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.
1. Histogram - Visualizing Data Distribution
A histogram is a useful graph for analyzing the distribution of data.
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()
Key Concepts
-
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.
2. Scatter Plot - Analyzing Relationships Between Data
Scatter plots are used to visually represent correlations between two variables.
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()
Key Concepts
-
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.
3. Pie Chart - Representing Proportions
Pie charts are used to visually represent the proportions of data.
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()
Key Concepts
-
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.
4. Subplots - Arranging Multiple Graphs
With Matplotlib, you can arrange multiple graphs on a single screen.
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()
Key Concepts
-
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.
Utilizing Matplotlib makes data analysis more intuitive and efficient.
Reference Materials
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