Data visualization (graphic drawing) under Pycharm tool

There are some differences in the data visualization of python under different tools. But the principle of data construction is the same. The data construction of this part can refer to the previous method.
Drawing mode:
pyplot: Classic level package (hereinafter pyplot manner)
pylab: Matplotlib and the combined Numpy module, programming environment Matlab simulation
object-oriented (Object-Oriented): base layer and others based approach

// An highlighted block
import numpy as np
import matplotlib.pyplot as plt

Scatter plot

Correlation between two variables

Drawing

// An highlighted block
height=[160,170,180,190,200]
weight=[50,51,52,53,54]
plt.scatter(height,weight)
plt.show()

Appearance adjustment

c, point size: s (area), transparency: alpha, point shape: marker

// An highlighted block
height=[160,170,180,190,200]
weight=[50,51,52,53,54]
plt.scatter(height,weight,s=300,c='r',marker='<',alpha=0.5)
plt.show( )

line chart

Observe the trend of data over time

Drawing

// An highlighted block
x=np.linspace(-10,10,5)
y=x**2
plt.plot(x,y)
plt.show()

Appearance adjustment

Realize the linear drawing of two lines at the same time
: linestyle color: color point shape: marker

// An highlighted block
x=np.linspace(-10,10,5)
y=x**2
y1=x*2
plt.plot(x,y,linestyle='-',c='r',marker='o')
plt.plot(x,y1,linestyle='--',c='g',marker='<')
plt.show()

Bar graph

Compare the data size of multiple item classifications and use smaller data sets for analysis

Drawing

// An highlighted block
y=[50,30,40,20,60]
index=np.arange(5)
pl=plt.bar(x=index,height=y)
plt.show()

Appearance adjustment

Bandwidth width color color

// An highlighted block
y=[50,30,40,20,60]
index=np.arange(5)
pl=plt.bar(x=index,height=y,width=0.5,color='r')
plt.show()

Drawing of two histograms

// An highlighted block
y1=[50,30,40,20,60]
y2=[55,35,45,25,65]
index=np.arange(5)
p1=plt.bar(x=index,height=y1,width=0.3,color='r')
p2=plt.bar(x=index+0.3,height=y2,width=0.3,color='g')
plt.show()

Superimpose

// An highlighted block
y1=[50,30,40,20,60]
y2=[55,35,45,25,65]
index=np.arange(5)
p1=plt.bar(x=index,height=y1,width=0.5,color='r')
p2=plt.bar(x=index,height=y2,width=0.5,color='g',bottom=y1)
plt.show()

Histogram

Distribution of data

Drawing

// An highlighted block
mu=100
sigma=20
x=mu+sigma*np.random.randn(2000)
#normed 标准化
plt.hist(x,bins=20)
plt.show()

Appearance adjustment

normed color

// An highlighted block
mu=100
sigma=20
x=mu+sigma*np.random.randn(2000)
#normed 标准化
plt.hist(x,bins=20,color='green',normed=True)
plt.show()

Bivariate histogram

// An highlighted block
x=np.random.randn(1000)+2
y=np.random.randn(1000)+3
plt.hist2d(x,y,bins=40)
plt.show()

Pie chart

The data points in the pie chart are displayed as a percentage of the entire pie chart

Drawing

// An highlighted block
labels='A','B','C','D'
fracs=[15,30,10,45]
plt.axes(aspect=1)
plt.pie(x=fracs,labels=labels,autopct='%0.2f')
plt.show()

highlight

Highlight the'B','C' in the graph, explode controls the distance from the graph to the center of the circle, and shadow increases the shadow

// An highlighted block
labels='A','B','C','D'
fracs=[15,30,10,45]
explode=[0,0.08,0.08,0]
plt.axes(aspect=1)
plt.pie(x=fracs,labels=labels,autopct='%0.2f',explode=explode,shadow=True)
plt.show()

Box plot

The dispersion of the displayed data is
composed of the upper edge, upper quartile, median, lower quartile, lower edge, and outliers

Drawing

// An highlighted block
np.random.seed(100)
data=np.random.normal(size=1000,loc=0,scale=1)
plt.boxplot(data)
plt.show()

Appearance adjustment

Shape of outlier points, length of whis imaginary value: adjust the length of outlier

// An highlighted block
np.random.seed(100)
data=np.random.normal(size=1000,loc=0,scale=1)
plt.boxplot(data,sym='o',whis=0.5)
plt.show()

Plot multiple sets of data at the same time

// An highlighted block
np.random.seed(100)
data=np.random.normal(size=(1000,4),loc=0,scale=1)
labels=['A','B','C','D']
plt.boxplot(data,labels=labels,sym='o',whis=0.5)
plt.show()

Style string

It is very convenient when drawing color, point type, and line type as a string.

// An highlighted block
x=np.linspace(-10,10,5)
y=x**2
y1=2*x
plt.plot(x,y,'cx--')
plt.plot(x,y1,'mo:')
plt.show()