Histograms in Data Analysis Python

Introduction

A histogram helps us see how data is spread out by grouping values into bars. It shows which values happen more often.

To understand the distribution of exam scores in a class.

To check how daily temperatures vary over a month.

To see the frequency of different ages in a group of people.

To analyze how many products fall into different price ranges.

Syntax

Data Analysis Python

import matplotlib.pyplot as plt

plt.hist(data, bins=number_of_bins, color='color_name', edgecolor='color_name')
plt.title('Title')
plt.xlabel('X-axis label')
plt.ylabel('Frequency')
plt.show()

data is a list or array of numbers you want to plot.

bins controls how many bars the histogram has.

Examples

This creates a histogram of ages with 5 bars, blue color, and black edges.

Data Analysis Python

import matplotlib.pyplot as plt

ages = [22, 25, 29, 22, 30, 25, 27, 29, 30, 22]
plt.hist(ages, bins=5, color='blue', edgecolor='black')
plt.show()

This shows test scores grouped into 4 ranges with labels and a title.

Data Analysis Python

import matplotlib.pyplot as plt

scores = [80, 85, 90, 75, 70, 95, 85, 80]
plt.hist(scores, bins=4, color='green')
plt.title('Test Scores')
plt.xlabel('Score Range')
plt.ylabel('Number of Students')
plt.show()

Sample Program

This program shows how heights are spread in 5 groups. The bars show how many people fall into each height range.

Data Analysis Python

import matplotlib.pyplot as plt

# Sample data: heights of 15 people in cm
heights = [160, 165, 170, 160, 175, 180, 170, 165, 160, 175, 180, 185, 170, 165, 160]

# Create histogram with 5 bins
plt.hist(heights, bins=5, color='purple', edgecolor='black')

# Add title and labels
plt.title('Height Distribution')
plt.xlabel('Height (cm)')
plt.ylabel('Number of People')

# Show the plot
plt.show()

Important Notes

If you choose too few bins, the histogram may hide details.

If you choose too many bins, the histogram may look noisy.

Always label your axes and add a title to explain the chart.

Summary

Histograms group data into bars to show frequency.

Use bins to control how detailed the groups are.

They help understand data distribution quickly.

Practice

1. What does a histogram primarily show when analyzing data?

easy

A. The average value of the dataset

B. The exact values of each data point

C. The relationship between two variables

D. The frequency distribution of data values grouped into bins

Solution

1. Step 1: Understand histogram purpose

   A histogram groups data into bins and counts how many data points fall into each bin.

2. Step 2: Identify what histogram shows

   This grouping shows the frequency distribution, not exact values or relationships.

3. Final Answer:

   The frequency distribution of data values grouped into bins -> Option D

4. Quick Check:

   Histogram = frequency distribution [OK]

Hint: Histograms show counts per bin, not individual values [OK]

Common Mistakes:

• Confusing histogram with scatter plot
• Thinking histogram shows exact data points
• Assuming histogram shows averages

2. Which of the following is the correct way to create a histogram using matplotlib in Python?

easy

A. plt.plot(data)

B. plt.hist(data)

C. plt.bar(data)

D. plt.scatter(data)

Solution

1. Step 1: Recall matplotlib histogram function

   Matplotlib uses plt.hist() to create histograms from data arrays.

2. Step 2: Differentiate from other plot types

   plt.plot() is for line plots, plt.bar() for bar charts, plt.scatter() for scatter plots.

3. Final Answer:

   plt.hist(data) -> Option B

4. Quick Check:

   Histogram function = plt.hist() [OK]

Hint: Use plt.hist() to plot histograms in matplotlib [OK]

Common Mistakes:

• Using plt.plot() instead of plt.hist()
• Confusing bar chart with histogram
• Trying plt.scatter() for histograms

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
import numpy as np

data = np.array([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])
plt.hist(data, bins=4)
plt.show()

medium

A. A histogram with 4 bars showing counts: 1, 2, 3, 4

B. A histogram with 4 bars showing counts: 1, 2, 3, 4 but bars are unevenly spaced

C. A histogram with 4 bars showing counts: 1, 2, 3, 4 but bars represent ranges, not exact values

D. An error because bins must be equal to the number of unique values

Solution

1. Step 1: Understand bins in histogram

   Bins divide the data range into intervals. Here, bins=4 divides data from 1 to 4 into 4 intervals.

2. Step 2: Analyze counts per bin

   Counts per bin correspond to how many data points fall into each interval, not just counts of exact values.

3. Final Answer:

   A histogram with 4 bars showing counts: 1, 2, 3, 4 but bars represent ranges, not exact values -> Option C

4. Quick Check:

   Bins = ranges, counts per range [OK]

Hint: Bins group ranges, not just unique values [OK]

Common Mistakes:

• Assuming bins equal unique values
• Thinking bars show exact counts per value
• Expecting error due to bins count

4. Identify the error in this code that tries to plot a histogram:

import matplotlib.pyplot as plt

values = [5, 7, 8, 5, 6]
plt.hist(values, bins='five')
plt.show()

medium

A. bins parameter should be an integer, not a string

B. plt.hist() does not accept lists as data

C. plt.show() is missing

D. values list must be converted to numpy array first

Solution

1. Step 1: Check bins parameter type

   bins must be an integer or sequence, not a string like 'five'.

2. Step 2: Verify other parts

   plt.hist accepts lists, plt.show() is present, numpy array conversion is optional.

3. Final Answer:

   bins parameter should be an integer, not a string -> Option A

4. Quick Check:

   bins must be int or sequence [OK]

Hint: bins must be int or list, not string [OK]

Common Mistakes:

• Passing bins as string causes error
• Thinking plt.hist needs numpy array
• Forgetting plt.show()

5. You have a dataset with ages: [22, 25, 29, 35, 40, 42, 45, 50, 55, 60]. You want to create a histogram with bins representing decades (20-29, 30-39, 40-49, 50-59, 60-69). Which bins parameter should you use in plt.hist() to get this grouping?

hard

A. [20, 30, 40, 50, 60, 70]

B. [20, 29, 39, 49, 59, 69]

C. [20, 25, 30, 35, 40, 45, 50, 55, 60, 65]

D. [21, 31, 41, 51, 61, 71]

Solution

1. Step 1: Define bin edges for decades

   Bins edges must cover ranges: 20-29, 30-39, etc. Using edges [20, 30, 40, 50, 60, 70] creates intervals [20-30), [30-40), etc.

2. Step 2: Confirm bins cover all ages

   These edges include all ages from 22 to 60 correctly in decade groups.

3. Final Answer:

   [20, 30, 40, 50, 60, 70] -> Option A

4. Quick Check:

   Bins edges = decade boundaries [OK]

Hint: Bins edges define intervals; use decade boundaries [OK]

Common Mistakes:

• Using bin edges that exclude upper bounds
• Using single values instead of edges
• Misaligning bins with decade ranges