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SQL atop numpy arrays represented as tables. Tables logic forked from github.com/BastiaanBergman/npsql

Project description

About Nptab

Lightweight, intuitive and fast data-tables.

Nptab data-tables are tables with columns and column names, rows and row numbers. Indexing and slicing your data is analogous to numpy array’s. The only real difference is that each column can have its own data type.

Design objectives

I got frustrated with pandas: it’s complicated slicing syntax (.loc, .x, .iloc, .. etc), it’s enforced index column and the Series objects I get when I want a numpy array. With Nptab I created the simplified pandas I need for many of my data-jobs. Just focussing on simple slicing of multi-datatype tables and basic table tools.

  • Intuitive simple slicing.
  • Using numpy machinery, for best performance, integration with other tools and future support.
  • Store data by column numpy arrays (column store).
  • No particular index column, all columns can be used as the index, the choice is up to the user.
  • Fundamental necessities for sorting, grouping, joining and appending tables.

Install

pip install npsql

Quickstart

init

To setup a Nptab:

>>> from npsql import Nptab
>>> npsql = Nptab([ ["John", "Joe", "Jane"],
...                [1.82,1.65,2.15],
...                [False,False,True]], columns = ["Name", "Height", "Married"])
>>> npsql
 Name   |   Height |   Married
--------+----------+-----------
 John   |     1.82 |         0
 Joe    |     1.65 |         0
 Jane   |     2.15 |         1
3 rows ['<U4', '<f8', '|b1']

Alternatively, Tabls can be setup from dictionaries, numpy arrays, pandas DataFrames, or no data at all. Database connectors usually return data as a list of records, the module provides a convenience function to transpose this into a list of columns.

slice

Slicing can be done the numpy way, always returning Nptab objects:

>>> npsql[1:3,[0,2]]
 Name   |   Married
--------+-----------
 Joe    |         0
 Jane   |         1
2 rows ['<U4', '|b1']

Slices will always return a Nptab except in three distinct cases, when:

  1. explicitly one column is requested, a numpy array is returned:
>>> npsql[1:3,'Name']       # doctest: +SKIP
array(['Joe', 'Jane'],
      dtype='<U4')
  1. explicitly one row is requested, a tuple is returned:
>>> npsql[0,:]
('John', 1.82, False)
  1. explicitly one element is requested:
>>> npsql[0,'Name']
'John'

In general, slicing is intuitive and does not deviate from what would expect from numpy. With the one addition that columns can be referred to by names as well as numbers.

set

Setting elements works the same as slicing:

>>> npsql = Nptab({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]})
>>> npsql[0,"Name"] = "Jos"
>>> npsql
 Name   |   Height |   Married
--------+----------+-----------
 Jos    |     1.82 |         0
 Joe    |     1.65 |         0
 Jane   |     2.15 |         1
3 rows ['<U4', '<f8', '|b1']

The datatype that the value is expected to have, is the same as the datatype a slice would result into.

Adding columns, works the same as setting elements, just give it a new name:

>>> npsql = Nptab({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]})
>>> npsql['new'] = [1,2,3]
>>> npsql
 Name   |   Height |   Married |   new
--------+----------+-----------+-------
 John   |     1.82 |         0 |     1
 Joe    |     1.65 |         0 |     2
 Jane   |     2.15 |         1 |     3
3 rows ['<U4', '<f8', '|b1', '<i8']

Or set the whole column to the same value:

>>> npsql = Nptab({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]})
>>> npsql['new'] = 13
>>> npsql
 Name   |   Height |   Married |   new
--------+----------+-----------+-------
 John   |     1.82 |         0 |    13
 Joe    |     1.65 |         0 |    13
 Jane   |     2.15 |         1 |    13
3 rows ['<U4', '<f8', '|b1', '<i8']

Just like numpy, slices are not actual copies of the data, rather they are references.

append Nptab and row

Tabls can be appended with other Tabls:

>>> npsql = Nptab({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]})
>>> npsql += npsql
>>> npsql
 Name   |   Height |   Married
--------+----------+-----------
 John   |     1.82 |         0
 Joe    |     1.65 |         0
 Jane   |     2.15 |         1
 John   |     1.82 |         0
 Joe    |     1.65 |         0
 Jane   |     2.15 |         1
6 rows ['<U4', '<f8', '|b1']

Or append rows as dictionary:

>>> npsql = Nptab({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]})
>>> npsql.row_append({'Height':1.81, 'Name':"Jack", 'Married':True})
>>> npsql
 Name   |   Height |   Married
--------+----------+-----------
 John   |     1.82 |         0
 Joe    |     1.65 |         0
 Jane   |     2.15 |         1
 Jack   |     1.81 |         1
4 rows ['<U4', '<f8', '|b1']

instance properties

Your data is simply stored as a list of numpy arrays and can be accessed or manipulated like that (just don’t make a mess):

>>> npsql = Nptab({'Name' : ["John", "Joe", "Jane"], 'Height' : [1.82,1.65,2.15], 'Married': [False,False,True]})
>>> npsql.columns
['Name', 'Height', 'Married']
>>> npsql.data        # doctest: +SKIP
[array(['John', 'Joe', 'Jane'],
      dtype='<U4'), array([ 1.82,  1.65,  2.15]), array([False, False,  True], dtype=bool)]

Further the basic means to asses the size of your data:

>>> npsql.shape
(3, 3)
>>> len(npsql)
3

pandas

For for interfacing with the popular datatable framework, going back and forth is easy:

>>> import pandas as pd
>>> df = pd.DataFrame({'a':range(3),'b':range(10,13)})
>>> df
   a   b
0  0  10
1  1  11
2  2  12

To make a Nptab from a DataFrame, just supply it to the initialize:

>>> npsql = Nptab(df)
>>> npsql
   a |   b
-----+-----
   0 |  10
   1 |  11
   2 |  12
3 rows ['<i8', '<i8']

The dict property of Nptab provides a way to make a DataFrame from a Nptab:

>>> df = pd.DataFrame(npsql.dict)
>>> df
   a   b
0  0  10
1  1  11
2  2  12

Dependencies

  • numpy
  • tabulate (optional, recommended)
  • pandas (optional, for converting back and forth to DataFrames)

Tested on:

  • Python 3.8.2; numpy 1.18.1

Contributing to Nptab

Nptab is perfect already, no more contributions needed. Just kidding!

See the repository for filing issues and proposing enhancements.

  • pytest

    cd npsql/test
    conda activate py38
    pytest
    
  • pylint

    cd npsql/
    ./pylint.sh
    
  • doctest

    cd npsql/docs
    make doctest
    
  • sphynx

    cd npsql/docs
    make html
    
  • setuptools/pypi

    python setup.py sdist bdist_wheel
    twine upload dist/npsql-*
    

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