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recsys-and-customer-segmentation

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change date range

66cc316 over 1 year ago

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	import streamlit as st
	import pandas as pd


	@st.cache
	def load_and_preprocess_data():
	df = pd.read_csv(
	"Data/OnlineRetail.csv",
	encoding="latin-1",
	)

	# Remove nans values
	df = df.dropna()

	# Use only positive quantites. This is not a robust approach,
	# but to keep things simple it quite good.
	df = df[df["Quantity"] > 0]

	# Parse the date column and add 10 years, just to better visualization
	df["InvoiceDate"] = pd.to_datetime(df["InvoiceDate"]).dt.floor(
	"d"
	) + pd.offsets.DateOffset(years=10)

	# Change customer id to int
	df["CustomerID"] = df["CustomerID"].astype(int)

	# Add price column
	df["Price"] = df["Quantity"] * df["UnitPrice"]

	# Get unique entries in the dataset of users and products
	users = df["CustomerID"].unique()
	products = df["StockCode"].unique()

	# Create a categorical type for users and product. User ordered to ensure
	# reproducibility
	user_cat = pd.CategoricalDtype(categories=sorted(users), ordered=True)
	product_cat = pd.CategoricalDtype(categories=sorted(products), ordered=True)

	# Transform and get the indexes of the columns
	user_idx = df["CustomerID"].astype(user_cat).cat.codes
	product_idx = df["StockCode"].astype(product_cat).cat.codes

	# Add the categorical index to the starting dataframe
	df["CustomerIndex"] = user_idx
	df["ProductIndex"] = product_idx

	return df, user_idx, product_idx