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• 图是这个网站的：https://www.python-graph-gallery.com/
• 话不多说，直接上代码，不懂直接联系我，私信会及时回复的
• Rardar:
• Wordcloud
• Circular Barplot
• Parallel
• Lollipop

import numpy as np
import matplotlib.pyplot as plt
import matplotlib
%config InlineBackend.figure_format='svg'#矢量图设置

matplotlib.rcParams['font.family']='SimHei'
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
labels = np.array(['综合','KDA','发育','推进','生存','输出'])
data = np.concatenate((data,[data[0]]))
angles = np.concatenate((angles,[angles[0]]))
labels=np.concatenate((labels,[labels[0]]))   #对labels进行封闭

nAttr = 6
data = np.array([7,5,6,9,8,7])
angles = np.linspace(0,2*np.pi,nAttr,endpoint=False)
data = np.concatenate((data,[data[0]]))
angles = np.concatenate((angles,[angles[0]]))
fig = plt.figure(facecolor="white")
plt.subplot(111,polar=True)
plt.plot(angles,data,'bo-',color ='g',linewidth=2)
plt.fill(angles,data,facecolor='g',alpha=0.25)
plt.thetagrids(angles*180/np.pi,labels)
plt.figtext(0.52,0.95,'DOTA能力值雷达图',ha='center')
plt.grid(True)
plt.show()

# Libraries
from wordcloud import WordCloud
import matplotlib.pyplot as plt
 
# Create a list of word
text=("Python Python Python Matplotlib Matplotlib Seaborn Network Plot Violin Chart Pandas Datascience Wordcloud Spider Radar Parrallel Alpha Color Brewer Density Scatter Barplot Barplot Boxplot Violinplot Treemap Stacked Area Chart Chart Visualization Dataviz Donut Pie Time-Series Wordcloud Wordcloud Sankey Bubble")
 
# Create the wordcloud object
wordcloud = WordCloud(width=480, height=480).generate(text)
 
# Display the generated image:
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.margins(x=0, y=0)
plt.show()

# import pandas for data wrangling
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%config InlineBackend.figure_format='svg'#矢量图设置
# Build a dataset
df = pd.Dataframe(
        {
            'Name': ['item ' + str(i) for i in list(range(1, 51)) ],
            'Value': np.random.randint(low=10, high=100, size=50)
        })

# Reorder the dataframe
df = df.sort_values(by=['Value'])

# initialize the figure
plt.figure(figsize=(20,10))
ax = plt.subplot(111, polar=True)
plt.axis('off')

# Constants = parameters controling the plot layout:
upperLimit = 100
lowerLimit = 30
labelPadding = 4

# Compute max and min in the dataset
max = df['Value'].max()

# Let's compute heights: they are a conversion of each item value in those new coordinates
# In our example, 0 in the dataset will be converted to the lowerLimit (10)
# The maximum will be converted to the upperLimit (100)
slope = (max - lowerLimit) / max
heights = slope * df.Value + lowerLimit

# Compute the width of each bar. In total we have 2*Pi = 360°
width = 2*np.pi / len(df.index)

# Compute the angle each bar is centered on:
indexes = list(range(1, len(df.index)+1))
angles = [element * width for element in indexes]
angles

# Draw bars
bars = ax.bar(
    x=angles, 
    height=heights, 
    width=width, 
    bottom=lowerLimit,
    linewidth=2, 
    edgecolor="white",
    color="#61a4b2",
)

# Add labels
for bar, angle, height, label in zip(bars,angles, heights, df["Name"]):

    # Labels are rotated. Rotation must be specified in degrees :(
    rotation = np.rad2deg(angle)

    # Flip some labels upside down
    alignment = ""
    if angle >= np.pi/2 and angle < 3*np.pi/2:
        alignment = "right"
        rotation = rotation + 180
    else: 
        alignment = "left"

    # Finally add the labels
    ax.text(
        x=angle, 
        y=lowerLimit + bar.get_height() + labelPadding, 
        s=label, 
        ha=alignment, 
        va='center', 
        rotation=rotation, 
        rotation_mode="anchor") 

import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import parallel_coordinates
from sklearn import datasets
%config InlineBackend.figure_format='svg'#矢量图设置
# 使用pandas中的parallel_coordinates可视化高维数据
# 导入数据
data_origin = datasets.load_iris()
# 处理数据
# 先把数据提出来
data = data_origin["data"]
# 处理类标签数据
target = data_origin["target"]
target_names = data_origin["target_names"]
target_labels = []
for class_num in target:
    target_labels.append(target_names[class_num])

feature_names = data_origin["feature_names"]
# 合成字典
data_dict = {}
column = 0
for feature_name in feature_names:
    data_dict[feature_name] = data[:, column]
    column += 1
data_dict["target_labels"] = target_labels
# 合成dataframe
pd_data = pd.Dataframe(data_dict)
# 画图
plt.figure()
parallel_coordinates(pd_data, "target_labels")
plt.show()

# libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%config InlineBackend.figure_format='svg'#矢量图设置
# Create a dataframe
df = pd.Dataframe({'group':list(map(chr, range(65, 85))), 'values':np.random.uniform(size=20) }) #定义数据从A-
 
# Reorder it based on the values:
ordered_df = df.sort_values(by='values')
my_range=range(1,len(df.index)+1)
 
# Make the plot
plt.stem(ordered_df['values'])
plt.xticks( my_range, ordered_df['group'])
plt.show()