Histograms in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Histogram Bin Counts with numpy

What is the output of the following code that creates a histogram bin count using numpy?

Data Analysis Python

import numpy as np

data = np.array([1, 2, 2, 3, 4, 4, 4, 5])
bins = [1, 3, 5]
counts, edges = np.histogram(data, bins=bins)
print(counts)

A [3 4]

B [3 5]

C [2 6]

D [4 4]

💡 Hint

Remember that numpy.histogram counts how many values fall into each bin interval, including the left edge but excluding the right edge except for the last bin.

The bins are [1,3) and [3,5]. Values 1,2,2 fall into the first bin (3 values). Values 3,4,4,4,5 fall into the second bin (5 values).

❓ data_output

intermediate

Pandas Histogram Value Counts

Given this pandas DataFrame, what is the output of the histogram value counts?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'scores': [10, 20, 20, 30, 40, 40, 40, 50]})
bins = [10, 30, 50]
hist = pd.cut(df['scores'], bins=bins).value_counts().sort_index()
print(hist)

A

[10-30)    3
30-50)     5
dtype: int64

B

[10-30)    2
30-50)     6
dtype: int64

C

[10-30)    4
30-50)     4
dtype: int64

D

[10-30)    3
30-50)     4
dtype: int64

💡 Hint

pd.cut bins are left-inclusive and right-exclusive by default.

Scores 10, 20, 20 fall into the first bin [10,30). Scores 30, 40, 40, 40, 50 fall into the second bin [30,50).

❓ visualization

advanced

Matplotlib Histogram Bin Width Effect

Which histogram plot shows the effect of changing bin width on the same data?

Data Analysis Python

import matplotlib.pyplot as plt
import numpy as np

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

plt.figure(figsize=(10,4))
plt.subplot(1,2,1)
plt.hist(data, bins=10, color='blue')
plt.title('Bins=10')

plt.subplot(1,2,2)
plt.hist(data, bins=30, color='green')
plt.title('Bins=30')

plt.tight_layout()
plt.show()

A Left plot is a scatter plot; right plot is a histogram.

B Left plot has narrower bins with more bars; right plot has wider bins with fewer bars.

C Both plots have the same number of bins and look identical.

D Left plot has wider bins with fewer bars; right plot has narrower bins with more bars.

💡 Hint

More bins means narrower bars and more detail.

Bins=10 means each bin covers a wider range, so fewer bars. Bins=30 means narrower bins, so more bars and more detail.

🔧 Debug

advanced

Fix the Histogram Edge Inclusion Bug

What error does this code raise and why? import numpy as np data = [1, 2, 3, 4, 5] bins = [1, 3, 5] counts, edges = np.histogram(data, bins=bins) print(counts) print(edges) # Then trying to access counts[3]

Data Analysis Python

print(counts[3])

A IndexError: index 3 is out of bounds for axis 0 with size 2

B TypeError: 'list' object is not subscriptable

C ValueError: bins must increase monotonically

D No error, prints the count of the fourth bin

💡 Hint

Check how many bins are created by np.histogram with 3 edges.

np.histogram with 3 bin edges creates 2 bins, so counts has length 2. Accessing counts[3] is out of range and raises IndexError.

🚀 Application

expert

Choosing Optimal Histogram Bins for Skewed Data

You have a dataset with a strong right skew. Which binning strategy below will best reveal the data distribution in a histogram?

A Use a single bin to summarize all data.

B Use equal-width bins spanning the entire data range.

C Use bins with widths increasing exponentially to capture skewness.

D Use bins with random widths unrelated to data distribution.

💡 Hint

Think about how bin widths can adapt to data skew to show detail where data is dense.

Equal-width bins may hide details in skewed data. Exponentially increasing bin widths capture dense and sparse areas better.