Why DataFrame is the core data structure in Data Analysis Python - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is a DataFrame considered the core data structure in data analysis?

Choose the best reason why DataFrame is central to data analysis tasks.

A It allows storing data in rows and columns, making it easy to manipulate and analyze structured data.

B It is a tool used only for visualizing data, not for storing or manipulating it.

C It is a simple list that stores only numbers for fast calculations.

D It stores data as plain text files without any structure.

💡 Hint

Think about how you work with tables in real life, like spreadsheets.

DataFrames organize data in rows and columns, similar to spreadsheets, which makes data easy to access, filter, and analyze.

❓ Predict Output

intermediate

What is the output shape of this DataFrame?

Given the code below, what is the shape (rows, columns) of the resulting DataFrame?

Data Analysis Python

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Cara'], 'Age': [28, 34, 29], 'City': ['NY', 'LA', 'SF']}
df = pd.DataFrame(data)
print(df.shape)

A (1, 3)

B (3, 2)

C (3, 3)

D (2, 3)

💡 Hint

Count the number of rows and columns in the data dictionary.

The DataFrame has 3 rows (one for each person) and 3 columns (Name, Age, City).

❓ data_output

advanced

What is the output of filtering this DataFrame?

What rows remain after filtering the DataFrame for Age > 30?

Data Analysis Python

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Cara', 'Dan'], 'Age': [28, 34, 29, 40]}
df = pd.DataFrame(data)
filtered = df[df['Age'] > 30]
print(filtered)

A

Empty DataFrame
Columns: [Name, Age]
Index: []

B

   Name  Age
1   Bob   34
3   Dan   40

C

   Name  Age
0  Anna   28
1   Bob   34
2  Cara   29
3   Dan   40

D

   Name  Age
0  Anna   28
2  Cara   29

💡 Hint

Look for rows where Age is greater than 30.

Only Bob (34) and Dan (40) have Age values greater than 30, so only their rows remain.

❓ visualization

advanced

Which plot best shows the distribution of ages in this DataFrame?

Given a DataFrame with ages, which plot type best visualizes the age distribution?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

data = {'Age': [22, 25, 29, 22, 30, 25, 28, 29, 30, 22]}
df = pd.DataFrame(data)

# Assume the plot is created here

A Histogram showing frequency of each age group

B Line plot connecting ages in order

C Scatter plot with Age on x-axis and index on y-axis

D Pie chart showing percentage of each age

💡 Hint

Think about how to show how many times each age appears.

A histogram groups ages into bins and shows how many values fall into each, making it ideal for distribution.

🔧 Debug

expert

What error does this DataFrame operation raise?

What error occurs when running this code?

Data Analysis Python

import pandas as pd

data = {'Name': ['Anna', 'Bob'], 'Age': [28, 34]}
df = pd.DataFrame(data)

# Attempt to access a non-existent column
print(df['Salary'])

A ValueError

B TypeError

C IndexError

D KeyError

💡 Hint

Check what happens when you try to access a column that is not in the DataFrame.

Accessing a column that does not exist in a DataFrame raises a KeyError.