tensor_to_pd

# Import statement

from hottbox.pdtools import tensor_to_pd

tensor_to_pd(tensor, col_name=None)

Represent tensor as a multi-index pandas dataframe

Parameters

tensorTensor

Tensor to be represented as a multi-index dataframe

col_namestr

Column label to use for resulting dataframe

Returns

dfpd.DataFrame

Multi-index data frame

Raises

TensorStateError

If tensor is not in normal state: tensor.in_normal_state is False.

Examples

1. Conversion of a tensor with default meta information

>>> import numpy as np
>>> from hottbox.core import Tensor
>>> from hottbox.pdtools import tensor_to_pd
>>> data = np.arange(8).reshape(2, 2, 2)
>>> tensor = Tensor(data)
>>> print(tensor.data)
    [[[0 1]
      [2 3]]
     [[4 5]
      [6 7]]]
>>> tensor.modes
    [Mode(name='mode-0', index=None),
     Mode(name='mode-1', index=None),
     Mode(name='mode-2', index=None)]
>>> df = tensor_to_pd(tensor)
>>> print(df)
                              Values
    mode-0 mode-1 mode-2
    0      0      0            0
                  1            1
           1      0            2
                  1            3
    1      0      0            4
                  1            5
           1      0            6
                  1            7

2. Conversion of a tensor with specified mode names

>>> import numpy as np
>>> from hottbox.core import Tensor
>>> from hottbox.pdtools import tensor_to_pd
>>> data = np.arange(8).reshape(2, 2, 2)
>>> tensor = Tensor(data, mode_names=["Year", "Month", "Day"])
>>> print(tensor.data)
    [[[0 1]
      [2 3]]
     [[4 5]
      [6 7]]]
>>> tensor.modes
    [Mode(name='Year', index=None),
     Mode(name='Month', index=None),
     Mode(name='Day', index=None)]
>>> df = tensor_to_pd(tensor)
>>> print(df)
                        Values
    Year Month Day
    0    0     0         0
               1         1
         1     0         2
               1         3
    1    0     0         4
               1         5
         1     0         6
               1         7

3. Conversion of a tensor with specified mode names and mode index

>>> import numpy as np
>>> from hottbox.core import Tensor
>>> from hottbox.pdtools import tensor_to_pd
>>> data = np.arange(8).reshape(2, 2, 2)
>>> mode_index = {0: [2005, 2010],
...               1: ["Jan", "Feb"],
...               2: ["Mon", "Wed"],
...              }
>>> tensor = Tensor(data, mode_names=["Year", "Month", "Day"])
>>> tensor.set_mode_index(mode_index)
>>> print(tensor.data)
    [[[0 1]
      [2 3]]
     [[4 5]
      [6 7]]]
>>> tensor.modes
    [Mode(name='Year', index=[2005, 2010]),
     Mode(name='Month', index=['Jan', 'Feb']),
     Mode(name='Day', index=['Mon', 'Wed'])]
>>> df = tensor_to_pd(tensor)
>>> print(df)
                      Values
    Year Month Day
    2005 Jan   Mon       0
               Wed       1
         Feb   Mon       2
               Wed       3
    2010 Jan   Mon       4
               Wed       5
         Feb   Mon       6
               Wed       7
>>> df = tensor_to_pd(tensor, col_name="Population")
>>> print(df)
                        Population
    Year Month Day
    2005 Jan   Mon           0
               Wed           1
         Feb   Mon           2
               Wed           3
    2010 Jan   Mon           4
               Wed           5
         Feb   Mon           6
               Wed           7