I want to do a t-test for the means of hourly wages of male and female staff.
`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()`
Truth is, I think I've got confused half way. I wanted to do a t-test so I wrote the code
`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}")`
It will only return errors.
I sort of went crazy trying different things...
`#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'])`
Please who can guide me to the right step to take?
You're passing mean values of each group as a and b parameters - that's why the error raises. Instead, you should pass arrays, as it is stated in the documentation.
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)