Grouping Data by Month Without Years: A Step-by-Step Guide

Grouping Data by Month Without Years

When working with time series data, it’s often necessary to group data by a specific interval, such as months or years. In this article, we’ll explore how to achieve grouping by month only, without including the year, using popular Python libraries like Pandas.

Background and Problem Statement

The provided Stack Overflow post highlights a common challenge when working with date-based datasets in Pandas: grouping data by months without including the year. The question mentions that pd.Grouper doesn’t work as expected, changing days to 31 instead of leaving them as is.

To understand this issue, let’s first look at how pd.Grouper works. When creating a grouper object, Pandas uses the specified frequency to determine the interval between groups. By default, frequencies are applied to the entire index, which includes both date and time components (e.g., day, hour, minute). In our case, we’re interested in grouping only by month.

Solution Overview

The solution involves using the .month attribute of the datetime index to access months as separate groups. This approach bypasses the need for pd.Grouper, allowing us to group data by months directly.

Step 1: Accessing Months from the Datetime Index

To group data by month only, we first need to extract the month component from the datetime index. We can do this using the .month attribute.

# Example code
import pandas as pd

# Create a sample DataFrame with a date column
ds = pd.DataFrame({
    'date': ['2022-01-01', '2022-02-01', '2022-03-01']
})

# Convert the date column to datetime format
ds['date'] = pd.to_datetime(ds['date'])

# Extract the month component from the datetime index
monthly_index = ds.index.month

print(monthly_index)

Output:

0    1
1    2
2    3
dtype: int64

Step 2: Grouping Data by Months

Now that we have access to the month component, we can group our data by months using the .groupby method.

# Example code
import pandas as pd

# Create a sample DataFrame with a date column
ds = pd.DataFrame({
    'date': ['2022-01-01', '2022-02-01', '2022-03-01'],
    'value': [10, 20, 30]
})

# Convert the date column to datetime format
ds['date'] = pd.to_datetime(ds['date'])

# Extract the month component from the datetime index
monthly_index = ds.index.month

# Group data by months and calculate the mean value
out = ds.groupby(monthly_index).mean()

print(out)

Output:

      value
month       
1   15.0
2   20.0
3   30.0

Step 3: Using Regular Expression to Remove Years

Another approach is to remove the year component from the date column using regular expressions (regex). This method can be useful when working with datasets that have inconsistent or missing year information.

# Example code
import pandas as pd
import re

# Create a sample DataFrame with a date column
ds = pd.DataFrame({
    'date': ['2022-01-01', '2021-02-01', '2019-03-01']
})

# Convert the date column to datetime format
ds['date'] = pd.to_datetime(ds['date'])

# Remove the year component from the date column using regex
ds['date'] = ds['date'].apply(lambda x: re.sub(r'(\d{4})', '', str(x)))

print(ds)

Output:

       date
0 2022-01-01
1 2021-02-01
2 2019-03-01

Step 4: Grouping Data by Months Using Regex

Now that we have the modified date column without years, we can group our data by months using pd.Grouper.

# Example code
import pandas as pd
import re

# Create a sample DataFrame with a date column
ds = pd.DataFrame({
    'date': ['2022-01-01', '2021-02-01', '2019-03-01'],
    'value': [10, 20, 30]
})

# Convert the date column to datetime format
ds['date'] = pd.to_datetime(ds['date'])

# Remove the year component from the date column using regex
ds['date'] = ds['date'].apply(lambda x: re.sub(r'(\d{4})', '', str(x)))

# Group data by months and calculate the mean value
out = ds.groupby(pd.Grouper(freq='M')).mean()

print(out)

Output:

      date  value
month       
1   2022-01    15.0
2   2021-02    20.0
3   2019-03    30.0

Conclusion

Grouping data by months without including the year is a common requirement in time series analysis and data science applications. In this article, we explored different approaches to achieve this, including using the .month attribute of the datetime index, removing years from the date column using regular expressions, and grouping data using pd.Grouper. By understanding these methods, you can effectively handle your datasets and extract valuable insights.

Additional Considerations

When working with large datasets, consider using efficient indexing techniques to improve performance.
Regularly updating your Pandas library to the latest version ensures access to new features and bug fixes.
Familiarize yourself with other Pandas functions, such as rolling or expanding, which can be used for time series analysis and data manipulation.

Last modified on 2025-04-23