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

Returns all field arrays

Methods

`add_field_array`(scalars, name[, deep])
`clear_field_arrays`()	removes all field arrays
`copy`([deep])	Returns a copy of the object
`copy_meta_from`(ido)	Copies pyvista meta data onto this object from another object
`get_data_range`([arr, preference])	Get the non-NaN min and max of a named scalar array
`head`([display, html])	Return the header stats of this dataset.
`save`(filename[, binary])	Writes this mesh to a file.

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

Bases: object

Methods common to all wrapped data objects

add_field_array(scalars, name, deep=True)¶

clear_field_arrays()¶: removes all field arrays

copy(deep=True)¶

Returns 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)¶: Copies pyvista meta data onto this object from another object

property field_arrays¶: Returns all field arrays

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

Get the non-NaN min and max of a named scalar 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 scalar 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

save(filename, binary=True)¶

Writes 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.

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`
`n_columns`
`n_rows`
`row_arrays`	Returns 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 scalar array
`items`()
`keys`()
`next`()	Get the next block from the iterator
`pop`(name)	Pops off an array by the specified name
`save`(args, *kwargs)	Writes this mesh to a file.
`to_pandas`()	Create a Pandas DataFrame from this Table
`update`(data)
`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 scalar 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 scalar type to search for in the dataset. Must be either 'row' or 'field'.

items()¶

keys()¶

property n_arrays¶

property n_columns¶

property n_rows¶

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

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

property row_arrays¶: Returns the all row arrays

save(*args, **kwargs)¶

Writes 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.

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

update(data)¶

values()¶