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

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this CSV read code?

Given a CSV file with columns name and age, what does this code output?

Pandas

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.head(1))

A

   name  age
0  Alice   30

B

   name  age
1    Bob   25

C

   name  age
0  Alice  30.0

D

   name  age
0  Alice  '30'

💡 Hint

Look at the first row printed by head(1).

The head(1) method prints the first row of the DataFrame. The age column is read as integer, so no decimal or quotes appear.

❓ data_output

intermediate

How many rows does this DataFrame have after reading JSON?

After reading this JSON string into a DataFrame, how many rows does it contain?

Pandas

import pandas as pd
json_str = '[{"city": "NY", "pop": 8000}, {"city": "LA", "pop": 4000}]'
df = pd.read_json(json_str)
print(len(df))

A 0

B 1

C 2

D Raises ValueError

💡 Hint

Count the number of objects in the JSON array.

The JSON string contains two objects, so the DataFrame has two rows.

🔧 Debug

advanced

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

What error will this code raise?

Pandas

import pandas as pd
from io import StringIO
csv_data = "name;age\nAlice;30\nBob;25"
df = pd.read_csv(StringIO(csv_data))

A TypeError: expected string or bytes-like object

B No error, DataFrame with one column

C ValueError: delimiter not supported

D ParserError: Error tokenizing data

💡 Hint

What is the default delimiter for read_csv?

Since the delimiter is not specified, pandas treats the whole line as one column, so no error occurs. The DataFrame has one column with the entire line as string.

❓ visualization

advanced

Which plot shows correct data after reading Excel file?

Given this Excel data read into a DataFrame, which plot correctly shows the sales column?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
from io import BytesIO
import numpy as np

# Simulate Excel file
excel_data = BytesIO()
df_original = pd.DataFrame({'month': ['Jan', 'Feb', 'Mar'], 'sales': [100, 150, 120]})
df_original.to_excel(excel_data, index=False)
excel_data.seek(0)

# Read Excel

df = pd.read_excel(excel_data)

plt.figure(figsize=(4,3))
plt.plot(df['month'], df['sales'], marker='o')
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.tight_layout()
plt.savefig('plot.png')
plt.close()

A Line plot with months Jan, Feb, Mar on x-axis and sales 100, 150, 120 on y-axis

B Bar plot with months Jan, Feb, Mar on x-axis and sales 100, 150, 120 on y-axis

C Line plot with months 0, 1, 2 on x-axis and sales 100, 150, 120 on y-axis

D Scatter plot with sales on x-axis and months on y-axis

💡 Hint

Look at the code: it uses plt.plot with month on x-axis.

The code creates a line plot with months as x-axis labels and sales as y-axis values.

🧠 Conceptual

expert

Why is efficient data I/O critical in data science projects?

Choose the best reason why efficient data input/output (I/O) matters in data science workflows.

A It allows data scientists to avoid learning programming languages.

B It ensures data is always stored in CSV format for compatibility.

C It guarantees that data will never have missing values after loading.

D It reduces the time spent waiting for data to load or save, speeding up analysis and iteration.

💡 Hint

Think about how data size and format affect workflow speed.

Efficient data I/O reduces waiting time, enabling faster data analysis and model development. Other options are incorrect because data format choice depends on context, missing values depend on data quality, and programming skills are still needed.