Get rows that have the same value across its columns in pandas

Question

In pandas, given a DataFrame D:

+-----+--------+--------+--------+   
|     |    1   |    2   |    3   |
+-----+--------+--------+--------+
|  0  | apple  | banana | banana |
|  1  | orange | orange | orange |
|  2  | banana | apple  | orange |
|  3  | NaN    | NaN    | NaN    |
|  4  | apple  | apple  | apple  |
+-----+--------+--------+--------+

How do I return rows that have the same contents across all of its columns when there are three columns or more such that it returns this:

+-----+--------+--------+--------+   
|     |    1   |    2   |    3   |
+-----+--------+--------+--------+
|  1  | orange | orange | orange |
|  4  | apple  | apple  | apple  |
+-----+--------+--------+--------+

Note that it skips rows when all values are NaN.

If this were only two columns, I usually do D[D[1]==D[2]] but I don't know how to generalize this for more than 2 column DataFrames.

Answer 1

Similar to Andy Hayden answer with check if min equal to max (then row elements are all duplicates):

df[df.apply(lambda x: min(x) == max(x), 1)]

Answer 2

My entry:

>>> df
        0       1       2
0   apple  banana  banana
1  orange  orange  orange
2  banana   apple  orange
3     NaN     NaN     NaN
4   apple   apple   apple

[5 rows x 3 columns]
>>> df[df.apply(pd.Series.nunique, axis=1) == 1]
        0       1       2
1  orange  orange  orange
4   apple   apple   apple

[2 rows x 3 columns]

This works because calling pd.Series.nunique on the rows gives:

>>> df.apply(pd.Series.nunique, axis=1)
0    2
1    1
2    3
3    0
4    1
dtype: int64

Note: this would, however, keep rows which look like [nan, nan, apple] or [nan, apple, apple]. Usually I want that, but that might be the wrong answer for your use case.