What is Python?

Python is a high-level, and interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. Python is widely used in web development, data science, machine learning, automation, and more.

Key Features of Python

• Easy to Learn : Python’s syntax is clean and similar to natural language.
• Cross-Platform: Works on Windows, macOS, and Linux.
• Extensive Libraries & Modules: Python has a vast collection of built-in and third-party modules.

Common Built-in Modules in Python

1. os – Interacts with the operating system.
2. sys – Provides system-specific functions.
3. math – Mathematical operations.
4. random – Generates random numbers.
5. datetime – Handles date and time.
6. json – Works with JSON data.
7. re – Regular expressions for pattern matching.
8. collections – Advanced data structures like deque, Counter.
9. threading – Handles multithreading.
10. socket – Networking capabilities.

Python also has thousands of third-party libraries like NumPy, Pandas, TensorFlow , Matplotlib .

How to setup python environment ? .

First of all we have to create python environment , create an folder structure in your directory and setup in that particular folder.

Syntax: conda create -p venv python==3.13

What is conda ? .

Conda is an open-source package management and environment management system that allows users to install, manage, and update packages and dependencies in a structured way. It’s widely used in data science, machine learning .

Key Features of Conda

1. Package Management:
   • Installs, updates, and removes software packages easily.
   • Works with Python packages and other software dependencies.
2. Environment Management:
   • Creates isolated environments to prevent conflicts between dependencies.
   • Allows different versions of Python or libraries to exist on the same system.
3. Platform Independence:
   • Works on Windows, macOS, and Linux.
4. Supports Multiple Languages:
   • Not limited to Python—it can manage packages for R, Ruby, Java, and more.
5. Dependency Resolution:
   • Automatically resolves conflicts between package dependencies.

To execute the specified command successfully, it is essential to install Anaconda beforehand. However, after completing the Anaconda installation, users may encounter a "command not found" error when attempting to run the command. This issue typically arises due to incorrect environment configuration or missing PATH settings. To resolve this, ensure that Anaconda is properly added to the system PATH or use the correct command syntax within the Anaconda environment, once environment setup done . once again run the below commands .

conda create -p venv python==3.12

Once installation and environment setup completed to activate the conda .

conda init

conda activate venv

Thrid party libraries details

For algo trading we must have to some basic knowledge of NumPy , pandas ,matplotlib .

1.NumPy

A NumPy array is a powerful data structure provided by the NumPy library in Python. It is used for storing and manipulating numerical data efficiently. Unlike Python lists, NumPy arrays are faster, more memory-efficient, and support advanced mathematical operations.

Key Features of NumPy Arrays

1. Homogeneous Data:
   • All elements in a NumPy array must be of the same data type (e.g., integers, floats).
2. Efficient Storage:
   • Uses contiguous memory allocation, making access and computations faster than Python lists.
3. Vectorized Operations:
   • Supports element-wise operations without needing loops (e.g., adding, multiplying arrays).
4. Multi-Dimensional Support:
   • NumPy arrays can be 1D (vector), 2D (matrix), or n-dimensional (tensor).
5. Indexing & Slicing:
   • Allows powerful ways to access and manipulate elements, similar to Python lists.
6. Mathematical Functions:
   • Includes built-in operations for linear algebra, statistics, and more.

2.Pandas

Pandas is a widely used open-source data analysis and manipulation library, essential for working with structured data like tables, spreadsheets, and databases.

Features of Pandas

1. Efficient DataFrame Handling:
   • Provides the DataFrame and Series structures for storing and manipulating tabular data.
2. Data Cleaning & Preprocessing:
   • Includes functions for handling missing data, transforming datasets, and filtering.
3. Powerful Indexing & Selection:
   • Supports advanced indexing techniques for fast and flexible data retrieval.
4. Data Aggregation & Grouping:
   • Enables easy grouping and aggregation with functions like .groupby() and .agg().
5. Built-in Data Visualization:
   • Works seamlessly with Matplotlib to generate plots directly from a DataFrame.
6. Handling Various File Formats:
   • Read and write CSV, Excel, JSON, SQL databases, and more using .read_csv(), .read_excel(), etc.
7. Time-Series Analysis:
   • Provides specialized tools for working with timestamps, date ranges, and time-based data.
8. Interoperability with Other Libraries:
   • Integrates well with NumPy, SciPy, Matplotlib, and other data science libraries

3.Matplotlib

Matplotlib is a popular Python library used for data visualization. It provides a wide range of functions to create static, animated, and interactive plots, making it an essential tool for data science, machine learning, and scientific computing.

Key Features of Matplotlib

1. Supports Multiple Plot Types:
   • Line plots, bar charts, histograms, scatter plots, pie charts, and more.
2. Highly Customizable:
   • Allows adjusting labels, titles, colors, legends, grids, and plot styles.
3. Integration with Pandas & NumPy:
   • Works seamlessly with Pandas DataFrames and NumPy arrays for efficient plotting.
4. Interactive & Animated Plots:
   • Supports zooming, panning, and dynamic updates in Jupyter Notebooks.
5. Subplots & Multi-Panel Figures:
   • Enables multiple plots in a single figure using plt.subplots().
6. Exporting Plots:
   • Saves figures in PNG, JPG, PDF, and SVG formats.

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