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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:
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)
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)
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)
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)
2. Application scenarios
The sample code is as follows:
import numpy as np
data = np.concatenate((train_data, test_data) , axis=0)
print(data.shape)
The sample code is as follows:
import numpy as np
flipped_sample = np.fliplr(sample)
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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