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Array manipulation routines

Basic operations

copyto(dst, src[, casting, where]) Copies values from one array to another, broadcasting as necessary.

Changing array shape

reshape(a, newshape[, order]) Gives a new shape to an array without changing its data.
ravel(a[, order]) Return a contiguous flattened array.
ndarray.flat A 1-D iterator over the array.
ndarray.flatten([order]) Return a copy of the array collapsed into one dimension.

Transpose-like operations

moveaxis(a, source, destination) Move axes of an array to new positions.
rollaxis(a, axis[, start]) Roll the specified axis backwards, until it lies in a given position.
swapaxes(a, axis1, axis2) Interchange two axes of an array.
ndarray.T Same as self.transpose(), except that self is returned if self.ndim < 2.
transpose(a[, axes]) Permute the dimensions of an array.

Changing number of dimensions

atleast_1d(*arys) Convert inputs to arrays with at least one dimension.
atleast_2d(*arys) View inputs as arrays with at least two dimensions.
atleast_3d(*arys) View inputs as arrays with at least three dimensions.
broadcast Produce an object that mimics broadcasting.
broadcast_to(array, shape[, subok]) Broadcast an array to a new shape.
broadcast_arrays(*args, **kwargs) Broadcast any number of arrays against each other.
expand_dims(a, axis) Expand the shape of an array.
squeeze(a[, axis]) Remove single-dimensional entries from the shape of an array.

Changing kind of array

asarray(a[, dtype, order]) Convert the input to an array.
asanyarray(a[, dtype, order]) Convert the input to an ndarray, but pass ndarray subclasses through.
asmatrix(data[, dtype]) Interpret the input as a matrix.
asfarray(a[, dtype]) Return an array converted to a float type.
asfortranarray(a[, dtype]) Return an array laid out in Fortran order in memory.
ascontiguousarray(a[, dtype]) Return a contiguous array in memory (C order).
asarray_chkfinite(a[, dtype, order]) Convert the input to an array, checking for NaNs or Infs.
asscalar(a) Convert an array of size 1 to its scalar equivalent.
require(a[, dtype, requirements]) Return an ndarray of the provided type that satisfies requirements.

Joining arrays

concatenate((a1, a2, ...)[, axis]) Join a sequence of arrays along an existing axis.
stack(arrays[, axis]) Join a sequence of arrays along a new axis.
column_stack(tup) Stack 1-D arrays as columns into a 2-D array.
dstack(tup) Stack arrays in sequence depth wise (along third axis).
hstack(tup) Stack arrays in sequence horizontally (column wise).
vstack(tup) Stack arrays in sequence vertically (row wise).

Splitting arrays

split(ary, indices_or_sections[, axis]) Split an array into multiple sub-arrays.
array_split(ary, indices_or_sections[, axis]) Split an array into multiple sub-arrays.
dsplit(ary, indices_or_sections) Split array into multiple sub-arrays along the 3rd axis (depth).
hsplit(ary, indices_or_sections) Split an array into multiple sub-arrays horizontally (column-wise).
vsplit(ary, indices_or_sections) Split an array into multiple sub-arrays vertically (row-wise).

Tiling arrays

tile(A, reps) Construct an array by repeating A the number of times given by reps.
repeat(a, repeats[, axis]) Repeat elements of an array.

Adding and removing elements

delete(arr, obj[, axis]) Return a new array with sub-arrays along an axis deleted.
insert(arr, obj, values[, axis]) Insert values along the given axis before the given indices.
append(arr, values[, axis]) Append values to the end of an array.
resize(a, new_shape) Return a new array with the specified shape.
trim_zeros(filt[, trim]) Trim the leading and/or trailing zeros from a 1-D array or sequence.
unique(ar[, return_index, return_inverse, ...]) Find the unique elements of an array.

Rearranging elements

fliplr(m) Flip array in the left/right direction.
flipud(m) Flip array in the up/down direction.
reshape(a, newshape[, order]) Gives a new shape to an array without changing its data.
roll(a, shift[, axis]) Roll array elements along a given axis.
rot90(m[, k]) Rotate an array by 90 degrees in the counter-clockwise direction.
doc_NumPy
doc_NumPy
2017-01-10 18:07:32
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