Working with Pandas DataFrames: Looping Through Columns to Find String Containment

In this article, we will explore how to use pandas and numpy to efficiently loop through columns of a DataFrame in Python. Our focus will be on finding if a string contains any string from a separate pandas DataFrame column.

Introduction to Pandas and Numpy

Pandas is a powerful library used for data manipulation and analysis in Python. It provides an efficient way to work with structured data, particularly tabular data such as spreadsheets and SQL tables.

NumPy (Numerical Python) is another essential library that we will be using throughout this article. NumPy provides support for large, multi-dimensional arrays and matrices, along with a wide range of high-performance mathematical functions to operate on these arrays.

Problem Statement

We have two pandas DataFrames: dfa and dfb. The dfa DataFrame contains a column called “sentenceCol,” which holds sentence-length strings. The “other column” in the same DataFrame contains numerical values, while the “country” column in the dfb DataFrame contains a list of countries.

Our goal is to loop through the “sentenceCol” column in the dfa DataFrame and assign the corresponding country from the “country” column in the dfb DataFrame. We want to find if any string in the sentence is contained within a country name, where there can be multiple countries per sentence.

Optimizing Looping Over DataFrames

Looping over a DataFrame is generally not recommended because it can be slow and inefficient. Instead, we will use optimized pandas or numpy methods to achieve our goal.

Extracting Countries from Sentences

For the first problem, we need to extract countries from sentences using the str.extract method. This method allows us to specify patterns to match in a string.

dfa['country'] = dfa['sentenceCol'].str.extract(f"({'|'.join(dfb['country'])})")

In this code snippet:

• We use str.extract to extract countries from sentences.
• The pattern "({'|'.join(dfb['country'])})" matches any string that contains the countries in the dfb['country'] list. The | character is used as an OR operator, and join combines the country names into a single string.

The resulting DataFrame will have a new “country” column containing the extracted countries.

Handling Multiple Countries per Sentence

For the second problem, we need to handle cases where there are multiple countries in a sentence. We can use the str.extractall method for this purpose.

dfa['country'] = (
    dfa['sentenceCol'].str.extractall(f"({'|'.join(dfb['country'])})")
        .drop_duplicates()
        .to_numpy()
)

In this code snippet:

• We use str.extractall to extract all matches of the pattern in each string.
• The .drop_duplicates() method removes duplicate rows based on the extracted values.
• The resulting array is then converted back to a DataFrame using to_numpy().
• This approach ensures that each sentence contains all relevant countries.

Combining Extraction with Grouping

If there are no duplicate sentences, we can use the GroupBy.agg method to combine the extracted values into a single row.

dfa['country'] = (
    dfa['sentenceCol'].str.extractall(f"({'|'.join(dfb['country'])})")
        .groupby(level=0)
        .agg(', '.join)
        .to_numpy()
)

In this code snippet:

• We use GroupBy.agg to combine the extracted values into a single string.
• The , .join` method joins the country names with commas.

Reverting to Original Rows

To return to our original DataFrame, we need to join it back to its extraction using the join method.

extraction = dfa['sentenceCol'].str.extractall(f"({'|'.join(dfb['country'])})").rename(columns={0: 'country'})

dfa = extraction.droplevel(1).join(dfa).reset_index(drop=True)

In this code snippet:

• We extract the original values back into a new DataFrame.
• We use droplevel to remove the extracted column and join it with the original DataFrame.

DataFrames Used

For demonstration purposes, we will create two sample DataFrames: dfa and dfb.

dfa = pd.DataFrame({'sentenceCol': ['this is from france and spain']*2,
                   'other column':[15]*2})

dfb = pd.DataFrame({'country':['france', 'spain']})

These DataFrames serve as an example of the input data we are working with.

Conclusion

In this article, we explored how to use pandas and numpy to efficiently loop through columns in a DataFrame. We discussed two problems: extracting countries from sentences without duplicates and handling multiple countries per sentence. By utilizing optimized pandas and numpy methods, such as str.extract and str.extractall, we can significantly improve the performance of our data manipulation tasks.

Additional Tips

When working with large DataFrames or complex patterns, it is crucial to consider the following tips:

• Use vectorized operations whenever possible to minimize loop overhead.
• Leverage pandas’ built-in methods for string extraction and matching.
• Explore numpy’s array operations for efficient numerical computations.

Last modified on 2024-08-04