Advanced Graphs and Applications - Using Matplotlib

In this lesson, we'll explore various ways to represent data using histograms, scatter plots, pie charts, and subplots.

A histogram is a useful graph for analyzing the distribution of data.

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import matplotlib.pyplot as plt
import numpy as np

# Generate random data (normal distribution)
data = np.random.randn(1000)

# Draw a histogram
plt.hist(data, bins=30, color='purple', alpha=0.7)

# Graph settings
plt.title("Histogram of Data Distribution")
plt.xlabel("Value")
plt.ylabel("Frequency")

plt.show()

• bins=30: Divides the data into 30 bins (intervals)

• alpha=0.7: Adjusts the transparency of the graph (0.0: transparent, 1.0: opaque)

Using a histogram, you can analyze whether data is concentrated around specific values or follows a normal distribution.

Scatter plots are used to visually represent correlations between two variables.

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import matplotlib.pyplot as plt
import numpy as np

# Generate random data
x = np.random.rand(50)
y = np.random.rand(50)

# Draw a scatter plot
plt.scatter(x, y, color='blue', alpha=0.5)

# Graph settings
plt.title("Scatter Plot")
plt.xlabel("X Value")
plt.ylabel("Y Value")

plt.show()

• plt.scatter(x, y): Represents the relationship between x-axis and y-axis data in points

• alpha=0.5: Adjusts point transparency to easily see overlapping areas

Scatter plots are useful for checking correlations between variables or exploring outliers.

Pie charts are used to visually represent the proportions of data.

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import matplotlib.pyplot as plt

labels = ["A", "B", "C", "D"]
values = [30, 20, 40, 10]

plt.pie(values, labels=labels, autopct="%1.1f%%", colors=['red', 'blue', 'green', 'orange'])

plt.title("Pie Chart Representing Proportions")

plt.show()

• labels: Specifies the name of each slice

• autopct="%1.1f%%": Displays percentage values (to one decimal place)

• colors: Specifies the color of each slice

Pie charts are effective when expressing the relative sizes of data.

With Matplotlib, you can arrange multiple graphs on a single screen.

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import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.figure(figsize=(10, 4))

# First graph (sine graph)
plt.subplot(1, 2, 1)
plt.plot(x, y1, color='blue')
plt.title("Sine Function")

# Second graph (cosine graph)
plt.subplot(1, 2, 2)
plt.plot(x, y2, color='red')
plt.title("Cosine Function")

plt.tight_layout()
plt.show()

• plt.subplot(rows, columns, position): Arranges multiple graphs

• figsize=(10, 4): Adjusts the overall graph size

• plt.tight_layout(): Automatically adjusts the spacing between graphs

Using subplots allows for easy comparison of multiple data sets at once.

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Utilizing Matplotlib makes data analysis more intuitive and efficient.

• Matplotlib Official Documentation