5 Effective Ways to Create a PeriodIndex and Get Days of the Week in Python Pandas

by

💡 Problem Formulation: In data analysis, managing dates and times is a common task. Given a date range, it is often necessary to create an index for each date and then determine the day of the week for those dates. This article specifically tackles how one can achieve this in Python using the Pandas library, demonstrating how to create a PeriodIndex and extract the day names.

Method 1: Using the pd.period_range Function with dayofweek Property

This method employs the pd.period_range Pandas function to create a range of periods, which inherently has the dayofweek property that corresponds to the days of the week. Each day is represented as an integer with Monday as 0 and Sunday as 6.

Here’s an example:

import pandas as pd

periods = pd.period_range(start='2023-01-01', end='2023-01-07', freq='D')
days_of_week = periods.dayofweek

Output:

Int64Index([6, 0, 1, 2, 3, 4, 5], dtype='int64')

This code snippet creates a PeriodIndex with daily frequency starting from January 1, 2023, to January 7, 2023. It then uses the dayofweek property to list the days of that period as integers representing the days of the week.

Method 2: Converting to DateTimeIndex and using strftime

Another approach is to convert the PeriodIndex to a DateTimeIndex using to_timestamp, and then apply the strftime method to obtain the day names.

Here’s an example:

periods = pd.period_range(start='2023-01-01', end='2023-01-07', freq='D')
days_of_week = periods.to_timestamp().strftime('%A')

Output:

Index(['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'], dtype='object')

By converting the PeriodIndex to DateTimeIndex we can utilize the strftime method to directly get the day names, which may be more readable and useful for some applications.

Method 3: Using day_name() Method on PeriodIndex

The Pandas library offers a convenient day_name() method that can directly be used on the PeriodIndex to retrieve the full names of the weekdays.

Here’s an example:

periods = pd.period_range(start='2023-01-01', end='2023-01-07', freq='D')
day_names = periods.day_name()

Output:

Index(['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'], dtype='object')

This snippet is straightforward and requires no conversion, as it uses the built-in day_name() method which is a part of the PeriodIndex class itself.

Method 4: Using dt Accessor with day_name() Method

If we first convert the periods into a Series object, we can leverage the dt accessor together with day_name() to extract the weekday names.

Here’s an example:

periods = pd.period_range(start='2023-01-01', end='2023-01-07', freq='D')
day_names = pd.Series(periods).dt.day_name()

Output:

0       Sunday
1       Monday
2      Tuesday
3    Wednesday
4     Thursday
5       Friday
6     Saturday
dtype: object

This method first converts the PeriodIndex to a Pandas Series to allow access to the dt accessor, from which it retrieves the day names using day_name().

Bonus One-Liner Method 5: List Comprehension with strftime

A quick and slightly more Pythonic approach could be to use a list comprehension to apply the strftime method on each period within a PeriodIndex.

Here’s an example:

periods = pd.period_range(start='2023-01-01', end='2023-01-07', freq='D')
day_names = [p.strftime('%A') for p in periods]

Output:

['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday']

This concise one-liner employs a list comprehension to format each period into the name of the day, producing a Python list of day names.

Summary/Discussion

  • Method 1: Using pd.period_range. Strengths: Simple and tied to Pandas’ PeriodIndex. Weaknesses: Only provides integer representation of weekdays.
  • Method 2: Converting to DateTimeIndex. Strengths: Allows for fully specified weekday names. Weaknesses: Requires conversion which could be extra overhead.
  • Method 3: Using day_name() Method. Strengths: Direct and readable with no conversion necessary. Weaknesses: Less flexible if other formats are needed.
  • Method 4: Using dt Accessor. Strengths: Useful in scenarios where the index needs to be part of a DataFrame. Weaknesses: Slightly more verbose with additional conversion to Series.
  • Method 5: List Comprehension with strftime. Strengths: Pythonic and concise one-liner. Weaknesses: Operates outside of Pandas’ specific functions, potentially less efficient for large datasets.