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`numpy.cross(a, b)`

# cross product of*a*and*b*(or vectors in*a*and*b*)`numpy.cross(a, b, axisa=-1)`

#cross product of vectors in*a*with*b*, s.t. vectors in a are laid out along axis*axisa*`numpy.cross(a, b, axisa=-1, axisb=-1, axisc=-1)`

# cross products of vectors in*a*and*b*, output vectors laid out along axis specified by*axisc*`numpy.cross(a, b, axis=None)`

# cross products of vectors in*a*and*b*, vectors in*a*,*b*, and in output laid out along axis*axis*

Column | Column |
---|---|

a,b | In simplest usage, `a` and `b` are two 2- or 3-element vectors. They can also be arrays of vectors (i.e. two-dimensional matrices). If `a` is an array and 'b' is a vector, `cross(a,b)` returns an array whose elements are the cross products of each vector in `a` with the vector `b` . The `b` is an array and `a` is a single vector, `cross(a,b)` returns an array whose elements are the cross products of `a` with each vector in `b` . `a` and `b` can both be arrays if they have the same shape. In this case, `cross(a,b)` returns `cross(a[0],b[0]), cross(a[1], b[1]), ...` |

axisa/b | If `a` is an array, it can have vectors laid out across the most quickly varying axis, the slowest varying axis, or something in between. `axisa` tells `cross()` how the vectors are laid out in `a` . By default, it takes the value of the most slowly varying axis. `axisb` works the same with input `b` . If the output of `cross()` is going to be an array, the output vectors can be laid out different array axes; `axisc` tells `cross` how to lay out the vectors in its output array. By default, `axisc` indicates the most slowly varying axis. |

axis | A convenience parameter that sets `axisa` , `axisb` , and `axisc` all to the same value if desired. If `axis` and any of the other parameters are present in the call, the value of `axis` will override the other values. |