5 Best Ways to Remove Columns with Duplicate Elements in Python

💡 Problem Formulation: When working with data in Python, especially in dataframes, it is common to encounter columns with duplicate elements. These duplicates can skew analysis and must be removed for accurate data interpretation. For example, if we have a dataframe with columns A, B, C, and D, where columns A and C contain duplicate elements, we would want to remove one of these columns to retain only unique data columns. This article discusses various methods to address this scenario in Python.

Method 1: Using Pandas DataFrame.drop_duplicates()

One effective way to remove columns with duplicate elements is by transposing the DataFrame and then applying the drop_duplicates() method. Transposing changes the original columns into rows, which allows drop_duplicates() to operate effectively, removing any rows with duplicates, which were originally your columns. This method ensures that the DataFrame structure is preserved while duplicates are excluded.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [1, 2, 3]
})

df = df.T.drop_duplicates().T
print(df)

Output:

The code snippet transposes the DataFrame, uses drop_duplicates() to remove duplicate rows, and transposes the DataFrame back to its original orientation. This results in a DataFrame with the duplicate column removed (column C was identical to column A and was therefore dropped).

Method 2: Using Collections Library

Another way to address duplicated columns is by using Python’s collections.Counter to keep track of column occurrences and remove any column that appears more than once. This is more of a manual approach, which offers fine control over which columns to keep during deduplication.

Here’s an example:

import pandas as pd
from collections import Counter

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [1, 2, 3]
})

col_counter = Counter()
for col in df.columns:
    col_tuple = tuple(df[col])
    if col_counter[col_tuple]:
        df = df.drop(col, axis=1)
    else:
        col_counter[col_tuple] += 1

print(df)

Output:

The code goes through each column, counts the occurrences of column data as tuples, and drops columns that have already been counted using the drop() function. This effectively removes duplicate columns (column C) from the DataFrame.

Method 3: Unique Columns via Set Operations

Set operations provide a simple yet powerful way to find unique columns in a DataFrame by converting columns into sets and comparing them. Although this method is succinct, it assumes that the data within your columns can be converted into a set, which means the data should be hashable and, therefore, immutable.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [1, 2, 3]
})

unique_cols = df.columns[[
    not set(df[col1]).issubset(set(df[col2])) or col1 == col2
    for col1 in df.columns
    for col2 in df.columns
]]
df = df[unique_cols]
print(df)

Output:

This example creates a unique set of column names after comparing each column against all other columns for uniqueness. If a column is a subset of another column (and they are not the same column), it is considered duplicate and is not included in the final DataFrame.

Method 4: Numpy’s Unique Function

numpy.unique() can be used to find unique column indices when the columns are treated as individual elements. By identifying unique combinations of a transposed matrix, and inverting the operation, we can obtain a DataFrame without duplicate columns. Keep in mind that numpy.unique() returns sorted unique elements, which might not always be desired if preserving the row order is important.

Here’s an example:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [1, 2, 3],
    'D': ['x', 'y', 'z']
})

_, idx = np.unique(df.T, axis=0, return_index=True)
df_unique = df.iloc[:, np.sort(idx)]
print(df_unique)

Output:

Using NumPy’s unique() function, we can extract unique column indices and then select those columns for the final DataFrame. This keeps only the unique columns, dropping any duplicates.

Bonus One-Liner Method 5: Lambda Function and any()

A concise one-liner approach utilizes a lambda function in conjunction with the any() built-in function to remove columns with duplicated elements. This leverages list comprehensions and functional programming to achieve the desired result in a single line of code.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [1, 2, 3]
})

df = df.loc[:, ~df.T.duplicated()]
print(df)

Output:

This concise code transposes the DataFrame and utilizes the duplicated() method to create a boolean mask with True for duplicated columns. The negated mask is then used to select non-duplicate columns.

Summary/Discussion

Method 1: Pandas drop_duplicates(). Strengths: Simple, uses built-in Pandas methods, and preserves DataFrame integrity. Weaknesses: Requires additional transpose operations which might not be efficient for large dataframes.
Method 2: Collections Library. Strengths: Provides fine control over column iteration and removal. Weaknesses: More verbose, and manually keeping track of column occurrences can be error-prone.
Method 3: Set Operations. Strengths: Clean and leverages Python’s powerful set functionality. Weaknesses: Only works with hashable and therefore immutable data elements.
Method 4: Numpy’s Unique Function. Strengths: Utilizes NumPy’s performance advantages, great for numerical data. Weaknesses: Sorts the data which might not be desired in all circumstances.
Bonus Method 5: Lambda Function and any(). Strengths: One-liner solution, very Pythonic. Weaknesses: Can be harder to read and understand compared to the other methods.