Why flexible I/O handles real-world data in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a CSV file using pandas.

Data Analysis Python

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

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

A'data.csv'

Bdata.csv

Cread.csv

Dcsv.read

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the file name

Using wrong function names

File names must be given as strings in quotes when reading files.

2fill in blank

medium

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

Data Analysis Python

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

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

Adata.json

B'data.json'

Cjsonfile

Dread.json

Attempts:

3 left

💡 Hint

Common Mistakes

Passing variable names without quotes

Using incorrect function names

The file name must be a string with quotes when reading JSON files.

3fill in blank

hard

Fix the error in the code to read a CSV file with a custom delimiter.

Data Analysis Python

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

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

A'delimiter'

B;

C';'

D,

Attempts:

3 left

💡 Hint

Common Mistakes

Using delimiter without quotes

Using wrong delimiter character

The delimiter must be a string character, so it needs quotes.

4fill in blank

hard

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

Data Analysis Python

words = ['data', 'is', 'fun', 'and', 'easy']
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 values

Incorrect condition in if clause

We want the length of each word as the value, and only include words with length greater than 3.

5fill in blank

hard

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

Data Analysis Python

words = ['data', 'is', 'fun', 'and', 'easy']
lengths = {{ [1]: [2] for word in words if [3] }}

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

Aword.upper()

Blen(word)

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Not converting words to uppercase

Using word instead of len(word) for values

Wrong filter condition

The keys are uppercase words, values are their lengths, filtered by length greater than 3.

Practice

1. Why is flexible input/output (I/O) important when working with real-world data?

easy

A. Because flexible I/O makes data always clean and error-free

B. Because real-world data comes in many formats and flexible I/O can handle them all

C. Because flexible I/O only works with CSV files

D. Because flexible I/O slows down data processing

Solution

1. Step 1: Understand real-world data formats

   Real-world data can be CSV, Excel, JSON, or others, so we need tools that can read many types.

2. Step 2: Role of flexible I/O

   Flexible I/O means using functions that can read and write different formats easily, like pandas' read_csv or to_excel.

3. Final Answer:

   Because real-world data comes in many formats and flexible I/O can handle them all -> Option B

4. Quick Check:

   Flexible I/O = handles many formats [OK]

Hint: Real data varies; flexible I/O reads many formats [OK]

Common Mistakes:

• Thinking flexible I/O only works with one file type
• Assuming flexible I/O cleans data automatically
• Believing flexible I/O slows down processing

2. Which of the following is the correct way to read a CSV file using pandas in Python?

easy

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

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

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

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

Solution

1. Step 1: Recall pandas CSV reading function

   The correct pandas function to read CSV files is read_csv.

2. Step 2: Check syntax correctness

   pd.read_csv('data.csv') is the correct syntax; others are invalid or do not exist.

3. Final Answer:

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

4. Quick Check:

   pandas CSV read = read_csv [OK]

Hint: Use pd.read_csv() to read CSV files [OK]

Common Mistakes:

• Using incorrect function names like readExcel or load_csv
• Confusing read_csv with open or load functions
• Missing the 'pd.' prefix for pandas

3. What will be the output of this code snippet?

import pandas as pd
from io import StringIO
csv_data = 'name,age\nAlice,30\nBob,25'
data = pd.read_csv(StringIO(csv_data))
print(data['age'].tolist())

medium

A. ["Alice", "Bob"]

B. ['30', '25']

C. Error: StringIO not supported

D. [30, 25]

Solution

1. Step 1: Understand StringIO usage

   StringIO allows reading CSV data from a string as if it were a file.

2. Step 2: Check data reading and output

   pd.read_csv reads the CSV string into a DataFrame; data['age'] column contains integers 30 and 25; .tolist() converts it to a list.

3. Final Answer:

   [30, 25] -> Option D

4. Quick Check:

   CSV string to list of ages = [30, 25] [OK]

Hint: StringIO reads string as file; ages are integers [OK]

Common Mistakes:

• Expecting strings instead of integers in the list
• Thinking StringIO is unsupported
• Confusing column names with values

4. Identify the error in this code that tries to save a DataFrame to Excel:

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df.to_excel('output.csv')

medium

A. File extension should be .xlsx, not .csv

B. to_excel() cannot save DataFrames

C. Missing import for Excel writer

D. DataFrame is empty

Solution

1. Step 1: Check file extension for to_excel()

   to_excel() expects an Excel file extension like .xlsx, not .csv.

2. Step 2: Understand file format mismatch

   Saving with .csv extension causes confusion; .csv is for comma-separated files, not Excel format.

3. Final Answer:

   File extension should be .xlsx, not .csv -> Option A

4. Quick Check:

   to_excel needs .xlsx extension [OK]

Hint: Use .xlsx extension with to_excel() [OK]

Common Mistakes:

• Using .csv extension with to_excel()
• Thinking to_excel() cannot save DataFrames
• Assuming missing imports cause error

5. You have a JSON file with nested data and want to convert it into a flat pandas DataFrame. Which approach best uses flexible I/O to handle this real-world data?

hard

A. Use pandas.read_csv() directly on the JSON file

B. Manually open the JSON file and parse it line by line without pandas

C. Use pandas.read_json() with the 'lines=True' parameter and then normalize nested fields

D. Convert JSON to Excel first, then read with pandas.read_excel()

Solution

1. Step 1: Understand JSON reading in pandas

   pandas.read_json() can read JSON files; 'lines=True' helps with line-delimited JSON.

2. Step 2: Flatten nested JSON data

   After reading, pandas.json_normalize() can flatten nested fields into columns for easy analysis.

3. Final Answer:

   Use pandas.read_json() with the 'lines=True' parameter and then normalize nested fields -> Option C

4. Quick Check:

   read_json + normalize = best for nested JSON [OK]

Hint: Use read_json with normalization for nested data [OK]

Common Mistakes:

• Trying to read JSON with read_csv()
• Ignoring nested structure flattening
• Converting formats unnecessarily