Matplotlib

Matplotlib is a low level graph plotting library in python that serves as a visualization utility.
Matplotlib was created by John D. Hunter. Matplotlib is open source and we can use it freely.
Matplotlib is mostly written in python, a few segments are written in C, Objective-C and Javascript for Platform compatibility.

Installation of Matplotlib

         
            pip install matplotlib

Import Matplotlib

         
            import matplotlib

Checking Matplotlib Version

         
            import matplotlibprint(matplotlib.__version__)

Matplotlib Pyplot

Pyplot
Most of the Matplotlib utilities lies under the pyplot submodule, and are usually imported under the plt alias

Now the Pyplot package can be referred to as plt.

Plotting x and y points
The plot() function is used to draw points (markers) in a diagram.
By default, the plot() function draws a line from point to point.

The function takes parameters for specifying points in the diagram.

Parameter 1 is an array containing the points on the x-axis.

Parameter 2 is an array containing the points on the y-axis.

The x-axis is the horizontal axis.

The y-axis is the vertical axis.

Plotting Without Line
To plot only the markers, you can use shortcut string notation parameter 'o', which means 'rings'.

                                                   
                                    import matplotlib.pyplot as plt

                                                   
                                    xpoints = np.array([1, 8])
                                    ypoints = np.array([3, 10])
                                    
                                    plt.plot(xpoints, ypoints)
                                    plt.show()

                                                   
                                    xpoints = np.array([1, 8])
                                    ypoints = np.array([3, 10])
                                    
                                    plt.plot(xpoints, ypoints, 'o')
                                    plt.show()

                                                     
                                    xpoints = np.array([1, 2, 6, 8])
                                    ypoints = np.array([3, 8, 1, 10])
                                    
                                    plt.plot(xpoints, ypoints)
                                    plt.show()

                                                     
                                    ypoints = np.array([3, 8, 1, 10, 5, 7])plt.plot(ypoints)
plt.show()

Matplotlib Markers

Markers
You can use the keyword argument marker to emphasize each point with a specified marker

Marker Reference

Marker	Description
'o'	Circle
'*'	Star
'.'	Point
','	Pixel
'x'	X
'X'	X (filled)
'+'	Plus
'P'	Plus (filled)
's'	Square
'D'	Diamond
'd'	Diamond (thin)
'p'	Pentagon
'H'	Hexagon
'h'	Hexagon
'v'	Triangle Down
'^'	Triangle Up
'<'	Triangle Left
'>'	Triangle Right
'1'	Tri Down
'2'	Tri Up
'3'	Tri Left
'4'	Tri Right
'\|'	Vline
'_'	Hline

Line Reference

Line Syntax	Description
'-'	Solid line
':'	Dotted line
'--'	Dashed line
'-.'	Dashed/dotted line

Color Reference

Color Syntax	Description
'r'	Red
'g'	Green
'b'	Blue
'c'	Cyan
'm'	Magenta
'y'	Yellow
'k'	Black
'w'	White

Marker Size

Marker Size
You can use the keyword argument markersize or the shorter version, ms to set the size of the markers

                    
                        ypoints = np.array([3, 8, 1, 10])                        plt.plot(ypoints, marker = 'o', ms = 20)
                        plt.show()

Line Color

Line Color
You can use the keyword argument color or the shorter c to set the color of the line

                    
                        ypoints = np.array([3, 8, 1, 10])                        plt.plot(ypoints, color = 'r')
                        plt.show()

Line Width

You can use the keyword argument linewidth or the shorter lw to change the width of the line.

The value is a floating number, in points

                    
                        ypoints = np.array([3, 8, 1, 10])                        plt.plot(ypoints, linewidth = '20.5')
                        plt.show()

Linestyle

You can use the keyword argument linestyle, or shorter ls, to change the style of the plotted line

Shorter Syntax:
The line style can be written in a shorter syntax:

linestyle can be written as ls.

                    
                        ypoints = np.array([3, 8, 1, 10])plt.plot(ypoints, linestyle = 'dotted')
plt.show()

                    
                        plt.plot(ypoints, ls = ':')

Marker Size

Marker Size
You can use the keyword argument markersize or the shorter version, ms to set the size of the markers

                    
                        ypoints = np.array([3, 8, 1, 10])                        plt.plot(ypoints, marker = 'o', ms = 20)
                        plt.show()

Marker Color

Marker Color
You can use the keyword argument markeredgecolor or the shorter mec to set the color of the edge of the markers

You can use the keyword argument markerfacecolor or the shorter mfc to set the color inside the edge of the markers

                    
                        ypoints = np.array([3, 8, 1, 10])plt.plot(ypoints, marker = 'o', ms = 20, mec = 'r')
plt.show()

    
        ypoints = np.array([3, 8, 1, 10])        plt.plot(ypoints, marker = 'o', ms = 20, mfc = 'r')
        plt.show()

Format Strings fmt

Format Strings fmt

Format Strings fmt
You can use also use the shortcut string notation parameter to specify the marker.

This parameter is also called fmt, and is written with this syntax:

marker|line|color

                                
                                    plt.title("  ")

                                
                                    plt.xlabel("    ")

                                
                                    plt.ylabel("     ")

                                
                                    font1 = {'family':'serif','color':'blue','size':20}
                                    font2 = {'family':'serif','color':'darkred','size':15}
                                    
                                    plt.title("Sports Watch Data", fontdict = font1)
                                    plt.xlabel("Average Pulse", fontdict = font2)
                                    plt.ylabel("Calorie Burnage", fontdict = font2)

                                
                                    arr = np.array([1, 2, 3, 4, 5])
                                    x = arr.copy()

Legal values are: 'left', 'right', and 'center'. Default value is 'center'.

                                
                                    plt.title("Data", loc = 'left')

                                
                                    plt.grid()

                                
                                    x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])plt.subplot(1, 2, 1)
plt.plot(x,y)#plot 2:
x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])plt.subplot(1, 2, 2)
plt.plot(x,y)plt.show()

plt.subplot(1, 2, 1) #the figure has 1 row, 2 columns, and this plot is the first plot.
plt.subplot(1, 2, 2) #the figure has 1 row, 2 columns, and this plot is the second plot.

                                
                                    plt.title("SALES")

                                
                                    plt.suptitle("MY SHOP")

                                
x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
                                    
                                    plt.scatter(x, y)
                                    plt.show()

Note: You cannot use the color argument for this, only the c argument.

                                
 x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
colors = np.array(["red","green","blue","yellow","pink","black","orange","purple","beige","brown","gray","cyan","magenta"])
                                    
                                    plt.scatter(x, y, c=colors)
                                    
                                    plt.show()

                                The Matplotlib module has a number of available colormaps.                                    A colormap is like a list of colors, where each color has a value that ranges from 0 to 100.
                                
                                    x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
                                    y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
                                    colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])
                                    
                                    plt.scatter(x, y, c=colors, cmap='viridis')
                                    
                                    plt.show()

                                
                                    plt.colorbar()

Name		Reverse
Accent	Try it »	Accent_r	Try it »
Blues	Try it »	Blues_r	Try it »
BrBG	Try it »	BrBG_r	Try it »
BuGn	Try it »	BuGn_r	Try it »
BuPu	Try it »	BuPu_r	Try it »
CMRmap	Try it »	CMRmap_r	Try it »
Dark2	Try it »	Dark2_r	Try it »
GnBu	Try it »	GnBu_r	Try it »
Greens	Try it »	Greens_r	Try it »
Greys	Try it »	Greys_r	Try it »
OrRd	Try it »	OrRd_r	Try it »
Oranges	Try it »	Oranges_r	Try it »
PRGn	Try it »	PRGn_r	Try it »
Paired	Try it »	Paired_r	Try it »
Pastel1	Try it »	Pastel1_r	Try it »
Pastel2	Try it »	Pastel2_r	Try it »
PiYG	Try it »	PiYG_r	Try it »
PuBu	Try it »	PuBu_r	Try it »
PuBuGn	Try it »	PuBuGn_r	Try it »
PuOr	Try it »	PuOr_r	Try it »
PuRd	Try it »	PuRd_r	Try it »
Purples	Try it »	Purples_r	Try it »
RdBu	Try it »	RdBu_r	Try it »
RdGy	Try it »	RdGy_r	Try it »
RdPu	Try it »	RdPu_r	Try it »
RdYlBu	Try it »	RdYlBu_r	Try it »
RdYlGn	Try it »	RdYlGn_r	Try it »
Reds	Try it »	Reds_r	Try it »
Set1	Try it »	Set1_r	Try it »
Set2	Try it »	Set2_r	Try it »
Set3	Try it »	Set3_r	Try it »
Spectral	Try it »	Spectral_r	Try it »
Wistia	Try it »	Wistia_r	Try it »
YlGn	Try it »	YlGn_r	Try it »
YlGnBu	Try it »	YlGnBu_r	Try it »
YlOrBr	Try it »	YlOrBr_r	Try it »
YlOrRd	Try it »	YlOrRd_r	Try it »
afmhot	Try it »	afmhot_r	Try it »
autumn	Try it »	autumn_r	Try it »
binary	Try it »	binary_r	Try it »
bone	Try it »	bone_r	Try it »
brg	Try it »	brg_r	Try it »
bwr	Try it »	bwr_r	Try it »
cividis	Try it »	cividis_r	Try it »
cool	Try it »	cool_r	Try it »
coolwarm	Try it »	coolwarm_r	Try it »
copper	Try it »	copper_r	Try it »
cubehelix	Try it »	cubehelix_r	Try it »
flag	Try it »	flag_r	Try it »
gist_earth	Try it »	gist_earth_r	Try it »
gist_gray	Try it »	gist_gray_r	Try it »
gist_heat	Try it »	gist_heat_r	Try it »
gist_ncar	Try it »	gist_ncar_r	Try it »
gist_rainbow	Try it »	gist_rainbow_r	Try it »
gist_stern	Try it »	gist_stern_r	Try it »
gist_yarg	Try it »	gist_yarg_r	Try it »
gnuplot	Try it »	gnuplot_r	Try it »
gnuplot2	Try it »	gnuplot2_r	Try it »
gray	Try it »	gray_r	Try it »
hot	Try it »	hot_r	Try it »
hsv	Try it »	hsv_r	Try it »
inferno	Try it »	inferno_r	Try it »
jet	Try it »	jet_r	Try it »
magma	Try it »	magma_r	Try it »
nipy_spectral	Try it »	nipy_spectral_r	Try it »
ocean	Try it »	ocean_r	Try it »
pink	Try it »	pink_r	Try it »
plasma	Try it »	plasma_r	Try it »
prism	Try it »	prism_r	Try it »
rainbow	Try it »	rainbow_r	Try it »
seismic	Try it »	seismic_r	Try it »
spring	Try it »	spring_r	Try it »
summer	Try it »	summer_r	Try it »
tab10	Try it »	tab10_r	Try it »
tab20	Try it »	tab20_r	Try it »
tab20b	Try it »	tab20b_r	Try it »
tab20c	Try it »	tab20c_r	Try it »
terrain	Try it »	terrain_r	Try it »
twilight	Try it »	twilight_r	Try it »
twilight_shifted	Try it »	twilight_shifted_r	Try it »
viridis	Try it »	viridis_r	Try it »
winter	Try it »	winter_r	Try it »

You can adjust the transparency of the dots with the alpha argument.

                                
                                    plt.scatter(x, y, s=sizes, alpha=0.5)

You can change the size of the dots with the s argument.

                                
x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
sizes = np.array([20,50,100,200,500,1000,60,90,10,300,600,800,75])plt.scatter(x, y, s=sizes)

                
                    x = np.array(["A", "B", "C", "D"])
                    y = np.array([3, 8, 1, 10])
                    
                    plt.bar(x,y)
                    plt.show()

                
                    x = np.array(["A", "B", "C", "D"])
y = np.array([3, 8, 1, 10])plt.barh(x, y)
plt.show()

                
                    from members.models import Member
x = Member.objects.all()[0]
x.phone = 5551234
x.joined_date = '2022-01-05'
x.save()

            
                plt.bar(x, y, width = 0.1)

            
                x = np.random.normal(170, 10, 250)                plt.hist(x)
                plt.show()

                                        
                                            y = np.array([35, 25, 25, 15])                                            plt.pie(y)
                                            plt.show()

                                        
                                            y = np.array([35, 25, 25, 15])
                                            mylabels = ["Apples", "Bananas", "Cherries", "Dates"]
                                            
                                            plt.pie(y, labels = mylabels)
                                            plt.show()

                                    
                                        y = np.array([35, 25, 25, 15])
                                        mylabels = ["Apples", "Bananas", "Cherries", "Dates"]
                                        
                                        plt.pie(y, labels = mylabels, startangle = 90)
                                        plt.show()

                                  
                                    y = np.array([35, 25, 25, 15])
                                    mylabels = ["Apples", "Bananas", "Cherries", "Dates"]
                                    myexplode = [0.2, 0, 0, 0]
                                    
                                    plt.pie(y, labels = mylabels, explode = myexplode)
                                    plt.show()

                                
                                    plt.pie(y, labels = mylabels, explode = myexplode, shadow = True)

                                    
                                        y = np.array([35, 25, 25, 15])
mylabels = ["Apples", "Bananas", "Cherries", "Dates"]
mycolors = ["black", "hotpink", "b", "#4CAF50"]plt.pie(y, labels = mylabels, colors = mycolors)
plt.show()

admin.py

                                    
                                        plt.legend()

                                    
                                        plt.legend(title = "Four Fruits:")

Matplotlib

Matplotlib

Matplotlib Pyplot

import matplotlib

Draw a line between two points

Plotting two points Without Line

Multiple Points

Plotting without x-points

Matplotlib Markers

Line Reference

Color Reference

Marker Size

Line Color

Line Width

Linestyle

Marker Size

Marker Color

Format Strings fmt

Create a Title for a Plot

Create Labels for a Plot

Set Font Properties for Title and Labels

Create a COPY

Position the Title

Legal values are: 'left', 'right', and 'center'. Default value is 'center'.

Add Grid Lines to a Plot

Matplotlib Subplot(Display Multiple Plots)

plt.subplot(1, 2, 1) #the figure has 1 row, 2 columns, and this plot is the first plot. plt.subplot(1, 2, 2) #the figure has 1 row, 2 columns, and this plot is the second plot.

Title

Super Title

Creating Scatter Plots

Color Each Dot

Note: You cannot use the color argument for this, only the c argument.

ColorMap

The Matplotlib module has a number of available colormaps. A colormap is like a list of colors, where each color has a value that ranges from 0 to 100.

include the colormap

built-in colormaps

Alpha

You can adjust the transparency of the dots with the alpha argument.

Size

You can change the size of the dots with the s argument.

Creating Bars

Horizontal Bars

Bar Width

Create Histogram

Creating Pie Charts

Add labels to the pie chart

Start the first wedge at 90 degrees

Pull the "Apples" wedge 0.2 from the center of the pie

Add a shadow

Specify a new color for each wedge

Add a legend

admin.py

Add a legend with a header

plt.subplot(1, 2, 1) #the figure has 1 row, 2 columns, and this plot is the first plot.
plt.subplot(1, 2, 2) #the figure has 1 row, 2 columns, and this plot is the second plot.