exploratory-data-analysis

## What I do - Investigate datasets systematically - Identify patterns, trends, and anomalies - Generate hypotheses from data - Create summary visualizations - Understand variable relationships - Assess data quality and requirements ## When to use me Use me when: - Starting any data science project - Understanding new datasets - Preparing for modeling - Answering ad-hoc analytical questions - Discovering insights in data ## Key Concepts ### EDA Workflow ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # 1. Data Overview df = pd.read_csv("data.csv") print(f"Shape: {df.shape}") print(f"Columns: {df.columns.tolist()}") print(df.info()) # 2. Summary Statistics print(df.describe(include="all")) # 3. Missing Values missing = df.isnull().sum() missing_pct = (missing / len(df)) * 100 print(pd.DataFrame({"Missing": missing, "%": missing_pct})) # 4. Univariate Analysis for col in df.select_dtypes(include=[np.number]).columns: fig, ax = plt.subplots(1, 2, figsize=(10, 4)) df[col].hist(ax=ax[0], bins=30) df.boxplot(column=col, ax=ax[1]) # 5. Bivariate Analysis numeric_cols = df.select_dtypes(include=[np.number]).columns corr = df[numeric_cols].corr() # 6. Categorical Analysis for col in df.select_dtypes(include="object").columns: print(df[col].value_counts().head(10)) ``` ### Visual EDA Techniques - **Histograms**: Distribution shape - **Box plots**: Outliers and quartiles - **Scatter plots**: Relationships - **Heatmaps**: Correlation - **Pair plots**: Multiple relationships - **Count plots**: Categorical counts