feat!: Use pandas custom data types for BigQuery DATE and TIME columns, remove date_as_object argument
#972
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…of _from_sequence
…from_sequence_of_strings
So we can force use of the default/backup NDArrayBackedExtensionArray for pandas earlier than 1.3. Also simplified version-checking code.
…for better speed.
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Co-authored-by: Leah E. Cole <6719667+leahecole@users.noreply.github.com>
Co-authored-by: Leah E. Cole <6719667+leahecole@users.noreply.github.com>
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Thanks @leahecole ! I'll let Kokoro run and check back on this PR with fresh eyes tomorrow before merging. |
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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
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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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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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Fixed #861
Fixes #862 🦕
BREAKING CHANGE: remove
date_as_objectargument fromto_dataframe. Thedbdatedtype is used by default with an automatic fallback toobjectwhen dates are not within the range of a nanosecond-precision pandas timestamp