Python：numpy库中的一些函数简介、使用方法之详细攻略

Python：numpy库中的一些函数简介、使用方法之详细攻略numpy库中的一些函数简介、使用方法1、np.concatenate()1.1、函数案例import numpy as npa=np.array([1,2,3])b=np.array([11,22,33])c=np.array([44,55,66])d=np.concatenate((a,b,c),axis=0) # 默认情况下，axis=0可以不写print(d) #输出array([ 1, 2, 3, 11, 22, 33, 44, 55, 66])，对于一维数组拼接，axis的值不影响最后的结果 1.2、函数用法concatenate Found at: numpy.core.multiarrayconcatenate((a1, a2, ...), axis=0, out=None)Join a sequence of arrays along an existing axis.Parameters----------a1, a2, ... : sequence of array_like. The arrays must have the same shape, except in the dimension  corresponding to `axis` (the first, by default).axis : int, optional. The axis along which the arrays will be joined.  Default is 0.out : ndarray, optional. If provided, the destination to place the result. The shape  must be correct, matching that of what concatenate would have   returned if no  out argument were specified.Returns-------res : ndarrayThe concatenated array.在:numpy.core.multiarray找到连接连接((a1, a2，…)，axis=0, out=None)沿着现有的轴连接数组序列。参数----------a1, a2,…:数组类型的序列。数组必须具有相同的形状，除了与“axis”对应的维度(默认情况下为第一个维度)。axis: int，可选。数组连接的轴线。默认值为0。out : ndarray，可选。如果提供，放置结果的目的地。形状必须正确，如果没有指定out参数，则匹配concatenate将返回的形状。返回-------res: ndarray连接后的字符串数组。See Also--------ma.concatenate : Concatenate function that preserves input   masks.array_split : Split an array into multiple sub-arrays of equal ornear-equal size.split : Split array into a list of multiple sub-arrays of equal size.hsplit : Split array into multiple sub-arrays horizontally   (column wise)vsplit : Split array into multiple sub-arrays vertically (row wise)dsplit : Split array into multiple sub-arrays along the 3rd axis  (depth).stack : Stack a sequence of arrays along a new axis.hstack : Stack arrays in sequence horizontally (column wise)vstack : Stack arrays in sequence vertically (row wise)dstack : Stack arrays in sequence depth wise (along third  dimension)Notes-----When one or more of the arrays to be concatenated is a  MaskedArray,   this function will return a MaskedArray object instead of an  ndarray, but the input masks are *not* preserved. In cases where a   MaskedArray  is expected as input, use the ma.concatenate function from  the masked  array module instead.另请参阅--------马。保存输入掩码的连接函数。array_split:将一个数组分割成多个相等或的子数组与大小。分割:将数组分割成相同大小的多个子数组。hsplit:水平(按列)将数组分割为多个子数组垂直(按行)将数组分割为多个子数组dsplit:沿着第三轴(深度)将数组分割成多个子数组。堆栈:将数组序列沿着一个新的轴进行堆栈。hstack:水平排列(按列排列)垂直(行向)按顺序堆叠数组。dstack:按深度顺序排列的堆栈数组(沿三维方向)笔记-----当一个或多个要连接的数组是一个MaskedArray时，这个函数将返回一个MaskedArray对象而不是ndarray，但是输入掩码*不*保留。在需要MaskedArray作为输入的情况下，使用ma。连接函数从掩码数组模块代替。Examples-------->>> a = np.array([[1, 2], [3, 4]])>>> b = np.array([[5, 6]])>>> np.concatenate((a, b), axis=0)array([[1, 2],[3, 4],[5, 6]])>>> np.concatenate((a, b.T), axis=1)array([[1, 2, 5],[3, 4, 6]])This function will not preserve masking of MaskedArrayinputs.>>> a = np.ma.arange(3)>>> a[1] = np.ma.masked>>> b = np.arange(2, 5)>>> amasked_array(data = [0 -- 2],mask = [False  True False],fill_value = 999999)>>> barray([2, 3, 4])>>> np.concatenate([a, b])masked_array(data = [0 1 2 2 3 4],mask = False,fill_value = 999999)>>> np.ma.concatenate([a, b])masked_array(data = [0 -- 2 2 3 4],mask = [False  True False False False False],fill_value = 999999)