الرسوم البيانية

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
revenue = [42, 48, 45, 61, 70, 78]            # thousands EGP

plt.figure(figsize=(7, 3.5))
plt.plot(months, revenue, marker="o", color="#3776AB", linewidth=2.5)
plt.title("Monthly revenue (K EGP)")
plt.ylabel("Revenue")
plt.grid(alpha=0.3)
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("orders.csv")
df["revenue"] = df["qty"] * df["price"]
by_city = df.groupby("city")["revenue"].sum().sort_values()

plt.figure(figsize=(7, 3.5))
plt.barh(by_city.index, by_city.values, color="#3776AB")
plt.title("Revenue by city (EGP)")
plt.show()

import numpy as np
import matplotlib.pyplot as plt

rng = np.random.default_rng(7)
delivery_days = rng.normal(loc=3, scale=1, size=500).clip(0.5, 8)

plt.figure(figsize=(7, 3.5))
plt.hist(delivery_days, bins=25, color="#FFD43B", edgecolor="#14213D")
plt.axvline(delivery_days.mean(), color="#D64545", linestyle="--", label="mean")
plt.title("Delivery time (days)")
plt.legend()
plt.show()

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

rng = np.random.default_rng(1)
area = rng.uniform(60, 200, 80)
city = rng.choice(["Cairo", "Giza"], 80)
price = area * 20 + np.where(city == "Cairo", 700, 0) + rng.normal(0, 150, 80)
homes = pd.DataFrame({"area": area, "price_k": price, "city": city})

plt.figure(figsize=(7, 3.8))
sns.scatterplot(data=homes, x="area", y="price_k", hue="city",
                palette=["#3776AB", "#E0A800"])
plt.title("Area vs price (K EGP)")
plt.show()