helpers.py

from mlxtend.frequent_patterns import apriori, association_rules


def check_df(dataframe):
    print("##################### Shape #####################")
    print(dataframe.shape)
    print("##################### Types #####################")
    print(dataframe.dtypes)
    print("##################### Head #####################")
    print(dataframe.head(3))
    print("##################### Tail #####################")
    print(dataframe.tail(3))
    print("##################### NA #####################")
    print(dataframe.isnull().sum())
    print("##################### Quantiles #####################")
    print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)


def outlier_thresholds(dataframe, variable):
    quartile1 = dataframe[variable].quantile(0.01)
    quartile3 = dataframe[variable].quantile(0.99)
    interquantile_range = quartile3 - quartile1
    up_limit = quartile3 + 1.5 * interquantile_range
    low_limit = quartile1 - 1.5 * interquantile_range
    return low_limit, up_limit


def replace_with_thresholds(dataframe, variable):
    low_limit, up_limit = outlier_thresholds(dataframe, variable)
    # dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
    dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit


def crm_data_prep(dataframe):
    dataframe.dropna(axis=0, inplace=True)
    dataframe = dataframe[~dataframe["Invoice"].str.contains("C", na=False)]
    dataframe = dataframe[dataframe["Quantity"] > 0]
    replace_with_thresholds(dataframe, "Quantity")
    replace_with_thresholds(dataframe, "Price")
    dataframe["TotalPrice"] = dataframe["Quantity"] * dataframe["Price"]
    return dataframe


def create_invoice_product_df(dataframe):
    return dataframe.groupby(['Invoice', 'StockCode'])['Quantity'].sum().unstack().fillna(0). \
        applymap(lambda x: 1 if x > 0 else 0)