Extracting Substrings from Lists of Strings in a Pandas DataFrame

Extracting a Substring from a List of Strings in a Pandas DataFrame

In this article, we’ll explore the process of extracting a substring from a list of strings in a pandas DataFrame. This task is common in data analysis and manipulation when dealing with text data.

Introduction to Pandas DataFrames

A pandas DataFrame is a two-dimensional table of data with rows and columns. It’s similar to an Excel spreadsheet or a SQL table. DataFrames are powerful data structures that provide efficient data manipulation, analysis, and visualization capabilities.

In this article, we’ll focus on the explode method in pandas, which allows us to extract elements from a list stored in a single column of a DataFrame.

Exploring the DataFrame

Let’s examine the provided DataFrame:

df = pd.DataFrame({'Name': {0: 'Mark', 1: 'John', 2: 'Rick'},
                   'Location': {0: ['Mark lives in UK',
                                  'Rick lives in France',
                                  'John Lives in US'],
                                1: ['Mark lives in UK', 'Rick lives in France', 'John Lives in US'],
                                2: ['Mark lives in UK', 'Rick lives in France', 'John Lives in US']}})

As we can see, the Location column contains lists of strings. The task at hand is to extract a specific substring from these lists.

Exploding the DataFrame

One way to achieve this is by using the explode method, which splits each list into individual rows:

df = df.explode('Location')

This results in a new DataFrame with multiple rows for each original row:

   Name Location
0  Mark     [Mark lives in UK]
1  John    [John Lives in US]
2  Rick     [Rick lives in France]
3  Mark      [Mark lives in UK]
4  Rick      [Rick lives in France]
5  John      [John Lives in US]

Extracting the Desired Substring

Now that we have exploded the DataFrame, we can extract the desired substring using various methods. One approach is to use the apply method with a lambda function:

df['Sorted'] = df.apply(lambda x: [idx for idx,s in enumerate(x.Location) if x.Name in s], axis=1)

However, this solution has some limitations and can be improved upon.

Alternative Solution Using `explode` and String Manipulation

A more efficient approach is to use the explode method followed by string manipulation:

df = df.explode('Location')
df['Person_IND'] = df['Location'].apply(lambda x: x.split(' ')[0])

This code first explodes the DataFrame, then extracts the first word from each location using string splitting. Finally, it filters the resulting DataFrame to keep only rows where the Name and Person_IND columns match:

df = df.loc[df['Name'] == df['Person_IND']]

Merging the DataFrames

If you really need the middle column (Location) in your final output, you can merge the original DataFrame with the exploded one using the Name column as a common key:

df1 = df.explode('Location')
df1['Person_IND'] = df1['Location'].apply(lambda x: x.split(' ')[0])
df1 = df1.loc[df1['Name'] == df1['Person_IND']]
df1 = df1[['Name', 'Location']]

df_merge = pd.merge(df, df1, on='Name')

This will produce the desired output:

   Name        Location
0  Mark      [Mark lives in UK]
1  John    [John Lives in US]
2  Rick     [Rick lives in France]

Conclusion

In this article, we explored the process of extracting a substring from a list of strings in a pandas DataFrame. We covered various methods and approaches to achieve this task, including using the explode method, string manipulation, and merging DataFrames.

By understanding how to work with DataFrames and lists of strings, you can efficiently extract and manipulate text data in your pandas-based projects.

Last modified on 2023-12-31