How to read a Parquet file into Pandas DataFrame?
PythonPandasDataframeParquetBlazePython Problem Overview
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.
Python Solutions
Solution 1 - Python
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).
Solution 2 - Python
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.
Solution 3 - Python
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
Solution 4 - Python
Parquet
Step 1: Data to play with
df = pd.DataFrame({
'student': ['personA007', 'personB', 'x', 'personD', 'personE'],
'marks': [20,10,22,21,22],
})
Step 2: Save as Parquet
df.to_parquet('sample.parquet')
Step 3: Read from Parquet
df = pd.read_parquet('sample.parquet')
Solution 5 - Python
Considering the .parquet
file named data
parquet_file = '../data.parquet'
open( parquet_file, 'w+' )
Then use pandas.to_parquet
(this function requires either the fastparquet or pyarrow library)
parquet_df.to_parquet(parquet_file)
Then, use pandas.read_parquet()
to get a dataframe
new_parquet_df = pd.read_parquet(parquet_file)
Solution 6 - Python
When writing to parquet, consider using brotli compression. I'm getting a 70% size reduction of 8GB file parquet file by using brotli compression. Brotli makes for a smaller file and faster read/writes than gzip, snappy, pickle. Although pickle can do tuples whereas parquet does not.
df.to_parquet('df.parquet.brotli',compression='brotli')
df = pd.read_parquet('df.parquet.brotli')
Solution 7 - Python
Parquet files are always large. so read it using dask.
import dask.dataframe as dd
from dask import delayed
from fastparquet import ParquetFile
import glob
files = glob.glob('data/*.parquet')
@delayed
def load_chunk(path):
return ParquetFile(path).to_pandas()
df = dd.from_delayed([load_chunk(f) for f in files])
df.compute()