read_csv parameters (sep, header, index_col) in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code reading CSV with custom separator?

Given the CSV content below and the code reading it with pandas, what will be the output DataFrame?

Pandas

import pandas as pd
from io import StringIO
csv_data = '''name|age|city
Alice|30|New York
Bob|25|Los Angeles
'''
df = pd.read_csv(StringIO(csv_data), sep='|')
print(df)

A

    name|age|city
0  Alice|30|New York
1    Bob|25|Los Angeles

B

    name  age         city
0  Alice   30     New York
1    Bob   25  Los Angeles

C

    name  age  city
0  Alice   30  New York
1    Bob   25  Los Angeles

D Error: ParserError due to wrong separator

💡 Hint

Check how the separator parameter affects how pandas splits columns.

The separator '|' tells pandas to split columns at the pipe character. Without it, pandas treats the whole line as one column.

❓ data_output

intermediate

What is the index of the DataFrame after reading CSV with index_col?

Given this CSV and code, what will be the index values of the resulting DataFrame?

Pandas

import pandas as pd
from io import StringIO
csv_data = '''id,name,score
101,Alice,88
102,Bob,92
'''
df = pd.read_csv(StringIO(csv_data), index_col='id')
print(df.index.tolist())

A [101, 102]

B [0, 1]

C ['id', 'name', 'score']

D Error: KeyError for index_col

💡 Hint

index_col sets which column becomes the row labels (index).

Setting index_col='id' makes the 'id' column the index, so index values are 101 and 102.

🔧 Debug

advanced

Why does this code raise a ParserError?

This code tries to read a CSV but raises a ParserError. What is the cause?

Pandas

import pandas as pd
from io import StringIO
csv_data = '''name,age,city
Alice,30
Bob,25,Los Angeles
'''
df = pd.read_csv(StringIO(csv_data))

A Because the separator is incorrect

B Because header is set to None

C Because index_col is missing

D Because the second row has fewer columns than the header

💡 Hint

Check if all rows have the same number of columns as the header.

The second row has only two values but the header has three columns, causing a parsing error.

🧠 Conceptual

advanced

How does the header parameter affect reading CSV files?

Which statement correctly describes the effect of setting header=None in pd.read_csv?

A The first row is skipped and not read

B The first row is treated as column names

C The first row is treated as data, and pandas assigns default integer column names

D The file is read without any columns

💡 Hint

Think about what happens if pandas does not use the first row as headers.

Setting header=None tells pandas not to treat the first row as column names, so it uses default numbers as column names and reads the first row as data.

🚀 Application

expert

Which code produces a DataFrame with 'date' as index and columns 'temp' and 'humidity' from a semicolon-separated CSV?

You have a CSV with columns: date;temp;humidity. Which code correctly reads it with 'date' as index?

A pd.read_csv('file.csv', sep=';', index_col='date')

B pd.read_csv('file.csv', sep=',', index_col='date')

C pd.read_csv('file.csv', sep=';', header=None, index_col=0)

D pd.read_csv('file.csv', index_col='date')

💡 Hint

Check the separator and index_col parameters carefully.

The CSV uses semicolon separator, so sep=';' is needed. index_col='date' sets the 'date' column as index. Option A uses wrong separator, C ignores header, D uses default separator.

Practice

1. What does the sep parameter do in pandas.read_csv()?

easy

A. It specifies which column to use as the index.

B. It sets which row to use as column headers.

C. It tells pandas to skip empty rows.

D. It defines the character that separates columns in the file.

Solution

1. Step 1: Understand the role of sep

   The sep parameter tells pandas what character separates the columns in the CSV file, like a comma or tab.

2. Step 2: Compare with other parameters

   header sets the row for column names, and index_col sets the index column, so they do not define separators.

3. Final Answer:

   It defines the character that separates columns in the file. -> Option D

4. Quick Check:

   sep = column separator [OK]

Hint: Remember: sep = separator character between columns [OK]

Common Mistakes:

• Confusing sep with header or index_col
• Thinking sep sets the index column
• Assuming sep skips rows

2. Which of the following is the correct way to read a CSV file with tab-separated values and no header row using pandas?

easy

A. pd.read_csv('file.csv', sep=',', header=0)

B. pd.read_csv('file.csv', sep='\t', header=None)

C. pd.read_csv('file.csv', sep=';', index_col=0)

D. pd.read_csv('file.csv', header=None, index_col=0)

Solution

1. Step 1: Identify tab separator and no header

   Tab-separated files use sep='\t'. No header means header=None.

2. Step 2: Check options for correct syntax

   pd.read_csv('file.csv', sep='\t', header=None) uses both correctly. Others use wrong separators or header settings.

3. Final Answer:

   pd.read_csv('file.csv', sep='\t', header=None) -> Option B

4. Quick Check:

   Tab sep + no header = sep='\t', header=None [OK]

Hint: Use header=None if file has no column names [OK]

Common Mistakes:

• Using default comma separator for tab files
• Not setting header=None when no header exists
• Confusing index_col with header

3. Given a CSV file with content:

id;name;age
1;Alice;30
2;Bob;25

What will be the index of the DataFrame after running:

df = pd.read_csv('file.csv', sep=';', index_col=0)

medium

A. [1, 2]

B. [0, 1]

C. ['id', 'name']

D. ['Alice', 'Bob']

Solution

1. Step 1: Understand index_col=0 usage

   Setting index_col=0 uses the first column ('id') as the row index.

2. Step 2: Identify index values from first column

   The first column values are 1 and 2, so the index will be [1, 2].

3. Final Answer:

   [1, 2] -> Option A

4. Quick Check:

   index_col=0 sets index to first column values [OK]

Hint: index_col=0 means first column becomes row labels [OK]

Common Mistakes:

• Thinking index is default 0-based integers
• Confusing column names with index values
• Ignoring sep parameter effect

4. You try to read a CSV file with pd.read_csv('data.csv', sep=';', header=1, index_col=0) but get wrong column names. What is the likely mistake?

medium

A. The header row is not the second row, so header=1 is incorrect.

B. The separator should be a comma, not a semicolon.

C. The index_col should be None, not 0.

D. The file is missing a header row entirely.

Solution

1. Step 1: Understand header parameter meaning

   header=1 means pandas uses the second row as column names.

2. Step 2: Check if header row matches file

   If the actual header is the first row, using header=1 shifts column names incorrectly, causing wrong names.

3. Final Answer:

   The header row is not the second row, so header=1 is incorrect. -> Option A

4. Quick Check:

   header=1 means second row is header [OK]

Hint: header=0 means first row is header; header=1 means second row [OK]

Common Mistakes:

• Assuming header=1 always works
• Ignoring actual file structure
• Changing sep without checking file

5. You have a CSV file where columns are separated by '|', the first row is a comment, the second row has column names, and the first column should be the index. Which read_csv call correctly reads this file?

hard

A. pd.read_csv('file.csv', sep='|', header=0, index_col=0)

B. pd.read_csv('file.csv', sep='|', header=1, index_col=0)

C. pd.read_csv('file.csv', sep='|', header=1, index_col=0, comment='#')

D. pd.read_csv('file.csv', sep='|', header=0, index_col=1, comment='#')

Solution

1. Step 1: Handle comment and header rows

   The first row is a comment, so use comment='#' to skip it. The second row has column names, so header=1.

2. Step 2: Set separator and index column

   Columns are separated by '|', so sep='|'. The first column is index, so index_col=0.

3. Step 3: Verify options

   pd.read_csv('file.csv', sep='|', header=1, index_col=0, comment='#') includes all correct parameters. Others miss comment or have wrong header/index_col.

4. Final Answer:

   pd.read_csv('file.csv', sep='|', header=1, index_col=0, comment='#') -> Option C

5. Quick Check:

   comment skips first row, header=1 reads second row as columns [OK]

Hint: Use comment='#' to skip comment lines, header=1 for second row headers [OK]

Common Mistakes:

• Not using comment to skip comment lines
• Wrong header row number
• Incorrect index_col setting