1 minute read

Having recently joined the corporate world I was affronted with figures made in Excel. I had to admit that these figures looked more at home in presentations than my bog-standard Matplotlib efforts.

Clearly, I want to still use Python to create my figures, but want to match the professional look of other’s plots. This also means that I won’t get any push back from my graphs being in a different format.

Matplotlib stylesheets provide a way to achieve a consistent styling to your figures, e.g.:

Example timeseries Example bar chart

The different elements of the stylesheet are described below, along with some helpful snippets for legends and tick formatting.

This part contains the base seaborn style

# Seaborn common parameters
# .15 = dark_gray
# .8 = light_gray
figure.facecolor: white
text.color: .15
axes.labelcolor: .15
legend.frameon: False
legend.numpoints: 1
legend.scatterpoints: 1
xtick.direction: out
ytick.direction: out
xtick.color: .15
ytick.color: .15
axes.axisbelow: True
image.cmap: Greys
font.family: sans-serif
font.sans-serif: Arial, Liberation Sans, DejaVu Sans, Bitstream Vera Sans, sans-serif
grid.linestyle: -
lines.solid_capstyle: round
lines.linewidth : 2
lines.markersize : 10

# Seaborn whitegrid parameters
axes.grid: True
axes.facecolor: white
grid.color: .8
xtick.major.size: 4
ytick.major.size: 0
xtick.minor.size: 2
ytick.minor.size: 0

Cycle through the branded colours of my organisation (Trainline):

#cycler
axes.prop_cycle: cycler('color', ['00a88f','ff9da1','160078','ffc508','ff6120','004ff9','ac3200'])

Set the grid style and labels:

# grid
axes.grid.axis:  y    # which axis the grid should apply to
axes.grid.which:  major   # grid lines at {major, minor, both} ticks

#font size
font.size : 18
axes.titlesize : 24
figure.titlesize: 24
axes.labelsize : 20
xtick.labelsize : 16
ytick.labelsize : 16
legend.fontsize : 16

# label pad
axes.labelpad:      8.0     # space between label and axis

Following the Excel style, only show the bottom spine

# spines
axes.spines.left:   False  # display axis spines
axes.spines.bottom: True
axes.spines.top:    False
axes.spines.right:  False

Set the date format

# DATES
date.autoformatter.year:        %y
date.autoformatter.month:       %m/%y
date.autoformatter.day:         %d/%m/%y
date.autoformatter.hour:        %m-%d %H
date.autoformatter.minute:      %d %H:%M
date.autoformatter.second:      %H:%M:%S
date.autoformatter.microsecond: %M:%S.%f

Extra Helpers

Format y axis to have comma separated numbers, e.g. $$100,000$

import matplotlib.ticker as ticker
# Just put a , between 000
axs.yaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))

# % symbol
axs.yaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.1f}%'))


# currency
axs.yaxis.set_major_formatter(ticker.StrMethodFormatter('£{x:,.2f}'))

or

from matplotlib.ticker import FuncFormatter
import matplotlib.pyplot as plt
def millions(x, pos):
    'The two args are the value and tick position'
    return f'£{x*1e-6:,.1f}m'


formatter = FuncFormatter(millions)

ax.yaxis.set_major_formatter(formatter)

from here

Legend on top of the plot (guide)

ax.legend(bbox_to_anchor=(0, 1, 1, 0), loc="lower left", mode="expand", ncol=2)

Legend on the right

ax.legend(bbox_to_anchor=(1, 1), loc="upper left")

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