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Introduction to NumPy

NumPy is the foundational library for numerical computing in Python, providing fast array operations and mathematical functions.

What is NumPy?

NumPy (Numerical Python) provides the ndarray, a fast, memory-efficient multi-dimensional array, along with a huge collection of mathematical functions to operate on these arrays. Install with pip install numpy.

Why NumPy Instead of Lists?

NumPy arrays are stored in contiguous memory and operations are implemented in C, making them dramatically faster than Python lists for numerical work. NumPy also supports vectorized operations — applying an operation to an entire array at once without explicit loops.

Creating Arrays

Arrays can be created from lists, or using helper functions like np.zeros(), np.ones(), np.arange(), and np.linspace().

Array Attributes

Every array has a shape (dimensions), dtype (data type), and ndim (number of dimensions).

Indexing and Slicing

NumPy arrays support slicing similar to lists, plus powerful boolean and fancy indexing for filtering data.

Vectorized Operations

Arithmetic operators (+, -, *, /) applied to arrays operate element-wise, without writing loops.

Common Functions

np.sum(), np.mean(), np.max(), np.min(), np.std(), and np.reshape() are widely used for analysis.

Syntax

<pre><code>import numpy as np

# Creating arrays
arr = np.array([1, 2, 3, 4, 5])
zeros = np.zeros((2, 3))      # 2x3 array of zeros
ones = np.ones((3, 3))        # 3x3 array of ones
range_arr = np.arange(0, 10, 2)   # [0 2 4 6 8]
lin = np.linspace(0, 1, 5)    # 5 evenly spaced values from 0 to 1

# Array attributes
print(arr.shape)   # (5,)
print(arr.dtype)   # int64
print(arr.ndim)    # 1

# Indexing and slicing
print(arr[0])      # 1
print(arr[1:4])    # [2 3 4]
print(arr[arr > 2])  # boolean indexing -> [3 4 5]

# Vectorized operations (no loops needed!)
doubled = arr * 2          # [2 4 6 8 10]
squared = arr ** 2         # [1 4 9 16 25]
sum_arrays = arr + doubled # element-wise addition

# Common functions
print(np.sum(arr))   # 15
print(np.mean(arr))  # 3.0
print(np.max(arr))   # 5
print(np.min(arr))   # 1

# 2D arrays (matrices)
matrix = np.array([[1, 2], [3, 4]])
print(matrix.shape)  # (2, 2)
print(matrix.reshape(4))  # flatten to [1 2 3 4]
</code></pre>

Revision Notes

• ndarray = NumPy's core data structure, faster than lists
• np.array(), np.zeros(), np.ones(), np.arange(), np.linspace() to create arrays
• shape, dtype, ndim describe array structure
• Operators (+, -, *, /) work element-wise (vectorized)
• Boolean indexing: arr[arr > x] filters elements
• np.sum/mean/max/min/std for aggregation, reshape() to change shape

NumPy Array Statistics

Easy

Using NumPy, write a function array_stats(data) that takes a list of numbers and returns a tuple (mean, max, min, sum) of the array, all computed using NumPy functions.

Input:

[10, 20, 30, 40, 50]

Output:

(30.0, 50, 10, 150)

Show Hint

Convert the list to a NumPy array with np.array(data), then use .mean(), .max(), .min(), .sum().

Solve this Challenge

Show Solution

import numpy as np

def array_stats(data):
    arr = np.array(data)
    return (arr.mean(), arr.max(), arr.min(), arr.sum())

print(array_stats([10, 20, 30, 40, 50]))

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