可以看到,我们给一个切片赋值了一个整数,结果切片中,每一个子元素都变成了这个数。这就是广播:它会自动将你做的赋值这种操作,根据一定规则,应用到整个区域内。 除了赋值操作符以外, 比较操作符如:>, <也支持广播,譬如: a = np.arange(20).reshape((4,5))print(a>10)""" [[False False False False F...
sort(a, axis=-1, kind='quicksort', order=None):返回排序后副本; unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None):去重并排序; return_index:返回一个指示每个元素在原序列中的位置序列; return_inverse:返回一个构造原序列的序列(标识出原序列每个元素在返回序列中...
return_inverse=True) # 同时返回原数组元素在新数组中的索引 print(x) x = np.unique(a, return_counts=True) # 同时返回新数组元素在原数组中的个数 print(x) """ (array([1, 2, 3, 4, 5, 6, 7]), array([1
subtypes ofndarray, e.g.numpy.ma.MaskedArray. See the typing docs for more information. (gh-26081) np.quantilewith methodclosest_observationchooses nearest even order statistic This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statist...
The NumPy quantile() function computes the q-th quantile (or percentile) of the data along a specified axis. A quantile is a value below which a given percentage of observations fall. For example, the 0.5 quantile (50th percentile) is the median of the data, while the 0.25 quantile (...
quantile(a, 0.5) 3.5 >>> np.quantile(a, 0.5, axis=0) Output: array([6.5, 4.5, 2.5]) >>> np.quantile(a, 0.5, axis=1) Output: array([7., 2.]) >>> np.quantile(a, 0.5, axis=1, keepdims=True) Output: array([[7.], [2.]]) >>> m = np.quantile(a, 0.5, axis=0)...
inv Compute the inverse of a square matrix pinv Compute the Moore-Penrose pseudo-inverse of a matrix qr Compute the QR decomposition svd Compute the singular value decomposition (SVD) solve Solve the linear system Ax = b for x, where A is a square matrix lstsq Compute the least-squares sol...
def calc_inv_vol_weights(returns): """ Calculates weights proportional to inverse volatility of each column. Returns weights that are inversely proportional to the column's volatility resulting in a set of portfolio weights where each position has the same level of volatility. Note, that assets ...
return_inverse:返回一个构造原序列的序列(标识出原序列每个元素在返回序列中的位置) return_counts:返回每个元素在原序列中重复数; intersect1d(ar1, ar2, assume_unique=False, return_indices=False):交集(去重排序); union1d(ar1, ar2):并集(去重排序); ...
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