SimplyEDA 0.1.7

A simple library for exploratory data analysis

SimpleEDA

SimpleEDA is a Python library for simple exploratory data analysis tasks. It provides functions to handle outliers, find special characters, calculate Variance Inflation Factor (VIF), detect duplicates, and visualize continuous data using box plots.

Installation

You can install SimpleEDA via pip:

pip install SimpleEDA

Usage

Below are examples of how to use the various functions provided by SimpleEDA.

Importing the Library

import SimpleEDA as eda
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 2, 4, 5, 6, 7, 8, 9, 10],
    'B': [11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
    'C': [21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
    'D': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
})

remove_outlier

This function removes outliers from a column based on the Interquartile Range (IQR) method.

lower, upper = eda.remove_outlier(df['A'])
print(f"Lower bound: {lower}, Upper bound: {upper}")

Parameters:

• col (pd.Series): The column from which to remove outliers.
• multiplier (float): The multiplier for the IQR to define outliers. Default is 1.5.

Returns:

• tuple: Lower and upper range for outlier detection.

find_specialchar

This function finds special characters in a DataFrame.

eda.find_specialchar(df)

Parameters:

• df (pd.DataFrame): The DataFrame to check.

Returns:

• None

vif_cal

This function calculates the Variance Inflation Factor (VIF) for each feature in the DataFrame.

eda.vif_cal(df[['A', 'B', 'C']])

Parameters:

• input_data (pd.DataFrame): The DataFrame for which to calculate VIF.

Returns:

• None

dups

This function shows a duplicate summary of a DataFrame.

eda.dups(df)

Parameters:

• df (pd.DataFrame): The DataFrame to check for duplicates.

Returns:

• None

boxplt_continous

This function plots boxplots for all continuous features in the DataFrame.

eda.boxplt_continous(df)

Parameters:

• df (pd.DataFrame): The DataFrame to plot.

Returns:

• None

Example

Here's a complete example of using SimpleEDA with a sample DataFrame:

import SimpleEDA as eda
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 2, 4, 5, 6, 7, 8, 9, 10],
    'B': [11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
    'C': [21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
    'D': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
})

# Remove outliers
lower, upper = eda.remove_outlier(df['A'])
print(f"Lower bound: {lower}, Upper bound: {upper}")

# Find special characters
eda.find_specialchar(df)

# Calculate VIF
eda.vif_cal(df[['A', 'B', 'C']])

# Detect duplicates
eda.dups(df)

# Plot boxplots for continuous features
eda.boxplt_continous(df)

enhance_summary

Provides an enhanced summary of a pandas DataFrame, including custom percentiles, IQR, outliers, duplicates, missing values, and skewness. It also handles both numerical and categorical variables.

summary = eda.enhance_summary(df, custom_percentiles=[5, 95])
print(summary)

Parameters:

dataframe (pd.DataFrame): The DataFrame to summarize. custom_percentiles (list, optional): A list of custom percentiles to include in the summary.

Returns:

pd.DataFrame: DataFrame containing the enhanced summary statistics.

Example

Here's a complete example of using SimplyEDA with a sample DataFrame:

import SimplyEDA as eda
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 2, 4, 5, 6, 7, 8, 9, 10],
    'B': [11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
    'C': [21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
    'D': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
})

# Remove outliers
lower, upper = eda.remove_outlier(df['A'])
print(f"Lower bound: {lower}, Upper bound: {upper}")

# Find special characters
eda.find_specialchar(df)

# Calculate VIF
vif = eda.vif_cal(df[['A', 'B', 'C']])
print(vif)

# Detect duplicates
eda.dups(df)

# Plot boxplots for continuous features
eda.boxplt_continous(df)

# Enhanced summary
summary = eda.enhance_summary(df, custom_percentiles=[5, 95])
print(summary)

Author

This project was created by M.R.Vijay Krishnan. You can reach me at [e-mail].

License

This project is licensed under the MIT License - see the LICENSE file for details.