What are the differences between feather and parquet? : <p>Both are <strong>columnar (disk-)storage formats</strong> for use in data analysis systems. 
Both are integrated within <a href="https://arrow.apache.org/" rel="noreferrer">Apache Arrow</a> (<a href="https://pypi.python.org/pypi/pyarrow" rel="noreferrer">pyarrow</a> package for python) and are
designed to correspond with <a href="https://arrow.apache.org/docs/python/index.html" rel="noreferrer">Arrow</a> as a columnar in-memory analytics layer.</p>

<p>How do both formats differ?</p>

<p>Should you always prefer feather when working with pandas when possible?</p>

<p>What are the use cases where <a href="https://arrow.apache.org/docs/python/ipc.html#feather-format" rel="noreferrer">feather</a> is more suitable than <a href="https://arrow.apache.org/docs/python/parquet.html" rel="noreferrer">parquet</a> and the 
other way round?</p>

<hr>

<p>Appendix</p>

<p>I found some hints here <a href="https://github.com/wesm/feather/issues/188" rel="noreferrer">https://github.com/wesm/feather/issues/188</a>,
but given the young age of this project, it's possibly a bit out of date.</p>

<p>Not a serious speed test because I'm just dumping and loading a whole
Dataframe but to give you some impression if you never
heard of the formats before:</p>

<pre><code> # IPython    
import numpy as np
import pandas as pd
import pyarrow as pa
import pyarrow.feather as feather
import pyarrow.parquet as pq
import fastparquet as fp


df = pd.DataFrame({'one': [-1, np.nan, 2.5],
                   'two': ['foo', 'bar', 'baz'],
                   'three': [True, False, True]})

print("pandas df to disk ####################################################")
print('example_feather:')
%timeit feather.write_feather(df, 'example_feather')
# 2.62 ms ± 35.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
print('example_parquet:')
%timeit pq.write_table(pa.Table.from_pandas(df), 'example.parquet')
# 3.19 ms ± 51 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
print()

print("for comparison:")
print('example_pickle:')
%timeit df.to_pickle('example_pickle')
# 2.75 ms ± 18.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
print('example_fp_parquet:')
%timeit fp.write('example_fp_parquet', df)
# 7.06 ms ± 205 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
print('example_hdf:')
%timeit df.to_hdf('example_hdf', 'key_to_store', mode='w', table=True)
# 24.6 ms ± 4.45 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)
print()

print("pandas df from disk ##################################################")
print('example_feather:')
%timeit feather.read_feather('example_feather')
# 969 µs ± 1.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
print('example_parquet:')
%timeit pq.read_table('example.parquet').to_pandas()
# 1.9 ms ± 5.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

print("for comparison:")
print('example_pickle:')
%timeit pd.read_pickle('example_pickle')
# 1.07 ms ± 6.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
print('example_fp_parquet:')
%timeit fp.ParquetFile('example_fp_parquet').to_pandas()
# 4.53 ms ± 260 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
print('example_hdf:')
%timeit pd.read_hdf('example_hdf')
# 10 ms ± 43.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

# pandas version: 0.22.0
# fastparquet version: 0.1.3
# numpy version: 1.13.3
# pandas version: 0.22.0
# pyarrow version: 0.8.0
# sys.version: 3.6.3
# example Dataframe taken from https://arrow.apache.org/docs/python/parquet.html
</code></pre>
