# Let your data speak! ### Data visualisation in matplotlib by Bartosz TeleŇĄczuk

Introduction

Anscombe dataset

Exploratory data analysis

The greatest value of a picture is when it forces us to notice what we never expected to see.

Visual presentation

Interactive presentation

John Hunter (1968 - 2012)

Patterns over time

Backends

TkAgg Qt4Agg WebAgg

Antigrain geometry

Raster (PNG) Vector (SVG)

pyplot interface

 
import matplotlib.pyplot as plt
plt.figure()
plt.subplot(...)
plt.plot(...) 
plt.show()
        
pyplot is a stateful interface that handles much of the boilerplate for creating figures and axes and connecting them to the backend of your choice, and maintains module-level internal data structures representing the current figure and axes to which to direct plotting commands.

Recap

plt.plot
plt.show
plt.xlabel
plt.ylabel
plt.xlim
plt.gca
plt.text
xaxis.set_minor_locator
yaxis.set_major_locator
YearsLocator
MonthLocator

Proportions

Artists

Design points

Signal-to-noise ratio

Recap

plt.bar
plt.xticks(pos, labels)
Spine.set_visible
Axis.set_ticks_position
Axes.set_axis_bgcolor
plt.grid
plt.title

Distributions

Stem-and-leaf plot

1.1 2.2 2.8 3.2 3.4 3.4 3.5 4.1 4.9 5.5 5.7

1. 1
2. 2 8
3. 2 4 4 5
4. 1 9
5. 5 7

Boxplots

Transforms

Recap

plt.hist
plt.boxplot
plt.legend
plt.text
plt.setp
plt.vlines
Axes.transAxes
Axes.transData
blended_transform_factory

Correlations

Small multiplies

Use of color

Visual illusion

Matplotlib colormaps

Recap

plt.subplots
Axes.set_axis_off
plt.imshow
rcParams

Finding patterns

Principal component analysis

K-means clustering

Visual encoding channels

Recap

plt.scatter
mcolors.Normalize
cm.get_cmap

Making maps

Map projections

Choropleth map

Recap

matplotlib plt.contourf
cartopy
cartopy.crs
cartopy.io
ax.coastlines
ax.add_feature
ax.set_extent
ax.add_image
ax.add_geometries