Mega Python – Pandas, Numpy, ML, APIs, GraphQL, AWS, PySpark

سرفصل های دوره

One Mega course which covers programming, web development, APIs, DevOps, Financial World, Machine Learning and much more

1. Getting Started

1. Install python

2. Python3 and python.html

3. The python interpreter

4. Writing our first python code

5. Python IDLE program

6. Installing Anaconda

7. Create your first python notebook

8. Jupyter Notebook - The Dashboard

9. Jupyter Notebook - Coding commands

10. Setting up IDE - Visual Studio Code

2. Python Strings and Numbers

1.1 variables.zip

1. Variables and Strings

2.1 comments.zip

2. Working with Comments

3. How to load sample jupyter notebook

4.1 strings.zip

4. Working with Strings and Numbers

5.1 strings.zip

5. String functions

6.1 string formatting.zip

6. String formatting

7.1 manipulating strings.zip

7. Manipulating String

8.1 intro to numbers.zip

8. Intro to Numbers

9.1 working with numbers.zip

9. Fun with Numbers

10.1 working with numbers.zip

10. Numbers - modulus and floor division

11.1 built-in functions for numbers.zip

11. Built-in functions for numbers

12.1 math functions in math module.zip

12. More math functions with math module

13.1 formatting numbers.zip

13. Formatting Numbers

14. The double equality sign

15.1 getting user input.zip

15. Getting User Input

16.1 python operators.zip

16. Python Operators

17.1 python operators.zip

17. Logical Operators

18. Comparison Operators

19.1 boolean - logical operators.zip

19. Boolean Operators

3. Python List

1.1 list.zip

1. Python List

2.1 list.zip

2. Adding and removing elements in a list

3. Popping items from a list

4. Removing an item by value

5. Sorting a list permanently or temporarily

6. Reverse a list

7. Avoiding Index errors

8.1 list constructor.zip

8. The list() constructor

9.1 working with list.zip

9. Looping an entire list

10. Indentation

11.1 numerical list.zip

11. Numerical List

12. min, max and sum functions

13.1 negative indexing.zip

13. Negative Indexing

14.1 multi-dimentional list.zip

14. Multi-diementional list

15.1 range function.zip

15. Range function

16.1 looping multi-dimentional list.zip

16. Looping multi-dimentional list

17.1 slicing of a list.zip

17. Slicing of a list

18.1 list slicing - part 2.zip

18. Slicing a List Part 2

19.1 iterate over multiple list at a time.zip

19. Iterate over multiple list

20.1 to check if an item exist in a list.screenflow.zip

20. Check if an item exist or not

21.1 Count total occurance of an item.screenflow.zip

21. Count total occurrence of an item

22.1 membership operators.zip

22. Membership operators

23.1 find most common item.zip

23. Find most common item

24.1 nested list.zip

24. Nested List

25.1 list comprehensions.zip

25. List Comprehensions

26. List Comprehensions with if clause

27. Nested List Comprehensions

28.1 flatten a list of lists.zip

28. Flatten a list of lists

29.1 remove duplicates from the list.zip

29. Remove duplicates from the list

30.1 combine lists.zip

30. Combine lists

4. Python Tuple

1.1 python tuple.zip

1. Introduction to Tuple

2. tuple constructor

3. Access tuple items

4. Nested Tuples

5. Slicing a tuple

6. Change Tuple item

7. Writing over a tuple

8. Concatenation and Repetition

9. Iterate through a tuple

10. Tuple Sorting

11. Tuple Packing & Unpacking

12.1 tuples count() method.zip

12. Tuple count() method

13.1 tuple index method.zip

13. Tuple index() method

14.1 tuple functions.zip

14. all() function with Tuples

15. any() function with tuples

16. sum() function with tuples

17. enumerate() function with tuples

5. Python Set

1. Create, Set Constructor, Add and remove methods

2. Find Length, clear all elements, and iterate all elements

3. Check if an item exist or not

4. pop method

6. NUMPY

1. Introduction to Numpy arrays

2. array attributes - shape

3. array attributes - ndim, size, dtype, nbytes

4. Array Data types

5. Create arrays from constant values

6. Create arrays from space values

7. Create arrays from set diagnals

8. Create arrays from functions

9. Indexing and slicing - Single dimension array

10. Indexing and slicing - Multi-dimension array

11. Creating views and copies

12. Array Indexing

13. Array indexing - multi dimensional array

14. Boolean indexing

15. Reshaping Numpy arrays

16. Joining arrays

17. Splitting arrays

18. Searching arrays

19. Sorting arrays

20. Sorting techniques

21. Sorting a matrix

22. Iterating 1-D, 2D, and 3-D arrays

23. Iterating arrays via nditer(), ndenumerate()

24. Arithmetic operations

25. Mathematical functions

26. Comparing arrays

27. Conditional functions

28. Aggregation functions

7. PANDAS

1. Creating a DataFrame from list or dictionaries

2. Creating an empty DataFrame

3. Create a dataframe from lists of lists

4. Rename DataFrame columns and indexes

5. Create a Dataframe from list of dictionaries

6. Create a Dataframe from tuples with zip function

8. Pandas Tricks

1.1 pandas_tricks.zip

1. Check Equality

2. Using asset_series for equality

3. Calculate memory usage

4. No of words in a column

5. Convert one set of values to another

6. Convert continuous data into categorical data

7. Create a datetime column from multiple columns

8. Resample by date time column

9. Create a cross-tabluation

10. Fill missing values using interpolation

11. Transpose a wide DataFrame

12. Create example DataFrames

13. Identify missing rows

14. Use query to avoid intermediate variables

15. Reshape a DataFrame from wide to long format

9. BUILDING APIs

1. Introduction to FastAPI

2. FastAPI installation and first route

3. Path parameters

4. Built-in documentation

5. Query Parameters

6. Request Body and Pydantic models

7. Setup SQLAlchemy, Postgresql on cloud

8. Setup Database configurations

9. Setup SQLAlchemy models

10. Setup Pydantic schemas

11. Populate all tables on postgreSQL

12. PUT Request

13. Defining HTTP Status codes

10. Python with GraphQL

1. What is GraphQL

2. Setting up GraphQL with Python Flask Server

3. Adding a cloud postgreSQL database

4. Creating a model

5. Creating a GraphQL Schema

6. GraphQL with Ariadne library

7. Setup Apollo GraphQL IDE

8. Write resolver for list all posts

9. Write resolver to list a query by id

10. Mutation - Create a new post

11. Mutation - Update a post

12. Mutation - Delete a post

11. Python with Amazon DynamoDB

1. Create a table

2. Get an item

3. Delete an item

4. Add an item

5. Update an item

12. Python with Apache Cassandra

1. Introduction to Cassandra

2. Setup Docker to spin cassandra

3. Read data

4. Read data via prepare statement

5. Write data synchronously and asynchronously

6. Docker cleanup

13. DATA VISUALIZATIONS

1. Introduction.html

14. Data Visualization with Dash

1.1 dash_medal_dashboard.zip

1. Introduction to Dash

2. Creating a quick Dash interactive application

3. Create a medal dashboard app - setting up layout

4. Creating Layout Components - Dropdown

5. Callback functions

6. Introduction.html

7. Create a Barchart component

8. Create interactive bar chart with callbacks

9. Applying styling

10.1 dash_callback.zip

10. Dash Callbacks - Simple

11. Callback function with graph and a slider

12. Callback function with multiple inputs

13. Cross Filtering - Interactive Graphing

15. Building Interactive data apps with Streamlit

1.1 app.zip

1. Crypto Currency Market Dashboard Application

16. Data Visualization with Plotly

1.1 data_visualization_with_plotly.zip

1. Scatter plots

2. Bar Chart

3. Facet Plots

4. Facet Plot Grid

5. Adding lines to Facets

6. Pie Charts

7. Bar Chart - Horizontal

8. Gant Chart

9. Sunburst Chart

10. Treemaps

11. Financial Charts

12. Histogram

13. Animations

17. REALTIME DATA APPLICATIONS

1. Welcome to Realtime Data Applications.html

18. Realtime Blog Activity Feed

1. Setup the system with Pusher

2. Setup Pusher and Routes

3. Define backend API endpoints

4. Create the blog post view

5. Handling form events for add, delete and deactivate

6. Add pusher events to blog post view

7. How data is broadcast via pusher

8. View Realtime blog post events

19. Asynchronous programming in Python

1.1 app.zip

1.2 get_urls_async.zip

1.3 test_async.zip

1.4 urls.zip

1. Coding program to run sequentially

2. Async programming with aiohttp and asyncio

3. 100s of Web site fetching asynchronously

20. DATA SCIENCE AND MACHINE LEARNING

1. Introduction to Machine Learning.html

2. Machine Learning Terminologies

3. Machine Learning Types

4.1 ml - data analysis using seaborn.zip

4. Data Exploration using seaborn

5.1 ml - data processing.zip

5. Data Processing

6.1 ml - simple linear regression.zip

6. Create your own Simple Linear Regression

21. DEV OPS

1. Introduction.html

22. Amazon S3 Services

1. Introduction.html

2.1 tutorial-s3.zip

2. Using Python for AWS S3

3. S3 - List Buckets

4. S3 - List objects

5. S3 - Upload a file

6. S3 - Download a file

7. S3 - Create a bucket

8. S3 - Get object metadata

23. Amazon SNS Services

1. Introduction to SNS

2. Create a topic

3. Publish messages

24. AWS Lambda Function

1. What is a Lambda Function

2. Create a function

3. Invoking Lambda function from another function - Create policy

4. Invoking Lambda function from another lambda function

25. AWS Step Functions

1. What are STEP Functions

2. Amazon step language (ASL)

3. Create lambda functions

4. Create state machine and trigger lambda functions

26. PySpark - SparkSQL and Dataframes

1. PySpark

2. Introduction to PySpark

3. Spark Components

4.1 my_pyspark1.zip

4. Setup python spark on google colabs

5. What is a dataframe

6. What is RDD

7.1 pyspark_rdd.zip

7. Creating RDDs

8. Creating Python functions and lambda functions

9. Apply transformation to RDD, map and filter methods

10. flatMap and Set transformations

11. Doing multiple transformations

12.1 pyspark_dataframes.zip

12. PySpark Dataframes

13. Create a dataframe from a schema

14. Create a dataframe from a CSV file

15. Convert PySpark Dataframe to Pandas dataframe

16. SparkSQL - Creating Dataframes

17. SparkSQL - applying groupBy and aggregation data

18. SparkSQL - multiple aggregation and filtering data

19. SparkSQL - filtering data with filter

20. SparkSQL - Apply pure SQL queries

21.1 yahoo finance stock data.zip

21. Get Stock Data from yahoo finance

27. Analyzing Sales Transaction Data

1.1 analyzing_sales_transaction_data.zip

1. Reading Sales transaction data

2. Running queries and find unique values

3. Introduction.html

4. Create custom function to plot heat maps and bar plot

5. Find total sales per payment method

6. Find average unit price per product

7. Find average purchase by client group

8. Find AverageTotal value by weeks and months

9. Find average per client group and warehouse

10. Find average per product group and warehouse

11. Find average per weekmonth and warehouse

12. Find average quantity with month and product group

13. Find correlations

28. Analyzing Employees Turnovers Churn Rates

1. The data and import libraries

2. Quick intro to pywaffle module

3. Import sample data

4. View distinct values and running queries

5. Create a waffle visual to display employee turnover

6. Visualize Employees Satisfaction score

7. Create employees groups per satisfaction bucket

8. Satisfaction Analysis

9. Number of employees vs department

10. Number of project vs employees analysis

11. What is the workload look like for those who left

12. What is the spread of tenure across employee segments

13. # of years employees stayed in the company

14. How long did the employees stay

15. # of years of service per employe segments

29. Global Earthquakes Analysis

1. Get Earthquake data with USGS API

2. Examine structure of the data

3. Summary statistics for categorial columns

4. Get stats for a particular column

5. Selecting column via list comprehensions, get and more

6. Slicing data

7. loc and iloc

8. Filtering information

9. Adding new data and using assign()

30. Monkey Pox Virus - Data Analysis

1.1 monkeypox virus - data analysis.zip

1. Reported cases by country

2. Plot cases distribution by country and city

3. Confirm Cases, and case status per country

4. Analyze frequency of symptomssigns

5. Time series analysis - cases per day

31. Thank You!

1. Your feedback is very valuable!.html

2. Bonus Lecture.html

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