Assigning Priority Scores Based on Location in a Pandas DataFrame

In this article, we will explore how to assign priority scores based on location in a pandas DataFrame. We will cover the problem statement, provide a generic approach using dictionaries and regular expressions, and discuss the code implementation.

Problem Statement

The problem is as follows: we have a DataFrame with two columns, “Business” and “Location”. The “Location” column can contain multiple locations separated by commas. We need to assign a priority score based on location. If the location contains Beirut or Saida, the priority score should be 1. If it contains Baalbeck or Sour, the priority score should be 2. Otherwise, the priority score should be 3.

Desired Output

The desired output is as follows:

Approach

To solve this problem, we will use a dictionary of words and regular expressions to match the locations. We will then use the np.select function from NumPy to assign the priority scores.

Dictionary of Words

First, let’s define a dictionary that maps location patterns to their corresponding priority scores.

priority = {1: ['Beirut', 'Saida'], 2: ['Baalbeck', 'Sour']}

In this dictionary, we have two key-value pairs. The first pair has the priority score 1 and contains the words “Beirut” and “Saida”. The second pair has the priority score 2 and contains the words “Baalbeck” and “Sour”.

Regular Expressions

Next, let’s define a dictionary that maps each priority score to its corresponding regular expression pattern.

patterns = {'|'.join(map(re.escape, l)): i for i, l in priority.items()}

In this line of code, we use the map function to escape each word in the list and then join them together with the pipe (|) character. The resulting string is used as a regular expression pattern.

Code Implementation

Now that we have defined our dictionary and regular expressions, let’s implement the code:

import re
import numpy as np

# Define the priority score and location patterns
priority = {1: ['Beirut', 'Saida'], 2: ['Baalbeck', 'Sour']}
patterns = {'|'.join(map(re.escape, l)): i for i, l in priority.items()}

# Create a new column with priority scores
df['Score'] = np.select([df['Location'].str.contains(pat, case=False) for pat in patterns], patterns.values(), 3)

In this code, we first import the necessary libraries: re for regular expressions and numpy as np. We then define our dictionary of priority scores and location patterns.

Next, we use a list comprehension to create a new column with priority scores. For each row in the DataFrame, we check if the location contains any of the patterns using the str.contains method. If it does, we assign the corresponding priority score; otherwise, we assign a default value of 3.

Example Output

Let’s take a look at an example output:

In this example, we have a DataFrame with four rows. The first row has the location “Beirut,Aley”, which contains the word “Beirut” and should be assigned a priority score of 1. Similarly, the second row has the location “Saida,Sour”, which also contains the words “Saida” and should be assigned a priority score of 1.

The third row has the location “Baalbeck,Tripoli”, which contains the word “Baalbeck” and should be assigned a priority score of 2. Finally, the fourth row has the location “Tripoli”, which does not contain any of the patterns and should be assigned a default value of 3.

Conclusion

In this article, we explored how to assign priority scores based on location in a pandas DataFrame. We used a dictionary of words and regular expressions to match the locations and then used the np.select function from NumPy to assign the priority scores. The resulting code is generic and can be easily adapted to other use cases.

Additional Tips and Variations

Here are some additional tips and variations:

• Use a more complex dictionary: If you have a large number of location patterns, you may want to consider using a more complex data structure such as a trie or a suffix tree.
• Use a different method: Instead of using regular expressions, you could use the str.contains method with the regex=True parameter.
• Add error handling: You may want to add error handling to handle cases where the location patterns are not recognized.

I hope this article has been helpful in explaining how to assign priority scores based on location in a pandas DataFrame.

Last modified on 2024-02-09