5 Best Ways to Get the Quarter of the Year from a Pandas Period Object

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💡 Problem Formulation: When working with time series data in Python, users frequently need to extract specific time components from their dates. Pandas, a powerful data manipulation library, provides the Period object for handling periods (time spans). This article will demonstrate how to retrieve the quarter of the year from a given Pandas Period object. For instance, inputting a Period object representing ‘2023-02’ should yield an output indicating ‘Q1’, as February falls in the first quarter of the year.

Method 1: Using the quarter Attribute

Every Period object in Pandas has an integral attribute named quarter that returns the quarter of the year the period belongs to. This method is both simple and efficient, as it directly accesses an attribute without requiring any additional computation.

Here’s an example:

import pandas as pd

# Create a Period object for February 2023
period = pd.Period('2023-02')

# Access the quarter attribute
quarter = period.quarter

# Display the result
print(f'The quarter for {period} is Q{quarter}')

Output:

The quarter for 2023-02 is Q1

This snippet creates a Period object representing February 2023, and by accessing the quarter attribute of the Period object, we can print that February 2023 falls in the first quarter of the year.

Method 2: Using the to_timestamp() Method

Pandas Period objects can be converted to Timestamp objects using the to_timestamp() method. The resulting Timestamp object can then have its quarter accessed via the quarter attribute, similar to Method 1.

Here’s an example:

import pandas as pd

# Create a Period object for May 2023
period = pd.Period('2023-05')

# Convert to Timestamp and access the quarter attribute
quarter = period.to_timestamp().quarter

# Display the result
print(f'The quarter for {period} is Q{quarter}')

Output:

The quarter for 2023-05 is Q2

In this code, a Period object for May 2023 is converted to a Timestamp using to_timestamp(), and then we retrieve the quarter from this Timestamp object. This method is useful if the Timestamp object is needed for further time manipulation tasks alongside getting the quarter information.

Method 3: Using the dt Accessor on PeriodIndex

When working with a collection of Period objects, such as within a Series or DataFrame, the dt accessor provides access to datetime properties. This includes obtaining the quarter of each Period object within a Pandas Series.

Here’s an example:

import pandas as pd

# Create a Series of Period objects
periods = pd.Series([pd.Period('2023-01'), pd.Period('2023-06'), pd.Period('2023-12')])

# Access the quarter attribute for each Period using dt accessor
quarters = periods.dt.quarter

# Display the result
print(quarters)

Output:

0    1
1    2
2    4
dtype: int64

This code creates a Pandas Series of Period objects for January, June, and December of 2023. The dt accessor is used to extract the quarter for each period, which are returned as a new Series containing the quarter numbers.

Method 4: Using Period Arithmetic

Due to the mathematical features implemented in Pandas Periods, it is possible to perform arithmetic to find out the quarter based on the month of the year the Period represents. This method is more manual and not recommended for typical use cases.

Here’s an example:

import pandas as pd

# Create a Period object for August 2023
period = pd.Period('2023-08')

# Calculate the quarter manually using arithmetic
month = period.month
quarter = (month - 1) // 3 + 1

# Display the result
print(f'The quarter for {period} is Q{quarter}')

Output:

The quarter for 2023-08 is Q3

In this code example, we use the month attribute of the Period object to determine the quarter by performing integer division and adjusting the result. Although this approach works, it bypasses the built-in utilities provided by Pandas and is less readable.

Bonus One-Liner Method 5: Lambda Function

A one-liner lambda function can be combined with the dt accessor for a quick inline calculation of quarters in a Series of Periods, offering a concise yet powerful method for extraction.

Here’s an example:

import pandas as pd

# Create a Series of Period objects
periods = pd.Series(pd.period_range('2023Q1', periods=4, freq='Q'))

# Use a lambda function to get the quarter for each period
quarters = periods.apply(lambda p: p.quarter)

# Display the result
print(quarters)

Output:

0    1
1    2
2    3
3    4
dtype: int64

This snippet creates a Series of Period objects representing each quarter of 2023. The apply() method with a lambda function succinctly extracts the quarter for each Period object.

Summary/Discussion

  • Method 1: Quarter Attribute. The most straightforward and readable method. It does not require conversion and is directly available as an attribute of the Period object.
  • Method 2: Conversion to Timestamp. Useful when the Timestamp object is required for additional time manipulation processes after obtaining the quarter.
  • Method 3: DT Accessor on PeriodIndex. Ideal for when dealing with a Series of Period objects, allowing batch extraction of quarters.
  • Method 4: Period Arithmetic. More manual and error-prone, less intuitive compared to using built-in attributes. This method should generally be avoided.
  • Method 5: Lambda Function. Offers a compact and inline approach when using a Series, however, it can be slightly less intuitive due to the use of apply() and lambda.