First data analysis walkthrough in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of basic data filtering

What is the output of this code that filters rows where age is greater than 30?

Data Analysis Python

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie', 'David'], 'age': [25, 35, 30, 40]}
df = pd.DataFrame(data)
filtered = df[df['age'] > 30]
print(filtered)

A

    name  age
1    Bob   35
3  David   40

B

Empty DataFrame
Columns: [name, age]
Index: []

C

    name  age
1    Bob   35
2  Charlie 30
3  David  40

D

    name  age
0  Alice   25
2  Charlie 30

💡 Hint

Look for rows where the age value is strictly greater than 30.

The code filters the DataFrame to keep only rows where the 'age' column is greater than 30. Bob (35) and David (40) meet this condition.

❓ data_output

intermediate

Count unique values in a column

What is the output of this code that counts unique values in the 'city' column?

Data Analysis Python

import pandas as pd

data = {'name': ['Anna', 'Ben', 'Cara', 'Dan'], 'city': ['NY', 'LA', 'NY', 'SF']}
df = pd.DataFrame(data)
count = df['city'].nunique()
print(count)

A 4

B 3

C 2

D Error: AttributeError

💡 Hint

Count how many different cities appear in the list.

The cities are NY, LA, NY, SF. Unique cities are NY, LA, SF, so count is 3.

❓ visualization

advanced

Identify the correct bar chart output

Which option shows the correct bar chart output for the count of fruits in this data?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

data = {'fruit': ['apple', 'banana', 'apple', 'orange', 'banana', 'banana']}
df = pd.DataFrame(data)
counts = df['fruit'].value_counts()
counts.plot(kind='bar')
plt.show()

A Bar chart with bars: banana=3, apple=2, orange=1

B Bar chart with bars: apple=3, banana=2, orange=1

C Bar chart with bars: orange=3, apple=2, banana=1

D Bar chart with bars: apple=1, banana=1, orange=1

💡 Hint

Count how many times each fruit appears in the list.

Banana appears 3 times, apple 2 times, orange 1 time. The bar chart shows these counts.

🔧 Debug

advanced

Identify the error in data aggregation code

What error does this code produce when trying to calculate the mean age grouped by city?

Data Analysis Python

import pandas as pd

data = {'name': ['Eve', 'Frank'], 'age': [28, 33], 'city': ['NY', 'LA']}
df = pd.DataFrame(data)
result = df.groupby('city')['age'].mean
print(result())

A AttributeError: 'DataFrame' object has no attribute 'groupby'

B KeyError: 'age'

C TypeError: 'method' object is not callable

D No error, prints mean ages

💡 Hint

Check if the mean function is called correctly.

The code assigns the method 'mean' to result but does not call it before printing. Calling result() will work, but the code as is calls result() correctly. However, the code as given is correct and will print the mean ages. But the code has result = df.groupby('city')['age'].mean (without parentheses), so result is a method, and then print(result()) calls it. This is correct usage, so no error occurs.

🚀 Application

expert

Determine the number of rows after multiple filters

Given this DataFrame, how many rows remain after filtering for age >= 30 and city == 'NY'?

Data Analysis Python

import pandas as pd

data = {'name': ['Gina', 'Hank', 'Ivy', 'Jack'], 'age': [29, 31, 30, 35], 'city': ['NY', 'NY', 'LA', 'NY']}
df = pd.DataFrame(data)
filtered = df[(df['age'] >= 30) & (df['city'] == 'NY')]
print(len(filtered))

A 0

B 3

C 1

D 2

💡 Hint

Check each row if both conditions are true.

Rows with age >= 30 and city 'NY' are Hank (31, NY) and Jack (35, NY), total 2 rows.