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# Matplotlib
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A comprehensive Python library for creating static, animated, and interactive visualizations. Matplotlib provides publication-quality figures across various output formats and interactive environments, serving as the foundational plotting library for the Python scientific ecosystem.
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## Package Information
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- **Package Name**: matplotlib
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- **Language**: Python
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- **Installation**: `pip install matplotlib`
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## Core Imports
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```python
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import matplotlib
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```
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Common for working with plots:
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```python
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import matplotlib.pyplot as plt
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```
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Object-oriented interface:
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```python
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from matplotlib.figure import Figure
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from matplotlib.axes import Axes
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import matplotlib.patches as mpatches
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import matplotlib.colors as mcolors
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```
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## Basic Usage
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```python
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import matplotlib.pyplot as plt
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import numpy as np
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# Create sample data
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x = np.linspace(0, 10, 100)
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y = np.sin(x)
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# Create a simple line plot
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plt.figure(figsize=(10, 6))
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plt.plot(x, y, 'b-', linewidth=2, label='sin(x)')  
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plt.xlabel('X values')
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plt.ylabel('Y values')
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plt.title('Simple Sine Wave Plot')
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plt.legend()
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plt.grid(True)
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plt.show()
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# Create a scatter plot
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plt.figure(figsize=(8, 6))
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plt.scatter(x[::10], y[::10], c='red', s=50, alpha=0.7)
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plt.xlabel('X values')
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plt.ylabel('Y values') 
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plt.title('Scatter Plot')
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plt.show()
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```
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## Architecture
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The Artist hierarchy enables matplotlib's flexibility and extensibility:
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- **Figure**: Top-level container managing canvas, subplots, and overall layout
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- **Axes**: Individual plot areas containing data graphics and formatting  
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- **Axis**: X and Y coordinate system components with ticks and labels
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- **Primitives**: Drawable elements (Line2D, Text, Rectangle, PathCollection, etc.)
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This design allows every visual element to be customized and provides the foundation for matplotlib's role as the base plotting library for the Python scientific ecosystem, including integration with NumPy, pandas, Jupyter notebooks, and hundreds of domain-specific visualization libraries.
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## Capabilities
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### Pyplot Interface
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State-based MATLAB-like interface providing simple plotting functions for quick visualizations. Includes figure management, basic plots, statistical plots, and 2D visualizations with automatic state handling.
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```python { .api }
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def plot(*args, **kwargs): ...
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def scatter(x, y, **kwargs): ...
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def bar(x, height, **kwargs): ...
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def hist(x, **kwargs): ...  
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def imshow(X, **kwargs): ...
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def figure(num=None, figsize=None, **kwargs): ...
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def show(**kwargs): ...
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def savefig(fname, **kwargs): ...
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```
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[Pyplot Interface](./pyplot.md)
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### Object-Oriented API
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Direct manipulation of Figure and Axes objects for complex layouts and fine control. This approach provides maximum flexibility for custom visualizations and programmatic plot generation.
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```python { .api }
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class Figure:
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    def add_subplot(self, *args, **kwargs): ...
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    def add_axes(self, rect, **kwargs): ...
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    def savefig(self, fname, **kwargs): ...
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class Axes:
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    def plot(self, *args, **kwargs): ...
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    def scatter(self, x, y, **kwargs): ...
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    def set_xlabel(self, xlabel, **kwargs): ...
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    def set_ylabel(self, ylabel, **kwargs): ...
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    def legend(self, **kwargs): ...
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```
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[Object-Oriented API](./object-oriented.md)
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### Color and Styling
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Comprehensive color management with built-in colormaps, color conversion utilities, and styling systems. Supports custom colormaps, normalization, and global style configuration.
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```python { .api }
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# Color conversion
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def to_rgba(c, alpha=None): ...
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def to_hex(c, keep_alpha=False): ...
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# Colormaps  
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class Colormap:
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    def __call__(self, X, alpha=None, bytes=False): ...
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# Configuration
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rcParams: dict
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def rc(group, **kwargs): ...
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def use(style): ...
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```
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[Color and Styling](./colors-styling.md)
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### Geometric Shapes
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Drawing primitive shapes and complex geometric objects. Includes rectangles, circles, polygons, arrows, and custom patches with styling options.
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```python { .api }
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class Rectangle:
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    def __init__(self, xy, width, height, **kwargs): ...
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class Circle:
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    def __init__(self, xy, radius, **kwargs): ...
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class Polygon:
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    def __init__(self, xy, **kwargs): ...
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class Arrow:
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    def __init__(self, x, y, dx, dy, **kwargs): ...
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```
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[Geometric Shapes](./shapes.md)
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### Animation and Interactivity  
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Creating animated plots and interactive visualizations. Supports function-based and artist-based animations with multiple export formats.
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```python { .api }
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class FuncAnimation:
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    def __init__(self, fig, func, frames=None, **kwargs): ...
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class ArtistAnimation:
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    def __init__(self, fig, artists, **kwargs): ...
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def save(self, filename, writer=None, **kwargs): ...
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```
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[Animation](./animation.md)
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### 3D Plotting
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Three-dimensional visualization capabilities through the mplot3d toolkit. Supports 3D line plots, surface plots, scatter plots, and wireframes.
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```python { .api }
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from mpl_toolkits.mplot3d import Axes3D
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class Axes3D:
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    def plot(self, xs, ys, zs, **kwargs): ...
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    def scatter(self, xs, ys, zs, **kwargs): ...
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    def plot_surface(self, X, Y, Z, **kwargs): ...
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    def plot_wireframe(self, X, Y, Z, **kwargs): ...
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```
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[3D Plotting](./3d-plotting.md)
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### Backends and Export
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Output format control and GUI integration. Supports multiple backends for different platforms and export formats including PNG, PDF, SVG, and EPS.
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```python { .api }
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def use(backend): ...
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def get_backend(): ...
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def switch_backend(newbackend): ...
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# Export formats
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def savefig(fname, dpi=None, format=None, bbox_inches=None): ...
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```
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[Backends and Export](./backends.md)
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## Global Configuration
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```python { .api }
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# Main configuration dictionary
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rcParams: dict
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# Configuration functions  
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def rc(group, **kwargs): ...
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def rcdefaults(): ...
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def rc_context(rc=None, fname=None): ...
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# Backend management
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def use(backend): ...
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def get_backend(): ...
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def interactive(b=None): ...
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```
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## Core Types
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```python { .api }
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from typing import Union, Optional, Sequence, Any
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from numpy.typing import ArrayLike
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# Common type aliases
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ColorType = Union[str, tuple, list]
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LineStyleType = Union[str, tuple]  
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MarkerType = Union[str, int]
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ArrayLike = Union[list, tuple, 'numpy.ndarray']
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```