A Python package for univariate and bivariate data analysis using PySpark
Project description
pyspark_eda
pyspark_eda
is a Python library for performing exploratory data analysis (EDA) using PySpark. It offers functionalities for both univariate and bivariate analysis, handling missing values, outliers, and visualizing data distributions.
Features
- Univariate analysis: Analyze numerical and categorical columns individually.
- Bivariate analysis: Includes correlation, Cramer's V, and ANOVA.
- Automatic handling: Deals with missing values and outliers seamlessly.
- Visualization: Provides graphical representation of data distributions and relationships.
Installation
You can install pyspark_eda
via pip:
pip install pyspark_eda
Example Usage
Univariate Analysis
from pyspark.sql import SparkSession
from pyspark_eda import get_univariate_analysis
# Initialize Spark session
spark = SparkSession.builder.appName('DataAnalysis').getOrCreate()
# Load your data into a PySpark DataFrame
df = spark.read.csv('your_data.csv', header=True, inferSchema=True)
# Perform univariate analysis
get_univariate_analysis(df,table_name="your_table_name",, print_graphs=1 id_list=['id_column'])
Bivariate Analysis
from pyspark.sql import SparkSession
from pyspark_eda import get_bivariate_analysis
# Initialize Spark session
spark = SparkSession.builder.appName('DataAnalysis').getOrCreate()
# Load your data into a PySpark DataFrame
df = spark.read.csv('your_data.csv', header=True, inferSchema=True)
# Perform bivariate analysis
get_bivariate_analysis(df,table_name="bivariate_analysis_results", print_graphs=1, id_columns=['id_column'], correlation_analysis=1, cramer_analysis=1, anova_analysis=1)
Functions
get_univariate_analysis
Parameters
- df (DataFrame): The input PySpark DataFrame.
- table_name (str): The base table name to save the results
- print_graphs (int, optional): Whether to print graphs (1 for yes, 0 for no),default value is 0.
- id_list (list, optional): List of columns to exclude from analysis.
Description
Performs univariate analysis on the DataFrame and prints summary statistics and visualizations.
get_bivariate_analysis
Parameters
- df (DataFrame): The input PySpark DataFrame.
- table_name (str): The base table name to save the results
- print_graphs (int, optional): Whether to print graphs (1 for yes, 0 for no),default value is 0.
- id_columns (list, optional): List of columns to exclude from analysis.
- correlation_analysis (int, optional): Whether to perform correlation analysis (1 for yes, 0 for no),default value is 1.
- cramer_analysis (int, optional): Whether to perform Cramer's V analysis (1 for yes, 0 for no), default value is 1.
- anova_analysis (int, optional): Whether to perform ANOVA analysis (1 for yes, 0 for no),default value is 1.
Description
Performs bivariate analysis on the DataFrame, including correlation, Cramer's V, and ANOVA.
Contact
- Author: Tanya Irani
- Email: tanyairani22@gmail.com
Project details
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