Histogram plots in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of histogram bin counts

What is the output of the following code that plots a histogram and returns the counts of values in each bin?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.Series([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])
counts, bins, patches = plt.hist(data, bins=4, edgecolor='black')
plt.close()
print(counts)

A [1. 2. 3. 4.]

B [2. 2. 3. 3.]

C [1. 3. 3. 3.]

D [0. 3. 4. 3.]

💡 Hint

Count how many values fall into each bin range when bins=4.

The data has 1 value in the first bin (1), 2 values in the second bin (2,2), 3 values in the third bin (3,3,3), and 4 values in the last bin (4,4,4,4).

❓ data_output

intermediate

Number of bins in histogram

How many bins will the histogram have in this code snippet?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.Series([5, 7, 8, 5, 6, 7, 8, 9, 10, 11])
plt.hist(data, bins=5)
plt.close()

A 10

B 4

C 5

D 6

💡 Hint

The bins parameter sets the number of bins directly.

The bins parameter is set to 5, so the histogram will have exactly 5 bins.

❓ visualization

advanced

Identify the histogram shape

Given this data and histogram code, what shape does the histogram plot show?

Pandas

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

data = pd.Series(np.concatenate([np.random.normal(0, 1, 500), np.random.normal(5, 1, 500)]))
plt.hist(data, bins=30, edgecolor='black')
plt.close()

A Skewed distribution with long tail

B Uniform distribution with flat shape

C Single peak normal distribution

D Bimodal distribution with two peaks

💡 Hint

The data is combined from two normal distributions centered at different points.

The data combines two normal distributions centered at 0 and 5, so the histogram shows two peaks, making it bimodal.

🔧 Debug

advanced

Error in histogram plotting code

What error does this code raise when trying to plot a histogram?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.Series(['a', 'b', 'c', 'a', 'b'])
plt.hist(data)
plt.close()

A No error, histogram plots categorical data

B TypeError: float() argument must be a string or a number, not 'Series'

C TypeError: unorderable types: str() < str()

D ValueError: bins must be positive, when an integer

💡 Hint

Histograms require numeric data to bin values.

Matplotlib histogram expects numeric data to create bins. Strings cannot be converted to float for binning, causing a TypeError.

🚀 Application

expert

Choosing bin size for histogram analysis

You have a dataset of 10,000 numeric values with a wide range. You want to plot a histogram to analyze the distribution clearly. Which bin size choice is best to balance detail and readability?

A Use 50 bins to balance detail and smoothness

B Use 1000 bins to capture every small variation

C Use 10 bins to get a broad overview with less noise

D Use 1 bin to see only the total count

💡 Hint

Too few bins hide details; too many bins create noise.

50 bins provide enough detail to see distribution shape without overwhelming noise or empty bins, suitable for 10,000 data points.