In machine learning, one ought to deal with a lot of data for analysis. Handling such gargantuan amounts of data might be a bit tricky but with the right functions, it is just a matter of time before we end up right where we want to be.

In this article, we shall explore one such function that will help the user to find whether the set of given values falls in between two given inputs. The function of interest is the** between( )** function from the pandas library in Python & we shall have a look at it in great detail in the following sections.

- Syntax of Pandas
*Series.between( )*Function - Using
*between( )*Function on a Series - Exercising the
*inclusive*Option in*between( )*Function

**Before moving any further let us import the pandas library using the below code:**

```
import pandas as pd
```

## Syntax of Pandas *Series.between()* Function

One can understand the workings of the *between( ) *function through its syntax which is given below. It contains all the basic constituents required for its effective functioning.

**Syntax:**

```
Series.between(left, right, inclusive = ‘both’)
```

**Parameters:**

input list or scalar entity which is declared to be one end of the boundary*left –*input list or scalar entity which is declared to be the other end of the boundary*right –*an optional construct set to*inclusive –**both*by default and is used to include or exclude the boundary values before comparing the inputs against the boundaries

## Using *between()* Function on a Series

In this section, we shall construct a series against which we shall apply the *between( ) *function to find which amongst the values in the series fall between the selected boundaries.

**Given below is the code to create a series using the pandas library in Python:**

```
sr1 = pd.Series([12, 36, 71, 99, 34, 28])
```

**Once done, the above series is then passed through the between( ) function as shown below:**

```
R = sr1.between(2,50)
print(R)
```

**Output:**

As seen in the above output, the *between( ) *function returns the Boolean value as a result for each entity in the series by comparing whether or not it lies within the specified boundaries.

## Exercising the *inclusive* Option in *between()* Function

In this section, we shall make use of the optional construct in the syntax of the *between( )* function to demonstrate its different variations.

**The inclusive option in this function can be deployed in three different ways, each of which is listed below:**

Both the boundary values are considered while analysing the input series*Both –*Both the boundary values are not considered while analysing the input series**Neither –**Only the left side boundary value is considered while ignoring the right side boundary value while analysing the input series**Left –**Only the right side boundary value is considered leaving aside the left side boundary value while analysing the input series**Right –**

Let us now have a look at examples for different types to understand the *inclusive *option better.

**Example 1:**

```
sr2 = pd.Series([5, 12, 36, 71, 99, 50, 34, 28])
R = sr2.between(5,50, inclusive = 'both')
print(R)
```

**Output:**

From the above result, it is evident that both boundary values 5 & 50 are included in the search thereby returning *True *for the 0^{th} and 5^{th} positions of the input series.

**Example 2:**

Let us now set the option as *neither *and see what happens.

```
R = sr2.between(5,50, inclusive = 'neither')
print(R)
```

**Output:**

Contrary to the previous results, it could be seen that the results are returned as *False *in both the 0th & 5th positions of the input series since neither of the boundary values are being considered in this case. The same can be expected for the leftmost boundary value is not considered when the left option is deployed or vice versa in the case of the *right *being deployed.

**If you’re new: Indentation in Python**

## Conclusion

Now that we have reached the end of this article, hope it has elaborated on the different ways of using the *between( ) *function from the *pandas *library. Here’s another article that explains how to use the *sqrt( ) *function from the *numpy *library in Python. There are numerous other enjoyable & equally informative articles in *CodeforGeek* that might be of great help to those who are looking to level up in Python.

## Reference

https://pandas.pydata.org/docs/reference/api/pandas.Series.between.html