Machine learning [Matplotlib]

One Matplotlib

Matplotlib Is dedicated to the development of 2D Chart ( Include 3D Chart ), Gradually 、 Interactive way to realize data visualization

Visualization is a key auxiliary tool in data mining , Can clearly understand the data , To adjust our analytical methods .

Can visualize data , More intuitive presentation , Make the data more objective 、 More persuasive

1 Simple implementation

matplotlib.pytplot It contains a series of things like matlab Drawing function of .

# Import required libraries 
import matplotlib.pyplot as plt
# Create a canvas 
plt.figure(figsize=(10, 10), dpi=100)
# Draw line chart 
plt.plot([1, 2, 3, 4, 5, 6 ,7], [17,17,18,15,11,11,13])
# Display images 
plt.show()

Some attribute descriptions

figsize: Specify the length and width of the graph 
dpi: The clarity of the image 
 return fig object 

2 Matplotlib Image structure

3 Basic drawing function

demand ： Draw a city 11 Point to 12 spot 1 Every... In an hour 5 Line chart of temperature change in minutes , The temperature range is 15 degree ~18 degree

（1） Prepare the data , Draw the initial line chart

import matplotlib.pyplot as plt
import random
# 0. Prepare the data 
x = range(60)
y_shanghai = [random.uniform(15, 18) for i in x]
# 1. Create a canvas 
plt.figure(figsize=(20, 8), dpi=80)
# 2. Draw line chart 
plt.plot(x, y_shanghai)
# 3. Display images 
plt.show()

（2） Add custom x,y scale

plt.xticks(x, **kwargs) x: The scale value to display

plt.yticks(y, **kwargs) y: The scale value to display

#  structure x Axis scale label 
#  take i The value of is filled in braces 
x_ticks_label = ["11 spot {} branch ".format(i) for i in x]
#  structure y Axis scale 
y_ticks = range(40)

#  modify x,y The scale display of axis coordinates 
#  from 0 To 60, The interval is 5,
plt.xticks(x[::5], x_ticks_label[::5])
#plt.xticks(x_ticks_label[::5]) # error , The first parameter must be a number 
# from 0 Start 40 end , The interval is 5
plt.yticks(y_ticks[::5])

（3） Add grid display

true Make the function effective ,linestyle Manage dashes （–） Or solid line （-）,alpha Mesh transparency

plt.grid(True, linestyle='--', alpha=0.5)

（4） Add a description

add to x Axis 、y Axis description information and title

adopt fontsize Parameter can modify the size of the font in the image

plt.xlabel(" Time ")
plt.ylabel(" temperature ")
plt.title(" At noon, 11 spot 0 be assigned to 12 Diagram of temperature change between points ", fontsize=20)

（5） Image saving

Save the image to the specified path

plt.show() Will release figure resources , If you save an image after it is displayed, you can only save an empty image .

plt.savefig("path")

4 Drawing multiple images in one coordinate system

demand ： Add another city's temperature change

Collect the temperature changes in Beijing on that day , The temperature is 1 C to 3 degree . Just need to do it again plot that will do , But you need to distinguish between lines ,

#  Increase the temperature data of Beijing 
y_beijing = [random.uniform(1, 3) for i in x]
#  Draw line chart 
plt.plot(x, y_shanghai,label=" Shanghai ")
#  Use it many times plot You can draw multiple broken lines 
plt.plot(x, y_beijing, color='r', linestyle='--',label=" Beijing ")
#  Show Legend , Description information , Which color line represents which city 
plt.legend(loc="best")

Use plt.legend() Show the legend , But it needs to be in plt.plot() Set in label value

（1） Graphic style

Color characters （color）	Style character （linestyle）
r Red	- Solid line
g green	- - Dotted line
b Blue	-. Point line
w white	: Dotted line
c Cyan	’ ’ leave a blank 、 Space
m Magenta
y yellow
k black

（2）loc Code

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

（3） Complete code

# 0. Prepare the data 
x = range(60)
y_shanghai = [random.uniform(15, 18) for i in x]
y_beijing = [random.uniform(1,3) for i in x]

# 1. Create a canvas 
plt.figure(figsize=(20, 8), dpi=100)

# 2. The plot 
plt.plot(x, y_shanghai, label=" Shanghai ")
plt.plot(x, y_beijing, color="r", linestyle="--", label=" Beijing ")

# 2.1  add to x,y Axis scale 
#  structure x,y Axis scale label 
x_ticks_label = ["11 spot {} branch ".format(i) for i in x]
y_ticks = range(40)

#  Scale display 
plt.xticks(x[::5], x_ticks_label[::5])
plt.yticks(y_ticks[::5])

# 2.2  Add grid display 
plt.grid(True, linestyle="--", alpha=0.5)

# 2.3  Add a description 
plt.xlabel(" Time ")
plt.ylabel(" temperature ")
plt.title(" At noon, 11 spot --12 Point to the temperature change map of a city ", fontsize=20)

# 2.4  Image saving 
plt.savefig("./test.png")

# 2.5  Add legend 
plt.legend(loc=0)


# 3. Image display 
plt.show()

5 Display images in multiple coordinate systems

matplotlib.pyplot.subplots(nrows=1, ncols=1, **fig_kw) Create one with multiple axes( Coordinate system / Drawing area ) Graph

Parameters:    

nrows, ncols :  There are several rows and columns of coordinate system 
    int, optional, default: 1, Number of rows/columns of the subplot grid.

Returns:    
fig :  Picture object 
axes :  Returns the corresponding number of coordinate systems , Use axes Draw the image 

 There are different ways to set the title ：
    set_xticks
    set_yticks
    set_xlabel
    set_ylabel

# 0. Prepare the data 
x = range(60)
y_shanghai = [random.uniform(15, 18) for i in x]
y_beijing = [random.uniform(1, 5) for i in x]
# 1. Create a canvas 
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8), dpi=100)
# 2. The plot 
axes[0].plot(x, y_shanghai, label=" Shanghai ")
axes[1].plot(x, y_beijing, color="r", linestyle="--", label=" Beijing ")
# 2.1  add to x,y Axis scale 
x_ticks_label = ["11 spot {} branch ".format(i) for i in x]
y_ticks = range(40)
#  Scale display 
axes[0].set_xticks(x[::5])
axes[0].set_yticks(y_ticks[::5])  
axes[0].set_xticklabels(x_ticks_label[::5])  #  Replace... With a string 
axes[1].set_xticks(x[::5])
axes[1].set_yticks(y_ticks[::5])
axes[1].set_xticklabels(x_ticks_label[::5])  #  Replace... With a string 
# 2.2  Add grid display 
axes[0].grid(True, linestyle="--", alpha=0.5)
axes[1].grid(True, linestyle="--", alpha=0.5)
# 2.3  Add a description 
axes[0].set_xlabel(" Time ")
axes[0].set_ylabel(" temperature ")
axes[0].set_title(" At noon, 11 spot --12 Point Beijing temperature change diagram ", fontsize=20)
axes[1].set_xlabel(" Time ")
axes[1].set_ylabel(" temperature ")
axes[1].set_title(" At noon, 11 spot --12 Point Shanghai temperature change diagram ", fontsize=20)
# # 2.4  Image saving 
plt.savefig("./test.png")
# # 2.5  Add legend 
axes[0].legend(loc=0)
axes[1].legend(loc=0)
# 3. Image display 
plt.show()

6 Application scenarios of line chart

• Present the company's products ( Different regions ) Number of active users per day
• present app Number of downloads per day
• Present the new functions of the product , The number of user clicks over time

Use plt.plot() draw sin Images

import numpy as np
# 0. Prepare the data 
x = np.linspace(-10, 10, 1000)  # from -10 To 10 Generate 1000 Data 
y = np.sin(x)
# y = x * x * x
# 1. Create a canvas 
plt.figure(figsize=(20, 8), dpi=100)
# 2. Draw function image 
plt.plot(x, y)
# 2.1  Add grid display 
plt.grid()
# 3. Display images 
plt.show()

7 Common graphic drawing

（1） Broken line diagram

A statistical chart showing the increase or decrease of statistical quantity by the rise or fall of a broken line

characteristic ： It can show the trend of data change , Reflect the change of things .( change )

api：plt.plot(x, y)

（2） Scatter plot

Two sets of data are used to form multiple coordinate points , Look at the distribution of coordinate points , Judge whether there is some association between two variables or summarize the distribution pattern of coordinate points .

characteristic ： Determine whether there is a quantitative correlation trend between variables , Show outliers ( The law of distribution )

api：plt.scatter(x, y)

（3） Histogram

Data arranged in columns or rows of a worksheet can be drawn into a histogram .

characteristic ： Draw even discrete data , Can see the size of each data at a glance , Compare the differences between the data .( Statistics / contrast )

api：plt.bar(x, width, align='center', **kwargs)

Parameters:    
x :  Data to be passed 

width :  The width of the histogram 

align :  The position alignment of each histogram 
    {‘center’, ‘edge’}, optional, default: ‘center’

**kwargs :
color: Choose the color of the histogram 

（4） Histogram

Data distribution is represented by a series of vertical stripes or line segments with different heights . Generally, the horizontal axis is used to represent the data range , The vertical axis shows the distribution .

characteristic ： Draw continuous data to show the distribution of one or more groups of data ( Statistics )

api：matplotlib.pyplot.hist(x, bins=None)

Parameters:    
x :  Data to be passed 
bins :  Group spacing 

（5） The pie chart

Used to indicate the proportion of different classifications , Compare various classifications by radian size .

characteristic ： The proportion of classified data ( Proportion )

api：plt.pie(x, labels=,autopct=,colors)

Parameters:  
x: Number , Automatically calculate percentage 
labels: The name of each part 
autopct: The percentage display specifies %1.2f%%
colors: Color of each part 

（6） Scatter plot

# 0. Prepare the data 
x = [225.98, 247.07, 253.14, 457.85, 241.58, 301.01,  20.67, 288.64,
       163.56, 120.06, 207.83, 342.75, 147.9 ,  53.06, 224.72,  29.51,
        21.61, 483.21, 245.25, 399.25, 343.35]
y = [196.63, 203.88, 210.75, 372.74, 202.41, 247.61,  24.9 , 239.34,
       140.32, 104.15, 176.84, 288.23, 128.79,  49.64, 191.74,  33.1 ,
        30.74, 400.02, 205.35, 330.64, 283.45]
# 1. Create a canvas 
plt.figure(figsize=(20, 8), dpi=100)
# 2. Draw a scatter plot 
plt.scatter(x, y)
# 3. Display images 
plt.show()

（7） Histogram drawing

demand ： Compare the box office receipts of each movie

# 0. Prepare the data 
#  The name of the movie 
movie_name = [' The thor 3： The gods at dusk ',' Justice League: Injustice for All ',' Murder on the Orient express ',' Dream seeking travel notes ',' Global storms ',' Legend of conquering the devil ',' chase ',' Seventy-seven days ',' Secret War ',' Wild beast ',' Other ']
#  Abscissa 
x = range(len(movie_name))
#  Box office data 
y = [73853,57767,22354,15969,14839,8725,8716,8318,7916,6764,52222]
# 1. Create a canvas 
plt.figure(figsize=(20, 8), dpi=100)
# 2. Draw a histogram 
plt.bar(x, y, width=0.5, color=['b','r','g','y','c','m','y','k','c','g','b'])
# 2.1  modify x The scale of the axis shows 
plt.xticks(x, movie_name)
# 2.2  Add grid display 
plt.grid(linestyle="--", alpha=0.5)
# 2.3  Add the title 
plt.title(" Movie box office revenue comparison ")
# 3. Display images 
plt.show()

[ Reference link ]：https://matplotlib.org/index.html