https://github.com/dhakalnirajan/python-workshop
Intensive Python Programming Workshop
https://github.com/dhakalnirajan/python-workshop
colab-notebook jupyter-notebook python
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Intensive Python Programming Workshop
- Host: GitHub
- URL: https://github.com/dhakalnirajan/python-workshop
- Owner: dhakalnirajan
- License: mit
- Created: 2023-07-16T15:43:38.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-19T06:13:55.000Z (almost 2 years ago)
- Last Synced: 2023-07-19T07:41:46.777Z (almost 2 years ago)
- Topics: colab-notebook, jupyter-notebook, python
- Homepage:
- Size: 208 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Python-Workshop
### Official Python Website:
[](https://python.org)
[](https://docs.python.org)
[](https://github.com/huggingface/diffusion-models-class/blob/main/LICENSE)
[](https://github.com/dhakalnirajan/Python-Workshop)
[](https://www.python.org/)
[](https://jupyter.org/try)
# Objectives of the Course
1. To make students understand the use of Python in Research.
2. To teach the usage of python and its modules like
* **NumPy**,
* **Pandas**,
* **Matplotlib**,
* **Seaborn**,
* **SymPy**
This course starts from July 18, 2023 and the first working days are dedicated to learning basics of Programming using Python.
### Course:
1. Python Introduction
- Python Syntax
- Data Types
- Operators
- Lists
- Control statements
- loops
- functions
- OOP: Python Class
- Modules: NumPy, Matplotlib, Pandas
2. NumPy
- Data as lists, arrays, and tuples.
- Find the average, stddev, quartiles, mode, etc. of the data.
3. Matplotlib
- Plotting Data from lists in NumPy.
- Plot curves of polynomial functions, trigonometriv functions, etc.
- Plotting subplots
- Plotting scatter plots, bar charts, histograms, pie charts, etc.
4. Pandas
- Read CSV
- Dataframes
- Analyzing Data
- Pandas Correlations
- Plotting Data
5. SymPy
- Defining Functions
- Derivatives
- Integrals
- Solving differential equations
# **Course Syllabus**:
## 1. **Python Programming**
- Introduction to Google Colab
- Python version and pip package manager
- Python Program
- Python Arithmatic Operators
- Using Python as calculators
- IEEE 754 standard for floating point arithmetic
- How to define a variable name and Variable Naming convention
- Changing and updating variable values in Python
- Data types in Python
- Number data type: int, float, complex
- Number data type with conditionals
- Anatomy of conditionals: if ... else statements
- Indentation
- Expression and Comparison operators
- Nesting and chaining(if... elif... else) of conditionals
- Logical Operators
- String data type in Python
- Single line strings and multi-line strings
- Indexing and slicing: How to access characters in a string?
- range() method
- for loop in python with range() method
- continue vs break vs pass statements
- characters vs substrings
- string methods: `.replace(), .lower(), .upper(), .lstrip(), .rstrip(), .split()`
- Sequence data type: List
- Indexing, slicing, for loop with and without `range()` , while loop, for loop vs while loop
- Calculating mean of list using loops
- Negative Indexing
- Membership operators: `in , not in`
- Mutable vs Immutable data type with exmaple
- List methods: `.insert(), .append(), .remove(), .pop(), .sort()`
* List comprehension
* Sequence data type: Tuple
* List vs tuple
* Typecasting data types
* loop in tuple
* Unpacking of tuples
* Sets: unordered, unindexed
* `.remove() , .add()` in sets
* Type conversion
* Set operation in Python : union, intersection, difference
* Mapping data type Dictionary
* Accessing dictionary items and add key value pair
* `keys() and values()` method in dictionary
* Updating dictionary: The `update()` method
* `pop()
* Looping in dictionary
* Nested Dictionary
* NoneType data type in Python
* Identity Operators
* Python Functions
* def keyword and function arguments
* return statement
* Default arguments and non default arguments
* Handling multiple return values
* Recursion and its advantage
* Object Oriented Programming in Python (OOP)
* Characterstics of OOP
* Class and Object --defining class and creating object
* . operator
* Instance attribute vs class attribute
* What is this `def __init__(self)` ?
* What is `self` parameter?
* `__new__()` and `__init__()`
* Object methods or user defined methods inside user defined class
* Inheritance in Python
* `super()` method
* Polymorphism and operator overloading
* Abstraction and Encapsulation
* limiting behaviour of variables : private, public and protected
## 2. **Numpy**
* Install and check version of the numpy
* How to import numpy?
* Vectors, the 1D Arrays
* What is array and Creating Numpy array: How do you know the shape and size of an array?
* What’s the difference between a Python list and a NumPy array?
* Array creation routines: `.zeros(), .ones() and .empty()`
* Array initilization using Monotonic sequence : `.arange() , .linspace()
* Creating random array: `np.random.randint(), np.random.rand(), np.random.uniform(), np.random.randn(), np.random.normal()`
* Indexing (fancy indexing) and slicing 1D numpy array
* Logic Functions: Truth value testing : `np.any() vs np.all()`
* Adding, concatenate, and sorting array elements `np.append() , np.sort(), np.concatenate()`
* Vector operations i.e. elementwise operations in 1D numpy array
* Broadcasting and its application in Image Processing
* Array Operation: `np.floor(), np.ceil(), np.round()`
* Statistics using numpy: `.max(), .min(), .argmax(), .argmin(), .sum(), .mean(), .std(), .var()`
* Matrices, the 2D Arrays, and 3D arrays + Introduction to Computer vision
* Creation of 2D numpy array using: `list of list and 1D array, .ones(), .zeros(), .full(), .eye(), .reshape()`
* Indexing, slicing and modifying values in 2D array
* Creating random matrix: `np.random.randint(), np.random.rand(), np.random.uniform(), np.random.randn(), np.random.normal()`
* Matrix multiplication: Dot product
* Cross Product
* Inverse, Transpose and determinant of matrix using numpy
* The `axis` argument in numpy: 2D: `axis = 0 vs axis = 1`
* Matrix statistics: `.min(), .min(axis = 1), .min(axis = 0), .argmin(), .argmin(axis = 1), .argmin(axis = 0), np.unravel_index(), `
* How morden day images are created? with Example of opencv library
## 3. **Matplotlib**
* Install and check version of matplotlib
* how to import matplotlib
* 2D plotting
* Line plot
* Scatter plot
* Bar plot
* Histogram
* Pie chart
* Box plot
* Density plot
* Meshgrid
* Contourplot
* Subplots
* Customizing plots
* Title, Axis labels, Legend, Figure size,
* Spines, Ticks, Grid, Color, Linewidth,
* Marker, Markerfacecolor, Markeredgecolor, Markeredgewidth
* Adding legends, labels to the plot
* Tight Layouting Images/ Padding the images, Saving the images
* Other plotting libraries like seaborn and plotly
## 4. **Pandas**
* Install and check version of pandas
* How to import pandas?
* Series:
* Creating Series
* Accessing elements,
* Indexing, Slicing,
* Operations,
* Missing values,
* Sorting,
* Statistics,
* Applying functions,
* Concatenating,
* Filtering,
* Grouping,
* Merging,
* Joining,
* Reshaping,
* Time series,
* Plotting
* DataFrames:
* Creating DataFrame,
* Accessing elements,
* Indexing,
* Slicing,
* Operations,
* Missing values,
* Sorting, Statistics,
* Applying functions,
* Concatenating,
* Filtering,
* Grouping,
* Merging,
* Joining,
* Reshaping
* Reading csv files, creating csv files from DataFrames
* Groupby In Pandas:
* Plotting in Pandas,
* Missing values in Pandas,
* Merging, Joining, Concatenating, and Reshaping DataFrames,
* Time Series in Pandas,
* Handling Missing values in Pandas,
* Reading and Writing Files in Pandas
* Joins in Pandas: types of database join
* Loc and iLoc in Pandas:
* Accessing elements in DataFrame,
* Pivot Tables in Pandas,
* Grouping and aggregating data
## 5. **Sympy**
* Introduction to Sympy
* importing Sympy
* Representing mathematical expressions
* Minor calculations using Sympy
* Plotting the equations and the solutions
* Derivatives In Sympy
* Expressing in Sympy
* Differentiation
* Integration
* Series expansion
* Limit
* Solving equations
* Solving differential equations
* Solving Initial Value Problems
* Solving Higher Order Derivatives
* Solving Partial Derivatives
* Integrals in Sympy
* Expressing the solution in Sympy
* Solving the integrals
* Solving Multiple integrals