Insert Pandas Data Frame into Teradata DB

[kylenater]

Hi all,

I am using the Teradata python module to read transaction data from Teradata into a Pandas data frame for analysis. Now that I have done my analysis, I need to load the new data frame (rules) back into Teradata. The new data frame does not need to be altered further and only needs to be inserted into Teradata the way it is. I appreciate any help you can provide! Here is my code so far...

#Import Libraries
import teradata
import pandas as pd

#Connect to Teradata 
udaExec = teradata.UdaExec (appName="Teradata_Test", version="1.0",
        logConsole=False)

session = udaExec.connect(method="odbc", system="****",
        username="****", password="****")

#Load the data into a Pandas dataframe  
query = """
   select from where blah blah 
    """

df_transactions_2018 = pd.read_sql(query,session) 

print(df_transactions_2018.head(10))                      
                         
#mlxtend is a machine learning extension package
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules

#Manipulate the transaction dataframe into a basket dataframe, reset index
df_basket_2018 = df_transactions_2018.groupby('TXN_ID').PROD_ID.apply(list).reset_index()

#Print head df_basket_2018 to check if transaction data is properly manipulated
#1
print(df_basket_2018.head(15))

#Transform Pandas dataframe into a sparse Numpy boolean array with TransactionEncoder to prepare for Apriori algorithm
te = TransactionEncoder()
dset = df_basket_2018.PROD_ID.values
te_ary = te.fit(dset).transform(dset, sparse=True)
StringCols = [str(i) for i in te.columns_]

#Find frequent itemsets through the Apriori algorithm and min_supp. threshold.
aprioriData = pd.SparseDataFrame(te_ary, columns=StringCols, default_fill_value=False)
Results = apriori(aprioriData, min_support=0.01, use_colnames=True, verbose=0)
#2
print(Results)

#Generate association rules from frequent itemsets using a minimum confidence or lift threshold
rules = association_rules(Results, metric="lift", min_threshold=1)

#3
print(rules)

#Load rules dataframe back into Teradata...