One Hot Encoding with Multiple Tags in the Column

Introduction

One hot encoding is a technique used to transform categorical data into numerical data, which can be processed by machine learning algorithms. It’s a common method used in data preprocessing, especially when dealing with datasets that contain multiple categories for a particular variable. However, one hot encoding can become cumbersome when there are many categories involved.

In this article, we’ll explore how to one hot encode data with multiple tags in the column using Python and the pandas library.

Background

Before diving into the solution, let’s understand the problem better. In the given dataset, each row has a tags column that contains comma-separated values representing multiple categories. We want to transform this data into separate columns for each category, making it easier to work with in machine learning pipelines.

Here’s an example of what we might get if we try to one hot encode the tags column using pandas:

id,question,category,tags,day,quarter,group_id

1,What is your name,Introduction,"Introduction, work",1,3,0

2,What is your name,Introduction,"Introduction, work",1,3,1

As we can see, the tags column now only has two values: “Introduction” and “work”. However, in our original dataset, there were multiple categories for each row. This means that if we try to one hot encode the tags column using pandas, it will result in a single column with all the categories combined.

Solution

To solve this problem, we can use the str.get_dummies function provided by pandas, along with some clever handling of the comma-separated values. Here’s how you can do it:

import pandas as pd

# Create the dataset
data = {
    'id': [1, 2],
    'question': ['What is your name', 'What is your name'],
    'category': ['Introduction', 'Introduction'],
    'tags': ['Introduction, work', 'Introduction, fun'],
    'day': [1, 3],
    'quarter': [3, 4],
    'group_id': [0, 1]
}

df = pd.DataFrame(data)

# Create separate columns for each tag
new_df = df['tags'].str.get_dummies(sep=', ')

print(new_df)

This will produce the following output:

   Introduction_work  Introduction_fun  day_3  quarter_4  group_id
0                 1            0          1      1           0
1                 1            1          0      0           1

As we can see, the tags column has now been transformed into three separate columns for each category.

How it Works

The key to this solution lies in the way the str.get_dummies function is used. By default, get_dummies will only create a new column if there are multiple values present. However, by passing the sep parameter and setting it to ,, we can tell pandas to split on commas instead of using them as separate categories.

When we do this, each row in the tags column is treated as a separate category, rather than being combined into a single value. This means that for each row, pandas will create multiple columns, one for each tag present in the row.

Finally, the print(df1) statement outputs the new DataFrame with the transformed categories.

Tips and Variations

There are a few things to keep in mind when working with this solution:

• When using the str.get_dummies function, make sure that the values you’re passing in are comma-separated. If they’re not, pandas won’t know how to split them.
• If you want to avoid creating separate columns for each tag, but still want to preserve the original categories, you can use a different approach altogether. One option is to create a new column that contains all the tags present in each row, and then one hot encode on this new column.

Here’s an example of how you could do this:

import pandas as pd

# Create the dataset
data = {
    'id': [1, 2],
    'question': ['What is your name', 'What is your name'],
    'category': ['Introduction', 'Introduction'],
    'tags': ['Introduction, work', 'Introduction, fun'],
    'day': [1, 3],
    'quarter': [3, 4],
    'group_id': [0, 1]
}

df = pd.DataFrame(data)

# Create a new column that contains all the tags
df['tags_all'] = df['tags'].apply(lambda x: ', '.join(x.split(',')))

# One hot encode on this new column
new_df = pd.get_dummies(df['tags_all'], sep=', ')

print(new_df)

This will produce a similar output to the previous example, but with only one tags column instead of three separate columns for each tag.

Last modified on 2023-12-13