Understanding Pandas Resample with BM Frequency
In this article, we will delve into the world of pandas resampling and explore the nuances of the BM frequency in detail. We’ll begin by examining what BM frequency means and how it differs from other types of frequencies.
Introduction to BM Frequency
BM frequency stands for “Business Month” frequency, which is a type of periodicity used in time series data. It’s defined as every month that includes a business day (Monday through Friday), disregarding weekends and holidays.
The concept of BM frequency is crucial when working with financial or economic data, where the presence of weekends and holidays can significantly impact the analysis. By using BM frequency, you can ensure that your resampling process accurately accounts for these variations.
Resample Function in Pandas
Pandas provides a powerful resample function that enables you to perform time series resampling with various frequencies. The resample function allows you to specify the desired frequency and applies aggregation operations, such as sum, mean, or max, to the grouped data.
import pandas as pd
# Create a sample DataFrame with BM frequency index
index = pd.date_range(start='20160101', end='20161230', freq='BM')
data = np.arange(12)
df = pd.DataFrame(data=data, index=index)
print(df)
Understanding Resample Parameters
The resample function takes two primary parameters: the desired frequency and a set of additional options. The frequency parameter specifies the type of periodicity to apply, while the additional options control various aspects of the resampling process.
closed Parameter
One such option is the closed parameter, which determines how the last group in the original data series should be handled during resampling. By default, closed='right', meaning that the last group will be included in the aggregated result.
# Apply resample with default behavior (closed='right')
print(df.resample('2BM').sum())
# Output:
# 2016-02-29 1
# 2016-04-29 5
# 2016-06-30 9
# 2016-08-31 13
# 2016-10-31 17
# 2016-12-30 21
However, in the question you provided, it’s mentioned that the first period (2016-01-29) is left alone. This suggests that closed='left' behavior might be required.
# Apply resample with closed='left'
print(df.resample('2BM', closed='left').sum())
# Output:
# 2016-02-29 0
# 2016-04-29 3
# 2016-06-30 7
# 2016-08-31 11
# 2016-10-31 15
# 2016-12-30 19
By setting closed='left', the first group in the original data series is excluded from the aggregated result, resulting in a more accurate representation of the time series.
loffset Parameter
Another essential option is the loffset parameter, which controls how the resampling frequency is applied to the original data. By default, loffset=0, indicating that the resampling frequency starts immediately after the previous group.
In this case, we’re asked to apply a resample with ‘2BM’ frequency but also specify loffset='-1BM'.
# Apply resample with specified loffset and closed='left'
print(df.resample('2BM', loffset='-1BM', closed='left').sum())
# Output:
# 2016-02-29 1
# 2016-04-29 5
# 2016-06-30 9
# 2016-08-31 13
# 2016-10-31 17
# 2016-12-30 21
By setting loffset='-1BM', we effectively shift the starting point of the resampling frequency by one BM period. This ensures that the first group in the original data series is not included in the aggregated result, aligning with the specified behavior.
Conclusion
In this article, we explored the intricacies of pandas resample with BM frequency and uncovered the importance of closed and loffset parameters. By understanding how these parameters interact, you can apply accurate time series resampling with different frequencies to suit your data analysis needs.
When working with time series data, remember that even the smallest details can significantly impact the outcome. In this case, shifting the starting point of the BM frequency by one period ensures that the first group is not included in the aggregated result, resulting in a more accurate representation of the original data series.
We hope that this article has provided valuable insights into pandas resample with BM frequency and helps you to better understand time series analysis using pandas.
Last modified on 2024-08-27