## TL;DR

Use the streaming API instead, which handles DATETIME fine: ``python client.insert\_rows\_from\_dataframe(table\_id, dataframe) ` 2. The BigQuery team later fixed the backend bug, but at the time the verified workarounds (confirmed by multiple reporters) were: 1.

## Steps

1. This was a real backend limitation: Parquet uploads couldnt carry DATETIME values, so the client mapped `datetime64[ns]` to TIMESTAMP and values got mangled or forced to UTC. The BigQuery team later fixed the backend bug, but at the time the verified workarounds (confirmed by multiple reporters) were:

2. Use the streaming API instead, which handles DATETIME fine:

```python
 client.insert_rows_from_dataframe(table_id, dataframe)
 ```

3. Serialize to CSV instead of Parquet for the load job:

```python
 job_config = bigquery.LoadJobConfig(
 schema=table_schema, source_format=bigquery.SourceFormat.CSV
 )
 client.load_table_from_dataframe(dataframe, table_id, job_config=job_config)
 ```

4. Or use pandas-gbq, which serializes to CSV rather than Parquet.

5. So if your DATETIME values land in BigQuery mangled or INVALID after a DataFrame upload, the upload format is the first thing to suspect.

## When to use

You are seeing this: Uploading a pandas DataFrame with a datetime column (e.g. Use this skill when you run into "BigQuery load_table_from_dataframe mangles DATETIME values (INVALID or wrong dates)".

## When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

## Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

## Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.