Matplotlib | Plot a step graph (step)

2023-08-12

A step graph is a graph of vertical and parallel lines connecting data.

It is used to visualize data trends, patterns of change, and step functions. It is especially suited for visualizing categorical or cumulative data.

It is also useful for highlighting changes or events that occur over time.

This article explains how to plot a step graph in Python’s Matplotlib

Table of Contents

Basic step graph

In Matplotlib, the step function draws a step graph with an array of x and y values.

Axes.step

Parameters

x (array) : 1D sequence of x positions.
y (array) : 1D sequence of y levels.
fmt (str) : A format string. Line type, line color, and marker can be defined at once
where (str) : Define where the steps should be placed: ‘pre’, ‘post’, ‘mid’

Returns

list of Line2D

Official Documentation

matplotlib.axes.Axes.step — Matplotlib 3.7.1 documentation

The following tabs explain the code and flowchart

import matplotlib.pyplot as plt
import numpy as np

# step1 Create data
x = np.arange(10)
y = np.sin(x)

# step2 Create graph frames
fig, ax = plt.subplots()

# step3 Plot a step graph
ax.step(x, y, label='Sample 1')
ax.step(x, y+2, label='Sample 2')

ax.set_xlabel('X label')
ax.set_ylabel('Y label')  
ax.legend()
ax.set_title('Step Line Chart')

plt.show()

STEP

Create data

x is an array with 10 elements starting from 0

x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

numpy.arange ([start, ]stop, [step, ]) : ndarray
Return evenly spaced values within a given interval.

STEP

Create graph frames

To draw a graph in Matplotlib, you need to create a graph frame

Create the main fig and the ax that actually draws the graph in pyplot.subplots

pyplot.subplots (nrows, ncols, sharex, sharey, subplot_kw, constrained_layout) : fig, ax
Create a figure and a set of subplots.

STEP

Plot a step graph with Axes.step

The graph is displayed with the x and y data created in step1

The code starting with ax.set sets the axis relationships for the graph

Set the format (fmt)

fmt (format string) can define marker, linetype, and color at once, as shown below.

fmt = '[marker][line][color]'

I created three different staircase graphs using fmt

# step3 Plot a step graph
# Circle + green + dashed line
ax.step(x, y, 'og--' ,label='fmt="og--"')
# Rectangle + red + single dotted line
ax.step(x, y+2, 'sr-.' ,label='fmt="sr-."')
# Star + cyan + dotted line
ax.step(x, y+4, '*c:' ,label='fmt="*c:"')

plt.show()

The marker center is white because rcParams[‘lines.markerfacecolor’] = ‘white

Change the marker

Markers can be specified from 25 different types

In the following I have drawn with three types of circles, squares and triangles.

# step3 Plot a step graph
# Markers
ax.step(x, y, 'o-' ,label='circle')
ax.step(x, y+2, 's-' ,label='square')
ax.step(x, y+4, '^-' ,label='triangle_up')

Change the line style

The line style can be defined from among four types: solid, dashed, dash, dotted, and dotted.

# step3 Plot a step graph
# Line styles
ax.step(x, y, '-' ,label='solid')
ax.step(x, y+2, '--' ,label='dashed')
ax.step(x, y+4, '-.' ,label='dash-dot')
ax.step(x, y+6, ':' ,label='dotted')

Change the color

Colors can be defined in three main ways

How to define colors

Defined by 1 character (8 types)
Defined by Hex strings ex) ‘#008000’
Defined by full name ex) palegreen, tab:olive

# step3 Plot a step graph
# Colors
ax.step(x, y, 'm' ,label='Magenta')
ax.step(x, y+2, '#0097a7' ,label='#0097a7')
ax.step(x, y+4, 'tomato' ,label='tomato')

Define where the steps (where)

There are three types of step arrangements

Step Types

pre : step is the same as the previous x. ex) y=1 when x=0~1
post : step is the same as x immediately after. ex) y=0 when x=0~1
mid : step is mid x-coordinate

The graph below was drawn by comparing three different step arrangements and line graphs.

# step3 Plot a step graph
ax.step(x, y, where='pre', label='pre (default)')
ax.plot(x, y, 'o--', color='grey', alpha=0.4)

ax.step(x, y+2, where='mid',label='mid')
ax.plot(x, y+2, 'o--', color='grey', alpha=0.4)

ax.step(x, y+4, where='post', label='post')
ax.plot(x, y+4, 'o--', color='grey', alpha=0.4)

plt.show()