read_csv parameters (sep, header, index_col) in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a CSV file using a comma as the separator.

Pandas

import pandas as pd

df = pd.read_csv('data.csv', sep=[1])

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

A,

B'|'

C';'

D'\t'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a semicolon or tab as separator when the file uses commas.

Leaving the separator blank when the file uses commas.

The sep parameter defines the delimiter used in the CSV file. A comma is the default separator for CSV files.

2fill in blank

medium

Complete the code to read a CSV file where the first row is the header.

Pandas

import pandas as pd

df = pd.read_csv('data.csv', header=[1])

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

A0

Bnull

C1

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using null when the file actually has headers.

Using 1 which means the second row is header.

The header=0 means the first row of the file is used as column names.

3fill in blank

hard

Fix the error in reading a CSV file by setting the correct index column.

Pandas

import pandas as pd

df = pd.read_csv('data.csv', index_col=[1])

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

Anull

B0

C1

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using null when the file has an index column.

Using 1 which selects the second column as index.

The index_col=0 tells pandas to use the first column as the row labels (index).

4fill in blank

hard

Fill both blanks to read a CSV file with a tab separator and no header row.

Pandas

import pandas as pd

df = pd.read_csv('data.csv', sep=[1], header=[2])

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

A'\t'

Bnull

C0

D','

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma as separator for tab-separated files.

Setting header to 0 when there is no header row.

Use sep='\t' for tab-separated files and header=null when there is no header row.

5fill in blank

hard

Fill all three blanks to read a CSV file with pipe separator, first row as header, and second column as index.

Pandas

import pandas as pd

df = pd.read_csv('data.csv', sep=[1], header=[2], index_col=[3])

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

A'|'

B0

C1

D','

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma as separator for pipe-separated files.

Setting index_col to 0 instead of 1.

Use sep='|' for pipe-separated files, header=0 for first row as header, and index_col=1 to use the second column as index.

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