Objects¶

The pyvista.DataObject class is a set of common methods and attributes for all PyVista types. These objects have no spatial reference, but simply hold data.

Attributes

`field_arrays`	Return all field arrays.
`memory_address`	Get address of the underlying C++ object in format ‘Addr=%p’.

Methods

`add_field_array`(scalars, name[, deep])	Add a field array.
`clear_field_arrays`()	Remove all field arrays.
`copy`([deep])	Return a copy of the object.
`copy_meta_from`(ido)	Copy pyvista meta data onto this object from another object.
`deep_copy`(to_copy)	Overwrite this mesh with the given mesh as a deep copy.
`get_data_range`([arr, preference])	Get the non-NaN min and max of a named array.
`head`([display, html])	Return the header stats of this dataset.
`save`(filename[, binary])	Write this mesh to a file.
`shallow_copy`(to_copy)	Shallow copy the given mesh to this mesh.

class pyvista.DataObject(*args, **kwargs)¶

Bases: object

Methods common to all wrapped data objects.

add_field_array(scalars, name, deep=True)¶: Add a field array.

clear_field_arrays()¶: Remove all field arrays.

copy(deep=True)¶

Return a copy of the object.

Parameters: deep (bool, optional) – When True makes a full copy of the object.
Returns: newobject – Deep or shallow copy of the input.
Return type: same as input

copy_meta_from(ido)¶: Copy pyvista meta data onto this object from another object.

deep_copy(to_copy)¶: Overwrite this mesh with the given mesh as a deep copy.

property field_arrays¶: Return all field arrays.

get_data_range(arr=None, preference='field')¶

Get the non-NaN min and max of a named array.

Parameters

arr (str, np.ndarray, optional) – The name of the array to get the range. If None, the active scalar is used
preference (str, optional) – When scalars is specified, this is the perfered array type to search for in the dataset. Must be either 'point', 'cell', or 'field'.

head(display=True, html=None)¶

Return the header stats of this dataset.

If in IPython, this will be formatted to HTML. Otherwise returns a console friendly string.

property memory_address¶: Get address of the underlying C++ object in format ‘Addr=%p’.

save(filename, binary=True)¶

Write this mesh to a file.

Parameters

filename (str) – Filename of mesh to be written. File type is inferred from the extension of the filename unless overridden with ftype.
binary (bool, optional) – Writes the file as binary when True and ASCII when False.

Notes

Binary files write much faster than ASCII and have a smaller file size.

shallow_copy(to_copy)¶: Shallow copy the given mesh to this mesh.

Table¶

The table class is a non-spatially referenced data object that can be used on VTK pipelines and holds arrays of data.

Attributes

`n_arrays`	Return the number of columns.
`n_columns`	Return the number of columns.
`n_rows`	Return the number of rows.
`row_arrays`	Return the all row arrays.

Methods

`get`(index)	Get an array by its name.
`get_data_range`([arr, preference])	Get the non-NaN min and max of a named array.
`items`()	Return the table items.
`keys`()	Return the table keys.
`next`()	Get the next block from the iterator.
`pop`(name)	Pops off an array by the specified name.
`save`(args, *kwargs)	Save the table.
`to_pandas`()	Create a Pandas DataFrame from this Table.
`update`(data)	Set the table data.
`values`()	Return the table values.

class pyvista.Table(*args, **kwargs)¶

Bases: vtkCommonDataModelPython.vtkTable, pyvista.core.common.DataObject

Wrapper for the vtkTable class.

Create by passing a 2D NumPy array of shape (n_rows by n_columns) or from a dictionary containing NumPy arrays.

Example

>>> import pyvista as pv
>>> import numpy as np
>>> arrays = np.random.rand(100, 3)
>>> table = pv.Table(arrays)

get(index)¶: Get an array by its name.

get_data_range(arr=None, preference='row')¶

Get the non-NaN min and max of a named array.

Parameters

arr (str, np.ndarray, optional) – The name of the array to get the range. If None, the active scalar is used
preference (str, optional) – When scalars is specified, this is the perfered array type to search for in the dataset. Must be either 'row' or 'field'.

items()¶: Return the table items.

keys()¶: Return the table keys.

property n_arrays¶

Return the number of columns.

Alias for: n_columns.

property n_columns¶: Return the number of columns.

property n_rows¶: Return the number of rows.

next()¶: Get the next block from the iterator.

pop(name)¶: Pops off an array by the specified name.

property row_arrays¶: Return the all row arrays.

save(*args, **kwargs)¶: Save the table.

to_pandas()¶: Create a Pandas DataFrame from this Table.

update(data)¶: Set the table data.

values()¶: Return the table values.