kopia lustrzana https://github.com/animator/learn-python
55 wiersze
1.6 KiB
Markdown
55 wiersze
1.6 KiB
Markdown
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# Numpy Array Shape and Reshape
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In NumPy, the primary data structure is the ndarray (N-dimensional array). An array can have one or more dimensions, and it organizes your data efficiently.
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Code to create a 2D array
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``` python
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import numpy as np
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numbers = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
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print(numbers)
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# Output:
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# array([[1, 2, 3, 4],[5, 6, 7, 8]])
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```
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## Changing Array Shape using Reshape()
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The `reshape()` function allows you to rearrange the data within a NumPy array.
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It take 2 arguements, row and columns. The `reshape()` can add or remove the dimensions. For instance, array can convert a 1D array into a 2D array or vice versa.
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``` python
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arr_1d = np.array([1, 2, 3, 4, 5, 6]) # 1D array
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arr_2d = arr_1d.reshape(2, 3) # Reshaping with 2rows and 3cols
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print(arr_2d)
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# Output:
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# array([[1, 2, 3],[4, 5, 6]])
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```
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## Changing Array Shape using Resize()
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The `resize()` function allows you to modify the shape of a NumPy array directly.
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It take 2 arguements, row and columns.
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``` python
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import numpy as np
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arr_1d = np.array([1, 2, 3, 4, 5, 6])
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arr_1d.resize((2, 3)) # 2rows and 3cols
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print(arr_1d)
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# Output:
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# array([[1, 2, 3],[4, 5, 6]])
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```
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## Reshape() VS Resize()
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| Reshape | Resize |
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| ----------- | ----------- |
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| Does not modify the original array | Modifies the original array in-place |
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| Creates a new array | Changes the shape of the array |
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| Returns a reshaped array | Doesn't return anything |
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| Compatibility between dimensions | Does not compatibility between dimensions |
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| Syntax: reshape(row,col) | Syntax: resize((row,col)) |
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