We'll now take an in-depth look at the Matplotlib package for visualization in Python. Matplotlib is a multi-platform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack. It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot from the IPython command line. IPython's creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months. John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003. It received an early boost when it was adopted as the plotting package of choice of the Space Telescope Science Institute (the folks behind the Hubble Telescope), which financially supported Matplotlib’s development and greatly expanded its capabilities.

One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends. Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish. This cross-platform, everything-to-everyone approach has been one of the great strengths of Matplotlib. It has led to a large user base, which in turn has led to an active developer base and Matplotlib’s powerful tools and ubiquity within the scientific Python world.

Just as we use the `np`

shorthand for NumPy and the `pd`

shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:

In [1]:

```
import matplotlib as mpl
import matplotlib.pyplot as plt
```

We will use the `plt.style`

directive to choose appropriate aesthetic styles for our figures.
Here we will set the `classic`

style, which ensures that the plots we create use the classic Matplotlib style:

In [2]:

```
plt.style.use('classic')
```

Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package.

The IPython notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements.

Plotting interactively within an IPython notebook can be done with the `%matplotlib`

command, and works in a similar way to the IPython shell.
In the IPython notebook, you also have the option of embedding graphics directly in the notebook, with two possible options:

`%matplotlib notebook`

will lead to*interactive*plots embedded within the notebook`%matplotlib inline`

will lead to*static*images of your plot embedded in the notebook

For this book, we will generally opt for `%matplotlib inline`

:

In [3]:

```
%matplotlib inline
```

In [11]:

```
import numpy as np
x = np.linspace(0, 10, 100)
fig = plt.figure()
plt.plot(x, np.sin(x), '-')
plt.plot(x, np.cos(x), '--'); # pay attention to the last ";" to ignore the Line2D object
```

In [8]:

```
type(_[0])
```

Out[8]:

matplotlib.lines.Line2D

One nice feature of Matplotlib is the ability to save figures in a wide variety of formats.
Saving a figure can be done using the `savefig()`

command.
For example, to save the previous figure as a PNG file, you can run this:

In [12]:

```
fig.savefig('my_figure.png')
```

We now have a file called `my_figure.png`

in the current working directory:

In [14]:

```
!ls -lh my_figure.png
```

-rw-r--r-- 1 mn staff 26K Apr 8 12:10 my_figure.png

`savefig()`

, the file format is inferred from the extension of the given filename.
Depending on what backends you have installed, many different file formats are available.
The list of supported file types can be found for your system by using the following method of the figure canvas object:

In [15]:

```
fig.canvas.get_supported_filetypes()
```

Out[15]:

{'eps': 'Encapsulated Postscript', 'jpg': 'Joint Photographic Experts Group', 'jpeg': 'Joint Photographic Experts Group', 'pdf': 'Portable Document Format', 'pgf': 'PGF code for LaTeX', 'png': 'Portable Network Graphics', 'ps': 'Postscript', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics', 'tif': 'Tagged Image File Format', 'tiff': 'Tagged Image File Format'}

A potentially confusing feature of Matplotlib is its dual interfaces: a convenient MATLAB-style state-based interface, and a more powerful object-oriented interface. We'll quickly highlight the differences between the two here.

Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact.
The MATLAB-style tools are contained in the pyplot (`plt`

) interface.
For example, the following code will probably look quite familiar to MATLAB users:

In [18]:

```
fig = plt.figure() # create a plot figure
# create the first of two panels and set current axis
plt.subplot(2, 1, 1) # (rows, columns, panel number)
plt.plot(x, np.sin(x))
# create the second panel and set current axis
plt.subplot(2, 1, 2)
plt.plot(x, np.cos(x), '--');
```

The object-oriented interface is available for these more complicated situations, and for when you want more control over your figure.
Rather than depending on some notion of an "active" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit `Figure`

and `Axes`

objects.
To re-create the previous plot using this style of plotting, you might do the following:

In [19]:

```
# First create a grid of plots
# ax will be an array of two Axes objects
fig, ax = plt.subplots(2)
# Call plot() method on the appropriate object
ax[0].plot(x, np.sin(x))
ax[1].plot(x, np.cos(x), '--');
```

Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$. Here we will take a first look at creating a simple plot of this type. As with all the following sections, we'll start by setting up the notebook for plotting and importing the packages we will use:

In [20]:

```
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
```

In [21]:

```
fig = plt.figure()
ax = plt.axes()
```

*figure* (an instance of the class `plt.Figure`

) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.
The *axes* (an instance of the class `plt.Axes`

) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.
Throughout this book, we'll commonly use the variable name `fig`

to refer to a figure instance, and `ax`

to refer to an axes instance or group of axes instances.

`ax.plot`

function to plot some data. Let's start with a simple sinusoid:

In [13]:

```
fig = plt.figure()
ax = plt.axes()
x = np.linspace(0, 10, 1000)
ax.plot(x, np.sin(x));
```

In [14]:

```
plt.plot(x, np.sin(x));
```

`plot`

function multiple times:

In [22]:

```
plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x), '--');
```

The first adjustment you might wish to make to a plot is to control the line colors and styles.
The `plt.plot()`

function takes additional arguments that can be used to specify these.
To adjust the color, you can use the `color`

keyword, which accepts a string argument representing virtually any imaginable color.

In [23]:

```
plt.plot(x, np.sin(x - 0), '--', color='blue') # specify color by name
plt.plot(x, np.sin(x - 1), color='g') # short color code (rgbcmyk)
plt.plot(x, np.sin(x - 2), color='0.75') # Grayscale between 0 and 1
plt.plot(x, np.sin(x - 3), color='#FFDD44') # Hex code (RRGGBB from 00 to FF)
plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1
plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported
```

Similarly, the line style can be adjusted using the `linestyle`

keyword:

In [25]:

```
plt.plot(x, x + 0, linestyle='solid')
plt.plot(x, x + 1, linestyle='dashed') # '--'
plt.plot(x, x + 2, linestyle='dashdot')
plt.plot(x, x + 3, linestyle='dotted');
# For short, you can use the following codes:
plt.plot(x, x + 4, linestyle='-') # solid
plt.plot(x, x + 5, linestyle='--') # dashed
plt.plot(x, x + 6, linestyle='-.') # dashdot
plt.plot(x, x + 7, linestyle=':'); # dotted
```

`linestyle`

and `color`

codes can be combined into a single non-keyword argument to the `plt.plot()`

function:

In [18]:

```
plt.plot(x, x + 0, '-g') # solid green
plt.plot(x, x + 1, '--c') # dashed cyan
plt.plot(x, x + 2, '-.k') # dashdot black
plt.plot(x, x + 3, ':r'); # dotted red
```

In [26]:

```
import this
```

Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.
The most basic way to adjust axis limits is to use the `plt.xlim()`

and `plt.ylim()`

methods:

In [19]:

```
plt.plot(x, np.sin(x))
plt.xlim(-1, 11)
plt.ylim(-1.5, 1.5);
```

In [20]:

```
plt.plot(x, np.sin(x))
plt.xlim(10, 0)
plt.ylim(1.2, -1.2);
```

`plt.axis()`

(note here the potential confusion between *axes* with an *e*, and *axis* with an *i*).
The `plt.axis()`

method allows you to set the `x`

and `y`

limits with a single call, by passing a list which specifies `[xmin, xmax, ymin, ymax]`

:

In [21]:

```
plt.plot(x, np.sin(x))
plt.axis([-1, 11, -1.5, 1.5]);
```

`plt.axis()`

method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot:

In [22]:

```
plt.plot(x, np.sin(x))
plt.axis('tight');
```

`x`

is equal to one unit in `y`

:

In [23]:

```
plt.plot(x, np.sin(x))
plt.axis('equal');
```

As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.

Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them:

In [24]:

```
plt.plot(x, np.sin(x))
plt.title("A Sine Curve")
plt.xlabel("x")
plt.ylabel("sin(x)");
```

`plt.legend()`

method.
Though there are several valid ways of using this, I find it easiest to specify the label of each line using the `label`

keyword of the plot function:

In [25]:

```
plt.plot(x, np.sin(x), '-g', label='sin(x)')
plt.plot(x, np.cos(x), ':b', label='cos(x)')
plt.axis('equal')
plt.legend();
```

While most `plt`

functions translate directly to `ax`

methods (such as `plt.plot()`

→ `ax.plot()`

, `plt.legend()`

→ `ax.legend()`

, etc.), this is not the case for all commands.
In particular, functions to set limits, labels, and titles are slightly modified.
For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:

`plt.xlabel()`

→`ax.set_xlabel()`

`plt.ylabel()`

→`ax.set_ylabel()`

`plt.xlim()`

→`ax.set_xlim()`

`plt.ylim()`

→`ax.set_ylim()`

`plt.title()`

→`ax.set_title()`

In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the `ax.set()`

method to set all these properties at once:

In [26]:

```
ax = plt.axes()
ax.plot(x, np.sin(x))
ax.set(xlim=(0, 10), ylim=(-2, 2), xlabel='x', ylabel='sin(x)', title='A Simple Plot'); # focus on metadata only
```

Another commonly used plot type is the simple scatter plot, a close cousin of the line plot. Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape. We’ll start by setting up the notebook for plotting and importing the functions we will use:

In [27]:

```
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
```

`plt.plot`

¶In the previous section we looked at `plt.plot`

/`ax.plot`

to produce line plots.
It turns out that this same function can produce scatter plots as well:

In [30]:

```
x = np.linspace(0, 10, 30)
y = np.sin(x)
plt.plot(x, y, 'o', color='black');
```

`'-'`

, `'--'`

to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of `plt.plot`

, or in Matplotlib's online documentation. Most of the possibilities are fairly intuitive, and we'll show a number of the more common ones here:

In [31]:

```
rng = np.random.RandomState(0)
for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:
plt.plot(rng.rand(5), rng.rand(5), marker, label="marker='{0}'".format(marker))
plt.legend(numpoints=1)
plt.xlim(0, 1.8);
```

In [32]:

```
plt.plot(x, y, '-ok');
```

`plt.plot`

specify a wide range of properties of the lines and markers:

In [31]:

```
plt.plot(x, y, '-p', color='gray',
markersize=15, linewidth=4,
markerfacecolor='white',
markeredgecolor='gray',
markeredgewidth=2)
plt.ylim(-1.2, 1.2);
```