Box plots in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple box plot data summary

What is the output of the following code snippet that creates a box plot summary using pandas?

Pandas

import pandas as pd
import numpy as np

data = pd.DataFrame({'values': np.array([1, 2, 2, 3, 4, 5, 6, 7, 8, 9])})
summary = data['values'].describe()
summary

A {'count': 10.0, 'mean': 4.7, 'std': 2.738613, 'min': 1.0, '25%': 2.5, '50%': 4.5, '75%': 7.0, 'max': 9.0}

B {'count': 10.0, 'mean': 5.0, 'std': 3.0, 'min': 1.0, '25%': 3.0, '50%': 5.0, '75%': 7.0, 'max': 9.0}

C {'count': 10.0, 'mean': 4.7, 'std': 2.738613, 'min': 1.0, '25%': 2.75, '50%': 4.5, '75%': 7.0, 'max': 9.0}

D {'count': 10.0, 'mean': 4.7, 'std': 2.738613, 'min': 1.0, '25%': 2.75, '50%': 5.0, '75%': 7.0, 'max': 9.0}

💡 Hint

Look carefully at the quartile values and mean calculated by pandas describe() method.

The pandas describe() method calculates count, mean, std, min, 25%, 50%, 75%, and max. The 25% quartile is 2.75, median (50%) is 4.5, and mean is 4.7 for this data.

❓ data_output

intermediate

Number of outliers detected in a box plot

Given the following data, how many outliers will be detected using the IQR method in a box plot?

Pandas

import pandas as pd
import numpy as np

data = pd.Series([10, 12, 12, 13, 12, 11, 14, 15, 100, 101, 102])
Q1 = data.quantile(0.25)
Q3 = data.quantile(0.75)
IQR = Q3 - Q1
outliers = data[(data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))]
outliers.count()

A 2

B 3

C 1

D 0

💡 Hint

Calculate Q1, Q3, then find values outside 1.5 times IQR range.

The outliers are values greater than Q3 + 1.5*IQR or less than Q1 - 1.5*IQR. Here, 100, 101, and 102 are outliers but only 2 exceed the upper bound based on calculation.

❓ visualization

advanced

Interpreting box plot visualization with multiple categories

You have this code to create box plots for two groups. Which statement correctly describes the visualization output?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
data = pd.DataFrame({'group': ['A']*50 + ['B']*50, 'value': np.concatenate([np.random.normal(0, 1, 50), np.random.normal(1, 1.5, 50)])})
data.boxplot(by='group', column='value')
plt.show()

A Group B has a higher median and wider spread than group A.

B Group A has a higher median and wider spread than group B.

C Both groups have the same median but different spreads.

D Group B has a lower median and narrower spread than group A.

💡 Hint

Look at the mean and standard deviation used to generate each group.

Group B data is generated with mean 1 and std 1.5, so its median is higher and spread wider than group A's mean 0 and std 1.

🧠 Conceptual

advanced

Understanding whiskers in box plots

In a box plot created by pandas, what do the whiskers represent by default?

A The minimum and maximum values in the data.

B The most extreme data points within 1.5 times the IQR from the quartiles.

C The 1st and 3rd quartiles.

D The mean plus or minus one standard deviation.

💡 Hint

Think about how box plots identify outliers.

Whiskers extend to the most extreme points within 1.5 times the interquartile range (IQR) from the quartiles. Points beyond are outliers.

🔧 Debug

expert

Identify the error in box plot code with missing data

What error will this code raise when trying to plot a box plot with missing values?

Pandas

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

data = pd.Series([1, 2, np.nan, 4, 5])
data.plot.box()
plt.show()

A AttributeError because plot.box() is not a valid method.

B ValueError due to NaN values in the data.

C TypeError because NaN is not a number.

D No error; the box plot will ignore NaN values and plot correctly.

💡 Hint

Check how pandas handles NaN values in plotting functions.

Pandas automatically ignores NaN values when plotting box plots, so no error occurs and the plot is generated correctly.