verbose=True) Streaming Insert is 10% Complete Streaming Insert is 20% Complete Streaming Insert is 30% Complete Streaming Insert is 40% Complete Streaming Insert is 50% Complete Streaming Insert is 60% 60% Complete Streaming Insert is 70% Complete Streaming Insert is 80% Complete Streaming Insert is 90% Complete Streaming Insert is 100% Complete Note: If an error occurs while streaming data to BigQuery a BigQuery table with the same name, but different table schema, you must wait 2 minutes before streaming data into the table. As a workaround, consider creating the new table with a different name. Refer

Python syntax (set([x,y])) (GH11215) • Improve the error message in pandas.io.gbq.to_gbq() when a streaming insert fails (GH11285) and when the DataFrame does not match the schema of the destination table verbose=True) Streaming Insert is 10% Complete Streaming Insert is 20% Complete Streaming Insert is 30% Complete Streaming Insert is 40% Complete Streaming Insert is 50% Complete Streaming Insert is 60% 60% Complete Streaming Insert is 70% Complete Streaming Insert is 80% Complete Streaming Insert is 90% Complete Streaming Insert is 100% Complete Note: If an error occurs while streaming data to BigQuery

Python syntax (set([x,y])) (GH11215) • Improve the error message in pandas.io.gbq.to_gbq() when a streaming insert fails (GH11285) and when the DataFrame does not match the schema of the destination table verbose=True) Streaming Insert is 10% Complete Streaming Insert is 20% Complete Streaming Insert is 30% Complete Streaming Insert is 40% Complete Streaming Insert is 50% Complete Streaming Insert is 60% 60% Complete Streaming Insert is 70% Complete Streaming Insert is 80% Complete Streaming Insert is 90% Complete Streaming Insert is 100% Complete Note: If an error occurs while streaming data to BigQuery

BigQuery table from a pandas DataFrame using the to_gbq() function. This function uses the Google streaming API which requires that your destination table exists in BigQuery. Given the BigQuery table already the defined table schema and column types. For simplicity, this method uses the Google BigQuery streaming API. The to_gbq method chunks data into a default chunk size of 10,000. Failures return the complete insert. There are several important limitations of the Google streaming API which are detailed at: https://developers.google.com/bigquery/streaming-data-into-bigquery. Parameters dataframe : DataFrame DataFrame

BigQuery table from a pandas DataFrame using the to_gbq() function. This function uses the Google streaming API which requires that your destination table exists in BigQuery. Given the BigQuery table already the defined table schema and column types. For simplicity, this method uses the Google BigQuery streaming API. The to_gbq method chunks data into a default chunk size of 10,000. Failures return the complete insert. There are several important limitations of the Google streaming API which are detailed at: https://developers.google.com/bigquery/streaming-data-into-bigquery. Parameters dataframe : DataFrame DataFrame

. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... Dask Name: read-parquet, 1 graph layer Inspecting the ddf object, we see a few things • There are familiar attributes like .columns analysis toolkit, Release 1.4.4 Rather than executing immediately, doing operations build up a task graph. In [26]: ddf Out[26]: Dask DataFrame Structure: id name x y npartitions=12 int64 object float64 Name: read-parquet, 1 graph layer In [27]: ddf["name"] Out[27]: Dask Series Structure: npartitions=12 object ... ... ... ... Name: name, dtype: object Dask Name: getitem, 2 graph layers In [28]: ddf["name"]

. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... Dask Name: read-parquet, 1 graph layer Inspecting the ddf object, we see a few things • There are familiar attributes like .columns hasn’t actually read the data yet. Rather than executing immediately, doing operations build up a task graph. In [38]: ddf Out[38]: Dask DataFrame Structure: id name x y npartitions=12 int64 object float64 Name: read-parquet, 1 graph layer In [39]: ddf["name"] Out[39]: Dask Series Structure: npartitions=12 object ... ... ... ... Name: name, dtype: object Dask Name: getitem, 2 graph layers In [40]: ddf["name"]

Python syntax (set([x, y])) (GH11215) • Improve the error message in pandas.io.gbq.to_gbq() when a streaming insert fails (GH11285) and when the DataFrame does not match the schema of the destination table Plotly Plotly’s Python API enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and D3.js. The library supports plotting directly from a pandas DataFrame existing packages such as PyTables, h5py, and pymongo to move data between non pandas formats. Its graph based approach is also extensible by end users for custom formats that may be too specific for the

Python syntax (set([x, y])) (GH11215) • Improve the error message in pandas.io.gbq.to_gbq() when a streaming insert fails (GH11285) and when the DataFrame does not match the schema of the destination table Plotly Plotly’s Python API enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and D3.js. The library supports plotting directly from a pandas DataFrame existing packages such as PyTables, h5py, and pymongo to move data between non pandas formats. Its graph based approach is also extensible by end users for custom formats that may be too specific for the

Python syntax (set([x, y])) (GH11215) • Improve the error message in pandas.io.gbq.to_gbq() when a streaming insert fails (GH11285) and when the DataFrame does not match the schema of the destination table Plotly Plotly’s Python API enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and D3.js. The library supports plotting directly from a pandas DataFrame existing packages such as PyTables, h5py, and pymongo to move data between non pandas formats. Its graph based approach is also extensible by end users for custom formats that may be too specific for the