swectral.Stats2d

class swectral.Stats2d(axis=0, measure=None)

Statistical measure calculator for 2D array-like data.

Each row or column of the input is treated as a sample, depending on the selected axis.

For parallel or standalone computation of individual statistics, prefer methods:

mean, std, skew, kurt, minimum, median, maximum

instead of factory methods:

summary and values

Attributes:

axisint, optional

Axis along which statistics are computed.

• 0 : treat each row as a sample

• 1 : treat each column as a sample

Default is 0.

measurestr or callable() or list of (str or callable()) or None, optional

Statistical measure(s) to be used by Stats2d.values().

• Common measures may be specified by name: "mean", "std", "skewness", "kurtosis", "min", "median", "max".

• Custom measures must be callables accepting a 2D array-like input and returning a statistical measure with same length as the number of variables.

• Multiple measures must be provided as a list or tuple of measure names or measure callbles.

If None, all common measures listed above are computed. Default is None.

Methods

summary(data_array_2d)	Computes the statistical measures in the measure of this Stats2d instance for a 2D data array.
values	Returns a function computing the values of a single specified statistical measure for the provided 2D array.
mean(data_array_2d)	Mean values of a 2D data array with each row or column as a sample.
std(data_array_2d)	Standard deviation values of a 2D data array with each row or column as a sample.
skew(data_array_2d)	Skewness values of a 2D data array with each row or column as a sample.
kurt(data_array_2d)	Kurtosis values of a 2D data array with each row or column as a sample.
minimum(data_array_2d)	Minimum values of a 2D data array with each row or column as a sample.
median(data_array_2d)	Median values of a 2D data array with each row or column as a sample.
maximum(data_array_2d)	Maximum values of a 2D data array with each row or column as a sample.

Examples

>>> stats2d = Stats2d(axis=0)

__init__(axis=0, measure=None)

Methods

__init__([axis, measure])
kurt(data_array_2d)	Kurtosis values of a 2D data array with each row or column as a sample.
maximum(data_array_2d)	Maximum values of a 2D data array with each row or column as a sample.
mean(data_array_2d)	Mean values of a 2D data array with each row or column as a sample.
median(data_array_2d)	Median values of a 2D data array with each row or column as a sample.
minimum(data_array_2d)	Minimum values of a 2D data array with each row or column as a sample.
skew(data_array_2d)	Skewness values of a 2D data array with each row or column as a sample.
std(data_array_2d)	Standard deviation values of a 2D data array with each row or column as a sample.
summary(data_array_2d)	Computes the statistical measures in the measure of this Stats2d instance for a 2D data array.
var(data_array_2d)	Variance values of a 2D data array with each row or column as a sample.

Attributes

values

Returns a function computing the values of a single specified statistical measure for the provided 2D array.

mean(data_array_2d)

Mean values of a 2D data array with each row or column as a sample.

Parameters:

data_array_2darray_like

2D array-like data. Nan value is omited.

Returns:

numpy.ndarray

Measure values of the samples.

Return type:

ndarray

See also

Stats2d

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> Stats2d(axis=0).kurt(demo_array)

std(data_array_2d)

Standard deviation values of a 2D data array with each row or column as a sample.

Parameters:

data_array_2darray_like

2D array-like data. Missing value is omited.

Returns:

numpy.ndarray

Measure values of the samples.

Return type:

ndarray

See also

Stats2d

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> Stats2d(axis=0).kurt(demo_array)

var(data_array_2d)

Variance values of a 2D data array with each row or column as a sample.

Parameters:

data_array_2darray_like

2D array-like data. Missing value is omited.

Returns:

numpy.ndarray

Measure values of the samples.

Return type:

ndarray

See also

Stats2d

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> Stats2d(axis=0).kurt(demo_array)

skew(data_array_2d)

Skewness values of a 2D data array with each row or column as a sample.

Parameters:

data_array_2darray_like

2D array-like data. Missing value is omited.

Returns:

numpy.ndarray

Measure values of the samples.

Return type:

ndarray

See also

Stats2d

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> Stats2d(axis=0).kurt(demo_array)

kurt(data_array_2d)

Kurtosis values of a 2D data array with each row or column as a sample.

Parameters:

data_array_2darray_like

2D array-like data. Missing value is omited.

Returns:

numpy.ndarray

Measure values of the samples.

Return type:

ndarray

See also

Stats2d

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> Stats2d(axis=0).kurt(demo_array)

minimum(data_array_2d)

Minimum values of a 2D data array with each row or column as a sample.

Parameters:

data_array_2darray_like

2D array-like data. Missing value is omited.

Returns:

numpy.ndarray

Measure values of the samples.

Return type:

ndarray

See also

Stats2d

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> Stats2d(axis=0).minimum(demo_array)

median(data_array_2d)

Median values of a 2D data array with each row or column as a sample.

Parameters:

data_array_2darray_like

2D array-like data. Missing value is omited.

Returns:

numpy.ndarray

Measure values of the samples.

Return type:

ndarray

See also

Stats2d

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> Stats2d(axis=0).median(demo_array)

maximum(data_array_2d)

Maximum values of a 2D data array with each row or column as a sample.

Parameters:

data_array_2darray_like

2D array-like data. Missing value is omited.

Returns:

numpy.ndarray

Measure values of the samples.

Return type:

ndarray

See also

Stats2d

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> Stats2d(axis=0).maximum(demo_array)

property values

Returns a function computing the values of a single specified statistical measure for the provided 2D array. The function is named as the specified measure.

Parameters:

data_array_2darray_like

2D array-like data. Missing value is omited.

Returns:

callable()

Function accepting 2D arraylike data that computes the specified statistical measure.

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> mean_function = Stats2d(axis=0, measure="mean").values
>>> mean_function(demo_array)
>>> mean_function.__name__

summary(data_array_2d)

Computes the statistical measures in the measure of this Stats2d instance for a 2D data array.

Parameters:

data_array_2darray_like

2D array-like data. Missing value is omited.

Returns:

dict

A dictionary mapping str measure names to resulting value arrays, where:

• Keysstr

  Names of the measures, e.g. “mean”

• Valuesnumpy.ndarray

  Results for each measure.

Return type:

dict[str, ndarray]

See also

Stats2d

Examples

>>> demo_array = np.random.randint(0, 1000, size=(10, 10))
>>> Stats2d(axis=0).summary(demo_array)

Customize computed measures: >>> Stats2d(axis=0, measure=[“mean”, “std”]).summary(demo_array)