Approximation – in a world of precision to the n^{th} decimal digit, there are indeed grounds where approximating values still holds good. In fact, nature is not very precise in carrying out things, rather it just does things within a ‘range’. The same can also be true when one sets afoot in data analysis.

Data scientists can themselves be all hubris about how precise their model is at delivering results, but they too can never deny the fact that their entire model is based on postulates with foundations on approximation!

*Also read: Numpy.Divide() – How to Use Numpy Divide in Python?*

In this article we would be exploring one such function available in the NumPy library of Python, that shall help one with rounding off any given number to the nearest integer towards zero. What this means is any given number, how many soever digits in decimals it might possess, would be rounded off into an integer without any decimal values left behind.

The function under study is *numpy.fix( )* and before getting on with a demonstration using sample data, let us first understand its syntax.

## Syntax of numpy.fix( )

Following is the syntax of *numpy.fix( ) *function.

```
numpy.fix(x, out=None)
```

where,

*x –*can be a single number or an array of numbers that are to be rounded off*out –*an optional feature that can be used to store the resultant value(s) within a destined array of the same dimensions as input, if specified.

It is to be noted that the output of the *numpy.fix( ) *function is returned in the format of floats.

One shall kick start things by importing the *numpy *library using the following code.

```
import numpy as np
```

## Rounding off a single number with numpy.fix( )

After the *numpy *library is imported, one shall get right into rounding the desired number into its nearest integer towards zero by using the following code.

```
import numpy as np
np.fix(3.14159365)
```

Those who are keen in their observation might have figured out by now that the number being rounded off in the above image is one that falls under the category of *irrational numbers. *If you have not guessed by now, it is the first eight decimals of *pi *(π)!

The result displayed is only ‘3.0’ which infers that how much soever might be the count of decimals, one shall get the result only as zero in the decimal keeping the whole number part of it intact.

*Also read: Numpy.subtract(): How to Use Subtract Numbers with NumPy in Python?*

## Rounding off an array of numbers with numpy.fix( )

Let us now get our hands with rounding a collection of numbers in an array. We shall get started by constructing one similar to the array given below.

```
ar = [[12.6, 3.4, 2.3],
[4.5, 7.6, 78.5]]
```

Once done, we shall deploy the *fix( )* function to use using the following code.

```
np.fix(ar)
```

Running the above code leads us to the following result.

## Using the Out Option on the numpy.fix( ) method

This section shall demonstrate how the *out *option described earlier in this article shall be used while rounding off the number(s). Since the provision to store the output should be of the same dimensions as the input, one shall assign a variable with an array of the same input dimensions in case multiple numbers are involved.

If the array is the same as used in the above section, then it has 2 rows and 3 columns, which makes us set the output array empty as follows, so that it could be replaced with the values of the result.

```
rr = np.empty([2,3])
```

Then while using the *fix( ) *function, assign the ‘rr’ to the *out *option as shown below.

```
np.fix(ar, out=rr)
```

Running the above code gives us the following result.

To verify whether the output array is updated, one can use the *print( ) *function.

**Conclusion**

Now that we have reached the end of this article, hope it has elaborated on how to round any given number to its nearest integer using the *numpy.fix( ) *function in Python. Here’s another article that details how to convert degrees to radians using *numpy *in Python. There are numerous other enjoyable & equally informative articles in AskPython that might be of great help to those who are looking to level up in Python. Whilst you enjoy those, *adios*!