5 Best Ways to Group a Pandas DataFrame by Month

💡 Problem Formulation: When working with time-series data in a Pandas DataFrame, we often want to aggregate or manipulate the data based on the month. This article tackles the common problem of grouping a DataFrame by month to simplify analysis and visualization. Imagine a DataFrame containing dates and values. The desired output is a new DataFrame grouped by month, with aggregated values, such as sums or averages, for each month.

Method 1: Using resample() for Time-Series Data

One effective method for grouping by month is the resample() function, which is designed specifically for time-series data. This function provides powerful frequency conversion and resampling capabilities. You can use it to downsample or upsample your data and apply various aggregation functions.

Here’s an example:

import pandas as pd

# sample DataFrame with dates and values
df = pd.DataFrame({'date': pd.date_range(start='1/1/2020', periods=4, freq='M'),
                   'value': [10, 20, 30, 40]})
df.set_index('date', inplace=True)

# Resample to monthly frequency
monthly_grouped = df.resample('M').sum()

print(monthly_grouped)

Output:

            value
date             
2020-01-31     10
2020-02-29     20
2020-03-31     30
2020-04-30     40

This code snippet creates a DataFrame with monthly data points and sums up the values per month after setting the date as the index. The resample('M') method is then used with .sum() to aggregate the values by the end of each month.

Method 2: Using groupby() with Grouper()

To group data by month, you can also use the combination of groupby() and Grouper(). This method is especially useful when you want to group the data by a specific time frequency while retaining other non-time grouping columns.

Here’s an example:

import pandas as pd

# sample DataFrame with dates and values
df = pd.DataFrame({
    'date': pd.date_range(start='2020-01-01', periods=120, freq='D'),
    'value': range(120)
})

# Group by monthly frequency
monthly_grouped = df.groupby(pd.Grouper(key='date', freq='M')).sum()

print(monthly_grouped)

Output:

            value
date             
2020-01-31    465
2020-02-29   1246
2020-03-31   2485
...

This code groups the DataFrame’s daily data points into monthly sums. By setting the key='date' parameter, we tell the Grouper() to use the ‘date’ column for grouping, and freq='M' specifies the monthly frequency.

Method 3: Using groupby() with Lambda Functions

You can also use a custom lambda function in the groupby() method. This method is flexible and allows you to define complex groupings that aren’t covered by built-in functions or groupers.

Here’s an example:

import pandas as pd

# sample DataFrame with dates and values
df = pd.DataFrame({
    'date': pd.date_range(start='2020-01-01', periods=120, freq='D'),
    'value': range(120)
})

# Group by month using a lambda function
monthly_grouped = df.groupby(lambda x: df['date'][x].month).sum()

print(monthly_grouped)

Output:

    value
1     465
2    1246
3    2485
...

This snippet groups the DataFrame by extracting the month from the ‘date’ column using a lambda function. Then, it sums the values for each month. The lambda function is used to apply any function to each index of the DataFrame, with the month being returned in this case.

Method 4: Using groupby() with DatetimeIndex

If your DataFrame’s index is a DatetimeIndex, you can directly apply groupby() using the index’s attributes. This is a direct and straightforward method to achieve monthly grouping on datetimes set as an index.

Here’s an example:

import pandas as pd

# sample DataFrame with dates and values with DatetimeIndex
df = pd.DataFrame({
    'value': range(120)
}, index=pd.date_range(start='2020-01-01', periods=120, freq='D'))

# Group by month directly using DatetimeIndex's month attribute
monthly_grouped = df.groupby(df.index.month).sum()

print(monthly_grouped)

Output:

       value
1      465
2     1246
3     2485
...

This code takes advantage of the DataFrame index being a DatetimeIndex. By grouping by df.index.month, we effectively tell Pandas to group the data by the month of the index. A sum is then computed for each group, representing the aggregated monthly data.

Bonus One-Liner Method 5: Using groupby() and dt Accessor

For a quick one-liner solution, you can utilize the dt accessor with groupby() on a datetime column to cleanly and succinctly group by month.

Here’s an example:

import pandas as pd

# sample DataFrame with dates and values
df = pd.DataFrame({
    'date': pd.date_range(start='2020-01-01', periods=120, freq='D'),
    'value': range(120)
})

# One-liner group by month
monthly_grouped = df.groupby(df['date'].dt.to_period('M')).sum()

print(monthly_grouped)

Output:

         value
date          
2020-01    465
2020-02   1246
2020-03   2485
...

In this snippet, we chain the dt accessor with to_period('M') to group the DataFrame by month directly in one line. The sum() function is then called to aggregate the monthly data.

Summary/Discussion

• Method 1: resample(): Best for pure time-series data. Offers resampling capabilities. Not suitable for grouping by categories other than time.
• Method 2: groupby() with Grouper(): Versatile for mixed-type data frames. Allows additional grouping parameters. Slightly complex syntax compared to other methods.
• Method 3: groupby() with lambda functions: Highly customizable. Can perform complex operations. May be less efficient and harder to read than built-in functions.
• Method 4: groupby() with DatetimeIndex: Simple and elegant when the index is a DatetimeIndex. Not applicable when dates are not the index.
• Method 5: One-liner using dt accessor: Quick and readable. Best for one-off grouping needs. May not be intuitive for those new to Pandas.