Added README

README.md

+# PGE Lectures
+
+## Installation
+
+```bash
+pip install -e .
+```
+
+## Usage
+
+Info:
+
+```bash
+python -m pge_lectures
+```
+
+Run a specific lesson:
+
+```bash
+python -m pge_lectures <lesson_number>
+```
+
+List available lessons:
+
+```bash
+python -m pge_lectures -l
+```

src/pge_lectures/lecture_01/l1_intro_strctures.qmd

+# Basic data structures, intro to asymptotic complexity
 ---
 title: "Lecture 1"
 format:
@@ -5,7 +6,6 @@ format:
     code-fold: false
 jupyter: python3
 ---
-# Basic data structures, intro to asymptotic complexity
 
 ## Basic data structures (in Python)
 
@@ -1082,9 +1082,6 @@ bogosort_data = {
 plot_data({'bogosort': (list(bogosort_data.values()), 'bogosort sort')}, 'bogosort sort', x_values=list(bogosort_data.keys()))
 ```
 
-
-### Comparison of operations with the basic data structures
-
 ##### Pre-allocation
 
 Pre-allocation of arrays is (slightly) faster than iterative appending. Although, in Python, both are relatively slow. Depending on the task, list comprehension may be more efficient. In general, if the appending overhead is insignificant, it will not have significant impact on runtime whether pre-allocation is used. However, with large loops it might cause memory issues and pre-allocation will be important with more efficient array implementations (e.g., NumPy arrays).