In NumPy, we can access specific rows or columns of a 2-D array using array indexing. Let's see an example. importnumpyasnp# create a 2D arrayarray1 = np.array([[1,3,5], [7,9,2], [4,6,8]])# access the second row
Integer array indexing: Select array elements with another array defindexing(): a= np.random.rand(5)print(a)#[ 0.71899463 0.50556877 0.8397599 0.37655158 0.88041567]indices = np.array([1,1,2,3])#access index of 1,1,2,3print(a[indices])#[ 0.50556877 0.50556877 0.8397599 0.37655158]if__name...
Know the shape of the array witharray.shape, then use slicing to obtain different views of the array:array[::2], etc. Adjust the shape of the array usingreshapeor flatten it withravel. Obtain a subset of the elements of an array and/or modify their values with masks Know miscellaneous ...
NumPy arrays can also be indexed with other arrays or other sequence-like objects like lists. NumPy数组也可以与其他数组或其他类似于序列的对象(如列表)建立索引。 Let’s take a look at a few examples. 让我们来看几个例子。 I’m first going to define my array z1. 我首先要定义我的数组z1。
When you try to index a numpy ndarray with a DeviceArray, the numpy array tries to interpret the jax array as a tuple. import numpy as onp import jax.numpy as np x = onp.zeros((5,7)) np_idx = onp.array([1,2,3]) jax_idx = np.array([1,2,3]) x[np_idx] x[jax_idx]...
Since we selected 2, we end up with the third value: 6Negative IndexingUse negative indexing to access an array from the end.Example Print the last element from the 2nd dim: import numpy as nparr = np.array([[1,2,3,4,5], [6,7,8,9,10]]) print('Last element from 2nd dim: ...
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你可以通过调用NumPy的array()函数将一维数据从列表转换为数组。 代码语言:txt AI代码解释 # one dimensional example from numpy import array # list of data data = [11, 22, 33, 44, 55] # array of data data = array(data) print(data) ...
2. 数组索引(Array indexing) 另外一个重要的就是数组索引,这里先介绍第一种方法: 切片(slicing),这里的切片和列表的切片方式一样,不同的是,由于数组是多维的,因此需要对不同维度分别切片。 importnumpyasnp# Create the following rank 2 array with shape (3, 4)# [[ 1 2 3 4]# [ 5 6 7 8]# ...
arr[arr>3])# 输出：[4 5]5.花式索引（Fancy Indexing）：# 使用索引数组提取元素indices=np.array...