Python 内置函 ndarray 访问和删除 (十三)

访问和删除 ndarray 中的元素及向其中插入元素。

修改

你已经知道如何创建各种 ndarray,现在将学习 NumPy 使我们如何有效地操纵 ndarray 中的数据。NumPy ndarray 是可变的,意味着 ndarray 中的元素在 ndarray 创建之后可以更改。NumPy ndarray 还可以切片,因此可以通过多种方式拆分 ndarray。例如,我们可以从 ndarray 中获取想要的任何子集。通常,在机器学习中,你需要使用切片拆分数据,例如将数据集拆分为训练集、交叉验证集和测试集。

我们首先将了解如何通过索引访问或修改 ndarray 中的元素。可以在方括号 [ ] 中添加索引来访问元素。在 NumPy 中,你可以使用正索引和负索引访问 ndarray 中的元素。正索引表示从数组的开头访问元素,负索引表示从数组的末尾访问元素。我们来看看如何访问秩为 1 的 ndarray 中的元素:

# We create a rank 1 ndarray that contains integers from 1 to 5
x = np.array([1, 2, 3, 4, 5])

# Let's access some elements with positive indices
print('This is First Element in x:', x[0]) 
print('This is Second Element in x:', x[1])
print('This is Fifth (Last) Element in x:', x[4])
print()

打印:

his is First Element in x: 1
This is Second Element in x: 2
This is Fifth (Last) Element in x: 5

注意,要访问 ndarray 中的第一个元素,我们需要使用索引 0,而不是 1。此外注意,可以同时使用正索引和负索引访问同一个元素。正如之前提到的,正索引用于从数组的开头访问元素,负索引用于从数组的末尾访问元素。

现在我们看看如何更改秩为 1 的 ndarray 中的元素。方法是访问要更改的元素,然后使用 = 符号分配新的值:

# We create a rank 1 ndarray that contains integers from 1 to 5
x = np.array([1, 2, 3, 4, 5])

# We print the original x
print()
print('Original:\n x = ', x)
print()

# We change the fourth element in x from 4 to 20
x[3] = 20

# We print x after it was modified 
print('Modified:\n x = ', x)
Original: x = [1 2 3 4 5]

Modified: x = [ 1 2 3 20 5]

同样,我们可以访问和修改秩为 2 的 ndarray 中的特定元素。要访问秩为 2 的 ndarray 中的元素,我们需要提供两个索引,格式为[row, column]

可以像针对秩为 1 的 ndarray 一样修改秩为 2 的 ndarray 中的元素。我们来看一个示例:

# We create a 3 x 3 rank 2 ndarray that contains integers from 1 to 9
X = np.array([[1,2,3],[4,5,6],[7,8,9]])

# We print the original x
print()
print('Original:\n X = \n', X)
print()

# We change the (0,0) element in X from 1 to 20
X[0,0] = 20

# We print X after it was modified 
print('Modified:\n X = \n', X)
Original:
X =
[[1 2 3]
 [4 5 6]
 [7 8 9]]

Modified:
X =
[[20 2 3]
 [ 4 5 6]
 [ 7 8 9]]

添加或删除

现在看看如何向 ndarray 中添加元素及删除其中的元素。我们可以使用np.delete(ndarray, elements, axis)函数删除元素。此函数会沿着指定的轴从给定 ndarray删除给定的元素列表。对于秩为 1 的 ndarray,不需要使用关键字 axis。对于秩为 2 的 ndarray,axis = 0 表示选择axis = 1 表示选择。我们来看一些示例:


# We create a rank 1 ndarray 
x = np.array([1, 2, 3, 4, 5])

# We create a rank 2 ndarray
Y = np.array([[1,2,3],[4,5,6],[7,8,9]])

# We print x
print()
print('Original x = ', x)

# We delete the first and last element of x
x = np.delete(x, [0,4])

# We print x with the first and last element deleted
print()
print('Modified x = ', x)

# We print Y
print()
print('Original Y = \n', Y)

# We delete the first row of y
w = np.delete(Y, 0, axis=0)

# We delete the first and last column of y
v = np.delete(Y, [0,2], axis=1)

# We print w
print()
print('w = \n', w)

# We print v
print()
print('v = \n', v)

打印:

Original x = [1 2 3 4 5]

Modified x = [2 3 4]

Original Y =
[[1 2 3]
 [4 5 6]
 [7 8 9]]

w =
[[4 5 6]
 [7 8 9]]

v =
[[2]
 [5]
 [8]]

现在我们来看看如何向 ndarray 中附加值。我们可以使用np.append(ndarray, elements, axis) 函数向 ndarray 中附加值。该函数会将给定的元素列表沿着指定的轴附加到 ndarray 中。我们来看一些示例:

# We create a rank 1 ndarray 
x = np.array([1, 2, 3, 4, 5])

# We create a rank 2 ndarray 
Y = np.array([[1,2,3],[4,5,6]])

# We print x
print()
print('Original x = ', x)

# We append the integer 6 to x
x = np.append(x, 6)

# We print x
print()
print('x = ', x)

# We append the integer 7 and 8 to x
x = np.append(x, [7,8])

# We print x
print()
print('x = ', x)

# We print Y
print()
print('Original Y = \n', Y)

# We append a new row containing 7,8,9 to y
v = np.append(Y, [[7,8,9]], axis=0)

# We append a new column containing 9 and 10 to y
q = np.append(Y,[[9],[10]], axis=1)

# We print v
print()
print('v = \n', v)

# We print q
print()
print('q = \n', q)

打印:

Original x = [1 2 3 4 5]

x = [1 2 3 4 5 6]

x = [1 2 3 4 5 6 7 8]

Original Y =
[[1 2 3]
 [4 5 6]]

v =
[[1 2 3]
 [4 5 6]
 [7 8 9]]

q =
[[ 1 2 3 9]
 [ 4 5 6 10]]

注意,当我们将行或列附加到秩为 2 的 ndarray 中时,行或列的形状必须正确,以与秩为 2 的 ndarray 的形状相符。

插入值

现在我们来看看如何向 ndarray 中插入值。我们可以使用 np.insert(ndarray, index, elements, axis)函数向 ndarray 中插入值。此函数会将给定的元素列表沿着指定的轴插入到 ndarray 中,并放在给定的索引前面。我们来看一些示例:

# We create a rank 1 ndarray 
x = np.array([1, 2, 5, 6, 7])

# We create a rank 2 ndarray 
Y = np.array([[1,2,3],[7,8,9]])

# We print x
print()
print('Original x = ', x)

# We insert the integer 3 and 4 between 2 and 5 in x. 
x = np.insert(x,2,[3,4])

# We print x with the inserted elements
print()
print('x = ', x)

# We print Y
print()
print('Original Y = \n', Y)

# We insert a row between the first and last row of y
w = np.insert(Y,1,[4,5,6],axis=0)

# We insert a column full of 5s between the first and second column of y
v = np.insert(Y,1,5, axis=1)

# We print w
print()
print('w = \n', w)

# We print v
print()
print('v = \n', v)

打印:

Original x = [1 2 5 6 7]

x = [1 2 3 4 5 6 7]

Original Y =
[[1 2 3]
 [7 8 9]]

w =
[[1 2 3]
 [4 5 6]
 [7 8 9]]

v =
[[1 5 2 3]
 [7 5 8 9]]

堆叠

NumPy 还允许我们将 ndarray 上下堆叠起来,或者左右堆叠。可以使用 np.vstack() 函数进行垂直堆叠,或使用 np.hstack() 函数进行水平堆叠。请务必注意,为了堆叠 ndarray,ndarray 的形状必须相符。我们来看一些示例:

# We create a rank 1 ndarray 
x = np.array([1,2])

# We create a rank 2 ndarray 
Y = np.array([[3,4],[5,6]])

# We print x
print()
print('x = ', x)

# We print Y
print()
print('Y = \n', Y)

# We stack x on top of Y
z = np.vstack((x,Y))

# We stack x on the right of Y. We need to reshape x in order to stack it on the right of Y. 
w = np.hstack((Y,x.reshape(2,1)))

# We print z
print()
print('z = \n', z)

# We print w
print()
print('w = \n', w)

打印:

x = [1 2]

Y =
[[3 4]
 [5 6]]

z =
[[1 2]
 [3 4]
 [5 6]]

w =
[[3 4 1]
 [5 6 2]]

为者常成,行者常至