Table of Contents
1. Basic reshape operation
2. Use -1 to automatically infer dimensions
3. Multidimensional reshape (for example, 1D → 3D)
4. reshape back to one-dimensional array
5. Note: reshape does not change the original array
Common error: element count mismatch
Summary: Key points for using reshape
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python numpy reshape example

Jul 29, 2025 am 02:24 AM
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numpy.reshape() is used to change the shape of the array without changing the data. 1. The basic operation is to convert a one-dimensional array of 6 elements into a (2,3) two-dimensional array; 2. Use -1 to automatically infer dimensions, such as reshape(2,-1) or reshape(-1,4); 3. Multi-dimensional conversion can be performed, such as converting a one-dimensional array of 24 elements into a three-dimensional array of (2,3,4); 4. Use reshape(-1) to efficiently flatten the array into one-dimensional; 5. Reshape does not modify the original array, return the new array, and the original array remains unchanged; note that the total number of new and old shape elements must be consistent, otherwise an error will be reported.

python numpy reshape example

numpy.reshape() is a very common method in NumPy, which is used to change the shape of an array without changing its data. The following is a few common examples to intuitively understand its usage.

python numpy reshape example

1. Basic reshape operation

Convert a one-dimensional array to a two-dimensional array (for example 1x6 → 2x3):

 import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6])
reshapeed = arr.reshape(2, 3)
print(reshaped)

Output:

python numpy reshape example
 [[1 2 3]
 [4 5 6]]

Note: The number of original elements is 6, and the shape (2,3) after reshape must also be 6 elements, otherwise an error will be reported.


2. Use -1 to automatically infer dimensions

You can use -1 to make NumPy automatically calculate the size of a certain dimension:

python numpy reshape example
 arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])

# Convert to 2 rows, automatically infer the number of columns reshaped = arr.reshape(2, -1)
print(reshaped)

Output:

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

It can also be written as (-1, 4) , indicating that the number of rows is automatically calculated, with 4 columns per row.


3. Multidimensional reshape (for example, 1D → 3D)

 arr = np.arange(24) # [0, 1, ..., 23]
reshapeed = arr.reshape(2, 3, 4)
print(reshaped.shape) # (2, 3, 4)
print(reshaped)

The output is a 3D array of 2 layers, 3 rows and 4 columns per layer.


4. reshape back to one-dimensional array

 arr = np.array([[1, 2], [3, 4], [5, 6]]) # shape (3, 2)
flat = arr.reshape(-1) # Flatten to one-dimensional print(flat) # [1 2 3 4 5 6]

This is equivalent to .flatten() , but reshape(-1) is more efficient and does not copy data (if possible).


5. Note: reshape does not change the original array

 arr = np.array([1, 2, 3, 4])
new_arr = arr.reshape(2, 2)
print(arr) # Still [1 2 3 4]
print(new_arr) # [[1 2], [3 4]]

reshape returns the new array, the original array remains unchanged (unless the view cannot be viewed, it will be copied).


Common error: element count mismatch

 arr = np.array([1, 2, 3])
# arr.reshape(2, 2) # Error! 3 elements cannot be turned into 2x2 (4 are required)

An error will be reported:

 ValueError: cannot reshape array of size 3 into shape (2,2)

Summary: Key points for using reshape

  • The total number of elements in old and new shapes must be consistent.
  • Use -1 to automatically infer a certain dimension (but only one -1).
  • Commonly used for data preprocessing, such as image and machine learning input adjustment.
  • reshape(-1) is an efficient way to flatten arrays.

Basically all that, not complicated but very practical.

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