# mars.dataframe.DataFrame.dot#

DataFrame.dot(other)#

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array.

It can also be called using `self @ other` in Python >= 3.5.

Parameters

other (Series, DataFrame or array-like) – The other object to compute the matrix product with.

Returns

If other is a Series, return the matrix product between self and other as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array.

Return type

`Series.dot`

Similar method for Series.

Notes

The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.

The dot method for Series computes the inner product, instead of the matrix product here.

Examples

Here we multiply a DataFrame with a Series.

```>>> import mars.tensor as mt
>>> import mars.dataframe as md
>>> df = md.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = md.Series([1, 1, 2, 1])
>>> df.dot(s).execute()
0    -4
1     5
dtype: int64
```

Here we multiply a DataFrame with another DataFrame.

```>>> other = md.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other).execute()
0   1
0   1   4
1   2   2
```

Note that the dot method give the same result as @

```>>> (df @ other).execute()
0   1
0   1   4
1   2   2
```

The dot method works also if other is an np.array.

```>>> arr = mt.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr).execute()
0   1
0   1   4
1   2   2
```

Note how shuffling of the objects does not change the result.

```>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2).execute()
0    -4
1     5
dtype: int64
```