一. 数据分布情况

我们观察单个变量，一般是观察该变量的分布情况。

1.1 构建一个随机变量

代码1:

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

import seaborn as sns
sns.set(color_codes=True)
np.random.seed(sum(map(ord, "distributions")))

x = np.random.normal(size=100)
sns.distplot(x,kde=False)

plt.show()

测试记录1:

image.png

此时我们想自己指定柱状图的个数:
代码2:

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

import seaborn as sns
sns.set(color_codes=True)
np.random.seed(sum(map(ord, "distributions")))

x = np.random.normal(size=100)
sns.distplot(x, bins=20, kde=False)

plt.show()

测试记录2:

image.png

二. 数据分布情况

代码1:

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

import seaborn as sns
sns.set(color_codes=True)
np.random.seed(sum(map(ord, "distributions")))

x = np.random.gamma(6, size=200)
sns.distplot(x, kde=False, fit=stats.gamma)

plt.show()

测试记录1:

image.png

根据均值和协方差生成数据
代码2:

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

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

测试记录2: