Question

我想遍历我的熊猫数据框并最终根据一些计算来创建直方图。我想对四个不同的值执行此操作，即40、60、80和100。我编写了脚本，以实现期望的值100。

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

number_of_trials = 10**6

true_averages = beta.rvs(81, 219, size=number_of_trials)
hits = np.random.binomial(300, true_averages, number_of_trials)
simulations = pd.DataFrame({'True_Average': true_averages, 'Hits': hits})

hit_100 = simulations['Hits'] == 100
hit_100_df = simulations[hit_100]
mu, sigma = np.mean(hit_100_df['True_Average']), np.std(hit_100_df['True_Average'])
x = np.linspace(min(hit_100_df['True_Average']), max(hit_100_df['True_Average']), 100)

plt.plot(x, mlab.normpdf(x, mu, sigma), color='k', linestyle='--')
n, bins, patches = plt.hist(hit_100_df['True_Average'], 25, normed=True, 
facecolor='grey', alpha=0.75)
plt.xlabel('Batting average of players who got 100 H / 300 AB')
plt.ylabel('Density')
plt.show()

现在，我想创建一个循环以在一个图中显示所有四个值的密度函数。我知道我可以为每个值重复该过程，但是我想更快地学习它。

hit = dict()
is_check = dict()
hits_df = pd.DataFrame()
hits = [40, 60, 80, 100]

for x in hits:
   hit[x] = x
   is_check[x] = simulations['Hits'] == hit[x]
   hits_df[x] = simulations(is_check[x]) # This line gives me an error
   print(hits_df[x])

任何帮助将不胜感激

遍历熊猫df并根据列表中的值获取不同的df

0 个答案: