Resampling¶

Resampling changes the frequency of time series data, either downsampling (e.g., daily to monthly) or upsampling (e.g., monthly to daily).

Basic Resampling¶

Change time series frequency.

1. Monthly Average¶

import pandas as pd

s = pd.Series(
    range(100),
    index=pd.date_range('2025-01-01', periods=100, freq='D')
)

monthly = s.resample('M').mean()
print(monthly)

2. Weekly Sum¶

weekly = s.resample('W').sum()

3. Quarterly Max¶

quarterly = s.resample('Q').max()

Common Frequencies¶

Resampling frequency strings.

1. Time-based¶

s.resample('D').mean()   # Daily
s.resample('W').mean()   # Weekly
s.resample('M').mean()   # Monthly
s.resample('Q').mean()   # Quarterly
s.resample('Y').mean()   # Yearly

2. Business Frequencies¶

s.resample('B').mean()   # Business day
s.resample('BM').mean()  # Business month end

3. Intraday¶

s.resample('H').mean()   # Hourly
s.resample('T').mean()   # Minute

OHLC Aggregation¶

Financial price data aggregation.

1. Open-High-Low-Close¶

prices = pd.Series(
    [100, 101, 99, 102, 98, 103],
    index=pd.date_range('2025-01-01', periods=6, freq='D')
)

ohlc = prices.resample('W').ohlc()
print(ohlc)

2. Standard for Financial Data¶

OHLC is standard for representing price bars.

3. With Volume¶

# For DataFrame with price and volume
df.resample('W').agg({
    'price': 'ohlc',
    'volume': 'sum'
})

Aggregation Functions¶

Apply various aggregations when resampling.

1. Built-in Functions¶

s.resample('M').mean()
s.resample('M').sum()
s.resample('M').first()
s.resample('M').last()
s.resample('M').count()

2. Multiple Functions¶

s.resample('M').agg(['mean', 'std', 'min', 'max'])

3. Custom Function¶

s.resample('M').apply(lambda x: x.max() - x.min())

Upsampling¶

Increase frequency (requires filling).

1. Daily to Hourly¶

daily = pd.Series([100, 101, 102], index=pd.date_range('2025-01-01', periods=3, freq='D'))
hourly = daily.resample('H').ffill()  # Forward fill

2. Fill Methods¶

s.resample('H').ffill()    # Forward fill
s.resample('H').bfill()    # Backward fill
s.resample('H').asfreq()   # No fill (NaN)

3. Interpolation¶

s.resample('H').interpolate()

Practical Examples¶

Financial analysis with resampling.

1. Monthly Returns¶

import yfinance as yf

aapl = yf.download('AAPL', start='2023-01-01', end='2024-01-01')
monthly_avg = aapl['Close'].resample('M').mean()
print(monthly_avg)

2. Rolling Analysis on Resampled Data¶

monthly_returns = aapl['Close'].resample('M').last().pct_change()

3. Plotting¶

import matplotlib.pyplot as plt

monthly_avg.plot(title='AAPL Monthly Average Closing Price')
plt.show()