SWMat - Storytelling With Matplotlib

(Make stunning graphs/charts using matplotlib in just a few lines of code)

Example plots made with this library

Index

1. Installation
2. Import
3. Usage
   • 3.1 Initialize Class
   • 3.2 Main features of Package
   • 3.3 Other Functions
     • Line Plot
     • Hist Plot
     • Bar Plot
     • Violin Plot (Dist Plot)
     • Text
4. References

1) Installation

Using Pip:

pip install SWMat

Download from pypi: SWMat.tar.gz

2) Import

from SWMat.SWMat import SWMat  # Will change it soon...

3) Usage

3.1 Initialize class

You can initialize SWMat class in two ways:

• Either you can only pass matplotlib.pyplot directly,

import matplotlib.pyplot as plt
swm = SWMat(plt) # Will take current Axes

• Or you can pass Axes with matplotlib.pyplot to beautify given Axes.

import matplotlib.pyplot as plt
fig, axs = plt.subplots(3, 4)
swm = SWMat(plt, ax=axs[0][1])

3.2 Main features of package

There are 'two' main differentiating features of this package:

• Base beautifications are done without much hassle,

swm = SWMat(plt) # And... The Base beautifications will be added.
plt.scatter(x, y)

• You can easily plot text on your plot using SWMat.text,

swm = SWMat(plt) # This function will initialize your plot
plt.scatter(x, y)
swm.text(some_text);  # That's all!! Don't worry there's much more you can do with it.

• And there is a highlight parameter available with every function in SWMat, to help you highlight some part of plot for that particular plot type.

3.3 Other Functions

3.3.1 Line Plot

Main parameters to look for here are: line_labels (Label for each line plot) and highlight (int or list of lines to highlight).

swm = SWMat(plt)
swm.line_plot(xs, ys, line_labels=["A", "B"], highlight=0, lw=3);

3.3.2 Hist Plot

Main parameters to look for here are: highlight (int orlist of index of bins to highlight).

swm = SWMat(plt)
swm.hist(data, highlight=3, bins=bins);

swm = SWMat(plt)
swm.hist(data, highlight=3, bins=bins, hide_y=True);

3.3.3 Bar Plot

Main parameters to look for here are: plot_type (horizontal, vertical etc), cat_labels (labels for categories in each data vector), data_labels (labels for each data vector) and highlight (dictionary taking int or list of ints for highlighting particular data bars or given category bars).

swm = SWMat(plt)
swm.bar(cats, heights, data_labels=["A", "B", "C", "D"], cat_labels=["One", "Two", "Three"], highlight={"data":1});

swm = SWMat(plt)
swm.bar(cats, heights, data_labels=["Alpha", "Beta"],  highlight={"data":1, "cat":1},
    cat_labels=["One", "Two", "Three"], plot_type="stacked100%");

3.3.4 Violin Plot (Dist PLot)

Main parameters to look for here are: show (where violin\dist plot(s) should be shown), highlight (dictionary taking list of tuples of starting and ending points inbetween which to highlight, with keys as index of violin plot. Actually you can plot many violin plots simultaneously.)

swm = SWMat(plt)
swm.violinplot(data, show="top", highlight={"0":[(0.7, 2.3), (4.7, 6)]})

3.3.5 Text

Main feature of text function of SWMat is that you can type in paragraphs of text without using matplotlib's plot function again and again, and that too while using HTML like attribute specification using prop tag. So you can configure your text as:

text = "Lorem <prop fontsize='20', color='#FF7700'>ipsum dolor</prop> sit amet." # All attribute's values needs to be inside quotes (As "value" or 'value').

prop tag can take all attributes of fontdict available in matplotlib.

4) References

1. Knaflic, Cole. Storytelling With Data: A Data Visualization Guide for Business Professionals, Wiley, © 2015
2. Matplotlib Library Docs