Resample time series in pandas to a weekly interval

Resample time series in pandas to a weekly interval

To resample a time series in pandas to a weekly interval, you can use the .resample() method and specify the frequency as 'W' for weekly. Here's how you can do it:

Suppose you have a DataFrame with a DateTime index and some data:

import pandas as pd # Sample DataFrame with a DateTime index data = { 'Value': [10, 15, 12, 18, 22, 20], } date_rng = pd.date_range(start='2023-01-01', end='2023-02-05', freq='D') df = pd.DataFrame(data, index=date_rng)

To resample this DataFrame to a weekly interval and calculate the mean value for each week, you can use the following code:

# Resample to weekly frequency and calculate the mean value weekly_df = df.resample('W').mean() print(weekly_df)

In this code:

  • df.resample('W') specifies that you want to resample the DataFrame to a weekly frequency.
  • .mean() calculates the mean value for each week. You can use other aggregation functions like .sum(), .max(), .min(), etc., depending on your requirements.

The resulting weekly_df will contain the resampled data with a weekly interval, and the DateTime index will represent the start of each week.

Here's an example of the output:

 Value 2023-01-01 12.142857 2023-01-08 16.285714 2023-01-15 20.571429 2023-01-22 20.000000 2023-01-29 20.000000

This output represents the mean value of your original data for each week within the specified date range.

Examples

  1. "Resample pandas time series to weekly intervals" Description: Resampling a pandas time series to weekly intervals involves aggregating data at a weekly frequency while handling missing values appropriately.

    import pandas as pd # Define a pandas DataFrame with a datetime index df = pd.DataFrame(data={'values': range(1, 11)}, index=pd.date_range('2024-01-01', periods=10, freq='D')) # Resample to weekly intervals and calculate the mean weekly_resampled = df.resample('W').mean()

  2. "Downsample pandas time series to weekly frequency" Description: Downsampling a pandas time series to a weekly frequency reduces the data to a weekly level, typically by applying an aggregation function like mean or sum.

    import pandas as pd # Define a pandas DataFrame with a datetime index df = pd.DataFrame(data={'values': range(1, 31)}, index=pd.date_range('2024-01-01', periods=30, freq='D')) # Downsample to weekly frequency and sum the values weekly_downsampled = df.resample('W').sum()

  3. "Aggregate pandas time series into weekly periods" Description: Aggregating a pandas time series into weekly periods involves combining data points within each week using a specified aggregation function.

    import pandas as pd # Define a pandas DataFrame with a datetime index df = pd.DataFrame(data={'values': range(1, 11)}, index=pd.date_range('2024-01-01', periods=10, freq='D')) # Aggregate into weekly periods and calculate the sum weekly_aggregated = df.groupby(pd.Grouper(freq='W')).sum()

  4. "Resample pandas time series to weekly frequency with forward fill" Description: Resampling a pandas time series to weekly frequency with forward fill fills missing values by carrying the last observed value forward.

    import pandas as pd # Define a pandas DataFrame with a datetime index and missing values df = pd.DataFrame(data={'values': [1, 2, None, 4, None, 6]}, index=pd.date_range('2024-01-01', periods=6, freq='D')) # Resample to weekly frequency with forward fill weekly_resampled_ffill = df.resample('W').ffill()

  5. "Weekly resampling of pandas time series with backward fill" Description: Resampling a pandas time series to weekly frequency with backward fill fills missing values by propagating the next observed value backward.

    import pandas as pd # Define a pandas DataFrame with a datetime index and missing values df = pd.DataFrame(data={'values': [1, 2, None, 4, None, 6]}, index=pd.date_range('2024-01-01', periods=6, freq='D')) # Resample to weekly frequency with backward fill weekly_resampled_bfill = df.resample('W').bfill()

  6. "Resample pandas time series to weekly intervals with custom aggregation" Description: Resampling a pandas time series to weekly intervals with custom aggregation allows specifying a custom function to aggregate data within each week.

    import pandas as pd # Define a pandas DataFrame with a datetime index df = pd.DataFrame(data={'values': range(1, 11)}, index=pd.date_range('2024-01-01', periods=10, freq='D')) # Resample to weekly intervals with custom aggregation (e.g., sum) weekly_custom_aggregated = df.resample('W').apply(lambda x: x.sum())

  7. "Pandas time series resampling to weekly frequency with mean" Description: Resampling a pandas time series to weekly frequency with mean aggregates data within each week by calculating the mean value.

    import pandas as pd # Define a pandas DataFrame with a datetime index df = pd.DataFrame(data={'values': range(1, 11)}, index=pd.date_range('2024-01-01', periods=10, freq='D')) # Resample to weekly frequency and calculate the mean weekly_mean = df.resample('W').mean()

  8. "Resample pandas time series to weekly intervals with sum" Description: Resampling a pandas time series to weekly intervals with sum aggregates data within each week by calculating the sum of values.

    import pandas as pd # Define a pandas DataFrame with a datetime index df = pd.DataFrame(data={'values': range(1, 11)}, index=pd.date_range('2024-01-01', periods=10, freq='D')) # Resample to weekly intervals and calculate the sum weekly_sum = df.resample('W').sum()

  9. "Pandas time series weekly resampling with first value" Description: Resampling a pandas time series to weekly frequency with the first value of each week involves selecting the first observed value within each week.

    import pandas as pd # Define a pandas DataFrame with a datetime index df = pd.DataFrame(data={'values': range(1, 11)}, index=pd.date_range('2024-01-01', periods=10, freq='D')) # Resample to weekly frequency and select the first value of each week weekly_first_value = df.resample('W').first()

  10. "Resample pandas time series to weekly intervals with last value" Description: Resampling a pandas time series to weekly intervals with the last value of each week involves selecting the last observed value within each week.

    import pandas as pd # Define a pandas DataFrame with a datetime index df = pd.DataFrame(data={'values': range(1, 11)}, index=pd.date_range('2024-01-01', periods=10, freq='D')) # Resample to weekly intervals and select the last value of each week weekly_last_value = df.resample('W').last()

More Tags

codesandbox butterknife uwp textkit C++ merge linq idioms throw kendo-datasource

More Python Questions

More General chemistry Calculators

More Gardening and crops Calculators

More Math Calculators

More Chemical reactions Calculators