Reading CSV files with read_csv in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a CSV file named 'data.csv' into a DataFrame.

Pandas

import pandas as pd
df = pd.[1]('data.csv')

Drag options to blanks, or click blank then click option'

Aread_table

Bread_excel

Cread_json

Dread_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using read_excel instead of read_csv

Using read_json which is for JSON files

Using read_table which expects tab-separated files

The read_csv function is used to read CSV files into a pandas DataFrame.

2fill in blank

medium

Complete the code to read 'data.csv' but only load the first 5 rows.

Pandas

import pandas as pd
df = pd.read_csv('data.csv', [1]=5)

Drag options to blanks, or click blank then click option'

Anrows

Brows

Chead

Dlimit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'head' which is a method on DataFrames, not a parameter

Using 'rows' or 'limit' which are not valid parameters

The nrows parameter limits the number of rows read from the CSV file.

3fill in blank

hard

Fix the error in the code to read a CSV file with a semicolon separator.

Pandas

import pandas as pd
df = pd.read_csv('data.csv', sep=[1])

Drag options to blanks, or click blank then click option'

A,

B;

C|

D:

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma when the file uses semicolons

Using pipe or colon which are different delimiters

The sep parameter defines the delimiter used in the CSV file. For semicolon-separated files, use ';'.

4fill in blank

hard

Fill both blanks to read 'data.csv' using only columns 'Name' and 'Age'.

Pandas

import pandas as pd
df = pd.read_csv('data.csv', usecols=[1], dtype=[2])

Drag options to blanks, or click blank then click option'

A['Name', 'Age']

B{'Name': str, 'Age': int}

C['Name', 'Salary']

D{'Name': int, 'Age': str}

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting wrong columns in usecols

Mixing up data types in dtype

usecols selects columns to load. dtype sets data types for columns.

5fill in blank

hard

Fill all three blanks to read 'data.csv' skipping the first row, setting index to 'ID', and parsing dates in 'Date' column.

Pandas

import pandas as pd
df = pd.read_csv('data.csv', skiprows=[1], index_col=[2], parse_dates=[3])

Drag options to blanks, or click blank then click option'

A1

B'ID'

C['Date']

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Not skipping the header row properly

Using wrong index column name

Not parsing dates correctly

skiprows=1 skips the first row, index_col='ID' sets the index column, and parse_dates=['Date'] parses the 'Date' column as dates.

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