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.

Practice

1. What does the read_csv function in pandas do?

easy

A. Saves a DataFrame to a CSV file

B. Loads data from a CSV file into a DataFrame

C. Deletes a CSV file from disk

D. Creates a new empty CSV file

Solution

1. Step 1: Understand the purpose of read_csv

   The read_csv function is used to load data from a CSV file into a pandas DataFrame, which is a table-like data structure.

2. Step 2: Differentiate from other file operations

   Saving or deleting files are done by other functions, not read_csv. This function only reads and loads data.

3. Final Answer:

   Loads data from a CSV file into a DataFrame -> Option B

4. Quick Check:

   read_csv = Load CSV data [OK]

Hint: Remember: read_csv means read and load CSV data [OK]

Common Mistakes:

• Confusing read_csv with saving functions
• Thinking it creates or deletes files
• Assuming it returns raw text instead of DataFrame

2. Which of the following is the correct syntax to load a CSV file named data.csv using pandas?

easy

A. pd.read_csv('data.csv')

B. pd.load_csv('data.csv')

C. pandas.read('data.csv')

D. pd.read('data.csv')

Solution

1. Step 1: Recall pandas function for reading CSV

   The correct pandas function to read CSV files is read_csv, called as pd.read_csv() if pandas is imported as pd.

2. Step 2: Check the syntax correctness

   The argument should be the filename as a string inside parentheses. So pd.read_csv('data.csv') is correct.

3. Final Answer:

   pd.read_csv('data.csv') -> Option A

4. Quick Check:

   Correct function name and syntax = pd.read_csv('data.csv') [OK]

Hint: Use pd.read_csv('filename.csv') to load CSV files [OK]

Common Mistakes:

• Using pd.load_csv which does not exist
• Using pd.read instead of read_csv
• Forgetting quotes around filename

3. Given the CSV file sample.csv with content:

name,age
Alice,30
Bob,25
Charlie,35

What will be the output of this code?

import pandas as pd
df = pd.read_csv('sample.csv')
print(df.head(2))

medium

A. Throws an error because head() needs no arguments

B. Prints all rows because head() defaults to all

C. Prints only the column names without data

D. Prints first 2 rows with columns 'name' and 'age'

Solution

1. Step 1: Understand what read_csv does here

   The code loads the CSV file into a DataFrame with columns 'name' and 'age' and three rows.

2. Step 2: Understand head(2) output

   The head(2) function returns the first 2 rows of the DataFrame, including column headers.

3. Final Answer:

   Prints first 2 rows with columns 'name' and 'age' -> Option D

4. Quick Check:

   head(2) = first 2 rows [OK]

Hint: head(n) shows first n rows of DataFrame [OK]

Common Mistakes:

• Thinking head() shows all rows
• Assuming head() requires no arguments
• Expecting only column names without data

4. What is wrong with this code snippet?

import pandas as pd
df = pd.read_csv(data.csv)
print(df.head())

medium

A. Missing quotes around filename 'data.csv'

B. Using print instead of display

C. read_csv cannot be used without specifying separator

D. head() cannot be called without an argument

Solution

1. Step 1: Check the argument passed to read_csv

   The filename must be a string, so it needs quotes around it. Here, data.csv is not quoted, causing a NameError.

2. Step 2: Verify other parts of the code

   Using print is fine, separator defaults to comma, and head() can be called without arguments.

3. Final Answer:

   Missing quotes around filename 'data.csv' -> Option A

4. Quick Check:

   Filename must be a string in quotes [OK]

Hint: Always put filename in quotes in read_csv [OK]

Common Mistakes:

• Forgetting quotes around filename
• Thinking head() needs an argument
• Assuming separator must always be specified

5. You have a CSV file records.csv where values are separated by semicolons (;). Which code correctly loads this file into a DataFrame?

hard

A. pd.read_csv('records.csv', sep=',')

B. pd.read_csv('records.csv', delimiter=',')

C. pd.read_csv('records.csv', sep=';')

D. pd.read_csv('records.csv', sep='|')

Solution

1. Step 1: Identify the separator used in the CSV file

   The file uses semicolons (;) to separate values, so the separator must be set to ';'.

2. Step 2: Use the correct parameter in read_csv

   The parameter sep=';' tells pandas to split columns on semicolons instead of commas.

3. Final Answer:

   pd.read_csv('records.csv', sep=';') -> Option C

4. Quick Check:

   Set sep to actual delimiter used [OK]

Hint: Use sep=';' if CSV uses semicolons as separators [OK]

Common Mistakes:

• Assuming comma is always the separator
• Using wrong sep parameter
• Confusing sep and delimiter parameters