Programming for Data Science, Lec 10: Data Visualization and Plotting in Python Using Matplotlib

Published: 04 March 2024
on channel: Dr. Data Science

337

#datascience #matplotlib #machinelearning
`Matplotlib` is one of the most essential and widely used libraries for creating static, interactive, and animated plots in Python.
It provides a versatile and comprehensive set of tools for creating a wide range of plots, including line plots, scatter plots, bar plots, histograms, 3D plots, and more.
Users have fine-grained control over the appearance of plots, enabling customization of colors, styles, labels, and annotations to convey information effectively.

To utilize Matplotlib, the library needs to be imported.
`matplotlib.pyplot` is a module within the Matplotlib library.

You can save a figure in Matplotlib using the `plt.savefig()` function. This function allows you to save the current figure to a file.
The `dpi` parameter in Matplotlib's `plt.savefig()` function stands for "dots per inch." It determines the resolution or quality of the saved figure by specifying how many dots (pixels) should be used per inch. The higher the DPI, the higher the resolution of the saved image.

A histogram is a graphical representation of the distribution of a dataset. It is used to visualize the underlying probability distribution of a continuous variable. The Probability Density Function (PDF) represents the likelihood of a variable taking on a specific value.

Here is the basic syntax for `plt.hist()`:
```
plt.hist(x, bins=None, range=None, density=False)
```

Scatter plots and line plots are both used to visualize data, but they serve different purposes and convey different types of information.
Scatter plots visualize the relationship between two variables.
Data points are represented as individual markers.

`x`: (array or sequence of arrays) The input data to be histogrammed. It can be a single array or a sequence of arrays if you want to plot multiple datasets.

`bins`: (int or sequence or str, optional) If bins is an integer, it defines the number of equal-width bins in the range.

`range`: The lower and upper range of the bins. If not provided, range is `(x.min(), x.max())`.

`density`: (bool, optional) If `True`, draw and return a probability density: each bin will display the bin's raw count divided by the total number of counts and the bin width, so that the area under the histogram integrates to 1.

3D plots

Creating 3D plots in Matplotlib allows you to visualize data in three-dimensional space. Here are the key points about 3D plots.

Importing the Required Module:

Import the `Axes3D` module `from mpl_toolkits.mplot3d` for creating 3D axes.

Creating 3D Axes:

Use `fig.add_subplot(111, projection='3d')` to add 3D axes to the figure.

Plotting 3D Data:

Use `ax.plot3D()` for line plots.

Adding Labels and Title:

Set labels for the x, y, and z-axes using `ax.set_xlabel()`, `ax.set_ylabel()`, and `ax.set_zlabel()`.

Customizing 3D Plot:

Adjust viewing angles using `ax.view_init(elev, azim)` where `elev` is the elevation angle and azim is the `azimuthal` angle.

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