["python","pandas","dataframe","parquet","blaze"]
How to read a Parquet file into Pandas DataFrame?

How to read a modestly sized Parquet data-set into an in-memory Pandas DataFrame without setting up a cluster computing infrastructure such as Hadoop or Spark? This is only a moderate amount of data that I would like to read in-memory with a simple Python script on a laptop. The data does not reside on HDFS. It is either on the local file system or possibly in S3. I do not want to spin up and configure other services like Hadoop, Hive or Spark. I thought Blaze/Odo would have made this possible: the Odo documentation mentions Parquet, but the examples seem all to be going through an external Hive runtime.
pandas 0.21 introduces new functions for Parquet: import pandas as pd pd.read_parquet('example_pa.parquet', engine='pyarrow') or import pandas as pd pd.read_parquet('example_fp.parquet', engine='fastparquet') The above link explains: These engines are very similar and should read/write nearly identical parquet format files. These libraries differ by having different underlying dependencies (fastparquet by using numba, while pyarrow uses a c-library).