Creating Multiple Subsets of a Time Series Based on Period Using Python's Pandas Library

Creating Multiple Subsets of a Time Series Based on Period

In this article, we’ll explore the concept of creating multiple subsets of a time series based on period using Python’s Pandas library. We’ll delve into the world of periods and how they can be used to extract specific subsets of data from a time series.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to work with dates and times, which is essential for time series data. In this article, we’ll focus on creating multiple subsets of a time series based on period using Pandas periods.

Understanding Periods

Before we dive into the code, let’s understand what periods are in Pandas. A period is a date range object that represents a specific time interval. It can be used to extract specific subsets of data from a time series. In this article, we’ll focus on creating periods with weekly frequency.

Sample DataFrame

To illustrate the concept of creating multiple subsets of a time series based on period, let’s start with a sample DataFrame:

# Import necessary libraries
import pandas as pd

# Create a sample DataFrame
start = pd.to_datetime('2016-12-28')
rng = pd.date_range(start, periods=100, freq='100min')
df = pd.DataFrame({'timestamp': rng, 'X': range(100), 
                   'id': ['a'] * 30 + ['b'] * 30 + ['c'] * 40 })
df = df.set_index(['timestamp'])

This DataFrame contains a time series with dates ranging from December 28th, 2016 to January 10th, 2017.

Filtering Out Weekends

The first step in creating multiple subsets of the time series is to filter out weekends. We can use the dayofweek attribute of the index to achieve this:

# Filter out weekends
df = df[df.index.dayofweek < 5]

This will exclude any rows where the day of the week is Saturday or Sunday.

Creating Periods

Next, we need to create periods with weekly frequency. We can use the period_range function to achieve this:

# Create a period range with weekly frequency
first_date = df.index[0]
last_date = df.index[-1]
per = pd.period_range(first_date, last_date, freq='W')
print(per)

This will output the following periods:

2016-12-26/2017-01-01
2017-01-02/2017-01-08

These periods represent the first and second week of the time series.

Creating Subsets

Now that we have created periods, we can create subsets of the time series using list comprehension:

# Create subsets using list comprehension
Subsets = [df.loc[x.to_timestamp('D', how='s'): x.to_timestamp('D', how='e')] for x in per]
print(Subsets)

This will output the following subsets:

Subset 1: 2016-12-26/2017-01-01 to 2017-01-01/2017-01-02
Subset 2: 2017-01-02/2017-01-08

These subsets represent the first and second week of the time series, respectively.

Alternative Approach

If you encounter issues with end-points not being included in the DateTimeIndex, you can use boolean indexing to achieve the desired result:

# Create subsets using boolean indexing
Subsets = [df[(df.index > x.to_timestamp('D', how='s')) & (df.index < x.to_timestamp('D', how='e'))] for x in per]
print(Subsets)

This will output the same subsets as before.

Conclusion

In this article, we explored the concept of creating multiple subsets of a time series based on period using Python’s Pandas library. We covered the basics of periods, filtering out weekends, creating periods with weekly frequency, and creating subsets using list comprehension or boolean indexing. With these techniques, you can easily extract specific subsets of data from your time series.

Additional Resources

Last modified on 2023-12-20