import scipy.stats as st

dist_list = [st.gamma, st.expon]
dist_params_list = []

for dist in dist_list:
    dist_params = dist.fit(df_test_1_1.values)
    dist_params_list.append(dist_params)


chisqs, p_values = [], []

hist_values, bounds = np.histogram(df_test_1_1.values, bins=100)
for dist, dist_params in zip(dist_list, dist_params_list):
    dist_theor = dist(*dist_params)



    hist_values = hist_values / hist_values.sum()

    theor_hist_values = []
    for low_bound, upper_bound in zip(bounds[:-1], bounds[1:]):
        theor_hist_values.append((dist_theor.cdf(upper_bound) + dist_theor.cdf(low_bound)) / 2)

    theor_hist_values = np.array(theor_hist_values)
    theor_hist_values = theor_hist_values / theor_hist_values.sum()

    chisq, p_value = st.chisquare(hist_values, f_exp=theor_hist_values)

    chisqs.append(chisq)
    p_values.append(p_value)

p_values