plydata.cat_tools.cat_inorder¶

plydata.cat_tools.cat_inorder(c, ordered=None)[source]¶

Reorder categorical by appearance

Parameters

clist-like: Values that will make up the categorical.
orderedbool: If True, the categorical is ordered.

Returns

outcategorical: Values

Notes

NaN or None are ignored when creating the categories.

Examples

>>> import numpy as np
>>> x = [4, 1, 3, 4, 4, 7, 3]
>>> cat_inorder(x)
[4, 1, 3, 4, 4, 7, 3]
Categories (4, int64): [4, 1, 3, 7]
>>> arr = np.array(x)
>>> cat_inorder(arr)
[4, 1, 3, 4, 4, 7, 3]
Categories (4, int64): [4, 1, 3, 7]
>>> c = ['b', 'f', 'c', None, 'c', 'a', 'b', 'e']
>>> cat_inorder(c)
['b', 'f', 'c', NaN, 'c', 'a', 'b', 'e']
Categories (5, object): ['b', 'f', 'c', 'a', 'e']
>>> s = pd.Series(c)
>>> cat_inorder(s)
['b', 'f', 'c', NaN, 'c', 'a', 'b', 'e']
Categories (5, object): ['b', 'f', 'c', 'a', 'e']
>>> cat = pd.Categorical(c)
>>> cat_inorder(cat)
['b', 'f', 'c', NaN, 'c', 'a', 'b', 'e']
Categories (5, object): ['b', 'f', 'c', 'a', 'e']
>>> cat_inorder(cat, ordered=True)
['b', 'f', 'c', NaN, 'c', 'a', 'b', 'e']
Categories (5, object): ['b' < 'f' < 'c' < 'a' < 'e']

By default, ordered categories remain ordered.

>>> ocat = pd.Categorical(cat, ordered=True)
>>> ocat
['b', 'f', 'c', NaN, 'c', 'a', 'b', 'e']
Categories (5, object): ['a' < 'b' < 'c' < 'e' < 'f']
>>> cat_inorder(ocat)
['b', 'f', 'c', NaN, 'c', 'a', 'b', 'e']
Categories (5, object): ['b' < 'f' < 'c' < 'a' < 'e']
>>> cat_inorder(ocat, ordered=False)
['b', 'f', 'c', NaN, 'c', 'a', 'b', 'e']
Categories (5, object): ['b', 'f', 'c', 'a', 'e']