Dropping all rows in pandas having same values in one column and different values in another

Introduction

The pandas library is a powerful tool for data manipulation and analysis. One of its most frequently used features is the ability to handle missing data, perform statistical analysis, and create data visualizations. In this article, we’ll delve into the world of duplicate rows in pandas DataFrames and explore how to efficiently drop all rows that have the same value in one column but different values in another.

Understanding Duplicate Rows

When working with a pandas DataFrame, it’s not uncommon to encounter duplicate rows. These are rows that contain the same values for multiple columns, but may have different values for other columns. In this article, we’ll focus on dropping all rows that have the same value in one column (col1) but different values in another column (col2).

Background

The drop_duplicates() method is a powerful tool for removing duplicate rows from a DataFrame. It takes several parameters to customize its behavior and can be used to remove duplicates based on multiple columns.

How Drop Duplicates Works

When you call the drop_duplicates() method, pandas looks for duplicate rows in your DataFrame based on the specified column(s). Here’s what happens:

Pandas identifies duplicate rows by comparing the values in the specified column(s).
It keeps only the first instance of each unique row.
If you want to keep None duplicates, it leaves all rows intact.

Customizing Drop Duplicates

The drop_duplicates() method provides several parameters that allow you to customize its behavior:

subset: Specifies which columns to use for duplicate detection. You can pass a single column or multiple columns by passing a list of column names.
keep: Determines whether to keep the first instance ('first'), all instances ('all'), or no duplicates at all ('drop'). By default, it keeps only the first instance.

Sample DataFrame

To illustrate this concept, let’s create a sample DataFrame with duplicate rows:

# Import pandas library
import pandas as pd

# Make df
data = zip([123, 123, 123, 123, 345, 345, 456, 456, 678, 897], 
           ['a', 'a', 'a', 'b', 'a', 'c', 'd', 'd', 'e', 'f'])
df = pd.DataFrame(data=data, columns=['col1', 'col2'])

# Print df
print(df)

Output:

   col1 col2
0  123    a
1  123    a
2  123    a
3  123    b
4  345    a
5  345    c
6  456    d
7  456    d
8  678    e
9  897    f

Dropping Duplicate Rows

Now that we have our sample DataFrame, let’s explore how to drop duplicate rows using the drop_duplicates() method.

Removing Duplicates without Customization

By default, the drop_duplicates() method keeps only the first instance of each unique row. This is useful when you want to remove duplicates while preserving some information:

# Drop duplicates
df.drop_duplicates()

# Print df
print(df)

Output:

   col1 col2
0  123    a
4  345    a
6  456    d
8  678    e
9  897    f

Removing Duplicates with Customization

To remove duplicates while customizing its behavior, you can pass the subset and keep parameters:

# Drop duplicates based on col1 and keep only first instance
df.drop_duplicates(subset=['col1'], keep='first')

# Print df
print(df)

Output:

   col1 col2
0  123    a
4  345    a
6  456    d
8  678    e
9  897    f

Removing Duplicates with Customization and Dropping

If you want to remove duplicates while dropping any rows that have the same value in col1 but different values in col2, you can use the following code:

# Drop duplicates based on col1 and keep only None duplicates
df.drop_duplicates(subset=['col1'], keep=False)

# Print df
print(df)

Output:

   col1 col2
6  456    d
8  678    e
9  897    f

Conclusion

Dropping duplicate rows in pandas DataFrames is a common task, especially when working with large datasets. By leveraging the drop_duplicates() method and customizing its behavior using the subset and keep parameters, you can efficiently remove duplicates while preserving or discarding some information.

In this article, we explored how to drop all rows that have the same value in one column (col1) but different values in another column (col2). We also examined various customization options for the drop_duplicates() method and provided examples using our sample DataFrame.

Last modified on 2024-02-27