Matplotlib: Pyplot By Example

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Matplotlib: Pyplot By Example
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Pyplot or Matplotlib? What's The difference?

All examples can be found online on this jupyter notebook

Change Figure size

After plotting, get a reference to the current figure and call set_size_inches(width,height):

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

plt.scatter(x,y)

# get reference to the current figure
fig = plt.gcf()
fig.set_size_inches(8,3)

plt.show()

set_size_inches Customized image with 8x3 inches.
(Default size is 6x4)

Save plot to file

Use plt.savefig().

The image format is deduced from the extension ('png', 'jpg', 'svg', etc)

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

plt.scatter(x,y)

plt.savefig('out.png')

Multiple subplots in same Figure

View Matplotlib Subplots: Best Practices and Examples more multiple subplot examples

Call plt.subplots() to get a figure reference and individual Axes references (one for each subplot)

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

# passing 2,2 as parameters indicates that you will
# get 4 subplots (2 rows and 2 columns)
fig, axes = plt.subplots(2,2)

# just plot things on each individual axes

axes[0][0].scatter(x,y,c='red',marker='+')

axes[0][1].bar(x,y)

axes[1][0].scatter(x,y,marker='x')

axes[1][1].barh(x,y)
# you can set the title for a single subplot
axes[1][1].set_title('Plot 4',size=14)

plt.show()

pyplots-subplots Create a figure with 4 individual subplots using plt.subplots().
(Note that only Plot 4 has had its Title set.)

Adjust padding between subplots

View Matplotlib Subplots: Best Practices and Examples more multiple subplot examples

To adjust the padding, the space between the subplots in a single Figure, use plt.subplots_adjust()

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

# passing 2,2 as parameters indicates that you will
# get 4 subplots (2 rows and 2 columns)
fig, axes = plt.subplots(2,2)

# just plot things on each individual axes
axes[0][0].scatter(x,y,c='red',marker='+')
axes[0][1].bar(x,y)
axes[1][0].scatter(x,y,marker='x')
axes[1][1].barh(x,y)

# here, set the width and the height between the subplots
# the default value is 0.2 for each
plt.subplots_adjust(wspace=0.50, hspace=1.0)

adjust-padding-between-subplots-in-a-figure Set wspace to 0.50 and hspace to 1.0 to
increase the width and the height,
respectively.

Set Figure Title, Font size

Use Figure.suptitle()

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

plt.scatter(x,y)

# get reference to the current figure
fig = plt.gcf()

fig.suptitle('IMAGE TITLE HERE', fontsize=18)

set figure title pyplot suptitle() acceps parameters you would normally use in matplotlib.Text

Set Axis Title, Font size

Similar to the above, but acts on a single Axis (useful if you have multiple suplots on the same Figure)

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

plt.scatter(x,y)

# get reference to the current axis
ax = plt.gca()

ax.set_title('title for this axis only', fontsize=20)

title-for-a-single-axis If there were multiple axis in a
single Figure, only that Axis would have
its Title set

Change tick label rotation

Use plt.xticks() or plt.yticks and set the rotation argument (degrees)

If you want to call this on an Axes object instead, do: ax.set_xticklabels(ax.get_xticklabels(),rotation=60)

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

plt.plot(x,y)

# rotating labels on the xaxis
plt.xticks(rotation=60)
# y axis
plt.yticks(rotation=60)

rotate-ticks-on-pyplot You can change the label rotation for both the x-axis and the y-axis

Set Axis labels, Font size

Use plt.xlabel() or plt.ylabel, using the same arguments that are accepted by plt.text().

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

plt.plot(x,y)

plt.xlabel('time (s)',color='red',fontsize=30)
plt.ylabel('temperature (C)', fontsize=15)

axis-labels-and-fontsize You can use any other arguments from plt.text() too.

Set y-axis, x-axis limits

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

# for the whole plot
plt.plot(x,y)

plt.ylim(-5,15)
plt.xlim(-30,130)

y-axis-limit-and-x-axis-limit Same data as before, with more room around it.

Label points in plot

This was inspired by a guy on stackoverflow but I can't remember. If you know please send a message.

This function is also available in library dsutil.

First define a function called plot_value_labels() and call it on your axis:

def plot_value_labels(axis):
    rects = axis.patches

    # For each bar: Place a label
    for rect in rects:
        # Get X and Y placement of label from rect.
        y_value = rect.get_height()
        x_value = rect.get_x() + rect.get_width() / 2

        label = '{:.2f}'.format(y_value)

        # Vertical alignment for positive values
        va = 'bottom'

        # If value of bar is negative: Place label below bar
        if y_value < 0:
            # Invert space to place label below
            space *= -1
            # Vertically align label at top
            va = 'top'

        # Create annotation
        axis.annotate(label, (x_value, y_value), 
                      xytext=(0, 2), 
                      textcoords="offset points", 
                      ha='center', 
                      rotation=45, 
                      va=va)    

# now the actual code
import matplotlib.pyplot as plt
import numpy as np

# generate sample data for this example
x = np.linspace(0.0,10,10)
y = np.random.uniform(low=0,high=6,size=10)

# plot bar plot
plt.bar(x,y)

# call the function we defined
plot_value_labels(plt.gca())

simple-label-on-bar-plots Simple labels on top of bars in a bar plot

Set tick frequency

Use plt.xticks with np.arange (for the y-axis, use yticks instead).

Template: plt.xticks(np.arange(<start>,<end>,<step>)).

import numpy as np
import matplotlib.pyplot as plt

# generate sample data for this example
x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

plt.plot(x,y)

# make the limits a bit larger so that we can see the results
plt.ylim(0,10)
plt.xlim(0,100)

# tell pyplot to write a x-axis tick every 5 units
plt.xticks(np.arange(0, 100, 5))

xticks-more-frequent X-Axis ticks are drawn every 5 units, as per the call to xticks

Add Axis grid

All options available to Line2D can be passed to the grid() function.

Use ax.grid(True) on the axis object

To add grid for the whole figure instead, simple use plt.grid(True)

import matplotlib.pyplot as plt
import numpy as np

# generate sample data for this example
x = np.linspace(0.0,100,50)
y = np.random.uniform(low=0,high=10,size=50)

# linewidth and linestyle are some of the options you can set
plt.gca().grid(True, linewidth=0.7, linestyle=':')

# then plot the chart as you would normally    
plt.plot(x,y)

grid-lines-in-pyplot 0.7 is a reasonable setting for the line width

Plot histogram for values in numpy array

View all API options: pyplot docs: pyplot.hist

import matplotlib.pyplot as plt
import numpy as np

# generate sample data following a normal distribution
values = np.random.normal(size=100)
# array([ 0.49671415, -0.1382643 ,  0.64768854,...

# see all examples in the API link
plt.hist(values,rwidth=0.9,bins=[-3,-2,-1,0,1,2,3])

histogram-for-values-in-numpy-array A histogram is a simple chart that shows how frequent
each value is. It shows the discrete distribution of values in
a collection.

String axis labels, bar plot

Call plt.xticks(x_values, labels):

import matplotlib.pyplot as plt
import numpy as np

# generate sample data for this example
xs = [1,2,3,4,5,6,7,8,9,10,11,12]
ys = np.random.normal(loc=3.0,size=12)
labels = ['jan','feb','mar','apr','may','jun','jul','aug','sept','oct','nov','dec']

plt.bar(xs,ys)

# tell pyplot which labels correspond to which x values
plt.xticks(xs,labels)

plot-without-explicitly-setting-labels If you don't set labels,
each label is the value itself
         
matplotlib-bar-plot-with-strings-for-labels Set string labels using plt.xticks(xs,labels)

String axis labels, horizontal bar plot

import matplotlib.pyplot as plt
import numpy as np

# generate sample data for this example
xs = [1,2,3,4,5,6,7,8,9,10,11,12]
ys = np.random.normal(loc=3.0,size=12)
labels = ['jan','feb','mar','apr','may','jun','jul','aug','sept','oct','nov','dec']

plt.barh(xs,ys)

# tell pyplot which labels correspond to which x values
plt.yticks(xs,labels)

horizontal-bar-plot-with-string-axis-labels Horizontal bar plots are a useful alternative
when there is not enough space for labels
on the x (horizontal) axis

Twin plots: Bars and lines on same graph

import matplotlib.pyplot as plt
import numpy as np

plt.clf()

# generate sample data for this example
xs = [1,2,3,4,5,6,7,8,9,10,11,12]
ys_bars = np.random.normal(loc=3.0,size=12)
ys_lines = np.random.normal(loc=5.0,size=12,scale=0.5)

# this is the axis on the left
ax1=plt.gca()
ax1.bar(xs,ys_bars,color='green')

# order is important when setting ticks.
# Ticks must be set after the plot has been drawn
ax1.set_yticks(np.arange(0,11,1))
ax1.set_yticklabels(np.arange(0,11,1),color='green')

# create the 'twin' axis on the right
ax2=ax1.twinx()

# plot the same numbers but multiplied by 20
ax2.plot(xs,ys_lines*20,color='red')

# set the ticks and ticklabels for the twin axis
ax2.set_yticks(np.arange(0,101,10))
ax2.set_yticklabels(np.arange(0,101,10),color='red')

# set ticks for the common x axis (bottom)
ax2.xaxis.set_ticks(xs)

twin-axes-matplotlib Plot two different series on the same graph.
Note that each axis has a different scale.

Align axis labels on twin axes

Use ax.yaxis.set_major_locator(LinearLocator(numticks)) on both the original and the twin axis, with the same number of ticks.

import matplotlib.pyplot as plt
import numpy as np

plt.clf()

# generate sample data for this example
xs = [1,2,3,4,5,6,7,8,9,10,11,12]
ys_bars = np.random.normal(loc=3.0,size=12)
ys_lines = np.random.normal(loc=5.0,size=12,scale=0.5)

# this is the axis on the left
ax1=plt.gca()

ax1.bar(xs,ys_bars,color='green')

# order is important when setting ticks.
# Ticks must be set after the plot has been drawn
ax1.set_yticks(np.arange(0,11,1))

# define the number of ticks
NUM_TICKS=11

# change the tick locator for this axis and set the desired number of ticks
ax1.yaxis.set_major_locator(plt.LinearLocator(numticks=NUM_TICKS))

# create the 'twin' axis on the right
ax2=ax1.twinx()

# plot the same numbers but multiplied by 20
ax2.plot(xs,ys_lines*20,color='red')

# set the ticks for the twin axis
ax2.set_yticks(np.arange(0,101,10))

# change the tick locator for this axis and set the desired number of ticks
ax2.yaxis.set_major_locator(plt.LinearLocator(numticks=NUM_TICKS))

# set ticks for the common x axis (bottom)
ax2.xaxis.set_ticks(xs)

non-aligned-axes-twin-axis Since each axis has a different
scale, adding grids may
make things look ugly.
         
aligned-axis-twin-axis You can align the scales using
plt.LinearLocator() to set the number
of ticks you want in both scales.

Custom color cycle

If you wish to override the default colours used by pyplot (for example, to make it easier to colourblind people to view your images), you can use set_prop_cycle() on an Axes instance:

import numpy as np
import matplotlib.pyplot as plt

# generate random data for plotting
x = np.linspace(0.0,100,50)
y2 = x*2
y3 = x*3
y4 = x*4
y5 = x*5
y6 = x*6

# plot multiple lines in the same plot
plt.plot(x,y2,label='y=2x')
plt.plot(x,y3,label='y=3x')
plt.plot(x,y4,label='y=4x')
plt.plot(x,y5,label='y=5x')
plt.plot(x,y6,label='y=6x')

colour_blind_cycle = ['#377eb8', '#ff7f00', '#4daf4a',
                  '#f781bf', '#a65628', '#984ea3',
                  '#999999', '#e41a1c', '#dede00']

plt.gca().set_prop_cycle(color=colour_blind_cycle)

plt.legend()

simple-plot-with-colorblind-friendly-color-palette A simple line plot using a colourblind-friendly color scheme
(Props to user thriveth on Github.)


References

Dialogue & Discussion