ecoscope.analysis.classifier

Module Contents

ecoscope.analysis.classifier.classification_methods

ecoscope.analysis.classifier.apply_classification(dataframe, input_column_name, output_column_name=None, labels=None, scheme='natural_breaks', label_prefix='', label_suffix='', label_ranges=False, label_decimals=1, **kwargs)

Classifies the data in a DataFrame column using specified classification scheme.

Args: dataframe (pd.DatFrame): The data. input_column_name (str): The dataframe column to classify. output_column_names (str): The dataframe column that will contain the classification.

Defaults to “<input_column_name>_classified”

labels (list[str]): labels of bins, use bin edges if labels==None. scheme (str): Classification scheme to use [equal_interval, natural_breaks, quantile, std_mean, max_breaks, fisher_jenks] label_prefix (str): Prepends provided string to each label label_suffix (str): Appends provided string to each label label_ranges (bool): Applicable only when ‘labels’ is not set

If True, generated labels will be the range between bin edges, rather than the bin edges themselves.

label_decimals (int): Applicable only when ‘labels’ is not set

Specifies the number of decimal places in the label

**kwargs:

Additional keyword arguments specific to the classification scheme, passed to mapclassify. See below

Applicable to equal_interval, natural_breaks, quantile, max_breaks & fisher_jenks: k (int): The number of classes required

Applicable only to natural_breaks: initial (int): The number of initial solutions generated with different centroids.

The best of initial results are returned.

Applicable only to max_breaks: mindiff (float): The minimum difference between class breaks.

Applicable only to std_mean: multiples (numpy.array): The multiples of the standard deviation to add/subtract

from the sample mean to define the bins.

anchor (bool): Anchor upper bound of one class to the sample mean.

For more information, see https://pysal.org/mapclassify/api.html

Returns: The input dataframe with a classification column appended.

ecoscope.analysis.classifier.apply_color_map(dataframe, input_column_name, cmap, output_column_name=None)

Creates a new column on the provided dataframe with the given cmap applied over the specified input column

Args: dataframe (pd.DatFrame): The data. input_column_name (str): The dataframe column who’s values will be inform the cmap values. cmap (str, list): Either a named mpl.colormap or a list of string hex values. output_column_name(str): The dataframe column that will contain the classification.

Defaults to “<input_column_name>_colormap”

Returns: The input dataframe with a color map appended.