Matplotlib

Introduction to Matplotlib

• External library for creating static visualizations in Python.
• Installed as: pip install matplotlib.
• Has axis and figure objects for flexible plotting. - axis: Represents a single plot area with x and y axes. - figure: Container for one or more axes (plots).
• Supports Charts like: line, bar, scatter, histogram, pie etc.
• figure and axis objects is created with plt.subplots()
• Using axis, we can customize titles, labels, grid, legends etc.
• Using figure, we can customize size, layout, save etc.

Plotting equations with Matplotlib

• import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2 * np.pi, 50) # 50 points from 0 to 2π y1 = np.sin(x) # Equation to plot y2 = np.cos(x) fig, ax = plt.subplots() ax.plot(x, y1, label="sin(x)") ax.plot(x, y2, label="cos(x)") ax.set_title("Trigonometric Functions") ax.set_xlabel("Angle (radians)") ax.set_ylabel("Function Value") ax.legend() plt.show()

Multiple plots in one figure

• plt.subplots(1, 2) creates a figure with 1 rows and 2 columns.
• We can plot different equations in each subplot.
• Example: fig, ax = plt.subplots(1, 2) x = np.linspace(0, 2 * np.pi, 50) y_sin = np.sin(x) y_cos = np.cos(x) # First subplot ax[0].plot(x, y_sin, label="sin(x)", color="blue") ax[0].set_title("Sine Function") ax[0].set_xlabel("Angle (radians)") ax[0].set_ylabel("sin(x)") # Second subplot ax[1].plot(x, y_cos, label="cos(x)", color="orange") ax[1].set_title("Cosine Function") ax[1].set_xlabel("Angle (radians)") ax[1].set_ylabel("cos(x)") plt.tight_layout() plt.show()

Customizing Matplotlib Charts

Barplot in Matplotlib

• Used to compare values across categories. i.e category vs numeric
• Created with ax.bar() or ax.barh().
• Example:products = ["Product A", "Product B", "Product C"] sales = [100, 200, 150] fig, ax = plt.subplots() sales_bar = ax.bar(products, sales, color=["red", "green", "blue"]) # ax.barh(sales, products) # For horizontal bar plot ax.bar_label(sales_bar, label_type='center') ax.set_title("Bar Plot Example") ax.set_xlabel("Category") ax.set_ylabel("Sales") plt.show()

Stacked Barplot in Matplotlib

• Used to show composition of categories with sub-categories.
• Created by stacking bars on top or right of other.
• Example: import numpy as np import matplotlib.pyplot as plt countries = ["Country A", "Country B", "Country C"] gold_mdl = [10, 20, 15] silver_mdl = [5, 10, 8] fig, ax = plt.subplots() silver_plt = ax.bar(countries, silver_mdl, label="Silver") gold_plt = ax.bar(countries, gold_mdl, bottom=silver_mdl, label="Gold") ax.bar_label(gold_plt, label_type='center') ax.bar_label(silver_plt, label_type='center') ax.set_title("Medal Counts by Country") ax.set_xlabel("Country") ax.set_ylabel("Medals") ax.legend() plt.show()

Pie Chart and Donut Chart in Matplotlib

• Used to show parts of a whole. category vs numeric.
• Created with ax.pie().
• Example: clubs = ["Man City", "Man United", "Liverpool", "Chelsea"] titles = [9, 13, 1, 5] fig, ax = plt.subplots() ax.pie(titles, labels=clubs, autopct="%1.1f%%") plt.show() # For donut chart ax.pie(titles, labels=clubs, autopct="%1.1f%%", wedgeprops={"width": 0.6}) ax.set_title("League Wins by Each Team") plt.show()

Plotting Histogram in Matplotlib

• Used to show distribution of a single continuous variable.
• Shows frequency of data points in specified ranges (bins).
• Created with ax.hist(). bins = √n shows proper distribution.
• cumulative=True => cumulative distribution, density=True => pdf.
• Example: import numpy as np import matplotlib.pyplot as plt data = np.random.randn(1000) fig, ax = plt.subplots() ax.hist(data, bins=30, color="skyblue", edgecolor="black") ax.set_title("Histogram Example") ax.set_xlabel("Value") ax.set_ylabel("Frequency") plt.show()

Box Plot in Matplotlib

• To show distribution & outliers of a continuous variable across categories.
• Displays median, quartiles, and potential outliers.
• Created with ax.boxplot().
• Example: import numpy as np import matplotlib.pyplot as plt data = [np.random.randn(100) + i for i in range(4)] fig, ax = plt.subplots() ax.boxplot(data, tick_labels=["Group 1", "Group 2", "Group 3", "Group 4"]) ax.set_title("Box Plot Example") ax.set_xlabel("Group") ax.set_ylabel("Value") plt.show()

Scatter Plot in Matplotlib

• Used to show relationship between two continuous variables.
• Created with ax.scatter().
• marker, size, color can be customized as per requirement.
• Example: import numpy as np import matplotlib.pyplot as plt x = np.random.rand(100) y = np.random.rand(100) fig, ax = plt.subplots() ax.scatter(x, y, color="purple", alpha=0.5) ax.set_title("Scatter Plot Example") ax.set_xlabel("X-axis") ax.set_ylabel("Y-axis") plt.show()

Setting Styles in Matplotlib

• Matplotlib has built-in styles to improve aesthetics.
• Set style with plt.style.use("style_name").

Saving Plots in Matplotlib

• Use plt.savefig() to save plots in various formats.
• Specify resolution with dpi parameter.
• Control layout with bbox_inches. "tight" removes extra whitespace.
• Format can be controlled with format. Values: png, pdf, svg, jpg etc.
• Figure size can be set with figsize in plt.subplots(). Values are in inches.
• Example: fig, ax = plt.subplots(2, 2, figsize=(10, 6)) plt.savefig("plot.png", dpi=300, bbox_inches="tight") plt.savefig("plot.pdf", format="pdf", bbox_inches="tight")