From eb78d69f8c270bcf641e08b6ad6e85af277473e6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 5 Feb 2025 13:21:24 +0900
Subject: [PATCH] update introduction

---
 .../python_for_robotics_main.rst              | 46 +++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
index 23f31da779..1ad5316f53 100644
--- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
+++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
@@ -1,4 +1,50 @@
 Python for Robotics
 ----------------------
 
+Python for general-purpose programming
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+`Python <https://www.python.org/>`_ is an general-purpose programming language developed by
+`Guido van Rossum <https://en.wikipedia.org/wiki/Guido_van_Rossum>`_ in the late 1980s.
+
+It features as follows:
+
+#. High-level
+#. Interpreted
+#. Dynamic type system (also type annotation is supported)
+#. Emphasizes code readability
+#. Rapid prototyping
+#. Batteries included
+#. Interoperability for C and Fortran
+
+Due to these features, Python is the most popular programming language
+for educational purposes for programming beginners.
+
+Python for Scientific Computing
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Python itself was not designed for scientific computing.
+However, scientists quickly recognized its strengths.
+For example,
+
+#. High-level and interpreted features enable scientists to focus on their problems without dealing with low-level programming tasks like memory management.
+#. Code readability, rapid prototyping, and batteries included features enables scientists who are not professional programmers, to solve their problems easily.
+#. The interoperability to wrap C and Fortran libraries enables scientists to access already existed powerful scientific computing libraries.
+
+To address the more needs of scientific computing, many libraries have been developed.
+
+- `NumPy <https://numpy.org/>`_ is the fundamental package for scientific computing with Python.
+- `SciPy <https://www.scipy.org/>`_ is a library that builds on NumPy and provides a large number of functions that operate on NumPy arrays and are useful for different types of scientific and engineering applications.
+- `Matplotlib <https://matplotlib.org/>`_ is a plotting library for the Python programming language and its numerical mathematics extension NumPy.
+- `Pandas <https://pandas.pydata.org/>`_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy.
+- `SymPy <https://www.sympy.org/>`_ is a Python library for symbolic mathematics.
+
+And more domain-specific libraries have been developed:
+- `Scikit-learn <https://scikit-learn.org/stable/>`_ is a free software machine learning library for the Python programming language.
+- `Scikit-image <https://scikit-image.org/>`_ is a collection of algorithms for image processing.
+
+Python for Robotics
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 TBD
+