如何从数据框中执行 T 检验？答案

【问题标题】：How do I perform a T-test from a dataframe?如何从数据框中执行 T 检验？
【发布时间】：2020-01-11 13:07:29
【问题描述】：

我想对男性和女性员工的小时工资均值进行 t 检验。

`df1 = df[["gender","hourly_wage"]] #creating a sub-dataframe with only the columns of gender and hourly wage
staff_wages=df1.groupby(['gender']).mean() #grouping the data frame by gender and assigning it to a new variable 'staff_wages'
staff_wages.head()`

说实话，我想我已经弄糊涂了。我想做一个t检验所以我写了代码

`mean_val_salary_female = df1[staff_wages['gender'] == 'female'].mean()
mean_val_salary_female = df1[staff_wages['gender'] == 'male'].mean()

t_val, p_val = stats.ttest_ind(mean_val_salary_female, mean_val_salary_male)

# obtain a one-tail p-value
p_val /= 2

print(f"t-value: {t_val}, p-value: {p_val}")`

它只会返回错误。

我有点疯狂地尝试不同的东西......

`#married_vs_dependents = df[['married', 'num_dependents', 'years_in_employment']]


#married_vs_dependents = df[['married', 'num_dependents', 'years_in_employment']]
#married_vs_dependents.head()

#my_data = df(married_vs_dependents)
#my_data.groupby('married').mean()

mean_gender = df.groupby("gender")["hourly_wage"].mean()
married_vs_dependents.head()

mean_gender.groupby('gender').mean()

mean_val_salary_female = df[staff_wages['gender'] == 'female'].mean()
mean_val_salary_female = df[staff_wages['gender'] == 'male'].mean()

#cat1 = mean_gender['male']==['cat1']
#cat2 = mean_gender['female']==['cat2']

ttest_ind(cat1['gender'], cat2['hourly_wage'])`

请谁能指导我采取正确的步骤？

【问题讨论】：

你的 mean_val_salary_male 是从哪来的（t_val, p_val = stats.ttest_ind(mean_val_salary_female, mean_val_salary_male)）？？？
你有两个 (mean_val_salary_female) 但没有 (mean_val_salary_male)
@MehdiHamzeloee 哦，我明白了。让我看看

标签： python pandas p-value t-test

【解决方案1】：

您将每个组的 平均值 作为 a 和 b 参数传递 - 这就是引发错误的原因。相反，您应该传递 arrays，正如 documentation 中所述。

df1 = df[["gender","hourly_wage"]]

m = df1.loc[df1["gender"].eq("male")]["hourly_wage"].to_numpy()
f = df1.loc[df1["gender"].eq("female")]["hourly_wage"].to_numpy()

stats.ttest_ind(m,f)

【讨论】：