问题描述
如何打印数据框,其中人口在平均值的 5% 以内? (低于 2.5%,高于 2.5%)
这是我尝试过的:
mean = df['population'].mean()
minimum = mean - (0.025*mean)
maximum = mean + (0.025*mean)
df[df.population < maximum]
解决方法
使用:
df.loc[(df['population'] > minimum) & (df['population'] < maximum)]
,
import pandas as pd
df = pd.read_csv("fileName.csv")
#suppose this dataFrame contains the population in the int format
mean = df['population'].mean()
minimum = mean - (0.025*mean)
maximum = mean + (0.025*mean)
ans = df.loc[(df['population']>minimum) & (df['population'] <maximum)]
ans
你可以使用这个
,我构建了这个数据框用于测试。
import numpy as np
import pandas as pd
random_data = np.random.randint(1_000_000,100_000_000,200)
random_df = pd.DataFrame(random_data,columns=['population'])
random_df
这是您所要求的具体答案。
pop = random_df.population
top_boundary = pop.mean() + pop.mean() * 0.025
low_boundary = pop.mean() - pop.mean() * 0.025
criteria_boundary_limits = random_df.population.between(low_boundary,top_boundary)
criteria_boundary_df = random_df.loc[criteria_boundary_limits]
criteria_boundary_df
但是,也许,使用分位数可以得到另一个答案。我使用了 40 个分位数,因为 1/40 = 0.025。
groups_list = list(range(1,41))
random_df['groups'] = pd.qcut(random_df['population'],40,labels = groups_list)
criteria_groups_limits = random_df.groups.between(20,21)
criteria_groups_df = random_df.loc[criteria_groups_limits]
criteria_groups_df