# Introduction to Binary Search and Complexity Analysis with Python

### Part 1 of "Data Structures and Algorithms in Python"

Data Structures and Algorithms in Python is beginner-friendly introduction to common data structures (linked lists, stacks, queues, graphs) and algorithms (search, sorting, recursion, dynamic programming) in Python, designed to help you prepare for coding interviews and assessments. Check out the full series here:

- Binary Search and Complexity Analysis
- Python Classes and Linked Lists
- Arrays, Stacks, Queues and Strings (coming soon)
- Binary Search Trees and Hash Tables (coming soon)
- Insertion Sort, Merge Sort and Divide-and-Conquer (coming soon)
- Quicksort, Partitions and Average-case Complexity (coming soon)
- Recursion, Backtracking and Dynamic Programming (coming soon)
- Knapsack, Subsequence and Matrix Problems (coming soon)
- Graphs, Breadth-First Search and Depth-First Search (coming soon)
- Shortest Paths, Spanning Trees & Topological Sorting (coming soon)
- Disjoint Sets and the Union Find Algorithm (coming soon)
- Interview Questions, Tips & Practical Advice (coming soon)

Earn a verified certificate of accomplishment for this course by signing up here: http://pythondsa.com .

Ask questions, get help & participate in discussions on the community forum: https://jovian.ai/forum/c/data-structures-and-algorithms-in-python/78

#### Prerequisites

This course assumes very little background in programming and mathematics, and you can learn the required concepts here:

- Basic programming with Python (variables, data types, loops, functions etc.)
- Some high school mathematics (polynomials, vectors, matrices and probability)
- No prior knowledge of data structures or algorithms is required

We'll cover any additional mathematical and theoretical concepts we need as we go along.

### How to Run the Code

The best way to learn the material is to execute the code and experiment with it yourself. This tutorial is an executable Jupyter notebook. You can *run* this tutorial and experiment with the code examples in a couple of ways: *using free online resources* (recommended) or *on your computer*.

##### Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing the code is to click the **Run** button at the top of this page and select **Run on Binder**. You can also select "Run on Colab" or "Run on Kaggle", but you'll need to create an account on Google Colab or Kaggle to use these platforms.

##### Option 2: Running on your computer locally

To run the code on your computer locally, you'll need to set up Python, download the notebook and install the required libraries. We recommend using the Conda distribution of Python. Click the **Run** button at the top of this page, select the **Run Locally** option, and follow the instructions.

Jupyter Notebooks: This notebook is made ofcells. Each cell can contain code written in Python or explanations in plain English. You can execute code cells and view the results instantly within the notebook. Jupyter is a powerful platform for experimentation and analysis. Don't be afraid to mess around with the code & break things - you'll learn a lot by encountering and fixing errors. You can use the "Kernel > Restart & Clear Output" menu option to clear all outputs and start again from the top.

Try executing the cells below:

```
# Import a library module
import math
```

```
# Use a function from the library
math.sqrt(49)
```

`7.0`