In the world of data analysis, it’s common to encounter large data sets that require data manipulation and processing. One such problem that often arises is filtering rows based on fuzzy values, particularly when dealing with textual data. Pandas, a popular Python library for data manipulation, provides an elegant solution to help tackle this issue. In this article, we’ll dive into how to use Pandas to filter rows using fuzzy values, explore the code step-by-step, and discuss relevant libraries and functions that can aid in solving similar problems.
To begin addressing this problem, we’ll leverage the Pandas library along with the fuzzywuzzy library which helps calculate the similarity between different strings. The fuzzywuzzy library uses the Levenshtein distance, a measure of similarity based on the number of edits (insertions, deletions, or substitutions) needed to transform one string into another.
Installing and Importing Required Libraries
To start, we’ll need to install and import the necessary libraries. You can use pip to install both Pandas and fuzzywuzzy:
pip install pandas pip install fuzzywuzzy
Once installed, import the libraries in your Python code:
import pandas as pd from fuzzywuzzy import fuzz, process
Filtering Rows Based on Fuzzy Values
Now that we’ve imported the required libraries, let’s create a fictional data set and showcase how to filter rows based on fuzzy values. In this example, our data set will consist of garment names and their corresponding styles.
data = {'Garment': ['T-shirt', 'Polo shirt', 'Jeans', 'Leather jacket', 'Winter coat'],
'Style': ['Casual', 'Casual', 'Casual', 'Biker', 'Winter']}
df = pd.DataFrame(data)
Assuming we want to filter rows containing garments with names similar to “Tee shirt”, we’ll need to employ the fuzzywuzzy library to accomplish this.
search_string = "Tee shirt"
threshold = 70
def filter_rows(df, column, search_string, threshold):
return df[df[column].apply(lambda x: fuzz.token_sort_ratio(x, search_string)) >= threshold]
filtered_df = filter_rows(df, 'Garment', search_string, threshold)
In the above code, we define a function filter_rows that takes four parameters: the DataFrame, the column name, the search string, and the similarity threshold. It returns a filtered DataFrame based on the specified threshold, which is calculated using the fuzz.token_sort_ratio function from the fuzzywuzzy library.
Understanding the Code Step-by-Step
- First, we create a DataFrame called df containing our data set.
- Next, we define our search string as “Tee shirt” and set a similarity threshold of 70. You can adjust the threshold value according to your desired level of similarity.
- We then create a function called filter_rows, which filters the DataFrame based on the Levenshtein distance between the search string and each row’s value in the specified column.
- Finally, we call the filter_rows function to obtain our filtered DataFrame, filtered_df.
In conclusion, Pandas, in combination with the fuzzywuzzy library, is an excellent tool for filtering rows based on fuzzy values. Understanding these libraries and their functions allows us to efficiently manipulate data and solve complex data processing tasks.
