Comparing pandas dataframe rows

Question

I have a dataframe like:

df = pd.DataFrame({30: {'2020-10-09': 12.79, '2020-10-12': 12.83, '2020-10-13': 12.88, '2020-10-14': 12.93, '2020-10-15': 12.99, '2020-10-16': 13.07, '2020-10-19': 13.16, '2020-10-20': 13.24, '2020-10-21': 13.32, '2020-10-22': 13.42}, 365: {'2020-10-09': 12.27, '2020-10-12': 12.27, '2020-10-13': 12.28, '2020-10-14': 12.29, '2020-10-15': 12.29, '2020-10-16': 13.07, '2020-10-19': 12.31, '2020-10-20': 12.32, '2020-10-21': 12.32, '2020-10-22': 12.33}})

I want to find the rows where the values are equal across all columns. I can do this with .loc but that would mean I have to hard code the column names, which I don't want to do as it is possible I will have more columns to compare in the future. I am quite close with

df.eq(df.iloc[:, 0], axis=0)

Which gives

             30     365
2020-10-09  True    False
2020-10-12  True    False
2020-10-13  True    False
2020-10-14  True    False
2020-10-15  True    False
2020-10-16  True    True
2020-10-19  True    False
2020-10-20  True    False
2020-10-21  True    False
2020-10-22  True    False

but I am unable to retrieve the rows with True in each column. I thought using df[df.eq(df.iloc[:, 0], axis=0)] should work but it gives a multi-index error. Thanks!

Answer 1

You can add DataFrame.all for test if all True s and use boolean indexing :

df = df[df.eq(df.iloc[:, 0], axis=0).all(axis=1)]
print (df)
              30     365
2020-10-16  13.07  13.07