Reading CSV files with read_csv in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code reading a CSV with a custom delimiter?

Consider a CSV file named data.csv with the following content:

name|age|city
Alice|30|New York
Bob|25|Los Angeles

What will be the output of this code snippet?

Pandas

import pandas as pd
from io import StringIO
csv_data = "name|age|city\nAlice|30|New York\nBob|25|Los Angeles"
df = pd.read_csv(StringIO(csv_data), delimiter='|')
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: delimiter parameter is invalid

💡 Hint

Check how the delimiter parameter changes how pandas splits columns.

The delimiter '|' tells pandas to split columns at the pipe character. So the DataFrame has columns 'name', 'age', and 'city' with correct values.

❓ data_output

intermediate

How many rows does this DataFrame have after reading a CSV with skiprows?

Given a CSV content:

id,value
1,100
2,200
3,300
4,400

What is the number of rows in the DataFrame after running this code?

Pandas

import pandas as pd
from io import StringIO
csv_data = "id,value\n1,100\n2,200\n3,300\n4,400"
df = pd.read_csv(StringIO(csv_data), skiprows=1)
print(len(df))

A 3

B 4

C 1

D 0

💡 Hint

skiprows=1 skips the first line of the file.

skiprows=1 skips the first line which is the header, so pandas treats the second line as header and reads 3 data rows.

🔧 Debug

advanced

What error does this code raise when reading a CSV with wrong header parameter?

This code tries to read a CSV but sets header=5:

import pandas as pd
from io import StringIO
csv_data = "a,b,c\n1,2,3\n4,5,6"
df = pd.read_csv(StringIO(csv_data), header=5)

What error will it raise?

Pandas

import pandas as pd
from io import StringIO
csv_data = "a,b,c\n1,2,3\n4,5,6"
df = pd.read_csv(StringIO(csv_data), header=5)

A pandas.errors.ParserError

B IndexError

C No error, DataFrame with empty columns

D ValueError: Passed header row index 5 is out of range

💡 Hint

Check what happens if header row index is beyond file lines.

Setting header=5 means pandas looks for the header at line 6 (0-based), but the file has only 3 lines, so it raises a ValueError.

🚀 Application

advanced

Which option correctly reads a CSV file ignoring the first two rows and using the third row as header?

You have a CSV file where the first two rows are metadata and the third row contains column names. Which code reads the file correctly?

A pd.read_csv('file.csv', header=2)

B pd.read_csv('file.csv', skiprows=[0,1], header=0)

C pd.read_csv('file.csv', skiprows=2, header=2)

D pd.read_csv('file.csv', skiprows=2, header=0)

💡 Hint

Remember skiprows can take a list of rows to skip.

skiprows=[0,1] skips the first two rows, then header=0 uses the next row as header. This correctly reads the third row as header.

🧠 Conceptual

expert

What is the effect of setting index_col=False in pd.read_csv?

When reading a CSV file, what happens if you set index_col=False?

A The CSV file is read but all columns become index columns.

B The first column is used as the index of the DataFrame.

C No column is used as the index; pandas uses default integer index.

D Raises a TypeError because index_col must be int or str.

💡 Hint

Check pandas documentation for index_col parameter behavior.

Setting index_col=False tells pandas not to use any column as index, so it uses the default integer index.

Practice

1. What does the pandas.read_csv() function do?

easy

A. Loads data from a CSV file into a pandas DataFrame

B. Saves a DataFrame to a CSV file

C. Creates a new CSV file from scratch

D. Deletes a CSV file from disk

Solution

1. Step 1: Understand the purpose of read_csv

   The read_csv function is designed to read data stored in CSV files.

2. Step 2: Identify the output of read_csv

   It loads the CSV data into a pandas DataFrame, which is a table-like data structure for analysis.

3. Final Answer:

   Loads data from a CSV file into a pandas DataFrame -> Option A

4. Quick Check:

   read_csv loads CSV data into DataFrame [OK]

Hint: Remember: read_csv reads files, not writes [OK]

Common Mistakes:

• Confusing read_csv with to_csv (saving files)
• Thinking it creates or deletes files
• Assuming it returns a list or dictionary

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

easy

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

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

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

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

Solution

1. Step 1: Recall the pandas function for reading CSV

   The correct function is read_csv from the pandas library, usually imported as pd.

2. Step 2: Match the syntax with the file name

   The syntax is pd.read_csv('filename.csv'), 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.read instead of pd.read_csv
• Using load_csv which does not exist
• Forgetting to put filename in quotes

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

name,age,city
Alice,30,New York
Bob,25,Los Angeles
Charlie,35,Chicago

What will be the output of this code?

import pandas as pd
df = pd.read_csv('sample.csv', usecols=['name', 'city'])
print(df)

medium

A. DataFrame with all columns: 'name', 'age', 'city'

B. Error because 'usecols' is not a valid argument

C. DataFrame with columns 'name' and 'city' only

D. Empty DataFrame with no columns

Solution

1. Step 1: Understand the usecols parameter in read_csv

   The usecols argument selects only specified columns from the CSV file.

2. Step 2: Apply usecols=['name', 'city'] to the CSV data

   Only the 'name' and 'city' columns will be loaded into the DataFrame, excluding 'age'.

3. Final Answer:

   DataFrame with columns 'name' and 'city' only -> Option C

4. Quick Check:

   usecols filters columns = DataFrame with columns 'name' and 'city' only [OK]

Hint: usecols picks only listed columns from CSV [OK]

Common Mistakes:

• Expecting all columns to load despite usecols
• Thinking usecols causes an error
• Confusing usecols with skiprows

4. What is wrong with this code snippet?

import pandas as pd
df = pd.read_csv('data.csv', sep=';')
print(df.head())

Assuming data.csv uses commas as separators.

medium

A. Missing header argument causes error

B. The separator sep=';' is incorrect for comma-separated data

C. read_csv cannot read files with commas

D. print(df.head()) is invalid syntax

Solution

1. Step 1: Check the separator argument in read_csv

   The code uses sep=';' which means it expects semicolon-separated values.

2. Step 2: Compare with actual CSV separator

   The file uses commas, so the separator should be sep=',' or omitted (default is comma).

3. Final Answer:

   The separator sep=';' is incorrect for comma-separated data -> Option B

4. Quick Check:

   Separator must match file format = The separator sep=';' is incorrect for comma-separated data [OK]

Hint: Match sep to actual CSV delimiter to avoid errors [OK]

Common Mistakes:

• Using wrong separator for the CSV file
• Assuming header argument is always needed
• Thinking print(df.head()) is invalid

5. You have a CSV file records.csv with no header row, and columns are separated by tabs. How do you correctly load it into pandas with column names ['ID', 'Name', 'Score']?

hard

A. pd.read_csv('records.csv', names=['ID', 'Name', 'Score'])

B. pd.read_csv('records.csv', sep=',', header=0, names=['ID', 'Name', 'Score'])

C. pd.read_csv('records.csv', sep='\t', header=0)

D. pd.read_csv('records.csv', sep='\t', header=None, names=['ID', 'Name', 'Score'])

Solution

1. Step 1: Identify the separator and header presence

   The file uses tabs as separators, so sep='\t' is needed. It has no header row, so header=None must be set.

2. Step 2: Assign column names manually

   Since there is no header, use names=['ID', 'Name', 'Score'] to name columns on load.

3. Final Answer:

   pd.read_csv('records.csv', sep='\t', header=None, names=['ID', 'Name', 'Score']) -> Option D

4. Quick Check:

   No header + tab sep + names = pd.read_csv('records.csv', sep='\t', header=None, names=['ID', 'Name', 'Score']) [OK]

Hint: Use header=None and names=... for files without headers [OK]

Common Mistakes:

• Using wrong separator (comma instead of tab)
• Not setting header=None for no header files
• Providing names without header=None