python - How to count duplicate rows in pandas dataframe?

Question

I am trying to count the duplicates of each type of row in my dataframe. For example, say that I have a dataframe in pandas as follows:

df = pd.DataFrame({'one': pd.Series([1., 1, 1]),
                   'two': pd.Series([1., 2., 1])})

I get a df that looks like this:

    one two
0   1   1
1   1   2
2   1   1

I imagine the first step is to find all the different unique rows, which I do by:

df.drop_duplicates()

This gives me the following df:

    one two
0   1   1
1   1   2

Now I want to take each row from the above df ([1 1] and [1 2]) and get a count of how many times each is in the initial df. My result would look something like this:

Row     Count
[1 1]     2
[1 2]     1

How should I go about doing this last step?

Edit:

Here's a larger example to make it more clear:

df = pd.DataFrame({'one': pd.Series([True, True, True, False]),
                   'two': pd.Series([True, False, False, True]),
                   'three': pd.Series([True, False, False, False])})

gives me:

    one three   two
0   True    True    True
1   True    False   False
2   True    False   False
3   False   False   True

I want a result that tells me:

       Row           Count
[True True True]       1
[True False False]     2
[False False True]     1

Answer 1

You can groupby on all the columns and call size the index indicates the duplicate values:

In [28]:
df.groupby(df.columns.tolist(),as_index=False).size()

Out[28]:
one    three  two  
False  False  True     1
True   False  False    2
       True   True     1
dtype: int64