data frames. This feature requires version 0.10.0 of the pandas-gbq library as well as the google-cloud-bigquery-storage and fastavro libraries. (GH26104) • Fixed memory leak in DataFrame.to_json() when writing blosc Compression for msgpack fastparquet 0.2.1 Parquet reading / writing gcsfs 0.2.2 Google Cloud Storage access html5lib HTML parser for read_html (see note) lxml 3.8.0 HTML parser for read_html msgpack, both of which are supported by pandas’ IO facilities. 4.20.10 Computation Numerical integration (sample-based) of a time series Correlation Often it’s useful to obtain the lower (or upper)

data frames. This feature requires version 0.10.0 of the pandas-gbq library as well as the google-cloud-bigquery-storage and fastavro libraries. (GH26104) • Fixed memory leak in DataFrame.to_json() when writing blosc Compression for msgpack fastparquet 0.2.1 Parquet reading / writing gcsfs 0.2.2 Google Cloud Storage access html5lib HTML parser for read_html (see note) lxml 3.8.0 HTML parser for read_html msgpack, both of which are supported by pandas’ IO facilities. 4.20.10 Computation Numerical integration (sample-based) of a time series 4.20. Cookbook 869 pandas: powerful Python data analysis toolkit

writing blosc Compression for HDF5 fastparquet 0.3.2 Parquet reading / writing gcsfs 0.2.2 Google Cloud Storage access html5lib HTML parser for read_html (see note) Continued on next page 46 Chapter parquet, both of which are supported by pandas’ IO facilities. 3.22.10 Computation Numerical integration (sample-based) of a time series Correlation Often it’s useful to obtain the lower (or upper) download query results quickly, but at an increased cost. To use this API, first enable it in the Cloud Con- sole. You must also have the bigquery.readsessions.create permission on the project you are billing

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.1.1 Integration with Apache Parquet file format . . . . . . . . . . . . . . . . . . . . . 8 1.2.1.2 infer_objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 3.5.2 Testing With Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 3.5.3 Test-driven development/code number of bug fixes. We recommend that all users upgrade to this version. Highlights include: • Integration with Apache Parquet, including a new top-level read_parquet() function and DataFrame. to_parquet()

DataFrame. To introduction tutorial To user guide Straight to tutorial... pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,...). Importing files aside from local and HTTP fastparquet 0.3.2 Parquet reading / writing gcsfs 0.6.0 Google Cloud Storage access html5lib HTML parser for read_html (see note) lxml 3.8.0 HTML parser for read_html parquet, both of which are supported by pandas’ IO facilities. 2.25.10 Computation Numerical integration (sample-based) of a time series Correlation Often it’s useful to obtain the lower (or upper)

DataFrame. To introduction tutorial To user guide Straight to tutorial... pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,...). Importing files aside from local and HTTP fastparquet 0.3.2 Parquet reading / writing gcsfs 0.6.0 Google Cloud Storage access html5lib HTML parser for read_html (see note) lxml 3.8.0 HTML parser for read_html parquet, both of which are supported by pandas’ IO facilities. 2.25.10 Computation Numerical integration (sample-based) of a time series Correlation Often it’s useful to obtain the lower (or upper)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2647 4.4.5 Testing with continuous integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2648 4.4.6 Test-driven development/code DataFrame. To introduction tutorial To user guide Straight to tutorial... pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,...). Importing toolkit, Release 1.3.2 Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.12.0 Google

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2726 4.4.5 Testing with continuous integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 4.4.6 Test-driven development/code DataFrame. To introduction tutorial To user guide Straight to tutorial... pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,...). Importing Windows Failed Failed Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.12.0 Google

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2726 4.4.5 Testing with continuous integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 4.4.6 Test-driven development/code DataFrame. To introduction tutorial To user guide Straight to tutorial... pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,...). Importing Windows Failed Failed Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.12.0 Google

DataFrame. To introduction tutorial To user guide Straight to tutorial... Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,...). Importing 0.5 Table 1 – continued from previous page Dependency Minimum Version Notes gcsfs 0.2.2 Google Cloud Storage access html5lib HTML parser for read_html (see note) lxml 3.8.0 HTML parser for read_html parquet, both of which are supported by pandas’ IO facilities. 2.22.10 Computation Numerical integration (sample-based) of a time series Correlation Often it’s useful to obtain the lower (or upper)