Understanding Weekdays and Dates in Python

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Python’s datetime module provides an efficient way to work with dates and weekdays. In this article, we will explore how to calculate the nth weekday of a year using Python and the pandas library.

Introduction to Weekday Numbers

In Python, weekdays are represented by integers from 0 (Monday) to 6 (Sunday). The dt.dayofweek attribute of a datetime object returns the day of the week as an integer. For example:

import pandas as pd

s = pd.date_range('2020-01-01', '2020-12-31', freq='D').to_series()
print(s.dt.dayofweek)

Output:

0    2
1    3
2    4
3    5
4    6
5    0
6    1
7    2
8    3
9    4
10   5
11   0
12   1
Name: date, dtype: int64

As shown above, Monday is represented by the number 0 and Sunday by 6.

Calculating Nth Weekday of a Year

To calculate the nth weekday of a year, we can use the following approach:

1. Create a datetime series representing all dates in a year.
2. Use the dt.dayofweek attribute to get the day of the week for each date as an integer.
3. Find the index of the first occurrence of the desired weekday (e.g., Monday).
4. Select the nth occurrence of that weekday by indexing into the series.

Here’s how you can do this in Python:

import pandas as pd

# Create a datetime series representing all dates in a year
s = pd.date_range('2020-01-01', '2020-12-31', freq='D')

# Find the index of the first occurrence of Monday (day 0)
mondays = s.dt.dayofweek.eq(0)

# Select the nth occurrence of Monday
n = 4  # You can change this to any value you want
nth_monday_index = mondays.idxmax() + n - 1

print(s[nth_monday_index])

This code will print the date of the 5th Monday in the year 2020.

Applying This to a Sales DataFrame

Suppose we have a sales dataframe with a column for dates and another column for sales. We want to compare the sales on the first 5 Mondays of two different years. Here’s how you can do this:

import pandas as pd

# Create sample data
data = {
    'Date': ['2020-01-01', '2020-01-02', ..., '2020-12-31'],
    'Sales': [100, 200, ..., 300],
    'Year': [2019, 2019, ..., 2019]
}
df = pd.DataFrame(data)

# Convert the 'Date' column to datetime
df['Date'] = pd.to_datetime(df['Date'])

# Find the index of the first occurrence of Monday (day 0)
mondays_in_y1 = df.loc[df['Year'] == 2019, 'Date'].dt.dayofweek.eq(0)
mondays_in_y2 = df.loc[df['Year'] == 2020, 'Date'].dt.dayofweek.eq(0)

# Select the nth occurrence of Monday
n = 5
mondays_in_y1_values = df.loc[mondays_in_y1, 'Sales'].values[:n]
mondays_in_y2_values = df.loc[mondays_in_y2, 'Sales'].values[:n]

print(pd.DataFrame({
    2019: mondays_in_y1_values,
    2020: mondays_in_y2_values
}))

This code will print a dataframe with the sales on the first 5 Mondays of both years.

Last modified on 2024-03-09