larray.Array.apply_map

Array.apply_map(mapping, dtype=None) → Union[Array, bool, int, float, str, bytes, generic, Tuple[Array, ...]][source]

Apply a transformation mapping to array elements.

Parameters

mappingmapping (dict): Mapping to apply to values of the array. A mapping (dict) must have the values to transform as keys and the new values as values, that is: {<oldvalue1>: <newvalue1>, <oldvalue2>: <newvalue2>, …}.
dtypetype, optional: Output dtype. Defaults to None (inspect all output values to infer it automatically).

Returns

Array: Axes will be the same as the original array axes.

Notes

To apply a transformation given as an Array (with current values as labels on one axis of the array and desired values as the array values), you can use: mapping_arr[original_arr].

Examples

First let us define a test array

>>> arr = Array([[0, 2, 1],
...              [3, 1, 5]], 'a=a0,a1;b=b0..b2')
>>> arr
a\b  b0  b1  b2
 a0   0   2   1
 a1   3   1   5

Now, assuming for a moment that the values of our test array above were in fact some numeric representation of names and we had the correspondence to the actual names stored in a dictionary:

>>> code_to_names = {0: 'foo', 1: 'bar', 2: 'baz',
...                  3: 'boo', 4: 'far', 5: 'faz'}

We could get back an array with the actual names by using:

>>> arr.apply_map(code_to_names)
a\b   b0   b1   b2
 a0  foo  baz  bar
 a1  boo  bar  faz