الترتيب والتقسيم إلى فئات

import pandas as pd

items = pd.read_csv("order_items.csv")
orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
items["revenue"] = items["quantity"] * items["unit_price"] * (1 - items["discount"])
orders = orders.merge(items.groupby("order_id", as_index=False)["revenue"].sum(), on="order_id", validate="one_to_one")
orders = orders[orders["status"] != "cancelled"]

cust = orders.groupby("customer_id").agg(orders=("order_id", "count"), spend=("revenue", "sum"))
print(len(cust), "customers with a non-cancelled order")
print(cust["spend"].describe().round(0))

import pandas as pd

items = pd.read_csv("order_items.csv")
orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
items["revenue"] = items["quantity"] * items["unit_price"] * (1 - items["discount"])
orders = orders.merge(items.groupby("order_id", as_index=False)["revenue"].sum(), on="order_id", validate="one_to_one")
orders = orders[orders["status"] != "cancelled"]
cust = orders.groupby("customer_id").agg(orders=("order_id", "count"), spend=("revenue", "sum"))

cust["rank"] = cust["spend"].rank(ascending=False, method="min")
cust["percentile"] = cust["spend"].rank(pct=True)
print(cust.sort_values("rank").head(5).round(2))

top20 = cust.nlargest(int(len(cust) * 0.2), "spend")
print(len(top20), "customers =", round(top20["spend"].sum() / cust["spend"].sum(), 3), "of revenue")

import pandas as pd

items = pd.read_csv("order_items.csv")
orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
customers = pd.read_csv("customers.csv")
items["revenue"] = items["quantity"] * items["unit_price"] * (1 - items["discount"])
orders = orders.merge(items.groupby("order_id", as_index=False)["revenue"].sum(), on="order_id", validate="one_to_one")
orders = orders[orders["status"] != "cancelled"]
cust = orders.groupby("customer_id", as_index=False).agg(spend=("revenue", "sum"))
cust = cust.merge(customers[["customer_id", "country"]], on="customer_id", validate="one_to_one")

cust["rank_in_country"] = cust.groupby("country")["spend"].rank(ascending=False, method="first")
print(cust[cust["rank_in_country"] == 1].sort_values("spend", ascending=False).round(0))

import numpy as np
import pandas as pd

items = pd.read_csv("order_items.csv")
orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
items["revenue"] = items["quantity"] * items["unit_price"] * (1 - items["discount"])
orders = orders.merge(items.groupby("order_id", as_index=False)["revenue"].sum(), on="order_id", validate="one_to_one")
orders = orders[orders["status"] != "cancelled"]
cust = orders.groupby("customer_id").agg(orders=("order_id", "count"), spend=("revenue", "sum"))

cust["band"] = pd.cut(cust["spend"], bins=[0, 250, 500, 1000, 2000, np.inf],
                      labels=["<250", "250-500", "500-1k", "1k-2k", "2k+"])
print(cust.groupby("band", observed=True)["spend"].agg(["count", "sum"]).round(0))

import pandas as pd

items = pd.read_csv("order_items.csv")
orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
items["revenue"] = items["quantity"] * items["unit_price"] * (1 - items["discount"])
orders = orders.merge(items.groupby("order_id", as_index=False)["revenue"].sum(), on="order_id", validate="one_to_one")
orders = orders[orders["status"] != "cancelled"]
cust = orders.groupby("customer_id").agg(orders=("order_id", "count"), spend=("revenue", "sum"))

cust["quartile"] = pd.qcut(cust["spend"], q=4, labels=["Q1 low", "Q2", "Q3", "Q4 high"])
share = cust.groupby("quartile", observed=True)["spend"].sum() / cust["spend"].sum()
print(share.round(3))
print(pd.qcut(cust["orders"], q=4, duplicates="drop").value_counts().sort_index())