Masked array operations

Constants

ma.MaskType

alias of numpy.bool_

Creation

From existing data

ma.masked_array

alias of numpy.ma.core.MaskedArray

ma.array(data[, dtype, copy, order, mask, …])

An array class with possibly masked values.

ma.copy(self, *args, **params) a.copy(order=)

Return a copy of the array.

ma.frombuffer(buffer[, dtype, count, …])

Interpret a buffer as a 1-dimensional array.

ma.fromfunction(function, shape, **dtype)

Construct an array by executing a function over each coordinate.

ma.MaskedArray.copy([order])

Return a copy of the array.

Ones and zeros

ma.empty(shape[, dtype, order, like])

Return a new array of given shape and type, without initializing entries.

ma.empty_like(prototype[, dtype, order, …])

Return a new array with the same shape and type as a given array.

ma.masked_all(shape[, dtype])

Empty masked array with all elements masked.

ma.masked_all_like(arr)

Empty masked array with the properties of an existing array.

ma.ones(shape[, dtype, order])

Return a new array of given shape and type, filled with ones.

ma.zeros(shape[, dtype, order, like])

Return a new array of given shape and type, filled with zeros.


Inspecting the array

ma.all(self[, axis, out, keepdims])

Returns True if all elements evaluate to True.

ma.any(self[, axis, out, keepdims])

Returns True if any of the elements of a evaluate to True.

ma.count(self[, axis, keepdims])

Count the non-masked elements of the array along the given axis.

ma.count_masked(arr[, axis])

Count the number of masked elements along the given axis.

ma.getmask(a)

Return the mask of a masked array, or nomask.

ma.getmaskarray(arr)

Return the mask of a masked array, or full boolean array of False.

ma.getdata(a[, subok])

Return the data of a masked array as an ndarray.

ma.nonzero(self)

Return the indices of unmasked elements that are not zero.

ma.shape(obj)

Return the shape of an array.

ma.size(obj[, axis])

Return the number of elements along a given axis.

ma.is_masked(x)

Determine whether input has masked values.

ma.is_mask(m)

Return True if m is a valid, standard mask.

ma.isMaskedArray(x)

Test whether input is an instance of MaskedArray.

ma.isMA(x)

Test whether input is an instance of MaskedArray.

ma.isarray(x)

Test whether input is an instance of MaskedArray.

ma.MaskedArray.all([axis, out, keepdims])

Returns True if all elements evaluate to True.

ma.MaskedArray.any([axis, out, keepdims])

Returns True if any of the elements of a evaluate to True.

ma.MaskedArray.count([axis, keepdims])

Count the non-masked elements of the array along the given axis.

ma.MaskedArray.nonzero()

Return the indices of unmasked elements that are not zero.

ma.shape(obj)

Return the shape of an array.

ma.size(obj[, axis])

Return the number of elements along a given axis.

ma.MaskedArray.data

Returns the underlying data, as a view of the masked array.

ma.MaskedArray.mask

Current mask.

ma.MaskedArray.recordmask

Get or set the mask of the array if it has no named fields.


Manipulating a MaskedArray

Changing the shape

ma.ravel(self[, order])

Returns a 1D version of self, as a view.

ma.reshape(a, new_shape[, order])

Returns an array containing the same data with a new shape.

ma.resize(x, new_shape)

Return a new masked array with the specified size and shape.

ma.MaskedArray.flatten([order])

Return a copy of the array collapsed into one dimension.

ma.MaskedArray.ravel([order])

Returns a 1D version of self, as a view.

ma.MaskedArray.reshape(*s, **kwargs)

Give a new shape to the array without changing its data.

ma.MaskedArray.resize(newshape[, refcheck, …])

Modifying axes

ma.swapaxes(self, *args, …)

Return a view of the array with axis1 and axis2 interchanged.

ma.transpose(a[, axes])

Permute the dimensions of an array.

ma.MaskedArray.swapaxes(axis1, axis2)

Return a view of the array with axis1 and axis2 interchanged.

ma.MaskedArray.transpose(*axes)

Returns a view of the array with axes transposed.

Changing the number of dimensions

ma.atleast_1d(*args, **kwargs)

Convert inputs to arrays with at least one dimension.

ma.atleast_2d(*args, **kwargs)

View inputs as arrays with at least two dimensions.

ma.atleast_3d(*args, **kwargs)

View inputs as arrays with at least three dimensions.

ma.expand_dims(a, axis)

Expand the shape of an array.

ma.squeeze(a[, axis])

Remove axes of length one from a.

ma.MaskedArray.squeeze([axis])

Remove axes of length one from a.

ma.stack(*args, **kwargs)

Join a sequence of arrays along a new axis.

ma.column_stack(*args, **kwargs)

Stack 1-D arrays as columns into a 2-D array.

ma.concatenate(arrays[, axis])

Concatenate a sequence of arrays along the given axis.

ma.dstack(*args, **kwargs)

Stack arrays in sequence depth wise (along third axis).

ma.hstack(*args, **kwargs)

Stack arrays in sequence horizontally (column wise).

ma.hsplit(*args, **kwargs)

Split an array into multiple sub-arrays horizontally (column-wise).

ma.mr_

Translate slice objects to concatenation along the first axis.

ma.row_stack(*args, **kwargs)

Stack arrays in sequence vertically (row wise).

ma.vstack(*args, **kwargs)

Stack arrays in sequence vertically (row wise).

Joining arrays

ma.concatenate(arrays[, axis])

Concatenate a sequence of arrays along the given axis.

ma.stack(*args, **kwargs)

Join a sequence of arrays along a new axis.

ma.vstack(*args, **kwargs)

Stack arrays in sequence vertically (row wise).

ma.hstack(*args, **kwargs)

Stack arrays in sequence horizontally (column wise).

ma.dstack(*args, **kwargs)

Stack arrays in sequence depth wise (along third axis).

ma.column_stack(*args, **kwargs)

Stack 1-D arrays as columns into a 2-D array.

ma.append(a, b[, axis])

Append values to the end of an array.


Operations on masks

Creating a mask

ma.make_mask(m[, copy, shrink, dtype])

Create a boolean mask from an array.

ma.make_mask_none(newshape[, dtype])

Return a boolean mask of the given shape, filled with False.

ma.mask_or(m1, m2[, copy, shrink])

Combine two masks with the logical_or operator.

ma.make_mask_descr(ndtype)

Construct a dtype description list from a given dtype.

Accessing a mask

ma.getmask(a)

Return the mask of a masked array, or nomask.

ma.getmaskarray(arr)

Return the mask of a masked array, or full boolean array of False.

ma.masked_array.mask

Current mask.

Finding masked data

ma.flatnotmasked_contiguous(a)

Find contiguous unmasked data in a masked array along the given axis.

ma.flatnotmasked_edges(a)

Find the indices of the first and last unmasked values.

ma.notmasked_contiguous(a[, axis])

Find contiguous unmasked data in a masked array along the given axis.

ma.notmasked_edges(a[, axis])

Find the indices of the first and last unmasked values along an axis.

ma.clump_masked(a)

Returns a list of slices corresponding to the masked clumps of a 1-D array.

ma.clump_unmasked(a)

Return list of slices corresponding to the unmasked clumps of a 1-D array.

Modifying a mask

ma.mask_cols(a[, axis])

Mask columns of a 2D array that contain masked values.

ma.mask_or(m1, m2[, copy, shrink])

Combine two masks with the logical_or operator.

ma.mask_rowcols(a[, axis])

Mask rows and/or columns of a 2D array that contain masked values.

ma.mask_rows(a[, axis])

Mask rows of a 2D array that contain masked values.

ma.harden_mask(self)

Force the mask to hard.

ma.soften_mask(self)

Force the mask to soft.

ma.MaskedArray.harden_mask()

Force the mask to hard.

ma.MaskedArray.soften_mask()

Force the mask to soft.

ma.MaskedArray.shrink_mask()

Reduce a mask to nomask when possible.

ma.MaskedArray.unshare_mask()

Copy the mask and set the sharedmask flag to False.


Conversion operations

> to a masked array

ma.asarray(a[, dtype, order])

Convert the input to a masked array of the given data-type.

ma.asanyarray(a[, dtype])

Convert the input to a masked array, conserving subclasses.

ma.fix_invalid(a[, mask, copy, fill_value])

Return input with invalid data masked and replaced by a fill value.

ma.masked_equal(x, value[, copy])

Mask an array where equal to a given value.

ma.masked_greater(x, value[, copy])

Mask an array where greater than a given value.

ma.masked_greater_equal(x, value[, copy])

Mask an array where greater than or equal to a given value.

ma.masked_inside(x, v1, v2[, copy])

Mask an array inside a given interval.

ma.masked_invalid(a[, copy])

Mask an array where invalid values occur (NaNs or infs).

ma.masked_less(x, value[, copy])

Mask an array where less than a given value.

ma.masked_less_equal(x, value[, copy])

Mask an array where less than or equal to a given value.

ma.masked_not_equal(x, value[, copy])

Mask an array where not equal to a given value.

ma.masked_object(x, value[, copy, shrink])

Mask the array x where the data are exactly equal to value.

ma.masked_outside(x, v1, v2[, copy])

Mask an array outside a given interval.

ma.masked_values(x, value[, rtol, atol, …])

Mask using floating point equality.

ma.masked_where(condition, a[, copy])

Mask an array where a condition is met.

> to a ndarray

ma.compress_cols(a)

Suppress whole columns of a 2-D array that contain masked values.

ma.compress_rowcols(x[, axis])

Suppress the rows and/or columns of a 2-D array that contain masked values.

ma.compress_rows(a)

Suppress whole rows of a 2-D array that contain masked values.

ma.compressed(x)

Return all the non-masked data as a 1-D array.

ma.filled(a[, fill_value])

Return input as an array with masked data replaced by a fill value.

ma.MaskedArray.compressed()

Return all the non-masked data as a 1-D array.

ma.MaskedArray.filled([fill_value])

Return a copy of self, with masked values filled with a given value.

> to another object

ma.MaskedArray.tofile(fid[, sep, format])

Save a masked array to a file in binary format.

ma.MaskedArray.tolist([fill_value])

Return the data portion of the masked array as a hierarchical Python list.

ma.MaskedArray.torecords()

Transforms a masked array into a flexible-type array.

ma.MaskedArray.tobytes([fill_value, order])

Return the array data as a string containing the raw bytes in the array.

Filling a masked array

ma.common_fill_value(a, b)

Return the common filling value of two masked arrays, if any.

ma.default_fill_value(obj)

Return the default fill value for the argument object.

ma.maximum_fill_value(obj)

Return the minimum value that can be represented by the dtype of an object.

ma.minimum_fill_value(obj)

Return the maximum value that can be represented by the dtype of an object.

ma.set_fill_value(a, fill_value)

Set the filling value of a, if a is a masked array.

ma.MaskedArray.get_fill_value()

The filling value of the masked array is a scalar.

ma.MaskedArray.set_fill_value([value])

ma.MaskedArray.fill_value

The filling value of the masked array is a scalar.


Masked arrays arithmetics

Arithmetics

ma.anom(self[, axis, dtype])

Compute the anomalies (deviations from the arithmetic mean) along the given axis.

ma.anomalies(self[, axis, dtype])

Compute the anomalies (deviations from the arithmetic mean) along the given axis.

ma.average(a[, axis, weights, returned])

Return the weighted average of array over the given axis.

ma.conjugate(x, /[, out, where, casting, …])

Return the complex conjugate, element-wise.

ma.corrcoef(x[, y, rowvar, bias, …])

Return Pearson product-moment correlation coefficients.

ma.cov(x[, y, rowvar, bias, allow_masked, ddof])

Estimate the covariance matrix.

ma.cumsum(self[, axis, dtype, out])

Return the cumulative sum of the array elements over the given axis.

ma.cumprod(self[, axis, dtype, out])

Return the cumulative product of the array elements over the given axis.

ma.mean(self[, axis, dtype, out, keepdims])

Returns the average of the array elements along given axis.

ma.median(a[, axis, out, overwrite_input, …])

Compute the median along the specified axis.

ma.power(a, b[, third])

Returns element-wise base array raised to power from second array.

ma.prod(self[, axis, dtype, out, keepdims])

Return the product of the array elements over the given axis.

ma.std(self[, axis, dtype, out, ddof, keepdims])

Returns the standard deviation of the array elements along given axis.

ma.sum(self[, axis, dtype, out, keepdims])

Return the sum of the array elements over the given axis.

ma.var(self[, axis, dtype, out, ddof, keepdims])

Compute the variance along the specified axis.

ma.MaskedArray.anom([axis, dtype])

Compute the anomalies (deviations from the arithmetic mean) along the given axis.

ma.MaskedArray.cumprod([axis, dtype, out])

Return the cumulative product of the array elements over the given axis.

ma.MaskedArray.cumsum([axis, dtype, out])

Return the cumulative sum of the array elements over the given axis.

ma.MaskedArray.mean([axis, dtype, out, keepdims])

Returns the average of the array elements along given axis.

ma.MaskedArray.prod([axis, dtype, out, keepdims])

Return the product of the array elements over the given axis.

ma.MaskedArray.std([axis, dtype, out, ddof, …])

Returns the standard deviation of the array elements along given axis.

ma.MaskedArray.sum([axis, dtype, out, keepdims])

Return the sum of the array elements over the given axis.

ma.MaskedArray.var([axis, dtype, out, ddof, …])

Compute the variance along the specified axis.

Minimum/maximum

ma.argmax(self[, axis, fill_value, out])

Returns array of indices of the maximum values along the given axis.

ma.argmin(self[, axis, fill_value, out])

Return array of indices to the minimum values along the given axis.

ma.max(obj[, axis, out, fill_value, keepdims])

Return the maximum along a given axis.

ma.min(obj[, axis, out, fill_value, keepdims])

Return the minimum along a given axis.

ma.ptp(obj[, axis, out, fill_value, keepdims])

Return (maximum - minimum) along the given dimension (i.e.

ma.MaskedArray.argmax([axis, fill_value, …])

Returns array of indices of the maximum values along the given axis.

ma.MaskedArray.argmin([axis, fill_value, …])

Return array of indices to the minimum values along the given axis.

ma.MaskedArray.max([axis, out, fill_value, …])

Return the maximum along a given axis.

ma.MaskedArray.min([axis, out, fill_value, …])

Return the minimum along a given axis.

ma.MaskedArray.ptp([axis, out, fill_value, …])

Return (maximum - minimum) along the given dimension (i.e.

Sorting

ma.argsort(a[, axis, kind, order, endwith, …])

Return an ndarray of indices that sort the array along the specified axis.

ma.sort(a[, axis, kind, order, endwith, …])

Return a sorted copy of the masked array.

ma.MaskedArray.argsort([axis, kind, order, …])

Return an ndarray of indices that sort the array along the specified axis.

ma.MaskedArray.sort([axis, kind, order, …])

Sort the array, in-place

Algebra

ma.diag(v[, k])

Extract a diagonal or construct a diagonal array.

ma.dot(a, b[, strict, out])

Return the dot product of two arrays.

ma.identity(n[, dtype])

Return the identity array.

ma.inner(a, b, /)

Inner product of two arrays.

ma.innerproduct(a, b, /)

Inner product of two arrays.

ma.outer(a, b)

Compute the outer product of two vectors.

ma.outerproduct(a, b)

Compute the outer product of two vectors.

ma.trace(self[, offset, axis1, axis2, …])

Return the sum along diagonals of the array.

ma.transpose(a[, axes])

Permute the dimensions of an array.

ma.MaskedArray.trace([offset, axis1, axis2, …])

Return the sum along diagonals of the array.

ma.MaskedArray.transpose(*axes)

Returns a view of the array with axes transposed.

Polynomial fit

ma.vander(x[, n])

Generate a Vandermonde matrix.

ma.polyfit(x, y, deg[, rcond, full, w, cov])

Least squares polynomial fit.

Clipping and rounding

ma.around

Round an array to the given number of decimals.

ma.clip(a, a_min, a_max[, out])

Clip (limit) the values in an array.

ma.round(a[, decimals, out])

Return a copy of a, rounded to ‘decimals’ places.

ma.MaskedArray.clip([min, max, out])

Return an array whose values are limited to [min, max].

ma.MaskedArray.round([decimals, out])

Return each element rounded to the given number of decimals.

Miscellanea

ma.allequal(a, b[, fill_value])

Return True if all entries of a and b are equal, using fill_value as a truth value where either or both are masked.

ma.allclose(a, b[, masked_equal, rtol, atol])

Returns True if two arrays are element-wise equal within a tolerance.

ma.apply_along_axis(func1d, axis, arr, …)

Apply a function to 1-D slices along the given axis.

ma.apply_over_axes(func, a, axes)

Apply a function repeatedly over multiple axes.

ma.arange([start,] stop[, step,][, dtype, like])

Return evenly spaced values within a given interval.

ma.choose(indices, choices[, out, mode])

Use an index array to construct a new array from a list of choices.

ma.ediff1d(arr[, to_end, to_begin])

Compute the differences between consecutive elements of an array.

ma.indices(dimensions[, dtype, sparse])

Return an array representing the indices of a grid.

ma.where(condition[, x, y])

Return a masked array with elements from x or y, depending on condition.