Pandas with Scikit-Learn: Converting a Categorical to Numeric Variable

Translating human language into combinations of binary digits that instruct machines to display content that is readable by humans, such as text, audio, and images, is an intricate process that computers are unable to accomplish without assistance. Additionally, prior to feeding data into any Machine Learning (ML) model, we need to ensure that it adheres to the correct format and solely comprises numerical variables. Nevertheless, categorical variables entail crucial information that can be transformed into numerical form by using libraries like Pandas and Scikit-learn.

Techniques to store and transfer data on categorized variables

Categorical variables, also known as “strings” or “categories,” have a finite size. Some examples include:

1. Cities where individuals dwell, like San Francisco, Chicago, Las Vegas, Seattle, and more.
2. The department where someone is employed, like HR, Merchandising, or Marketing.
3. The most advanced degree earned by a person, whether it be a Bachelor’s, Master’s, Doctorate, and so on.

Categorical data comprises ordinal data and nominal data as illustrations.

• Statistical Analysis Based on Ordinal Variables:

  When encoding ordinal data, it is crucial to acknowledge and preserve the predetermined hierarchy of values. Failure to do so can result in erroneous conclusions and data. For instance, when examining an individual’s academic qualifications, the degrees should be assigned in the correct order, rather than arbitrarily. It is vital to maintain the hierarchical arrangement of the data to ensure precision.

• Facts and Statistics:

  Presently, there is no feasible approach to organise the diverse categories in a logical manner. Nominal data is assumed to hold some degree of hierarchy, and only the existence or non-existence of a particular characteristic needs to be considered. For instance, in the preceding scenario, it would be advantageous to include the person’s city of residence. Even if they prefer Chicago or Las Vegas, their place of residence should still be included.

With our current comprehension of categorical variables, we can examine encoding possibilities with the help of the Python’s Pandas and Scikit-learn libraries.

Utilize the Find/Replace Functionality

The search and replace process is the simplest way to encode categorical data. To replace all instances of an old character with a new one in a string, the replace() function may be used.

Using the Pandas library, we can tackle the problem of textual labels representing numerical values in the data set’s “number of cylinders” column. Since the highest number of cylinders in any vehicle is 4, any numerical values entered in this column will be rounded down to the nearest 4. To accomplish this, we may quickly replace the textual labels like “two” or “one” with their corresponding numerical values using the replace function.

A mapping dictionary is being created to convert all string values to their corresponding integer values. Since preserving the hierarchy of ordinal data is necessary, this approach is extremely advantageous.

In the aforementioned instance of “an individual’s degree,” the highest degree may be linked with the largest numerical value, while the lowest degree may be assigned the smallest numerical value.

Data Encoding for Labels

With the help of this approach, every label is allotted a unique number based on its alphabetical sequence. We can execute this process using the Scikit-learn package.

Efficient Encoding

To overcome the limitation of label encoding, one-hot encoding is frequently employed as a substitute. This approach entails converting each category into a unique column, with a value of either 1 or 0 assigned. This process is commonly known as the creation of dummy datasets.

Using Pandas to Convert Categorical Data into Numeric Format

Below are the methods utilised to convert categorical data in Pandas into numeric format.

1. Method 1: Utilising get_dummies()
2. Method 2: Replacement

Using Scikit-learn to Convert “Categorical to Numerical” Data

Scikit-learn offers several techniques for converting categorical data into a numeric format.

1. Method 1: Label Encoding
2. Method 2: One-Hot Encoding

So, Which Encoding Technique Should You Choose?

The appropriate encoding technique to use is determined by our understanding of the data. We will then need to select a specific method.

It is worth noting that utilizing the support vector machine (SVM) method for training could significantly increase the required time. This is due to the relatively slow SVM approach, which could result in increased complexity of the model with fifteen or more categorical features, further extending the training time.

When deciding on an encoding technique, it is important to keep in mind some critical factors such as:

Using Find-and-Replace Technique

1. Use this approach when preserving the existing order is crucial.
2. If the data is quantitative or ordinal, assigning numeric values to the range of magnitudes can be an effective technique. For instance, if a variable contains values of small, medium, and large, it can be assigned values of 1, 2, and 3 in that order.

Using One-Hot Encoding

1. If the properties of the underlying category are not ordinal, one-hot encoding can be applied. For instance, when representing data in relevant columns regarding a person’s current city of residence, one-hot encoding is preferred to avoid any inherent rank order.
2. With fewer distinct categories, the model’s complexity and the training time increase as more features are incorporated. Therefore, it is crucial to evaluate the trade-off between the model’s accuracy and the resources required to train it.

Encoding Labels

1. If a categorical property has a natural order or hierarchy, it is considered ordinal. For instance, the ordinal positions in an army often employ high integers to denote rank, with rank ‘1’ designated for the highest position.
2. When there is a plethora of diverse options to select from.

This article will delve into multiple approaches to encoding categorical data, highlighting their advantages, disadvantages, and frequent uses. Being informed of the merits and potential drawbacks of each technique is critical since encoding is an essential aspect of the feature engineering process, which is pivotal for building a successful model.