Introduction to Python Pandas Sorting Alphanumeric Data

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In this article, we will explore the process of sorting alphanumeric data using the popular Python library pandas. Specifically, we will focus on how to sort a column containing strings with mixed alphanumeric and non-alphanumeric characters.

Understanding Lexicographical Order

When sorting columns of type string, pandas uses lexicographical order by default. This means that the sorting is done alphabetically, character by character, without considering the numerical values associated with some characters (e.g., ‘0’ to ‘9’).

For example, in the case of the titleId column from the IMDB dataset provided, the sorting result we obtained earlier was:

tt1037178   1   Women's Studies US  \N  \N  \N  0
tt10371782  1   Episodio #1.67  IT  it  \N  \N  0
tt10371782  2   एपिसोड #1.67    IN  hi  \N  \N  0
tt10371782  3   エピソード #1.67 JP  ja  \N  \N  0

However, we expected the result to be:

tt1037178   1   Women's Studies US  \N  \N  \N  0
tt1037179   1   Wood Simps  US  \N  \N  \N  0
tt10371782  1   Episodio #1.67  IT  it  \N  \N  0

The difference in sorting order comes from the fact that we are dealing with alphanumeric data, where some characters have numerical values associated with them.

Extracting Numerical Values and Sorting

To achieve the desired result, we need to extract the numerical value associated with the titleId column and sort based on this value. One way to do this is by applying a lambda function to the titleId column using the apply() method.

Here’s how you can modify your code:

df['titleId_number'] = df['titleId'].apply(lambda x: int(x.split('tt')[1]))
a = df.sort_values(by='titleId_number')

In this modified code, we first create a new column called titleId_number and assign it the numerical value extracted from the titleId column using the lambda function. We then sort the dataframe based on this new column.

Explanation of the Lambda Function

Let’s take a closer look at the lambda function used to extract the numerical value:

lambda x: int(x.split('tt')[1])

Here’s what each part of the lambda function does:

• x: This is the input parameter, which in this case is the string value from the titleId column.
• split('tt'): This splits the string into two parts at the ’tt’ substring. The resulting list will contain only one element because there’s no more ’tt’ after the first occurrence.
• [1]: This selects the second element of the resulting list, which corresponds to the numerical value associated with the titleId.
• int(...): This converts the extracted string into an integer.

Example Use Case

Suppose we have a dataframe containing movie titles along with their corresponding titleId values:

We can apply the lambda function to extract the numerical value from the titleId column and sort based on this value:

import pandas as pd

data = {
    'titleId': ['tt123', 'tt456', 'tt789'],
    'title': ['Movie Title 1', 'Movie Title 2', 'Movie Title 3']
}

df = pd.DataFrame(data)

def extract_number(x):
    return int(x.split('tt')[1])

# Apply the lambda function to extract numerical values
df['titleId_number'] = df['titleId'].apply(extract_number)

# Sort the dataframe based on the extracted numerical values
sorted_df = df.sort_values(by='titleId_number')

print(sorted_df)

Output:

The output shows that the dataframe has been sorted based on the extracted numerical values from the titleId column.

Conclusion

In this article, we explored how to sort alphanumeric data using pandas. We discussed the limitations of lexicographical order and introduced a solution by extracting numerical values from strings and sorting based on these values. The provided example demonstrates how to apply this approach to real-world data.

We also took a closer look at the lambda function used in the solution, explaining each part of its functionality.

I hope you found this article informative and helpful! If you have any questions or need further clarification, please don’t hesitate to ask.

Last modified on 2024-06-05