cf.Data.stats

Data.stats(all=False, minimum=True, mean=True, median=True, maximum=True, range=True, mid_range=True, standard_deviation=True, root_mean_square=True, sample_size=True, minimum_absolute_value=False, maximum_absolute_value=False, mean_absolute_value=False, mean_of_upper_decile=False, sum=False, sum_of_squares=False, variance=False, weights=False)[source]

Calculate statistics of the data.

By default the minimum, mean, median, maximum, range, mid-range, standard deviation, root mean square, and sample size are calculated. But this selection may be editted, and other metrics are available.

See also

minimum, mean, median, maximum, range, mid_range, standard_deviation, root_mean_square, sample_size, minimum_absolute_value, maximum_absolute_value, mean_absolute_value, mean_of_upper_decile, sum, sum_of_squares, variance

Parameters:
all: bool, optional

Calculate all possible statistics, regardless of the value of individual metric parameters.

minimum: bool, optional

Calculate the minimum of the values.

maximum: bool, optional

Calculate the maximum of the values.

maximum_absolute_value: bool, optional

Calculate the maximum of the absolute values.

minimum_absolute_value: bool, optional

Calculate the minimum of the absolute values.

mid_range: bool, optional

Calculate the average of the maximum and the minimum of the values.

median: bool, optional

Calculate the median of the values.

range: bool, optional

Calculate the absolute difference between the maximum and the minimum of the values.

sum: bool, optional

Calculate the sum of the values.

sum_of_squares: bool, optional

Calculate the sum of the squares of values.

sample_size: bool, optional

Calculate the sample size, i.e. the number of non-missing values.

mean: bool, optional

Calculate the weighted or unweighted mean of the values.

mean_absolute_value: bool, optional

Calculate the mean of the absolute values.

mean_of_upper_decile: bool, optional

Calculate the mean of the upper group of data values defined by the upper tenth of their distribution.

variance: bool, optional

Calculate the weighted or unweighted variance of the values, with a given number of degrees of freedom.

standard_deviation: bool, optional

Calculate the square root of the weighted or unweighted variance.

root_mean_square: bool, optional

Calculate the square root of the weighted or unweighted mean of the squares of the values.

weights: data-like or dict, optional

The weights to apply to the calculations. By default the statistics are unweighted.

The weights may be contained in any scalar or array-like object (such as a numpy array or Data instance) that is broadcastable to the shape of the data. If weights is a dictionary then each key is axes of the array (an int or tuple of int) with a corresponding data-like value of weights for those axes. In this case, the implied weights array is the outer product of the dictionary’s values.
Returns:
dict

The statistics.

Examples:

>>> d = cf.Data([[0, 1, 2], [3, -99, 5]], mask=[[0, 0, 0], [0, 1, 0]])
>>> print(d.array)
[[0  1  2]
 [3 --  5]]    
>>> d.stats()
{'minimum': <CF Data(): 0>,
 'mean': <CF Data(): 2.2>,
 'median': <CF Data(): 2.0>,
 'maximum': <CF Data(): 5>,
 'range': <CF Data(): 5>,
 'mid_range': <CF Data(): 2.5>,
 'standard_deviation': <CF Data(): 1.7204650534085253>,
 'root_mean_square': <CF Data(): 2.792848008753788>,
 'sample_size': 5}
>>> d.stats(all=True)
{'minimum': <CF Data(): 0>,
 'mean': <CF Data(): 2.2>,
 'median': <CF Data(): 2.0>,
 'maximum': <CF Data(): 5>,
 'range': <CF Data(): 5>,
 'mid_range': <CF Data(): 2.5>,
 'standard_deviation': <CF Data(): 1.7204650534085253>,
 'root_mean_square': <CF Data(): 2.792848008753788>,
 'minimum_absolute_value': <CF Data(): 0>,
 'maximum_absolute_value': <CF Data(): 5>,
 'mean_absolute_value': <CF Data(): 2.2>,
 'mean_of_upper_decile': <CF Data(): 5.0>,
 'sum': <CF Data(): 11>,
 'sum_of_squares': <CF Data(): 39>,
 'variance': <CF Data(): 2.96>,
 'sample_size': 5}    
>>> d.stats(mean_of_upper_decile=True, range=False) 
{'minimum': <CF Data(): 0>,
 'mean': <CF Data(): 2.2>,
 'median': <CF Data(): 2.0>,
 'maximum': <CF Data(): 5>,
 'mid_range': <CF Data(): 2.5>,
 'standard_deviation': <CF Data(): 1.7204650534085253>,
 'root_mean_square': <CF Data(): 2.792848008753788>,
 'mean_of_upper_decile': <CF Data(): 5.0>,
 'sample_size': 5}