baypy¶

dot_product(data: pd.DataFrame, regressors: dict[str, float | int]) → np.ndarray¶

Computes the dot product between columns of data and values of regressors.

Parameters¶

datapandas.DataFrame: The dataframe to be used for the dot product. It cannot be empty. It must contain all regressors keys.
regressorsdict: Dictionary with regressors’ values. It cannot be empty. All regressors and relative values must be a key-value pair.

Returns¶

numpy.ndarray: Array of computed dot product. Each element is the dot product of a data row with respect to each regressors. It has the same length of data.

Raises

TypeError

If data is not a pandas.DataFrame,
if regressors is not a dict,
if a regressors value is not a int or a float.

KeyError

If a regressors key is not a column of data.

ValueError

If data is an empty pandas.DataFrame,
if regressors is an empty dict.

Examples

>>> import pandas as pd
>>> data = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
>>> regressors = {'a': 2, 'b': -1}
>>> dot_product(data = data, regressors = regressors)
>>> array([-2, -1,  0])

flatten_matrix(matrix: ndarray) → ndarray¶

Flattens a matrix of dimensions (M, N) to (M*N, ).

Parameters¶

matrixnumpy.ndarray: Matrix with M rows and N columns.

Returns¶

numpy.ndarray: Flatten array with M*N elements.

Raises

TypeError: If matrix is not a numpy.ndarray.
ValueError: If matrix is an empty numpy.ndarray.

Examples

>>> import numpy
>>> a = numpy.array([[1, 2], [3, 4], [5, 6]])
>>> a
>>> array([[1, 2],
...        [3, 4],
...        [5, 6]])
>>> flatten_matrix(a)
>>> array([1, 2, 3, 4, 5, 6])

matrices_to_frame(matrices_dict: dict[str, ndarray]) → DataFrame¶

Organizes a dictionary of matrices in a pandas.DataFrame. Each matrix becomes a frame column, with column name equal to the matrix’ relative key in the dictionary. If the matrix has dimensions (M, N), then the relative frame column has length M*N.

Parameters¶

matrices_dictdict: Dictionary of matrices to be organized. Matrices and relative names are key-value pairs. Each matrix is a numpy.ndarray with dimensions (M, N).

Returns¶

pandas.DataFrame: Reorganized matrices frame. Matrices are organized in a pandas.DataFrame, one for each column. The length of the frame is M*N.

Raises

TypeError

If matrices_dict is not a dict,
if a matrices_dict value is not a numpy.ndarray.

ValueError

If a matrices_dict value is an empty numpy.ndarray.

Examples

>>> import numpy
>>> a = numpy.array([[1, 2], [3, 4], [5, 6]])
>>> b = numpy.array([[7, 8], [9, 10], [11, 12]])
>>> d = {'a': a, 'b': b}
>>> matrices_to_frame(d)
>>>    a   b
>>> 0  1   7
>>> 1  2   8
>>> 2  3   9
>>> 3  4  10
>>> 4  5  11
>>> 5  6  12