!pip install pyspark

from google.colab import drive drive.mount('/content/datasets')

# This code creates a filterable and interactive data table like excel for pandas dataframes from google.colab import data_table data_table.enable_dataframe_formatter()

import seaborn as sns import matplotlib.pyplot as plt from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('OUTLIER REMOVAL').getOrCreate() spark

df = spark.read.csv('/content/datasets/MyDrive/Datasets/UCLA_Module2_FinalProject/customer_data.csv',header = True) df.show()

df.count()

len(df.columns)

df.printSchema()

numeric_cols = df.columns[2:] numeric_cols

from pyspark.sql import functions as f from pyspark.sql.types import IntegerType

for column in numeric_cols: df = df.withColumn(column,f.col(column).cast(IntegerType()))

df.printSchema()

numeric_columns = [column[0] for column in df.dtypes if column[1]=='int'] numeric_columns

def find_outliers(df): # Identifying the numerical columns in a spark dataframe numeric_columns = [column[0] for column in df.dtypes if column[1]=='int'] # Using the `for` loop to create new columns by identifying the outliers for each feature for column in numeric_columns: less_Q1 = 'less_Q1_{}'.format(column) more_Q3 = 'more_Q3_{}'.format(column) Q1 = 'Q1_{}'.format(column) Q3 = 'Q3_{}'.format(column) # Q1 : First Quartile ., Q3 : Third Quartile Q1 = df.approxQuantile(column,[0.25],relativeError=0) Q3 = df.approxQuantile(column,[0.75],relativeError=0) # IQR : Inter Quantile Range # We need to define the index [0], as Q1 & Q3 are a set of lists., to perform a mathematical operation # Q1 & Q3 are defined seperately so as to have a clear indication on First Quantile & 3rd Quantile IQR = Q3[0] - Q1[0] #selecting the data, with -1.5*IQR to + 1.5*IQR., where param = 1.5 default value less_Q1 = Q1[0] - 1.5*IQR more_Q3 = Q3[0] + 1.5*IQR isOutlierCol = 'is_outlier_{}'.format(column) df = df.withColumn(isOutlierCol,f.when((df[column] > more_Q3) | (df[column] < less_Q1), 1).otherwise(0)) # Selecting the specific columns which we have added above, to check if there are any outliers selected_columns = [column for column in df.columns if column.startswith("is_outlier")] # Adding all the outlier columns into a new colum "total_outliers", to see the total number of outliers df = df.withColumn('total_outliers',sum(df[column] for column in selected_columns)) # Dropping the extra columns created above, just to create nice dataframe., without extra columns df = df.drop(*[column for column in df.columns if column.startswith("is_outlier")]) return df

new_df = find_outliers(df) new_df.show()

new_df_with_no_outliers = new_df.filter(new_df['total_Outliers']<=1) new_df_with_no_outliers = new_df_with_no_outliers.select(*df.columns) new_df_with_no_outliers.show()

new_df_with_no_outliers.count()

data_with_outliers = new_df.filter(new_df['total_Outliers']>=2) data_with_outliers.show()

# Selecting the numerical columns from the original dataframe and converting into pandas numeric_columns

original_numerical_df = df.select(*numeric_columns).toPandas() original_numerical_df.head(10)

# Plotting the box for the dataset after removing the outliers dataset_after_removing_outliers = new_df_with_no_outliers.toPandas() dataset_after_removing_outliers.head(10)

numeric_columns

fig,ax = plt.subplots(2,6,figsize=(15,8)) for i,df in enumerate([original_numerical_df,dataset_after_removing_outliers]): for j, col in enumerate(numeric_columns): sns.boxplot(data = df, y=col,ax=ax[i][j])