Statistical plot enhancements in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Effect of adding a KDE curve to a histogram

What will be the output of this code snippet that plots a histogram with a KDE curve using matplotlib and seaborn?

Matplotlib

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

data = np.random.normal(loc=0, scale=1, size=1000)

sns.histplot(data, kde=True, color='skyblue')
plt.title('Histogram with KDE curve')
plt.show()

A A histogram with blue bars only, no KDE curve

B A KDE curve only, no histogram bars

C A histogram with blue bars and a smooth KDE curve overlayed in black

D A scatter plot of the data points

💡 Hint

The 'kde=True' parameter adds a smooth curve over the histogram.

The sns.histplot function with kde=True draws both the histogram bars and a smooth kernel density estimate curve on top. The bars are colored skyblue, and the KDE curve is black by default.

❓ data_output

intermediate

Number of bins in a histogram with automatic binning

Given this code, how many bins will the histogram have approximately?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.random.uniform(0, 10, 500)
plt.hist(data, bins='auto')
plt.show()

A About 50 bins

B About 20 bins

C About 30 bins

D About 10 bins

💡 Hint

The 'auto' binning method tries to find a good number of bins based on data size and distribution.

The 'auto' option in matplotlib's hist function usually selects a bin count around the square root of the number of data points or uses other heuristics. For 500 points, this is roughly 20 bins.

❓ visualization

advanced

Customizing boxplot whiskers to show 5th and 95th percentiles

Which code snippet correctly creates a boxplot where whiskers extend to the 5th and 95th percentiles instead of the default 1.5 IQR?

A plt.boxplot(data, whis=(5, 95))

B plt.boxplot(data, whis=[0.05, 0.95])

C plt.boxplot(data, whis=(0.05, 0.95))

D plt.boxplot(data, whis=[5, 95])

💡 Hint

The 'whis' parameter accepts percentiles (0-100) when given as a tuple.

To set whiskers at the 5th and 95th percentiles, use whis=(5, 95). Passing values between 0 and 100 in a tuple tells matplotlib to use those percentiles. Passing small fractions like 0.05 will not work as intended.

🔧 Debug

advanced

Identify the error in this violin plot code

What error will this code produce when run?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = [np.random.normal(size=100), np.random.normal(loc=2, size=100)]
plt.violinplot(data, positions=[1, 2, 3])
plt.show()

A ValueError: positions length does not match data length

B TypeError: data must be a 1D array

C No error, plot shows correctly

D IndexError: list index out of range

💡 Hint

Check if the number of positions matches the number of datasets.

The violinplot function expects the positions list length to match the number of datasets. Here, data has length 2 but positions has length 3, causing a ValueError.

🚀 Application

expert

Enhancing a scatter plot with marginal histograms

Which code snippet correctly creates a scatter plot with histograms on the top and right margins using matplotlib's gridspec?

A Use plt.subplots with gridspec_kw={'width_ratios':[1,4],'height_ratios':[1,4]} and plot scatter on top-left, histograms on bottom-right and top-right

B Use plt.subplots with gridspec_kw={'width_ratios':[1,4],'height_ratios':[4,1]} and plot scatter on top-right, histograms on bottom-left and top-left

C Use plt.subplots with gridspec_kw={'width_ratios':[4,1],'height_ratios':[4,1]} and plot scatter on bottom-right, histograms on top-right and bottom-left

D Use plt.subplots with gridspec_kw={'width_ratios':[4,1],'height_ratios':[1,4]} and plot scatter on bottom-left, histograms on top-left and bottom-right

💡 Hint

The scatter plot should occupy the larger bottom-left area, histograms on top and right margins.

To create marginal histograms, the figure is divided into a 2x2 grid with width_ratios and height_ratios set so the scatter plot is larger (bottom-left). Histograms go on top-left (top margin) and bottom-right (right margin).