Understanding the Problem

The problem at hand involves finding the average of consecutive days in a pandas DataFrame, specifically for Saturdays, Sundays, and Mondays.

Given a DataFrame df with columns ‘Date’, ‘Val’, and ‘WD’ (day of the week), we need to create a new column in the same DataFrame, denoted as df2, where the values are updated to be the average of consecutive Saturday, Sunday, and Monday values.

Background

To tackle this problem, we’ll leverage pandas’ built-in functionality for grouping and aggregating data. Specifically, we’ll use the CustomBusinessDay class from the pandas.tseries.offsets module to define a custom business day frequency.

The main concept employed here is grouping data by specific intervals, in this case, consecutive Saturdays, Sundays, and Mondays within the same week. We’ll also utilize the ngroup method to assign a unique group identifier to each interval.

Solution Overview

Our approach involves:

1. Importing necessary libraries and defining custom business days.
2. Creating a new column for grouping data by consecutive Saturday, Sunday, and Monday intervals.
3. Applying the mean aggregation function to calculate the average values for these intervals.

Step-by-Step Implementation

Import Libraries and Define Custom Business Days

# Import necessary libraries
import pandas as pd
from pandas.tseries.offsets import CustomBusinessDay

# Define custom business days for Saturday, Sunday, and Monday
days = CustomBusinessDay(weekmask='Tue Wed Thu Fri Sat')

Create Grouping Column

We’ll use pd.grouper to group data by the defined custom business day frequency. The key parameter specifies the column we want to group by.

# Group data by consecutive Saturday, Sunday, and Monday intervals
df['group_col'] = df.groupby(pd.Grouper(key='Date', freq=days)).ngroup()

Apply Mean Aggregation

Next, we’ll apply the mean aggregation function to calculate the average values for each group.

# Calculate the mean of 'Val' for groups with exactly three consecutive days (Sat, Sun, Mon)
df.update(df[df.groupby('group_col')['Val'].transform('size').eq(3)].groupby('group_col').transform('mean'))

This will result in a new DataFrame df2 where each value is the average of consecutive Saturday, Sunday, and Monday values for the corresponding date.

Final Result

The final updated DataFrame df2 with the desired calculation:

# Print the resulting DataFrame df2
Date          Val          WD     group_col
0   2019-01-03  2.650000    Thursday    0
1   2019-01-04  2.510000    Friday      1
2   2019-01-05  3.243333    Saturday    2
3   2019-01-06  3.243333    Sunday      2
4   2019-01-07  3.243333    Monday      2
5   2019-01-12  2.783333    Saturday    7
6   2019-01-13  2.783333    Sunday      7
7   2019-01-14  2.783333    Monday      7
8   2019-01-15  3.810000    Tuesday     8
9   2019-01-16  3.750000    Wednesday   9
10  2019-01-17  3.690000    Thursday    10
11  2019-01-18  3.470000    Friday      11

Note that this implementation only considers groups with exactly three consecutive days (Sat, Sun, Mon) within the same week. If you want to find the mean of any combination of these days in the same week, you can modify the aggregation function as shown in the original solution.

Last modified on 2024-03-04