Why data I/O matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a CSV file into a pandas DataFrame.

Pandas

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

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

Aread_csv

Bto_csv

Cread_excel

Dto_excel

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_csv instead of read_csv

Using read_excel for CSV files

Use pd.read_csv to load CSV data into a DataFrame.

2fill in blank

medium

Complete the code to save a DataFrame to an Excel file.

Pandas

import pandas as pd
df = pd.DataFrame({'A': [1, 2]})
df.[1]('output.xlsx')

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

Aread_excel

Bto_excel

Cread_csv

Dto_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using read_excel instead of to_excel

Using to_csv for Excel files

Use to_excel to save a DataFrame as an Excel file.

3fill in blank

hard

Fix the error in the code to correctly read a JSON file into a DataFrame.

Pandas

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

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

Ato_json

Bread_csv

Cread_json

Dto_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_json instead of read_json

Using read_csv for JSON files

Use read_json to load JSON data into a DataFrame.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

Pandas

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for the value

Using word > 3 which compares strings to numbers

The dictionary maps each word to its length using len(word). The condition len(word) > 3 filters words longer than 3 letters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is less than 5.

Pandas

words = ['apple', 'bat', 'carrot', 'dog']
result = { [1]: [2] for w in words if [3] }

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

Aw.upper()

Blen(w)

Clen(w) < 5

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of w.upper()

Using len(w) > 5 instead of less than 5

The keys are uppercase words using w.upper(). The values are their lengths with len(w). The condition filters words shorter than 5 letters.

Practice

1. What does pandas.read_csv() do in data science?

easy

A. It reads data from a CSV file into a DataFrame.

B. It saves a DataFrame to a CSV file.

C. It creates a new empty DataFrame.

D. It deletes a CSV file from disk.

Solution

1. Step 1: Understand the function name

   The function name read_csv suggests it reads CSV files.

2. Step 2: Recall pandas I/O functions

   read_csv loads data from a CSV file into a pandas DataFrame for analysis.

3. Final Answer:

   It reads data from a CSV file into a DataFrame. -> Option A

4. Quick Check:

   read_csv = read data [OK]

Hint: Remember: read_csv reads data in, to_csv writes data out [OK]

Common Mistakes:

• Confusing read_csv with to_csv
• Thinking it creates empty DataFrame
• Assuming it deletes files

2. Which of the following is the correct syntax to save a DataFrame df to a CSV file named data.csv?

easy

A. df.to_csv('data.csv')

B. df.read_csv('data.csv')

C. pandas.save_csv(df, 'data.csv')

D. df.save('data.csv')

Solution

1. Step 1: Identify the correct pandas method for saving

   The method to save a DataFrame to CSV is to_csv.

2. Step 2: Check the syntax

   The syntax is df.to_csv('filename.csv'), which matches df.to_csv('data.csv').

3. Final Answer:

   df.to_csv('data.csv') -> Option A

4. Quick Check:

   to_csv saves DataFrame to CSV [OK]

Hint: Use df.to_csv('filename.csv') to save data [OK]

Common Mistakes:

• Using read_csv instead of to_csv
• Using non-existent methods like save_csv or save
• Wrong method call order

3. What will be the output of this code?

import pandas as pd
from io import StringIO
csv_data = 'name,age\nAlice,30\nBob,25'
df = pd.read_csv(StringIO(csv_data))
print(df.shape)

medium

A. (3, 3)

B. (3, 2)

C. (2, 3)

D. (2, 2)

Solution

1. Step 1: Understand the CSV data

   The CSV string has 2 rows of data (Alice and Bob) and 2 columns (name, age).

2. Step 2: Check DataFrame shape

   The shape is (rows, columns) = (2, 2).

3. Final Answer:

   (2, 2) -> Option D

4. Quick Check:

   2 rows and 2 columns = (2, 2) [OK]

Hint: Count rows and columns in CSV to find shape [OK]

Common Mistakes:

• Counting header as a data row
• Mixing rows and columns in shape
• Confusing number of columns

4. Identify the error in this code snippet that tries to read a CSV file:

import pandas as pd
df = pd.read_csv('data.txt')

medium

A. read_csv cannot read text files

B. No error, code is correct

C. File extension should be .csv, not .txt

D. Missing import statement

Solution

1. Step 1: Examine the code for syntax issues

   The code has proper import and correct read_csv call. No syntax errors.

2. Step 2: Understand file handling

   pandas read_csv reads files based on content, not extension. 'data.txt' works if it contains CSV data.

3. Final Answer:

   No error, code is correct -> Option B

4. Quick Check:

   read_csv ignores file extension [OK]

Hint: File extension (.csv or .txt) doesn't matter for read_csv if format is correct [OK]

Common Mistakes:

• Insisting on .csv extension only
• Believing read_csv can't handle .txt files
• Overlooking the present import statement

5. You have a DataFrame df with columns ['A', 'B', 'C']. You want to save only columns A and C to a CSV file named subset.csv. Which code correctly does this?

hard

A. df.to_csv('subset.csv', columns=['A', 'C'])

B. df.to_csv('subset.csv', cols=['A', 'C'])

C. df[['A', 'C']].to_csv('subset.csv', index=False)

D. df.to_csv('subset.csv').select(['A', 'C'])

Solution

1. Step 1: Recall to_csv column selection

   Slicing df[['A', 'C']] creates a subset DataFrame with only desired columns.

2. Step 2: Proper saving syntax

   df[['A', 'C']].to_csv('subset.csv', index=False) saves exactly A and C without index column.

3. Step 3: Evaluate alternatives

   df.to_csv('subset.csv', columns=['A', 'C']) saves index + A + C (extra column). df.to_csv('subset.csv', cols=['A', 'C']) invalid cols param. df.to_csv('subset.csv').select(['A', 'C']) invalid method chain.

4. Final Answer:

   df[['A', 'C']].to_csv('subset.csv', index=False) -> Option C

5. Quick Check:

   df[cols].to_csv(index=False) excludes index [OK]

Hint: df[['A','C']].to_csv('subset.csv', index=False) for only those columns [OK]

Common Mistakes:

• Using columns= without index=False (adds index column)
• Using invalid cols parameter
• Chaining non-existent .select() method