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PDF打印问题

scipy matplotlib python

user8834780 · 技术社区 · 6 年前

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()

我在stackoverflow上找到了以下方法:

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()

正如你所看到的,我得到完全不同的值-概率是正确的,为1,但对2,他们不(也不加起来100%),和Y轴(%)的直方图是基于6个箱,而不是3。

你能解释一下我怎样才能得到2的正确概率吗?

1 回复 | 直到 6 年前

ImportanceOfBeingErnest 6 年前

第一种方法给你一个概率群众功能。第二个给你一个概率密度 -因此命名为概率密度函数(pdf)。因此两者都是正确的,它们只是显示出一些不同的东西。

如果在更大的范围内(例如,标准偏差的10倍)计算pdf,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()