parse_date¶

parse_date(date_string)

Description¶

Parse date with automatic format detection (inspired from Sebabrata BigQuery tutorial)

Usage¶

Call or Deploy parse_date ?

Call parse_date directly

The easiest way to use bigfunctions

parse_date function is deployed in 39 public datasets for all of the 39 BigQuery regions.
It can be called by anyone. Just copy / paste examples below in your BigQuery console. It just works!
(You need to use the dataset in the same region as your datasets otherwise you may have a function not found error)

Public BigFunctions Datasets

Region	Dataset
`eu`	`bigfunctions.eu`
`us`	`bigfunctions.us`
`europe-west1`	`bigfunctions.europe_west1`
`asia-east1`	`bigfunctions.asia_east1`
...	...

Deploy parse_date in your project

Why deploy?

You may prefer to deploy parse_date in your own project to build and manage your own catalog of functions.
This is particularly useful if you want to create private functions (for example calling your internal APIs).
Get started by reading the framework page

Deployment

parse_date function can be deployed with:

pip install bigfunctions
bigfun get parse_date
bigfun deploy parse_date

Examples¶

EUUSeurope-west1

select bigfunctions.eu.parse_date("2021-01-20 ")

select bigfunctions.us.parse_date("2021-01-20 ")

select bigfunctions.europe_west1.parse_date("2021-01-20 ")

+--------------------+
| cleaned_date       |
+--------------------+
| date('2021-01-20') |
+--------------------+

EUUSeurope-west1

select bigfunctions.eu.parse_date("2021-1-20 ")

select bigfunctions.us.parse_date("2021-1-20 ")

select bigfunctions.europe_west1.parse_date("2021-1-20 ")

+--------------------+
| cleaned_date       |
+--------------------+
| date('2021-01-20') |
+--------------------+

EUUSeurope-west1

select bigfunctions.eu.parse_date("2021/01/20 ")

select bigfunctions.us.parse_date("2021/01/20 ")

select bigfunctions.europe_west1.parse_date("2021/01/20 ")

+--------------------+
| cleaned_date       |
+--------------------+
| date('2021-01-20') |
+--------------------+

EUUSeurope-west1

select bigfunctions.eu.parse_date("2021/1/20 ")

select bigfunctions.us.parse_date("2021/1/20 ")

select bigfunctions.europe_west1.parse_date("2021/1/20 ")

+--------------------+
| cleaned_date       |
+--------------------+
| date('2021-01-20') |
+--------------------+

EUUSeurope-west1

select bigfunctions.eu.parse_date("01/20/21")

select bigfunctions.us.parse_date("01/20/21")

select bigfunctions.europe_west1.parse_date("01/20/21")

+--------------------+
| cleaned_date       |
+--------------------+
| date('2021-01-20') |
+--------------------+

EUUSeurope-west1

select bigfunctions.eu.parse_date("1/20/21")

select bigfunctions.us.parse_date("1/20/21")

select bigfunctions.europe_west1.parse_date("1/20/21")

+--------------------+
| cleaned_date       |
+--------------------+
| date('2021-01-20') |
+--------------------+

EUUSeurope-west1

select bigfunctions.eu.parse_date("Wed Jan 20 21:47:00 2021")

select bigfunctions.us.parse_date("Wed Jan 20 21:47:00 2021")

select bigfunctions.europe_west1.parse_date("Wed Jan 20 21:47:00 2021")

+--------------------+
| cleaned_date       |
+--------------------+
| date('2021-01-20') |
+--------------------+

Use cases¶

You have a table containing date strings in various formats, and you need to standardize them into a consistent DATE type in BigQuery for analysis. The parse_date function can automatically detect and convert these different formats.

Scenario:

You're analyzing customer orders, and the order_date column contains date values, but they were entered using different formats due to various data sources or input methods:

order_id	order_date
1	2023-10-26
2	10/27/2023
3	Oct 28, 2023
4	28/10/23
5	Fri Oct 29 08:00:00 2023

Query using parse_date:

SELECT
    order_id,
    bigfunctions.us.parse_date(order_date) AS standardized_order_date
FROM
    your_project.your_dataset.your_table;

(Replace bigfunctions.us with the appropriate dataset for your region.)

Result:

order_id	standardized_order_date
1	2023-10-26
2	2023-10-27
3	2023-10-28
4	2023-10-28
5	2023-10-29

Now all your dates are in a standard DATE format, allowing you to perform date-based calculations, filtering, and aggregations consistently without having to manually handle the different formats. For example, you could then easily query for all orders placed in October:

SELECT
    *
FROM
    your_project.your_dataset.your_table
WHERE
    standardized_order_date BETWEEN '2023-10-01' AND '2023-10-31';

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report a bug so that it can be improved.
or open the discussion with the community on Slack.

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