[box type=”note” align=”” class=”” width=””]This is an excerpt from the book titled Matplotlib 2.x By Example written by Allen Chi Shing Yu, Claire Yik Lok Chung, and Aldrin Kay Yuen Yim,. The book covers basic know-how on how to create and customize plots by Matplotlib. It will help you learn to visualize geographical data on maps and implement interactive charts. [/box]

The article talks about how you can manipulate ticks in Matplotlib 2.0. It includes steps to adjust tick spacing, customizing tick formats, trying out the ticker locator and formatter, and rotating tick labels.

What are Ticks

Ticks are dividers on an axis that help readers locate the coordinates. Tick labels allow estimation of values or, sometimes, labeling of a data series as in bar charts and box plots.

Adjusting tick spacing

Tick spacing can be adjusted by calling the locator methods:

ax.xaxis.set_major_locator(xmajorLocator) ax.xaxis.set_minor_locator(xminorLocator) ax.yaxis.set_major_locator(ymajorLocator) ax.yaxis.set_minor_locator(yminorLocator)

Here, ax refers to axes in a Matplotlib figure.

Since set_major_locator() or set_minor_locator() cannot be called from the pyplot interface but requires an axis, we call pyplot.gca() to get the current axes. We can also store a figure and axes as variables at initiation, which is especially useful when we want multiple axes.

Removing ticks

NullLocator: No ticks

Drawing ticks in multiples

Spacing ticks in multiples of a given number is the most intuitive way. This can be done by using MultipleLocator space ticks in multiples of a given value.

Automatic tick settings

MaxNLocator: This finds the maximum number of ticks that will display nicely
AutoLocator: MaxNLocator with simple defaults
AutoMinorLocator: Adds minor ticks uniformly when the axis is linear

Setting ticks by the number of data points

IndexLocator: Sets ticks by index (x = range(len(y))

Set scaling of ticks by mathematical functions

• • LinearLocator: Linear scale
  • LogLocator: Log scale
  • SymmetricalLogLocator: Symmetrical log scale, log with a range of linearity
  • LogitLocator: Logit scaling

Locating ticks by datetime

There is a series of locators dedicated to displaying date and time:

MinuteLocator: Locate minutes HourLocator: Locate hours

DayLocator: Locate days of the month

WeekdayLocator: Locate days of the week

MonthLocator: Locate months, for example, 8 for August YearLocator: Locate years that in multiples

RRuleLocator: Locate using matplotlib.dates.rrulewrapper

The rrulewrapper is a simple wrapper around a

dateutil.rrule (dateutil) that allows almost arbitrary date

tick specifications

AutoDateLocator: On autoscale, this class picks the best

MultipleDateLocator to set the view limits and the tick locations

Customizing tick formats

Tick formatters control the style of tick labels. They can be called to set the major and minor tick formats on the x and y axes as follows:

ax.xaxis.set_major_formatter( xmajorFormatter ) ax.xaxis.set_minor_formatter( xminorFormatter ) ax.yaxis.set_major_formatter( ymajorFormatter ) ax.yaxis.set_minor_formatter( yminorFormatter )

Removing tick labels

NullFormatter: No tick labels

Fixing labels

FixedFormatter: Labels are set manually

Setting labels with strings

IndexFormatter: Take labels from a list of strings
StrMethodFormatter: Use the string format method

Setting labels with user-defined functions

FuncFormatter: Labels are set by a user-defined function

Formatting axes by numerical values

ScalarFormatter: The format string is automatically selected for scalars by default

The following formatters set values for log axes:

LogFormatter: Basic log axis
LogFormatterExponent: Log axis using exponent = log_base(value)
LogFormatterMathtext: Log axis using exponent = log_base(value) using Math text
LogFormatterSciNotation: Log axis with scientific notation
LogitFormatter: Probability formatter

Trying out the ticker locator and formatter

To demonstrate the ticker locator and formatter, here we use Netflix subscriber data as an example. Business performance is often measured seasonally. Television shows are even more “seasonal”. Can we better show it in the timeline?

import numpy as np

import matplotlib.pyplot as plt import matplotlib.ticker as ticker

"""

Number for Netflix streaming subscribers from 2012-2017

Data were obtained from Statista on https://www.statista.com/statistics/250934/quarterly-number-of-netflix-stre aming-subscribers-worldwide/ on May 10, 2017. The data were originally published by Netflix in April 2017.

"""

# Prepare the data set x = range(2011,2018)

y = [26.48,27.56,29.41,33.27,36.32,37.55,40.28,44.35,

48.36,50.05,53.06,57.39,62.27,65.55,69.17,74.76,81.5,

83.18,86.74,93.8,98.75] # quarterly subscriber count in millions

# Plot lines with different line styles plt.plot(y,'^',label = 'Netflix subscribers',ls='-')

# get current axes and store it to ax ax = plt.gca()

# set ticks in multiples for both labels ax.xaxis.set_major_locator(ticker.MultipleLocator(4)) # set major marks

# every 4 quarters, ie once a year ax.xaxis.set_minor_locator(ticker.MultipleLocator(1)) # set minor marks

# for each quarter ax.yaxis.set_major_locator(ticker.MultipleLocator(10)) # ax.yaxis.set_minor_locator(ticker.MultipleLocator(2))

# label the start of each year by FixedFormatter  ax.get_xaxis().set_major_formatter(ticker.FixedFormatter(x))

plt.legend() plt.show()

From this plot, we see that Netflix has a pretty linear growth of subscribers from the year 2012 to 2017. We can tell the seasonal growth better after formatting the x axis in a quarterly manner. In 2016, Netflix was doing better in the latter half of the year. Any TV shows you watched in each season?

Rotating tick labels

A figure can get too crowded or some tick labels may get skipped when we have too many tick labels or if the label strings are too long. We can solve this by rotating the ticks, for example, by pyplot.xticks(rotation=60):

import matplotlib.pyplot as plt import numpy as np

import matplotlib as mpl mpl.style.use('seaborn')

techs = ['Google Adsense','DoubleClick.Net','Facebook Custom

Audiences','Google Publisher Tag', 'App Nexus'] y_pos = np.arange(len(techs))

# Number of websites using the advertising technologies

# Data were quoted from builtwith.com on May 8th 2017 websites = [14409195,1821385,948344,176310,283766]

plt.bar(y_pos, websites, align='center', alpha=0.5)

# set x-axis tick rotation plt.xticks(y_pos, techs, rotation=25) plt.ylabel('Live site count')

plt.title('Online advertising technologies usage') plt.show()

Use pyplot.tight_layout() to avoid image clipping. Using rotated labels can sometimes result in image clipping, as follows, if you save the figure by pyplot.savefig(). You can call pyplot.tight_layout() before pyplot.savefig() to ensure a complete image output.

We saw how ticks can be adjusted, customized, rotated and formatted in Matplotlib 2.0 for easy readability, labelling and estimation of values.

To become well-versed with Matplotlib for your day to day work, check out this book Matplotlib 2.x By Example.