# Data Science with NumPy and Pandas

Python has become the dominant language for data science, largely due to its powerful libraries. **NumPy** and **Pandas** are two of the most fundamental libraries in the Python data science ecosystem.

---

## NumPy (Numerical Python)

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

### 1. Installation

```bash
pip install numpy
```

### 2. The NumPy Array

The main object in NumPy is the homogeneous multidimensional array.

```python
import numpy as np

a = np.array([1, 2, 3])
print(a)            # Output: [1 2 3]
print(type(a))      # Output: <class 'numpy.ndarray'>
```

### 3. Basic Operations

Mathematical operations on arrays apply element-wise.

```python
import numpy as np

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

c = a * b
print(c) # Output: [ 10  40  90 160]
```

---

## Pandas

Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

### 1. Installation

```bash
pip install pandas
```

### 2. DataFrames

The primary data structure in Pandas is the **DataFrame**, which is essentially a programmable spreadsheet.

```python
import pandas as pd

data = {
    'apples': [3, 2, 0, 1], 
    'oranges': [0, 3, 7, 2]
}

df = pd.DataFrame(data)
print(df)
```

### 3. Reading Data

Pandas can read data from various file formats like CSV, JSON, SQL, etc.

```python
import pandas as pd

df = pd.read_csv('data.csv')

# View the first 5 rows
print(df.head())

# Get basic statistics
print(df.describe())
```

[[programming/python/python]]