Window with GroupBy¶

Combine window functions with groupby to apply rolling, expanding, or EWM calculations within groups.

Rolling Within Groups¶

Apply rolling calculations per group.

1. Basic Pattern¶

import pandas as pd

df = pd.DataFrame({
    'Ticker': ['AAPL', 'AAPL', 'AAPL', 'MSFT', 'MSFT', 'MSFT'],
    'Date': pd.date_range('2024-01-01', periods=3).tolist() * 2,
    'Close': [150, 152, 151, 350, 355, 353]
})

df['5D_MA'] = df.groupby('Ticker')['Close'].rolling(window=2).mean().reset_index(0, drop=True)
print(df)

2. Using transform¶

df['MA'] = df.groupby('Ticker')['Close'].transform(
    lambda x: x.rolling(2).mean()
)

3. Preserves Original Index¶

The transform approach maintains alignment with original DataFrame.

Expanding Within Groups¶

Cumulative calculations per group.

1. Cumulative Mean per Group¶

df['Cum_Mean'] = df.groupby('Ticker')['Close'].transform(
    lambda x: x.expanding().mean()
)

2. Running Max per Group¶

df['Running_Max'] = df.groupby('Ticker')['Close'].transform(
    lambda x: x.expanding().max()
)

3. Since-Inception Return per Asset¶

df['Since_Start'] = df.groupby('Ticker')['Close'].transform(
    lambda x: x / x.iloc[0] - 1
)

EWM Within Groups¶

Exponentially weighted calculations per group.

1. EWMA per Asset¶

df['EWMA'] = df.groupby('Ticker')['Close'].transform(
    lambda x: x.ewm(span=10).mean()
)

2. EWM Volatility per Asset¶

returns = df.groupby('Ticker')['Close'].pct_change()
df['EWM_Vol'] = returns.groupby(df['Ticker']).transform(
    lambda x: x.ewm(span=20).std()
)

3. Independent Decay per Group¶

Each group's EWM is calculated independently.

Practical Example¶

Multi-asset portfolio analysis.

1. Sample Data¶

import numpy as np

np.random.seed(42)
dates = pd.date_range('2024-01-01', periods=50)
tickers = ['AAPL', 'MSFT', 'GOOGL']

data = []
for ticker in tickers:
    prices = 100 + np.cumsum(np.random.randn(50))
    for date, price in zip(dates, prices):
        data.append({'Ticker': ticker, 'Date': date, 'Close': price})

df = pd.DataFrame(data)

2. Add Rolling Stats per Asset¶

df['MA20'] = df.groupby('Ticker')['Close'].transform(
    lambda x: x.rolling(20).mean()
)

df['Vol20'] = df.groupby('Ticker')['Close'].transform(
    lambda x: x.pct_change().rolling(20).std()
)

3. Cross-sectional Rank¶

# Rank within each date
df['Rank'] = df.groupby('Date')['Close'].rank()

Performance Tip¶

Efficient grouped window operations.

1. Single transform Call¶

# Faster: single grouped operation
df['MA'] = df.groupby('Ticker')['Close'].transform(
    lambda x: x.rolling(20).mean()
)

2. Avoid Looping¶

# Slower: don't loop over groups
# for ticker in df['Ticker'].unique():
#     ...

3. Use Built-in When Possible¶

GroupBy rolling is optimized internally.