NumPy Tutorial for Beginners 2026

# Install NumPy with pip
pip install numpy

# Or install a specific version
pip install numpy==2.0.0

# Verify installation
python -c "import numpy; print(numpy.__version__)"

# Install NumPy with conda (Anaconda or Miniconda)
conda install numpy

# Or create an environment with NumPy
conda create -n myenv python=3.11 numpy
conda activate myenv

# Verify installation
python -c "import numpy; print(numpy.__version__)"

import numpy as np

# From a Python list
a = np.array([1, 2, 3, 4, 5])
print(a)  # [1 2 3 4 5]

# 2D array (matrix)
b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)

# Array shape and dtype
print(a.shape)   # (5,)
print(b.shape)   # (2, 3)
print(a.dtype)   # int64 (or int32)

import numpy as np

# Zeros and ones
zeros = np.zeros((2, 3))
ones = np.ones((3, 3))

# Range and linspace
r = np.arange(0, 10, 2)     # [0 2 4 6 8]
ls = np.linspace(0, 1, 5)   # [0. 0.25 0.5 0.75 1.]

# Random numbers
rand = np.random.rand(3, 3)  # uniform [0, 1)
randn = np.random.randn(3)    # standard normal

# Identity matrix
eye = np.eye(3)

import numpy as np

a = np.array([1, 2, 3, 4])
b = np.array([10, 20, 30, 40])

# Element-wise operations
print(a + b)     # [11 22 33 44]
print(a * b)     # [10 40 90 160]
print(a ** 2)    # [1 4 9 16]
print(a / 2)     # [0.5 1.0 1.5 2.0]

# Matrix multiplication (2D)
m1 = np.array([[1, 2], [3, 4]])
m2 = np.array([[5, 6], [7, 8]])
print(m1 @ m2)   # [[19 22] [43 50]]

import numpy as np

a = np.array([1, 2, 3, 4])

# Aggregations
print(a.sum())    # 10
print(a.mean())   # 2.5
print(a.min())    # 1
print(a.max())    # 4
print(a.std())    # ~1.118

# Along axis (2D)
m = np.array([[1, 2, 3], [4, 5, 6]])
print(m.sum(axis=0))  # [5 7 9]  (column sums)
print(m.sum(axis=1))  # [6 15]   (row sums)
print(m.mean(axis=0)) # [2.5 3.5 4.5]

import numpy as np

a = np.array([10, 20, 30, 40, 50])

# Basic indexing
print(a[0])       # 10
print(a[-1])      # 50

# Slicing
print(a[1:4])     # [20 30 40]
print(a[::2])     # [10 30 50]

# 2D indexing
m = np.array([[1, 2, 3], [4, 5, 6]])
print(m[0, 1])    # 2
print(m[:, 1])    # [2 5]  (column 1)
print(m[1, :])    # [4 5 6]  (row 1)

import numpy as np

a = np.array([10, 20, 30, 40, 50])

# Boolean indexing
mask = a > 25
print(mask)       # [False False  True  True  True]
print(a[mask])    # [30 40 50]

# Fancy indexing
print(a[[0, 2, 4]])  # [10 30 50]

# Modify with mask
a[a > 25] = 0
print(a)          # [10 20  0  0  0]

# np.where for conditional selection
b = np.array([1, 2, 3, 4, 5])
result = np.where(b > 3, b, 0)
print(result)     # [0 0 0 4 5]

import numpy as np

# Scalar + array
a = np.array([1, 2, 3])
print(a + 10)     # [11 12 13]

# 2D + 1D (broadcast along rows)
m = np.array([[1, 2, 3], [4, 5, 6]])
row = np.array([10, 20, 30])
print(m + row)
# [[11 22 33]
#  [14 25 36]]

# 2D + column vector
col = np.array([[100], [200]])
print(m + col)
# [[101 102 103]
#  [204 205 206]]

import numpy as np

# Normalise each row of a matrix
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Subtract row means
row_means = data.mean(axis=1, keepdims=True)
print(row_means.shape)   # (3, 1)
normalised = data - row_means
print(normalised)
# [[-1.  0.  1.]
#  [-1.  0.  1.]
#  [-1.  0.  1.]]

# Standardise each column
col_means = data.mean(axis=0)
col_stds = data.std(axis=0)
standardised = (data - col_means) / col_stds