What Is the Fastest Way to Upload a Big CSV File in Notebook to Work with Python Pandas

What is the fastest way to upload a big csv file in notebook to work with python pandas?

Here are results of my read and write comparison for the DF (shape: 4000000 x 6, size in memory 183.1 MB, size of uncompressed CSV - 492 MB).

Comparison for the following storage formats: (CSV, CSV.gzip, Pickle, HDF5 [various compression]):

                  read_s  write_s  size_ratio_to_CSV
storage
CSV               17.900    69.00              1.000
CSV.gzip          18.900   186.00              0.047
Pickle             0.173     1.77              0.374
HDF_fixed          0.196     2.03              0.435
HDF_tab            0.230     2.60              0.437
HDF_tab_zlib_c5    0.845     5.44              0.035
HDF_tab_zlib_c9    0.860     5.95              0.035
HDF_tab_bzip2_c5   2.500    36.50              0.011
HDF_tab_bzip2_c9   2.500    36.50              0.011

reading

writing/saving

file size ratio in relation to uncompressed CSV file

RAW DATA:

CSV:

In [68]: %timeit df.to_csv(fcsv)
1 loop, best of 3: 1min 9s per loop

In [74]: %timeit pd.read_csv(fcsv)
1 loop, best of 3: 17.9 s per loop

CSV.gzip:

In [70]: %timeit df.to_csv(fcsv_gz, compression='gzip')
1 loop, best of 3: 3min 6s per loop

In [75]: %timeit pd.read_csv(fcsv_gz)
1 loop, best of 3: 18.9 s per loop

Pickle:

In [66]: %timeit df.to_pickle(fpckl)
1 loop, best of 3: 1.77 s per loop

In [72]: %timeit pd.read_pickle(fpckl)
10 loops, best of 3: 173 ms per loop

HDF (format='fixed') [Default]:

In [67]: %timeit df.to_hdf(fh5, 'df')
1 loop, best of 3: 2.03 s per loop

In [73]: %timeit pd.read_hdf(fh5, 'df')
10 loops, best of 3: 196 ms per loop

HDF (format='table'):

In [37]: %timeit df.to_hdf('D:\\temp\\.data\\37010212_tab.h5', 'df', format='t')
1 loop, best of 3: 2.6 s per loop

In [38]: %timeit pd.read_hdf('D:\\temp\\.data\\37010212_tab.h5', 'df')
1 loop, best of 3: 230 ms per loop

HDF (format='table', complib='zlib', complevel=5):

In [40]: %timeit df.to_hdf('D:\\temp\\.data\\37010212_tab_compress_zlib5.h5', 'df', format='t', complevel=5, complib='zlib')
1 loop, best of 3: 5.44 s per loop

In [41]: %timeit pd.read_hdf('D:\\temp\\.data\\37010212_tab_compress_zlib5.h5', 'df')
1 loop, best of 3: 854 ms per loop

HDF (format='table', complib='zlib', complevel=9):

In [36]: %timeit df.to_hdf('D:\\temp\\.data\\37010212_tab_compress_zlib9.h5', 'df', format='t', complevel=9, complib='zlib')
1 loop, best of 3: 5.95 s per loop

In [39]: %timeit pd.read_hdf('D:\\temp\\.data\\37010212_tab_compress_zlib9.h5', 'df')
1 loop, best of 3: 860 ms per loop

HDF (format='table', complib='bzip2', complevel=5):

In [42]: %timeit df.to_hdf('D:\\temp\\.data\\37010212_tab_compress_bzip2_l5.h5', 'df', format='t', complevel=5, complib='bzip2')
1 loop, best of 3: 36.5 s per loop

In [43]: %timeit pd.read_hdf('D:\\temp\\.data\\37010212_tab_compress_bzip2_l5.h5', 'df')
1 loop, best of 3: 2.5 s per loop

HDF (format='table', complib='bzip2', complevel=9):

In [42]: %timeit df.to_hdf('D:\\temp\\.data\\37010212_tab_compress_bzip2_l9.h5', 'df', format='t', complevel=9, complib='bzip2')
1 loop, best of 3: 36.5 s per loop

In [43]: %timeit pd.read_hdf('D:\\temp\\.data\\37010212_tab_compress_bzip2_l9.h5', 'df')
1 loop, best of 3: 2.5 s per loop

PS i can't test feather on my Windows notebook

DF info:

In [49]: df.shape
Out[49]: (4000000, 6)

In [50]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4000000 entries, 0 to 3999999
Data columns (total 6 columns):
a    datetime64[ns]
b    datetime64[ns]
c    datetime64[ns]
d    datetime64[ns]
e    datetime64[ns]
f    datetime64[ns]
dtypes: datetime64[ns](6)
memory usage: 183.1 MB

In [41]: df.head()
Out[41]:
                    a                   b                   c  \
0 1970-01-01 00:00:00 1970-01-01 00:01:00 1970-01-01 00:02:00
1 1970-01-01 00:01:00 1970-01-01 00:02:00 1970-01-01 00:03:00
2 1970-01-01 00:02:00 1970-01-01 00:03:00 1970-01-01 00:04:00
3 1970-01-01 00:03:00 1970-01-01 00:04:00 1970-01-01 00:05:00
4 1970-01-01 00:04:00 1970-01-01 00:05:00 1970-01-01 00:06:00

                    d                   e                   f
0 1970-01-01 00:03:00 1970-01-01 00:04:00 1970-01-01 00:05:00
1 1970-01-01 00:04:00 1970-01-01 00:05:00 1970-01-01 00:06:00
2 1970-01-01 00:05:00 1970-01-01 00:06:00 1970-01-01 00:07:00
3 1970-01-01 00:06:00 1970-01-01 00:07:00 1970-01-01 00:08:00
4 1970-01-01 00:07:00 1970-01-01 00:08:00 1970-01-01 00:09:00

File sizes:

{ .data }  » ls -lh 37010212.*                                                                          /d/temp/.data
-rw-r--r-- 1 Max None 492M May  3 22:21 37010212.csv
-rw-r--r-- 1 Max None  23M May  3 22:19 37010212.csv.gz
-rw-r--r-- 1 Max None 214M May  3 22:02 37010212.h5
-rw-r--r-- 1 Max None 184M May  3 22:02 37010212.pickle
-rw-r--r-- 1 Max None 215M May  4 10:39 37010212_tab.h5
-rw-r--r-- 1 Max None 5.4M May  4 10:46 37010212_tab_compress_bzip2_l5.h5
-rw-r--r-- 1 Max None 5.4M May  4 10:51 37010212_tab_compress_bzip2_l9.h5
-rw-r--r-- 1 Max None  17M May  4 10:42 37010212_tab_compress_zlib5.h5
-rw-r--r-- 1 Max None  17M May  4 10:36 37010212_tab_compress_zlib9.h5

Conclusion:

Pickle and HDF5 are much faster, but HDF5 is more convenient - you can store multiple tables/frames inside, you can read your data conditionally (look at where parameter in read_hdf()), you can also store your data compressed (zlib - is faster, bzip2 - provides better compression ratio), etc.

PS if you can build/use feather-format - it should be even faster compared to HDF5 and Pickle

PPS: don't use Pickle for big data frames, as you may end up with SystemError: error return without exception set error message. It's also described here and here.

Using pandas to efficiently read in a large CSV file without crashing

You should consider using the chunksize parameter in read_csv when reading in your dataframe, because it returns a TextFileReader object you can then pass to pd.concat to concatenate your chunks.

chunksize = 100000
tfr = pd.read_csv('./movielens/ratings.csv', chunksize=chunksize, iterator=True)
df = pd.concat(tfr, ignore_index=True)

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If you just want to process each chunk individually, use,

chunksize = 20000
for chunk in pd.read_csv('./movielens/ratings.csv', 
                         chunksize=chunksize, 
                         iterator=True):
    do_something_with_chunk(chunk)

Improving time efficiency of code, working with a big Data Set - Python

It shouldn't be necessary to use any type of for loop for your code. You are reading the CSV using pandas, but you don't seem to specify the correct parameters.

import pandas as pd

path_data = '" *insert a path here* "'
file = path_data + ' *name of the .csv file* '

df = pd.read_csv(file, sep=';', parse_dates=[0], engine='c', header=None)

time_UTC = df.iloc[:, 0]
vmag2D = df.iloc[:, 1]
vdir = df.iloc[:, 2]

If you run this, your resulting variables (time_UTC, ...) will be of type pandas.Series. You can convert those to list with .to_list() or access the numpy array using .values.

Note that I am specifying engine='c' here in the pandas CSV parser, which is using a native C parser that is faster than its python equivalent, as you are processing a large file here.

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