3NynKn8L3NunY19CuQ5l3xYnrca6tfoQpbfrAEovzQ2Nct2M5xzka2ZQb/I5mHX6/gK4rRoicQ/wyKZ9X6dcSq8X+rLYdkRMbzxvhnm+PUS3Y4YjYpPvU5n5vm7qqPTlKjotjqJc8eUwyjIxzbNGX0b5AtatV1EmNlxPWaZlBTCT8sVrD8pM1tmUheUZa7jLzK9GuR71yWw4Df9xymLor80BrPww2Th5osaqAcdvYfgBzV6GpUlE/JYyeeKbA6jrlZRZePv16HRF87Hns+k10rrqEarWSxuujmjdl5NguRuAiPgE8MHM3GhttmrZm/dnj9Yy7LeIWEYZQP+VakLNdzLzHdW+4yhfYro+Zd+vZS+iXJ7umZn5vU0834Dun2PVIP1W21LeO9cAa3oxoaUuA/SjXNXo3sZzqmXfmcC2mfm6iPgIMCcz/7IHdT6J0onwe8oqBV0HmIg4eTTlM7PXV+7pOYPdOIiIv6b0cs2mzIp6ZjXD5wTg6l7MJqq+4XyCMlP10Or3zSjd1quAT2dm1z1pg9bnmXenA49ufCj1U0R8kTIWaRvKIr7tVus/uN/tGKuIeAOjWzx6pEk8ndTX99dMVc+DwDMy83tt9j2NsgZYT0JqRDyKcjWFv6NMAPkN5bTpGZl5Vw+OfyawWWa+PSJeS5lI9V3KKd/nAaf24kMq+rTYdkS8HrgwM3/XyfOt2+fYCO14PmXdxDdXEwS6Pd51lP+XIcrpzL4N0K8majyNMv7xE9XkiV2Bu7LLhZ6jrC348szcaOxbNdb2S5m5bTXL+6uZOaZeyiiLHV/RixA3zPE3tcB2s8wuF9seBE/FDlj1xvc1yimKT/PQLvL7gXdQxkd062jK+nILKcHuo9UH4TaUU3U9WeoE/ryw6zspp+CGm77f7am/Qcy8uxV4RfXGu5j2EzR6tbDvDmyY6fUwyimMSWOQYzgH+JppGO4DZA/K6aauRcSzKadD/0R5PS6lnMI8DHhHRMzJzO90Wc17KKfkyczPRFl8tTFE4nDKIP6uZeaxTb8vrh5b14ttNwe1TT3foixi2xeZeVVEnEZ5f+lF4LqePo/jjIitKV/mD6QE7M0pp1/vpJwivRX41y6ruY8yVrjdpIZns2EmewDdXK3lUuDOiPgScG5mXtPFsTbS72E+48EeuwGLiB9RVhh/S/VmtI4Na/K8lLKoZC+6+9dQ1ha7MiIeAPbJzCurfS8DTs/MWd3WUx1vEWUQ64UMfxmWbk/9/TdlZtwrGGbmXXZ52arY9OWYchKdUhzUNW/7rt+vmYh4J+WLCcBOlA+/+1uKbUkZs/bJzHzzWOtqqvOHlBmq+2fTJcqqD+QLgRmZ+bfd1lMXEfGhzDx+mH1bUdYy26+P9b+I0gO1db/q6KVqSZP9gNdSrqZzHxteM2+gLB68R5d1fBA4lhJ4L+ChY+wOo6wH+b5q7OJzMnOvMdbzFMokvFdQ3s9uo0wGOTer9VL1UPbYDd5ubPim1JqqV/PQAc7dWM2Gy3v9mrKMwpXV7aAMcu+VgyiXdTq1h8dsNYiZd+N14fmeiode8/Y5tLnmbR/q7Od4zn6/ZpZSJhIEZfmZK9jwPGtYR1l2pVezmHejjLF6SE9GZq6JiH+jN8udDFT0YbHtJkdExNrM/HBLnVtT1jTsxaWr2r3+G0vQvI/y958sDgDemZlXRFnUt9mv6MH/V2bOi7J4+NGUHuDGmZQ7gaObJkt8gdJ7ONZ6bqD8/78vIvakhLyDgKOiXAbu3OFC/1j8//bOO1yuqurD7yIICQFBQBCQLiiCfojSBRGkGMBIIBRpUURALB9FLEgoIk1RUYooCMhHEZASaggdKRIJKCACKl2qlBAgCZDf98faw5xMZm6ZU2buvet9nnnuzJxz99535u5z1l57rd+qKuyjTMKwq57ncQHSZqyKu8iLYDLwcVyHbQI+Kd7Gb1Dj8VibojDKz+YtJfOuk6Rt8dG0NobajX86AjiBes3bQzR7zdub2my3KQ3xnBvTJJ4zZxelzhlJk/DtUMwLm58m6ek8bfaBv9Naa3AJ2jQiUgLQ5pLuMbMX6DkuTXgQ+mS8ykpbn6OVK7ZdYzRwhZm9WTMYzIvCX4tfGzbM2T74tnirz+tFZpfZyYWZLYfvNrSa+3llaEbg5faasQBe8is3kn5uZifgMXy1mrdPSpqVOacwXThJ9wL34uLxW+HhBN/HZX1y04Gwj1IIw656zsf1hf6OSzUAKGX7fBc4vaB+jqa+Khufnp+C33Ank78SRJbf4mKnkwpss5FHqVcYeAAXca3pQG1NgSK1VkEJJjNbEb94jABG4tsYC+Nz8mVc8LVdw+6j+IWulkk4ElwLLGXMHk5+YytL2fGcVc2Z3CED/eCbQC3u7VJJM5LHaxvge3h4QTuchIco1J73FmuzAB4PdwZeTqsdyhTbBiB5nsYAl5qXsLuU+vVmA0nPFtDN15nz85qOe7n/JKlxe74tUhLOLfiCZGV8UbwgXgf1KeCfBXQzGf8faiZrsh1+7SmEZMQ9Tr1qR2kkY35b3Gv3GVwyqsjyf0fj4Ra1sI+sYXcvvsXc9USMXcWki/cf8dXts7ix8hS+2rkW2EYllcxKfc+rAuQ1bHbl/GG4btKj+MW2WYZnrqSDsjLvGvrotQRTgRmRE3AjeyweWPwpvKTZDvjFZTu1Ke5rFde8LTues+o5Y2bb0XMiUBGVJ17AExtq7U/Da5SCGxONW7SlSROlrMVLJPWqFdfi91+lPLHtxr6+gM/R/+IhJptLernsfovEzG7Ajbo9mL3u6Xq4mPteSlqaOfrYAL8W/wnf1j8ZN1I+jBt2G7Z7fWnoZ0m8wkyzuSLlrwaEmb0XX/DsgC8+3sa3388HrpI0vYdf729f0/Fr2XU2Z13ajYBr2s3urZLw2FVMWvVtZV68eBPqmjzXpy2hMpkPWNbMHixg9dlMOX8ZfBXViHBvYds0ybxbj+ILTldVggk8u+6r1IP055H0DnCumS2Kb6Wu12bbldW8TZQaz1nlnEkezfH4Z1hWuSfomzetKm4B8pTTKkVsOy1CGnkb99BsDfwMWNfM118FetOXxyVCahI0d6uJrmEOVseFfWvblcMBJN1uZofjnu9chp2kW9N8OQa/Vhvuqb+TgirCpEXbefjC/nnmnCu5qwElXsA/q4nAOGBCY2xqgVQVKlUukuIxCB/4JD4m83pjvALBO/jNd9VOj7HbHsC/cTX1YfiFZM3MseOBCwrs6xV81QwevzOm4bt6PUfbo4B90/Ol8FqKs9LjCeCTBX9ul+HB0uB6X8/giRS745Iukzr93fbjb3kSz+br+FgK+nu2ww2hW4C7Gh8F9bETvhB6b8Fjn5WuV7P68HingP5G4lp/b6U2Z6afb+GhC/MX9Hf9F/hsev4ssFPm2KZ55n6L/kbgNZXnK7jdB/F4tIWLbLdJP7vjYsel9ZHp67j0nXw6cx/4BL5l/gQuUF76OPI+wmNXAdZzseQ5UJtBzA3sjJd4qnE8fvE9HPgx7p3KVWOvSnr5DIsqOF1VCSaAh6nHQN4D7G1mV+E3sj2A/7TbsKqteQu9x3P2Oy6lQ3MGPObs+oLa6ihleh/NrHHbdRngcXMNyKLEtqvWFzsJjzfcC7hY0itmthAe0/Uz/DozroB+/g6siGdf3wHsZ2Z/wb+fg6jrWxaCpDcpp3zh0sA3JRUW39wMlSQ83YJD8BjlW6hnxl9GPezjqBa/11WEYVcNj9G/bZci4riWxD1QmNnSwP/gsRt3JbHNM/I0bq5o/i950PdHeztf+eUOHqP8gtNPkmre4jVit8Ld/+C6eYXFcuDxIasDZ+MXk4n4luYs/PsfV1RH8qVoWTVvkXQnKcta0ivA6ALiOR+j+jkD/r1sQQnGXU4FH3YAACAASURBVDKGvi/pX00MozlQ/szIPXCv/Q9yttOMRkHtwsW2Jb0bjG9mw9VDLJWZLVVAl2Nwz/O70hzp//l0cxH24yhmXv6G+kLoB7jBUMuCfh33svYb83J4fUb5S+Pdjod8NBMoLpQKsohr7dTCPjYGPke1oVKFEYZdNWydef5e/ALxIHAxvqe/GL4q/AieYVgEr+GZVuBbey+rXiZpOkmRPgf3A+vgWzr303vd0Lw33ioKTk/CJ/MleAHts5K3690STDn/hneR9LPM8zvNbDXcoBgB3CAplzJ9FUHNmb7mqK+aLpAzrP36qpXNmYZYruvw/6FFaZ4INJtHtJ+8Hzd8wMdfdoxdad5HSZ8to90emGBmWzXzNqcM8+vI7+F7g9YxVE9Q0MJO0tmZ5w+a2Sp4PO1w4E5Jz7fZ9McaXi+D/889T33OLIZfO4vIYN0fOCclT7WaK7krHFWURYx5sOam+H1t8fT2U7hXtXTjtUgiK7ZizOxMvHDyPk2O/RoYKWnXAvq5AI+vOgoPoJ1cu7ma2T54AfBVcrT/GTyoeFrKFuqtjmMuUVyroOC0mc2Hx6G8mF5vQ70E0yTgVGX0mfJgZotIaqUzlbftXoOaVWC9Qyu5vmrZc8bqBeatt3MZWNVHfg28JqmoxWKrfsbj2n9zhA+Y2RJ4xmyuutRm9g88fGGMpLcz76+Kz82/SdoiZx+H40kkWyqTZW1m8+DSSn+WdEiePqrCzLYGfgHsqkwJLvNyb2cB+0uakLOP7LWw6fW/iLlSURbxJ3Bv/YfwcJgXqSd+DcN3PHaU6+h1PWHYVYy5uO62zdy6limcXEA/S+HbfGvi+jvbS3omHbsDvxAWqWWX7XshPIbkyRyrz8Y2Kyk4XRVmNgOP3TgDmFiUwZjafhC/EI0rO/4l9TcLWFtNMu3MC7kfK6mVGG9f2i91ziSvYp/JbhF2M2Y2Fvdy30zx3sdsP+/g6vylGPapnaXwv2MKfoOdZWafwrNH/4Rf33LFEJrZkfhW67DUbs3LtQWejXsmdQNGkg6ds5U+9zUc3wVo5VHPKw/1AHCkpPOaHPsSLlre9sI+tTOO3hf0uePjzKtb7IRvWb+Dlye7PR37Cu6kWD1H+4vjSgHP4DGON6Udh5rU0sb4PFoc+FhR97Qyia3Y6nkTz7hptl+/AcW5+5/G/yGbsXkR/ZjZjniw8XvwYONzzOwQPG5knnTOpcBuyp+eXlXB6arYC7+JXIEXuD4LF8Z8uIC2Sw9qttnrqwoXjm1ZXzVnd6XOmU4Zar1sl6P2K4/U+EP6uRyeWThHFxQTm1gLt2jGB3HJoFykJKDP4cbdWWZ2Or4wugK/vhRRSSFbA7jR+zc3Lk/07pCYXby2z5jZp/FErFaxiLnloXDJjlbboG/g/xO5UNLGrAABMyXJvLLKstQFlp8EVsrZ/jfxa8wGjTHBycC7OjlD7sVLp43P2V/phGFXPacAh5jZInisWG1VOBq/2f+4rI7N7CN4TNJdOYLaa23tiZdzmYzH852RVtDjcMPuQTzm4+D0yBvA/Rvqn1vTgtPpvPXwLMC+/h2Vl2CCdy+KZ5rZCvhnthvwXTO7Ey/J9QdJ09psvoqg5irrq1Y2Z1K804IpIQTzAvO1TLnrJf2qoH76ogGW17ArLas0eWJrxqKAU5JnNctw/BpwbRF9SnosBbXfgsfc/lZSYZUAJC3R+1mF8Es8sW0z4O8qR5B+CnCYmd1V26mBdxcThwF3F9VRSp77JL6g/J2kZ83sQ8Bzkl4roIuys4g3w6vltLwnyjOkT8HjervesIut2A6QvB0H4QKY2cLJx0n6RUF9nIq79PdOr3cA/g+/kUwDtsjGXrTR/t+A6yTtn17vgsdufFvSiZnz9gP2bifmrUmf++GB8h9g9s/tJ6rXj1wV14F6rI9tHorfIP5jLg/R1xJMj0tqtwRTq7FsjF9018dXkBfhWb5T+vC72WSYFYBzcImG0oKaM30fSsn1VauYM6mfG4HbJR2cXp8IfBm4Fd86O1TSTwrop9Lt8qJJ27y1TMRt8Ztu499RM+xPbiee1MyOa3HoI3gsXDazXyowIahMzKWUxkia2OvJ7fexKm5QL4obcbXF0CdxHb3NlD9Ba358EbodHvs2N679OSXFeD8h6cA8faR+dgWWlXRkWnhdi6s+QMoiltT24iFt9e7QLNSj4bzP4VqmC/d0XjcQhl2HMLO56KFwcgHtP45LK5ybXj+MS1IcBPwKF5Vs2zBJF6etJN2YXi+A1zddX9IdmfM2wAVqC4l5K/tz6+MYcpVgatLefPhNchxuPDyA18LcDM/0/V5vxkQmAeDdt9LP0oKaq6aK7z55bb8s6QpziYsXgQMl/dbM/hcP1M4Vm5T6mQZ8sVnMaFFYNTJEmNkZeFb0v/O21dBuf6o9SG0kBKXF1F3yJLBWoSvZTm7obx9N+rwD+HUR8We99DMc+AoeZ12bM5OBM+Tadnnb/w0uhr4rcBseElFLbBiHz5vV8vbTpN/5ccN+BPmyiGvttYwRbThvbeA2SV2/09n1AxysqPzCyYvh8QeY2Up4ts+Y5Cb/DfX4m3YZweyxbDUPUGOc1UzqEg+5afzcUqLGHB6pkslbggkAM9sQ9wZtixtg5wMHZS4wh5jZQXhR+N68RF+hgyWqzGxdPEaplc5U7vqqFcwZ8OoDtS2ZddLri9PrKdT1x/JSxXZ5TzJENXIb+JK+3PieebH2ZYG2yxdKqkKg+Drqsk3X0XN2dFExifvgYRiPKadaQE/Idf9OLqt9XPfv25JuNK+rmuVxipsrs5FCVIrUlOspRrTZuV1PGHYdIHm3RtP6Jpg3tgZ8W6SmxfM54NmM690o5gLVbDKUYlyYS7QsIOm49Hp1PHB6CTO7Fxgt6amcfUym9yyvtdJqt89xfC36+hcewHw78C3cxd9se/R6XK6mR/oTyGxmhc77lJl6FT7WTwNX44b/+rgOVO6bV0VzBuBR/EZ/C14r+J7MNuKieDxpEVShAdZMa+59ePLU5vj/XW7MZULmlfS99HpjPLFhPjwxaDNJDxTRVwmsgoth156XQpP43ZHADWY2kyb/U5IWK7DvuWg+Z/L+f43At3WbsQCewdoW1rxOcEuUP7t7opm93cs5A8ZeGjADHSyYi2nejk+KkXgSwML4d/Eyvp1ZxE3qauCIlMp9ELMHsK9G/WKWh2aT4fqG94r6H/smHnRc45d42a0D8ULTx+DK5Hl4gDkNu/fhCRlvUqzY60V4oPFDPZ0k6W68NFefMbMjJf2wxbEReNJDvy6cvXAEcAL+PbyFSylMMZcRmQjclKfxCucMeFziKSmG7BO4R7XGRrg4ahHU2jmD1ouJXIuvHrxBl5pLe2yPL47yUkr5wnRz/5OkqX250bdzc6/NP3NZi62AayXd1992+sBJVOhRNzPD58SetE6iybu4n4wnfTXTkNuOeuZqO1zBnN7TVt7UvJ7Uw3P8blcShl31/ByfEGPxrcxRuPdnB/wC2E5NxWYckPraG/c+ZFPztyH/FlDVk2EZoHYRfj/uDdpE0k1p1XtiT7/cFySNa/Z+iumYQL4LVba94bjRsATpbyqYb5nZG5Jmq2uY/o4rKX6L5KPAD/FyaMKNLyQ9nhJSDseLqLdLVXMGSaeb2SN4XNL3JGWN+Zdw0dci6OjWOZ7scHGvZ/WNssoXXkF9m7TZjT5Lrpu7vDTiEcBf2m2jl/YPK6PdHvgWHsJxHG5cH4l70HbEpaiKqHl6CDDJzK4DLsS/g1EpyW07PF64XRqN0bnxZKOt8RCDwpA06Aw7JMWjwgcewLoV7oWpKfbXjn0Lz8grq28DNgFOB17q9GfRz7H/F5clAfc0vAYMS683At4ouf8tgUcLbO9l3DAtY6yfxQ2g/TLvvQ83jh4Blim4v+eATdPzp/BMz9qxUXiWcp72OzZnBusD90w+XuD3v3V6vjvw38yxTYBpbba7LDBP5nmPjwL+jltwsduyP/u9ejhmeJZ+3j7uT3NjWJoza6T358IXd8cU9Lesj2eMv5X6eQdPpFi/4M9str8jHj0/wmNXPcOBqXLl9Jeop22DT8b/KbpDM1sHV+4ei8fdvYRraA0k7gL2NbOn8AvWNaqLkq6Ab8uWyUK4cVQUE3DZlMJrecqDmcfgW25v4hm2tWDjDSQ9W3CXf8UTASbhf8/3zexpPHHmCFzVPQ+dmDMfpHU8X+5qDVWQJCcamQeXC1mJ/NqSNW4Gvpcysw/E4+tqrExK4uovyghHqxoR6W8D55vZG3jM6HM0eFVVTBb2Ccmjfnb2zZSAcA6eDb9nzj6WB+6V9I6ZvYVfv0hz6GTgNNyjlwtJtwEbpBCP9wGvqEAppaA9wrCrnoepb4XdA+xtZlfhK509KMhAMbOP4cbcjqm/mfhFfX/gJGXqLQ4QDsCFie/DbxTZovI74KvEXLSI45kHD6reD9++KoqJwE/Ma2m2uom0bUBImpi0Cy/At0yexj2euasANOEX1LdOfoB/TzWNrqfwrf88VDJn4N0kjQvwmys0l40pRComfT970tqAzBtA36yywXTcw7J/gQbqfnj5wvNxdf6DM8d2wz1hhZHkgfbADdRngd8XZPjVRHtP7eGcIr77cXhW7HRJFwKY16O9GA8B6FV2pQ/8F5g/PX8CD/2oSbW8D49XLQx5QlluCZWgGMKwq57zgdXxC+Eh+A1wKu5mHoZP+rYwr2KwU3qsgtc3vDb1czM+we8ZgEYdcr2tFc2rD7yk5J9PHIhf4PPSKo7nLdwL8Y0C+qjxf+nnmPRopF8xQy2M0reBc/G4lJ8B63pMdbFep2xb8tJPn8TldUYA/1DOGp6UOGeacDQez7kBngCwDb5tvgt+w92piE7M63X+Di+3tnF6PheeZPAKOWISk/dkFKlcHS4k/lzOIbdEJZUvNLPj8S3elTPvLYCHFKyEfy8LAgeY2VrKX47v61QQ9yjp/PQd/d7MpuMG1+W4gb+RpAcL6OY23Ei8Cr8GHGZmC+ML/H3JsVNgZgvick/vAW6WJ7eshldkWAVfeJ8i6fJ8f0LQLiFQ3GFSsPEW+E3wBuVQA8+I1P4Zv1H8seahSZPxZfzCUegKerBgzYvBTweeV8ETpUVfs9EfL0Tmu++LzpI0AAWKaxQ5Z5q0/W88EeQPuEG/tqTJ6djxwNKStu+hib72cw+eGX1M6qcm7LoAvqV9kaSfttHuCnhi1HKZt1/FlfULKe1VFWY2BbhU0hGZ9w7HjfuvSvpdSqSaBNwnadc2+tgQmKL2y/e1jZntjScGPYJ71zaR1B9R5p7a/jCwlKQbUsbvcXhCwwj88/qm2hD2NbOP41mwH0hvvYiH+FyKa9f9A0+mWhXYVtKlbY7/6w1vzYVn3h/HnFv7kpS3tu6gIgy7CknZkL8CTleqRVlw+4/iW1bT8Biu84CJkt4eiIadeUmhX0p6ylqXF6ohtVlSyMyWB97Mxp41ubC81hgT0030xVDMUnTMkvVc0D7Pd1PqnGnS3+t4ub1bzew1vFzRxHRsE3yxtFAB/UzDK7fclGKgNpV0Uzq2DfBzScu10e5FuHdzd3xrcXlcpHY5lSj4ayUIVKd4yl0lXZl57/7U3mqZ93YFDld7lSf6VHUgL9a6Csi3cY/9znjYApCvIkjSrVsCeLVog9XMrsS3+HfH7zPH4WELl0n6Sua8s4EVJa3XZj/9iWUc0AvVMoit2AqRNN3MdsQDZMtof/mUKPElfBX1JeBlM7sY17UbaFb8WPyzegrPhO1p/MJ11PqFeXmwq/AtsCvTe8OYUz5FZvZc0V4Pc7HgZWh+M+zzxb1mqKXV+YHAFZJyiSj3FetbQfu2DLuy50wTnsSFiME9KVtRjxdcmza3FZswFZg3PX8a38K6Kb02YJE2210XOCAFtQM8aGZ7pZ9LKFMQviisPIHqucl83mkrcRVcEy7LY9Q9SP2lqkoCraqA1Pq/OvM6ry7bXPhnsjXNNebysCbwtdp2sXllnB3w7d4s5zR5r89I6pd2ZzA7YdhVzw24HMVNZTSevBp3mte1rMUEbYuvpgXsmTKyStFrKpKsh6Ed70Uf+SpwddYrkOFTkqYApEyyPfGYxdyY1yH9Jb7ynbfFaf2+uMv1uA7G48Oq4ij8cxmncgralzpnGpiEV2q5BN8mOyvFDM7AdbmOL6ifycDHcaNxAjDeXNh7Jh6r1K53cgmSplyGf+EGwweAwg07yhOofhiXMqrFg22Vfk5sOG8xPNO/m2lWBaQU0g7N43jlj6JZlNmTlWrPGz//Wvxj0AHCsKuek4DTzGwkrbMhcxfmTmn51wHXpViOUXiG7DbAl8zsYRVQzLwqzGw34ErVyztljy2Mb2u1E3C+Ln3zJk1kTk9BHsbjN6o98NXtvrj23C7AiniljXb5M7AGBZTy6iNL4zE7Zd1cK5kzie+SboiSzk5bprXYpG/Qc8Zkfziaeqbv+PT8FNzbMhkXFm+Xqj3zZQlUnwj8NoWRPIfLHD3KnIurzcgnWjvKzD7SlxPbvMagEmvCtuBY4GAzu0XSixX3HXSYiLGrmCaxA9kvwCg5XiBJBXwR2FFSv8v8dIqeYmGSR+Wudj43M5uBBy3/qeH9A3AZhRfS608D10tq5V3rb78P4fEpZ+JejjXl5cMws7OA6ZL2arPtNfFtkBNobQgVpjVlZtfiMTZFGr7Z9iuZM2kbezv8f+mRvO212f+8kqbmaGMWnlXbmPm+aLP3C5BUwcyeA3aRNMlcZ/KHSrWLU7b2hZJGttn29/FFz0LAFGBfZUp+peSJ+/AYu34H0Hc6lstKquNqZhfiW+EL4rGWjdcASep3xZb0eZ1D3fNruBTV2amPGksAX4rYt84QHrvq2ZgOxrqlC8a55Ih/6BA9xcIsgscstcNrNIlnktS45VZkAXhwL9fDcgHR6cwuflyLT2nLsMM9duBbvSe0OKfIC27ZBe0rmTNpG/s0POO2cMPOzMb38bw0HP2ojW46UR6pNIFqSUfj3s1Wx1+g/fi6Gp+lpFJizTCrpI7rosxernDRVif2kyfwOMosj9O8fNgTBfUZ9JMw7CqmlvkW9I6ZjQZGZ946xMxeaDhtOK45NrnNbu7Gg4wv6+W8rakLmBbBMyQ1eHx7aUPq9XtXzNl21XVISy1oX/GcuQ/P7Cxj6+wwXMT1dXoP2hfQb8NOnal7WbZAddm8Ken1CvsrvY6rpFJi+kqMdQ4KJAy7ikhbEvvg+lLP4irjv1bshffEYsDHMq9XZM7V+Uw85ubINvs4BbjIzG5uJWdiZrvgCvrbtdlHM27CDdLLgd/iVSg+hAfp70COkm+1bbAK+XIZjXZozuyHVwV4Bi9bV6SY97/wWLq7cdHliyUV6QXuCCpfoHqwsSdwKB47+mNcq2+Kmf0Ivx6slKfx9Pkvhy8ep0gqKpO7cpLc0QTgqHCK9J2IsasAMxuLC54+jJfcWQ5PGz9e0kEdHNqAwcxuBPaR9I8S2v45rid1F24kPol7TD6IB2avDZwgaf8C+/wAsKiSuK6Z7cfsAqJHVOxFaIuU3bsW8KikIkt7dWTOJI/wfLgnWHh2X2N8YttxaWb2Kdwzsz2+PXYNbsRfIS/LFFRIihlbp2wdu4Y+Xwc+L+mWFOP7eUk3pGNbAqdJWqKNdhfDdx7Wou4RfhwYOxBUEFphZi/jepKF19UerIRhVwFmNhm/Qe1S8zYk/Z8jgPkL9goMSlJ80mnNjAfzeqt7KqNQ30b7o3Hjbl3q8iMzgDuAX0ia0G7bncDKr0Na62cufHvx3ZtTQe12ZM6kLM4eL4pFbXeaVz3YEZcjmg/3TJyqASAgbi7gfaGkF2xOMe9GhMthTOlEUkq3YWZPAHtLusrMHsG90MenY7sAJ6oNEWwz+x2wJR7zWhOnPg54R9Lqhf0BFZOSyaZKyqMUMKQIw64CzGwqMEbSdZn33ocXav5wXOx6p6ys2CZtDaOeTPFfSe/kbTPT9mT6EfumNtT6Uz/ZOqRfo0kd0jxGcJP+7se3SgpLyBlKc8a8APyP8W3gCZKa1Q7uKrKern5kls4CDpT0ixKH1vWY2Xn4FvXh5iXS9scTnWp1XG+VtG0b7T4G/EjS6Zn3VseNvMUHquxJup79BF9kt8ryL6z29WAgYuyqYX7mzNqsvV6g4rEMVGqK7M34IL5llptkyPW7hmIfeYA5pTp2w4u1z6HPl4Pv4IH3x+CG3cmavQ5pYVIniYOBY83svqwURU4G/Zwxs/Vxj912+N90ER7z2fUoUxlAfagSYGbz44kVB+DJFkOZw4Cl0vOj8CSqcWTquLbZ7tL4NSbLA/h1Zim8rutA5P/SzzHp0UjeSh2DjjDsqmM9M8umnM+F/0Oun+Kt3iVWH46Z7Y5XZgD/rE5Jnpwsw/EEi64vcC5pXPa1eTmx3YDDlCpcFMRKwG1JSuUd4L2p/9fM7Fi8okK/C8z3wA9xL+e9Seai2Yq6He9jR+aMlVD3NNP2GrgxtwOwOB5jV/PUFW1wdw2SpqUttT4JAQ9WUujCNDyeF0kz8BCQbxfRPJ5dm6XmTS28RFfZ4TEZSqtxPFgJw646ftbi/UadsVh91HmDuifLgFeZs3TNTLzO4skVjqsoyoqDKKsOaSvuJ5/yfysqnzNWXt3Tmij18niJtEPxrNi2xYi7DTP7IK2N4askPURzj8tQosw6ruAZ3c2Srs42s9kWDnkWKIlD8b+hWdLUkul4bsNOqQ520HfCsKuGWHG0gaQLgQsBzOwMPFP00c6OakBQVh3SpkgqQ+6kU3OmrLqn4J7U6cAn8ZJvxyUx4qYUleBSNmmL/wI8gxzqGZnZhUssVim9jutZLd5v3J4tikrCY97tzHc4lqH5wqGokoKDgjDsKiBWHPkpyXgYrPRWh7TdihaV0cE5U1bdU+hMVYgqOBq/4W4A/AkXJH4Zr3u8MbBT54bWlZRSx7WKa2QnwmOSpNIvU7+tSjrGwiFDGHbBgMHMlsNvFq22e7aveEhFUeiWrKQ7SV45Sa8Ao4uoQ9oTg+i7mQ7MJUlJpHhF4NZ0bCruiWiLDlWFqIJRuDFcK2X3H0mTgVvM7Hg8mWegfP9VsBleS/VxMyusjmtFdCI8ZjywFR73eg6eOfw6fr1ZkfaTTQYtYdgFA4IkaXILXn9wZbyM1YK4cO1TwD87Nrg+ksRvmxlx16et0tkocisuBWnPKKq9LIPhu8lQWt3TQcziwJMpWed1YOHMsauAP3ZmWF1LWXVcS6dJeMyPJP275G63xzOJL8ANu7sk3Q38PiXkjMb/z4JEGHbBQOEn+AVlDzz2aY8U+7Qertx/XCcH10dOoqIarqnCwRjcw1S2B20wfDc1Bnrd007wJHXj5BHcu1L7zNbGvaBBQiXVca2aZlu/SWtyWeDBtJgsgqWBh9PCYTrwvsyxc4BzGQDhJVUShl0wUFgdj02ppe8PB5B0exL5PIZysswKQ9JhVfRjZvsAJ+JbJo/g3qYyGfDfTY2oe9oWk4DPAZfgUjpnpc9tBvAZipXWCbqENLfnlfS99HpjvKTZfMCzZraZpCISN57Btf4AHgU2BGrC5SsW0P6gIwy7YKAgYGaKfXoeXxXeno49Sc7C2YOMA4Ez8LJFVZSrG7TfTSpnNmiqXJTEd0lZnpLONrNp1Ose7wv8uoNj60pSJvFoWsektl0POWnlLQG8Kmla24PsnZ2BIzOvj8eTZw7HK6kcjVe7yctNeGLO5cBvgZ+Y2YfwhcMO+K5AkCFKinWI3jSfqh9Rd2NmtwJnSTrNzC7BldR3xr1Rp+Elcz7eyTF2C2b2GvDFqopmV/XdVDVnzGxJfDux2Ta2JH23qL4GO8mL8x1Jn+/0WLoFM1sRX/iMwLOuX8DjEufGs4lflbRCjvbnxus3by2pNE950sXbQtItZrY08Dj1MnNbAmcUESecxMgXlXR/er0f9YXDJFwGq5l235AlPHYVE5pPbfMb6hIeP8BT6f+RXr+OT/TAuRqPbarEsKP37yZXPF+Vc8bMtsE9AMPw0nKNW6/CPVRDHjNbCNgCj4H6N1494610bCz+OX2C8Hg28nNcdmgsPj9G4Uk7O+BerlwZsSVr5WV5DU+SApe1eTlTy3t6Uf1LehZ4NvP65/hnGLQgDLvqCc2nNpB0dub5g2a2CrAe7lG5U1JZ9V0HBGb20czLk4DfJP2nScArjefnFfQ0s00lTUptNftu1sVX1Hfigr95PAdVzpmjcMN0nKRGGYcgYWY1nbLFM29PMbNt8WD2dXFh3F2AP1Q/wq5mLeCr1LPU50k1qs9NJfROwK9teShFK6+Bm4HvmdksPPzjssyxlfEwjKADxFZsxZjZv3HNpz/gGYRrJ80nkubT0gNI8yvoEtLFNTuZm3m1au9LUi4PV9qG2VbS1T2cMxIPqP+MpFbCon3pq7I5k+LDvijpul5PHsKY2eX4zXs33Nu0LPArPJFmXmBfSf/XuoWhi5m9AnwhbWG+CHxN0sXp2MbA5ZJG5uzjQrwM3oJAKVp5ZrYUcDawJnAvsL2kZ9KxO4C/ScqdrWpmj9JaTWAWri/5V+DEJIMy5AmPXfWE5lObmNlwPCOqVezTKdWPqmvYmIqkVBKXAJeY2Y6SLm08mDwPV+OVHPLWB61yztyO69iFYdcznwK+LakmSvxQysZ+BDdUwqhrzcPUQxfuAfY2s6uAd3DJoGa1V/tL6Vp5kp7GrzvN2JziZG7+iIdzjMDn5QvA+4FN8a3sv+De/F3MbCtJE1s1NFQIw656QvOpDczs0/gEf3+LU4SXzRqSSLqp4i53wf9X/2Bmu0q6oHYgVaGYiP+ff07SHTn7KnXOmFk2Fmh/4JzkuWu1jf1G43tDkMXxYvZZaq//WulIBh7n457Ns4FD8P/lqbj3aW7qJbvapkqtvKRdtxoea3m1pJfx2NSiMvKfx43hrSS9O9fNbASeKftE6n8CnpEbP4FdXwAAGDBJREFUhl2nBzAE6UnzaUM8ZTyYk1/iAdqbAX+vBWkHTg9VLRoRftF9gvQ/mOJ7+kWSAdkjCYaeY2bDJf3ezD6Oe+pmARsUVJy77DkzjTm3sc+g9ecZyU1Oq8+nComdAYukn2We32lmq+FJKCOAG2rZn92OmQ3D41/3xccufFv2ZXwR/hfg0AK6+hbuBZ5tASfpTTP7OXCapCPN7Le4YPGQJwy76ulJ8+kbwKkdHFs382FgjKTwBjSnP1UtRuDCnj/CtzV/0G6nkvY1sxnA75JR91V8K2lzSUUFT5c9Z75CtdvYg4WJzUrh0aREXpHl8QY6ZrYhMKWmMZfmyW/TsfnNbENJtxTQT2laeYmjgD3xOXgjvvCucRmwN8UYdgsxe5JOlsWB+dPzV/Ht7CFPGHYVkgqxbwPcBbwIIOkS3BMR9MzfgA90ehDdSjtVLczsG3g2W9uGXep7/5RM8X28EPyWaTumENLW5xuZ14XOGUlnFtXWEOLwTg9gAHMjnjV8V5NjH07H8yY39aqVB+Q17HYDvifpjOS9y/IvoG0tvgauAI4zs1eBKyTNNLN5cPHj49JxgI+lfoc8YdhViKQZZnYa7nYPbaf+sQ9wppk9JunmTg9mkHADbSY2tNj6NWAVPJB+tgN5PDZmdjdwOnBekQZj0D6SwrBrH+vh2PxkFjE5KFUrL7EQrQ2peSguZGFv4CzgIkBJgH0B/HO8HL83gO8U5FqkDhbCsKue+3DXeBgnvdDEeBgJ3GBmM3FxzNmI7Z7+keLfWmW19UZ/tn7zch+uy3W8mU3AjbxJKkmrycx2wLeYWm1hxf9Z0C/S9utGmbe+amZbNJw2HNgS/3/PSxVaeffjW73NMsg/D0zJ2T4Akl4BRpvZqng29gdwweK/ZGvRSrqoiP4GA2HYVc9+uOfpGeCaimp5DlSqNB6CftDO1m+OvsaZ2b64l2F3XOz4aTP7PV626J9F9WVmXwJ+B5yJG72/A+bCt31eAX5fVF/BkGJt4JuZ12OZM8lkJl6x5TsF9DccmCpplpm9BCyZOXY/8D8F9HEk8MeUnXohfq1ePVVv2Yti6sS+SzLiHuj1xCAEiqsmeaHmwyee8HiH2b6E8AgEQWtS/NDueIzP0sBtuAF2fmPmXBtt34Nv+RyDiyF/StKUFIg+CbhI0k/z9BEMbZLg7hfLTAQzs7uAX6Vko0n4PeYLeHLBmcBaklYqoJ/t8Ti3ZTJvPw0ckJVAKoKor953wmNXPeGFCoJ8vEN9Dr2Dx9qcDByTNPUm5Wh7JeC2JIb8DvBeAEmvmdmxeOxSGHZBHg4Fnmp2wMwWxvXa8nqGS9fKA0jG2wVmtjKuNfkS8FCRYRJRX73/hMcuGDCY2XZ4sH+zyhNIWqvyQQWVkESExwLjcJX5f+JeurMkPZduiCcC60hqOxvPzP4DfFnSRDN7DDi2VtHEzMak/hbI9ccEQ5q0YFhX0hxZsUmf8a68Jf+atLs0BWjlmdk6wN1V6oia2YnAZ/G416a1omslBgMnPHbBgMDMDgPG45ldf8fjUYIhgJn9DtetmwvfJh0v6dbsOZJeMrMTgB1zdjcZ+Dju4ZgAjE+abDPx/787c7YfBD1lxS6Ce9bydVCeVt7twPSUqX5betwu6b95x9wDo/Ba0bXydf9JhtwtqVb0d/CSY0EiDLsOEJ6nttgDOEZSpLMPPVbF9fbOkzRHNnSGB/CVfR6Opl7Hc3x6fgpuVE7GpReCoF+Y2Wg8g7TGISneOstw3BtdhPepLK28zVO76+EJEgfhEiSPkIw8PJThH+0MugVRX72fhGFXMeF5apsFgOs7PYigeiSt3cfzppFTRkjSnSSvXEZmYV5gXkm5PSnBkGUxXEC3xorMKbg+E7gWzzbNSylaeSl+dRKAuVjlqsD6uLG3AfBl3NB7GffkFZEZG/XV+0nE2FWMmT0JnB2ep/5hZr8GXpNUhBRAMABJBtZSNPdyt12T1szG9+N0SfpRu30FgZndCOxTsFerUSvvMOA05kzSqGnlvS4pr45dszFshHvXPw9QRKygmf0KmCuVL9wVFyu+k0yt6ALKow0qwrCrGDN7BdhWUnifesHMRmVezoen1d+MrxhfaTw/Ut4HJ2a2FF4P9vPNDuPGVts3EDObBbyJK/T35Okg9RVyREG/SHGirXgbeB64RdK1Ofr4DvUyYYvgZcNaauVJyiUgbGYjcY/ZeumxDr6z8nfgDuAOSWfl6SP1Mx8wn6QX0+ttqNeKngScKmlW3n4GE2HYVUx4nvpOuuH2lVw396B7MbOrgDXw+Lem4Qt5ysyl+KBl8a3+84GLe4nlC4J+YWY9xc0NA5bAY8n+BIyqJT3k6K8Urbwk4F0z5D6GG4+18IU78IzemDsdJgy7CgjPU3uY2bK9n1VH0uNljSXoHKn4955FC5429PEpPKN2ezye5xrgPLzo+Jtl9RsENcxsbTwT+zxJ/5uzrd2AK5tlq+bRykuL7dfxCiwnZ0t6Bd1DGHYVkCaD6H2bB8Lz9C5mtgSuTfYbSRNbnLM58DU8ZuX5KscXVEPyqO0v6fKK+tsQN/K2xRdiE/DtnnbkIYKgz6TSed+VtEyvJ/fcTilaeUmke128ZquAu0nbrniyRCHX4ORx7KtxIkkrFtHvYCEMuwoIz1N7JI2ijYE1WimZp8ysu/Gi8N+tcnxBNZjZTsC++BZVZZmpZjYP8GO8vvMESWOq6jsYmpjZZ/Ea4vPmbGcWLtbdzLDbDPiDpPflaH9u4BPUpU/Ww+W7HqVu6N3Rbhyfmf2U3g27NXB5o3CGNBByJxUQhlrbbAX8rKfyNJJkZqfiN98w7AYJZta47boM8HiKVWoMX5CkHQrse33cY7cdHgx+Ea5lFwRlsyxelqvfVKmVJ+nt1MZk4Jep/6VwA2934IR0als2hqQDWx1L1S8OwY26B4Cj2uljMBOGXQWYWb/c6pKeKGssA4xl8WD53ngQWK7coQQV8/6G1/9KP9/T5FhuzGwN3JjbAQ9iv4a6p64tza8g6A8p9OSHwNVtNlG1Vl5NgmhN6l67danPz0LvY8mb+UNc0mUKMEbSpUX2MViIrdgKyMTY9YlwKztJ5HIXSVf2ct4o4Jw8WwvB0MXMHgKWB26gnhUbYsRBYTTxQGcZhhtgn8TFeDeQ9GzO/m4Evi7pwTztNGl3SepG3HrA6sA8uKzKvdSrT9wu6emC+hwFHIwbjbcBP5Z0TRFtD1bCsKsAM9sy8/K9eFbsg8DFuH7RYnig9kdwfaHzKx9kF2Jm1wP/lLRXL+edCnxI0ibVjCwYTKSF13Q826/XC2Lo2AX9JRlarXgbeAG4Ffi9pNerGVX/yTgpXiYlTODG1uSis8fNbFvgB3gs33W4QZerssxQIQy7ijGzM4E3Je3T5NivgZGSdq18YF1Imth/APZoJXSZ0vpPA3aQdEmV4wvKo5/VIJB0RI6+Du1nX4e321cQlEVFIshfwb1xhVbNaNLPA7ij4yZ82/jPPZ0f4RKzE4ZdxZjZVLzyxKQmxzYFLpK0YPUj605SZux+eObrNXjchvBg+s3xtPuf9xRsGww8mgR9j8ClRwCm4fUuwWtevhFetGCoU7UIcpk0iNP3xYse4UsZInmiet4EPk0qpNzABkRB49mQdICZ3QT8L16DsCYDMAPfAhgt6YoODS8oCUnvJkiY2brAOXjg9CWS3jSzEcAY4EfAzp0ZZRB0D5LW7O2cjAjykfg1tVv5cqcHMJAJj13FmNlheKr2KfgEq8XYjQb2wuMI+rU1NFRI2kmLpJf/TSn3wSAneSJOlXRak2NfA/aWtEb1IwuCgUdRIshB9xIeu4qRdFjK9jwI+Dr1ihTPAgdK+kUnx9fNJEPuuU6PI6ic1YD/tDj2NLBKhWMJgoHO3/Et2WCQEh67DmFmcwFL42nuzwJPSupP0fsgGBKY2V/xrMEtJc3IvD8cuBJYRNLqnRpfEAwkzGwccLSkJTo9lqAcwrALgqCrSbVbr8LjUydRD1/YFE+o+HzUcQ2C3kkiyLfi2bFf6fR4gnIIw64DJJHHrfDaesMbDitqngbB7KQb0n64yn3Nyz0Z+IWkVtu0QTBkqFoEOehewrCrGDPbBjgPn2jP4yVeskjSCpUPLAiCIBiwDBYR5CA/YdhVjJk9CDwCjJPUVrHnIAiCIBhqpPJit0p6LfPegZJ+2sFhdR1h2FWMmU0Dvijpuk6PJQgGCma2A7AnsDJzhi9Ema8gGAJkyv+dAYwHDgX2DYHi2Zmr0wMYgtwOfLjTgwiCgYKZfQk4C/gnHpc6AbgCv35NBU7s3OiCIKiQ+YGtgRWAp4BxwJc6OaBuJAy76tkf+JqZ7W5mS5rZfI2PTg8wCLqM7+AVJvZNr09OGX3LAy/iZcWCIBgkmNlKadt1NlJN2NuAd/C4wXeAOyoeXtcTW7EV05caeOFWDoI6KXxhK0k3mdlbwKaSbkrHtsFrBS/XwSEGQVAgZnYl8KKk3RveXxiXPloK+DxwBF6FaM/qR9m9ROWJ6vkKfShqHATBu0ylXiO4VmnipvTaqJeZC4JgcLAW8NXsG2a2LDAxvfy0pMfN7DwgqjU1EIZdxUg6s6fjZvaeioYSBAOFycDH8Yv6BGC8mb2NSwWNB+7s4NiCICieYWRCxcxsddxT9xQwStKL6dDLxMJuDsKw6wLMzICNgZ2AMcDCnR1REHQVRwPLpufj0/NT8Av/ZGCvDo0rCIJyuA74SYo5Xwk4EK8XvVGKs6uxGfB4B8bX1USMXQcxs3VwY24sXpT5JeACSfv2+ItBMMQxs3mBeSVN7fRYgiAoFjNbHDgH+CweunQpngk7GfgpMA3YHjgWOFzS0R0aalcShl3FmNnHcGNuR9zzMBOYB8+WPUnS2x0cXhAEQRB0BWY2Epgl6U0z+wBu4K1ZOwz8AdhF0judGmM3EoZdBZjZCrgxtxMe+P02cC1wPnAz8ATuYo5C5kEAmNn4fpwuST8qbTBBEHQNZrYesAzwoKS/dno83UgYdhWQJE4E/Bn4HfBHSS+nYwviAaBh2AVBIs2ZN4HX8ZV5TygqTwRBEDiRPFENj+PbrqsBGwHPmNnE2HYNgpb8C58zd+Oe7Yuz9SGDIAiC5kTliQqQtDywHnAmsAlwOfCcmf02vQ63aRBkkLQSPmcewKtOPGdmF5vZWDMb0dnRBUEQdC+xFVsxZjYXdWmTbYCFcMPuXOAESX/p4PCCoCsxsw3xhKNtgflwPbtTI3whCIJgdsKw6yBJjHgUfsPaGhgBPCxplY4OLAi6FDObB/gxsB8wQdKYDg8pCIKgqwjDrktIQoxfBHaU9IVOjycIugkzWx9fAG0HLABcAZwi6eaODiwIgqDLCMMuCIKuxMzWwI25HXAB72vwRIoJDerzQRAMYsxsAWA0sDIwvPG4pIMqH1QXE4ZdEARdh5k9BCwP3EA9KzaqTATBEMPMVgRux0OVRgIv4GU358alwl6VtELnRth9hGEXBEHXkXTspuM6dr1epELHLggGJ2Y2AVfwGItfDz4F/BX35B8NbCdpcudG2H2Ejl0QBN3I4Z0eQBAEXcFawFeBGen1PKmE2LlmtihwAi6NFCTCsAuCoOuQFIZdEATgMXVTJc0ys5eAJTPH7gf+pzPD6l5CoDgIgiAIgm7lYbwKDcA9wN5mNjzJhe0B/KdjI+tSwmMXBEEQBEG3cj6wOnA2cAgwEZgKzAKGAeM6NrIuJZIngiAIgiAYEJjZ0sAWeJbsDZLu7/CQuo4w7IIgCIIg6DrMbDjwK+B0SXd2ejwDhYixC4IgCIKg65A0HRcpn0OUOGhNGHZBEARBEHQrNwCf7fQgBhKRPBEEQRAEQbdyEnCamY0ErgKeo0G0XNLfOzGwbiVi7IIgCIIg6EpSFZosWaPFAEkaVuGQup7w2AVBEARB0K3ENmw/CY9dEARBEATBICGSJ4IgCIIgCAYJsRUbBEEQBEHXkOLq+rydGDF2sxOGXRAEQRAE3cS3qBt27wEOAKYBlwHPA4sDo4GRwPGdGGA3EzF2QRAEQRB0JWb2M2AZYKwyBouZGXAh8LSkb3dqfN1IGHZBEARBEHQlZvYisLOkiU2ObQ6cK2mR6kfWvUTyRBAEQRAE3cowYJUWx1Yl7Jg5iBi7IAiCIAi6lXOAo8xsbmACHmO3GB5jdwRwegfH1pXEVmwQBEEQBF2Jmc0DHAvsBcybOTQD+A1wkKSZnRhbtxKGXRAEQRAEXY2ZLQx8DPgA8Cxwn6SXOjuq7iQMuyAIgiAIgkFCxNgFQRAEQdDVmNmngZWB4Y3HJJ1c/Yi6l/DYBUEQBEHQlZjZ4sD1wEdx0WJLh941XqLyxOxEmnAQBEEQBN3K8cCrwNK4Ubc2sBxwCPAI7sULMsRWbBAEQRAE3cpngG8Dz6TXJukJXAJlLuBkYPNODa4bCY9dEARBEATdykLAC5JmAVNxDbsatwPrdWRUXUwYdkEQBEEQdCuPAkuk5w8AO2eObQ2E5EkDsRUbBEEQBEG3ciWwGXABcCRwmZk9BbwFLAN8t4Nj60oiKzYIgiAIggGBma0JfBEYAUySdHWHh9R1hGEXBEEQBEEwSIit2CAIgiAIug4zM2BTYB1g8fT2c3jSxPUKz1RTwmMXBEEQBEFXYWafAM4HPgS8A7yI69gtgjulHgZ2lHRvxwbZpURWbBAEQRAEXUOqNjERmA6MAhaQtKSkJYAFgC2BmcBEM1usdUtDkzDsgiAIgiDoJr4JvAlsIGmipBm1A5JmpISJDdM53+jQGLuWMOyCIAiCIOgmNgNOljS11QmSXgFOAbaobFQDhDDsgiAIgiDoJj4ETOnDeXenc4MMYdgFQRAEQdBNLAi82ofzXgPeW/JYBhxh2AVBEARB0E0Y0FfJDitzIAORkDsJgiAIgqBrMLNZwCvA272cOjewoKRh5Y9q4BACxUEQBEEQdBOHd3oAA5nw2AVBEARBEAwSIsYuCIIgCIJgkBCGXRAEQRAEwSAhDLsgCIIgCIJBQhh2QRAMScxsnJndbWavmdnLZnaPmf0sc3wxMzvMzJZro+150u+uXuSYgyAIeiMMuyAIhhxm9n3gNLzQ+BhgN+Ay4AuZ0xYDDgWWa6OLedLvhmEXBEGlhNxJEARDkW8Ap0r6Qea9y80sZBaCIBjQhMcuCIKhyELAs41vKuk/pe3X+9LbN5qZzKx2bKSZnWhmD5nZG2b2qJmdZGbZ0kavpZ9n1H63tqVrZsPN7Dgze9LMZpjZX81sVHYcZvaFtE38etom/rOZfabAvz8IgkFKGHZBEAxFpgDfNLPdzWyRJsefAXZOz/cF1k0PgPmAYcDBwOeBQ4CNgQszv79x+nlk5nefSe9dBIwDjgK2BiYDE2rxeGa2YjrnhnR8Z+AKYOG2/9ogCIYMIVAcBMGQw8w+DlwKLI/XpHwQ+CPwU0lT0zmr4V67z0q6qYe25gbWBv4ELCvpCTObH/fafVnSmZlzNwGuAzaSdHPm/VuA5ySNNbPt8G3iZgZnEARBj4THLgiCIYekvwGr4MkSJ+OFxA8B/pKMsh4xs11TFu004C3cqANYuZdf/Ry+BXybmc1dewDXA59K59wHLGhmZ5nZZmY2sr9/XxAEQ5cw7IIgGJJImiHpcknfkPRR4KvASsAePf2emW0D/B64AxgLrANskw4P76XbRYEP4MZg9nEYsHQa10PAaGAF4CrgRTM718ze39+/MQiCoUdkxQZBEACSTjez44CP9HLqWODPkr5ee6MfiQ0vAU8DX+xlLFcCV5rZgsCWwC+AXwE79rGfIAiGKGHYBUEw5DCzxSQ93/De+4EFgefSWzPTz0Yv3AhgRsN7Oze8bvW71wMHANMk/aO3cUp6FTg3GY7r9nZ+EARBGHZBEAxF7jOzy4BrgeeBZYEDgTeAs9I5TwBvArub2avAW5L+AkwCTjKzg4E/A6OATbKNS5ppZo8C25vZ/cB04G/pdycCk8zsWOAB4L24kPFwSd83s71wI+4a4D/49vBYfPs3CIKgRyIrNgiCIYeZ7YvHsa2Gy4g8C9wOHJH1pJnZztSrT7xHkpnZMOBYvFrFcNxYOw64E9ha0hXpdzcDfoonVMwLLC/pMTObF/gB7uVbBt+evRf4laQrzWxd4IfAJ9LYngEuAMZLavQUBkEQzEYYdkEQBEEQBIOEyIoNgiAIgiAYJIRhFwRBEARBMEgIwy4IgiAIgmCQEIZdEARBEATBICEMuyAIgiAIgkFCGHZBEARBEASDhDDsgiAIgiAIBglh2AVBEARBEAwS/h9tyFTBWFXfcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=np.arange(len(label2)) \n",
    "plt.bar(index, df2_shortage_pd[\"chc_short\"])  # Or change to df2\n",
    "plt.xlabel(\"States\",size=15)\n",
    "plt.ylabel(\"CHC - Shortage\", size=15)\n",
    "plt.xticks(index, label2, fontsize=15, rotation=90)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}