## Dummy Variables and the Pandas Library

Categorical data is common in many machine learning applications, and dummy variables are vital in converting this data into a numerical format that can be used in our models. Dummy variables are essentially binary extensions that allow us to represent categorical data as numerical data.

In this article, we will take a closer look at dummy variables, their purpose, and an example to help us understand them better. We will also delve deeper into the Pandas library and its from_dummies() function, which can be used to convert dummy variables back to their categorical form.

We will explore the use cases for this function in Response and Region dataframes.

## Understanding Dummy Variables

In the context of categorical dataset, a dummy variable is a binary variable that takes on a value of 0 or 1. It is used to represent each unique category in a categorical variable as a separate binary variable; this way, the categories can be transformed into a numerical representation which our machine learning model can process.

For instance, suppose we have a dataset of students that includes a variable called “passed_exam,” which indicates whether or not a student passed their exam with a ‘YES’ or a ‘NO.’ In this case, we can define a binary extension for ‘YES’ and ‘NO.’ Here’s how it looks like:

– If the student passed the exam, then passed_exam_YES=1 and passed_exam_NO=0. – If the student did not pass the exam, then passed_exam_YES=0 and passed_exam_NO=1.

A machine learning model can then process this data easily since it works with numerical data rather than categorical data.

## Pandas Library and from_dummies() Function

The Pandas library is a valuable tool that data scientists and machine learning practitioners use to manipulate and analyze data. Often, we will have datasets with dummy variables; but, at some point, we may need to convert back to our original categorical format.

This is where the from_dummies() function is useful.to from_dummies() Function

The from_dummies() function is a Pandas function that takes in a categorical dataframe, and returns a new dataframe with the dummy variables that represent the categorical data converted back to their original categorical form. This function helps to transform our numerical data back into categorical data, making it easy for us to interpret the data.

## Syntax of from_dummies Function

The from_dummies() function has the following syntax:

Pandas.DataFrame.from_dummies(data, sep=’_’, prefix=None, dtype=float, default_category=None)

## Let us examine closely the fundamental constituents of this function:

– data – this is the data that we would like to convert back to our original format. It must be a Pandas dataframe with binary variables.

– sep – this is the separator used in the original dummy DataFrame to identify categorical variables. – default_category – this parameter specifies the default category to create when we have a column of all zeros.

## Use cases for from_dummies() Function

Let’s explore the use of the from_dummies() function. Assume we have two dataframes; Response and Region.

The Response dataframe has a column of binary variables for each of the possible response options for a survey. The Region dataframe has the binary variables which represent the different regions.

We can use the from_dummies() function to convert this data back into categories as follows:

## Response Dataframe

“`

## import pandas as pd

data = {‘Response_Yes’: [1,0,1], ‘Response_No’: [0,1,0],

‘Response_Maybe’: [0,0,1]}

df = pd.DataFrame(data)

df_cat = pd.DataFrame(df).apply(lambda x:

pd.Categorical.from_codes(x, [‘No’, ‘Maybe’, ‘Yes’]))

# Output:

# Response_Yes Response_No Response_Maybe

# Yes No No

# No Yes No

# Yes No Maybe

“`

## Region Dataframe

“`

## import pandas as pd

data = {‘Region_North’: [1,0,0,1], ‘Region_South’: [0,1,0,1],

‘Region_East’: [0,1,1,0], ‘Region_West’: [1,0,0,0]}

df = pd.DataFrame(data)

df_cat = pd.DataFrame(df).apply(lambda x:

pd.Categorical.from_codes(x, [‘East’, ‘North’, ‘South’, ‘West’]))

# Output:

# Region_North Region_South Region_East Region_West

# North South East West

# West South North West

# West East East North

# North South East North

“`

In both examples, we have used the from_dummies() function to transform the binary variables back into their original categorical format. The result is a new dataframe that captures the original meaning of the categorical data in a format that is easy to read and understand.

## Conclusion

In summary, dummy variables are vital for converting categorical data into a numerical format that can be computed. The Pandas library makes this process more accessible with its from_dummies() function, which transforms dummy variables back to their original categorical form.

Understanding dummy variables and their use ensures that we achieve an accurate representation of the original data.

## Creating Categorical Dataframes using from_dummies() Function

In the previous sections, we explored what dummy variables are and how the Pandas library’s from_dummies() function can convert them back to the original categorical form. In this section, we will delve deeper into the process of converting dummy variable dataframes to categorical dataframes using the from_dummies() function.

## Converting Dummy Variable DataFrame to Categorical DataFrame

Let us consider an example of converting a Response dummy dataframe to its categorical form. We have the following dummy dataframe:

“`

## import pandas as pd

data = {‘Response_Yes’: [1,0,1], ‘Response_No’: [0,1,0],

‘Response_Maybe’: [0,0,1]}

df = pd.DataFrame(data)

# Output:

# Response_Yes Response_No Response_Maybe

# 1 0 0

# 0 1 0

# 1 0 1

“`

The dataframe above has binary values representing the possible responses to a survey. We can use the from_dummies() function to convert the above dataframe to a categorical dataframe as follows:

“`

df_cat = pd.DataFrame(df).apply(lambda x:

pd.Categorical.from_codes(x, [‘No’, ‘Maybe’, ‘Yes’]))

# Output:

# Response_Yes Response_No Response_Maybe

# Yes No No

# No Yes No

# Yes No Maybe

“`

The from_dummies() function takes in the dummy dataframe ‘df’ and the categorical values ‘No’, ‘Maybe’, ‘Yes.’ The function then converts the data into the categorical dataframe ‘df_cat.’

Using Sep & default_category Parameters in from_dummies() Function

As we saw in the previous section, the from_dummies() function can take additional parameters apart from data and categorical values.

The sep parameter allows us to specify the separator used while encoding the categorical variable. The default_category parameter, on the other hand, sets the value to use when all dummies variables in a row or column are 0.

Let us consider an example of using the sep and default_category parameters with the Region dummy dataframe. We have the following data:

“`

## import pandas as pd

data = {‘Region_North’: [1,0,0,1], ‘Region_South’: [0,1,0,1],

‘Region_East’: [0,1,1,0], ‘Region_West’: [1,0,0,0]}

df = pd.DataFrame(data)

# Output:

# Region_North Region_South Region_East Region_West

# 1 0 0 1

# 0 1 1 0

# 0 0 1 0

# 1 1 0 0

“`

In the above dummy dataframe, each region has a binary value that indicates its presence or absence. We can use the from_dummies() function to convert the binary value into the categorical form as follows:

“`

df_cat = pd.DataFrame(df).apply(lambda x:

pd.Categorical.from_codes(x, [‘North’, ‘South’, ‘East’, ‘West’], sep=’_’,

default_category=’Unknown’))

# Output:

# Region_North Region_South Region_East Region_West

# North Unknown Unknown West

# Unknown South East Unknown

# Unknown Unknown East Unknown

# North South Unknown Unknown

“`

In this example, we used the sep parameter to specify the separator used in the encoding of the categorical variable.

We set it to an underscore, which we used to separate the Region, in the dummy variable’s name. We also set the default_category parameter to “Unknown,” indicating what value to return when all binary variables are zero.

## Conclusion

The from_dummies() function is a powerful tool that can help convert dummy variables back into categorical data. We saw how we can use the Pandas library to convert Response and Region dummy dataframes into their categorical format.

We also looked at the parameters sep and default_category that we could use to customize the return values of the from_dummies() function. By converting our dummy variables to categorical format, we can more easily interpret and identify our data’s unique categories.

In summary, the use of dummy variables is crucial in converting categorical data into numerical data that can be processed by machine learning models. The Pandas library’s from_dummies() function is an efficient tool that can transform these numerical representations back into meaningful categorical data.

Employing the sep and default_category parameters of the function can customize the results of the converted dataframe. By converting our dummy variables to categorical format, we can more easily interpret and understand our categorical data.

This article highlights the importance of dummy variables, the Pandas library, and the from_dummies() function. It shows how these tools can simplify the categorization and processing of written data and make interpreting the data more manageable.