def __init__(self,
df: pd.DataFrame,
mn: str,
un: str,
dim: str = 'col',
digits: int = 5):
"""
Constructor / Initiate the class
Parameters
----------
df : pandas.DataFrame
DataFrame used for analysis
mn : str
string with all the results from the multivariate normality tests
un : str
string with all the results from the univariate normality tests
dim : str
indicate whether one wants to test for normality along the columns 'col' or rows
'row', default is 'col'
digits : int
number of decimal places to round down
"""
super().__init__(dim=dim, digits=digits)
Assertor.evaluate_pd_dataframe(df)
Assertor.evaluate_numeric_df(df)
Assertor.evaluate_data_type({mn: str, un: str, dim: str, digits: int})
self.df = df
self.mn = mn
self.un = un
self.dim = dim
self.digits = digits
def __init__(self, df: pd.DataFrame):
"""
Constructor / Initiate the class
Parameters
----------
df : pandas.DataFrame
Dataframe for which one wants to test for normality
"""
Assertor.evaluate_pd_dataframe(df)
Assertor.evaluate_numeric_df(df)
if np.prod(df.shape) < 400:
raise ValueError(
"pd.DataFrame must have at least 400 observations, i.e. (20 x 20) in order to "
"conduct any meaningful normality tests, got {}".format(
df.shape))
self.df = df
def __init__(self, df: pd.DataFrame, dim: str = 'col', digits: int = 5):
"""
Constructor / Initiate the class
Parameters
----------
df : pandas.DataFrame
Dataframe for which one wants to generate / test
dim : str
indicate whether one wants to test for normality along the columns 'col' or rows
'row', default is 'col'
digits : int
number of decimal places to round down
"""
super().__init__(dim=dim, digits=digits)
Assertor.evaluate_pd_dataframe(df)
Assertor.evaluate_numeric_df(df)
Assertor.evaluate_data_type({dim: str, digits: int})
self.df = df
self.dim = dim
self.digits = digits