Reading CSV files (read_csv) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of reading CSV with specific delimiter

What is the output DataFrame when reading the CSV content with a semicolon delimiter?

Data Analysis Python

import pandas as pd
from io import StringIO
csv_data = '''name;age;city
Alice;30;New York
Bob;25;Los Angeles
Charlie;35;Chicago'''
df = pd.read_csv(StringIO(csv_data), delimiter=';')
df

A

Empty DataFrame
Columns: [name, age, city]
Index: []

B [["name", "age", "city"], ["Alice", 30, "New York"], ["Bob", 25, "Los Angeles"], ["Charlie", 35, "Chicago"]]

C

   name;age;city
0  Alice;30;New York
1  Bob;25;Los Angeles
2  Charlie;35;Chicago

D

   name  age         city
0  Alice   30     New York
1    Bob   25  Los Angeles
2 Charlie   35      Chicago

💡 Hint

Check how the delimiter parameter affects parsing.

Using delimiter=';' correctly splits the columns. The DataFrame shows three columns with proper data types.

❓ data_output

intermediate

Number of rows after reading CSV with skiprows

How many rows does the DataFrame have after reading the CSV while skipping the first data row?

Data Analysis Python

import pandas as pd
from io import StringIO
csv_data = '''name,age,city
Alice,30,New York
Bob,25,Los Angeles
Charlie,35,Chicago'''
df = pd.read_csv(StringIO(csv_data), skiprows=[1])
len(df)

A 1

B 3

C 2

D 0

💡 Hint

skiprows removes the specified rows from the file before parsing.

Skipping row 1 removes the first data row 'Alice,30,New York', so only Bob and Charlie remain, total 2 rows.

🔧 Debug

advanced

Identify the error when reading CSV with wrong header parameter

What error occurs when running this code?

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), header=2)
df

A pandas.errors.ParserError

B Empty DataFrame with columns [Bob, 25, Los Angeles]

C Empty DataFrame with columns [Alice, 30, New York]

D IndexError

💡 Hint

header=2 means the third line is used as header.

Since the CSV has only 3 lines, header=2 uses the third line 'Bob,25,Los Angeles' as header, resulting in an empty DataFrame with those column names.

🚀 Application

advanced

Reading CSV with missing values and filling them

Given a CSV with missing values, which code correctly reads it and fills missing ages with 0?

Data Analysis Python

import pandas as pd
from io import StringIO
csv_data = '''name,age,city
Alice,30,New York
Bob,,Los Angeles
Charlie,35,Chicago'''
df = pd.read_csv(StringIO(csv_data))
df['age'] = df['age'].fillna(0)
df

A

   name   age         city
0  Alice  30.0     New York
1    Bob   0.0  Los Angeles
2 Charlie 35.0      Chicago

B

   name  age         city
0  Alice   30     New York
1    Bob  NaN  Los Angeles
2 Charlie 35      Chicago

C

   name  age         city
0  Alice  30.0     New York
1    Bob  NaN  Los Angeles
2 Charlie 35.0      Chicago

D

   name  age         city
0  Alice  30.0     New York
1    Bob  0     Los Angeles
2 Charlie 35.0      Chicago

💡 Hint

fillna replaces NaN values with the given value.

fillna(0) replaces missing ages with 0. The age column becomes float because of NaN presence.

🧠 Conceptual

expert

Effect of dtype parameter in read_csv

What is the effect of setting dtype={'age': str} when reading a CSV file?

A The 'age' column is read as strings, preserving leading zeros and preventing numeric conversion.

B The 'age' column is converted to integers, removing any decimal points.

C The entire DataFrame is read as strings regardless of column names.

D The 'age' column is ignored and not included in the DataFrame.

💡 Hint

dtype controls the data type of specific columns during import.

Setting dtype={'age': str} forces pandas to treat the 'age' column as strings, useful to keep formatting like leading zeros.