Data Visualization: Matplotlib aur Seaborn

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
sales = [120, 135, 128, 160, 175, 190]   # hazaar rupaye

plt.plot(months, sales, marker="o", color="#2563eb")
plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales (Rs '000)")
plt.grid(alpha=0.3)
plt.show()

categories = ["Electronics", "Clothing", "Grocery", "Books"]
revenue = [5400, 3200, 2100, 900]

plt.bar(categories, revenue, color="#f97316")
plt.title("Category wise Revenue")
plt.ylabel("Revenue (Rs)")
plt.show()

import numpy as np

rng = np.random.default_rng(1)
marks = rng.normal(65, 12, size=500).clip(0, 100)

plt.hist(marks, bins=20, color="#10b981", edgecolor="white")
plt.axvline(marks.mean(), color="black", linestyle="--", label="Mean")
plt.title("Exam marks distribution")
plt.xlabel("Marks")
plt.legend()
plt.show()

experience = rng.integers(0, 16, size=100)
salary = 30000 + experience * 4000 + rng.normal(0, 6000, size=100)

plt.scatter(experience, salary, alpha=0.7, color="#8b5cf6")
plt.title("Experience vs Salary")
plt.xlabel("Experience (years)")
plt.ylabel("Salary (Rs)")
plt.show()

fig, axes = plt.subplots(1, 2, figsize=(10, 4))

axes[0].bar(categories, revenue, color="#f97316")
axes[0].set_title("Revenue")
axes[0].tick_params(axis="x", rotation=30)

axes[1].pie(revenue, labels=categories, autopct="%1.0f%%")
axes[1].set_title("Revenue share")

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd

rng = np.random.default_rng(7)
n = 200
department = rng.choice(["IT", "Sales", "HR", "Finance"], size=n, p=[0.4, 0.3, 0.1, 0.2])
experience = rng.integers(0, 16, size=n)
education = rng.choice(["Graduate", "Post Graduate"], size=n, p=[0.65, 0.35])

base = {"IT": 45000, "Sales": 35000, "HR": 32000, "Finance": 42000}
salary = (np.array([base[d] for d in department])
          + experience * 4000
          + np.where(education == "Post Graduate", 8000, 0)
          + rng.normal(0, 6000, size=n)).round(-2)

emp = pd.DataFrame({"department": department, "experience": experience,
                    "education": education, "salary": salary})

import seaborn as sns

sns.boxplot(data=emp, x="department", y="salary", hue="education")
plt.title("Department aur Education ke hisaab se Salary")
plt.show()

emp["is_pg"] = (emp["education"] == "Post Graduate").astype(int)
corr = emp[["experience", "is_pg", "salary"]].corr()

sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", vmin=-1, vmax=1)
plt.title("Correlation Heatmap")
plt.show()