Find Duplicated and Distinct Rows in Pandas | Python Data Filtering Tips #pandasdataframe #coding

"Welcome to Hack Coding with Uday! In this short video, I will explain how to efficiently find duplicated and distinct rows in a Pandas DataFrame using Python. Pandas, a powerful data analysis library in Python, provides easy-to-use methods to identify duplicates and extract unique records from large datasets, which is crucial for data cleaning, analysis, and preprocessing.

Why is Finding Duplicates and Distinct Rows Important?
Data duplication is a common problem that occurs during data collection or data integration. When you're working with datasets, duplicates can skew analysis, leading to inaccurate insights. Whether you're cleaning raw data or preparing datasets for machine learning models, detecting and handling duplicates is a necessary step in your workflow.

Similarly, identifying distinct rows (unique records) allows you to focus on data points that are not repeated, ensuring you are working with unique observations. These operations can significantly enhance the quality and reliability of your analysis.

What is Pandas?
For those who are new to Pandas, it’s an open-source Python library primarily used for data manipulation and analysis. Pandas provide powerful data structures such as DataFrames, which are 2-dimensional labeled data structures that are similar to SQL tables or Excel spreadsheets.

With Pandas, you can load, manipulate, and analyze structured data effortlessly. From data cleaning to complex data operations, Pandas is widely used in data science, machine learning, and data engineering.

Finding Duplicates in Pandas:
The duplicated() function in Pandas allows you to detect duplicate rows in your DataFrame. It returns a Series with True for duplicate rows and False for non-duplicates. Here’s how you can use it:

python
import pandas as pd

# Sample DataFrame
data = {
'Name': ['Alice', 'Bob', 'Charlie', 'Alice'],
'Age': [25, 32, 25, 25],
'Gender': ['F', 'M', 'F', 'F']
}

df = pd.DataFrame(data)

# Detecting duplicate rows
print(duplicates)
In this example, Pandas detects the duplicate rows based on all columns. You can also specify certain columns to check for duplicates:

python
# Find duplicates based on a single column
print(duplicates_in_name)
How to Remove Duplicates in Pandas:
Once duplicates are detected, you can remove them using the drop_duplicates() function:

python
# Remove duplicates
print(df_without_duplicates)
You can also control which duplicates are removed using the keep parameter:

keep='first' (default): Keeps the first occurrence of each duplicate.
keep='last': Keeps the last occurrence of each duplicate.
keep=False: Removes all duplicates.
Finding Distinct Rows in Pandas:
To find distinct or unique rows in Pandas, you can use the drop_duplicates() function with the default setting, which will give you all unique rows:

python
# Find distinct rows (unique records)
print(distinct_rows)
In this example, drop_duplicates() removes all duplicates, leaving only distinct records.

Common Use Cases:
Data Cleaning: Removing duplicate entries from datasets before performing analysis.
Data Validation: Ensuring that only unique rows are included in datasets, such as customer data, product information, or financial records.
Data Integrity: Checking for redundant data in large datasets to maintain accuracy.
These methods ensure your dataset is clean, consistent, and ready for further analysis.

Additional Tips for Working with Duplicates in Pandas:
Detect Duplicates for Specific Columns: You can limit the duplicate check to specific columns using the subset parameter. For instance:

Keep the Last Occurrence: If you want to keep the last occurrence of a duplicate and remove the earlier ones, you can do so with:

Best Practices for Handling Duplicates:
Back Up Data: Always back up your original DataFrame before dropping duplicates or performing irreversible operations.
Check All Columns: Ensure you’re checking all necessary columns when detecting duplicates.
Performance Considerations: For large datasets, removing duplicates can be a time-consuming process. Always benchmark and optimize your operations when dealing with millions of rows.
Why Watch This Short?
In this video, you’ll learn how to easily identify both duplicate and unique rows using Pandas. It’s a fundamental step in data analysis that will help ensure your datasets are clean and free from redundancy. Whether you’re just starting with Pandas or looking to refine your data-cleaning skills, this video will give you the tools you need to work more effectively with your data.

If you found this video helpful, make sure to check out more content on Hack Coding with Uday, where I break down useful Python functions, coding tips, and data science tricks.