Read this article to understand the numpy array splicing method and application scenarios

Overview:
In data processing and analysis, it is often necessary to splice multiple numpy arrays. for further processing and analysis. The numpy library provides a variety of array splicing methods. This article will introduce the numpy array splicing methods and their application scenarios, and give specific code examples.

1. Numpy array splicing method:

1. np.concatenate
   The np.concatenate function can splice two or more arrays together along the specified axis to form a new array. The syntax is as follows:
   np.concatenate((a1, a2, ...), axis=0, out=None)

Among them, a1, a2, ...: need to be spliced Array;
axis: specifies the axis of splicing, the default is 0, which means splicing along the first axis;
out: the array of splicing result output, if not provided, a new array is created and returned.

The sample code is as follows:
import numpy as np

a = np.array([[1, 2], [3, 4]])
b = np. array([[5, 6]])

c = np.concatenate((a, b), axis=0)
print(c)

Output result:

[[1 2]

[3 4]

[5 6]]

2. np.vstack and np.row_stack
   np The .vstack function stacks two or more arrays together vertically (rows) to form a new array. The syntax is as follows:
   np.vstack(tup)

Among them, tup: the array tuple that needs to be stacked.

The np.row_stack function has the same function as the np.vstack function.

The sample code is as follows:
import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5 , 6])

c = np.vstack((a, b))
print(c)

Output result:

[[1 2 3]

[4 5 6]]

3. np.hstack and np.column_stack
   np.hstack function stacks two or more arrays horizontally (column) in together, forming a new array. The syntax is as follows:
   np.hstack(tup)

Among them, tup: the array tuple that needs to be stacked.

The np.column_stack function has the same function as the np.hstack function, but can handle one-dimensional arrays.

The sample code is as follows:
import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5 , 6])

c = np.hstack((a, b))
print(c)

Output result:

[1 2 3 4 5 6]

4. np.dstack
   np.dstack function stacks two or more arrays together in the depth direction (along the Z axis) to form a new array. The syntax is as follows:
   np.dstack(tup)

Among them, tup: the array tuple that needs to be stacked.

The sample code is as follows:
import numpy as np

a = np.array([[1, 2], [3, 4]])
b = np. array([[5, 6], [7, 8]])

c = np.dstack((a, b))
print(c)

Output result:

[[[1 5]

[2 6]]

[[3 7]

[4 8]]]

2. Application scenarios

1. Data merging
   When multiple arrays need to be merged according to certain rules to form a large array, you can use the numpy splicing method. For example, in machine learning, training and test sets are often separated and they need to be merged into a single data set.

The sample code is as follows:
import numpy as np

Assume that the training set has been loaded into the variable train_data, and the shape is (m, n1)

Assumption The test set has been loaded into the variable test_data, and the shape is (k, n1)

Merge the training set and the test set into one data set

data = np.concatenate((train_data, test_data) , axis=0)
print(data.shape)

2. Data expansion
   In deep learning, data expansion of training samples is a common method to improve the generalization ability of the model. . Multiple augmented samples of a sample can be combined using numpy's splicing method.

The sample code is as follows:
import numpy as np

Assume that the sample has been loaded into the variable sample, and the shape is (n, m)

For the sample Perform horizontal flip expansion

flipped_sample = np.fliplr(sample)

Merge the expanded samples

augmented_sample = np.hstack((sample, flipped_sample))
print(augmented_sample.shape)

Summary:
This article introduces the splicing method of numpy arrays and its application scenarios. By using numpy's splicing method, we can merge multiple arrays for data processing and analysis. The splicing methods include np.concatenate, np.vstack, np.row_stack, np.hstack, np.column_stack and np.dstack. You can choose the appropriate method according to specific needs. These methods are very common in application scenarios such as data merging and data augmentation, and can help us better process and analyze data.

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