Basic EDA Workflow

This example demonstrates a complete exploratory data analysis (EDA) workflow using edaflow. Follow these steps to analyze your dataset:

1. Load Data

import pandas as pd
import edaflow as eda
df = pd.read_csv('your_data.csv')

2. Assess Data Quality

eda.check_null_columns(df)
eda.display_column_types(df)

3. Visualize Distributions

eda.display_boxplot(df, column='age')
eda.display_histogram(df, column='income')

4. Handle Missing Values & Outliers

df = eda.impute_numerical_median(df, column='income')
df = eda.handle_outliers_median(df, column='score')

5. Explore Relationships

eda.display_correlation_matrix(df)
eda.display_scatter_matrix(df, columns=['age', 'income', 'score'])

6. Summarize Insights

eda.summarize_eda_insights(df)

7. Analyze Categorical Features

eda.analyze_categorical_columns(df, columns=['gender', 'region'])
eda.display_barplot(df, column='region')

8. Feature Engineering

df['income_per_age'] = df['income'] / df['age']
eda.display_scatter(df, x='income_per_age', y='score')

9. Generate Summary Report

eda.create_model_report(df)

This workflow helps you quickly assess, clean, and understand your data before modeling. For more advanced analysis, see the Advanced Visualization and Data Cleaning Pipeline examples.