Pandas: A Comprehensive Guide to Combining Multiple Categories into One Using Python, NumPy, and DataFrames

Are you trying to assign different categories to different values in your pandas DataFrame?

For instance, you might have categories ranging from 1 to 10, and you wish to map ‘red’ to values 3 to 5, ‘green’ to 1,6, and 7, and ‘blue’ to 2, 8, 9, and 10. It might seem challenging initially, especially if you encounter an error while renaming categories using the df.cat.rename_categories(['red','green','blue'] ) function, as it expects an equal number of new and old categories.

This blog post provides you with a comprehensive guide on how to combine multiple categories into one using Python, pandas, and NumPy. We’ll walk you through each step, exploring some errors you may encounter along the way, and suggesting elegant solutions for the same.

Table of Contents:

1. Understanding the Challenge
2. Simple and Elegant Solution
3. Another Approach: pandas.explode
4. Slightly Simplified Method
5. Using np.select for More Readability
6. Reverse Mapping using pandas.DataFrame.explode

Understanding the Challenge

The challenge seems straightforward: you want to assign certain values to specific categories, but the method you’re using to rename these categories isn’t working as expected. It’s essential to grasp why this error is occurring before we can address it. The rename_categories function in pandas expects the same number of new and old categories, which is not the case here.

Here’s the error that you may encounter:

ValueError: new categories need to have the same number of items than the old categories!

Now, if you try to resolve this by including duplicate values like this:

code
df.cat.rename_categories(['green','blue','red', 'red', 'red', 'green', 'green', 'blue', 'blue', 'blue'] )

You will again encounter an error stating that there are duplicate values, which is another limitation of this function.

So, what is the solution here?

Simple and Elegant Solution

One elegant way to address this problem is by using a Python dictionary. You can create a dictionary where each color maps to the respective values. You can then use this dictionary to build a new categorical series.

Here’s how to do this:

code


m = {"red": [3,4,5] , "green": [1,6,7] , "blue": [2,8,9,10] 

}
m2 = {v: k for k,vv in m.items() for v in vv}
df.cat = df.cat.map(m2).astype("category", categories=set(m2.values()))

Another Approach: pandas.explode

An alternative approach is to use the pandas.explode function, which was introduced in pandas version 0.25.0. This function transforms each element of a list-like to a row, replicating the index values. This approach helps avoid any loops and makes the code more readable and efficient.

Here’s an example of how to use it:

code

m = {"red": [3,4,5] , "green": [1,6,7] , "blue": [2,8,9,10] 
}
s = pd.Series(m).explode().sort_values()

Slightly Simplified Method

Another simplified method involves creating a dictionary that maps the old categories to new ones. Then, use the map function to apply this mapping to your column. This way is very readable and straightforward:

code




.
  } for _ in range(len(answers)) }

I hope this article has made it clear how to tackle the problem of combining multiple categories into one in pandas. While pandas doesn't provide a direct function for this kind of task, the flexibility of Python and pandas allows us to approach the problem in different ways.

Remember, while these solutions are efficient, they may not always be the best fit for your dat

Using np.select for More Readability

A more detailed and possibly readable approach involves using numpy’s np.select function. This function takes two lists: a list of conditions and a list of choices and returns an array that is based on these conditions and choices.

Here’s an example:

code
















import numpy as np

conditions = 
[
    df['cat'] 
.between(3, 5),
    df['cat'] .isin([1,6,7] 
),
    df['cat'] .isin([2,8,9,10] 


)
]

choices = 



[
    'red',
    'green',
    'blue'
] 



df['cat']  = np.select(conditions, choices, default='black')

This script defines three conditions, and then it assigns the colors ‘red’, ‘green’, and ‘blue’ based on those conditions. Any values that don’t match any of the conditions will be assigned ‘black’. It’s more verbose, but also more clear about what is happening.

Reverse Mapping using pandas.DataFrame.explode

While this post has largely focused on mapping multiple categories into one, there might be a need to do the opposite. You might have a DataFrame where each row has multiple categories, and you want to create a separate row for each category.

You can do this using the explode method, which was introduced in pandas 0.25.0. This method will transform each element of a list-like to a row, replicating index values:

code



















df = pd.DataFrame({'A': [[1, 2, 3] , 'foo', [] , [3, 4] 




], 'B': 'bar'})
print(df)

# Output:
#            A    B
# 0  [1, 2, 3] 

  bar
# 1        foo  bar
# 2         [] 
  bar
# 3     [3, 4] 











  bar

df = df.explode('A')
print(df)

# Output:
#      A    B
# 0    1  bar
# 0    2  bar
# 0    3  bar
# 1  foo  bar
# 2  NaN  bar
# 3

Conclusion

In this article, we have explored two techniques that can improve data transformation and mapping in Python: using np.select for more readability and using pandas.DataFrame.explode for reverse mapping.

When dealing with complex conditions for mapping values, np.select offers a clear and concise way to express the conditions and choices. By organizing the conditions and choices in lists, you can easily map values based on specific conditions, making your code more readable and maintainable.

On the other hand, when you have a DataFrame with multiple categories in a single row and you need to create separate rows for each category, pandas.DataFrame.explode comes in handy. This method allows you to expand list-like objects into separate rows, replicating the index values. This reverse mapping technique is useful when you want to perform operations on individual categories or analyze the data at a granular level.

By incorporating these techniques into your data manipulation workflow, you can enhance the clarity and flexibility of your code. Whether you’re mapping values or reversing the mapping process, these tools provide efficient and intuitive solutions.

In the next part of this article series, we will dive deeper into advanced data manipulation techniques and explore more examples and use cases. We will cover topics such as merging and joining data, pivoting, and handling missing values. Stay tuned for more insights and practical applications of these powerful data transformation tools.