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Fluent interface for data processing, advanced toolkit for data science

Project description


This package is an data science-specific update for yo_fluq that introduces:

  • querying and output for pandas data structures and files in Queryable
  • handy feed-based extension methods.

The main reason for separating yo_fluq_ds from yo_fluq is that data science functionality requires huge packages like pandas and matplotlib, which I didn't want to include in a basic package.

Small useful classes

  • Obj is an ordered dict with a member-like access: obj.a=12 works exactly as obj['a']=12
  • OrderedEnum is Enum with ordering, it's useful when using enums in pandas, because the basic enumeration cannot be used as keys for group_by

Pull-queries updates


Query.combinatorics has some useful method to create lazy combinatorics enumerations:

  • cartesian(en1,en2,...) will create a cartesian product of enumerations in en1, en2, etc.
  • grid(field1=en1,field2=en2) will create an enumeration of Obj with fields field1, field2 that runs over cartesian product of en1, en2, etc.
  • triangle is query-like replacement for loops i=0..N, j=0..i
  • powerset produces all the subsets of a given set

File system

Adds several aggregators/query sources to work with files.

  • to_text_file/Query.file.text: text file, its lines are interpreted as enumeration's objects
  • to_zip_file/Query.file.zipped_text: zipped text file
  • to_pickle_file/Query.file.pickle: a internal format, lazily writes a sequence of objects in pickle format in one file.
  • to_zip_folder/Query.to_zipped_folder: representation for KeyValuePair: filenames are keys, its concent is values

Adds FileIO class with one-line instruction to read text, json, pickle, jsonpickle, yaml files.

Adds Query.folder method to create enumeration of Path objects from folder


  • Adds to_series, to_dataframe and to_ndarray aggregators
  • Adds Query.series to convert series in KeyValuePair enumeration
  • Adds Query.df to convert dataframe in Obj (or dict) enumeration

Adds feed method to DataFrame, Series, DataFrameGroupBy and SeriesGroupBy by monkey-patching. It is now possible to write something like:

    .feed(lambda z: groupby('M')))

When calling lambda inside feed, z will be assigned to the dataframe after filtering out.

This technique allows longer fluent instructions for pandas, which is otherwise impossible due to filtering.

feed-extension methods.

Some methods from yo_fluq_ds are not incorporated into Queryables, because they are used not that often and I want to avoid overloading Queryable with such methods. So, they are accessible only via feed method.

All of them are inside fluq module.

For Queryable

  • fluq.with_progress_bar is a Queryable-friendly wrapping over tqdm. It automatically detects notebooks/console environments. The total (length of enumerable) in most cases is known from Queryable.length field, but sometimes needs to be provided.
  • fluq.with_plots(columns,figsize) will create plots for each of elements in enumerable and return the enumerable of ItemWithAx. Very handy to draw several plots at once, e.g. for different columns in dataframe
  • fluq.pairwise converts enumerable to the enumerable of pair of neighbouring elements

For pandas

  • fluq.fractions can be used where size is normally used to determine the relative size of the groups
  • fluq.trimmer can be used to trim too high/too low values from the series, thus facilitating histograms' creation.

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