Handling duplicate column names in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of DataFrame with duplicate columns after selection

What is the output of the following code snippet when selecting column 'A' from a DataFrame with duplicate column names?

Data Analysis Python

import pandas as pd

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

A [5, 6]

B KeyError: 'A'

C [1, 2]

D DataFrame with two columns named 'A'

💡 Hint

When duplicate column names exist, the last one overwrites previous ones in dictionary initialization.

In pandas, when creating a DataFrame from a dictionary with duplicate keys, the last key-value pair is used. So the column 'A' contains [5, 6]. Selecting df['A'] returns that column as a Series.

❓ data_output

intermediate

Number of columns after reading CSV with duplicate headers

Given a CSV file with headers: 'X,Y,X', what will be the number of columns in the DataFrame after reading it with pandas default settings?

Data Analysis Python

import pandas as pd
from io import StringIO

csv_data = "X,Y,X\n1,2,3\n4,5,6"
df = pd.read_csv(StringIO(csv_data))
print(len(df.columns))

A Raises an error

B 2

C 1

D 3

💡 Hint

pandas allows duplicate column names by default when reading CSV files.

pandas does not automatically rename duplicate columns when reading CSV files. It keeps all columns, so the DataFrame has 3 columns.

🔧 Debug

advanced

Identify error when accessing duplicate columns by attribute

What error occurs when trying to access a duplicate column by attribute in pandas DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "A"])
print(df.A)

A AttributeError: 'DataFrame' object has no attribute 'A'

B Returns a DataFrame with both 'A' columns

C Raises a KeyError

D Returns the last 'A' column as a Series

💡 Hint

Attribute access raises AttributeError when duplicate column names exist.

In pandas, when duplicate columns exist, attribute access (df.A) raises AttributeError because it requires unique column names.

🚀 Application

advanced

Resolving duplicate columns after concatenation

After concatenating two DataFrames with overlapping column names, which method correctly renames duplicate columns to unique names?

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({"A": [1], "B": [2]})
df2 = pd.DataFrame({"A": [3], "B": [4]})
df_concat = pd.concat([df1, df2], axis=1)
# Which code renames duplicates correctly?

A df_concat.columns = list(set(df_concat.columns))

B df_concat.columns = df_concat.columns.unique()

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

D df_concat.columns = df_concat.columns.drop_duplicates()

💡 Hint

Use enumeration to append index to each column name for uniqueness.

Option C appends an index to each column name, making all column names unique. Other options either remove duplicates or cause errors.

🧠 Conceptual

expert

Effect of duplicate columns on groupby aggregation

If a DataFrame has duplicate column names and you perform a groupby aggregation on one of these columns, what is the expected behavior?

A Aggregation applies only to the last occurrence of the column name

B Aggregation applies to all columns with that name, returning multiple results per group

C Aggregation applies only to the first occurrence of the column name

D Raises a ValueError due to ambiguous column names

💡 Hint

pandas treats columns as labels; duplicate names mean multiple columns with same label.

When grouping by a column name that appears multiple times, pandas applies aggregation to all columns with that name, resulting in multiple aggregated columns.