Formatting Dates with to_pydatetime() in Spark DataFrames

In this article, we will explore how to format dates with to_pydatetime() function in Spark DataFrames, specifically when working with dates stored in the “yyyy/MM/dd” format.

Background and Context

The to_pydatetime() function is used to convert a date string into a datetime object. While it can be useful for certain tasks, it has limitations when it comes to formatting dates as desired.

In this article, we will delve into how to use to_pydatetime() in combination with other Spark functions and how to format dates using the strftime() function. We will also explore alternative approaches to achieve the desired output.

The Problem

The question arises when working with dates stored in the “yyyy/MM/dd” format. When using to_pydatetime(), these dates are returned without leading zeros for months and days, resulting in:

[
  datetime.datetime(2018, 3, 6, 0, 0),
  datetime.datetime(2018, 3, 7, 0, 0),
  datetime.datetime(2018, 3, 8, 0, 0),
  ...
]

Instead of:

[
  datetime.datetime(2018, 03, 06, 0, 0),
  datetime.datetime(2018, 03, 07, 0, 0),
  datetime.datetime(2018, 03, 08, 0, 0),
  ...
]

Solution

One approach to solve this problem is by using the strftime() function in combination with to_pydatetime(). Here’s an example:

from datetime import datetime
import pandas as pd

def return_date_range(start_date, end_date):
    return pd.date_range(start=start_date, end=end_date).strftime("%Y/%m/%d").tolist()

date_range = return_date_range(start_date='2018-03-06', end_date='2018-03-12')
print(date_range)

Output:

[
  '2018/03/06',
  '2018/03/07',
  '2018/03/08',
  ...
]

By using strftime("%Y/%m/%d"), we can format the dates with leading zeros for months and days.

Implications for Spark

When working with dates in Spark, it’s essential to consider how they are stored and processed. The “yyyy/MM/dd” format is commonly used in data storage systems, but it may not be suitable for all applications.

In some cases, you may need to convert these dates into a more standard format, such as the ISO 8601 format (yyyy-mm-dd).

Alternative Approaches

There are alternative approaches to achieve the desired output. One option is to use the date_range() function without converting it to a list:

from datetime import datetime, timedelta

def return_date_range(start_date, end_date):
    start_date = datetime.strptime(start_date, "%Y-%m-%d")
    end_date = datetime.strptime(end_date, "%Y-%m-%d")
    
    while start_date <= end_date:
        yield start_date.strftime("%Y/%m/%d")
        
        start_date += timedelta(days=1)

This approach can be useful when working with dates in a specific format.

Best Practices

When working with dates in Spark, it’s essential to consider the following best practices:

• Always use the strftime() function or similar functions to format dates as desired.
• Be aware of the implications of date storage and processing formats on your application.
• Consider using alternative approaches when necessary.

By following these guidelines and exploring different approaches, you can effectively work with dates in Spark DataFrames.

Last modified on 2023-08-03