Data_visualization_in_pySpark .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no inbuilt plotting API in Spark. Here I use matplotlib to make plots. But we have to be very careful to use them. Usually Spark is meant to handle large data which can not fit into memory. Python plot library requires all the data loaded in momory before making any plots. But we can be careful here and do the calculation beforehand and feed them in matplotlib. For example from data with billions of rows and hundreds of columns if we are interested to make bar plot of top 10 values of one feature, we can select the top 10 value of that feature from spark query and feed that in matplotlib. In this way the heavy lifting is done by spark and allow matplotlib to handle few 10s to processed data. \n",
    "\n",
    "The plot functions presented below are designed carefully for the case basis. I tried to generalize it to some extent but I don't claim them to be general. But it gives you the idea what can be or should be done to generate visualization from Spark. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()\n",
    "\n",
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark = SparkSession.builder.appName('Name').config('config_option', 'value').getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+----+----------+----------------+---------------+-------------+-------+----------------------+-----------+--------+-----+------------------+-----+-----------------+-----------+---------+----------+-----------+---------------------+-------------------+-------------------------------+---------+\n",
      "|    Country|Year|    Status|Life expectancy |Adult Mortality|infant deaths|Alcohol|percentage expenditure|Hepatitis B|Measles | BMI |under-five deaths |Polio|Total expenditure|Diphtheria | HIV/AIDS|       GDP| Population| thinness  1-19 years| thinness 5-9 years|Income composition of resources|Schooling|\n",
      "+-----------+----+----------+----------------+---------------+-------------+-------+----------------------+-----------+--------+-----+------------------+-----+-----------------+-----------+---------+----------+-----------+---------------------+-------------------+-------------------------------+---------+\n",
      "|Afghanistan|2015|Developing|            65.0|            263|           62|   0.01|           71.27962362|         65|    1154| 19.1|                83|    6|             8.16|         65|      0.1| 584.25921|3.3736494E7|                 17.2|               17.3|                          0.479|     10.1|\n",
      "|Afghanistan|2014|Developing|            59.9|            271|           64|   0.01|           73.52358168|         62|     492| 18.6|                86|   58|             8.18|         62|      0.1|612.696514|   327582.0|                 17.5|               17.5|                          0.476|     10.0|\n",
      "+-----------+----+----------+----------------+---------------+-------------+-------+----------------------+-----------+--------+-----+------------------+-----+-----------------+-----------+---------+----------+-----------+---------------------+-------------------+-------------------------------+---------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = spark.read.option('inferSchema', True).csv('Life_Expectancy.csv', header=True)\n",
    "df.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tabular visualization\n",
    "\n",
    "# Pandas was better! But we can convert Spark dataframe into\n",
    "# pandas dataframe. But since we just want to see the top few \n",
    "# rows it makes sense to convert only top rows of the Spark \n",
    "# dataframe to Pandas. It saves the computer memory consumption. \n",
    "# Usually Spark dataframe is meant to handle really large data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "0  Afghanistan  2015  Developing              65.0              263   \n",
       "1  Afghanistan  2014  Developing              59.9              271   \n",
       "2  Afghanistan  2013  Developing              59.9              268   \n",
       "3  Afghanistan  2012  Developing              59.5              272   \n",
       "4  Afghanistan  2011  Developing              59.2              275   \n",
       "\n",
       "   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   ...  \\\n",
       "0             62     0.01               71.279624           65      1154  ...   \n",
       "1             64     0.01               73.523582           62       492  ...   \n",
       "2             66     0.01               73.219243           64       430  ...   \n",
       "3             69     0.01               78.184215           67      2787  ...   \n",
       "4             71     0.01                7.097109           68      3013  ...   \n",
       "\n",
       "   Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0      6               8.16           65        0.1  584.259210  33736494.0   \n",
       "1     58               8.18           62        0.1  612.696514    327582.0   \n",
       "2     62               8.13           64        0.1  631.744976  31731688.0   \n",
       "3     67               8.52           67        0.1  669.959000   3696958.0   \n",
       "4     68               7.87           68        0.1   63.537231   2978599.0   \n",
       "\n",
       "    thinness  1-19 years   thinness 5-9 years  \\\n",
       "0                   17.2                 17.3   \n",
       "1                   17.5                 17.5   \n",
       "2                   17.7                 17.7   \n",
       "3                   17.9                 18.0   \n",
       "4                   18.2                 18.2   \n",
       "\n",
       "   Income composition of resources  Schooling  \n",
       "0                            0.479       10.1  \n",
       "1                            0.476       10.0  \n",
       "2                            0.470        9.9  \n",
       "3                            0.463        9.8  \n",
       "4                            0.454        9.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.limit(5).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Country: string (nullable = true)\n",
      " |-- Year: integer (nullable = true)\n",
      " |-- Status: string (nullable = true)\n",
      " |-- Life expectancy : double (nullable = true)\n",
      " |-- Adult Mortality: integer (nullable = true)\n",
      " |-- infant deaths: integer (nullable = true)\n",
      " |-- Alcohol: double (nullable = true)\n",
      " |-- percentage expenditure: double (nullable = true)\n",
      " |-- Hepatitis B: integer (nullable = true)\n",
      " |-- Measles : integer (nullable = true)\n",
      " |--  BMI : double (nullable = true)\n",
      " |-- under-five deaths : integer (nullable = true)\n",
      " |-- Polio: integer (nullable = true)\n",
      " |-- Total expenditure: double (nullable = true)\n",
      " |-- Diphtheria : integer (nullable = true)\n",
      " |--  HIV/AIDS: double (nullable = true)\n",
      " |-- GDP: double (nullable = true)\n",
      " |-- Population: double (nullable = true)\n",
      " |--  thinness  1-19 years: double (nullable = true)\n",
      " |--  thinness 5-9 years: double (nullable = true)\n",
      " |-- Income composition of resources: double (nullable = true)\n",
      " |-- Schooling: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.select('Country', 'Year', 'Alcohol', 'GDP', 'Population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+----+-------+----------+-----------+\n",
      "|    Country|Year|Alcohol|       GDP| Population|\n",
      "+-----------+----+-------+----------+-----------+\n",
      "|Afghanistan|2015|   0.01| 584.25921|3.3736494E7|\n",
      "|Afghanistan|2014|   0.01|612.696514|   327582.0|\n",
      "|Afghanistan|2013|   0.01|631.744976|3.1731688E7|\n",
      "+-----------+----+-------+----------+-----------+\n",
      "only showing top 3 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Histogram \n",
    "\n",
    "# This might be the easiest of all. Spark RDDs has .histogram() \n",
    "# method which gives histogram values for specified number of bins. \n",
    "# We just need to plot these bins in python. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def histogram(df, col, bins=10, xname=None, yname=None):\n",
    "    \n",
    "    '''\n",
    "    This function makes a histogram from spark dataframe named \n",
    "    df for column name col. \n",
    "    '''\n",
    "    \n",
    "    # Calculating histogram in Spark \n",
    "    vals = df.select(col).rdd.flatMap(lambda x: x).histogram(bins)\n",
    "    \n",
    "    # Preprocessing histogram points and locations \n",
    "    width = vals[0][1] - vals[0][0]\n",
    "    loc = [vals[0][0] + (i+1) * width for i in range(len(vals[1]))]\n",
    "    \n",
    "    # Making a bar plot \n",
    "    plt.bar(loc, vals[1], width=width)\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel(yname)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jug1YSDZmcTJw/9aeUNLbgOtzof2AL5B95/uvyQbTAT4bEbelfeYCZwCvAJ+IiDu29vxmZrblGnVDfTA3/zzwrjQ/BIzZ2hNGxApgOoCkUcBzwE3A6cBFEXF+fntJ04BZwAHAvsBPJL01941uMzOr2IjFIiJOb8H5jwZWRcTTkkbaZiZwXURsBJ6StBI4FLi7BfmZmRnl7oaaAvwvYHJ++ya9onwWsCC3fI6kU4EB4NMR8SLZu6juyW0zmGL1cp0NzAaYNMmf4DAza5YyA9w3A6uBb5PdGVWbtomknYATyJ4IB7gU2J+si2pt7hz1LjnqPucREfMjoj8i+vv6+rY1RTMzS8rcOvv/IuLiCs79fuCBiHgeoPYLIOl7wK1pcRCYmNtvArCmgnzMzGwEZa4s/rekL0o6XNLBtakJ5z6FXBeUpHG5dScBtW9/LwJmSdo5dYlNBe5rwvnNzKykMlcWfwx8BHgP8GqKRVreKukDSu8je9dUzTclTU/HXl1bFxHLJC0EHgM2AWf7Tigzs9YqUyxOAvbLv6Z8W0XEy8Cew2IfabD9PGBes85vZmZbpkw31ENkD8yZmVmPKnNlsQ/wuKT7gY21YJNunTUzsy5Qplh8sfIszMyso5X5nsUvWpGImZl1rjJPcG9g80NwOwE7Ai9FxOgqEzMzs85R5spi9/yypBPJ3s1kZmY9oszdUK8RETezDc9YmJlZ9ynTDfWh3OIbgH5GeDeTmZltn8rcDZX/rsUmsqerZ1aSjZmZdaQyYxat+K6FmZl1sEafVf1Cg/0iIr5aQT5mZtaBGl1ZvFQnthvZt7D3BFwszMx6RKPPqv7hA0eSdgfOJftO9nU04eNHZmbWPRqOWUgaC3wK+EvgKuDg9KlTMzPrIY3GLL4FfAiYD/xxRPxny7IyM7OO0uihvE8D+wKfB9ZIWp+mDZLWtyY9MzPrBI3GLLb46W4zM9s+taUgSFot6RFJSyUNpNhYSXdKeiL9jsltP1fSSkkrJB3bjpzNzHpZO68e3h0R0yOiPy3PARZHxFRgcVpG0jRgFnAAMAO4RNKodiRsZtarOqmraSbZHVek3xNz8esiYmNEPAWsxG+9NTNrqXYViwB+LGmJpNkptk9ErAVIv3un+Hjg2dy+gyn2OpJmSxqQNDA0NFRR6mZmvafMiwSrcERErJG0N3CnpMcbbKs6sbpvvY2I+WS3+tLf3+8345qZNUlbriwiYk36XQfcRNat9LykcQDpd13afBCYmNt9ArCmddmamVnLi4Wk3dLrQ5C0G3AM8CiwCDgtbXYacEuaXwTMkrSzpCnAVOC+1mZtZtbb2tENtQ9wk6Ta+a+NiB9Juh9YKOkM4BngZICIWCZpIfAY2fc0zo6IV9qQt5lZz2p5sYiIJ4E/qRP/NXD0CPvMA+ZVnJqZmY2gk26dNTOzDuViYWZmhVwszMyskIuFmZkVcrEwM7NCLhZmZlbIxcLMzAq5WJiZWSEXCzMzK+RiYWZmhVwszMyskIuFmZkVcrEwM7NCLhZmZlbIxcLMzAq5WJiZWSEXCzMzK9SOb3BPlPQzScslLZN0bop/SdJzkpam6bjcPnMlrZS0QtKxrc7ZzKzXteMb3JuAT0fEA5J2B5ZIujOtuygizs9vLGkaMAs4ANgX+Imkt/o73GZmrdPyK4uIWBsRD6T5DcByYHyDXWYC10XExoh4ClgJHFp9pmZmVtPWMQtJk4GDgHtT6BxJD0u6QtKYFBsPPJvbbZDGxcXMzJqsbcVC0puAG4FPRsR64FJgf2A6sBa4oLZpnd1jhGPOljQgaWBoaKiCrM3MelNbioWkHckKxTUR8QOAiHg+Il6JiFeB77G5q2kQmJjbfQKwpt5xI2J+RPRHRH9fX191DTAz6zHtuBtKwOXA8oi4MBcfl9vsJODRNL8ImCVpZ0lTgKnAfa3K18zM2nM31BHAR4BHJC1Nsc8Cp0iaTtbFtBo4EyAilklaCDxGdifV2b4TysystVpeLCLiV9Qfh7itwT7zgHmVJWVmZg2148qi50ye88OmH3P1eR9o+jHNzEbi132YmVkhFwszMyvkYmFmZoVcLMzMrJCLhZmZFXKxMDOzQi4WZmZWyMXCzMwKuViYmVkhFwszMyvkYmFmZoVcLMzMrJBfJNilmv1yQr+Y0MwacbGwSriYmW1f3A1lZmaFfGVhQDXf3GgmfxPErL1cLKxnuavMrLyu6YaSNEPSCkkrJc1pdz5mZr2kK64sJI0CvgO8DxgE7pe0KCIea29mZpv5SsW2Z11RLIBDgZUR8SSApOuAmYCLhW23On0cqRt0Q8Htlv/I6JZiMR54Nrc8CLxj+EaSZgOz0+J/SloxbJO9gH+vJMPWc1s6k9vSQfSNP8x2fVtyGrYl1+at9ZZ6wW4pFqoTi9cFIuYD80c8iDQQEf3NTKxd3JbO5LZ0Jrdl23XLAPcgMDG3PAFY06ZczMx6TrcUi/uBqZKmSNoJmAUsanNOZmY9oyu6oSJik6RzgDuAUcAVEbFsKw41YhdVF3JbOpPb0pnclm2kiNd1/ZuZmb1Gt3RDmZlZG7lYmJlZoZ4oFp36qhBJEyX9TNJyScsknZviYyXdKemJ9Dsmt8/c1I4Vko7NxQ+R9Ehad7EkpfjOkq5P8XslTa6wPaMkPSjp1m5uRzrfHpJukPR4+v/n8G5tj6S/Sf98PSppgaRduqUtkq6QtE7So7lYS3KXdFo6xxOSTquoLd9K/4w9LOkmSXt0bFsiYrueyAbEVwH7ATsBDwHT2p1Xym0ccHCa3x34V2Aa8E1gTorPAb6R5qel/HcGpqR2jUrr7gMOJ3sm5Xbg/Sn+P4HL0vws4PoK2/Mp4Frg1rTcle1I57gK+B9pfidgj25sD9kDrU8Bu6blhcBHu6UtwJHAwcCjuVjluQNjgSfT75g0P6aCthwD7JDmv9HJbansX7ZOmdL/qHfklucCc9ud1wi53kL2/qsVwLgUGwesqJc72d1hh6dtHs/FTwG+m98mze9A9uSnKsh9ArAYeA+bi0XXtSMdfzTZH1gNi3dde9j89oOx6Ty3pj9QXdMWYDKv/QNbee75bdK67wKnNLstw9adBFzTqW3phW6oeq8KGd+mXEaULhkPAu4F9omItQDpd++02UhtGZ/mh8dfs09EbAJ+A+xZQRP+Afhb4NVcrBvbAdlV6BDw/dSt9k+SdqML2xMRzwHnA88Aa4HfRMSPu7EtOa3IvR1/Nz5GdqXwmryGnb9tbemFYlHqVSHtJOlNwI3AJyNifaNN68SiQbzRPk0j6XhgXUQsKbtLnVjb25GzA1l3waURcRDwEll3x0g6tj2pP38mWVfGvsBukv6q0S4j5NX2tpTQzNxb2iZJnwM2AddsQ16VtqUXikVHvypE0o5kheKaiPhBCj8vaVxaPw5Yl+IjtWUwzQ+Pv2YfSTsAbwZeaHIzjgBOkLQauA54j6R/7sJ21AwCgxFxb1q+gax4dGN73gs8FRFDEfF74AfAf+3SttS0IveW/d1IA87HA38ZqZ+owfnb1pZeKBYd+6qQdBfD5cDyiLgwt2oRULtj4TSysYxafFa662EKMBW4L12Kb5B0WDrmqcP2qR3rvwE/zf0D2RQRMTciJkTEZLL/fX8aEX/Vbe3IteffgGclvS2FjiZ7HX43tucZ4DBJb0w5HA0s79K21LQi9zuAYySNSVdnx6RYU0maAXwGOCEiXh7Wxs5qS7MGoTp5Ao4ju9NoFfC5dueTy+vPyC4HHwaWpuk4sn7GxcAT6Xdsbp/PpXasIN0FkeL9wKNp3T+y+en8XYB/AVaS3UWxX8VtOorNA9zd3I7pwED6/+ZmsrtIurI9wJeBx1Me/4fsDpuuaAuwgGys5fdk/4V8RqtyJxtDWJmm0ytqy0qy8YTav/+XdWpb/LoPMzMr1AvdUGZmto1cLMzMrJCLhZmZFXKxMDOzQi4WZmZWyMXCrIkk7SPpWklPSloi6W5JJ0k6StJv0utDVki6Kz35XtvvS5Kek7RU2dthT2hnO8yG64rPqpp1g/SQ1M3AVRHx31PsLcAJwIvALyPi+BSfDtws6bcRsTgd4qKIOF/SHwG/lLR3RLz6+jOZtZ6vLMya5z3A7yLislogIp6OiG8P3zAilgJfAc6ps2452XuC9qowV7Mt4mJh1jwHAA9swfYPAP9leFDSO8je3jvUpLzMtpmLhVlFJH1H0kOS7h9pk2HLfyNpKdkrxf8i/HoF6yAeszBrnmXAn9cWIuJsSXuRvWOqnoPIXupXc1FEnF9hfmZbzVcWZs3zU2AXSWflYm+st6GktwN/B3ynFYmZbStfWZg1SUSEpBOBiyT9LdmYw0tkr6AGeKekB8kKyDrgE7k7ocw6mt86a2ZmhdwNZWZmhVwszMyskIuFmZkVcrEwM7NCLhZmZlbIxcLMzAq5WJiZWaH/D000DwOgH2rnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the GDP histogram \n",
    "# There are a lots of lots of very very poor country. \n",
    "# The world is skewwwwwwwd!\n",
    "\n",
    "histogram(df, 'GDP', bins=15, yname='Number of countries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram(df, 'Alcohol', bins=15, yname='Number of countries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram(df, 'Population', bins=15, yname='Number of countries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+--------------------+\n",
      "|summary|          Population|\n",
      "+-------+--------------------+\n",
      "|  count|                2286|\n",
      "|   mean|1.2753375120052498E7|\n",
      "| stddev| 6.101209650842794E7|\n",
      "|    min|                34.0|\n",
      "|    25%|            195553.0|\n",
      "|    50%|           1384969.0|\n",
      "|    75%|           7423289.0|\n",
      "|    max|       1.293859294E9|\n",
      "+-------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.select('Population').summary().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram(df.filter(df['Population']<100000000), \\\n",
    "          'Population', bins=15, yname='Number of countries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Line plot \n",
    "\n",
    "# This type of plots are usually represented for the time series. \n",
    "# Sometimes distribution functions are also represented as line plot. \n",
    "# First we group by the data in time (or on the variable of which \n",
    "# distribution is of interest) and then we apply some sort of averaging \n",
    "# on the grouped data. I have included few averaging in my implemantation. \n",
    "# Others can be added similarly or can be done manually outside funtion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lineplot(df, x, y, measure='mean'):\n",
    "    \n",
    "    '''\n",
    "    This function makes a line plot from Spark dataframe df with \n",
    "    independent variable x and averaged over y.\n",
    "    '''\n",
    "    \n",
    "    if measure == 'mean':\n",
    "        \n",
    "        # Grouping the dataframe by x and averaging over y\n",
    "        # Sorting is important because Spark shuffle the values \n",
    "        # when doing the calculation\n",
    "        \n",
    "        pd_df = df.groupBy(x).mean(y).toPandas().sort_values(by=x)\n",
    "        \n",
    "        # Making plot using pandas \n",
    "        pd_df.plot(x, 'avg({})'.format(y), legend=False)\n",
    "        \n",
    "    elif measure == 'total':\n",
    "        pd_df = df.groupBy(x).sum(y).toPandas().sort_values(by=x)\n",
    "        pd_df.plot(x, 'sum({})'.format(y), legend=False)\n",
    "        \n",
    "    elif measure == 'count':\n",
    "        pd_df = df.groupBy(x).count().toPandas().sort_values(by=x)\n",
    "        pd_df.plot(x, 'count', legend=False)\n",
    "    \n",
    "    plt.ylabel(y)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lineplot(df, 'Year', 'GDP', measure='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lineplot(df, 'Year', 'Population', measure='total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The world has become no alcoholic all of sudden??!\n",
    "\n",
    "lineplot(df, 'Year', 'alcohol', measure='total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bar plot \n",
    "\n",
    "# In bar plot usually we are interested on top few entries \n",
    "# if thre are many. For this we can order the dataframe to \n",
    "# get the top entries on top. Then we limit the number of \n",
    "# entries, which is also the number of bars we want to see in \n",
    "# the plot, before converting it to the pandas datafrmae. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def barplot(df, col, lim=10, yname=None):\n",
    "    \n",
    "    '''\n",
    "    This function makes a bar plot for the Spark dataframe df \n",
    "    for categorical column col only selecting top categories \n",
    "    as specified by lim. \n",
    "    '''\n",
    "    \n",
    "    # Grouping by the categories, counting on each categories \n",
    "    # and ordering them by the count\n",
    "    classes = df.groupBy(col).count().orderBy('count', ascending=False)\n",
    "    \n",
    "    # Take first 'lim' number of rows and convert to pandas  \n",
    "    pd_df = classes.limit(lim).toPandas()\n",
    "    \n",
    "    # Making plot \n",
    "    pd_df.plot(kind='bar', x=col, legend=False)\n",
    "    plt.ylabel(yname)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Everybody is participating every year (almost)\n",
    "\n",
    "barplot(df, 'Year', lim=20, yname='Number of data per Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Most of the world population consumes very little alcohol. \n",
    "\n",
    "barplot(df, 'Alcohol', lim=20, yname='Number of countries')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which country has the largest population?\n",
    "# Which country has the largest GDP?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pie chart \n",
    "\n",
    "# Idea of bar chart is exactly similar to the bar plot \n",
    "# Just replace bar plot at the end by pir chart. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pieplot(df, col, lim=10, yname=None):\n",
    "    \n",
    "    '''\n",
    "    This function makes a pie chart for the Spark dataframe df \n",
    "    for categorical column col only selecting top categories \n",
    "    as specified by lim. \n",
    "    '''\n",
    "    \n",
    "    # Grouping by the categories, counting on each categories \n",
    "    # and ordering them by the count\n",
    "    classes = df.groupBy(col).count().orderBy('count', ascending=False)\n",
    "    \n",
    "    # Take first 'lim' number of rows and convert to pandas  \n",
    "    pd_df = classes.limit(lim).toPandas()\n",
    "    \n",
    "    # Making plot \n",
    "    pd_df.plot(kind='pie', x=col, y='count', \\\n",
    "           labels=pd_df[col], legend=False)\n",
    "    plt.ylabel(None)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pieplot(df, 'Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pieplot(df, 'Alcohol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pieplot(df, 'Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Box plot \n",
    "\n",
    "# Box plot can be computationally very heavy. Usually it takes all \n",
    "# the data, calculates the quartiles and also indicates the outlier. \n",
    "# It could be done easier if we ignore the outlier part. \n",
    "# However, outliers are import part of box plot. So, I choose sampling \n",
    "# method. I choose a fraction of sample of the total data and make a \n",
    "# Box plot out of that. \n",
    "\n",
    "# An alternative approach could be using quartiles and outliers which \n",
    "# is not done here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def boxplot(df, col, group_by, samp_size=10000): \n",
    "    \n",
    "    '''\n",
    "    This function makes a box plot for a Spark dataframe df \n",
    "    grouping them by a column name called group_by and their\n",
    "    aggrigate on another column called col. \n",
    "    '''\n",
    "    \n",
    "    # Selecting sample for the large dataset \n",
    "    # For samll data sampling is not done. For the large data \n",
    "    # sample size is the specified number. \n",
    "    \n",
    "    true_samp_size = min(samp_size/df.count(), 1.0)\n",
    "    pd_df = df.sample(False, true_samp_size).toPandas()\n",
    "    \n",
    "    # Making plot \n",
    "    pd_df.boxplot(col, by=group_by, figsize=(8, 5))\n",
    "    plt.ylabel(col)\n",
    "#     plt.xticks([2000, 2005, 2010, 2015])\n",
    "    plt.title(None)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The rich countries are outliers in our world. \n",
    "# Being poor is the norm. \n",
    "\n",
    "boxplot(df, 'GDP', 'Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(df.filter(df['Population']<10000000), 'Population', 'Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}