Python 如何从数据帧执行T测试？

我想对男女员工的小时工资平均值进行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()`

事实是，我想我已经糊涂了一半。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'])`

请问谁能指导我采取正确的步骤？

您将每组的平均值作为

a

和

b

参数传递-这就是错误产生的原因。相反，您应该传递数组，如中所述

---

df1=df[[“性别”，“小时工资”]]
m=df1.loc[df1[“性别”].eq（“男性”）][“小时工资”].to_numpy（）
f=df1.loc[df1[“性别”].eq（“女性”）][“小时工资”].to_numpy（）
统计数据测试索引（m，f）

在这一行中，你的平均工资男性来自哪里（t_val，p_val=stats.ttest_ind（平均工资女性，平均工资男性）？？你有两个（平均工资女性），但没有（平均工资男性）@MehdiHamzeloee哦，我明白了。让我看看