What does a typical exploratory data analysis (EDA) workflow look like in Python?

EDA is the first thing you do with a new dataset before any modelling. The goal is to understand the data's structure, quality, and relationships, and to spot problems (wrong dtypes, missing values, outliers, data leakage) before they propagate into a model.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# 1. Load and inspect
df = pd.read_csv('housing.csv')
print(df.shape)          # (rows, cols)
print(df.dtypes)         # types per column
print(df.head())         # first 5 rows
print(df.info())         # dtypes + non-null counts
print(df.describe())     # summary stats for numeric cols

# 2. Missing value audit
missing = df.isnull().sum().sort_values(ascending=False)
print(missing[missing > 0])

# 3. Duplicate rows
print(df.duplicated().sum())
df = df.drop_duplicates()

# 4. Distribution of each numeric column
df.select_dtypes('number').hist(bins=30, figsize=(16, 10))
plt.tight_layout(); plt.show()

# 5. Correlation heatmap
corr = df.select_dtypes('number').corr()
sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm')
plt.title('Correlation Matrix'); plt.show()

# 6. Target variable distribution
target = 'price'
sns.histplot(df[target], kde=True)
print(f'Skewness: {df[target].skew():.2f}')

# 7. Categorical breakdown
for col in df.select_dtypes('object').columns:
    print(df[col].value_counts())

# 8. Outlier detection
for col in df.select_dtypes('number').columns:
    Q1, Q3 = df[col].quantile([0.25, 0.75])
    IQR = Q3 - Q1
    n_out = ((df[col] < Q1-1.5*IQR)|(df[col] > Q3+1.5*IQR)).sum()
    if n_out > 0: print(f'{col}: {n_out} outliers')

EDA is iterative — findings in step 4 send you back to step 2, insights in the correlation matrix raise questions answered by group analysis. Keep a notebook with your observations alongside the code so you and your team can understand what was found and why certain preprocessing decisions were made.

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