Resampling time series data in pandas is like tuning your data stream to just the right frequency.
Basics of Resampling
Resampling in pandas is essential for changing the frequency of your time series data, which is great for making summaries or preparing data for further analysis:
import pandas as pd
# Create a time series DataFrame
dates = pd.date_range('20240101', periods=6, freq='D')
df = pd.DataFrame({'Data': [100, 101, 102, 103, 104, 105]}, index=dates)
# Resample the data to a weekly sum
weekly_sum = df.resample('W').sum()
print(weekly_sum)
This snippet changes the daily data into weekly sums, giving you a broader view of your data’s trends.
Adjusting the tempo of your time series data with pandas’ resampling lets you spotlight trends and patterns at just the right scale, making your data dance to your analysis tune.