metbit.utility
Statistics and utilities module in metbit 4.0.6.
import metbit.utilityClasses
lazypair
Methods
__init__(self, dataset, column_name)
get_index(self)
get_name(self)
get_meta(self)
get_column_name(self)
get_dataset(self)
gen_page
Methods
__init__(self, data_path)
This function takes in the path to the data folder and returns the HTML files for the OPLS-DA plots.
Parameters
data_pathstrThe path to the data folder. gen_page(data_path).get_files()
get_files(self)
oplsda_path
Methods
__init__(self, data_path)
make_path(self)
get_path(self)
Normality_distribution
Methods
__init__(self, data: pd.DataFrame)
plot_distribution(self, feature)
pca_distributions(self)
Normalise
Methods
__init__(self, data: pd.DataFrame, compute_missing: bool=True)
This function takes in a dataframe and returns the normalised dataframe.
Parameters
datapandas dataframeThe dataframe to be used. Normalise(data).normalise()
pqn_normalise(self, plot: bool=True)
decimal_place_normalisation(self, decimals: int=2)
This function returns the dataframe with values rounded to a specified number of decimal places.
Parameters
decimalsintThe number of decimal places to round to.
z_score_normalisation(self)
This function returns the dataframe normalized using Z-Score.
linear_normalisation(self)
This function returns the dataframe normalized using Min-Max (linear normalization).
normalize_to_100(self)
This function returns the dataframe with values normalized to 100.
clipping_normalisation(self, lower: float, upper: float)
This function returns the dataframe with values clipped to the specified range.
Parameters
lowerfloatThe lower bound for clipping.
upperfloatThe upper bound for clipping.
standard_deviation_normalisation(self)
This function returns the dataframe normalized using Standard Deviation.