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docs: Update bigframes links to new homepage #1028

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tswast merged 2 commits into from
Feb 20, 2026
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docs: Update bigframes links to new homepage #1028

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docs: Update bigframes links to new homepage
TrevorBergeron Feb 17, 2026
13b2fbf
Merge branch 'main' into bigframes_new_home_link
TrevorBergeron Feb 20, 2026
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4 changes: 2 additions & 2 deletions docs/index.rst
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Expand Up @@ -23,8 +23,8 @@ Note: The canonical version of this documentation can always be found on the
`BigQuery sandbox <https://cloud.google.com/bigquery/docs/sandbox>`__ to
try the service for free.

Also, consider using BigQuery DataFrames
(`bit.ly/bigframes-intro <https://bit.ly/bigframes-intro>`__)
Also, consider using `BigQuery DataFrames
<https://dataframes.bigquery.dev>`__
Comment on lines +26 to +27

@gemini-code-assist gemini-code-assist Bot Feb 17, 2026

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medium

For improved readability and maintainability of the reStructuredText source, I recommend using a substitution for this link. This approach keeps the main body of the text cleaner and centralizes link definitions.

You can replace the inline link with a substitution reference here, and then add the corresponding definition at the end of the file:

.. |BigQuery DataFrames| replace:: `BigQuery DataFrames <https://dataframes.bigquery.dev>`__
Suggested change
Also, consider using `BigQuery DataFrames
<https://dataframes.bigquery.dev>`__
Also, consider using |BigQuery DataFrames|

to process large results with pandas compatible APIs with transparent SQL
pushdown to BigQuery engine. This provides an opportunity to save on costs
and improve performance.
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2 changes: 1 addition & 1 deletion pandas_gbq/core/read.py
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Expand Up @@ -146,7 +146,7 @@ def download_results(
num_gib = num_bytes / pandas_gbq.constants.BYTES_IN_GIB
warnings.warn(
f"Recommendation: Your results are {num_gib:.1f} GiB. "
"Consider using BigQuery DataFrames (https://bit.ly/bigframes-intro)"
"Consider using BigQuery DataFrames (https://dataframes.bigquery.dev)"
"to process large results with pandas compatible APIs with transparent SQL "
"pushdown to BigQuery engine. This provides an opportunity to save on costs "
"and improve performance. "
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