Python：卡方列联检验（如何解释）

Question

I've done practicing a chi-squared contingency test as below but i'm having a problem on how to interpret the result. The result of below test says p-val = 0. So does it means that two variables are not independent?? As it's a small data, I thought it's pretty sure that the variables are independent. And it seems weird the p-val is 0. Did I do something wrong??

import pandas as pd
df = pd.DataFrame({
    "~60m2" : [54, 577, 143, 782],
    "60~85m2" : [2, 735, 1437, 1],
    "85m2~" : [0, 142, 44, 0],
    })
df.index = ["A", "B", "C", "D"]
df.columns.names = ["size"]
df.index.names = ["city"]

from scipy import stats
stats.chi2_contingency(df)

the output

(2064.576731417199,
 0.0,
 6,
 array([[ 22.24559612,  31.09522594,   2.65917794],
        [577.59101353, 807.36533061,  69.04365586],
        [645.12228746, 901.76155221,  77.11616033],
        [311.04110288, 434.77789124,  37.18100587]]))

Answer 1

I think it is correct. Your cities are very different. Just try to normalize by row:

(df.T / df.sum(axis=1)).T                                             

size     ~60m2   60~85m2     85m2~
city                              
A     0.964286  0.035714  0.000000
B     0.396836  0.505502  0.097662
C     0.088054  0.884852  0.027094
D     0.998723  0.001277  0.000000

each row is very different from the others, so yes cities seems to be different, ie sampled from different population.