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:
pd.read_parquet('example_pa.parquet', engine='pyarrow')
or
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).
Update: since the time I answered this there has been a lot of work on this look at Apache Arrow for a better read and write of parquet. Also: http://wesmckinney.com/blog/python-parquet-multithreading/
There is a python parquet reader that works relatively well: https://github.com/jcrobak/parquet-python
It will create python objects and then you will have to move them to a Pandas DataFrame so the process will be slower than pd.read_csv for example.
Aside from pandas, Apache pyarrow also provides way to transform parquet to dataframe
The code is simple, just type:
import pyarrow.parquet as pq
df = pq.read_table(source=your_file_path).to_pandas()
For more information, see the document from Apache pyarrow Reading and Writing Single Files
Source: Stackoverflow.com