如何从数据帧执行 T 检验?
How do I perform a T-test from a dataframe?
我想做一个男女员工小时工资均值的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,我想我已经半途而废了。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 return 个错误。
我尝试不同的东西有点疯狂...
`#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'])`
谁能指导我采取正确的步骤?
您将每个组的 平均值 作为 a
和 b
参数传递 - 这就是出现错误的原因。相反,您应该传递 arrays,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)
我想做一个男女员工小时工资均值的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,我想我已经半途而废了。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 return 个错误。
我尝试不同的东西有点疯狂...
`#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'])`
谁能指导我采取正确的步骤?
您将每个组的 平均值 作为 a
和 b
参数传递 - 这就是出现错误的原因。相反,您应该传递 arrays,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)