Skip to content
Randomly sample lines from a csv, tsv, or other line-based data file
Python Shell
Branch: master
Clone or download

Latest commit

Fetching latest commit…
Cannot retrieve the latest commit at this time.


Type Name Latest commit message Commit time
Failed to load latest commit information.
subsample bump to 0.0.6 May 30, 2014
tests added doc for stdin support (fixes #3); version bump to 0.0.5 May 12, 2014
util translate code for Python 3.x compat Feb 28, 2014
LICENSE added manifest Aug 23, 2013 fixed manifest Aug 31, 2013
Makefile added basic regression tests Aug 31, 2013
README.rst added doc for stdin support (fixes #3); version bump to 0.0.5 May 12, 2014 added basic regression tests Aug 31, 2013



subsample is a command-line tool for sampling data from a large, newline-separated dataset (typically a CSV-like file).


subsample is distributed with pip. Once you've installed pip, simply run:

> pip install subsample

and subsample will be installed into your Python environment.


subsample requires one argument, the input file. If the input file is -, data will be read from standard input (in this case, only the reservoir and approximate algorithms can be used).

Simple Example

To take a sample of size 1000 from the file big_data.csv, run subsample as follows:

> subsample -n 1000 big_data.csv

This will print 1000 random lines from the file to the terminal.

File Redirection

Usually we want to save the sample to another file instead. subsample doesn't have file output built-in; instead it relies on the output redirection features of your terminal. To save to big_data_sample.csv, run the following command:

> subsample -n 1000 big_data.csv > big_data_sample.csv

Sampling from STDIN

To use standard input as the source, use - as the filename, eg:

> subsample -n 1000 < big_data.csv > big_data_sample.csv

Note that only reservoir sampling supports stdin because the other sampling algorithms require a seekable input stream.

Header Rows

CSV files often have a header row with the column names. You can pass the -r flag to subsample to preserve the header row:

> subsample -n 1000 big_data.csv -r > big_data_sample.csv

Rarely, you may need to sample from a file with a header spanning multiple rows. The -r argument takes an optional number of rows to preserve as a header:

> subsample -n 1000 -r 3 data_with_header.csv > sample_with_header.csv

Note that if the -r argument is directly before the input filename, it must have an argument or else it will try to interpret the input filename as the number of header rows and fail. Putting the -r argument after the input filename will avoid this.

Random Seed

The output of subsample is random and depend on the computer's random state. Sometimes you may want to take a sample in a way that can be reproduced. You can pass a random seed to subsample with the -s flag to accomplish this:

> subsample -s 45906345 data_file.csv > reproducable_sample.csv

Sampling Algorithms

Algorithm Comparison

subsample implements three sampling algorithms, each with their own strengths and weaknesses.

  Reservoir Approximate Two-pass
flag -res -app -tp
stdin-compatible yes yes no
space complexity O(ss*rs) O(1) O(1)
fixed sample size compatible not compatible compatible
fractional sample size not compatible compatible compatible
sample order random source source

For space complexity, ss is the number of records in the sample and rs is the maximum size of a record.

Sample order is the order of the records returned. Only reservoir sampling gives results in random order; approximate and two-pass return results in the same order as the source data.

Reservoir Sampling

Reservoir sampling (Random Sampling with a Reservoir (Vitter 85)) is a method of sampling from a stream of unknown size where the sample size is fixed in advance. It is a one-pass algorithm and uses space proportional to the amount of data in the sample.

Reservoir sampling is the default algorithm used by subsample. For consistency, it can also be invoked with the argument --reservoir.

When using reservoir sampling, the sample size must be fixed rather than fractional.


> subsample --reservoir -n 1000 big_data.csv > sample_data.csv

Approximate Sampling

Approximate sampling simply includes each row in the sample with a probability given as the sample proportion. It is a stateless algorithm with minimal space requirements. Samples will have on average a size of fraction * population_size, but it will vary between each invocation. Because of this, approximate sampling is only useful when the sample size does not have to be exact (hence the name).


> subsample --approximate -f 0.15 my_data.csv > my_sample.csv

Equivalently, supply a percentage instead of a fraction by switching the -f to a -p:

> subsample --approximate -p 15 my_data.csv > my_sample.csv

Two-Pass Sampling

As the name implies, two-pass sampling uses two passes: the first is to count the number of records (ie. the population size) and the second is to emit the records which are part of the sample. Because of this it is not compatible with stdin as an input.


> subsample --two-pass -n 1000 my_data.csv > my_sample.csv

Two-pass sampling also accepts the sample size as a fraction or percent:

> subsample --two-pass -p 15 my_data.csv > my_sample.csv


A simple GNU Make-driven testing script is included. Run make test from subsample's base directory after installing to run some regression tests.

Due to the randomness inherent to random sampling, testing is limited to checking that the output is the same when the random seed is unchanged. This serves mainly to find new bugs introduced by changes in the future and does not imply that the code itself is correct (in the sense that the sample is truly random).

You can’t perform that action at this time.