Seaborn实现单变量分析

import numpy as np
import pandas as pd
from scipy import stats,integrate
import matplotlib.pyplot as plt
import seaborn as sns

# # 绘制直方图
# sns.set(color_codes=True)
# np.random.seed(sum(map(ord,"distributions")))
# # 生成高斯数据
# x = np.random.normal(size = 100)
# #
# # sns.distplot(x,kde = False)
# #  x 数据   kde 是否做密度估计
# #  将数据划分为 15 份 bins = 15
# sns.distplot(x,kde = False,bins = 15)
# plt.show()

# # 查看数据分布状况,根据某一个指标画一条线
# x = np.random.gamma(6,size = 200)
# sns.distplot(x,kde = False,fit = stats.gamma)
# plt.show()

# mean,cov = [0,1],[(1,5),(0.5,1)]
# data = np.random.multivariate_normal(mean,cov,200)
# df = pd.DataFrame(data,columns=["x","y"])

# # 单变量使用直方图,关系使用散点图
# 关系 joinplot (x,y,data)
# sns.jointplot(x = "x",y = "y",data = df)
# # 绘制散点图和直方图
# plt.show()

# # hex 图,数据越多 色越深
# mean,cov = [0,1],[(1,8),(0.5,1)]
# x,y = np.random.multivariate_normal(mean,cov,500).T
# # 注意 .T 进行倒置
# with sns.axes_style("white"):
#     sns.jointplot(x = x,y = y,kind = "hex",color = "k")
# plt.show()

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2020-04-24