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()