# numpy.matrix¶

class numpy.matrix

Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations. It has certain special operators, such as * (matrix multiplication) and ** (matrix power).

Parameters : data : array_like or string If data is a string, it is interpreted as a matrix with commas or spaces separating columns, and semicolons separating rows. dtype : data-type Data-type of the output matrix. copy : bool If data is already an ndarray, then this flag determines whether the data is copied (the default), or whether a view is constructed.

array

Examples

```>>> a = np.matrix('1 2; 3 4')
>>> print a
[[1 2]
[3 4]]
```
```>>> np.matrix([[1, 2], [3, 4]])
matrix([[1, 2],
[3, 4]])
```

Methods

 all(a[, axis, out]) Test whether all array elements along a given axis evaluate to True. any(a[, axis, out]) Test whether any array element along a given axis evaluates to True. argmax(a[, axis]) Indices of the maximum values along an axis. argmin(a[, axis]) Return the indices of the minimum values along an axis. argsort(a[, axis, kind, order]) Returns the indices that would sort an array. astype byteswap choose(a, choices[, out, mode]) Construct an array from an index array and a set of arrays to choose from. clip(a, a_min, a_max[, out]) Clip (limit) the values in an array. compress(condition, a[, axis, out]) Return selected slices of an array along given axis. conj(x[, out]) Return the complex conjugate, element-wise. conjugate(x[, out]) Return the complex conjugate, element-wise. copy(a) Return an array copy of the given object. cumprod(a[, axis, dtype, out]) Return the cumulative product of elements along a given axis. cumsum(a[, axis, dtype, out]) Return the cumulative sum of the elements along a given axis. diagonal(a[, offset, axis1, axis2]) Return specified diagonals. dot(a, b[, out]) Dot product of two arrays. dump dumps fill flatten getA getA1 getH getI getT getfield item itemset max(a[, axis, out]) Return the maximum of an array or maximum along an axis. mean(a[, axis, dtype, out]) Compute the arithmetic mean along the specified axis. min(a[, axis, out]) Return the minimum of an array or minimum along an axis. newbyteorder nonzero(a) Return the indices of the elements that are non-zero. prod(a[, axis, dtype, out]) Return the product of array elements over a given axis. ptp(a[, axis, out]) Range of values (maximum - minimum) along an axis. put(a, ind, v[, mode]) Replaces specified elements of an array with given values. ravel(a[, order]) Return a flattened array. repeat(a, repeats[, axis]) Repeat elements of an array. reshape(a, newshape[, order]) Gives a new shape to an array without changing its data. resize(a, new_shape) Return a new array with the specified shape. round(a[, decimals, out]) Round an array to the given number of decimals. searchsorted(a, v[, side]) Find indices where elements should be inserted to maintain order. setasflat setfield setflags sort(a[, axis, kind, order]) Return a sorted copy of an array. squeeze(a) Remove single-dimensional entries from the shape of an array. std(a[, axis, dtype, out, ddof]) Compute the standard deviation along the specified axis. sum(a[, axis, dtype, out]) Sum of array elements over a given axis. swapaxes(a, axis1, axis2) Interchange two axes of an array. take(a, indices[, axis, out, mode]) Take elements from an array along an axis. tofile tolist tostring trace(a[, offset, axis1, axis2, dtype, out]) Return the sum along diagonals of the array. transpose(a[, axes]) Permute the dimensions of an array. var(a[, axis, dtype, out, ddof]) Compute the variance along the specified axis. view

numpy.all