• data_check
• grab_columns_names
• numerical_summary
• cat_summary
• outliers_thresholds
• check_outlier
• grab_outliers
• remove_outliers
• replace_with_thresholds

argument:

• data: pandas DataFrame

output:

• .shape -----> return a tuple representing the dimensionality of the DataFrame.
• .head() -----> returns the first 5 rows for the DataFrame.
• .info() -----> prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.
• .describe() ------> Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
• .isnull().sum() -----> Detects and sums the missing values of each column.

arguments

• data: pandas DataFrame
• cat_th : int value. If int and float variables have less than cat_th unique values, these variables are categorical.
• car_th : int value. If int and float variables have more than car_th unique values, these variables are cardinal.

return: cat_cols, num_cols, cat_but_car

• cat_cols: List of Categorical columns.
• num_cols: List of Numerical columns.
• cat_but_car: List of Cardinal columns. Like age variables, its is numerical but u can also use like categorical.