pandas-save-profiler 0.0.0

Tools to evaluate pandas performance when saving dataframes in different file formats.

Pandas Save Profiler

pandas_save_profiler helps you evaluating and comparing the performance of different pandas read and write methods.

Install

pip install pandas-save-profiler

Usage

Load pandas and a dataframe you want to save.

import pandas as pd
data = pd.util.testing.makeMissingDataframe()

Load pandas_save_profiler and use it to evaluate pandas performance saving a pickle file:

import pandas_save_profiler
data.save_profiler('to_pickle')

The output is a pandas series:

format                                                 pickle
writer                                              to_pickle
reader                                            read_pickle
writer_args                      {'path': '/tmp/tmppk7nkivk'}
reader_args        {'filepath_or_buffer': '/tmp/tmppk7nkivk'}
writer_time                                         0.0798338
reader_time                                         0.0294895
writer_memory                                     1.09087e+08
reader_memory                                     1.09118e+08
df_memory                                                 288
file_size                                                1122
writer_memory_h                                      109.1 MB
reader_memory_h                                      109.1 MB
df_memory_h                                         288 Bytes
file_size_h                                            1.1 kB
repeats                                                     5
reads_the_same                                           True
dtype: object

Values in the series indicate:

• The format used to persist the dataframe and the writing and reading options.
• Writing and reading times in seconds.
• Writing and reading memory increment.
• Size of the dataframe in memory.
• Size of the saved file.

Memory values are in bytes but a "humanized" version is also reported. The saving and reloading process is repeated 5 times and average values are returned. The flag reads_the_same indicates whether the reloaded file is exactly the same as the original one or has some differences.

To compare several writing options you can use the save_profiler function on each of them and combine the results into a results dataframe:

pd.DataFrame([
    data.save_profiler('to_csv'),
    data.save_profiler('to_pickle'),
    data.save_profiler('to_parquet'),
])

returns:

    format      writer        reader                          writer_args  \
0      csv      to_csv      read_csv  {'path_or_buf': '/tmp/tmpsedehjob'}   
1   pickle   to_pickle   read_pickle         {'path': '/tmp/tmp_vhue2q7'}   
2  parquet  to_parquet  read_parquet         {'path': '/tmp/tmp0zn8qsnk'}   

                                  reader_args  writer_time  reader_time  \
0  {'filepath_or_buffer': '/tmp/tmpsedehjob'}     0.031842     0.039830   
1  {'filepath_or_buffer': '/tmp/tmp_vhue2q7'}     0.025705     0.028469   
2                {'path': '/tmp/tmp0zn8qsnk'}     0.039009     0.052447   

   writer_memory  reader_memory  df_memory  file_size writer_memory_h  \
0    110149632.0    110599372.8        288        139        110.1 MB   
1    110813184.0    110813184.0        288       1122        110.8 MB   
2    116892467.2    118014771.2        288       3449        116.9 MB   

  reader_memory_h df_memory_h file_size_h  repeats  reads_the_same  
0        110.6 MB   288 Bytes   139 Bytes        5           False  
1        110.8 MB   288 Bytes      1.1 kB        5            True  
2        118.0 MB   288 Bytes      3.4 kB        5            True