diff options
Diffstat (limited to 'numpy/core/fromnumeric.py')
| -rw-r--r-- | numpy/core/fromnumeric.py | 82 |
1 files changed, 49 insertions, 33 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index 99b837ba2..176ef3e6f 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -256,15 +256,14 @@ def repeat(a, repeats, axis=None): def put(a, ind, v, mode='raise'): """ - Changes specific elements of one array by replacing from another array. + Replaces specified elements of an array with given values. - The indexing works on the flattened target array, `put` is roughly + The indexing works on the flattened target array. `put` is roughly equivalent to: :: - for i, val in zip(ind, v): - x.flat[i] = val + a.flat[ind] = v Parameters ---------- @@ -292,14 +291,14 @@ def put(a, ind, v, mode='raise'): Examples -------- - >>> x = np.arange(5) - >>> np.put(x, [0, 2], [-44, -55]) - >>> x + >>> a = np.arange(5) + >>> np.put(a, [0, 2], [-44, -55]) + >>> a array([-44, 1, -55, 3, 4]) - >>> x = np.arange(5) - >>> np.put(x, 22, -5, mode='clip') - >>> x + >>> a = np.arange(5) + >>> np.put(a, 22, -5, mode='clip') + >>> a array([ 0, 1, 2, 3, -5]) """ @@ -1086,10 +1085,14 @@ def compress(condition, a, axis=None, out=None): """ Return selected slices of an array along given axis. + When working along a given axis, a slice along that axis is returned in + `output` for each index where `condition` evaluates to True. When + working on a 1-D array, `compress` is equivalent to `extract`. + Parameters ---------- - condition : array_like - Boolean 1-D array selecting which entries to return. If len(condition) + condition : 1-D array of bools + Array that selects which entries to return. If len(condition) is less than the size of `a` along the given axis, then output is truncated to the length of the condition array. a : array_like @@ -1109,18 +1112,31 @@ def compress(condition, a, axis=None, out=None): See Also -------- - ndarray.compress: Equivalent method. + take, choose, diag, diagonal, select + ndarray.compress : Equivalent method. Examples -------- - >>> a = np.array([[1, 2], [3, 4]]) + >>> a = np.array([[1, 2], [3, 4], [5, 6]]) + >>> a + array([[1, 2], + [3, 4], + [5, 6]]) >>> np.compress([0, 1], a, axis=0) array([[3, 4]]) - >>> np.compress([1], a, axis=1) - array([[1], - [3]]) - >>> np.compress([0,1,1], a) - array([2, 3]) + >>> np.compress([False, True, True], a, axis=0) + array([[3, 4], + [5, 6]]) + >>> np.compress([False, True], a, axis=1) + array([[2], + [4], + [6]]) + + Working on the flattened array does not return slices along an axis but + selects elements. + + >>> np.compress([False, True], a) + array([2]) """ try: @@ -1306,6 +1322,8 @@ def any(a,axis=None, out=None): """ Test whether any array element along a given axis evaluates to True. + Returns single boolean unless `axis` is not ``None`` + Parameters ---------- a : array_like @@ -1322,8 +1340,8 @@ def any(a,axis=None, out=None): Returns ------- - any : ndarray, bool - A new boolean or array is returned unless `out` is + any : bool, ndarray + A new boolean or `ndarray` is returned unless `out` is specified, in which case a reference to `out` is returned. See Also @@ -1429,12 +1447,10 @@ def cumsum (a, axis=None, dtype=None, out=None): Parameters ---------- a : array_like - Input array or object that can be converted to an array. + Input array. axis : int, optional Axis along which the cumulative sum is computed. The default - (`axis` = `None`) is to compute the cumsum over the flattened - array. `axis` may be negative, in which case it counts from the - last to the first axis. + (None) is to compute the cumsum over the flattened array. dtype : dtype, optional Type of the returned array and of the accumulator in which the elements are summed. If `dtype` is not specified, it defaults @@ -1459,11 +1475,12 @@ def cumsum (a, axis=None, dtype=None, out=None): Examples -------- - >>> a = np.array([[1,2,3],[4,5,6]]) + >>> a = np.array([[1,2,3], [4,5,6]]) >>> np.cumsum(a) array([ 1, 3, 6, 10, 15, 21]) - >>> np.cumsum(a,dtype=float) # specifies type of output value(s) + >>> np.cumsum(a, dtype=float) # specifies type of output value(s) array([ 1., 3., 6., 10., 15., 21.]) + >>> np.cumsum(a,axis=0) # sum over rows for each of the 3 columns array([[1, 2, 3], [5, 7, 9]]) @@ -2122,14 +2139,13 @@ def std(a, axis=None, dtype=None, out=None, ddof=0): Returns ------- - standard_deviation : {ndarray, scalar}; see dtype parameter above. + standard_deviation : ndarray, see dtype parameter above. If `out` is None, return a new array containing the standard deviation, otherwise return a reference to the output array. See Also -------- - numpy.var : Variance - numpy.mean : Average + var, mean Notes ----- @@ -2145,7 +2161,7 @@ def std(a, axis=None, dtype=None, out=None, ddof=0): is the square root of the estimated variance, so even with ``ddof=1``, it will not be an unbiased estimate of the standard deviation per se. - Note that, for complex numbers, std takes the absolute + Note that, for complex numbers, `std` takes the absolute value before squaring, so that the result is always real and nonnegative. Examples @@ -2153,9 +2169,9 @@ def std(a, axis=None, dtype=None, out=None, ddof=0): >>> a = np.array([[1, 2], [3, 4]]) >>> np.std(a) 1.1180339887498949 - >>> np.std(a, 0) + >>> np.std(a, axis=0) array([ 1., 1.]) - >>> np.std(a, 1) + >>> np.std(a, axis=1) array([ 0.5, 0.5]) """ |