Reading CSV with options (sep, header, encoding) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a CSV file named 'data.csv' using pandas.

Data Analysis Python

import pandas as pd

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

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

Aseparator=';'

Bdelimiter=','

Csep=','

Dsplit=','

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'delimiter' instead of 'sep' which is not recognized by pandas.read_csv.

Using 'split' which is not a parameter for read_csv.

The sep parameter specifies the delimiter used in the CSV file. The correct parameter name is sep.

2fill in blank

medium

Complete the code to read a CSV file without a header row.

Data Analysis Python

import pandas as pd

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

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

ANone

B-1

C0

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 which means the first row is the header.

Using False which is not accepted for header parameter.

Setting header=None tells pandas that the CSV file has no header row, so it will assign default column names.

3fill in blank

hard

Fix the error in the code to read a CSV file with UTF-8 encoding.

Data Analysis Python

import pandas as pd

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

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

A'utf8'

B'utf-8'

C'utf_8'

D'UTF8'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'utf8' without hyphen which may cause encoding errors.

Using uppercase or underscore variants which are not standard.

The correct encoding string for UTF-8 in pandas is 'utf-8' with a hyphen.

4fill in blank

hard

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

Data Analysis Python

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';'

BNone

C0

D','

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma as separator when the file uses semicolon.

Using 0 for header which assumes first row is header.

Use sep=';' to specify semicolon separator and header=None to indicate no header row.

5fill in blank

hard

Fill all three blanks to read a CSV file with tab separator, no header, and UTF-8 encoding.

Data Analysis Python

import pandas as pd

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

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

A'\t'

BNone

C'utf-8'

D','

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma instead of tab as separator.

Using 0 instead of None for header.

Using incorrect encoding strings like 'utf8' or 'UTF8'.

Use sep='\t' for tab separator, header=None for no header, and encoding='utf-8' for UTF-8 encoding.

Practice

1. What does the sep parameter do when reading a CSV file with pandas?

easy

A. It sets the encoding type of the file.

B. It tells pandas which row to use as column headers.

C. It specifies the character that separates columns in the file.

D. It defines the number of rows to skip at the start.

Solution

1. Step 1: Understand the role of sep

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

2. Step 2: Differentiate from other parameters

   Other parameters like header and encoding serve different purposes, so sep is specifically for column separation.

3. Final Answer:

   It specifies the character that separates columns in the file. -> Option C

4. Quick Check:

   sep = column separator [OK]

Hint: Remember: sep means separator character [OK]

Common Mistakes:

• Confusing sep with encoding
• Thinking sep sets header row
• Assuming sep skips rows

2. Which of the following is the correct syntax to read a CSV file named data.csv with semicolon-separated columns and no header row using pandas?

easy

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

B. pd.read_csv('data.csv', sep=';', header=None)

C. pd.read_csv('data.csv', encoding='utf-8')

D. pd.read_csv('data.csv', sep=',', header=None)

Solution

1. Step 1: Set separator to semicolon

   Use sep=';' to tell pandas columns are separated by semicolons.

2. Step 2: Specify no header row

   Use header=None to indicate the file has no header row and pandas should assign default column names.

3. Final Answer:

   pd.read_csv('data.csv', sep=';', header=None) -> Option B

4. Quick Check:

   sep=';' and header=None for no headers [OK]

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

Common Mistakes:

• Using header=0 when no header exists
• Forgetting to set sep for semicolon
• Confusing encoding with separator

3. Given a CSV file file.csv with tab-separated values and UTF-16 encoding, what will be the output of this code?

import pandas as pd

df = pd.read_csv('file.csv', sep='\t', encoding='utf-16')
print(df.columns.tolist())

Assuming the first row contains column names: 'Name', 'Age', 'City'.

medium

A. ['Name', 'Age', 'City']

B. ['Unnamed: 0', 'Unnamed: 1', 'Unnamed: 2']

C. Error due to wrong encoding

D. ['Name\tAge\tCity']

Solution

1. Step 1: Read CSV with correct separator and encoding

   The code uses sep='\t' for tab separation and encoding='utf-16' matching the file's format, so pandas reads it correctly.

2. Step 2: Extract column names

   Since the first row has column names, pandas sets them as columns. Printing df.columns.tolist() shows ['Name', 'Age', 'City'].

3. Final Answer:

   ['Name', 'Age', 'City'] -> Option A

4. Quick Check:

   Correct sep and encoding give proper columns [OK]

Hint: Match sep and encoding to file format [OK]

Common Mistakes:

• Using wrong encoding causing errors
• Not setting sep for tab-separated file
• Expecting data instead of column names

4. What is wrong with this code snippet if the CSV file uses pipe (|) as separator and has no header row?

import pandas as pd

df = pd.read_csv('data.csv', sep='|')
print(df.head())

medium

A. Encoding is missing, so it will fail to read.

B. It will raise a FileNotFoundError.

C. The separator is incorrect; should be comma.

D. It assumes the first row is header, causing first data row to be column names.

Solution

1. Step 1: Check separator usage

   The code correctly uses sep='|' matching the file's separator.

2. Step 2: Check header assumption

   By default, pandas assumes the first row is header. Since the file has no header, the first data row becomes column names, which is wrong.

3. Final Answer:

   It assumes the first row is header, causing first data row to be column names. -> Option D

4. Quick Check:

   Set header=None if no header row [OK]

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

Common Mistakes:

• Forgetting header=None for no header files
• Assuming default separator is pipe
• Thinking encoding is always required

5. You have a CSV file saved with semicolon separators, no header row, and encoded in ISO-8859-1. You want to read it into pandas and assign column names ['A', 'B', 'C']. Which code snippet correctly does this?

hard

A. pd.read_csv('file.csv', sep=';', header=None, encoding='ISO-8859-1', names=['A', 'B', 'C'])

B. pd.read_csv('file.csv', sep=',', header=0, encoding='utf-8', names=['A', 'B', 'C'])

C. pd.read_csv('file.csv', sep=';', encoding='utf-8')

D. pd.read_csv('file.csv', sep=';', header=0, encoding='ISO-8859-1')

Solution

1. Step 1: Set separator and encoding correctly

   Use sep=';' for semicolon and encoding='ISO-8859-1' to match file encoding.

2. Step 2: Specify no header and assign column names

   Use header=None because file has no header row, and names=['A', 'B', 'C'] to assign column names manually.

3. Final Answer:

   pd.read_csv('file.csv', sep=';', header=None, encoding='ISO-8859-1', names=['A', 'B', 'C']) -> Option A

4. Quick Check:

   Match sep, encoding, header, and names [OK]

Hint: Use names= with header=None to set columns [OK]

Common Mistakes:

• Using wrong separator or encoding
• Not setting header=None for no header files
• Assigning names without header=None