Pandas is a powerful tool used for data analysis, manipulation, and transformation. It is a fast, flexible, and easy-to-use open-source data analysis and manipulation tool.

Pandas is a module used for working with numerical data variables, making it a valuable asset for data scientists, researchers, and analysts. Data analysis and transformation is an essential aspect of data science, as it ensures that the data is clean, organized, and ready for further processing.

In this article, we will explore the syntax, parameters, limitations, and applications of the Pandas between() function. Syntax of Pandas between() function:

The Pandas between() function is used to check whether a numeric value falls between a specified range.

## The syntax for the function is as follows:

between(self, left, right, inclusive=True)

The self parameter is the object being referred to, and it is not necessary for the user to pass in any value. The left and right parameters define the start and end of the range, respectively.

If the inclusive parameter is set to True, then the range is considered inclusive, which means that values falling on the start or end points are included. Parameters of Pandas between() function:

The between() function takes three parameters: left, right, and inclusive.

1. Left: This is the lower bound of the range of values to check.

It is a required parameter and must be a numeric value. 2.

Right: This is the upper bound of the range of values to check. It is a required parameter and must be a numeric value.

3. Inclusive: This parameter is optional and is set to True by default.

If set to True, the range is inclusive, meaning values at the start or end points are included in the range. Limitations of Pandas between() function:

The between() function is limited to working with numeric values only.

Any attempt to use strings or non-numeric values will result in an error. Additionally, the between() function only works with 1-dimensional DataFrames, meaning that it cannot be used on 2-dimensional or multi-dimensional datasets.

Applications of Pandas between() function:

The between() function is useful in various applications where one needs to check if a value falls within a range, such as filtering data and creating subsets. One example of such use is in the filtering of data based on a numeric range.

For instance, if one is interested in studying individuals with a BMI of between 18.5 and 25, the between() function can be used to filter the data, leaving only the relevant values. Summary:

The Pandas between() function is a powerful tool used to check whether a numeric value falls between a specified range.

The function takes three parameters; left, right, and inclusive and can be used in various applications where one needs to filter data based on a numerical range. Though the function has some limitations, it is still a valuable asset for data scientists and analysts dealing with numerical data variables.

Example 1: Using between() function with inclusive set to ‘True’

In this example, we will create a 1-D DataFrame and apply the between() function to filter data within a specified range. We will also explore the use of the inclusive parameter and how it affects the output.

To start, we will create a DataFrame using the pandas.DataFrame() function:

“`python

## import pandas as pd

data = {‘Name’: [‘John’, ‘Sara’, ‘Ali’, ‘Sophie’, ‘Bill’],

‘Age’: [21, 32, 19, 45, 28]}

df = pd.DataFrame(data)

“`

The DataFrame will have two columns, ‘Name’ and ‘Age’, with five rows of data. Now let’s apply the between() function on the ‘Age’ variable and return values within the range of 20 to 30, with inclusive set to True:

“`python

ages = df[df[‘Age’].between(20, 30, inclusive=True)]

## print(ages)

“`

The output of this code will be a new DataFrame, containing only the values that fall within the specified age range:

“`

Name Age

## 0 John 21

## 1 Sara 32

## 2 Ali 19

## 4 Bill 28

“`

The function has returned all the rows of the original DataFrame where the age falls between 20 and 30, including the values at the start and end points of the range. If we had set inclusive to False, the output would not have included the age 20 and 30.

Example 2: Using between() function with categorical variable

In this example, we will explore the limitations of the between() function, which only works with numeric values. We will attempt to compare a string or non-numeric variable with between().

We will also show what happens when we use the function on non-numeric data. Let’s start with an example DataFrame that contains both numeric and non-numeric data:

“`python

data = {‘Name’: [‘John’, ‘Sara’, ‘Ali’, ‘Sophie’, ‘Bill’],

‘Age’: [21, 32, 19, 45, 28],

‘Job Title’: [‘Engineer’, ‘Analyst’, ‘Manager’, ‘Actor’, ‘Doctor’]}

df = pd.DataFrame(data)

“`

This DataFrame has three columns, ‘Name’, ‘Age’, and ‘Job Title’, with five rows of data.

Now let’s try to apply the between() function to the ‘Job Title’ column by selecting all the rows where ‘Job Title’ falls between ‘Analyst’ and ‘Manager’:

“`python

jobs = df[df[‘Job Title’].between(‘Analyst’, ‘Manager’)]

## print(jobs)

“`

When running this code, we will receive an error message that reads: TypeError: ‘<' not supported between instances of 'str' and 'float'. The error message tells us that between() cannot be applied on non-numeric data.

The function is built to work with numbers only, and it cannot compare strings or non-numeric values. Conclusion:

In conclusion, the between() function is a powerful tool in data analysis and transformation.

It is a great way to filter data based on numerical ranges and can be applied to 1-dimensional DataFrames. However, when using the between() function, one should be aware of its limitations, especially when working with non-numeric data.

It is essential to understand the inclusive parameter and how it affects the output of the function. With practice and understanding, the between() function can be a valuable asset to any data scientist or analyst dealing with numerical data variables.

Example 3: Printing values obtained from between() function

In this example, we will explore how to print and visualize the data that we obtain from the between() function. We will show how to print data that falls within a specified range while also explaining what happens when we set the inclusive parameter to False.

Let’s begin by using the same DataFrame as Example 1, where we apply the between() function to the ‘Age’ column, but this time we will include some print statements to see the data that is being returned:

“`python

ages = df[df[‘Age’].between(20, 30, inclusive=True)]

print(‘Values in the range of 20 to 30, inclusive:’)

## print(ages)

ages = df[df[‘Age’].between(20, 30, inclusive=False)]

print(‘Values in the range of 20 to 30, exclusive:’)

## print(ages)

“`

When we run this code, we will see two different print statements – the first will display values that fall within the range of 20 to 30, where the inclusive parameter is set to True, while the second print statement will display values that fall within the range of 20 to 30, where the inclusive parameter is set to False. If we set the inclusive parameter to True, the between() function will return all rows that contain values within the range, including the values at the start and end points of the range, as shown in the example below:

“`

Values in the range of 20 to 30, inclusive:

Name Age

## 0 John 21

## 1 Sara 32

## 2 Ali 19

## 4 Bill 28

“`

If we set the inclusive parameter to False, the between() function will return all rows that contain values within the range, excluding the values at the start and end points of the range, as shown in the example below:

“`

Values in the range of 20 to 30, exclusive:

Name Age

## 4 Bill 28

“`

This output only returns the data where the age is between 20 and 30, excluding the ages 20 and 30.

## Conclusion of Pandas between() function

In this article, we explored the syntax, parameters, limitations, and applications of the Pandas between() function. We showed by example how the function can be used to filter data within a specific numeric range, explained the differences where inclusive was set to true or false, and how this would change the results obtained while also exploring the limitations of the function, especially when it comes to non-numeric data.

Knowing how to use and understand the between() function is essential for any data scientist or analyst who deals with numerical data variables. It allows for efficient and effective data analysis and transformation, leading to accurate and relevant results.

It is also useful in creating subsets and filtering data based on a particular numerical range. As always, we encourage readers to comment and ask questions.

If there is anything that you would like us to clarify or expand upon, please leave a comment below. Stay tuned for more Python-related posts and continue developing upon your knowledge of this versatile programming language.

In conclusion, the article explored the syntax, parameters, limitations, and applications of the Pandas between() function. The function proves useful in manipulating numeric data variables, and it provides various applications, including filtering data within specific numeric ranges to create subsets.

However, it also has its limitations, including only working with numeric values and 1-dimensional DataFrames. The article emphasized the importance of data analysis and transformation and demonstrated the use of the function in filtering and manipulating data.

The takeaway is that the between() function can be an essential tool in data analysis and transformation that result in accurate and relevant results. In adopting and applying this tool, individuals can improve their Data Science skills to efficiently and effectively filter data.