Matplotlib Quick Start
14 Sep 2021 | beginner pythonIntroduction
matplotlib是Python語言及其數值計算庫NumPy的繪圖庫。它提供了一個物件導向的API，用於使用通用GUI工具包（如Tkinter、wxPython、Qt或GTK）將繪圖嵌入到應用程式中。
Types of plots
| Function | Description |
|---|---|
| bar | Make a bar plot(長條圖). |
| barh | Make a horizontal bar plot(橫長條圖). |
| boxplot | Make a box and whisker plot(箱形圖). |
| hist | Plot a histogram(直方圖). |
| hist2d | Make a 2D histogram plot(2D直方圖). |
| pie | Plot a pie chart(派餅圖). |
| plot | Plot lines and/or markers to the Axes(曲/折線圖). |
| polar | Make a polar plot(極座標圖). |
| scatter | Make a scatter plot of x vs y(散佈圖). |
| stackplot | Draws a stacked area plot(堆疊面積圖). |
| stem | Create a stem plot(枝葉圖). |
| step | Make a step plot(階梯圖). |
| quiver | Plot a 2-D field of arrows(向量圖). |
Image functions
| Function | Description |
|---|---|
| imread | Read an image from a file into an array. |
| imsave | Save an array as in image file. |
| imshow | Display an image on the axes. |
Axis functions
| Function | Description |
|---|---|
| axes | Add axes to the figure. |
| text | Add text to the axes. |
| title | Set a title of the current axes. |
| xlabel | Set the x axis label of the current axis. |
| xlim | Get or set the x limits of the current axes. |
| xscale | Set the scaling of the x-axis. |
| xticks | Get or set the x-limits of the current tick locations and labels. |
| ylabel | Set the y axis label of the current axis. |
| ylim | Get or set the y-limits of the current axes. |
| yscale | Set the scaling of the y-axis. |
| yticks | Get or set the y-limits of the current tick locations and labels. |
Figure functions
| Function | Description |
|---|---|
| figtext | Add text to figure |
| figure | Creates a new figure. |
| show | Display a figure. |
| savefig | Save the current figure. |
| close | Close a figure window. |
Plot sine wave
import matplotlib.pyplot as plot
import numpy as np
import math #needed for definition of pi
# %matplotlib inline required in jupyter notebook
x = np.arange(0, math.pi*2, 0.05)
y = np.sin(x)
plt.plot(x,y)
plt.xlabel("angle")
plt.ylabel("sine")
plt.title('sine wave')
plt.show() # comment it in jupyter notebook
Using object-oriented interface
import matplotlib.pyplot as plot
import numpy as np
import math #needed for definition of pi
x = np.arange(0, math.pi*2, 0.05)
y = np.sin(x)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1]) # A 4-length sequence of [left, bottom, width, height] quantities.
ax.plot(x,y)
ax.set_title("sine wave")
ax.set_xlabel('angle')
ax.set_ylabel('sine')
plt.show()
Figure class
fig = plt.figure()
parameter
| name | |
|---|---|
| Figsize | (width,height) tuple in inches |
| Dpi | Dots per inches |
| Facecolor | Figure patch facecolor |
| Edgecolor | Figure patch edge color |
| Linewidth | Edge line width |
Axes class
ax = fig.add_axes([0,0,1,1])
legend
ax.legend(handles, labels, loc)
| Location string | Location code |
|---|---|
| best | 0 |
| upper right | 1 |
| upper left | 2 |
| lower left | 3 |
| lower right | 4 |
| right | 5 |
| center left | 6 |
| center right | 7 |
| lower center | 8 |
| upper center | 9 |
| center | 10 |
axes.plot()
Color code
| Character | Color |
|---|---|
| ‘b’ | blue |
| ‘g’ | green |
| ‘r’ | red |
| ‘c’ | cyan |
| ‘m’ | magenta |
| ‘y’ | yellow |
| ‘k’ | black |
| ‘w’ | white |
Marker code
| Character | Description |
|---|---|
| ’.’ | point marker |
| ‘o’ | circle marker |
| ‘x’ | x marker |
| ‘D’ | diamond marker |
| ‘H’ | hexagon marker |
| ’s’ | square marker |
| ’+’ | plus marker |
Line styles
| Character | Description |
|---|---|
| ’-‘ | solid line |
| ’–’ | dashed line |
| ’-.’ | dash-dot line |
| ’:’ | dotted line |
Example
import matplotlib.pyplot as plt
y = [1, 4, 9, 16, 25,36,49, 64]
x1 = [1, 16, 30, 42,55, 68, 77,88]
x2 = [1,6,12,18,28, 40, 52, 65]
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
l1=ax.plot(x1,y,'ys-') # solid line with yellow colour and square marker
l2=ax.plot(x2,y,'go--') # dash line with green colour and circle marker
ax.legend(labels=('tv', 'Smartphone'), loc='lower right') # legend placed at lower right
ax.set_title("Advertisement effect on sales")
ax.set_xlabel('medium')
ax.set_ylabel('sales')
plt.show()
Miltiplots
plt.subplot(subplot(nrows, ncols, index))
Example 1
import matplotlib.pyplot as plt
# plot a line, implicitly creating a subplot(111)
plt.plot([1,2,3])
# now create a subplot which represents the top plot of a grid with 2 rows and 1 column.
#Since this subplot will overlap the first, the plot (and its axes) previously created, will be removed
plt.subplot(211)
plt.plot(range(12))
plt.subplot(212, facecolor='y') # creates 2nd subplot with yellow background
plt.plot(range(12))
plt.show()
Example 2
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot([1,2,3])
ax2 = fig.add_subplot(221, facecolor='y')
ax2.plot([1,2,3])
plt.show()
Example 3
import matplotlib.pyplot as plt
import numpy as np
import math
x=np.arange(0, math.pi*2, 0.05)
fig=plt.figure()
axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes
axes2 = fig.add_axes([0.55, 0.55, 0.3, 0.3]) # inset axes
y=np.sin(x)
axes1.plot(x, y, 'b')
axes2.plot(x,np.cos(x),'r')
axes1.set_title('sine')
axes2.set_title("cosine")
plt.show()
subplots()
plt,subplots(nrows, ncols)
import matplotlib.pyplot as plt
import numpy as np
fig, a = plt.subplots(2,2)
x=np.arange(1,5)
a[0][0].plot(x,x*x)
a[0][0].set_title('square')
a[0][1].plot(x,np.sqrt(x))
a[0][1].set_title('square root')
a[1][0].plot(x,np.exp(x))
a[1][0].set_title('exp')
a[1][1].plot(x,np.log10(x))
a[1][1].set_title('log')
plt.show()
subplot2grid()
plt.subplot2grid(shape, location, rowspan, colspan)
import matplotlib.pyplot as plt
import numpy as np
a1 = plt.subplot2grid((3,3),(0,0),colspan=2)
a2 = plt.subplot2grid((3,3),(0,2), rowspan=3)
a3 = plt.subplot2grid((3,3),(1,0),rowspan=2, colspan=2)
x=np.arange(1,10)
a1.plot(x, np.exp(x))
a1.set_title('exp')
a2.plot(x, x*x)
a2.set_title('square')
a3.plot(x, np.log(x))
a3.set_title('log')
plt.tight_layout()
plt.show()
Grids
import matplotlib.pyplot as plt
import numpy as np
fig, axes = plt.subplots(1,3, figsize=(12,4))
x=np.arange(1,11)
axes[0].plot(x, x**3, 'g',lw=2)
axes[0].grid(True)
axes[0].set_title('default grid')
axes[1].plot(x, np.exp(x), 'r')
axes[1].grid(color='b', ls='-.', lw=0.25)
axes[1].set_title('custom grid')
axes[2].plot(x,x)
axes[2].set_title('no grid')
fig.tight_layout()
plt.show()
Formatting axes
import matplotlib.pyplot as plt
import numpy as np
fig, axes = plt.subplots(1, 2, figsize=(10,4))
x=np.arange(1,5)
axes[0].plot( x, np.exp(x))
axes[0].plot(x,x**2)
axes[0].set_title("Normal scale")
axes[1].plot (x, np.exp(x))
axes[1].plot(x, x**2)
axes[1].set_yscale("log")
axes[1].set_title("Logarithmic scale (y)")
axes[0].set_xlabel("x axis")
axes[0].set_ylabel("y axis")
axes[0].xaxis.labelpad = 10
axes[1].set_xlabel("x axis")
axes[1].set_ylabel("y axis")
plt.show()
edge setting
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.spines['bottom'].set_color('blue')
ax.spines['left'].set_color('red')
ax.spines['left'].set_linewidth(2)
ax.spines['right'].set_color(None)
ax.spines['top'].set_color(None)
ax.plot([1,2,3,4,5])
plt.show()
Setting limits
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
a1 = fig.add_axes([0,0,1,1])
x = np.arange(1,10)
a1.plot(x, np.exp(x),'r')
a1.set_title('exp')
a1.set_ylim(0,10000)
a1.set_xlim(0,10)
plt.show()
Setting ticks and tick labels
import matplotlib.pyplot as plt
import numpy as np
import math
x = np.arange(0, math.pi*2, 0.05)
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes
y = np.sin(x)
ax.plot(x, y)
ax.set_xlabel(‘angle’)
ax.set_title('sine')
ax.set_xticks([0,2,4,6])
ax.set_xticklabels(['zero','two','four','six'])
ax.set_yticks([-1,0,1])
plt.show()
Twin axes
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
a1 = fig.add_axes([0,0,1,1])
x = np.arange(1,11)
a1.plot(x,np.exp(x))
a1.set_ylabel('exp')
a2 = a1.twinx()
a2.plot(x, np.log(x),'ro-')
a2.set_ylabel('log')
fig.legend(labels=('exp','log'),loc='upper left')
plt.show()
Bar plot
ax.bar(x, height, width, bottom, align)
Parameter
| Name | Description |
|---|---|
| x | sequence of scalars representing the x coordinates of the bars. align controls if x is the bar center (default) or left edge. |
| height | scalar or sequence of scalars representing the height(s) of the bars |
| width | scalar or array-like, optional. the width(s) of the bars default 0.8 |
| bottom | scalar or array-like, optional. the y coordinate(s) of the bars default None |
| align | {‘center’, ‘edge’}, optional, default ‘center’ |
Example 1
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
langs=['C', 'C++', 'Java', 'Python', 'PHP']
students=[23,17,35,29,12]
ax.bar(langs,students)
plt.show()
Example 2
import numpy as np
import matplotlib.pyplot as plt
data = [[30, 25, 50, 20],
[40, 23, 51, 17],
[35, 22, 45, 19]]
X = np.arange(4)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)
ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)
ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)
ax.set_xticks([0.25,1.25,2.25,3.25])
ax.set_xticklabels([2015,2016,2017,2018])
ax.legend(labels=['CS','IT','E&TC'])
plt.show()
Example 3
import numpy as np
import matplotlib.pyplot as plt
N = 5
width = 0.35
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
ind = np.arange(N) # the x locations for the groups
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.bar(ind, menMeans, width, color='r')
ax.bar(ind, womenMeans, width,bottom=menMeans, color='b')
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
ax.set_yticks(np.arange(0, 81, 10))
ax.legend(labels=['Men', 'Women'])
plt.show()
Histogram
ax.hist(x, bins [,range, density, cumulative, histtype])
Parameter
| Name | Description |
|---|---|
| x | array or sequence of arrays |
| bins | integer or sequence or ‘auto’, optional |
| range | The lower and upper range of the bins. |
| density | If True, the first element of the return tuple will be the counts normalized to form a probability density |
| cumulative | If True, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. |
| histtype | The type of histogram to draw. Default is ‘bar’ |
Example
import matplotlib.pyplot as plt
import numpy as np
fig,ax = plt.subplots(1,1)
a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])
ax.hist(a, bins = [0,25,50,75,100])
ax.set_title("histogram of result")
ax.set_xticks([0,25,50,75,100])
ax.set_xlabel('marks')
ax.set_ylabel('no. of students')
plt.show()
Pie chart
ax.pie(x, labels, colors, autopct)
Parameter
| Name | Description |
|---|---|
| x | array-like. The wedge sizes. |
| labels | list. A sequence of strings providing the labels for each wedge |
| colors | A sequence of matplotlibcolorargs through which the pie chart will cycle. If None, will use the colors in the currently active cycle. |
| autopct | string, used to label the wedges with their numeric value. The label will be placed inside the wedge. The format string will be fmt%pct. |
Example
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.axis('equal')
langs=['C', 'C++', 'Java', 'Python', 'PHP']
students=[23,17,35,29,12]
ax.pie(students, labels=langs,autopct='%1.2f%%')
plt.show()
Scatter plot
Example
import matplotlib.pyplot as plt
girls_grades = [89, 90, 70, 89, 100, 80, 90, 100, 80, 34]
boys_grades = [30, 29, 49, 48, 100, 48, 38, 45, 20, 30]
grades_range = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.scatter(grades_range, girls_grades, color='r')
ax.scatter(grades_range, boys_grades, color='b')
ax.set_xlabel('Grades Range')
ax.set_ylabel('Grades Scored')
ax.set_title('scatter plot')
plt.show()
Contour plot
Example
import numpy as np
import matplotlib.pyplot as plt
xlist = np.linspace(-3.0, 3.0, 100)
ylist = np.linspace(-3.0, 3.0, 100)
X, Y = np.meshgrid(xlist, ylist)
Z = np.sqrt(X**2 + Y**2)
fig,ax = plt.subplots(1,1)
cp = ax.contourf(X, Y, Z)
fig.colorbar(cp) # Add a colorbar to a plot
ax.set_title('Filled Contours Plot')
#ax.set_xlabel('x (cm)')
ax.set_ylabel('y (cm)')
plt.show()
Quiver plot
ax.quiver(x,y,u,v,c)
Parameter
| Name | Description |
|---|---|
| x | 1D or 2D array, sequence. The x coordinates of the arrow locations |
| y | 1D or 2D array, sequence. The y coordinates of the arrow locations |
| u | 1D or 2D array, sequence. The x components of the arrow vectors |
| v | 1D or 2D array, sequence. The y components of the arrow vectors |
| c | 1D or 2D array, sequence. The arrow colors |
Example
import matplotlib.pyplot as plt
import numpy as np
x,y = np.meshgrid(np.arange(-2, 2, .2), np.arange(-2, 2, .25))
z = x*np.exp(-x**2 - y**2)
v, u = np.gradient(z, .2, .2)
fig, ax = plt.subplots()
q = ax.quiver(x,y,u,v)
plt.show()
Box plot
Example
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(10)
collectn_1 = np.random.normal(100, 10, 200)
collectn_2 = np.random.normal(80, 30, 200)
collectn_3 = np.random.normal(90, 20, 200)
collectn_4 = np.random.normal(70, 25, 200)
data_to_plot = [collectn_1, collectn_2, collectn_3, collectn_4]
fig = plt.figure()
# Create an axes instance
ax = fig.add_axes([0,0,1,1])
# Create the boxplot
bp = ax.boxplot(data_to_plot)
plt.show()
Violin plot
Example
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(10)
collectn_1 = np.random.normal(100, 10, 200)
collectn_2 = np.random.normal(80, 30, 200)
collectn_3 = np.random.normal(90, 20, 200)
collectn_4 = np.random.normal(70, 25, 200)
## combine these different collections into a list
data_to_plot = [collectn_1, collectn_2, collectn_3, collectn_4]
# Create a figure instance
fig = plt.figure()
# Create an axes instance
ax = fig.add_axes([0,0,1,1])
# Create the boxplot
bp = ax.violinplot(data_to_plot)
plt.show()
Three-dimensional plotting
Example
import mpl_toolkits.mplot3d
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes(projection='3d')
z = np.linspace(0, 1, 100)
x = z * np.sin(20 * z)
y = z * np.cos(20 * z)
ax.plot3D(x, y, z, 'gray')
ax.set_title('3D line plot')
plt.show()
Example 3D scatter plot
import mpl_toolkits.mplot3d
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes(projection='3d')
z = np.linspace(0, 1, 100)
x = z * np.sin(20 * z)
y = z * np.cos(20 * z)
c = x + y
ax.scatter(x, y, z, c=c)
ax.set_title('3d Scatter plot')
plt.show()
3D contour plot
Example
import mpl_toolkits.mplot3d
import numpy as np
import matplotlib.pyplot as plt
def f(x, y):
return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.contour3D(X, Y, Z, 50, cmap='binary')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.set_title('3D contour')
plt.show()
3D wireframe plot
Example
import mpl_toolkits.mplot3d
import numpy as np
import matplotlib.pyplot as plt
def f(x, y):
return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_wireframe(X, Y, Z, color='black')
ax.set_title('wireframe')
plt.show()
3D surface plot
Example
import mpl_toolkits.mplot3d
import numpy as np
import matplotlib.pyplot as plt
x = np.outer(np.linspace(-2, 2, 30), np.ones(30))
y = x.copy().T # transpose
z = np.cos(x ** 2 + y ** 2)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_surface(x, y, z,cmap='viridis', edgecolor='none')
ax.set_title('Surface plot')
plt.show()
Working with text
Parameter
| Name | Description |
|---|---|
| text | Add text at an arbitrary location of the Axes. |
| annotate | Add an annotation, with an optional arrow, at an arbitrary location of theAxes. |
| xlabel | Add a label to the Axes’s x-axis. |
| ylabel | Add a label to the Axes’s y-axis. |
| title | Add a title to the Axes. |
| figtext | Add text at an arbitrary location of the Figure. |
| suptitle | Add a title to the Figure. |
Example
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.set_title('axes title')
ax.set_xlabel('xlabel')
ax.set_ylabel('ylabel')
ax.text(3, 8, 'boxed italics text in data coords', style='italic', bbox={'facecolor': 'red'})
ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)
ax.text(4, 0.05, 'colored text in axes coords', verticalalignment='bottom', color='green', fontsize=15)
ax.plot([2], [1], 'o')
ax.annotate('annotate', xy=(2, 1), xytext=(3, 4), arrowprops=dict(facecolor='black', shrink=0.05))
ax.axis([0, 10, 0, 10])
plt.show()
Mathematical expressions
You can use a subset TeXmarkup in any Matplotlib text string by placing it inside a pair of dollar signs ($).
# math text
plt.title(r'$\alpha > \beta$')
Example
import numpy as np
import matplotlib.pyplot as plt
t = np.arange(0.0, 2.0, 0.01)
s = np.sin(2*np.pi*t)
plt.plot(t,s)
plt.title(r'$\alpha_i> \beta_i$', fontsize=20)
plt.text(0.6, 0.6, r'$\mathcal{A}\mathrm{sin}(2 \omega t)$', fontsize=20)
plt.text(0.1, -0.5, r'$\sqrt{2}$', fontsize=10)
plt.xlabel('time (s)')
plt.ylabel('volts (mV)')
plt.show()
Working with images
Example
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
img = mpimg.imread('mtplogo.png')
plt.imsave("logo.png", img, cmap='gray', origin='lower')
imgplot = plt.imshow(img)
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