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Python Pandas - Time Span Representation

Pandas provides the Period object to represent time spans, also known as regular intervals of time, such as days, months, or years. This is particularly useful when working with time series data that focuses on time periods rather than exact timestamps.

In this tutorial, we will learn about the basics of working with Period objects in Pandas, including how to create and manipulate them, perform arithmetic operations, convert them to Timestamp objects, and obtain formatted string representations.

The Period object in Pandas represents a fixed time span (a period of time), such as a month, a quarter, or a day. You can define the span of a period using the freq parameter, which is set with a frequency alias like "D" for days, "M" for months, or "Y" for years.

Creating a Period object in Pandas can be done by using the pandas.Period() class.

Following is the syntax of the Period class−

class pandas.Period(value=None, freq=None, ordinal=None, year=None, month=None, quarter=None, day=None, hour=None, minute=None, second=None)

Here is an example of creating the Period object for different frequencies using the pandas.Period() class.

import pandas as pd

# Creating Period objects with different frequencies
# Yearly frequency 
p1 = pd.Period("2024", freq="Y")  
# Daily frequency
p2 = pd.Period("2024-01-01", freq="D") 
# Hourly frequency
p3 = pd.Period("2024-01-01 19:00", freq="H")

print("Output Yearly frequency:", p1)
print("Output Daily frequency:", p2)
print("Output Hourly frequency:", p3)

On executing the above code you will get the following output −

Output Yearly frequency: 2024
Output Daily frequency: 2024-01-01
Output Hourly frequency: 2024-01-01 19:00

Pandas Period object support arithmetic operations, like addition and subtraction, based on their defined frequency. You can add or subtract integers from a Period to shift it to that own frequency. Note that arithmetic operations between different frequencies are not allowed.

The following example demonstrates applying the arithmetic operations to Period object with integers.

import pandas as pd

# Creating Period objects with different frequencies
# Yearly frequency 
p1 = pd.Period("2024", freq="Y")  
# Daily frequency
p2 = pd.Period("2024-01-01", freq="D") 
# Hourly frequency
p3 = pd.Period("2024-01-01 19:00", freq="H")

# Adding and subtracting intervals
print("2024+1 =", p1 + 1) 
print("2024 - 3 =", p1 - 3) 
print("2024-01-01 + 2 =", p2 + 2) 
print("2024-01-01 - 2 =", p2 - 2) 
print("2024-01-01 19:00 + 4 =", p3 + 4) 
print("2024-01-01 19:00 - 3 =", p3 - 3) 

Following is the output of the above code −

2024+1 = 2025
2024 - 3 = 2021
2024-01-01 + 2 = 2024-01-03
2024-01-01 - 2 = 2023-12-30
2024-01-01 19:00 + 4 = 2024-01-01 23:00
2024-01-01 19:00 - 3 = 2024-01-01 16:00

If a Period frequency is daily or higher (e.g., hourly, minute, seconds, etc.), you can add time offsets, such as pd.offsets.Hour(2) or datetime.timedelta objects to the Period.

This example demonstrates combining time offsets with a Period object.

import pandas as pd
from datetime import timedelta
import numpy as np

# Create a period object
p = pd.Period("2024-11-01 09:00", freq="H")

# Adding hours using different timedelta like objects
print(p + pd.offsets.Hour(2)) 
print(p + timedelta(minutes=120)) 
print(p + np.timedelta64(7200, "s")) 

Following is the output of the above code −

2024-11-01 11:00
2024-11-01 11:00
2024-11-01 11:00

Note: you will get the IncompatibleFrequency error, if you try to add a time offset which results can have the different frequency.

The pandas.Period() class provides the strftime() method to convert a period object to a string representation, with custom formatting based on date and time directives.

This example demonstrates the process of formatting and retrieving the string representation of the Period object using the Period.strftime() method.

import pandas as pd

period = pd.Period(freq="S", year=2024, month=11, day=5, hour=8, minute=20, second=45)
print("Period:", period)

# Period object to String representation with specific format
print("Formatted String:", period.strftime('%d-%b-%Y'))

# Using multiple formatting directives
print("Detailed Format:", period.strftime('%b. %d, %Y was a %A'))

Following is the output of the above code −

Period: 2024-11-05 08:20:45
Formatted String: 05-Nov-2024
Detailed Format: Nov. 05, 2024 was a Tuesday

The pandas.Period() class provides the to_timestamp() method to convert period object to timestamp with the specified frequencies.

Here is the basic example of using the pandas.Period.to_timestamp() method to convert period object to timestamp object.

import pandas as pd

period = pd.Period(freq="S", year=2024, month=11, day=5, hour=8, minute=20, second=45)
print("Period:", period)

# Convert to Timestamp
print("Converted to Timestamp:", period.to_timestamp(freq='T'))

Following is the output of the above code −

Period: 2024-11-05 08:20:45
Converted to Timestamp: 2024-11-05 08:20:00

This example shows how to convert a Period object to a Timestamp with Daily Frequency.

import pandas as pd

period = pd.Period(freq="S", year=2024, month=11, day=5, hour=8, minute=20, second=45)
print("Period:", period)

# Convert to daily frequency Timestamp
print("Daily Timestamp:", period.to_timestamp(freq='D'))

Following is the output of the above code −

Period: 2024-11-05 08:20:45
Daily Timestamp: 2024-11-05 00:00:00

Here is another example of converting the Period object to Timestamp with minutely frequency.

import pandas as pd

period = pd.Period(freq="S", year=2024, month=11, day=5, hour=8, minute=20, second=45)
print("Period:", period)

# Convert to minutely frequency Timestamp
print("Minutely Timestamp:", period.to_timestamp(freq='T'))

Following is the output of the above code −

Period: 2024-11-05 08:20:45
Minutely Timestamp: 2024-11-05 08:20:00

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