Modern Data Wrangling with AI and Python - Beginner to Pro

Install Visual Studio Code, VSC Copilot, anacondas or Conures, and git to set up the course tools and enable AI-assisted coding.

Install the Python and Jupyter extensions in VS Code to boost functionality, and access the Python extension via the left block icon or command shift x.

Sign up for a GitHub account by visiting github.com, entering your email, and confirming via email; explore documentation and tutorials to familiarize yourself with GitHub throughout the course.

learn how to enable GitHub Copilot from the extensions, start a 60-day free trial by logging into GitHub and entering credentials and a credit card, and decide whether to continue.

Test copilot by checking the bottom-right bear icon and any stripes, then open a new Python file to see auto-completion. Press tab to accept suggestions and you've installed copilot.

Install GitHub Desktop, create a local repository copy of the course code, then open it in Visual Studio Code to access the project you will study.

Download git for Mac or Windows to manage code versions in a central repository. Learn how to use git in later sections.

Explore the data by asking what you can tell me about it, and examine four key points, beginning with spreadsheet wrangling in the data exploration section.

Explores how a spreadsheet program handles wide, poorly formatted sales data and guides you to shape data for insights like top five customers by order number.

Spreadsheets fit in the data toolkit but face scale and traceability limits. Explore data enrichment, quality, duplicates, and when to pivot to alternative solutions.

Access the course source code and open it in Visual Studio Code to explore the data in the exploration folder, where a Jupyter notebook contains the first section's code.

Pandas is the package used to read csv files and files that resemble spreadsheets. Learn how to run code, select a kernel, and handle a missing location when reading.

Explore the pandas library and its tabular data focus, use read_csv to load CSV into a dataframe, and follow getting started tutorials, with Excel and clipboard options.

Structured data uses tabular formats with rows and columns, like sheets and csv files, with referable coordinates. The lecture contrasts structured, unstructured (PDF), and semi-structured data and explains extraction differences.

Import excel data into a dataframe with the Open Pi Excel package, load workbook, and view sheet names to read the second sheet's data after the description sheet.

Learn how to read Excel and CSV data into a data frame, view the head and column names, and describe data using familiar methods that apply across structured data.

Learn how SQL databases store data in tables, with albums table columns album id, title, and artist id, and query by selecting from albums and limiting to five rows.

Import sql data into a pandas dataframe in python via a sqlite3 database and select all columns, then describe data, top rows, and list column names.

Explore functions as reusable code blocks, examine properties as object qualities, practice for loops for repeated execution, and learn to get help with LMS docstrings, signatures, and GitHub Copilot.

Explore how functions and object methods enable reusable code in Python, and see how to apply a function across multiple Excel files to count sheets.

Import the library inside the function, access workbook data, assign the path for bank personal loan modeling, retrieve the number of sheets, and print the sheet count.

Create a list of numbers from 1 to 10 with range and list methods, then print them using a for loop.

Master using GitHub Copilot chat to explain functions like Read.csv and perform context-sensitive operations through a conversational workflow, including properties in Python and follow-up questions.

Explore functions, properties, and for loops, including passing a date argument and iterating over lists. Get help from LMS documentation and Copilot for unstructured data tasks with PDFs.

Explore unstructured data such as pdfs, images, and videos; learn to import pdf data into python, extract tables into dataframes, access elements, rename, split, and drop columns, and extract text.

Learn how to extract table data from PDF documents using Python, and understand how table data falls along structured, semi-structured, and unstructured categories.

Learn to rename dataframe columns by concatenating first and last names into a new column, keep rows intact, and comment out unwanted drops while running code to apply consistent names.

Rename columns in a dataframe using zero-based indexing: extract values from the second and third columns and first two rows, combine them, reset the index, and verify the rename.

Learn how to split a column in Python using split by a character (percent sign), extract first and second numeric parts, remove the percent sign, and clean up column names.

Explore how GitHub Copilot completes code and enforces consistent names. Describe the data frame to reveal meaningful values reflecting one year and compute statistics.

Learn how to extract text from PDFs using Python and Pypdf2, open in read-only mode, read the first page, and slice characters to retrieve specific text.

Explore unstructured data techniques, including extracting tables from PDFs to pandas dataframes and extracting text with Python. Reference and manipulate dataframe values, then transition to semi-structured data and APIs.

Learn to call the cat facts api using a random fact endpoint, make get requests, and interpret the response body and length, using curl and a display add-on.

Explore JSON payloads and learn how APIs return data using JSON, with strings, integers, and a schema that maps values such as title and length.

Begin by making a get request in Postman, explore http methods such as get, post, patch, and delete, and view the json payload returned by a cat facts api.

Learn to access nested json data in Python by using square bracket notation to retrieve the glossary title from a json payload.

Learn to access deeply nested values in json, handle list results via square brackets, and iterate over list elements to access gml and xml.

Convert a json object to a pandas dataframe using the DataFrame method, handle nested dictionaries by creating new columns, and apply lambda to extract city into a city column.

Discover how APIs enable internet-wide communication, with examples like cat API and Google API, and how JSON payloads become pandas dataframes for analysis.

Learn how to create and manipulate Python lists, store numbers 1 to 5, iterate with for loops, append items, view contents with print, and explore list methods in the documentation.

Dictionaries let you look up data by key in Python, returning values like name, age, and city, and you can print values with a for loop and the values method.

Explore lambdas, small anonymous functions used as inline alternatives to regular functions in Python, and perform squaring with the power operator ** while comparing to a named function like square.

Review the theoretical aspects of lists, dictionaries, and datatypes in Python, showing how lists group items and dictionaries use keys for lookups. Learn about data types and lambdas.