Topic: Python Matplotlib – From Easy to Top: Part 4 of 6: Advanced Charts – Histograms, Pie, Box, Area, and Error Bars

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### 1. Histogram: Visualizing Data Distribution

Histograms show frequency distribution of numerical data.

import matplotlib.pyplot as plt
import numpy as np

data = np.random.randn(1000)

plt.hist(data, bins=30, color='skyblue', edgecolor='black')
plt.title("Normal Distribution Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.grid(True)
plt.show()

Customizations:

• bins=30 – controls granularity
• density=True – normalize the histogram
• alpha=0.7 – transparency

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### 2. Pie Chart: Showing Proportions

labels = ['Python', 'JavaScript', 'C++', 'Java']
sizes = [45, 30, 15, 10]
colors = ['gold', 'lightgreen', 'lightcoral', 'lightskyblue']
explode = (0.1, 0, 0, 0)  # explode the 1st slice

plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%',
        startangle=140, explode=explode, shadow=True)
plt.title("Programming Language Popularity")
plt.axis('equal')  # Equal aspect ratio ensures pie is circular
plt.show()

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### 3. Box Plot: Summarizing Distribution Stats

Box plots show min, Q1, median, Q3, max, and outliers.

data = [np.random.normal(0, std, 100) for std in range(1, 4)]

plt.boxplot(data, patch_artist=True, labels=['std=1', 'std=2', 'std=3'])
plt.title("Box Plot Example")
plt.grid(True)
plt.show()

Tip: Use vert=False to make a horizontal boxplot.

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### 4. Area Chart: Cumulative Trends

x = np.arange(1, 6)
y1 = np.array([1, 3, 4, 5, 7])
y2 = np.array([1, 2, 4, 6, 8])

plt.fill_between(x, y1, color="skyblue", alpha=0.5, label="Y1")
plt.fill_between(x, y2, color="orange", alpha=0.5, label="Y2")
plt.title("Area Chart")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.legend()
plt.show()

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### 5. Error Bar Plot: Showing Uncertainty

x = np.arange(0.1, 4, 0.5)
y = np.exp(-x)
error = 0.1 + 0.2 * x

plt.errorbar(x, y, yerr=error, fmt='-o', color='teal', ecolor='red', capsize=5)
plt.title("Error Bar Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.grid(True)
plt.show()

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### 6. Horizontal Bar Chart

langs = ['Python', 'Java', 'C++', 'JavaScript']
popularity = [50, 40, 30, 45]

plt.barh(langs, popularity, color='plum')
plt.title("Programming Language Popularity")
plt.xlabel("Popularity")
plt.show()

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### 7. Stacked Bar Chart

labels = ['2019', '2020', '2021']
men = [20, 35, 30]
women = [25, 32, 34]

x = np.arange(len(labels))
width = 0.5

plt.bar(x, men, width, label='Men')
plt.bar(x, women, width, bottom=men, label='Women')

plt.ylabel('Scores')
plt.title('Scores by Year and Gender')
plt.xticks(x, labels)
plt.legend()
plt.show()

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### 8. Summary

• Histograms show frequency distribution
• Pie charts are good for proportions
• Box plots summarize spread and outliers
• Area charts visualize trends over time
• Error bars indicate uncertainty in measurements
• Stacked and horizontal bars enhance categorical data clarity

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### Exercise

• Create a pie chart showing budget allocation of 5 departments.
• Plot 3 histograms on the same figure with different distributions.
• Build a stacked bar chart for monthly expenses across 3 categories.
• Add error bars to a decaying function and annotate the max point.

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#Python #Matplotlib #DataVisualization #AdvancedCharts #Histograms #PieCharts #BoxPlots

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