Python画3D图_Python_移动技术网

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)

df=pd.DataFrame({'X': range(1,101), 'Y': np.random.randn(100)*15+range(1,101), 'Z': (np.random.randn(100)*15+range(1,101))*2 })
 
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(df['X'], df['Y'], df['Z'], c='skyblue', s=60)
ax.view_init(30, 185)
plt.show()

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)

url = 'https://python-graph-gallery.com/wp-content/uploads/volcano.csv'
data = pd.read_csv(url)

df=data.unstack().reset_index() 
df.columns=["X","Y","Z"]

df['X']=pd.Categorical(df['X'])
df['X']=df['X'].cat.codes

fig = plt.figure()
ax = fig.gca(projection='3d')
surf=ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=plt.cm.viridis, linewidth=0.2)
fig.colorbar( surf, shrink=0.5, aspect=5)
plt.show()

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)

url = 'https://python-graph-gallery.com/wp-content/uploads/volcano.csv'
data = pd.read_csv(url)

df=data.unstack().reset_index() 
df.columns=["X","Y","Z"]

df['X']=pd.Categorical(df['X'])
df['X']=df['X'].cat.codes

fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=plt.cm.jet, linewidth=0.01)
plt.show()

import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

import seaborn as sns
sns.set_style("white")
df = sns.load_dataset('iris')

my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)

df['species']=pd.Categorical(df['species'])
my_color=df['species'].cat.codes
df = df.drop('species', 1)

pca = PCA(n_components=3)
pca.fit(df)

result=pd.DataFrame(pca.transform(df), columns=['PCA%i' % i for i in range(3)], index=df.index)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(result['PCA0'], result['PCA1'], result['PCA2'], c=my_color, cmap="Set2_r", s=60)

xAxisLine = ((min(result['PCA0']), max(result['PCA0'])), (0, 0), (0,0))     
ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r') 
yAxisLine = ((0, 0), (min(result['PCA1']), max(result['PCA1'])), (0,0)) 
ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r') 
zAxisLine = ((0, 0), (0,0), (min(result['PCA2']), max(result['PCA2']))) 
ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r') 
 
ax.set_xlabel("PC1") 
ax.set_ylabel("PC2")
ax.set_zlabel("PC3")
ax.set_title("PCA on the iris data set")
plt.show()

import matplotlib.pyplot as plt
import pandas as pd
from math import pi

df = pd.DataFrame({
    'group': ['A','B','C','D'],
    'var1': [38, 1.5, 30, 4], 
    'var2': [29, 10, 9, 34], 
    'var3': [8, 39, 23, 24], 
    'var4': [7, 31, 33, 14], 
    'var5': [28, 15, 32, 14]
    })

def make_spider( row, title, color):
    
    categories=list(df)[1:]
    N = len(categories)

    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]

    ax = plt.subplot(2,2,row+1, polar=True, )

    ax.set_theta_offset(pi / 2)
    ax.set_theta_direction(-1)

    plt.xticks(angles[:-1], categories, color='grey', size=8)

    ax.set_rlabel_position(0)
    plt.yticks([10,20,30], ["10","20","30"], color="grey", size=7)
    plt.ylim(0,40)

    values=df.loc[row].drop('group').values.flatten().tolist()
    values += values[:1]
    ax.plot(angles, values, color=color, linewidth=2, linestyle='solid')
    ax.fill(angles, values, color=color, alpha=0.4)

    # 添加标题
    plt.title(title, size=11, color=color, y=1.1)


my_dpi=96
plt.figure(figsize=(1000/my_dpi, 1000/my_dpi), dpi=my_dpi)

my_palette = plt.cm.get_cmap("Set2", len(df.index))

for row in range(0, len(df.index)):    
    make_spider( row=row, title='group '+df['group'][row], color=my_palette(row))
plt.show()