Today, I tried a data transformation that seemed so obvious: splitting the string values of a Pandas column on a delimiter and one-hot encode the resulting strings. However, it took me quite some time to figure out how to do it elegantly.
Here’s what I wanted to achieve. I had a DataFrame like this:
| index | string_column |
| 1 | apple,pear,banana |
| 2 | apple |
| 3 | pear,apple |
I wanted to turn it into this:
| Index | string_column | apple | pear | banana |
| 1 | apple,pear,banana | 1 | 1 | 1 |
| 2 | apple | 1 | 0 | 0 |
| 3 | pear,apple | 0 | 1 | 1 |
To break it down, this can be achieved by doing two transformations:
- Split string on a delimiter
- One-hot encode the resulting values
Although it looks seemingly easy, I had a hard time imagining how one goes from the intermediate state (columns with the first, second and third string after splitting them) to the final state.
Luckily, Pandas has an out-of-the-box method for achieving both transformations at once. That method is the get_dummies Series method, which differs a lot from Pandas’ general function with the same name.
By using the sep parameter, one can apply one-hot encoding to a single Series that has multiple values split by a delimiter:
df['string_column'].str.get_dummies(sep = ',')
Simple as that!
By the way, I didn’t necessarily come up with this solution myself. Although I’m grateful you’ve visited this blog post, you should know I get a lot from websites like StackOverflow and I have a lot of coding books. This one by Matt Harrison (on Pandas 1.x!) has been updated in 2020 and is an absolute primer on Pandas basics. If you want something broad, ranging from data wrangling to machine learning, try “Mastering Pandas” by Stefanie Molin.
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Great post, Roel! The step-by-step explanation on splitting columns and one-hot encoding in Pandas is really helpful. I especially appreciated the clear examples you provided. This simplifies a process that can sometimes seem overwhelming. Thanks for sharing your insights!
Great post, Roel! The step-by-step explanation on splitting the column and one-hot encoding was super helpful. I appreciate the clear examples—this will definitely streamline my data processing tasks. Keep up the good work!
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Great post, Roel! I found the section on one-hot encoding particularly helpful. It’s amazing how efficiently Pandas can handle data manipulation. Thanks for the clear examples!
Great post, Roel! The step-by-step approach to splitting the Pandas column and then one-hot encoding it was incredibly helpful. I appreciate the clear examples and explanations. This technique will definitely enhance my data preprocessing workflow. Thank you!
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Great post, Roel! The step-by-step approach to splitting the column and then applying one-hot encoding is really helpful. I especially appreciated the clear explanations and examples. This will definitely make my data preprocessing tasks easier. Thanks for sharing!
Great post, Roel! Your step-by-step explanation makes it really easy to follow. I never thought splitting a column and one-hot encoding could be so straightforward. Thanks for sharing the code snippets!
Great post, Roel! The step-by-step explanation on splitting columns and applying one-hot encoding was really helpful. I love how you included practical examples. Thanks for sharing!
Great post, Roel! The step-by-step explanation on splitting the Pandas column and one-hot encoding it is super helpful. I particularly appreciated the clear examples. Thanks for sharing!
Great post, Roel! I found the step-by-step guide very helpful, especially the example on one-hot encoding after splitting the column. It’s a clear and practical way to handle such data transformations in Pandas. Thanks for sharing!
Great post, Roel! The step-by-step approach for splitting columns and one-hot encoding in Pandas was incredibly helpful. I especially appreciated the code snippets. Thanks for sharing!
Great post, Roel! The step-by-step guide on splitting the Pandas column and applying one-hot encoding is really clear and helpful. I especially appreciated the use of the `str.get_dummies()` method—it’s a real game changer. Thanks for sharing!
Great post, Roel! The step-by-step approach to splitting columns and one-hot encoding in Pandas is super helpful. I especially appreciated the clear examples. Keep up the good work!
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This post is incredibly helpful! The step-by-step approach to split the Pandas column on a delimiter and then one-hot encode it makes the process clear and easy to follow. I appreciate the code snippets too—very useful for implementing in my own projects. Thank you, Roel!
Great tutorial! The examples were really clear, and I appreciate how you broke down the process of splitting columns and applying one-hot encoding. This will definitely help streamline my data preprocessing. Thanks for sharing!
Great post, Roel! The step-by-step instructions on splitting columns and one-hot encoding were super clear and easy to follow. I especially appreciated the code snippets you provided. They saved me a lot of time. Thanks for sharing!
Great post, Roel! Your explanation on splitting the Pandas column and one-hot encoding is super clear and easy to follow. I appreciate the practical examples you provided. They really help in understanding the process better. Thanks for sharing!
Great post! I really appreciate the clear examples on splitting columns and one-hot encoding in Pandas. This will definitely help streamline my data preprocessing. Thank you!
This article is really helpful! The method for splitting a Pandas column by a delimiter and one-hot encoding it using str.get_dummies() is very efficient. It’s a practical solution for data analysis and preparing datasets for machine learning models.
Great post, Roel! The examples you provided really clarified the one-hot encoding process after splitting a column in Pandas. I’ve been looking for a straightforward way to handle this, and your step-by-step guide was super helpful. Thanks for sharing!
Great post, Roel! The step-by-step approach you outlined for splitting the Pandas column and one-hot encoding it is super helpful. I particularly appreciated the clear examples you provided. Can’t wait to implement this in my own projects!