📊 Day 8: Histogram in Python

 

📊 Day 8: Histogram in Python

🔹 What is a Histogram?

A Histogram is a chart used to visualize the distribution of numerical data.
It groups data into bins (ranges) and shows how frequently values fall into each range.

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🔹 When Should You Use It?

Use a histogram when:

• You want to understand data distribution

• You need to detect skewness, spread, or outliers

• You’re working with continuous numerical data

• You want to analyze frequency patterns

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🔹 Example Scenario

Suppose you are analyzing:

• Exam scores

• Website response times

• Sensor readings

• Randomly generated data

A histogram helps you quickly see:

• Where most values lie

• Whether data is normally distributed

• Presence of extreme values

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🔹 Key Idea Behind It

👉 Data is divided into bins
👉 Bar height shows frequency count
👉 Helps understand shape of data

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🔹 Python Code (Histogram)

import matplotlib.pyplot as plt
import numpy as np 

data = np.random.randn(1000)

plt.hist(data, bins=30, color='lightgreen', edgecolor='black')

plt.title('Data Distribution (2026)')
plt.xlabel('Values')
plt.ylabel('Frequency')

plt.show()

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🔹 Output Explanation

• X-axis shows value ranges

• Y-axis shows frequency

• Taller bars indicate more data points

• Shape reveals:

  • Center concentration

  • Spread

  • Symmetry or skewness

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🔹 Histogram vs Bar Chart

Feature	Histogram	Bar Chart
Data type	Continuous	Categorical
Bars	Touching	Separate
Purpose	Distribution	Comparison

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🔹 Key Takeaways

• Histograms show how data is distributed

• Best for numerical & continuous data

• Bin size affects readability

• Essential for data analysis & statistics