10 Advanced Plots for Effective Data Visualization with Matplotlib

Introduction

Matplotlib is a fundamental Python library, empowers you to create various visualizations to explore and communicate your data effectively. While basic plots like bar charts and scatter plots are essential, delving into advanced visualizations can unlock deeper insights and enhance your storytelling.

Here are the top 10 advanced plots you can create with Matplotlib!

3D Surface Plots

3D Surface plots are useful for exploring relationships between three variables. With Matplotlib, creating a 3D plot is straightforward, allowing for the visualization of landscapes or surfaces. These plots are particularly useful in fields like meteorology, engineering, and finance, where understanding the interaction between variables is crucial.

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

# Define the figure and 3D axis

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

# Create data

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

x, y = np.meshgrid(x, y)

z = np.sin(np.sqrt(x**2 + y**2))

# Plot the surface

surf = ax.plot_surface(x, y, z, cmap='viridis')

# Add a color bar which maps values to colors

fig.colorbar(surf, shrink=0.5, aspect=5)

# Set labels

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

# Show the plot

plt.show()

Heatmaps

Heatmaps are a great way to represent data where the colors represent the magnitude of a phenomenon. They are widely used for representing correlation matrices, where they can highlight relationships between variables, or for displaying geographical data density.

import matplotlib.pyplot as plt

import numpy as np

# Generating data

data = np.random.rand(10, 10) # Random data for demonstration

# Creating the heatmap

plt.imshow(data, cmap='hot', interpolation='nearest')

# Customization

plt.colorbar()

plt.title('Heatmap Example')

plt.show()

Also Read: How to Plot Heatmaps in Seaborn?

Contour Plots

Contour plots are useful for representing three-dimensional data in two dimensions using contour lines. These lines connect points of equal value. Contour plots are commonly used in meteorology and geography to represent elevation or for visualizing the optimization landscapes in machine learning.

import matplotlib.pyplot as plt

import numpy as np

# Generate coordinate matrices from coordinate vectors

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

X, Y = np.meshgrid(x, y)

# Define the function z = sin(sqrt(x^2 + y^2))

Z = np.sin(np.sqrt(X**2 + Y**2))

# Create a contour plot

plt.figure(figsize=(8, 6))

contour = plt.contour(X, Y, Z, colors='black')

# Plot contour labels

plt.clabel(contour, inline=True, fontsize=8)

# Adding titles and labels

plt.title('Contour Plot')

plt.xlabel('x axis')

plt.ylabel('y axis')

# Display filled contours

plt.contourf(X, Y, Z, cmap='viridis') # Add a color fill

plt.colorbar() # Show color scale

plt.show()

Stream Plots

Stream plots, or flow fields, are ideal for representing fluid flow, air currents, or any other vector field. They give a sense of movement and direction, showing how a vector field flows across a plane, which can be crucial for engineering and environmental studies.

Time Series Analysis

Visualizing time series data is a fundamental aspect of data analysis in finance, meteorology, and engineering. Matplotlib can plot complex time series data, making it easier to identify trends, cycles, and seasonality.

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

# Generate a date range

dates = pd.date_range(start='2021-01-01', end='2021-12-31', freq='D')

# Generate random data to simulate a time series

data = np.random.randn(len(dates)).cumsum()

# Create a DataFrame

df = pd.DataFrame(data, index=dates, columns=['Value'])

# Creating the time series plot

plt.figure(figsize=(10, 6))

plt.plot(df.index, df['Value'], marker='o', linestyle='-', markersize=2)

# Customization

plt.title('Time Series Plot Example')

plt.xlabel('Date')

plt.ylabel('Value')

plt.grid(True)

# Rotate date labels for better readability

plt.xticks(rotation=45)

# Display the plot

plt.show()

Violin Plots

Violin plots represent numeric data and combine a box plot with a kernel density plot. They prove particularly useful for comparing data distributions across different categories.

import matplotlib.pyplot as plt

import seaborn as sns

import numpy as np

# Generate sample data

np.random.seed(10)  # For reproducible results

data = np.random.normal(loc=0, scale=1, size=100)

categories = ['A']*50 + ['B']*50

data = np.append(data, np.random.normal(loc=0.5, scale=1.5, size=100))

categories = np.array(categories + ['A']*50 + ['B']*50)

# Create a DataFrame for easy plotting with Seaborn

import pandas as pd

df = pd.DataFrame({'Category': categories, 'Value': data})

# Creating the violin plot

plt.figure(figsize=(8, 6))

sns.violinplot(x='Category', y='Value', data=df)

# Customization

plt.title('Violin Plot Example')

plt.xlabel('Category')

plt.ylabel('Value')

# Display the plot

plt.show()

Vector Field Plots

Visualize the direction and magnitude of a vector field using arrows. This is valuable for understanding phenomena like wind patterns, fluid flow, or magnetic fields.

import numpy as np

import matplotlib.pyplot as plt

# Define the grid of points

x, y = np.meshgrid(np.linspace(-5, 5, 20), np.linspace(-5, 5, 20))

# Define the vector field

u = -y

v = x

# Creating the vector field plot

plt.figure(figsize=(8, 6))

plt.quiver(x, y, u, v, color='r')

# Customization

plt.title('Vector Field Plot')

plt.xlabel('X axis')

plt.ylabel('Y axis')

plt.xlim([-5, 5])

plt.ylim([-5, 5])

# Display the plot

plt.show()

Subplots and GridSpec

Creating multiple subplots in a single figure is an advanced feature of Matplotlib that allows for the comparison of different datasets or different views of the same data. With GridSpec, you can customize the layout of multiple plots within a figure, controlling their placement, size, and aspect ratio.

import matplotlib.pyplot as plt

import matplotlib.gridspec as gridspec

import numpy as np

# Create figure and GridSpec layout

fig = plt.figure(figsize=(10, 8))

gs = gridspec.GridSpec(3, 3, fig) # 3 rows, 3 columns

# Create subplots in the layout

ax1 = fig.add_subplot(gs[0, :]) # Top row, span all columns

ax2 = fig.add_subplot(gs[1, :-1]) # Middle row, span first two columns

ax3 = fig.add_subplot(gs[1:, 2]) # Last two rows, last column

ax4 = fig.add_subplot(gs[2, 0]) # Bottom row, first column

ax5 = fig.add_subplot(gs[2, 1]) # Bottom row, second column

# Generate random data for plots

data1 = np.random.randn(100).cumsum()

data2 = np.random.rand(10)

data3 = np.random.rand(10,10)

data4 = np.random.randn(100)

data5 = np.random.randn(100).cumsum()

# Plot data

ax1.plot(data1, label='Line Plot')

ax1.set_title('Top Span Plot')

ax1.legend()

ax2.bar(np.arange(len(data2)), data2, color='orange', label='Bar Plot')

ax2.set_title('Bar Plot')

ax2.legend()

ax3.imshow(data3, cmap='viridis', aspect='auto', label='Heatmap')

ax3.set_title('Heatmap')

ax4.hist(data4, bins=20, color='green', label='Histogram')

ax4.set_title('Histogram')

ax4.legend()

ax5.scatter(np.arange(len(data5)), data5, color='red', label='Scatter Plot')

ax5.set_title('Scatter Plot')

ax5.legend()

# Adjust layout to make room for titles and labels

plt.tight_layout()

# Show plot

plt.show

Also Read: Tableau for Beginners – Data Visualisation made easy

Choropleth Maps

Though primarily achieved through extensions like GeoPandas, one can use Matplotlib to create choropleth maps shading or patterning areas in proportion to a statistical variable. This functionality proves particularly useful for geographic data visualization, illustrating variations in a measurement across a geographic area.

import plotly.express as px

import pandas as pd

# Sample data: State names and a random value for demonstration

data = {

   "State": ["CA", "TX", "NY", "FL", "IL", "PA", "OH", "GA", "NC", "MI"],

   "Value": [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

}

df = pd.DataFrame(data)

# Create the choropleth map

fig = px.choropleth(df,

                   locations='State',          # DataFrame column with locations

                   locationmode="USA-states",   # Set to plot as US States

                   color='Value',               # DataFrame column with color values

                   color_continuous_scale=px.colors.sequential.Plasma,  # Color scale

                   scope="usa",                 # Focus map on the USA

                   labels={'Value':'Sample Value'})  # Label for the colorbar

fig.update_layout(title_text = 'USA States Choropleth Map')  # Add a title

# Show the plot

fig.show()

Quiver Plots

Similar to vector field plots, quiver plots represent vectors using arrows, but with additional control over arrow properties like size and color. This allows for customization based on specific data characteristics.

import numpy as np

import matplotlib.pyplot as plt

# Define the grid of points

x, y = np.meshgrid(np.linspace(-2, 2, 20), np.linspace(-2, 2, 20))

# Define the vector components

u = -y

v = x

# Creating the quiver plot

plt.figure(figsize=(6, 6))

plt.quiver(x, y, u, v, color='blue')

# Customization

plt.title('Quiver Plot Example')

plt.xlabel('X axis')

plt.ylabel('Y axis')

plt.xlim([-2, 2])

plt.ylim([-2, 2])

plt.axhline(0, color='black',linewidth=0.5)

plt.axvline(0, color='black',linewidth=0.5)

plt.grid(True)

# Show the plot

plt.show()

Conclusion

Matplotlib’s versatility makes it an invaluable tool for advanced data visualization. By mastering these ten types of plots, you can uncover deeper insights into your data and convey complex ideas more effectively. Remember, the key to successful data visualization lies not just in choosing the right type of plot but also in customizing it to best represent your data’s story.

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