HI all, I keep getting this error with parsing My date formatting is fine, I imported from my SQL Server database with a DATE function. It happens while I'm running a Python script which works in notebooks.

Question

HI all,  I keep getting this error with parsing  My date formatting is fine, I imported from my SQL Server database with a DATE function.  It happens while I'm running a Python script which works in notebooks.

Michael Gillespie · Accepted Answer

Check your python code.  Something is putting a hidden character somewhere, is my guess.

Tableau Community Forums Member (Inactive) · Answer

hmm you're right, it could Lifetimes package but it's working in my local instance just fine, here's the code I insert as a scripting step   # Importing necessary libraries import pandas as pd import numpy as np   import lifetimes import datetime   import warnings warnings.filterwarnings('ignore') pd.options.display.float_format = '{:.2f}'.format from dateutil import parser   def preprocess_and_train(df):     # Feature selection     features = ['Elysium_Customer_Id', 'Order_Number', 'Order_Date', 'Gross_Revenue_incl_Shipping']     df = df[features]     df = df[pd.notnull(df['Elysium_Customer_Id'])]     df = df[df['Gross_Revenue_incl_Shipping'] > 0]     df = lifetimes.utils.summary_data_from_transaction_data(df, 'Elysium_Customer_Id', 'Order_Date', 'Gross_Revenue_incl_Shipping')     df = df.reset_index()     df = df.rename(columns={"Elysium_Customer_Id": "Customer ID"})     # Fitting the BG/NBD model     bgf = lifetimes.BetaGeoFitter(penalizer_coef=0.0)     bgf.fit(df['frequency'], df['recency'], df['T'])     # Compute the customer alive probability     df['probability_alive'] = bgf.conditional_probability_alive(df['frequency'], df['recency'], df['T'])     df['probability_churned'] = 1 - df['probability_alive']     #Predict future transaction for the next t days based on historical data     df['predicted_orders_30'] = round(bgf.conditional_expected_number_of_purchases_up_to_time(30, df['frequency'], df['recency'], df['T']),2)     df['predicted_orders_90'] = round(bgf.conditional_expected_number_of_purchases_up_to_time(90, df['frequency'], df['recency'], df['T']),2)     df['predicted_orders_180'] = round(bgf.conditional_expected_number_of_purchases_up_to_time(180, df['frequency'], df['recency'], df['T']),2)     df['predicted_orders_365'] = round(bgf.conditional_expected_number_of_purchases_up_to_time(365, df['frequency'], df['recency'], df['T']),2)       # Modeling the monetary value using Gamma-Gamma Model     return_customers_df = df[df['frequency'] > 0]       ggf = lifetimes.GammaGammaFitter(penalizer_coef=0.001)     ggf.fit(return_customers_df['frequency'],            return_customers_df['monetary_value'])       # Calculating the conditional expected average value for each customer per order       df = df[df['monetary_value'] > 0]       df['predicted_AOV'] = ggf.conditional_expected_average_profit(df['frequency'],                                            df['monetary_value'])     # Predicting Customer Lifetime Value for the next 30 days     df['predicted_clv_30'] =      ggf.customer_lifetime_value(bgf,                                                                    df['frequency'],                                                                    df['recency'],                                                                    df['T'],                                                                    df['monetary_value'],                                                                    time=1,     # lifetime in months                                                                    freq='D',   # frequency in which the data is present(T)                                                                          discount_rate=0.01) # discount rate       df['predicted_clv_90'] =      ggf.customer_lifetime_value(bgf,                                                                    df['frequency'],                                                                    df['recency'],                                                                    df['T'],                                                                    df['monetary_value'],                                                                    time=3,     # lifetime in months                                                                    freq='D',   # frequency in which the data is present(T)                                                                          discount_rate=0.01) # discount rate       df['predicted_clv_180'] =      ggf.customer_lifetime_value(bgf,                                                                    df['frequency'],                                                                    df['recency'],                                                                    df['T'],                                                                    df['monetary_value'],                                                                    time=6,     # lifetime in months                                                                    freq='D',   # frequency in which the data is present(T)                                                                          discount_rate=0.01) # discount rate       df['predicted_clv_365'] =      ggf.customer_lifetime_value(bgf,                                                                    df['frequency'],                                                                    df['recency'],                                                                    df['T'],                                                                    df['monetary_value'],                                                                    time=12,     # lifetime in months                                                                    freq='D',   # frequency in which the data is present(T)                                                                          discount_rate=0.01) # discount rate       # Inserting days since last purchase column       df['days_since_last_purchase'] = df['T'] - df['recency']     return df

HI all, I keep getting this error with parsing My date formatting is fine, I imported from my SQL Server database with a DATE function. It happens while I'm running a Python script which works in notebooks.

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