And what if simply changing the library would unlock all the processor cores without rewriting the code?

pandas runs joins on a single core, leaving the others idle when working with large tables.

Polars distributes join operations across all available cores and, as a result, is significantly faster than pandas on large data sets.

Why is Polars so fast:
• Processes rows in batches in parallel
• Uses all CPU cores
• Does not require any configuration

Article - pandas vs polars vs DuckDB
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Basics of NumPy: a powerful array instead of lists

NumPy is a fundamental library for scientific computing in Python. Its core is the ndarray (N-dimensional array), which works much faster than ordinary lists.

Briefly about the essence:
NumPy arrays store data of one type in a continuous block of memory. This allows operations to be performed on entire arrays without loops (vectorization), which gives a huge speed boost.

Where it's really useful:

🔵 Mathematical and statistical calculations
🔵 Processing large amounts of data
🔵 Machine learning and computer vision
🔵 Scientific modeling and engineering

An important nuance:

import numpy as np
arr = np.array([1, 2, 3])  # Creating an array
arr * 2  # Vectorization: [2, 4, 6]
list = [1, 2, 3]
list * 2  # Repetition: [1, 2, 3, 1, 2, 3]

Example in Python:

# Fast operations on the entire array
import numpy as np

# Creating an array
data = np.array([1, 4, 9, 16, 25])

# Vectorized operations
sqrt_data = np.sqrt(data) # Square root of each element
mean_value = np.mean(data) # Average value
filtered = data[data > 10] # Filtering (boolean indexing)

print(sqrt_data) # [1. 2. 3. 4. 5.]
print(mean_value) # 11.0
print(filtered)   # [16 25]

⚡️Comparison of approaches:

🟢 NumPy array vs Python list:
🟢 NumPy - operations on the entire array at once (vectorization)
🟢 List - cycles are required for element-by-element operations
绿色 NumPy - efficient storage in memory
绿色 List - stores references to objects
绿色 NumPy - built-in mathematics (linear algebra, statistics)
绿色 List - basic functionality

When to use:

🔴 Working with numerical data
🔴 High performance is required
🔴 Mathematics is needed: matrices, statistics, algebra
🔴 Processing images or audio
🔴 Integration with ML libraries (SciPy, Pandas, TensorFlow)

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This repository collects everything you need to use AI and LLM in your projects.

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