Handling duplicate column names in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a DataFrame with duplicate column names.

Data Analysis Python

import pandas as pd

data = {'A': [1, 2], [1]: [3, 4]}
df = pd.DataFrame(data)
print(df)

Drag options to blanks, or click blank then click option'

A'A'

B'B'

C'C'

D'D'

Attempts:

3 left

💡 Hint

Common Mistakes

Using different keys will not create duplicate columns.

Forgetting to use quotes around column names.

The dictionary keys are column names. Using 'A' twice creates duplicate columns.

2fill in blank

medium

Complete the code to rename duplicate columns by adding suffixes.

Data Analysis Python

df.columns = [col + '_1' if df.columns.duplicated()[i] else col for i, col in enumerate([1])]
print(df.columns)

Drag options to blanks, or click blank then click option'

Adf.columns

Bdf.head()

Cdf.values

Ddf.index

Attempts:

3 left

💡 Hint

Common Mistakes

Using df.index instead of df.columns.

Trying to rename without checking duplicates.

We check duplicates in column names using df.columns.duplicated() and rename accordingly.

3fill in blank

hard

Fix the error in the code to select the first 'A' column from a DataFrame with duplicate columns.

Data Analysis Python

first_A = df[[1]]
print(first_A)

Drag options to blanks, or click blank then click option'

A'A'

B['A']

Cdf['A']

Ddf.columns[0]

Attempts:

3 left

💡 Hint

Common Mistakes

Using df.loc[:, 'A'] which may cause ambiguity with duplicates.

Using a list instead of a string for column selection.

Using df['A'] selects the first column named 'A' even if duplicates exist.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that keeps only columns with unique names.

Data Analysis Python

unique_cols = {col: df[col] for col in df.columns if df.columns.[1](keep=False)[df.columns.[2](col)] == False}

Drag options to blanks, or click blank then click option'

Aduplicated

Bunique

Ctolist

Dget_loc

Attempts:

3 left

💡 Hint

Common Mistakes

Using unique() which returns unique values, not a boolean mask.

Using tolist() which converts to list, not for checking duplicates.

df.columns.duplicated(keep=False)[df.columns.get_loc(col)] returns True if the column name appears more than once. We keep columns where this is False.

5fill in blank

hard

Fill all three blanks to create a DataFrame with unique column names by appending suffixes to duplicates.

Data Analysis Python

cols = []
counter = {}
for col in df.columns:
    if col in counter:
        counter[col] += 1
        cols.append(col + [1] + str(counter[col]))
    else:
        counter[col] = 0
        cols.append(col + [2])
df.columns = cols
print(df.columns)

Drag options to blanks, or click blank then click option'

A'_'

B''

C'-'

D'#'

Attempts:

3 left

💡 Hint

Common Mistakes

Adding suffix to the first occurrence.

Using no separator between name and number.

We add an underscore and a number suffix to duplicate columns. The first occurrence has no suffix.

Practice

1. What happens if a pandas DataFrame has duplicate column names and you try to select one column by name using df['column_name']?

easy

A. It merges the columns with the same name into one.

B. It returns all columns with that name as a DataFrame.

C. It returns only the first column with that name.

D. It raises a KeyError.

Solution

1. Step 1: Understand pandas behavior with duplicate columns

   When selecting a column by name, pandas returns all columns with that name as a DataFrame if duplicates exist.

2. Step 2: Confirm selection result type

   Instead of a Series, pandas returns a DataFrame containing all matching columns.

3. Final Answer:

   It returns all columns with that name as a DataFrame. -> Option B

4. Quick Check:

   Duplicate columns selection = DataFrame [OK]

Hint: Selecting duplicate columns returns all as DataFrame, not error [OK]

Common Mistakes:

• Assuming it raises an error
• Thinking it returns only the first column
• Believing columns merge automatically

2. Which of the following is the correct way to rename duplicate columns in a pandas DataFrame df to make them unique?

easy

A. df.columns = pd.io.parsers.ParserBase({'names':df.columns})._maybe_dedup_names(df.columns)

B. df.columns = df.columns + '_1'

C. df.rename_duplicates()

D. df.columns = list(set(df.columns))

Solution

1. Step 1: Identify pandas method for deduplication

   pandas internal method _maybe_dedup_names can rename duplicate columns to unique names.

2. Step 2: Check each option

   A uses pandas internal deduplication correctly; B adds suffix to all columns, not just duplicates; C is invalid method; D removes duplicates losing data.

3. Final Answer:

   df.columns = pd.io.parsers.ParserBase({'names':df.columns})._maybe_dedup_names(df.columns) -> Option A

4. Quick Check:

   Use pandas internal deduplication = df.columns = pd.io.parsers.ParserBase({'names':df.columns})._maybe_dedup_names(df.columns) [OK]

Hint: Use pandas internal _maybe_dedup_names to rename duplicates [OK]

Common Mistakes:

• Trying to rename with set() which loses order and data
• Using non-existent method rename_duplicates()
• Adding suffix to all columns blindly

3. Given the DataFrame df with columns ['A', 'B', 'A'] and data:

   A  B  A
0  1  2  3
1  4  5  6

What is the output of df['A']?

medium

A. A Series with values [1, 4]

B. A DataFrame with columns 'A' and 'B'

C. A DataFrame with two columns both named 'A' containing values [[1, 3], [4, 6]]

D. Raises a KeyError

Solution

1. Step 1: Check columns and duplicates

   DataFrame has duplicate columns named 'A' at positions 0 and 2.

2. Step 2: Selecting df['A'] returns all columns named 'A'

   pandas returns a DataFrame with both 'A' columns, values [[1, 3], [4, 6]].

3. Final Answer:

   A DataFrame with two columns both named 'A' containing values [[1, 3], [4, 6]] -> Option C

4. Quick Check:

   Duplicate column selection = DataFrame with both columns [OK]

Hint: Selecting duplicate columns returns DataFrame with all matching columns [OK]

Common Mistakes:

• Expecting a single Series output
• Thinking it raises an error
• Confusing columns returned with all DataFrame columns

4. You have a DataFrame df with duplicate columns. You try to rename duplicates by running:

df.columns = df.columns + '_dup'

but it renames all columns, not just duplicates. How to fix this?

medium

A. Use df.columns = pd.io.parsers.ParserBase({'names':df.columns})._maybe_dedup_names(df.columns).

B. Drop duplicate columns before renaming.

C. Use df.rename(columns=lambda x: x+'_dup').

D. Use a loop to add suffix only to duplicate columns.

Solution

1. Step 1: Understand why all columns get renamed

   Adding '_dup' suffix to all columns renames every column, not just duplicates.

2. Step 2: Fix by renaming only duplicates

   Use a loop or comprehension to detect duplicates and rename only those columns.

3. Final Answer:

   Use a loop to add suffix only to duplicate columns. -> Option D

4. Quick Check:

   Rename duplicates selectively = loop method [OK]

Hint: Rename only duplicates by looping and checking counts [OK]

Common Mistakes:

• Renaming all columns blindly
• Using rename() without condition
• Dropping duplicates losing data

5. You have a DataFrame df with columns ['X', 'Y', 'X', 'Z', 'Y']. You want to rename duplicates by adding suffixes _1, _2, ... to make all column names unique. Which code correctly achieves this?

hard

A. df.columns = pd.Series(df.columns).groupby(df.columns).cumcount().add(1).astype(str).radd(df.columns + '_')

B. df.columns = pd.Series(df.columns).groupby(df.columns).cumcount().astype(str) + df.columns

C. df.columns = [f'{col}_{i}' for i, col in enumerate(df.columns)]

D. df.columns = list(set(df.columns))

Solution

1. Step 1: Understand the goal

   We want to add suffixes _1, _2, ... to duplicate columns to make names unique.

2. Step 2: Analyze each option

   A correctly groups by column names and adds suffixes starting at 1; B adds suffix before column name incorrectly; C adds suffixes by position, not duplicates; D removes duplicates losing data.

3. Final Answer:

   df.columns = pd.Series(df.columns).groupby(df.columns).cumcount().add(1).astype(str).radd(df.columns + '_') -> Option A

4. Quick Check:

   Groupby cumcount + suffix = unique names [OK]

Hint: Use groupby cumcount to add numeric suffixes to duplicates [OK]

Common Mistakes:

• Using set() which loses order and data
• Adding suffixes by position, not duplicates
• Ignoring duplicates and renaming all columns