Extracting Weeks from Datetime in Python Pandas

When working with datetime data in pandas, extracting the week information can be a useful feature. In this article, we will explore how to extract weeks from datetime objects and how to create another column showing year-week combinations.

Understanding Datetime Objects

A datetime object is a fundamental data type in pandas that represents a specific point in time. It can include date, time, and timezone information. When working with datetime objects, it’s essential to understand the different components of these objects.

• Date: The day of the month, month, and year.
• Time: The hour, minute, second, and microsecond (for fractional seconds).
• Timezone: The offset from UTC in hours and minutes.

Extracting Weeks from Datetime Objects

When extracting weeks from datetime objects, we can use the dt accessor, which provides a convenient way to access various components of a datetime object. In this case, we’re interested in the week number.

The dt.week attribute returns the week number of the year. However, when combining two columns (year and week) in rows 5 through 9, the result is not accurate. This is because pandas will always put the smaller date first in these cases.

To accurately combine year and week columns, we need to re-order our data and then concatenate the year and week.

Using dt.strftime for Week Extraction

One approach to extract weeks from datetime objects is by using the strftime method. The %Y-%W format specifier returns a string in the format “YYYY-Www”, where “yyyy” represents the full year, and “Www” represents the week number.

Here’s an example:

import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    'time': ['2013-12-28 00:17', '2013-12-28 00:20', '2013-12-28 00:26', 
             '2013-12-29 00:20', '2013-12-29 00:26', '2013-12-30 00:31',
             '2013-12-30 00:31', '2013-12-31 00:17', '2013-12-31 00:20', 
             '2013-12-31 00:26', '2014-01-01 04:30', '2014-01-01 04:34',
             '2014-01-01 04:37', '2014-01-02 04:30', '2014-01-02 05:30',
             '2014-01-03 04:30', '2014-01-03 04:34', '2014-01-03 04:37']
})

# Extract weeks from datetime objects using dt.strftime
df['week'] = df.time.dt.strftime('%Y-%W')

print(df)

This code will create a new column “week” with the week numbers in the format “YYYY-Www”.

Combining Year and Week Columns

When combining year and week columns, we need to ensure that our data is ordered correctly. We can achieve this by converting our datetime objects to strings before extracting weeks.

Here’s an updated code snippet:

import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    'time': ['2013-12-28 00:17', '2013-12-28 00:20', '2013-12-28 00:26',
             '2013-12-29 00:20', '2013-12-29 00:26', '2013-12-30 00:31',
             '2013-12-30 00:31', '2013-12-31 00:17', '2013-12-31 00:20',
             '2013-12-31 00:26', '2014-01-01 04:30', '2014-01-01 04:34',
             '2014-01-01 04:37', '2014-01-02 04:30', '2014-01-02 05:30',
             '2014-01-03 04:30', '2014-01-03 04:34', '2014-01-03 04:37']
})

# Convert time to datetime objects
df['time'] = pd.to_datetime(df['time'])

# Sort the dataframe by date
df = df.sort_values(by='time')

# Extract weeks from datetime objects using dt.strftime
df['week'] = df['time'].dt.strftime('%Y-%W')

print(df)

In this code, we first convert our time column to datetime objects and then sort our dataframe based on these datetime objects. This ensures that our data is ordered correctly.

We can then extract the week numbers using dt.strftime with the %Y-%W format specifier.

Final Code Snippet

Here’s a final code snippet that combines all the concepts discussed above:

import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    'time': ['2013-12-28 00:17', '2013-12-28 00:20', '2013-12-28 00:26',
             '2013-12-29 00:20', '2013-12-29 00:26', '2013-12-30 00:31',
             '2013-12-30 00:31', '2013-12-31 00:17', '2013-12-31 00:20',
             '2013-12-31 00:26', '2014-01-01 04:30', '2014-01-01 04:34',
             '2014-01-01 04:37', '2014-01-02 04:30', '2014-01-02 05:30',
             '2014-01-03 04:30', '2014-01-03 04:34', '2014-01-03 04:37']
})

# Convert time to datetime objects
df['time'] = pd.to_datetime(df['time'])

# Sort the dataframe by date
df = df.sort_values(by='time')

# Extract weeks from datetime objects using dt.strftime
df['year_week'] = df['time'].dt.strftime('%Y-%W')

print(df)

This code snippet extracts the week numbers in the format “YYYY-Www” and combines them with the year to create a new column “year_week”. The final output is a sorted dataframe with a new column containing the year-week combinations.

Last modified on 2024-02-22