Filtering a DataFrame Using Keywords from Another DataFrame

Introduction

Data manipulation is an essential part of data analysis and machine learning. When working with large datasets, it’s often necessary to filter the data based on conditions defined in another dataset. In this article, we’ll explore how to achieve this using pandas, a popular Python library for data manipulation.

We’ll consider a simple example where we have two DataFrames: df1 and df2. The first DataFrame contains information about items (e.g., name, price, stock), while the second DataFrame contains keywords along with their minimum stock values. Our goal is to filter df1 based on df2 using these keywords.

Understanding the Problem

Suppose we have a DataFrame df1 that looks like this:

And another DataFrame df2 that looks like this:

We want to filter df1 such that only the rows where item_name contains either of the keywords in the “Key Word” column are included in the result. The expected output would be:

Solving the Problem

To solve this problem, we’ll employ a few advanced pandas techniques: regex, str.extract, and boolean indexing.

Step 1: Extracting Keywords

First, we need to extract the keywords from df2. We can do this by splitting the “Key Word” column into individual words using the split method, exploding each word into separate rows, setting a new index on these exploded words, and then selecting only the minimum stock value for each word.

s = (df2.assign(kw=df2['Key Word'].str.split(';'))
     .explode('kw')
     .set_index('kw')['Min_stock']
)

Step 2: Creating a Regex Pattern

Next, we create a regex pattern by joining the extracted keywords with the | character, which represents “or” in regular expressions. This will allow us to match any keyword in the “item_name” column.

regex = '|'.join(s.index)

Step 3: Applying Boolean Indexing

Now, we use boolean indexing to filter df1. We extract each keyword from the “item_name” column using the regex pattern and map it to the minimum stock value from s. Then, we apply a mask to df1 that includes only rows where the corresponding minimum stock value is not missing (notna()) and the stock level is greater than or equal to this value (ge()).

mask = df1['item_name'].str.extract(f'({regex})', expand=False).map(s)
out = df1[mask.notna()&df1['stock'].ge(mask)]

Step 4: Final Result

After applying the mask, we obtain the filtered DataFrame out, which includes only the rows where the item name contains either of the keywords in the “Key Word” column.

Example Use Cases

This filtering technique can be applied to a wide range of use cases, including:

• Filtering data based on multiple criteria
• Applying constraints from one dataset to another
• Performing data preprocessing and cleaning tasks

By mastering this technique, you’ll become more efficient in handling complex data manipulation tasks.

Conclusion

In conclusion, we’ve demonstrated how to filter a DataFrame using keywords from another DataFrame. By leveraging pandas’ advanced features like regex, str.extract, and boolean indexing, we can efficiently perform data filtering and preprocessing tasks.

With this knowledge, you’ll be better equipped to tackle complex data analysis challenges and extract valuable insights from your datasets.

Common Use Cases for Pandas

• Data cleaning: Handling missing values, removing duplicates, and performing data normalization
• Data transformation: Converting data types, merging datasets, and performing data aggregation
• Data analysis: Grouping data, calculating statistics, and performing data visualization

By applying these techniques, you’ll become proficient in working with pandas and enhance your productivity when handling large datasets.

Last modified on 2023-12-22