0.5.0 / 2018-06-15

  • Project ID parameter is optional in read_gbq and to_gbq when it can inferred from the environment. Note: you must still pass in a project ID when using user-based authentication. (GH#103)
  • Progress bar added for to_gbq, through an optional library tqdm as dependency. (GH#162)
  • Add location parameter to read_gbq and to_gbq so that pandas-gbq can work with datasets in the Tokyo region. (GH#177)


Internal changes

  • Tests now use nox to run in multiple Python environments. (GH#52)
  • Renamed internal modules. (GH#154)
  • Refactored auth to an internal auth module. (GH#176)
  • Add unit tests for get_credentials(). (GH#184)

0.4.1 / 2018-04-05

  • Only show verbose deprecation warning if Pandas version does not populate it. (GH#157)

0.4.0 / 2018-04-03

  • Fix bug in read_gbq when building a dataframe with integer columns on Windows. Explicitly use 64bit integers when converting from BQ types. (GH#119)
  • Fix bug in read_gbq when querying for an array of floats (GH#123)
  • Fix bug in read_gbq with configuration argument. Updates read_gbq to account for breaking change in the way google-cloud-python version 0.32.0+ handles query configuration API representation. (GH#152)
  • Fix bug in to_gbq where seconds were discarded in timestamp columns. (GH#148)
  • Fix bug in to_gbq when supplying a user-defined schema (GH#150)
  • Deprecate the verbose parameter in read_gbq and to_gbq. Messages use the logging module instead of printing progress directly to standard output. (GH#12)

0.3.1 / 2018-02-13

  • Fix an issue where Unicode couldn’t be uploaded in Python 2 (GH#106)
  • Add support for a passed schema in :func:to_gbq instead inferring the schema from the passed DataFrame with DataFrame.dtypes (GH#46)
  • Fix an issue where a dataframe containing both integer and floating point columns could not be uploaded with to_gbq (GH#116)
  • to_gbq now uses to_csv to avoid manually looping over rows in a dataframe (should result in faster table uploads) (GH#96)

0.3.0 / 2018-01-03

  • Use the google-cloud-bigquery library for API calls. The google-cloud-bigquery package is a new dependency, and dependencies on google-api-python-client and httplib2 are removed. See the installation guide for more details. (GH#93)
  • Structs and arrays are now named properly (GH#23) and BigQuery functions like array_agg no longer run into errors during type conversion (GH#22).
  • to_gbq() now uses a load job instead of the streaming API. Remove StreamingInsertError class, as it is no longer used by to_gbq(). (GH#7, GH#75)

0.2.1 / 2017-11-27

  • read_gbq() now raises QueryTimeout if the request exceeds the query.timeoutMs value specified in the BigQuery configuration. (GH#76)
  • Environment variable PANDAS_GBQ_CREDENTIALS_FILE can now be used to override the default location where the BigQuery user account credentials are stored. (GH#86)
  • BigQuery user account credentials are now stored in an application-specific hidden user folder on the operating system. (GH#41)

0.2.0 / 2017-07-24

  • Drop support for Python 3.4 (GH#40)
  • The dataframe passed to `.to_gbq(...., if_exists='append')` needs to contain only a subset of the fields in the BigQuery schema. (GH#24)
  • Use the google-auth library for authentication because oauth2client is deprecated. (GH#39)
  • read_gbq() now has a auth_local_webserver boolean argument for controlling whether to use web server or console flow when getting user credentials. Replaces –noauth_local_webserver command line argument. (GH#35)
  • read_gbq() now displays the BigQuery Job ID and standard price in verbose output. (GH#70 and GH#71)

0.1.6 / 2017-05-03

  • All gbq errors will simply be subclasses of ValueError and no longer inherit from the deprecated PandasError.

0.1.4 / 2017-03-17

  • InvalidIndexColumn will be raised instead of InvalidColumnOrder in read_gbq() when the index column specified does not exist in the BigQuery schema. (GH#6)

0.1.3 / 2017-03-04

  • Bug with appending to a BigQuery table where fields have modes (NULLABLE,REQUIRED,REPEATED) specified. These modes were compared versus the remote schema and writing a table via to_gbq() would previously raise. (GH#13)

0.1.2 / 2017-02-23

Initial release of transfered code from pandas

Includes patches since the 0.19.2 release on pandas with the following:

  • read_gbq() now allows query configuration preferences pandas-GH#14742
  • read_gbq() now stores INTEGER columns as dtype=object if they contain NULL values. Otherwise they are stored as int64. This prevents precision lost for integers greather than 2**53. Furthermore FLOAT columns with values above 10**4 are no longer casted to int64 which also caused precision loss pandas-GH#14064, and pandas-GH#14305