fixed timedelta occurences

docs/tools/lint_setup/template.py

@@ -130,8 +130,8 @@
 MAX_PAGE_SIZE: int = 100
 API_VERSION: str = ""
 FIRST_DAY_OF_MILLENNIUM: TAnyDateTime = pendulum.DateTime(2000, 1, 1)
-START_DATE: DateTime = pendulum.DateTime(2024, 1, 1)
-END_DATE: DateTime = pendulum.DateTime(2024, 12, 31)
+START_DATE: pendulum.DateTime = pendulum.DateTime(2024, 1, 1)
+END_DATE: pendulum.DateTime = pendulum.DateTime(2024, 12, 31)
 START_DATE_STRING: str = ""
 API_KEY: str = ""
 ITEMS_PER_PAGE: int = 100

docs/website/docs/dlt-ecosystem/destinations/bigquery.md

@@ -304,7 +304,6 @@ The adapter updates the DltResource with metadata about the destination column a
 Here is an example of how to use the `bigquery_adapter` method to apply hints to a resource on both the column level and table level:
 
 ```py
-from datetime import date, timedelta
 
 import dlt
 from dlt.destinations.adapters import bigquery_adapter
@@ -319,7 +318,7 @@ from dlt.destinations.adapters import bigquery_adapter
 )
 def event_data():
     yield from [
-        {"event_date": date.today() + timedelta(days=i)} for i in range(100)
+        {"event_date": datetime.date.today() + datetime.timedelta(days=i)} for i in range(100)
     ]
 
 

docs/website/docs/dlt-ecosystem/verified-sources/zendesk.md

@@ -354,7 +354,7 @@ verified source.
     min_start_date = pendulum.DateTime(year=2023, month=1, day=1).in_timezone("UTC")
     max_end_date = pendulum.today()
     # Generate tuples of date ranges, each with 1 week in between.
-    ranges = make_date_ranges(min_start_date, max_end_date, timedelta(weeks=1))
+    ranges = make_date_ranges(min_start_date, max_end_date, datetime.timedelta(weeks=1))
 
     # Run the pipeline in a loop for each 1-week range
     for start, end in ranges:

docs/website/docs/general-usage/data-enrichments/currency_conversion_data_enrichment.md

@@ -159,7 +159,7 @@ API token.
 
         # Update the exchange rate if it's older than 12 hours
         if (currency_pair_state.get("rate") is None or
-            (datetime.datetime.utcnow() - currency_pair_state["last_update"] >= timedelta(hours=12))):
+            (datetime.datetime.utcnow() - currency_pair_state["last_update"] >= datetime.timedelta(hours=12))):
             url = f"https://v6.exchangerate-api.com/v6/{api_key}/pair/{base_currency}/{target_currency}"
             response = requests.get(url)
             if response.status_code == 200:

docs/website/docs/general-usage/data-enrichments/user_agent_device_data_enrichment.md

@@ -111,7 +111,6 @@ The first step is to register on [SerpAPI](https://serpapi.com/) and obtain the
 
 1. Create the `fetch_average_price()` function as follows:
    ```py
-   from datetime import datetime, timedelta
    import requests
 
    # Uncomment transformer function if it is to be used as a transformer,
@@ -159,7 +158,7 @@ The first step is to register on [SerpAPI](https://serpapi.com/) and obtain the
            device_data.get('timestamp', datetime.datetime.min)
        )
        # Check if the device is not in state or data is older than 180 days
-       if device not in device_info or last_updated > timedelta(days=180):
+       if device not in device_info or last_updated > datetime.timedelta(days=180):
            try:
                # Make an API request to fetch device prices
                response = requests.get("https://serpapi.com/search", params={