NumPy Arrays

import numpy as np

# Create arrays
arr = np.array([45000, 18000, 62000, 9000, 51000])
zeros = np.zeros(5)          # [0. 0. 0. 0. 0.]
ones  = np.ones(5)           # [1. 1. 1. 1. 1.]
rng   = np.arange(1, 11)     # [1 2 3 4 5 6 7 8 9 10]
lin   = np.linspace(0, 1, 5) # [0.  0.25 0.5  0.75 1.  ]

# Shape and info
arr.shape    # (5,)  — 1D array with 5 elements
arr.dtype    # dtype('int64')
arr.size     # 5

# 2D array (like a table)
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix.shape  # (2, 3) — 2 rows, 3 columns
matrix[0, 1]  # 2 — row 0, col 1

import numpy as np

sales = np.array([45000, 18000, 62000, 9000, 51000])

# Operations apply to every element instantly
sales * 1.1           # add 10% to all: [49500, 19800, ...]
sales / 100000        # convert to lakhs: [0.45, 0.18, ...]
sales > 20000         # [True, False, True, False, True]

# Filtering with boolean mask
high = sales[sales > 20000]   # [45000, 62000, 51000]

# Aggregations
np.sum(sales)     # 185000
np.mean(sales)    # 37000.0
np.median(sales)  # 45000.0
np.std(sales)     # standard deviation
np.min(sales)     # 9000
np.max(sales)     # 62000
np.percentile(sales, 75)   # 75th percentile

# This vectorised approach is 100x faster than a Python for loop
# on large arrays — this is why Pandas is fast