(phJSSKrSSKJr SSKJr \"SSS/5rSSS.S jrg) N) coo_matrix)_make_tuple_bunchCrosstabResultelementscountF)levelssparsec ^[U5nUS:Xa [S5e[US5m[U4SjUSS55(d [S5eU(aUS:wa [S5eUc0[ UVs/sHn[ R "US S 9PM sn6upVGOr[U5U:wa [S 5eUVs/sHn[ R"U5PM nn[ R"UT4[ RS 9n[ R"UT4[ RS 9n /n[[ X55Hun upU c'[ R "US S 9uoU SS24'S XSS24'OjU[ R"U 5RS S5:Hn [ R"U SS9XSS24'U RR5n U SXU S4'UR!U 5 M URSS9n[#U SS2U45nU(aI[%[ R&"[US5[(S 9USUS445nUR+5 OTUVs/sHn[U5PM nn[ R"U[(S 9n[ R,R/XS5 [1X_5$s snfs snfs snf)au Return table of counts for each possible unique combination in ``*args``. 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 matrix. The matrix will be an instance of the `scipy.sparse.coo_matrix` class. Because SciPy's sparse matrices must be 2-d, only two input sequences are allowed when `sparse` is True. Default is False. Returns ------- res : CrosstabResult An object containing the following attributes: elements : tuple of numpy.ndarrays. 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 : numpy.ndarray or scipy.sparse.coo_matrix Counts of the unique elements in ``zip(*args)``, stored in an array. Also known as a *contingency table* when ``len(args) > 1``. See Also -------- numpy.unique Notes ----- .. versionadded:: 1.7.0 References ---------- .. [1] "Contingency table", http://en.wikipedia.org/wiki/Contingency_table 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 array(['A', 'B'], dtype='>> xvals array(['X', 'Y', 'Z'], dtype='>> 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 [array([2, 3, 4]), [1, 2, 3, 4]] >>> 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 [array([2, 3, 4]), [1, 2]] >>> res.count array([[1, 1], [1, 4], [0, 3]]) Finally, let's repeat the first example, but return a sparse matrix: >>> res = crosstab(a, x, sparse=True) >>> res.count >>> res.count.toarray() array([[2, 3, 0], [1, 0, 4]]) rz(At least one input sequence is required.c3@># UHn[U5T:Hv M g7f)N)len).0alen0s H/var/www/html/venv/lib/python3.13/site-packages/scipy/stats/_crosstab.py crosstab..s0x!s1v~xsNz.All input sequences must have the same length.zrCs1#/#z7+  A0rB