5 Best Ways to Extract the Month of the Year from a Period in Python Pandas

💡 Problem Formulation: When working with time-series data in Python using Pandas, there might be cases when you need to extract the month component from a Period object for analysis or data preprocessing. Knowing how to obtain just the month can help in performing monthly aggregations, comparisons, and visualizations. For instance, if you have a Period object representing ‘2023-05’, the desired output would be ‘5’ for May.

Method 1: Using the .month Attribute

The .month attribute of a Pandas Period object directly provides the month component as an integer. This attribute is straightforward and efficient for extracting the month from a single Period object or applying it over a Series of Periods.

Here’s an example:

import pandas as pd

# Create a single period
single_period = pd.Period('2023-05')

# Extract the month
print(single_period.month)

Output:

5

This code snippet creates a Period object representing May 2023, and then accesses the .month attribute to print the month component. It is the simplest way to get the month from a period.

Method 2: Using the dt Accessor for Series

The dt accessor is designed to work specifically with Series containing datetime-like objects in Pandas. It provides an easy way to access date and time properties for each element in the Series. By utilizing the dt.month property, you can vectorize the operation over the entire Series of Periods.

Here’s an example:

import pandas as pd

# Create a series of periods
period_series = pd.Series([pd.Period('2023-05'), pd.Period('2024-06')])

# Extract the months
print(period_series.dt.month)

Output:

0    5
1    6
dtype: int64

In this code, we first create a Series of Period objects, then extract the month of each Period using the dt.month accessor. This method efficiently handles multiple Period objects in a Series.

Method 3: Using apply() Function with Lambda

If you prefer a more explicit approach or wish to apply a custom function to each Period in a Series, you can use the apply() function with a lambda expression. The lambda function will extract the month from each Period object as it is applied across the Series.

Here’s an example:

import pandas as pd

# Create a series of periods
period_series = pd.Series([pd.Period('2023-05'), pd.Period('2024-06')])

# Extract months using apply()
print(period_series.apply(lambda p: p.month))

Output:

0    5
1    6
dtype: int64

The code leverages the apply() function and a lambda to extract the month from each Period object in a Series. It is a flexible method that can be easily extended with additional logic.

Method 4: Using String Formatting

String formatting can be used if you’re dealing with Period objects represented as strings and want to extract the month using textual operations. This involves converting the period to a string and then using string slicing or splitting to retrieve the month component.

Here’s an example:

import pandas as pd

# Create a single period as a string
period_string = '2023-05'

# Extract the month using string operations
month = period_string.split('-')[1]
print(month)

Output:

'05'

This code takes a string representing a period, splits it at the hyphen, and prints the second element of the resulting list, which corresponds to the month. It’s a manual approach, more useful when dealing with string representations than with Period objects.

Bonus One-Liner Method 5: Using List Comprehension

If you have a list of Period objects and want to extract the month with minimal code, a list comprehension offers a compact solution. This method efficiently iterates through a list of Periods and creates a new list containing just the month components.

Here’s an example:

import pandas as pd

# Create a list of period objects
periods = [pd.Period('2023-05'), pd.Period('2024-06')]

# Extract months using list comprehension
months = [p.month for p in periods]
print(months)

Output:

[5, 6]

This code constructs a new list by iterating over a list of Period objects and selecting the month from each, resulting in a list containing the months. List comprehensions are a concise and pythonic way to achieve this.

Summary/Discussion

• Method 1: Direct attribute access. Strengths: Simple and direct. Weaknesses: Limited to a single Period object or a Series with a loop.
• Method 2: dt accessor. Strengths: Vectorized operation over a Series. Weaknesses: Only applicable to Series objects, not individual Periods.
• Method 3: apply() with lambda. Strengths: Flexible and can incorporate more complex functions. Weaknesses: Potentially slower than vectorized operations.
• Method 4: String formatting. Strengths: Useful for string representations. Weaknesses: Requires manual parsing and is not as clean as using Period attributes.
• Bonus Method 5: List comprehension. Strengths: Compact and pythonic. Weaknesses: Requires a list of Period objects and does not directly apply to a Series.