Python – R chisquare goodness-of-fit test code cannot be converted to equivalent python

UCLA has this awesome statistical testing website

https://stats.idre.ucla.edu/r/whatstat/what-statistical-analysis-should-i-usestatistical-analyses-using-r/#1sampt

But the code is all in R. I’m trying to convert code to Python equivalents, but it’s not a simple process for someone like chi-square goodness-of-fit. This is the R version:

hsb2 <- within(read.csv("https://stats.idre.ucla.edu/stat/data/hsb2.csv"), {
  race <- as.factor(race)
  schtyp <- as.factor(schtyp)
  prog <- as.factor(prog)
})
chisq.test(table(hsb2$race), p = c(10, 10, 10, 70)/100)

My Python attempt is like this:

import numpy as np
import pandas as pd
from scipy import stats

df = pd.read_csv("https://stats.idre.ucla.edu/stat/data/hsb2.csv")
# convert to category
df["race"] = df["race"].astype("category")

t_race = pd.crosstab(df.race, columns = 'race')
p_tests = np.array((10, 10, 10, 70))
p_tests = ptests/100
# tried this
stats.chisquare(t_race, p_tests)
# and this
stats.chisquare(t_race. T, p_tests)

But none of the stats.chisquare outputs come close to the R version. Can anyone guide me in the right direction? Time difference

chisq.test uses probability vectors; stats.chisquare uses the expected frequency ( docs )。

> results = chisq.test(c(24, 11, 20, 145), p=c(0.1, 0.1, 0.1, 0.7))
> results

Chi-squared test for given probabilities

data:  c(24, 11, 20, 145)
X-squared = 5.028571429, df = 3, p-value = 0.169716919

Contrast

In [49]: obs = np.array([24, 11, 20, 145])

In [50]: prob = np.array([0.1, 0.1, 0.1, 0.7])

In [51]: stats.chisquare(obs, f_exp=obs.sum() * prob)
Out[51]: Power_divergenceResult(statistic=5.0285714285714285, pvalue=0.16971691923343338)