diff options
Diffstat (limited to 'numpy/core/fromnumeric.py')
| -rw-r--r-- | numpy/core/fromnumeric.py | 65 |
1 files changed, 32 insertions, 33 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index f3e9f9b5d..a6f4203be 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -294,7 +294,7 @@ def swapaxes(a, axis1, axis2): Returns ------- a_swapped : ndarray - If `a` is an ndarray, then a view on `a` is returned, otherwise + If `a` is an ndarray, then a view of `a` is returned; otherwise a new array is created. Examples @@ -1176,11 +1176,9 @@ def product (a, axis=None, dtype=None, out=None): """ Return the product of array elements over a given axis. - Refer to `numpy.prod` for full documentation. - See Also -------- - prod : equivalent function + prod : equivalent function; see for details. """ try: @@ -1390,11 +1388,10 @@ def cumproduct(a, axis=None, dtype=None, out=None): """ Return the cumulative product over the given axis. - See `cumprod` for full documentation. See Also -------- - cumprod + cumprod : equivalent function; see for details. """ try: @@ -1449,7 +1446,7 @@ def ptp(a, axis=None, out=None): def amax(a, axis=None, out=None): """ - Return the maximum along a given axis. + Return the maximum along an axis. Parameters ---------- @@ -1463,19 +1460,19 @@ def amax(a, axis=None, out=None): Returns ------- - amax : {ndarray, scalar} + amax : ndarray A new array or a scalar with the result, or a reference to `out` if it was specified. Examples -------- - >>> x = np.arange(4).reshape((2,2)) - >>> x + >>> a = np.arange(4).reshape((2,2)) + >>> a array([[0, 1], [2, 3]]) - >>> np.amax(x,0) + >>> np.amax(a, axis=0) array([2, 3]) - >>> np.amax(x,1) + >>> np.amax(a, axis=1) array([1, 3]) """ @@ -1488,7 +1485,7 @@ def amax(a, axis=None, out=None): def amin(a, axis=None, out=None): """ - Return the minimum along a given axis. + Return the minimum along an axis. Parameters ---------- @@ -1502,19 +1499,21 @@ def amin(a, axis=None, out=None): Returns ------- - amin : {ndarray, scalar} + amin : ndarray A new array or a scalar with the result, or a reference to `out` if it was specified. Examples -------- - >>> x = np.arange(4).reshape((2,2)) - >>> x + >>> a = np.arange(4).reshape((2,2)) + >>> a array([[0, 1], [2, 3]]) - >>> np.amin(x,0) + >>> np.amin(a) # Minimum of the flattened array + 0 + >>> np.amin(a, axis=0) # Minima along the first axis array([0, 1]) - >>> np.amin(x,1) + >>> np.amin(a, axis=1) # Minima along the second axis array([0, 2]) """ @@ -1640,7 +1639,7 @@ def cumprod(a, axis=None, dtype=None, out=None): Parameters ---------- - a : array-like + a : array_like Input array. axis : int, optional Axis along which the cumulative product is computed. By default the @@ -1658,7 +1657,7 @@ def cumprod(a, axis=None, dtype=None, out=None): Returns ------- - cumprod : ndarray. + cumprod : ndarray A new array holding the result is returned unless `out` is specified, in which case a reference to out is returned. @@ -1925,21 +1924,21 @@ def mean(a, axis=None, dtype=None, out=None): a : array_like Array containing numbers whose mean is desired. If `a` is not an array, a conversion is attempted. - axis : {None, int}, optional + axis : int, optional Axis along which the means are computed. The default is to compute the mean of the flattened array. - dtype : {None, dtype}, optional - Type to use in computing the mean. For integer inputs the default - is float64; for floating point inputs it is the same as the input + dtype : dtype, optional + Type to use in computing the mean. For integer inputs, the default + is float64; for floating point, inputs it is the same as the input dtype. - out : {None, ndarray}, optional + out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output but the type will be cast if necessary. Returns ------- - mean : {ndarray, scalar}, see dtype parameter above + mean : ndarray, see dtype parameter above If `out=None`, returns a new array containing the mean values, otherwise a reference to the output array is returned. @@ -2050,27 +2049,27 @@ def var(a, axis=None, dtype=None, out=None, ddof=0): Parameters ---------- a : array_like - Array containing numbers whose variance is desired. If a is not an + Array containing numbers whose variance is desired. If `a` is not an array, a conversion is attempted. axis : int, optional Axis along which the variance is computed. The default is to compute the variance of the flattened array. dtype : dtype, optional Type to use in computing the variance. For arrays of integer type - the default is float32, for arrays of float types it is the same as + the default is float32; for arrays of float types it is the same as the array type. out : ndarray, optional Alternative output array in which to place the result. It must have - the same shape as the expected output but the type will be cast if + the same shape as the expected output but the type is cast if necessary. ddof : positive int,optional - Means Delta Degrees of Freedom. The divisor used in calculation is + "Delta Degrees of Freedom": the divisor used in calculation is N - ddof. Returns ------- - variance : {ndarray, scalar}, see dtype parameter above - If out=None, returns a new array containing the variance, otherwise + variance : ndarray, see dtype parameter above + If out=None, returns a new array containing the variance; otherwise a reference to the output array is returned. See Also @@ -2081,7 +2080,7 @@ def var(a, axis=None, dtype=None, out=None, ddof=0): Notes ----- The variance is the average of the squared deviations from the mean, - i.e. var = mean(abs(x - x.mean())**2). The computed variance is biased, + i.e., var = mean(abs(x - x.mean())**2). The computed variance is biased, i.e., the mean is computed by dividing by the number of elements, N, rather than by N-1. Note that for complex numbers the absolute value is taken before squaring, so that the result is always real and nonnegative. |