利用python绘制散点图,python画概率密度图
散点密度图是在散点图的基础上，计算了每个散点周围分布了多少其他的点，并通过颜色表现出来。本文主要介绍了计算机编程语言绘制散点密度图的三种方式，需要的可以参考下
目录
方式一方式二方式三
方式一
将matplotlib.pyplot作为血小板计数导入
将数组作为铭牌导入
从scipy.stats导入高斯_kde
从mpl_toolkits.axes_grid1导入make_axes_locatable
从绘制精美的图表导入rcParams
config={ font。family : Times New Roman ， font.size: 16， mathtext.fontset:stix}
rcParams.update(配置)
# 读取数据
进口熊猫作为螺纹中径
filename=r f :/Rpython/lp37/testdata。xlsx
df2=pd.read_excel(文件名)#读取文件
x=df2[data1].价值观念
y=df2[data2].价值观念
xy=np.vstack([x，y])
z=高斯kde(xy)(xy)
idx=z.argsort()
x，y，z=x[idx]，y[idx]，z[idx]
fig，ax=plt.subplots(figsize=(12，9)，dpi=100)
scatter=ax.scatter(x，y，marker=o ，c=z，edgecolors= ，s=15，label=LST ，cmap=Spectral_r )
cbar=plt.colorbar(scatter，shrink=1，orientation=vertical ，extend=both ，pad=0.015，aspect=30，label= frequency )# orientation= horizontal
font3={family:SimHei ， size:16， color:k}
plt.ylabel(估计值，fontdict=font3)
plt.xlabel(预测值，fontdict=font3)
PLT。保存图( f :/Rpython/lp37/plot 70。png ，dpi=800，bbox_inches=tight ，pad_inches=0)
plt.show()
方式二
从统计数据导入均值
将matplotlib.pyplot作为血小板计数导入
从sklearn.metrics导入解释变量得分，r2得分，中位数绝对误差，均方误差，平均绝对误差
从科学计算导入统计
将数组作为铭牌导入
从绘制精美的图表导入rcParams
config={ font。family : Times New Roman ， font.size: 16， mathtext.fontset:stix}
rcParams.update(配置)
def scatter_out_1(x，y): ## x，y为两个需要做对比分析的两个量。
#==========计算评价指标==========
偏差=平均值(x - y)
MSE=均方误差(x，y)
RMSE=np.power(均方误差，0.5)
R2=r2_score(x，y)
MAE=均值绝对误差(x，y)
EV=解释变量得分(x，y)
打印(==========算法评价指标==========)
打印( BIAS: ， %.3f % (BIAS))
打印(解释的差异(电动汽车): ， %.3f %(电动汽车))
打印(平均绝对误差(美): ， %.3
f % (MAE))
print(Mean squared error(MSE):, %.3f % (MSE))
print(Root Mean Squard Error(RMSE):, %.3f % (RMSE))
print(R_squared:, %.3f % (R2))
# ===========Calculate the point density==========
xy = np.vstack([x, y])
z = stats.gaussian_kde(xy)(xy)
# ===========Sort the points by density, so that the densest points are plotted last===========
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]
def best_fit_slope_and_intercept(xs, ys):
m = (((mean(xs) * mean(ys)) - mean(xs * ys)) / ((mean(xs) * mean(xs)) - mean(xs * xs)))
b = mean(ys) - m * mean(xs)
return m, b
m, b = best_fit_slope_and_intercept(x, y)
regression_line = []
for a in x:
regression_line.append((m * a) + b)
fig,ax=plt.subplots(figsize=(12,9),dpi=600)
scatter=ax.scatter(x,y,marker=o,c=z*100,edgecolors=,s=15,label=LST,cmap=Spectral_r)
cbar=plt.colorbar(scatter,shrink=1,orientation=vertical,extend=both,pad=0.015,aspect=30,label=frequency)
plt.plot([0,25],[0,25],black,lw=1.5) # 画的1:1线，线的颜色为black，线宽为0.8
plt.plot(x,regression_line,red,lw=1.5) # 预测与实测数据之间的回归线
plt.axis([0,25,0,25]) # 设置线的范围
plt.xlabel(OBS,family = Times New Roman)
plt.ylabel(PRE,family = Times New Roman)
plt.xticks(fontproperties=Times New Roman)
plt.yticks(fontproperties=Times New Roman)
plt.text(1,24, $N=%.f$ % len(y), family = Times New Roman) # text的位置需要根据x,y的大小范围进行调整。
plt.text(1,23, $R^2=%.3f$ % R2, family = Times New Roman)
plt.text(1,22, $BIAS=%.4f$ % BIAS, family = Times New Roman)
plt.text(1,21, $RMSE=%.3f$ % RMSE, family = Times New Roman)
plt.xlim(0,25) # 设置x坐标轴的显示范围
plt.ylim(0,25) # 设置y坐标轴的显示范围
plt.savefig(F:/Rpython/lp37/plot71.png,dpi=800,bbox_inches=tight,pad_inches=0)
plt.show()
方式三
import pandas as pd
import numpy as np
from scipy import optimize
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import Normalize
from scipy.stats import gaussian_kde
from matplotlib import rcParams
config={"font.family":Times New Roman,"font.size":16,"mathtext.fontset":stix}
rcParams.update(config)
# 读取数据
filename=rF:/Rpython/lp37/testdata.xlsx
df2=pd.read_excel(filename)#读取文件
x=df2[data1].values.ravel()
y=df2[data2].values.ravel()
N = len(df2[data1])
#绘制拟合线
x2 = np.linspace(-10,30)
y2 = x2
def f_1(x,A,B):
return A*x + B
A1,B1 = optimize.curve_fit(f_1,x,y)[0]
y3 = A1*x + B1
# Calculate the point density
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)
norm = Normalize(vmin = np.min(z), vmax = np.max(z))
#开始绘图
fig,ax=plt.subplots(figsize=(12,9),dpi=600)
scatter=ax.scatter(x,y,marker=o,c=z*100,edgecolors=,s=15,label=LST,cmap=Spectral_r)
cbar=plt.colorbar(scatter,shrink=1,orientation=vertical,extend=both,pad=0.015,aspect=30,label=frequency)
cbar.ax.locator_params(nbins=8)
cbar.ax.set_yticklabels([0.005,0.010,0.015,0.020,0.025,0.030,0.035])#0,0.005,0.010,0.015,0.020,0.025,0.030,0.035
ax.plot(x2,y2,color=k,linewidth=1.5,linestyle=--)
ax.plot(x,y3,color=r,linewidth=2,linestyle=-)
fontdict1 = {"size":16,"color":"k",family:Times New Roman}
ax.set_xlabel("PRE",fontdict=fontdict1)
ax.set_ylabel("OBS",fontdict=fontdict1)
# ax.grid(True)
ax.set_xlim((0,25))
ax.set_ylim((0,25))
ax.set_xticks(np.arange(0,25.1,step=5))
ax.set_yticks(np.arange(0,25.1,step=5))
plt.savefig(F:/Rpython/lp37/plot72.png,dpi=800,bbox_inches=tight,pad_inches=0)
plt.show()
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