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TextDirectory allows you to combine multiple text files into one aggregated file. TextDirectory also supports matching files for certain criteria and applying transformations to the aggregated text.

TextDirectory can be used as a mere tool (via the CLI) and as a Python library.

Of course, everything TextDirectory does could be achieved in bash or PowerShell. However, there are certain use-cases (e.g. when used as a library) in which it might be useful.


  • Aggregating multiple text files
  • Filtering documents/texts based on various parameters such as length, content, and random sampling
  • Transforming the aggregated text (e.g. transforming the text to lowercase)
Version Filters Transformations
0.1.0 filter_by_max_chars(n int); filter_by_min_chars(n int); filter_by_max_tokens(n int); filter_by_min_tokens(n int); filter_by_contains(str); filter_by_not_contains(str); filter_by_random_sampling(n int; replace=False) transformation_lowercase
0.1.1 filter_by_chars_outliers(n sigmas int) transformation_remove_nl
0.1.2 filter_by_filename_contains(str) transformation_usas_en_semtag; transformation_uppercase; transformation_postag(spacy_model str)
0.1.3 filter_by_similar_documents(reference_file str; threshold float) transformation_remove_non_ascii; transformation_remove_non_alphanumerical
0.2.0 filter_by_max_filesize(max_kb int); filter_by_min_filesize(min_kb int) transformation_to_leetspeak; transformation_crude_spellchecker(language model str)
0.2.1 None transformation_remove_stopwords(stopwords_source str; stopwords str [en]; spacy_model str; custom_stopwords str); transformation_remove_htmltags
0.3.0 None transformation_remove_weird_tokens(spaCy model; remove_double_space=False); transformation_lemmatizer(spaCy model)
0.3.2 None transformation_expand_english_contractions


Install TextDirectory via pip: pip install textdirectory

TextDirectory, as exemplified below, works with a two-stage model. After loading in your data (directory) you can iteratively select the files you want to process. In a second step you can perform transformations on the text before finally aggregating it.


As a Command-Line Tool

TextDirectory comes equipped with a CLI.

The syntax for both the filters and tranformations works similarly. They are chained by adding slashes (/) and parameters are passed via commas (,): filter_by_min_tokens,5/filter_by_random_sampling,2.

Example 1: A Very Simple Aggregation

textdirectory --directory testdata --output_file aggregated.txt

This will take all files (.txt) in testdata and then aggregates the files into a file called aggregated.txt.

You could also use '*' as a wildcard for filetype if you need to include all files and not just .txt.

textdirectory --directory testdata --output_file aggregated.txt --filetype *

Example 2: Applying Filters and Transformations

In this example we want to filter the files based on their token count, perform a random sampling and finally transform all text to lowercase.

textdirectory --directory testdata --output_file aggregated.txt --filters filter_by_min_tokens,5/filter_by_random_sampling,2 --transformations transformation_lowercase

After passing two filters (filter_by_min_tokens and filter_by_random_sampling) we've applied the transform_lowercase transformation.

The resulting file will contain the content of two files that each have at least five tokens.

As a Python Library

In order to demonstrate TextDirectory as a Python library, we'll recreate the second example from above:

import textdirectory
td = textdirectory.TextDirectory(directory='testdata')
td.load_files(recursive=False, filetype='txt', sort=True)

If we don't have special requirements, we can also call td = textdirectory.TextDirectory(directory='testdata', autoload=True) to skip manually callin load_files. If we wanted to keep working with the actual aggregated text, we could have called text = td.aggregate_to_memory() instead of aggregate_to_file.

import textdirectory
td = textdirectory.TextDirectory(directory='testdata', autoload=True)

Sometimes we might want to get the actual text of a given file. This can be achieved as seen above. The get_text method will return the transformed text if it is available. Otherwise, it will simply read the file and return the text.

Every applied filter will create a state (i.e. a checkpoint). If we want to go back to a previous state, we can print all states by calling td.print_saved_states(). Previous states can then be loaded by calling td.load_aggregation_state(state=0).

It's also possible to pass arguments to the individual transformations. In order to do this (at the moment) you have to adhere to the correct order of arguments.

# def transformation_remove_stopwords(text, stopwords_source='internal', stopwords='en', spacy_model='en_core_web_sm', custom_stopwords=None, *args)
td.stage_transformation(['transformation_remove_stopwords', 'internal', 'en', 'en_core_web_sm', 'dolor'])

In the above example, we are adding additional custom stopwords to the transformer.

Notes for Developers

If you want to run tests, please use python test.


  • Increasing test coverage
  • Writing better documentation
  • Adding better error handling (raw exception are, well ...)
  • Adding logging
  • Better handling of non-unicode files (e.g. by detecting and reporting the encoding)
  • Contemplating whether it makes sense to stage filters similarly to transformations
  • Allowing users to pass keyword arguments to transformers
  • Implementing autodoc (via Sphinx)


We are not holding the actual texts in memory. This leads to much more disk read activity (and time inefficiency), but saves memory. Of course, this is not the case when using aggregate_to_memory.

transformation_usas_en_semtag relies on the web version of Paul Rayson's USAS Tagger. Don't use this transformation for large amounts of text, give credit, and consider using their commercial product Wmatrix.


This package is based on the audreyr/cookiecutter-pypackage coockiecutter template. The crude spellchecker (transformation) is implemented following Peter Norvig's excellent tutorial.