5 Best Ways to Display Only Non-Duplicate Values from a DataFrame in Python

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💡 Problem Formulation: When working with data in Python, it’s common to come across the challenge of identifying and displaying unique values within a DataFrame. This process can be crucial for data analysis, ensuring that repeated entries do not skew the results. Suppose you have a DataFrame where a particular column, say “Product_ID,” has duplicates. The goal is to extract and display only the unique, non-repeated Product_IDs. This article provides a guide on how to accomplish this task using different methods in Python.

Method 1: Using drop_duplicates()

The drop_duplicates() function in Pandas returns a DataFrame with duplicate rows removed based on specified columns. It’s a straightforward method that can be tailored through parameters to control how duplicates are identified and which ones to drop.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Product_ID': [101, 102, 103, 101, 104, 105, 102],
    'Product_Name': ['WidgetA', 'WidgetB', 'WidgetC', 'WidgetA', 'WidgetD', 'WidgetE', 'WidgetB']
})

# Display non-duplicate Product_ID values
unique_products = df.drop_duplicates(subset='Product_ID')
print(unique_products)

Output:

   Product_ID Product_Name
0        101       WidgetA
1        102       WidgetB
2        103       WidgetC
4        104       WidgetD
5        105       WidgetE

In this snippet, the drop_duplicates() method is called on the dataframe with the subset parameter specifying the column “Product_ID.” This ensures that only the non-duplicate entries based on this column are retained. What’s left is a DataFrame containing unique Product_IDs.

Method 2: Using Boolean Indexing

Boolean indexing is a powerful technique where you can filter data based on a boolean condition. When applied to DataFrames, it can be used to eliminate duplicate values by creating a mask that selects only the unique entries.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Product_ID': [101, 102, 103, 101, 104, 105, 102],
    'Product_Name': ['WidgetA', 'WidgetB', 'WidgetC', 'WidgetA', 'WidgetD', 'WidgetE', 'WidgetB']
})

# Boolean mask to filter out duplicates
mask = ~df.duplicated(subset='Product_ID')
unique_products = df[mask]
print(unique_products)

Output:

   Product_ID Product_Name
0        101       WidgetA
1        102       WidgetB
2        103       WidgetC
4        104       WidgetD
5        105       WidgetE

The ~df.duplicated() function creates a boolean mask, where True corresponds to non-duplicate entries. By applying this mask to the DataFrame (df[mask]), only the unique values are displayed.

Method 3: Using groupby() and first()

The groupby() method groups a DataFrame by one or more columns and allows aggregation functions to be applied to the grouped data. Here, the first() function can be used after grouping to return the first occurrence of each group, effectively showing only non-duplicate values.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Product_ID': [101, 102, 103, 101, 104, 105, 102],
    'Product_Name': ['WidgetA', 'WidgetB', 'WidgetC', 'WidgetA', 'WidgetD', 'WidgetE', 'WidgetB']
})

# Group by Product_ID and keep the first occurrence
unique_products = df.groupby('Product_ID').first().reset_index()
print(unique_products)

Output:

   Product_ID Product_Name
0        101       WidgetA
1        102       WidgetB
2        103       WidgetC
3        104       WidgetD
4        105       WidgetE

This snippet groups the DataFrame by the column “Product_ID” and then uses the first() method to get the first instance of each Product_ID, which gives us only non-duplicate values. Note that reset_index() is used to restore “Product_ID” as a column.

Method 4: Using value_counts() and Filtering

The value_counts() method returns a Series containing count of unique values in the specified column. By further filtering this Series to include counts that are equal to one, we can identify non-duplicate values.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Product_ID': [101, 102, 103, 101, 104, 105, 102],
    'Product_Name': ['WidgetA', 'WidgetB', 'WidgetC', 'WidgetA', 'WidgetD', 'WidgetE', 'WidgetB']
})

# Identify non-duplicate Product_IDs
value_counts = df['Product_ID'].value_counts()
non_duplicates = value_counts[value_counts == 1].index
unique_products = df[df['Product_ID'].isin(non_duplicates)]
print(unique_products)

Output:

   Product_ID Product_Name
2        103       WidgetC
4        104       WidgetD
5        105       WidgetE

Here, value_counts() is used to determine the frequency of each Product_ID. The ones appearing only once are considered non-duplicates. The isin() method is then used to filter the original DataFrame for non-duplicate Product_IDs.

Bonus One-Liner Method 5: Using a List Comprehension and unique()

This one-liner approach makes use of the unique() function to retrieve unique values from a column, along with a list comprehension to filter only the non-duplicate values by checking the count of each value in the original column.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Product_ID': [101, 102, 103, 101, 104, 105, 102],
    'Product_Name': ['WidgetA', 'WidgetB', 'WidgetC', 'WidgetA', 'WidgetD', 'WidgetE', 'WidgetB']
})

# One-liner to display non-duplicate Product_IDs
unique_products = df[df['Product_ID'].isin([id for id in df['Product_ID'].unique() if df['Product_ID'].list().count(id) == 1])]
print(unique_products)

Output:

   Product_ID Product_Name
2        103       WidgetC
4        104       WidgetD
5        105       WidgetE

This code uses a list comprehension within the isin() method to filter out the Product_IDs that appear exactly once in the DataFrame, thereby displaying only the non-duplicate values.

Summary/Discussion

  • Method 1: Using drop_duplicates(). Strengths: Straightforward and flexible with various parameters. Weaknesses: Could be less efficient with large datasets if not necessary to drop entire rows.
  • Method 2: Using Boolean Indexing. Strengths: Intuitive and easy to adapt for multiple conditions. Weaknesses: Requires understanding of Boolean operations and masking.
  • Method 3: Using groupby() and first(). Strengths: Good for complex grouping and aggregation tasks. Weaknesses: Might be overkill for simple tasks.
  • Method 4: Using value_counts() and Filtering. Strengths: Concise and useful for further analysis of value distribution. Weaknesses: Requires additional steps to filter the original DataFrame.
  • Method 5: Bonus One-Liner. Strengths: Concise and clever use of list comprehension. Weaknesses: Can be less readable, especially to those unfamiliar with list comprehensions.