Python for ML: NumPy & Pandas

1. NumPy Introduction

NumPy is a powerful library for numerical computing in Python. It provides support for arrays, matrices, and a collection of mathematical functions to operate on these data structures.

Key features of NumPy include:

• Supports multi-dimensional arrays and matrices.
• Offers a variety of mathematical operations.
• Facilitates linear algebra, Fourier transforms, and random number generation.

2. Using NumPy

To use NumPy, you need to install it first. You can install it using pip:

pip install numpy

Here is a simple example of creating an array and performing operations:

import numpy as np

# Create an array
array = np.array([1, 2, 3, 4, 5])

# Perform operations
mean = np.mean(array)
sum_array = np.sum(array)

print("Mean:", mean)
print("Sum:", sum_array)

3. Pandas Introduction

Pandas is a data manipulation and analysis library that provides data structures such as Series and DataFrame, making it easy to work with structured data.

Key features of Pandas include:

• DataFrame for handling tabular data.
• Easy data manipulation and cleaning.
• Integration with many data formats (CSV, Excel, SQL, etc.).

4. Using Pandas

To use Pandas, install it via pip:

pip install pandas

Example of creating a DataFrame and performing basic operations:

import pandas as pd

# Create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [24, 30, 22]}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

# Calculate mean age
mean_age = df['Age'].mean()
print("Mean Age:", mean_age)

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