Pandas Sum for the Rest of the Month

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In this article, we will explore a pandas DataFrame and learn how to calculate the sum of sales for the rest of the month. We will use various techniques such as sorting values by date, grouping data based on months, and applying cumulative sums.

Problem Statement

Given a DataFrame with dates, days left in the month, and sales figures, we need to find the sum of sales for each day until the end of the month.

For example, if the current date is January 28th, we want to calculate the sum of sales from February 1st to December 31st. Similarly, for January 29th, we want to calculate the sum of sales from February 2nd to December 31st.

Current Solution

The current solution involves using a non-pythonic looping approach, which is not efficient and can be prone to errors.

for i in range(len(df_test)):
    days_left = df_test.loc[i].days_left_in_m

    if days_left == 0:
        sales_temp_list.append(0)
    else:
        if (i+1) + days_left <= len(df_test):
            sales_temp_list.append(sum(df_test.loc[(i+1):(i+days_left)].sales))
        else:
            sales_temp_list.append(np.nan)

Proposed Solution

We can solve this problem more efficiently by using pandas’ built-in functions.

First, we need to convert the date column to datetime format to use the Series.dt.month accessor. Then, we sort the DataFrame by date in descending order and group by month. We calculate the cumulative sum of sales for each month using the groupby.cumsum() method.

df_test['date'] = pd.to_datetime(df_test['date'], format='%d-%m-%Y')
months = df_test['date'].dt.month

df_test['required'] = (df_test.sort_values('date', ascending=False)
                        .groupby(months)['sales'].cumsum())

Alternatively, if we don’t want to convert the date column to datetime format, we can use the pd.to_datetime() function and extract the month from there.

months = pd.to_datetime(df_test['date'], format='%d-%m-%Y').dt.month
df_test['required'] = (df_test.sort_values('date', ascending=False)
                        .groupby(months)['sales'].cumsum())

Explanation

1. Sorting by Date: We sort the DataFrame by date in descending order using sort_values('date', ascending=False). This ensures that we calculate the sum of sales for each day until the end of the month.
2. Grouping by Month: We group the sorted DataFrame by month using groupby(months). This allows us to calculate the cumulative sum of sales for each month.
3. Calculating Cumulative Sum: We use the cumsum() method to calculate the cumulative sum of sales for each month.

Example Use Case

Suppose we have a DataFrame with dates, days left in the month, and sales figures:

import pandas as pd

date = ['28-01-2017','29-01-2017','30-01-2017','31-01-2017','01-02-2017','02-02-2017']
sales = [1,2,3,4,1,2]
days_left_in_m = [3,2,1,0,29,28]

df_test = pd.DataFrame({'date': date,'days_left_in_m':days_left_in_m,'sales':sales})

print(df_test)

Output:

    date  days_left_in_m  sales
0  28-01-2017               3     1
1  29-01-2017               2     2
2  30-01-2017               1     3
3  31-01-2017               0     4
4  01-02-2017              29     1
5  02-02-2017              28     2

We can use the proposed solution to calculate the sum of sales for each day until the end of the month:

df_test['date'] = pd.to_datetime(df_test['date'], format='%d-%m-%Y')
months = df_test['date'].dt.month

df_test['required'] = (df_test.sort_values('date', ascending=False)
                        .groupby(months)['sales'].cumsum())

print(df_test)

Output:

    date  days_left_in_m  sales  required
0 2017-01-28               3     1       10
1 2017-01-29               2     2        9
2 2017-01-30               1     3        7
3 2017-01-31               0     4        4
4 2017-02-01              29     1        3
5 2017-02-02              28     2        2

Note that the required column now contains the sum of sales for each day until the end of the month.

Last modified on 2024-06-20