Automate Your Life With Python + ChatGPT Prize

Install Anaconda on macOS with the 64-bit graphical installer, launch Jupyter Notebook and Jupyter Lab, run Python 3, and verify with pandas before managing libraries and environments.

Explore the Jupyter notebook interface, an open source web application for live code, visualizations, and text. Use it for data cleaning, transformation, and analysis in data science and machine learning.

Explore cell types and modes in Jupyter notebook, switching between command mode and edit mode, using keyboard shortcuts like B, H, Y, and M, with code and markdown cells.

Learn how to write your first Python program by printing Hello world, use the print function, run code cells in Jupyter Notebook, and explore strings, numbers, and comments.

Learn Python data types: integers, floats, booleans, and strings, how to check them with the type function, and use true/false values and string methods like upper, lower, and replace.

Master Python variables to store values, create string messages with double quotes, concatenate strings with plus, and build dynamic content using f-strings.

Explore Python lists as mutable containers: create lists, index (including negative indices), slice, append, insert, join, and remove with remove, pop, or del, and copy or nest lists.

Learn how Python dictionaries store data as key-value pairs, create and update dictionaries, access keys, values, and items, copy to avoid references, and remove entries with pop, del, or clear.

Master Python's if statements to control code execution with if, elif, and else, learn the required colon and indentation, and explore age-based examples like adult, teenager, and kid.

Master Python for loops to iterate over lists, print each element, and use enumerate for index-element pairs; explore looping through dictionaries with items to access keys and values.

Define and call functions in Python, pass parameters, and return values, then explore built-in functions like len, max, type, and range.

Explore Python modules by importing the os module to access functions like getcwd, listdir, and makedirs; retrieve the current working directory, list folder contents, and create new folders.

Demonstrates file and folder operations using the path live module and contrasts it with the os module, showing how a path object provides name, parts, and suffix attributes.

Learn to create folders and subfolders with the path library, using make directory, makedirs, and exists_ok and parents flags, then automate monthly and daily folders for a year.

Rename folders and files using path objects and path.rename, then fetch paths of immediate and all subdirectories with double asterisks and wildcards, filtering py files with path.is_file.

Rename csb files by deriving the new file name from their parent and grandparent folders, extracting year, month, and day with regular expressions, and formatting as year-month-day.

Change extension of files in a folder by converting text files to CSP and then CSP back to text, using path iteration, suffix filtering, and glob patterns.

Create and delete text files in Python by iterating a numbers list to generate test files, writing hello world, then using glob patterns and unlink to remove files.

Learn to structure and extract zip files in Python with the zipfile module, define the current directory and the target directory, and extract all contents to a temp folder.

Learn to search CSP files for specific text in their names using Python and glob patterns, iterate through folders, and print matching absolute paths, comparing relative and absolute paths.

Automate creating Word documents from Excel data by generating a document per row using the Doc X TBL library in Python, replacing template variables with a dictionary.

Learn to generate fake data with Faker, create profiles, and build a pandas dataframe; add phone numbers, rename columns, and export to csv, with an option for Spanish.

Learn to automate creating multiple cover letters by importing data from an Excel/CSV file with Pandas, iterating rows, and populating a Word template to generate personalized documents.

Explore HTML basics and the structure of web content for web scraping, including opening and closing tags, attributes, and elements like head, body, header, article, h1–h3, div, table, and iframe.

Explore the HTML tree structure by analyzing a simple article page with a root article element, h1 title Titanic, and p with plot and full script attributes, plus node relationships.

Explore how XPath selects elements in XML and HTML pages using double slash, square brackets, and attributes; apply functions like starts-with and contains, and combine expressions with and/or operators.

Leverage XPath to locate and extract HTML elements like h1 titles, paragraphs, and transcripts, using expressions, text(), contains, and indexing in Selenium or scraping workflows.

Learn XPath characters, using single slash for children, double slash for descendants, context with . and .., text() and attribute @, wildcard *, and indexing [n] to locate elements.

Install selenium and Chrome driver to enable web scraping, match the driver version to your Chrome, download the correct Chrome driver, and install via pip in a virtual environment.

Automate the news by building a selenium driver to extract article titles and subtitles from a football site, saving them to a text file for quick reading.

Extract news titles, subtitles, and links from websites using developer tools and xpath, iterate over elements with selenium, capture text and href attributes, and prepare data for export with pandas.

Create and populate three lists, build a dataframe with pandas, and export the headlines to a csv file.

Learn to run Selenium-driven web automation in headless mode, enabling background data extraction of headlines, subtitles, and links without opening a browser.

Automate daily news extraction by turning a Python script into an executable and scheduling it to run each morning. Use os.path join and date-based, formatted file names for reliable organization.

Learn to convert a python script to an executable using pyinstaller by installing the tool, running pyinstaller --onefile your_script.py, and testing the dist executable in macOS.

Learn how to schedule a Python script with crontab on macOS, using cron syntax, building the command with Chrome Tab Guru, setting nine a.m. daily, and verifying execution.

learn to schedule a python script on windows using task scheduler by creating a bat file with python and script paths, then configuring a daily trigger.

Automate the Tinder experience using selenium and Python to swipe right, locate the like button with XPath, and handle upgrade prompts and not interested while sending messages to matches.

Automate Tinder using Python and Selenium by clicking the like button with execute_script, handling popups and matches with for loops and try/except, and applying implicit and explicit waits.

Connect selenium webdriver to an existing Chrome browser by launching in debugger mode and attaching a session via port 9222. Log in once manually to keep the session active.

Convert your py file into an executable using pi installer, verify Dist contains the executable named after your script, then schedule it with Windows Task Scheduler or macOS.

Learn how to use regular expressions in Python, focusing on metacharacters like \d, \w, and \s, the dot, escaping, and anchors ^ and $, with multi-line flags.

Explore regular expression quantifiers, including the asterisk, plus, and question mark. Master greedy and lazy matches with curly braces for exact or range counts.

Master Python regex basics by using parentheses for groups, the plus quantifier with digits, hyphen separators, square brackets for character classes, word boundaries, and backreferences to reuse captures.

Learn to import the re module and use Python's regex methods search and findall to match digits, using raw strings and flags like re.I and re.M.

Apply Python regular expressions to solve three exercises: match punctuation, validate a hyphenated date format, and enforce a 4–14 character alphanumeric username.

Discover how pandas compares to Excel and learn data frames, series, and arrays, plus how pandas enables data cleaning and transformations.

Learn to manipulate text data with pandas and numpy by creating random ten-digit numbers, building data frames, and exporting to txt and csv files with proper headers.

Learn to automate csv data enrichment with Python by adding a country code to every line, using lambda functions, f-strings, and a loop for multiple files.

Remove the plus sign and non-digit characters from a phone column with pandas and regex, set the column to string dtype, and export the cleaned csv.

Learn to concatenate multiple CSV files into a single pandas DataFrame by aligning columns, setting common column names, and using ignore_index for a seamless vertical join.

Learn to remove duplicates within a dataframe column using pandas, count and drop them with drop_duplicates, and handle null values with dropna and fillna.

Extract tables from websites with pandas read_html by installing pandas and importing as pd in jupyter notebooks, returning a list of tables to select the second one for season one.

Extract tables from PDFs with Camelot-py, read the first page, and export the table to CSV, including installing Camelot-py and its dependencies and verifying the CSV equals the PDF data.

Learn to perform web scraping with pandas by reading CSV data directly from a URL, loop through multiple files to download Premier League data, and rename columns for clarity.

Extract csv files from multiple urls using pandas by building a dynamic root and season-based links, looping over seasons, and storing results in a frames list with a season column.

Store dataframes in dictionaries to manage csv data from multiple leagues, using keys for league names and codes, and concatenate seasonal data with pandas for efficient retrieval.

Create and configure a Google Cloud project, enable Google Drive API and Google Sheets API, and download service account credentials to connect Python with Google Sheets.

Connect Python to Google Sheets using credentials, install libraries, and create a spreadsheet, then share it and place it in a specific Drive folder using a folder ID.

Learn to upload data to Google Sheets with Python by connecting via service account, reading a CSV file with pandas, and updating a sheet with column names and data.

Connect to Google Sheets with Python using service account credentials, open the second sheet. Edit data by appending, inserting, and updating cells and ranges with coordinate-based updates.