Stratified Train-Test Split in pandas

When your target variable is imbalanced—think churn prediction with only 6 % churners or a fraud model with 0.3 % positives— a random train–test split can leave you with too few minority-class examples for model training or evaluation. A stratified split solves this by sampling each class proportionally, ensuring both training and test sets mirror the overall distribution.

What Is a Stratified Split?

A stratified split is a sampling technique that divides a dataset into two or more subsets, but does so stratum-by-stratum. A stratum is usually the target label, but it can also be any categorical column (e.g., geography, customer segment). By preserving class proportions, stratification guarantees that rare classes are represented in all subsets.

Why Not Just Use train_test_split()?

The vanilla train_test_split() from sklearn.model_selection uses uniform random sampling. On highly imbalanced data, this can lead to a test set that contains zero examples of the minority class, making metrics such as recall or AUC meaningless. Stratification maintains statistical power without needing to oversample or otherwise manipulate the data.

When Is Stratification Essential?

• Binary classification with class imbalance
• Multiclass problems with uneven class frequencies
• Time-agnostic splitting (non-time-series data)
• Any experiment where subgroup representation must be preserved (gender, region, etc.)

Implementation in pandas + scikit-learn

Although pandas itself does not ship a dedicated stratified splitter, we can marry pandas with scikit-learn’s StratifiedShuffleSplit or train_test_split() with the stratify argument.

Step-by-Step Walkthrough

1. Import librariesimport pandas as pd
from sklearn.model_selection import train_test_split

2. Create or load DataFramedf = pd.read_csv("transactions.csv")

3. Choose the stratification column
   Typically y, but any categorical column works:y = df["is_fraud"] # Minority class ~0.3%

4. Split with stratificationtrain_df, test_df = train_test_split(
df, # full DataFrame
test_size=0.2, # 20% test
stratify=y, # preserve class distribution
random_state=42 # reproducibility
)

5. Verify distributionprint(df["is_fraud"].value_counts(normalize=True))
print(train_df["is_fraud"].value_counts(normalize=True))
print(test_df["is_fraud"].value_counts(normalize=True))


Under the Hood

Scikit-learn’s splitter computes indices for each class separately and then concatenates them. Because DataFrames are index-aligned, the resulting train_df and test_df maintain the original row order inside each stratum unless shuffle=False is specified.

Practical Tips & Best Practices

Set a random_state

Always specify random_state for reproducible experiments. Without it, every run will yield different splits.

Mind Rare Labels

If a class has fewer than n_splits samples, stratification will error out. Consider combining tiny classes or using cross-validation with grouping constraints.

Use StratifiedKFold for CV

For k-fold cross-validation, use StratifiedKFold to achieve the same benefits across folds.

Don’t Stratify Time Series

For purely temporal data, use forward-chaining splits instead. Stratification can leak future information into the past.

End-to-End Example

import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Load sample data
X, y = load_breast_cancer(return_X_y=True, as_frame=True)
df = X.copy()
df["target"] = y

# Stratified split
train_df, test_df = train_test_split(
df,
test_size=0.3,
stratify=df["target"],
random_state=0,
)

# Train model
clf = RandomForestClassifier(random_state=0)
clf.fit(train_df.drop("target", axis=1), train_df["target"])

# Evaluate
preds = clf.predict(test_df.drop("target", axis=1))
print(classification_report(test_df["target"], preds))


Common Mistakes and How to Fix Them

Using Wrong Column for stratify

If you mistakenly pass a continuous column, scikit-learn will complain or coerce it to unique strata, defeating the purpose. Always use a categorical or discrete label.

Forgetting to Reset Index

After splitting, indices are preserved; downstream concatenation might duplicate indices. Use .reset_index(drop=True) if index uniqueness matters.

Applying Sampling After Split

Some practitioners oversample/undersample before the split, causing train–test leakage. Always split first, then apply resampling only on the training data.

Relation to Galaxy

Galaxy is primarily a SQL editor. A stratified split happens in Python during model development, not in SQL, so Galaxy isn’t directly involved. However, once you’ve trained a model, you might store results in a database and analyze them via Galaxy’s lightning-fast SQL editor.

Key Takeaways

• Stratification preserves class balance, producing more reliable metrics.
• Use train_test_split(..., stratify=target) for quick one-off splits.
• Always validate that the distribution in both subsets matches the original.