In-place type conversion of a NumPy array

Learn, how to implement in-place type conversion of a NumPy array? By Pranit Sharma Last updated : October 08, 2023

NumPy is an abbreviated form of Numerical Python. It is used for different types of scientific operations in python. Numpy is a vast library in python which is used for almost every kind of scientific or mathematical operation. It is itself an array which is a collection of various methods and functions for processing the arrays.

NumPy Array - In-place Type Conversion

Whenever we apply a function to any array, we sometimes pass a parameter called inplace=True. The question is what is the significance of this parameter?

Whenever we make some changes to an array either index-wise or axis-wise (in the case of the multi-dimensional array), we either use the method in which we change the original value and return the changed value or we use a method where we do not change the entire data set and do the specific changes wherever required.

inplace=True means the data will be modified without returning a copy of the data or the original data.

Syntax:

```dataframe.some_operation(inplace=True)
```

But if inplace=False is passed then it will modify the value and also returns a copy of the data.

Syntax:

```dataframe.some_operation(inplace=False)
```

Here, we will use the astype() method to convert the data type where we can pass the argument copy=False.

Let us understand with the help of an example,

Python program to demonstrate in-place type conversion of a NumPy array

```# Import numpy
import numpy as np

# Creating a numpy arrays
arr = np.array([1,2,4,5,3,6,8,9,7,10])

# Display original array
print("Original array:\n",arr,"\n")

# Convert the data type of the array
arr = arr.astype('float',copy=False)

# Display result
print("Data Type of the array:\n",arr.dtype)
```

Output

The output of the above program is: