larray.Array.all

Array.all(*axes_and_groups, out=None, skipna=None, keepaxes=False, **explicit_axes)[source]

Test whether all selected elements evaluate to True.

Parameters
*axes_and_groupsNone or int or str or Axis or Group or any combination of those

Axis(es) or group(s) along which the AND reduction is performed. The default (no axis or group) is to perform the AND reduction over all the dimensions of the input array.

An axis can be referred by:

  • its index (integer). Index can be a negative integer, in which case it counts from the last to the first axis.

  • its name (str or AxisReference). You can use either a simple string (‘axis_name’) or the special variable X (X.axis_name).

  • a variable (Axis). If the axis has been defined previously and assigned to a variable, you can pass it as argument.

You may not want to perform the AND reduction over a whole axis but over a selection of specific labels. To do so, you have several possibilities:

  • ([‘a1’, ‘a3’, ‘a5’], ‘b1, b3, b5’) : labels separated by commas in a list or a string

  • (‘a1:a5:2’) : select labels using a slice (general syntax is ‘start:end:step’ where is ‘step’ is optional and 1 by default).

  • (a=’a1, a2, a3’, X.b[‘b1, b2, b3’]) : in case of possible ambiguity, i.e. if labels can belong to more than one axis, you must precise the axis.

  • (‘a1:a3; a5:a7’, b=’b0,b2; b1,b3’) : create several groups with semicolons. Names are simply given by the concatenation of labels (here: ‘a1,a2,a3’, ‘a5,a6,a7’, ‘b0,b2’ and ‘b1,b3’)

  • (‘a1:a3 >> a123’, ‘b[b0,b2] >> b12’) : operator ‘ >> ‘ allows to rename groups.

outArray, optional

Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if dtype(out) is float, the result will consist of 0.0’s and 1.0’s). Axes and labels can be different, only the shape matters. Defaults to None (create a new array).

skipnabool, optional

Whether to skip NaN (null) values. If False, resulting cells will be NaN if any of the aggregated cells is NaN. Defaults to True.

keepaxesbool or label-like, optional

Whether reduced axes are left in the result as dimensions with size one. If True, reduced axes will contain a unique label representing the applied aggregation (e.g. ‘sum’, ‘prod’, …). It is possible to override this label by passing a specific value (e.g. keepaxes=’summation’). Defaults to False.

Returns
Array of bool or bool

See also

Array.all_by, Array.any, Array.any_by

Examples

>>> arr = ndtest((4, 4))
>>> arr
a\b  b0  b1  b2  b3
 a0   0   1   2   3
 a1   4   5   6   7
 a2   8   9  10  11
 a3  12  13  14  15
>>> barr = arr < 6
>>> barr
a\b     b0     b1     b2     b3
 a0   True   True   True   True
 a1   True   True  False  False
 a2  False  False  False  False
 a3  False  False  False  False
>>> barr.all()
False
>>> # along axis 'a'
>>> barr.all('a')
b     b0     b1     b2     b3
   False  False  False  False
>>> # along axis 'b'
>>> barr.all('b')
a    a0     a1     a2     a3
   True  False  False  False

Select some rows only

>>> barr.all(['a0', 'a1'])
b    b0    b1     b2     b3
   True  True  False  False
>>> # or equivalently
>>> # barr.all('a0,a1')

Split an axis in several parts

>>> barr.all((['a0', 'a1'], ['a2', 'a3']))
  a\b     b0     b1     b2     b3
a0,a1   True   True  False  False
a2,a3  False  False  False  False
>>> # or equivalently
>>> # barr.all('a0,a1;a2,a3')

Same with renaming

>>> barr.all((X.a['a0', 'a1'] >> 'a01', X.a['a2', 'a3'] >> 'a23'))
a\b     b0     b1     b2     b3
a01   True   True  False  False
a23  False  False  False  False
>>> # or equivalently
>>> # barr.all('a0,a1>>a01;a2,a3>>a23')