# Python NumPy Module

NumPy (Numerical Python) is the fundamental package for scientific computing in Python. It provides a high-performance multidimensional array object and tools for working with these arrays.

## Installation

```bash
pip install numpy
```

## Importing the Module

Standard convention is to import it as `np`.

```python
import numpy as np
```

## Creating Arrays

NumPy arrays are faster and more compact than Python lists.

```python
# From a list
arr = np.array([1, 2, 3])

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

# Range of values (start, stop, step)
r = np.arange(0, 10, 2) # [0, 2, 4, 6, 8]

# Linear space (start, stop, number of items)
l = np.linspace(0, 1, 5) # [0., 0.25, 0.5, 0.75, 1.]

# Zeros and Ones
z = np.zeros((2, 3)) # 2x3 matrix of zeros
o = np.ones((2, 3))  # 2x3 matrix of ones
```

## Array Inspection

```python
a = np.array([[1, 2, 3], [4, 5, 6]])

print(a.ndim)   # 2 (Dimensions)
print(a.shape)  # (2, 3) (Rows, Columns)
print(a.size)   # 6 (Total elements)
print(a.dtype)  # int64 (Data type)
```

## Operations

Operations are element-wise by default.

```python
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b)  # [5, 7, 9]
print(a * b)  # [4, 10, 18]
print(a * 2)  # [2, 4, 6] (Broadcasting)
```

## Indexing and Slicing

```python
a = np.array([[1, 2, 3], [4, 5, 6]])

# Element at row 0, column 1
print(a[0, 1]) # 2

# Slice: All rows, column 1
print(a[:, 1]) # [2, 5]
```

## Basic Statistics

```python
a = np.array([[1, 2, 3], [4, 5, 6]])

print(np.mean(a))       # 3.5
print(np.max(a))        # 6
print(np.sum(a, axis=0)) # [5, 7, 9] (Sum columns)
```

[[programming/python/python]]