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Category：Languages

I have a dataframe like this

`df = pd.DataFrame({'a' : [1,1,0,0], 'b': [0,1,1,0], 'c': [0,0,1,1]}) `

I want to get

` a b c a 2 1 0 b 1 2 1 c 0 1 2 `

where a,b,c are column names, and I get the values counting '1' in all columns when the filter is '1' in another column. For ample, when df.a == 1, we count a = 2, b =1, c = 0 etc

I made a loop to solve

`matrix = [] for name, values in df.iteritems(): matrix.append(pd.DataFrame( df.groupby(name, as_index=False).apply(lambda x: x[x == 1].count())).values.tolist()[1]) pd.DataFrame(matrix) `

But I think that there is a simpler solution, isn't it?

You appear to want the matrix product, so leverage `DataFrame.dot`

:

`df.T.dot(df) a b c a 2 1 0 b 1 2 1 c 0 1 2 `

Alternatively, if you want the same level of performance without the overhead of pandas, you could compute the product with `np.dot`

:

`v = df.values pd.DataFrame(v.T.dot(v), index=df.columns, columns=df.columns) `

Or, if you want to get cute,

`(lambda a, c: pd.DataFrame(a.T.dot(a), c, c))(df.values, df.columns) `

` a b c a 2 1 0 b 1 2 1 c 0 1 2 `