Introduction to Python for Biologists.
- Why Python for Biology?
- Biological Data Types and Python
- Sequence Analysis - Part 1
- Sequence Analysis - Part 2
- Image Analysis - Part 1
- Image Analysis - Part 2
- Database Management and Python
- Statistical Analysis in Python
- Bioinformatics and Python
- Data Visualization in Python
- Machine Learning for Biology with Python
- Project Planning and Design
- Implementing a Biological Project with Python
Why Python for Biology?
Python Basics: A Refresher for Biologists
General-purpose programming language.
Python is a versatile and powerful programming language that has found extensive use in various fields, including biology. This article serves as a refresher on Python basics, focusing on aspects that are particularly relevant to biological research.
Python Data Types
Python has several built-in data types that can be used to represent different kinds of information. Here are the most commonly used ones:
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Numbers: Python supports integers, floating point numbers, and complex numbers. They are defined as
int,float, andcomplexin Python. -
Strings: Strings in Python are arrays of bytes representing Unicode characters. They can be created by enclosing characters inside a single quote or double-quotes.
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Lists: A list in Python is a collection of items that can be of different data types. It is ordered and changeable, allowing duplicate members.
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Tuples: A tuple is a collection of items that is ordered but unchangeable. Tuples are written with round brackets.
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Dictionaries: A dictionary in Python is an unordered collection of items. Each item of a dictionary has a key/value pair.
Python Control Structures
Control structures in Python help to control the flow of your program. The primary control structures in Python are:
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If-Else Statements: These are used for decision making and executing different blocks of code based on different conditions.
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For Loops: These are used for iterating over a sequence (like a list, tuple, dictionary, set, or string) or other iterable objects.
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While Loops: These are used for repeated execution as long as a certain condition holds true.
Python Functions
Functions in Python are blocks of reusable code that perform a specific task. You can define your own functions using the def keyword. Functions help to break our program into smaller and modular chunks, making it organized and manageable.
Python Libraries
Python libraries are collections of functions and methods that allow you to perform many actions without writing your code. Some of the Python libraries that are particularly useful in biological research include:
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NumPy: This library is used for numerical computations and supports large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
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Pandas: This library is used for data manipulation and analysis. It provides data structures and functions needed to manipulate structured data.
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Matplotlib: This library is used for creating static, animated, and interactive visualizations in Python.
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BioPython: This library is a set of tools for biological computation. It provides the ability to parse bioinformatics files into Python utilizable data structures, among other features.
Python Environments
Python environments are where you write and execute your Python code. Some popular Python environments suitable for biological research include:
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Jupyter Notebooks: This is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.
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Anaconda: This is a distribution of Python and R for scientific computing and data science. It simplifies package management and deployment and is particularly useful when dealing with large datasets and complex computational requirements.
In conclusion, Python is a powerful tool for biological research due to its simplicity, versatility, and the wide range of libraries it offers. Whether you're analyzing genomic sequences, visualizing protein structures, or modeling ecological systems, Python has the tools to make it easier.