Learning (ML) pipelines and ETL processes. If you’re already familiar with Python and libraries such as Pandas, then PyFlink makes it simpler to leverage the full capabilities of the Flink ecosystem. Depending data: STRING Create a Table from a Pandas DataFrame [5]: import pandas as pd df = pd.DataFrame({'id': [1, 2], 'data': ['Hi', 'Hello']}) table = table_env.from_pandas(df) table.get_schema() /Users/du 2 rows in set PyFlink Table also provides the conversion back to a pandas DataFrame to leverage pandas API. [14]: table.to_pandas() [14]: id data 0 1 Hi 1 2 Hello 16 Chapter 1. How to build docs locally

Learning (ML) pipelines and ETL processes. If you’re already familiar with Python and libraries such as Pandas, then PyFlink makes it simpler to leverage the full capabilities of the Flink ecosystem. Depending data: STRING Create a Table from a Pandas DataFrame [5]: import pandas as pd df = pd.DataFrame({'id': [1, 2], 'data': ['Hi', 'Hello']}) table = table_env.from_pandas(df) table.get_schema() /Users/du 2 rows in set PyFlink Table also provides the conversion back to a pandas DataFrame to leverage pandas API. [14]: table.to_pandas() [14]: id data 0 1 Hi 1 2 Hello 16 Chapter 1. How to build docs locally