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Question

我尝试了以下手动方法：

dict = {'id': ['a','b','c','d'], 'testers_time': [10, 30, 15, None], 'stage_1_to_2_time': [30, None, 30, None], 'activated_time' : [40, None, 45, None],'stage_2_to_3_time' : [30, None, None, None],'engaged_time' : [70, None, None, None]} 
df = pd.DataFrame(dict, columns=['id', 'testers_time', 'stage_1_to_2_time', 'activated_time', 'stage_2_to_3_time', 'engaged_time'])

df= df.dropna(subset=['testers_time']).sort_values('testers_time')

prob = df['testers_time'].value_counts(normalize=True)
print(prob)
#0.333333,  0.333333,  0.333333
plt.plot(df['testers_time'], prob, marker='.', linestyle='-') 

plt.show()

我尝试了以下在stackoverflow上发现的方法：

dict = {'id': ['a','b','c','d'], 'testers_time': [10, 30, 15, None], 'stage_1_to_2_time': [30, None, 30, None], 'activated_time' : [40, None, 45, None],'stage_2_to_3_time' : [30, None, None, None],'engaged_time' : [70, None, None, None]} 
df = pd.DataFrame(dict, columns=['id', 'testers_time', 'stage_1_to_2_time', 'activated_time', 'stage_2_to_3_time', 'engaged_time'])

df= df.dropna(subset=['testers_time']).sort_values('testers_time')

fit = stats.norm.pdf(df['testers_time'], np.mean(df['testers_time']), np.std(df['testers_time']))  
print(fit)
#0.02902547,  0.04346777,  0.01829513]
plt.plot(df['testers_time'], fit, marker='.', linestyle='-')
plt.hist(df['testers_time'], normed='true')      

plt.show()

如您所见，我得到了完全不同的值-概率对于＃1是正确的，但对于＃2则不是（也不是总计为100％），直方图的y轴（％）基于6个容器，而不是3个。

您能解释一下我如何获得＃2的正确概率吗？

Answer 1

第一种方法为您提供概率质量函数。第二个参数为您提供概率密度-因此命名为概率密度函数（pdf）。因此，两者都是正确的，只是表现出一些不同。

如果您在更大范围内（例如，标准偏差的10倍）评估pdf，则它看起来很像预期的高斯曲线。

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

dict = {'id': ['a','b','c','d'], 'testers_time': [10, 30, 15, None], 'stage_1_to_2_time': [30, None, 30, None], 'activated_time' : [40, None, 45, None],'stage_2_to_3_time' : [30, None, None, None],'engaged_time' : [70, None, None, None]} 
df = pd.DataFrame(dict, columns=['id', 'testers_time', 'stage_1_to_2_time', 'activated_time', 'stage_2_to_3_time', 'engaged_time'])

df= df.dropna(subset=['testers_time']).sort_values('testers_time')

mean = np.mean(df['testers_time'])
std = np.std(df['testers_time'])
x = np.linspace(mean - 5*std, mean + 5*std)

fit = stats.norm.pdf(x, mean, std)  
print(fit)

plt.plot(x, fit, marker='.', linestyle='-')
plt.hist(df['testers_time'], normed='true')      

plt.show()