this shows us that the top five items are responsible for of the entire sales and only the top items are responsible for over of the salesthis is important for usas we don' want to find association rules for items which are bought very infrequently with this information we can limit the items we want to explore for creating our association rules this also helps us in keeping our possible itemset number to manageable figure mining rules we will be using the orange and the orange -associate frameworkswhich can be installed using the commands conda install orange and pip install orange -associate the orange -associate package contains the implementation of fp growth provided by the group who developed for the orange data mining packagethe bioinformatics laboratory at the university of ljubljanaslovenia (##note we encourage you to experiment with the orange packagewhich is available at biolab siit is gui-driven data mining framework written in python and highly conducive for learning data analysis in an interactive way before we go on using the package for finding association ruleswe will discuss about the way data is represented in orange library the data representation is little tricky but we will help you modify the existing data into the format required by orange primarily we will focus on how to convert our pandas dataframes to the orange table data structure orange table data structure the table data structure is the primary way to represent any tabular data in orange although it is similar in some way to numpy array or pandas dataframeit differs from them in the way it stores metadata about the actual data in our case we can easily convert our pandas dataframe to the table data structure by providing the metadata about our columns we need to define the domain for each of our variables the domain means the possible set of values that each of our variables can use this information will be stored as metadata and will be used in later transformation of the data as our columns are only having binary values-- either or --we can easily create the domain by using this information the following code snippet helps us convert our dataframe to an orange table from orange data import domaindiscretevariablecontinuousvariable from orangecontrib associate fpgrowth import input_assoc_rules grocery_df domain_grocery domain([discretevariable make(name=itemvalues=[' '' ']for item in input_assoc_rules columns]data_gro_ orange data table from_numpy(domain=domain_groceryx=input_assoc_rules as_matrix(),ynonehere we defined the domain of our data by specifying each variable as discretevariable having values as ( then using this domainwe created our table structure for our data