bundles / scipy latest / scipy / stats / _crosstab / crosstab

function

`scipy.stats._crosstab:crosstab`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_crosstab.py:11

Signature

def crosstab(*args, levels = None, sparse = False)

Summary

Return table of counts for each possible unique combination in *args.

Extended Summary

When len(args) > 1, the array computed by this function is often referred to as a contingency table ^[1].

The arguments must be sequences with the same length. The second return value, count, is an integer array with len(args) dimensions. If levels is None, the shape of count is (n0, n1, ...), where nk is the number of unique elements in args[k].

Parameters

*args : sequences: A sequence of sequences whose unique aligned elements are to be counted. The sequences in args must all be the same length.
levels : sequence, optional: If levels is given, it must be a sequence that is the same length as args. Each element in levels is either a sequence or None. If it is a sequence, it gives the values in the corresponding sequence in args that are to be counted. If any value in the sequences in args does not occur in the corresponding sequence in levels, that value is ignored and not counted in the returned array count. The default value of levels for args[i] is np.unique(args[i])
sparse : bool, optional: If True, return a sparse array. The array will be an instance of the scipy.sparse.coo_array class. Default is False.

Returns

res : CrosstabResult

An object containing the following attributes:

elements: Tuple of length len(args) containing the arrays of elements that are counted in count. These can be interpreted as the labels of the corresponding dimensions of count. If levels was given, then if levels[i] is not None, elements[i] will hold the values given in levels[i].
count: Counts of the unique elements in zip(*args), stored in an array. Also known as a contingency table when len(args) > 1.

Notes

Examples

from scipy.stats.contingency import crosstab

✓

Given the lists `a` and `x`, create a contingency table that counts the frequencies of the corresponding pairs.

a = ['A', 'B', 'A', 'A', 'B', 'B', 'A', 'A', 'B', 'B']
x = ['X', 'X', 'X', 'Y', 'Z', 'Z', 'Y', 'Y', 'Z', 'Z']
res = crosstab(a, x)
avals, xvals = res.elements
avals
xvals
res.count

✓

array([[2, 3, 0],
       [1, 0, 4]])

So ``('A', 'X')`` occurs twice, ``('A', 'Y')`` occurs three times, etc. Higher dimensional contingency tables can be created.

p = [0, 0, 0, 0, 1, 1, 1, 0, 0, 1]
res = crosstab(a, x, p)

✓

res.count

✗

array([[[2, 0],
        [2, 1],
        [0, 0]],

       [[1, 0],
        [0, 0],
        [1, 3]]])

res.count.shape

✓

(2, 3, 2)

The values to be counted can be set by using the `levels` argument. It allows the elements of interest in each input sequence to be given explicitly instead finding the unique elements of the sequence. For example, suppose one of the arguments is an array containing the answers to a survey question, with integer values 1 to 4. Even if the value 1 does not occur in the data, we want an entry for it in the table.

q1 = [2, 3, 3, 2, 4, 4, 2, 3, 4, 4, 4, 3, 3, 3, 4]  # 1 does not occur.
q2 = [4, 4, 2, 2, 2, 4, 1, 1, 2, 2, 4, 2, 2, 2, 4]  # 3 does not occur.
options = [1, 2, 3, 4]
res = crosstab(q1, q2, levels=(options, options))
res.count

✓

array([[0, 0, 0, 0],
       [1, 1, 0, 1],
       [1, 4, 0, 1],
       [0, 3, 0, 3]])

If `levels` is given, but an element of `levels` is None, the unique values of the corresponding argument are used. For example,

res = crosstab(q1, q2, levels=(None, options))
res.elements
res.count

✓

array([[1, 1, 0, 1],
       [1, 4, 0, 1],
       [0, 3, 0, 3]])

If we want to ignore the pairs where 4 occurs in ``q2``, we can give just the values [1, 2] to `levels`, and the 4 will be ignored:

res = crosstab(q1, q2, levels=(None, [1, 2]))
res.elements
res.count

✓

array([[1, 1],
       [1, 4],
       [0, 3]])

Finally, let's repeat the first example, but return a sparse array:

res = crosstab(a, x, sparse=True)

✓

res.count

✗

<COOrdinate sparse array of dtype 'int64'
	with 4 stored elements and shape (2, 3)>

res.count.toarray()

✓

array([[2, 3, 0],
       [1, 0, 4]])

Aliases

scipy.stats._crosstab.crosstab