edaSol 0.1.0

Library to help accelerataing Exploratory Data Analysis (EDA)

edaSol

A Python library to accelerate Exploratory Data Analysis (EDA).

edaSol provides simple, intuitive functions to quickly understand your dataset's structure, quality, and distributions without writing repetitive boilerplate code.

Features

• Quick Data Summary - Get data types, null counts, unique values at a glance
• Data Quality Reports - Comprehensive analysis of missing values, duplicates, and memory usage
• Outlier Detection - IQR-based outlier identification
• Categorical Analysis - Statistics for categorical columns
• Visualization Suite - Distribution plots, correlation heatmaps, boxplots, and more

Installation

pip install edaSol

Or install from source:

git clone https://github.com/SoloWPM23/edaSol.git
cd edaSol
pip install -e .

Quick Start

import pandas as pd
from edaSol import quick_summary, data_quality_report, plot_numerical_dist

# Load your data
df = pd.read_csv('your_data.csv')

# Get a quick summary of all columns
summary = quick_summary(df)
print(summary)

# Generate a comprehensive quality report
report = data_quality_report(df)
print(f"Shape: {report['shape']}")
print(f"Duplicates: {report['duplicates']}")
print(f"Memory: {report['memory_usage']} MB")

# Visualize numeric distributions
plot_numerical_dist(df)

API Reference

Core Functions

quick_summary(df, columns=None)

Returns a summary DataFrame with key statistics for each column.

from edaSol import quick_summary

summary = quick_summary(df)
# Returns: Data Type, Null Count, Null Percent, Unique Count, Sample Value

detect_outliers_iqr(df, column, return_bounds=False)

Detects outliers using the IQR (Interquartile Range) method.

from edaSol import detect_outliers_iqr

# Get outlier indices
outliers = detect_outliers_iqr(df, 'price')

# Get outliers with bounds
outliers, lower, upper = detect_outliers_iqr(df, 'price', return_bounds=True)

describe_categorical(df, columns=None)

Returns descriptive statistics for categorical columns.

from edaSol import describe_categorical

cat_stats = describe_categorical(df)
# Returns: Count, Unique, Top Value, Top Frequency, Top Percent

detect_duplicates(df, subset=None, keep='first')

Finds and returns duplicate rows.

from edaSol import detect_duplicates

duplicates = detect_duplicates(df)
duplicates_by_cols = detect_duplicates(df, subset=['name', 'email'])

data_quality_report(df)

Generates a comprehensive data quality report.

from edaSol import data_quality_report

report = data_quality_report(df)
# Returns dict with: shape, memory_usage, dtypes, missing, duplicates,
# numeric_summary, categorical_summary

Visualization Functions

plot_numerical_dist(df, columns=None, figsize=(12, 4), show=True)

Creates histogram with KDE plots for numeric columns.

from edaSol import plot_numerical_dist

plot_numerical_dist(df)
plot_numerical_dist(df, columns=['age', 'salary'])

plot_correlation_heatmap(df, figsize=(10, 8), annot=True, mask_upper=True, show=True)

Creates a correlation heatmap for numeric columns.

from edaSol import plot_correlation_heatmap

plot_correlation_heatmap(df)
plot_correlation_heatmap(df, mask_upper=False)  # Show full matrix

plot_missing_matrix(df, figsize=(12, 6), show=True)

Visualizes missing values as a heatmap matrix.

from edaSol import plot_missing_matrix

plot_missing_matrix(df)

plot_categorical_dist(df, columns=None, figsize=(12, 4), top_n=10, show=True)

Creates bar plots for categorical columns.

from edaSol import plot_categorical_dist

plot_categorical_dist(df)
plot_categorical_dist(df, top_n=5)  # Show only top 5 categories

plot_boxplots(df, columns=None, figsize=(12, 4), show=True)

Creates boxplots to visualize distributions and outliers.

from edaSol import plot_boxplots

plot_boxplots(df)
plot_boxplots(df, columns=['age', 'income'])

plot_pairplot(df, columns=None, hue=None, diag_kind='kde', show=True)

Creates pairwise scatter plots for numeric columns.

from edaSol import plot_pairplot

plot_pairplot(df)
plot_pairplot(df, hue='category')  # Color by category

Requirements

• Python >= 3.8
• pandas >= 1.5.0
• numpy >= 1.20.0
• matplotlib >= 3.5.0
• seaborn >= 0.11.0

License

MIT License

Author

Solo Manurung ([e-mail])

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.