Adding a column to a Pandas DataFrame to check if a date range falls on a given month in any year can be achieved using various techniques.

Pandas DataFrames and Date Operations in Python

Adding a column to a Pandas DataFrame to check if a date range falls on a given month in any year can be achieved using various techniques. In this article, we will explore the different approaches and provide code examples for each.

Introduction

Pandas is a powerful library in Python that provides data structures and functions for efficiently handling structured data, including tabular data such as spreadsheets and SQL tables. One of its key features is its ability to handle dates and times, which can be used to perform various date-related operations.

In this article, we will focus on adding a column to a Pandas DataFrame that checks if a given month falls within the range of dates in the DataFrame. We will explore different methods for achieving this goal and provide code examples for each approach.

Method 1: Using Boolean Masks

One approach to add a column to a Pandas DataFrame is by using boolean masks. This method involves creating conditions that determine whether a given month falls within the date range of the DataFrame.

import pandas as pd
import datetime as dt

# Create a sample DataFrame with start and finish dates
df = pd.DataFrame(
    {
        "start": [dt.datetime(2020,1,1), dt.datetime(2020,8,1), dt.datetime(2020,8,1)],
        "finish": [dt.datetime(2021,12,1), dt.datetime(2021,6,1), dt.datetime(2022,6,1)]
    }
)

# Create a boolean mask for the first condition (July of start year)
m1 = df['start'].add(pd.DateOffset(month=7)).between(df['start'], df['finish'])

# Create a boolean mask for the second condition (more than one year elapsed between start and finish dates)
m2 = df['finish'].add(pd.DateOffset(month=7)).between(df['start'], df['finish'])

# Create a boolean mask for the third condition
m3 = df['finish'].sub(df['start']).gt('1Y')

# Use the bitwise OR operator to combine the three conditions into one boolean mask
df['existed_in_july'] = m1|m2|m3

print(df)

Method 2: Using Pandas’ Built-in Date Functions

Another approach is by using Pandas’ built-in date functions, such as dt.year and dt.month.

import pandas as pd
import datetime as dt

# Create a sample DataFrame with start and finish dates
df = pd.DataFrame(
    {
        "start": [dt.datetime(2020,1,1), dt.datetime(2020,8,1), dt.datetime(2020,8,1)],
        "finish": [dt.datetime(2021,12,1), dt.datetime(2021,6,1), dt.datetime(2022,6,1)]
    }
)

# Create a boolean mask for the first condition (July of start year)
m1 = df['start'].dt.month == 7

# Create a boolean mask for the second condition (more than one year elapsed between start and finish dates)
m2 = df['finish'].dt.year != df['start'].dt.year

# Use the bitwise OR operator to combine the two conditions into one boolean mask
df['existed_in_july'] = m1|m2

print(df)

Method 3: Using Date Ranges and List Comprehensions

A third approach is by using date ranges and list comprehensions.

import pandas as pd
import datetime as dt

# Create a sample DataFrame with start and finish dates
df = pd.DataFrame(
    {
        "start": [dt.datetime(2020,1,1), dt.datetime(2020,8,1), dt.datetime(2020,8,1)],
        "finish": [dt.datetime(2021,12,1), dt.datetime(2021,6,1), dt.datetime(2022,6,1)]
    }
)

# Create a list of boolean masks
m = [(df['start'].between(df['start'], df['finish'], inclusive=True) & (df['finish'].between(df['start'], df['finish'], inclusive=True))) or 
     ((dt.date(df['start']).month == 7) & (dt.date(df['finish']) >= dt.date(df['start']).replace(day=1)))]

# Use the bitwise OR operator to combine the masks into one boolean mask
df['existed_in_july'] = [any(m) for m in m]

print(df)

Conclusion

In this article, we explored different approaches to add a column to a Pandas DataFrame that checks if a given month falls within the date range of the DataFrame. We used various methods such as boolean masks, Pandas’ built-in date functions, and date ranges with list comprehensions.

Each method has its own advantages and disadvantages, and the choice of which one to use depends on the specific requirements of the problem being solved.