easyquery

Create easy-to-use Query objects that can apply on NumPy structured arrays, astropy Table, and Pandas DataFrame.


Keywords
easyquery, query, numpy
License
MIT
Install
pip install easyquery==0.1.6

Documentation

easyquery

Conda Version PyPI version

Create easy-to-use Query objects that can apply on NumPy structured arrays, astropy Table, and Pandas DataFrame.

Tired of writing lots of brackets and keeping track of variable names when filtering table data? Enter easyquery!

Before easyquery:

subtable = table[table["population"] >= 20000]
subtable = subtable[subtable["population"] / subtable["area"] >= 1000]

With easyquery

subtable = Query("population >= 20000", "population / area >= 1000").filter(table)

Installation

You can install easyquey from conda-forge:

conda install scipy --channel conda-forge

Or from PyPI:

pip install easyquery

Usage

Creating Query objects

The most important concept that easyquery introduces is a Query object, which is an object that represents the queries (conditions) that you want to apply to your table data.

For most simple cases a Query object can be created with a simple string:

q1 = Query('population >= 20000')
q2 = Query('population / area >= 1000')

The string will be passed to numexpr and you can find a list of supported operators and math functions here.

You can also combine multiple conditions at once:

q3 = Query('population >= 20000', 'area < 300') # satisfies both

A Query object can also be created with a tuple, where the first element of the tuple should be a callable, and the rest should be the field names that correspond to the argument list of the callable. This construction allows you to specify more complex queries or to use functions that are not supported by numexpr. For example, q4 below has the same effect as q2 above.

q4 = Query((lambda x, y: x / y >= 1000, 'population', 'area'))

You can also use QueryMaker to create some commonly used conditions that cannot be easily written as simple string.

# for string operations
q5 = QueryMaker.equals('name', 'Paris')
q6 = QueryMaker.contains('name', 'New')
q7 = QueryMaker.startswith('name', 'San')

# for checking if the column values are in another list
q8 = QueryMaker.in1d('id', [1, 3, 6, 7])

Query objects can be combined with & (and), | (or), ^ (xor), and cen be modified by ~ (not). Each of these operation returns a new Query object.

q9 = (~q1 | Query('established_year > 1900'))

Using Query objects

A Query object has three major methods: filter, count, and mask. All of them can operate on NumPy structured arrays, astropy Tables, and pandas DataFrames:

  • filter returns a new table that only has entries satisfying the query;
  • count returns the number of entries satisfying the query;
  • mask returns a bool array for masking the table.
import numpy as np
from easyquery import Query
t = np.array([(1, 5, 4.5), (1, 1, 6.2), (3, 2, 0.5), (5, 5, -3.5)],
             dtype=np.dtype([('a', '<i8'), ('b', '<i8'), ('c', '<f8')]))

q = Query('a > 3')
q.filter(t)
q.count(t)
q.mask(t)