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# NumPy | reshape method

NumPy
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schedule Jul 1, 2022
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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([, , , ]) ```

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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