Reading CSV with options (sep, header, encoding) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of reading a CSV with a custom separator?

Given the CSV content below saved as data.csv with semicolon separators, what will be the output DataFrame when read with sep=';'?

Data Analysis Python

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 affects parsing columns.

Using sep=';' correctly splits columns by semicolon, producing a DataFrame with columns 'name', 'age', and 'city'.

❓ data_output

intermediate

What happens if header is set to None when reading CSV?

Consider this CSV content without a header row. What will be the DataFrame output when read with header=None?

Data Analysis Python

import pandas as pd
from io import StringIO

csv_data = '''Alice,30,New York
Bob,25,Los Angeles
'''
df = pd.read_csv(StringIO(csv_data), header=None)
print(df)

A

    Alice  30  New York
0    Bob  25  Los Angeles

B

       0   1            2
0  Alice  30     New York
1    Bob  25  Los Angeles

C Error: Missing header row

D

  Alice 30 New York
0 Bob 25 Los Angeles

💡 Hint

When header is None, pandas assigns numeric column names.

Setting header=None tells pandas the CSV has no header row, so it uses default numeric column names 0,1,2.

❓ Predict Output

advanced

What error occurs when reading a CSV with wrong encoding?

Given a CSV file encoded in UTF-8 with special characters, what error will occur if read with encoding='ascii'?

Data Analysis Python

import pandas as pd
from io import StringIO

csv_data = 'name,city\nAna,Sevilla\nJürgen,München'
df = pd.read_csv(StringIO(csv_data), encoding='ascii')
print(df)

A ValueError: Unknown encoding 'ascii'

B DataFrame with names and cities correctly loaded

C UnicodeDecodeError: 'ascii' codec can't decode byte 0x... in position ...

D Empty DataFrame with columns 'name' and 'city'

💡 Hint

ASCII encoding cannot decode special characters like ü.

Using encoding='ascii' on UTF-8 data with special characters causes a UnicodeDecodeError.

❓ visualization

advanced

Which option correctly reads a CSV with no header and custom separator, then shows the first row?

You have a CSV with pipe '|' separator and no header. Which code snippet produces the correct first row output?

Data Analysis Python

import pandas as pd
from io import StringIO

csv_data = 'Alice|30|New York\nBob|25|Los Angeles'
df = pd.read_csv(StringIO(csv_data), sep='|', header=None)
print(df.iloc[0])

A

0       Alice
1          30
2    New York
Name: 0, dtype: object

B

name    Alice
age        30
city  New York
Name: 0, dtype: object

C Error: Missing header row

D

0    Alice|30|New York
Name: 0, dtype: object

💡 Hint

Check how header=None affects column names and how iloc selects rows.

With header=None, columns are numbered 0,1,2. iloc[0] returns first row as Series with these columns.

🔧 Debug

expert

Why does this code fail to read a CSV with UTF-16 encoding?

This code tries to read a UTF-16 encoded CSV but raises an error. What is the cause?

Data Analysis Python

import pandas as pd
from io import StringIO

csv_data = 'name,age\nAlice,30\nBob,25'.encode('utf-16')

# Incorrect usage:
df = pd.read_csv(StringIO(csv_data.decode('utf-16')), encoding='utf-16')
print(df)

A TypeError: StringIO requires a string, but bytes were given

B DataFrame loads correctly with names and ages

C UnicodeDecodeError due to double decoding

D ValueError: encoding parameter conflicts with decoded input

💡 Hint

Check how encoding and decoding interact when reading bytes with StringIO.

Decoding bytes before passing to StringIO and also specifying encoding causes conflict; pandas expects raw bytes or string without encoding mismatch.

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