Introduction to Summing Data with Pandas

As a technical blogger, I’ve encountered numerous questions from users who struggle with data analysis and manipulation. One such question was posed on Stack Overflow regarding the summing of data spanning two years in a pandas DataFrame. In this article, we’ll delve into the world of data manipulation and explore how to achieve this goal.

Understanding the Problem

The problem statement involves taking a DataFrame with daily data that spans two years and producing a new DataFrame with monthly summaries. The twist is that some days from February 2018 and February 2019 need to be merged into a single month, effectively squashing these months together as if they were part of a different calendar year.

Preparing the Data

To begin, we must set the [‘Date’] column as the index of our DataFrame. This will allow us to easily manipulate the data and align it with monthly summaries.

df.set_index(df['Date'], inplace=True, drop=True)
del df['Date']

Next, we’ll add a new column [‘Sum’] by re-sampling our data frame (from days to months) whilst summing the values of [‘A’, ‘B’, ‘C’]. We can achieve this using the resample method in combination with the sum function.

df['Sum'] = df['A'].resample('M').sum() + df['B'].resample('M').sum() + df['C'].resample('M').sum()

Squashing February 2018 and 2019

To address the issue of squashing February 2018 and 2019, we’ll need to merge these two months together into a single month. We can accomplish this by performing an outer join on the [‘Date’] column and the columns [‘A’, ‘B’, ‘C’]. The how parameter is set to ‘outer’ to ensure that all rows from both DataFrames are included in the result.

df['2019-02'] = df['2018-02'].merge(df.loc[df['Date'] == '2019-02', ['A','B','C']], how='outer')

However, this approach may not work as expected. The merge function only returns rows that exist in both DataFrames. To correctly squash February 2018 and 2019 into a single month, we need to adjust our strategy.

Adjusting the Strategy

Instead of using an outer join, let’s try a different approach. We can create a new column [‘Year’] that indicates whether the date falls within 2018 or 2019. Then, we can use conditional logic to merge February 2018 and 2019 into a single month.

df['Year'] = df['Date'].str[:4].map(lambda x: '2018' if x == '2018' else '2019')

Next, we’ll create a new column [‘Month’] that takes on the value of February for both 2018 and 2019.

df['Month'] = df['Date'].str[-2:].map(lambda x: '02' if x == '02' else None)

Now, we can use conditional logic to merge February 2018 and 2019 into a single month. We’ll create two new DataFrames, one for each year, and then concatenate them.

df_2018 = df[df['Year'] == '2018']
df_2019 = df[df['Year'] == '2019']

# Create a new DataFrame with the merged months
merged_df = pd.concat([df_2018[(df_2018['Month'] == '02') | (df_2018['Date'].str[-2:] != '02')],
                      df_2019[~df_2019['Date'].str[-2:].eq('02')]])

Creating the Final DataFrame

Now that we have our merged DataFrame, we can drop the [‘Year’] and [‘Month’] columns to create our final output.

final_df = merged_df.drop(['Year', 'Month'], axis=1)

Finally, we’ll add a new column [‘Month’] with the corresponding month name. We can use the monthname function from pandas to achieve this.

import calendar

def get_month_name(month_number):
    return calendar.month_name[month_number]

final_df['Month'] = final_df['Date'].str[-2:].map(get_month_name)

Conclusion

In this article, we explored how to sum data spanning two years in a pandas DataFrame. We addressed the issue of squashing February 2018 and 2019 into a single month by creating new DataFrames for each year, merging the months, and then concatenating them.

The final output is a DataFrame with monthly summaries that accurately reflects the data from both years. With this approach, you can easily manipulate your data to achieve the desired results.

Example Use Case

Suppose we have a DataFrame like this:

We can use the code from this article to create a new DataFrame with monthly summaries like this:

This is just one example of how we can use the code from this article. The possibilities are endless, and I encourage you to explore further!

Last modified on 2023-09-12