Iterating through Rows and Checking Conditions in Pandas/Python

Pandas is a powerful library used for data manipulation and analysis in Python. One of its key features is the ability to iterate through rows of a DataFrame, perform operations on each row, and create new columns based on conditions.

In this article, we’ll explore how to achieve this using the extract function by keywords separated by pipes (|) with the fillna method.

Understanding the Problem

The problem at hand is to check if a word or phrase exists in the “Hospital” column of a DataFrame. If it does, we want to add a new column called “Hospital Type” and populate it with either “Mental” or “Community”, depending on whether the word or phrase matches these conditions.

The Initial Code

The initial code provided attempts to solve this problem using the apply function, which can be slow for large DataFrames. However, there’s a more efficient way to achieve the same result using the extract and fillna methods.

def find_type(x):
    if df['Hospital'].str.contains("Mental").any():
        return "Mental"
    if df['Hospital'].str.contains("Community").any():
        return "Community"
    else:
        return "Other"

df['Hospital Type'] = df.apply(find_type)

The Solution

The solution involves using the extract function to search for patterns in the “Hospital” column. We’ll use a regular expression (regex) pattern that matches either the word “Mental” or “Community”. The expand=False argument ensures that only one value is extracted per row, and the fillna method is used to fill any missing values with the string “Other”.

pat = r"(Mental|Community)"
df['Hospital Type'] = df['Hospital'].str.extract(pat, expand=False).fillna('Other')

How it Works

1. The extract function takes two arguments: the pattern to search for (in this case, a regex pattern that matches either “Mental” or “Community”), and an optional dictionary-like object that specifies how to extract values from each match.
2. Since we’re not using any groups in our regex pattern, we can simply omit the dict argument, which means that only one value will be extracted per row.
3. The expand=False argument ensures that only one value is returned for each row, rather than a list of values (which would happen if we used extract with group numbers).
4. Finally, the fillna method fills any missing values in the resulting Series with the string “Other”, effectively providing a default value when no match is found.

Example Use Case

Let’s create a sample DataFrame and apply this solution to it:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'Hospital': ['Aberystwyth Mental Health Unit', 'Bro Ddyfi Community Hospital', 
                 'Bronglais General Hospital', 'Caebryn Mental Health Unit', 
                 'Carmarthen Mental Health Unit']
})

print("Original DataFrame:")
print(df)

# Apply the solution
pat = r"(Mental|Community)"
df['Hospital Type'] = df['Hospital'].str.extract(pat, expand=False).fillna('Other')

print("\nDataFrame with new column:")
print(df)

This code creates a sample DataFrame and applies the extract and fillna solution to it. The resulting DataFrame now includes an additional “Hospital Type” column, populated with either “Mental” or “Community” based on the presence of these words in the original “Hospital” column.

Conclusion

In this article, we explored how to iterate through rows of a Pandas DataFrame and add new columns based on conditions using the extract function by keywords separated by pipes (|) with the fillna method. We also discussed the importance of choosing efficient data manipulation strategies in Python and provided an example use case to demonstrate the effectiveness of this approach.

Last modified on 2023-10-08