Background¶

this notebook is focused on documenting the use of Matplotlib and Seaborn to plot various data in Python.

You can find the docs at:

Setup¶

for plotting in Jupyter, see https://ipython.readthedocs.io/en/stable/interactive/reference.html#plotting-with-matplotlib

That documentation states:

If IPython is already running, you can run the `%matplotlib` magic. If no arguments are given, IPython will automatically detect your choice of matplotlib backend. You can also request a specific backend with `%matplotlib backend`, where `backend` must be one of: ‘tk’, ‘qt’, ‘wx’, ‘gtk’, ‘osx’. In the web notebook and Qt console, ‘inline’ is also a valid backend value, which produces static figures inlined inside the application window instead of matplotlib’s interactive figures that live in separate windows.

https://github.com/matplotlib/jupyter-matplotlib is the extension for interactive To install ipympl with pip:

pip install ipympl
# If using JupyterLab
# Install nodejs: https://nodejs.org/en/download/
jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install jupyter-matplotlib

To enable the jupyter-matplotlib backend, simply use the matplotlib Jupyter magic:

%matplotlib widget

In [1]:

# magic for plotting in notebook
%matplotlib inline

In [2]:

import matplotlib.pyplot as plt

In [4]:

import seaborn as sns

Get data¶

In [7]:

# Seaborn has some datasets available
sns.get_dataset_names()

/usr/lib/python3/dist-packages/seaborn/utils.py:376: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("lxml"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.

The code that caused this warning is on line 376 of the file /usr/lib/python3/dist-packages/seaborn/utils.py. To get rid of this warning, pass the additional argument 'features="lxml"' to the BeautifulSoup constructor.

  gh_list = BeautifulSoup(http)

Out[7]:

['anscombe',
 'attention',
 'brain_networks',
 'car_crashes',
 'diamonds',
 'dots',
 'exercise',
 'flights',
 'fmri',
 'gammas',
 'geyser',
 'iris',
 'mpg',
 'penguins',
 'planets',
 'tips',
 'titanic']

In [8]:

iris = sns.load_dataset('iris')

In [18]:

type(iris)

Out[18]:

pandas.core.frame.DataFrame

In [9]:

# datasets are pandas dataframes
iris

Out[9]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa
5	5.4	3.9	1.7	0.4	setosa
6	4.6	3.4	1.4	0.3	setosa
7	5.0	3.4	1.5	0.2	setosa
8	4.4	2.9	1.4	0.2	setosa
9	4.9	3.1	1.5	0.1	setosa
10	5.4	3.7	1.5	0.2	setosa
11	4.8	3.4	1.6	0.2	setosa
12	4.8	3.0	1.4	0.1	setosa
13	4.3	3.0	1.1	0.1	setosa
14	5.8	4.0	1.2	0.2	setosa
15	5.7	4.4	1.5	0.4	setosa
16	5.4	3.9	1.3	0.4	setosa
17	5.1	3.5	1.4	0.3	setosa
18	5.7	3.8	1.7	0.3	setosa
19	5.1	3.8	1.5	0.3	setosa
20	5.4	3.4	1.7	0.2	setosa
21	5.1	3.7	1.5	0.4	setosa
22	4.6	3.6	1.0	0.2	setosa
23	5.1	3.3	1.7	0.5	setosa
24	4.8	3.4	1.9	0.2	setosa
25	5.0	3.0	1.6	0.2	setosa
26	5.0	3.4	1.6	0.4	setosa
27	5.2	3.5	1.5	0.2	setosa
28	5.2	3.4	1.4	0.2	setosa
29	4.7	3.2	1.6	0.2	setosa
...	...	...	...	...	...
120	6.9	3.2	5.7	2.3	virginica
121	5.6	2.8	4.9	2.0	virginica
122	7.7	2.8	6.7	2.0	virginica
123	6.3	2.7	4.9	1.8	virginica
124	6.7	3.3	5.7	2.1	virginica
125	7.2	3.2	6.0	1.8	virginica
126	6.2	2.8	4.8	1.8	virginica
127	6.1	3.0	4.9	1.8	virginica
128	6.4	2.8	5.6	2.1	virginica
129	7.2	3.0	5.8	1.6	virginica
130	7.4	2.8	6.1	1.9	virginica
131	7.9	3.8	6.4	2.0	virginica
132	6.4	2.8	5.6	2.2	virginica
133	6.3	2.8	5.1	1.5	virginica
134	6.1	2.6	5.6	1.4	virginica
135	7.7	3.0	6.1	2.3	virginica
136	6.3	3.4	5.6	2.4	virginica
137	6.4	3.1	5.5	1.8	virginica
138	6.0	3.0	4.8	1.8	virginica
139	6.9	3.1	5.4	2.1	virginica
140	6.7	3.1	5.6	2.4	virginica
141	6.9	3.1	5.1	2.3	virginica
142	5.8	2.7	5.1	1.9	virginica
143	6.8	3.2	5.9	2.3	virginica
144	6.7	3.3	5.7	2.5	virginica
145	6.7	3.0	5.2	2.3	virginica
146	6.3	2.5	5.0	1.9	virginica
147	6.5	3.0	5.2	2.0	virginica
148	6.2	3.4	5.4	2.3	virginica
149	5.9	3.0	5.1	1.8	virginica

150 rows × 5 columns

In [ ]:

Colors¶

seaborns's color palettes is documented at https://seaborn.pydata.org/tutorial/color_palettes.html#palette-tutorial

color_palette() will accept the name of any seaborn palette or matplotlib colormap (except jet, which you should never use). It can also take a list of colors specified in any valid matplotlib format (RGB tuples, hex color codes, or HTML color names). The return value is always a list of RGB tuples.

In [26]:

# get default color palette tuples
sns.color_palette()

Out[26]:

[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),
 (1.0, 0.4980392156862745, 0.054901960784313725),
 (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),
 (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),
 (0.5803921568627451, 0.403921568627451, 0.7411764705882353),
 (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),
 (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),
 (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),
 (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),
 (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]

In [28]:

# or to look at them
sns.palplot(sns.color_palette())

In [35]:

# predefined palettes `Blues`
sns.palplot(sns.color_palette("Blues"))

In [41]:

# predefined palettes dark version
sns.palplot(sns.color_palette("Blues_d"))

In [39]:

# set size of palette
sns.palplot(sns.color_palette("Blues", 10))

In [40]:

# predefined palettes
sns.palplot(sns.color_palette("Greens"))

In [ ]:

Scatter¶

seaborns's scatterplot() is documented at https://seaborn.pydata.org/generated/seaborn.scatterplot.html#seaborn.scatterplot

In [21]:

# start simple
sns.scatterplot(x="petal_length", y="petal_width", data=iris[iris.species == "setosa"])

Out[21]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fc2350fce10>

In [20]:

# can separate another column by color using `hue`
sns.scatterplot(x="petal_length", y="petal_width", data=iris, hue="species")

Out[20]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fc23511b898>

In [30]:

# can choose colors redefining the `palette`
sns.scatterplot(x="petal_length", y="petal_width", data=iris, hue="species", palette=["r", "g", "b"])

Out[30]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fc234ea2be0>

In [22]:

# can also separate another column by size using `size`
sns.scatterplot(x="petal_length", y="petal_width", data=iris, size="species")

Out[22]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fc2350764a8>

In [33]:

# can also separate another column by markers using `style`
sns.scatterplot(x="petal_length", y="petal_width", data=iris, style="species")

Out[33]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fc234c566a0>

In [34]:

# can mix combinations
sns.scatterplot(x="petal_length", y="petal_width", data=iris, hue="species", style="species")

Out[34]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fc234bd9390>

In [ ]: