Univariate_EDA_functions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all required libraries\n",
    "from pyspark.sql import SparkSession\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "import os\n",
    "from pyspark.ml.feature import Bucketizer\n",
    "from pyspark.ml.feature import QuantileDiscretizer\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import isnan\n",
    "import pyspark.sql.functions as f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building Spark session necessary to do before starting any spark code\n",
    "spark = SparkSession.builder.appName(\"CG_analysis\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spark datatypes to contruct schema\n",
    "from pyspark.sql.types import  (StructType, \n",
    "                                StructField, \n",
    "                                DateType, \n",
    "                                BooleanType,\n",
    "                                DoubleType,\n",
    "                                IntegerType,\n",
    "                                StringType,\n",
    "                               TimestampType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_schema=StructType([StructField(\"PassengerId\", IntegerType(), True),\n",
    "                            StructField(\"Survived\", IntegerType(), True),\n",
    "                            StructField(\"Pclass\", IntegerType(), True ),\n",
    "                            StructField(\"Name\", StringType(), True),\n",
    "                            StructField(\"Sex\", StringType(), True),\n",
    "                            StructField(\"Age\", DoubleType(), True),\n",
    "                            StructField(\"SibSp\", IntegerType(), True),\n",
    "                            StructField(\"Parch\", IntegerType(), True),\n",
    "                            StructField(\"Ticket\", StringType(), True),\n",
    "                            StructField(\"Fare\", DoubleType(), True),\n",
    "                            StructField(\"Cabin\", StringType(), True),\n",
    "                            StructField(\"Embarked\", StringType(), True)\n",
    "                            ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = spark.read.csv(\"titanic.csv\",\n",
    "                       header = True, \n",
    "                        schema = data_schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(PassengerId=1, Survived=0, Pclass=3, Name='Braund, Mr. Owen Harris', Sex='male', Age=22.0, SibSp=1, Parch=0, Ticket='A/5 21171', Fare=7.25, Cabin=None, Embarked='S'),\n",
       " Row(PassengerId=2, Survived=1, Pclass=1, Name='Cumings, Mrs. John Bradley (Florence Briggs Thayer)', Sex='female', Age=38.0, SibSp=1, Parch=0, Ticket='PC 17599', Fare=71.2833, Cabin='C85', Embarked='C'),\n",
       " Row(PassengerId=3, Survived=1, Pclass=3, Name='Heikkinen, Miss. Laina', Sex='female', Age=26.0, SibSp=0, Parch=0, Ticket='STON/O2. 3101282', Fare=7.925, Cabin=None, Embarked='S'),\n",
       " Row(PassengerId=4, Survived=1, Pclass=1, Name='Futrelle, Mrs. Jacques Heath (Lily May Peel)', Sex='female', Age=35.0, SibSp=1, Parch=0, Ticket='113803', Fare=53.1, Cabin='C123', Embarked='S'),\n",
       " Row(PassengerId=5, Survived=0, Pclass=3, Name='Allen, Mr. William Henry', Sex='male', Age=35.0, SibSp=0, Parch=0, Ticket='373450', Fare=8.05, Cabin=None, Embarked='S')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "eda_op_dict={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_treatment_process(df,c):\n",
    "    q1,q3 = df.approxQuantile(c, [0.25, 0.75], 0)\n",
    "    iqr = q3 - q1\n",
    "    #print(q1,q3)\n",
    "    minimum = q1 - (iqr * 1.5)\n",
    "    maximum = q3 + (iqr * 1.5)\n",
    "    return minimum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discrete_count_plot(result,column):\n",
    "    mpl.style.use('seaborn')\n",
    "    plt.bar(range(len(result)), list(result.loc[:,'count'].values), align='center')\n",
    "    plt.xlabel(column,fontsize=15)\n",
    "    plt.ylabel('Counts',fontsize=15)\n",
    "    plt.xticks(range(len(result)), list(result.loc[:,column].values),fontsize=15,rotation=65, horizontalalignment='right')\n",
    "    plt.title('Count Histogram Representation for '+column,fontsize=15)\n",
    "    plt.savefig(column+'.png',bbox_inches = \"tight\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def continuous_count_plot(result,column,labels=None):\n",
    "    mpl.style.use('seaborn')\n",
    "    if labels is None:\n",
    "        labels=list(result.loc[:,'labels'].values)\n",
    "        \n",
    "    #plt.figure(figsize=(16,300))\n",
    "    plt.bar(range(len(result)), list(result.loc[:,'count'].values), align='center')\n",
    "    plt.xlabel(column,fontsize=15)\n",
    "    plt.ylabel('Counts',fontsize=15)\n",
    "    plt.xticks(range(len(result)), labels,fontsize=15,rotation=65, horizontalalignment='right')\n",
    "    plt.title('Count Histogram Representation for '+column,fontsize=15)\n",
    "    plt.savefig(column+'.png',bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import col\n",
    "def get_continuous_lab(unlabel_list,inputCol,outputCol,query_frame,get_scientfic=False):\n",
    "    label_df=pd.DataFrame()\n",
    "    label_list=[]\n",
    "    for i in unlabel_list:\n",
    "        re=query_frame.filter(col(outputCol).isin([i]))\n",
    "        mini=re.agg({inputCol: \"min\"}).collect()[0]['min('+inputCol+')']\n",
    "        if get_scientfic:\n",
    "            if mini is not None:\n",
    "                mini='%e' % mini\n",
    "        maxm=re.agg({inputCol: \"max\"}).collect()[0]['max('+inputCol+')']\n",
    "        if get_scientfic:\n",
    "            if maxm is not None:\n",
    "                maxm='%e' % maxm\n",
    "        label_list.append(str(mini)+'-'+str(maxm))\n",
    "    label_df[outputCol]=unlabel_list\n",
    "    label_df['labels']=label_list\n",
    "    return label_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discrete_univariate_analysis(data,column,max_cat_len=5,save_csv=False):\n",
    "        df=data.groupBy(column).count().toPandas()\n",
    "        #sorting in descending\n",
    "        df=df.sort_values(by='count',ascending=False)\n",
    "        if len(df)>max_cat_len:\n",
    "            #taking required max length \n",
    "            df_max_cat_len=df[:max_cat_len]\n",
    "            #making dict out of df\n",
    "            dict_df_max=dict(zip(df.loc[:,column], df.loc[:,'count']))\n",
    "            #converting the keys to str\n",
    "            dict_df_max_string = dict([(str(k), v) for k, v in dict_df_max.items()])\n",
    "            #saving values of nan \n",
    "            if ('nan' in dict_df_max_string):\n",
    "                df_nan=pd.DataFrame({column:'nan','count':dict_df_max_string['nan']},index=[1])\n",
    "            else:\n",
    "                df_nan=pd.DataFrame()\n",
    "            #taking sum of other values and putting in other category\n",
    "            sum_other_category_val=pd.DataFrame({column:'others','count':sum(df.loc[max_cat_len:,'count'])},index=[1])\n",
    "            #concatenating all of them\n",
    "            df_shorted_max = pd.concat([df_max_cat_len,df_nan,sum_other_category_val])\n",
    "            #plotting dataframe\n",
    "            discrete_count_plot(df_shorted_max,column)\n",
    "            if save_csv:\n",
    "                df_shorted_max.to_csv(column+'.csv',index=False)\n",
    "            # Update the dictionary\n",
    "            #eda_op_dict.update({'chart_id'+str(len(eda_op_dict)):{'column_name':column,'img_path':os.getcwd()+'/'+column+'.png','count':df}})\n",
    "        else:\n",
    "            #plotting dataframe\n",
    "            discrete_count_plot(df,column)\n",
    "        # Update the dictionary\n",
    "            if save_csv:\n",
    "                df.to_csv(column+'.csv',index=False)\n",
    "        eda_op_dict.update({'chart_id'+str(len(eda_op_dict)):{'column_name':column,'img_path':os.getcwd()+'/'+column+'.png','count':df}})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(data,\"Sex\",save_csv=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'chart_id0': {'column_name': 'Sex',\n",
       "  'img_path': '/home/nbuser/library/Sex.png',\n",
       "  'count':       Sex  count\n",
       "  1    male    577\n",
       "  0  female    314},\n",
       " 'chart_id1': {'column_name': 'Cabin',\n",
       "  'img_path': '/home/nbuser/library/Cabin.png',\n",
       "  'count':                Cabin  count\n",
       "  45              None    687\n",
       "  31           B96 B98      4\n",
       "  73                G6      4\n",
       "  99       C23 C25 C27      4\n",
       "  15                F2      3\n",
       "  25           C22 C26      3\n",
       "  71              E101      3\n",
       "  103              F33      3\n",
       "  67                 D      3\n",
       "  34               D20      2\n",
       "  125               C2      2\n",
       "  69              C124      2\n",
       "  37                E8      2\n",
       "  104               B5      2\n",
       "  90               D17      2\n",
       "  128  B57 B59 B63 B66      2\n",
       "  121             C125      2\n",
       "  120              D26      2\n",
       "  105              E24      2\n",
       "  65               C93      2\n",
       "  63               C68      2\n",
       "  48              C123      2\n",
       "  50               C83      2\n",
       "  60              E121      2\n",
       "  53               D35      2\n",
       "  109              C92      2\n",
       "  111              D36      2\n",
       "  40               B20      2\n",
       "  28               E33      2\n",
       "  95               D33      2\n",
       "  ..               ...    ...\n",
       "  42               B80      1\n",
       "  43               D48      1\n",
       "  44               E49      1\n",
       "  46                 T      1\n",
       "  85               B37      1\n",
       "  84               D19      1\n",
       "  83               D30      1\n",
       "  82               A10      1\n",
       "  81              C148      1\n",
       "  79               A31      1\n",
       "  78               B71      1\n",
       "  76               D47      1\n",
       "  75                B3      1\n",
       "  1                B79      1\n",
       "  72               A26      1\n",
       "  70               C30      1\n",
       "  68               E46      1\n",
       "  66               E36      1\n",
       "  64               C50      1\n",
       "  62               B19      1\n",
       "  61               D46      1\n",
       "  59               C54      1\n",
       "  57               D15      1\n",
       "  55             F G63      1\n",
       "  54              C111      1\n",
       "  52              C103      1\n",
       "  51           C62 C64      1\n",
       "  49               D37      1\n",
       "  47               A36      1\n",
       "  147              B41      1\n",
       "  \n",
       "  [148 rows x 2 columns]},\n",
       " 'chart_id2': {'column_name': 'SibSp',\n",
       "  'img_path': '/home/nbuser/library/SibSp.png',\n",
       "  'count':    SibSp  count\n",
       "  6      0    608\n",
       "  0      1    209\n",
       "  5      2     28\n",
       "  3      4     18\n",
       "  1      3     16\n",
       "  4      8      7\n",
       "  2      5      5}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda_op_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(data,\"Cabin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "discrete_univariate_analysis(data,\"SibSp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def continuous_univariate_analysis(data,column,outlier_treat=False,Buckets=5,outlier_treatment=False,save_csv=False,get_scientfic_val=False):\n",
    "    out_col=\"bucket_labels\"# just given a hardcoded naming to output of label column,this does not affect code \n",
    "    # here I am selecting the column \n",
    "    data_col=data.select(column)\n",
    "    # Treating the outlier in left of ditribution anything less than q1 - (iqr * 1.5) is removed  .\n",
    "    if outlier_treatment:\n",
    "        minimum=outlier_treatment_process(data,column)\n",
    "        data_col=data_col.filter(column+'>'+str(minimum))\n",
    "        \n",
    "     \n",
    "    #QuantileDiscretizer is used when no labels are passed and only number of buckets needed are given \n",
    "    # or by default 5 is what I have taken for this function , here separate column for nan will be formed \n",
    "    # as I have used handleInvalid parameter, this gives us labels for each values in column\n",
    "    discretizer = QuantileDiscretizer(numBuckets=Buckets, inputCol=column, outputCol=out_col,handleInvalid=\"error\")\n",
    "    result_buckets = discretizer.setHandleInvalid(\"keep\").fit(data_col).transform(data_col)\n",
    "    # we get the labels from QuantileDiscretizer and grouby by the labels to get count for each labels\n",
    "    label_count_df=result_buckets.groupBy(out_col).count().toPandas().sort_values(by=out_col,ascending=True)\n",
    "    # now we need to know which labels represents which range so we find out firstly distinct labels\n",
    "    labels_distinct=list(label_count_df[out_col])\n",
    "    # now get_continuous_lab helps us to get the min and max values of particular label and gives list of \n",
    "    #labels as output\n",
    "    output_labels=get_continuous_lab(labels_distinct,column,out_col,result_buckets,get_scientfic=get_scientfic_val)\n",
    "    merged_df_labels=pd.merge(output_labels,label_count_df,on=out_col,how=\"inner\")\n",
    "    #print(merged_df_labels)\n",
    "    continuous_count_plot(merged_df_labels,column)# plotting the dataframe\n",
    "    # Update the dictionary\n",
    "    if save_csv:\n",
    "        merged_df_labels.to_csv(column+'.csv',index=False)\n",
    "    \n",
    "    eda_op_dict.update({'chart_id'+str(len(eda_op_dict))+'_continuous_data':\\\n",
    "    {'column_name':column,'img_path':os.getcwd()+'/'+column+'.png'},'count_frame':label_count_df})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "continuous_univariate_analysis(data,\"Age\",Buckets=10,outlier_treatment=False,save_csv=True,get_scientfic_val=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_univariate_analysis(data,\"Fare\",Buckets=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_univariate_analysis(data,\"Fare\",Buckets=5,outlier_treatment=True,save_csv=True,get_scientfic_val=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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