我们经常会使用jupyter处理一些数据绘图，在pycharm中比较麻烦，在一次学校布置的作业中，要求完成图形的绘画处理，结果根据查找的绘图方法。发现利用seabron模块渲染运行之后，没有出现图形，出现了
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r
原代码：
import numpy as np
import sympy as sp
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
# 利用seaborn 进行渲染处理
# 读取数据
path = "D:\\PyCharmProjects\\pythonProject\\pythonHIgh\\resourse\\Admission_Predict_Ver1.1.csv"
path1 = "D:\\PyCharmProjects\\pythonProject\\pythonHIgh\\resourse\\Admission_Predict.csv"
data_df = pd.read_csv(path)
data_df1 = pd.read_csv(path1)
# 重置索引
data_df.index = data_df['Serial No.']
data_df1.index = data_df1['Serial No.']
# 合并数据
part_data_df = data_df[['GRE Score','TOEFL Score','University Rating','SOP','LOR','CGPA','Research','Chance of Admit']]
part_data_df1 = data_df1[['GRE Score','TOEFL Score','University Rating','SOP','LOR','CGPA','Research','Chance of Admit']]
combine_data_df = pd.concat([part_data_df,part_data_df1],axis = 0)
combine_data_df.head(5)
fig, axes = plt.subplots(figsize = (7, 6))
sns.heatmap(combine_data_df.corr(), ax=axes, annot = True, fmt='.2f',linewidths=0.03,cmap="magma")解决办法如下：
添加以下代码：
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
plt.show()
添加代码重新运行之后，运行结果如下：
更改后的代码如下：
import numpy as np
import sympy as sp
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
# 利用seaborn 进行渲染处理
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
# 读取数据
path = "D:\\PyCharmProjects\\pythonProject\\pythonHIgh\\resourse\\Admission_Predict_Ver1.1.csv"
path1 = "D:\\PyCharmProjects\\pythonProject\\pythonHIgh\\resourse\\Admission_Predict.csv"
data_df = pd.read_csv(path)
data_df1 = pd.read_csv(path1)
# 重置索引
data_df.index = data_df['Serial No.']
data_df1.index = data_df1['Serial No.']
# 合并数据
part_data_df = data_df[['GRE Score','TOEFL Score','University Rating','SOP','LOR','CGPA','Research','Chance of Admit']]
part_data_df1 = data_df1[['GRE Score','TOEFL Score','University Rating','SOP','LOR','CGPA','Research','Chance of Admit']]
combine_data_df = pd.concat([part_data_df,part_data_df1],axis = 0)
combine_data_df.head(5)
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
plt.show()
fig, axes = plt.subplots(figsize = (7, 6))
sns.heatmap(combine_data_df.corr(), ax=axes, annot = True, fmt='.2f',linewidths=0.03,cmap="magma")