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.