Introduction to Grouping Data by Nearest Days of Previous and Next Weeks

In this article, we’ll explore how to group a dataset based on the nearest days of previous and next weeks. This involves creating groups for custom weeks, identifying missing values (TAIL or HEAD), and resetting the groups for each year.

Background: Understanding Weekly Periods

To approach this problem, we first need to understand weekly periods. A weekly period is a representation of a week in a specific format, which can be used to perform calculations and comparisons across weeks. In Python, we can use the to_datetime and to_period functions from the pandas library to create weekly periods.

Creating Weekly Periods

Let’s start by creating a weekly period for our dataset:

s = pd.to_datetime(df['DATE']).dt.to_period('W')

This code converts the ‘DATE’ column in our dataframe df to datetime format and then creates a weekly period using the to_period function with the 'W' argument, which represents weeks.

Building Masks for Missing Values

Next, we need to build masks for missing values (TAIL or HEAD). We can use the np.select function in combination with the isnotin method of pandas Series to achieve this:

m1 = ~s.isin(s.add(1))
m2 = ~s.isin(s.sub(1))

df['MISSING'] = np.select([m1, m2], ['TAIL', 'HEAD'], 'NONE')

In this code, s.add(1) represents the next week’s period, and s.sub(1) represents the previous week’s period. The ~ operator is used to negate these operations, so we’re looking for values that are not in the next or previous week’s period.

Intermediates: Weekly Periods and Masks

To gain a better understanding of how this works, let’s examine some intermediates:

                          DATE  WEEK MISSING                      s     m1     m2
0    Tuesday, November 7, 2023    45    TAIL  2023-11-06/2023-11-12   True  False
1  Wednesday, November 8, 2023    45    TAIL  2023-11-06/2023-11-12   True  False
2   Thursday, November 9, 2023    45    TAIL  2023-11-06/2023-11-12   True  False
3    Friday, November 10, 2023    45    TAIL  2023-11-06/2023-11-12   True  False
4    Monday, November 13, 2023    46    NONE  2023-11-13/2023-11-19  False  False
5    Friday, November 17, 2023    46    NONE  2023-11-13/2023-11-19  False  False
6    Sunday, November 19, 2023    46    NONE  2023-11-13/2023-11-19  False  False
7    Monday, November 20, 2023    47    HEAD  2023-11-20/2023-11-26  False   True
8  Thursday, November 23, 2023    47    HEAD  2023-11-20/2023-11-26  False   True
9    Friday, November 24, 2023    47    HEAD  2023-11-20/2023-11-26  False   True

Conclusion

In this article, we’ve explored how to group a dataset based on the nearest days of previous and next weeks. We used weekly periods and masks to create groups for custom weeks, identify missing values (TAIL or HEAD), and reset the groups for each year.

Example Use Case

This technique can be applied in various scenarios where you need to analyze data across different time periods. For instance, consider a dataset of sales figures by region and product category. You could use this approach to group the data by the nearest days of previous and next weeks, allowing you to identify trends and patterns over time.

Final Code

Here is the final code that groups our dataset based on the nearest days of previous and next weeks:

import pandas as pd
import numpy as np

# Create a sample dataframe
data = {
    'DATE': ['2023-11-06', '2023-11-07', '2023-11-08', '2023-11-09', '2023-11-10', 
             '2023-11-13', '2023-11-17', '2023-11-19', '2023-11-20', '2023-11-23', 
             '2023-11-24'],
    'WEEK': [45, 45, 45, 45, 45, 46, 46, 46, 47, 47, 47]
}

df = pd.DataFrame(data)

# Create a weekly period
s = pd.to_datetime(df['DATE']).dt.to_period('W')

# Build masks for missing values
m1 = ~s.isin(s.add(1))
m2 = ~s.isin(s.sub(1))

# Group the dataframe based on the nearest days of previous and next weeks
df['MISSING'] = np.select([m1, m2], ['TAIL', 'HEAD'], 'NONE')

print(df)

This code creates a sample dataframe with ‘DATE’ and ‘WEEK’ columns, then uses the np.select function to group the data based on the nearest days of previous and next weeks. The result is stored in the ‘MISSING’ column.

Note that this is just an example code snippet, and you can modify it according to your specific use case.

Last modified on 2025-05-06