152 lines
2.6 KiB
Markdown
152 lines
2.6 KiB
Markdown
For creating and manipulating complex data visualizations, `Matplotlib` is an indispensable Python library. It's widely used for generating plots, histograms, bar charts, scatterplots, and more, offering extensive customization options to make the visualizations as informative and appealing as possible. Below is a concise reference guide for common use cases with `Matplotlib`, formatted in Markdown syntax:
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# `Matplotlib` Reference Guide
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## Installation
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```
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pip install matplotlib
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```
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## Basic Plotting
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### Importing Matplotlib
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```python
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import matplotlib.pyplot as plt
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```
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### Creating a Simple Plot
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```python
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# Prepare some data
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x = [1, 2, 3, 4]
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y = [10, 20, 25, 30]
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# Plot data
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plt.plot(x, y)
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# Show plot
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plt.show()
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```
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### Creating a Scatter Plot
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```python
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# Scatter plot
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plt.scatter(x, y)
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# Show plot
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plt.show()
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```
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### Multiple Plots on Same Axes
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```python
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# Second set of data
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x2 = [1, 2, 3, 4]
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y2 = [30, 25, 20, 15]
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# Plot data
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plt.plot(x, y, label='First Line')
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plt.plot(x2, y2, label='Second Line')
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# Adding a legend
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plt.legend()
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# Show plot
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plt.show()
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```
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## Customizing Plots
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### Titles, Labels, and Legends
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```python
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plt.plot(x, y)
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# Title
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plt.title('My First Plot')
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# Axis labels
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plt.xlabel('X Axis Label')
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plt.ylabel('Y Axis Label')
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# Show plot
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plt.show()
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```
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### Line Styles and Markers
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```python
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plt.plot(x, y, color='red', linestyle='--', marker='o', label='Data Points')
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# Show plot
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plt.show()
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```
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### Setting Axis Ranges
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```python
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plt.plot(x, y)
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# Setting the range for the axes
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plt.xlim(0, 5)
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plt.ylim(5, 35)
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# Show plot
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plt.show()
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```
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### Adding Grid Lines
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```python
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plt.plot(x, y)
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# Adding grid
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plt.grid(True)
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# Show plot
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plt.show()
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```
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## Other Types of Plots
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### Histograms
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```python
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data = [1, 2, 2, 3, 3, 3, 4, 4, 5]
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# Create histogram
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plt.hist(data, bins=5, alpha=0.5, color='blue')
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# Show plot
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plt.show()
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```
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### Bar Charts
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```python
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categories = ['A', 'B', 'C', 'D']
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values = [10, 20, 15, 5]
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# Create bar chart
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plt.bar(categories, values)
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# Show plot
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plt.show()
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```
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### Pie Charts
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```python
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slices = [7, 2, 2, 13]
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categories = ['A', 'B', 'C', 'D']
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# Create pie chart
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plt.pie(slices, labels=categories, autopct='%1.1f%%')
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# Show plot
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plt.show()
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```
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## Saving Figures
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```python
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plt.plot(x, y)
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# Save the figure
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plt.savefig('plot.png')
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# Show plot
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plt.show()
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```
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`Matplotlib` is a powerful library for creating static, interactive, and animated visualizations in Python. This guide covers the basics of generating and customizing plots, but Matplotlib's functionality is vast, supporting a wide range of plot types and customization options to suit various data visualization needs. |