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…frame` To override this behavior, specify the types for the desired columns with the `dtype` argument.
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I'll take a closer look at #776 before finishing this one, as it might mean fewer code paths to cover. I think the BQ Storage API will always be used for to_dataframe after that PR. |
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I did a little bit of experimentation to see what the intermediate pyarrow.Table types are for both REST and BQ Storage API on all scalar types. They do align, so that's good. Also, floating points appear to be handled correctly, even with null values in the table. It appears https://issuetracker.google.com/144712110 was fixed for FLOAT columns in #314 as of google-cloud-bigquery >= 2.2.0 (That was technically a breaking change [oops]) I might still keep this open so that we can have some explicit tests for different data types. Also, we're relying on PyArrow -> Pandas to pick the right data types, so maybe there's some dtype defaults we can help with still. |
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…python-bigquery into b144712110-nullable-pandas-types
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Re: system test failure: _____________ TestBigQuery.test_load_avro_from_uri_then_dump_table _____________ . . . E google.api_core.exceptions.RetryError: Deadline of 120.0s exceeded while calling functools.partial(<bound method PollingFuture._done_or_raise of <google.cloud.bigquery.job.load.LoadJob object at 0x7ff6eae07f40>>), last exception: Didn't we increase the default deadline to 10 minutes? Maybe v3 branch needs a sync? |
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Two nits, but not essential, looks good.
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| pip install --upgrade pandas | ||
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| Alternatively, you can install the BigQuery python client library with | ||
| Alternatively, you can install the BigQuery Python client library with |
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(nit)
Since already at this, there's at least on other occurrence of "python" not capitalized (line 69), which can also be fixed.
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| loss-of-precision. | ||
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| Returns: | ||
| Dict[str, str]: mapping from column names to dtypes |
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(nit) Can be expressed as the annotation of the function return type.
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…' into b144712110-nullable-pandas-types
| ("max_results",), ((None,), (10,),) # Use BQ Storage API. # Use REST API. | ||
| ) | ||
| def test_list_rows_nullable_scalars_dtypes(bigquery_client, scalars_table, max_results): | ||
| df = bigquery_client.list_rows( |
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Note to self: I'll need to exclude the INTERVAL column next time we sync with master
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deps!: BigQuery Storage and pyarrow are required dependencies (#776) fix!: use nullable `Int64` and `boolean` dtypes in `to_dataframe` (#786) feat!: destination tables are no-longer removed by `create_job` (#891) feat!: In `to_dataframe`, use `dbdate` and `dbtime` dtypes from db-dtypes package for BigQuery DATE and TIME columns (#972) fix!: automatically convert out-of-bounds dates in `to_dataframe`, remove `date_as_object` argument (#972) feat!: mark the package as type-checked (#1058) feat!: default to DATETIME type when loading timezone-naive datetimes from Pandas (#1061) feat: add `api_method` parameter to `Client.query` to select `INSERT` or `QUERY` API (#967) fix: improve type annotations for mypy validation (#1081) feat: use `StandardSqlField` class for `Model.feature_columns` and `Model.label_columns` (#1117) docs: Add migration guide from version 2.x to 3.x (#1027) Release-As: 3.0.0
deps!: BigQuery Storage and pyarrow are required dependencies (googleapis#776) fix!: use nullable `Int64` and `boolean` dtypes in `to_dataframe` (googleapis#786) feat!: destination tables are no-longer removed by `create_job` (googleapis#891) feat!: In `to_dataframe`, use `dbdate` and `dbtime` dtypes from db-dtypes package for BigQuery DATE and TIME columns (googleapis#972) fix!: automatically convert out-of-bounds dates in `to_dataframe`, remove `date_as_object` argument (googleapis#972) feat!: mark the package as type-checked (googleapis#1058) feat!: default to DATETIME type when loading timezone-naive datetimes from Pandas (googleapis#1061) feat: add `api_method` parameter to `Client.query` to select `INSERT` or `QUERY` API (googleapis#967) fix: improve type annotations for mypy validation (googleapis#1081) feat: use `StandardSqlField` class for `Model.feature_columns` and `Model.label_columns` (googleapis#1117) docs: Add migration guide from version 2.x to 3.x (googleapis#1027) Release-As: 3.0.0
deps!: BigQuery Storage and pyarrow are required dependencies (googleapis#776) fix!: use nullable `Int64` and `boolean` dtypes in `to_dataframe` (googleapis#786) feat!: destination tables are no-longer removed by `create_job` (googleapis#891) feat!: In `to_dataframe`, use `dbdate` and `dbtime` dtypes from db-dtypes package for BigQuery DATE and TIME columns (googleapis#972) fix!: automatically convert out-of-bounds dates in `to_dataframe`, remove `date_as_object` argument (googleapis#972) feat!: mark the package as type-checked (googleapis#1058) feat!: default to DATETIME type when loading timezone-naive datetimes from Pandas (googleapis#1061) feat: add `api_method` parameter to `Client.query` to select `INSERT` or `QUERY` API (googleapis#967) fix: improve type annotations for mypy validation (googleapis#1081) feat: use `StandardSqlField` class for `Model.feature_columns` and `Model.label_columns` (googleapis#1117) docs: Add migration guide from version 2.x to 3.x (googleapis#1027) Release-As: 3.0.0
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To override this behavior, specify the types for the desired columns with the
dtype argument.
BREAKING CHANGE: uses Int64 type by default to avoid loss-of-precision in results with large integer values
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Fixes https://issuetracker.google.com/144712110 🦕
Fixes #793