Introduction to NumPy

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What is NumPy?

NumPy (Numerical Python) is an open-source Python library used for scientific computing and numerical operations.
It provides support for multi-dimensional arrays, matrices, and a large collection of high-level mathematical functions to operate on these arrays efficiently.

NumPy is the core foundation of many scientific and data-related libraries such as:

• Pandas
• Matplotlib
• SciPy
• Scikit-Learn
• TensorFlow

Why NumPy over Python Lists?

NumPy is preferred over Python lists because it provides faster execution, lower memory consumption, and optimized mathematical operations through vectorization and efficient memory management. Python lists are general-purpose containers, whereas NumPy arrays are specialized for numerical computation.

Key Reasons to Use NumPy

1.Speed

• NumPy operations are implemented in C internally.
• They execute faster than Python lists, which use Python loops.

2.Memory Efficiency

• NumPy stores elements of the same data type in contiguous memory.
• Python lists store references to objects results in more memory usage.

3.Vectorized Operations

• NumPy allows element-wise operations without loops.
• This is called vectorization.

4.Mathematical & Scientific Support

• Built-in functions for:
  • Linear algebra
  • Statistics
  • Random number generation
  • Fourier transforms

Importing NumPy

NumPy is commonly imported using the alias np.

```

Hide

import numpy as np

```

Example

```

import numpy as np

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

print(arr)

```

NumPy Array vs Python List

Python List Example

```

list1 = [1, 2, 3, 4]

list2 = [5, 6, 7, 8]

result = []

for i in range(len(list1)):

result.append(list1[i] + list2[i])

print(result)

```

NumPy Array Example

```

import numpy as np

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

b = np.array([5, 6, 7, 8])

print(a + b)

```

Speed Comparison (Practical)

```

import numpy as np

import time

# Python list

list_data = list(range(1000000))

start = time.time()

sum([x * x for x in list_data])

print("List time:", time.time() - start)

# NumPy array

array_data = np.arange(1000000)

start = time.time()

np.sum(array_data ** 2)

print("NumPy time:", time.time() - start)

```

Result

• NumPy runs much faster due to vectorization and C-level execution.

Memory Comparison

```

import sys

import numpy as np

list_data = [1, 2, 3, 4]

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

print(sys.getsizeof(list_data))

print(array_data.nbytes)

```

Observation

• Python list consumes more memory
• NumPy array uses compact memory

Tabular Comparison

Practical Applications of NumPy

• Data analysis
• Machine learning
• Image processing
• Signal processing
• Scientific simulations

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