NumPy and pandas

import numpy as np

a = np.array([1, 2, 3, 4])
b = np.arange(12).reshape(3, 4)

print(a * 2)              # [2 4 6 8]   (vectorised, no loop)
print(b.shape, b.mean())  # (3, 4) 5.5
print(b[:, 1])            # second column
print(a[a > 2])           # boolean mask -> [3 4]

matrix = np.ones((3, 4))
row = np.array([1, 2, 3, 4])
print(matrix + row)       # row is added to every row

import pandas as pd

df = pd.DataFrame({
    "name": ["Asha", "Ravi", "Meera", "Kabir"],
    "dept": ["CS", "EE", "CS", "ME"],
    "marks": [91, 74, 88, 69],
})

print(df.head())
print(df.describe())

high = df[df["marks"] > 80]                     # filter rows
df["grade"] = pd.cut(df["marks"], [0, 70, 85, 100], labels=["C", "B", "A"])

avg = df.groupby("dept")["marks"].mean()

depts = pd.DataFrame({"dept": ["CS", "EE", "ME"], "head": ["Rao", "Iyer", "Shah"]})
merged = df.merge(depts, on="dept", how="left")

df = pd.read_csv("students.csv")
df.to_csv("clean.csv", index=False)