data_prep

cltv.py

replace_with_thresholds(df, 'Price')
replace_with_thresholds(df, 'Quantity')

df["TotalPrice"] = df["Quantity"] * df["Price"]

today_date = dt.datetime(2011, 12, 11) # Data we use is old. We need a proper analysis date.

cltv_df = df.groupby('Customer ID').agg(
    {'InvoiceDate': [lambda date: (date.max() - date.min()).days,
                     lambda date: (today_date - date.min()).days],
     'Invoice': lambda num: num.nunique(),
     'TotalPrice': lambda TotalPrice: TotalPrice.sum()})

cltv_df.columns = cltv_df.columns.droplevel(0)
cltv_df.columns = ['recency', 'T', 'frequency', 'monetary']

cltv_df["monetary"] = cltv_df["monetary"] / cltv_df["frequency"]
cltv_df = cltv_df[(cltv_df['frequency'] > 1)]
cltv_df["recency"] = cltv_df["recency"] / 7
cltv_df["T"] = cltv_df["T"] / 7  # Tenure on weekly scale

cltv_df.index = cltv_df.index.astype('int64')