# How to convert numpy int to float with separate numpy array?

Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about How to convert numpy int to float with separate numpy array in Python. So Here I am Explain to you all the possible Methods here.

## How to convert numpy int to float with separate numpy array?

1. How to convert numpy int to float with separate numpy array?

`images[0:5].astype(numpy.float32)` creates a `float` copy of your slice, but the result is converted back to `int` when assigned back to the `images` slice since `images` is of `dtype` `int`.

2. convert numpy int to float with separate numpy array

`images[0:5].astype(numpy.float32)` creates a `float` copy of your slice, but the result is converted back to `int` when assigned back to the `images` slice since `images` is of `dtype` `int`.

## Method 1

You can’t modify the `dtype` of a slice only. When you do

```images[0:5] = images[0:5].astype(numpy.float32)
```

`images[0:5].astype(numpy.float32)` creates a `float` copy of your slice, but the result is converted back to `int` when assigned back to the `images` slice since `images` is of `dtype` `int`.

What you could do is create a temporary copy of your slice and convert it to float:

```copied_slice = images[0:5].astype(numpy.float32)
```

do all the computation you need on this smaller part of your data, save whatever result you need, then move on to the next (copied and converted) slice.

## Summery

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.