Merging DataFrames with Trailing Path Elements

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In this article, we will explore the process of merging two pandas DataFrames based on the trailing part of the path or filename. We’ll dive into the use of regular expressions and string manipulation techniques to achieve this.

Overview

When working with file paths or filenames in data analysis, it’s common to need to join two datasets based on certain criteria. This article will focus on using pandas’ merge function with regular expressions to extract the trailing part of the path from one DataFrame and use it as a key to merge with another DataFrame.

Setting Up the Data

To illustrate this concept, let’s first create some sample DataFrames:

df1 = pd.DataFrame({'PATH':[r'C:\FODLER\Test1.jpg',
                            r'C:\A\FODLER\Test2.jpg',
                            r'C:\A\FODLER\Test3.jpg',
                            r'C:\A\FODLER\Test4.jpg'],
                    'VALUE':[45,23,45,2]})

df2 = pd.DataFrame({'F_NAME': [r'FODLER\Test1.jpg',
                               r'FODLER\Test2.jpg',
                               r'FODLER\\Test6.jpg',
                               r'FODLER\\Test3.jpg',
                               r'FODLER\\Test4.jpg',
                               r'FODLER\\Test9.jpg'],
                    'VALUE_X': ['12', '25', '97', '33', '123', '0'],
                    'CORDS': ['1', '2', '3', '4', '5', '6']})

Using Regular Expressions to Extract Trailing Path Elements

One approach to solving this problem is by using regular expressions (regex) with the str.extract function from pandas. This method allows us to extract specific parts of a string.

We’ll define a pattern that matches any of the values in the F_NAME column:

pattern = f"({'|'.join(df2['F_NAME'].apply(re.escape))})$"

This pattern, ([FODLER\\Test1\.jpg|FODLER\\Test2\.jpg|FODLER\\Test6\.jpg|FODLER\\Test3\.jpg|FODLER\\Test4\.jpg|FODLER\\Test9\.jpg])$,, uses the pipe character (|) to match any of the values within the parentheses. The $ symbol at the end ensures that we’re only matching the trailing part of the path.

Performing the Merge

Now, we’ll use the merge function with the left_on parameter set to the extracted pattern and right_on set to the F_NAME column:

df3 = df1.merge(df2, left_on=df1['PATH'].str.extract(pattern, expand=False),
                right_on='F_NAME', how='left')

The expand=False argument tells pandas not to add a new column for each match, instead returning the value as is.

Output

Running this code produces the following output:

                    PATH  VALUE            F_NAME VALUE_X CORDS
0    C:\FODLER\Test1.jpg     45  FODLER\Test1.jpg      12     1
1  C:\A\FODLER\Test2.jpg     23  FODLER\Test2.jpg      25     2
2  C:\A\FODLER\Test3.jpg     45  FODLER\Test3.jpg      33     4
3  C:\A\FODLER\Test4.jpg      2  FODLER\Test4.jpg     123     5

Alternative Method: Extracting Trailing Path Elements Before Merging

Another approach is to extract the trailing part of the path from df1 before performing the merge. This can be achieved by creating a new column in df1 using the str.extract function:

df3 = (df1
    .assign(F_NAME=df1['PATH'].str.extract(r'([^\\]+\\[^\\]+)$', expand=False))
    .merge(df2, how='left'))

This time, we’re using a slightly different pattern to extract the trailing path element: ([^\\]+\\[^\\]+)$. The [ ] characters are used to group the preceding characters together. This ensures that only the last two elements of the path (before the \) are matched.

Conclusion

In this article, we explored how to merge two pandas DataFrames based on the trailing part of a file path or filename using regular expressions and string manipulation techniques from pandas. We covered both methods for achieving this: directly extracting the pattern in the merge function and assigning a new column with the extracted value before merging.

Resources

Regular Expressions Demo: For practicing regex patterns.
Pandas Merge Documentation: For detailed information on pandas’ merge function.

Last modified on 2024-02-13