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# NumPy | reshape method

Programming
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Python
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NumPy
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Documentation
schedule Jul 1, 2022
Last updated
local_offer PythonNumPy
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NumPy's reshape(~) method returns a new NumPy array with the desired shape. If the reshaped array contains more values than the original array, then numbers will be repeated.

# Parameters

1. a | array-like

The input array.

2. shape | int or tuple of int

The desired shape. If -1 is passed, then the shape is inferred. Check our examples below for clarification.

# Return value

A new NumPy array with the desired shape.

# Examples

## Converting from 1D to 2D

Consider the following 1D array:

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

To change this to a 2D array with 2 rows and 3 columns:

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

To convert a 1D array to a 2D array with one element:

np.reshape(a, (1, a.size))
array([[1, 2, 3, 4, 5, 6]])

## Converting from 2D to 1D

Suppose we have the following 2D array:

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

To obtain the 1D representation:

np.reshape(a, 4)
array([1, 2, 3, 4])

Here, we could simply use the ravel(~) method to flatten the array:

a = np.array([[1,2],[3,4]])
a.ravel()
array([1, 2, 3, 4])

## Inferring the shape

The value -1 in shape tells NumPy to infer the suitable shape. For instance:

np.reshape([1,2,3,4], [-1,1])
array([[1],
[2],
[3],
[4]])

We could have manually specified [4,-1] as the shape, but the number of resulting rows can be inferred from:

• the number of values there are in the array

• the number of columns (1 in this case).

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