Prompt Engineering for Data Analysis Python, Pandas, ChatGPT

Explore GPT-4's advanced language capabilities, compare it with GPT-3, and learn prompt engineering basics—from gathering requirements to crafting and refining prompts—and apply them through project-based exercises.

Learn how to sign up for ChatGPT, upgrade to premium GPT four, and craft basic prompts for comparison, summarization, and follow-up prompts.

Draft a high quality prompt by setting context, adopting a persona, choosing precise verbs, and defining length and output type to guide ChatGPT effectively.

Learn to craft prompts by shaping tone, audience, references, and purpose to tailor ChatGPT outputs; set difficulty levels, use data and keywords to guide the response.

Discover how to leverage iterative follow up prompts to train ChatGPT for data analysis, using Bing and Google search, summarizing sources, drafting prompts, formatting, tone adjustments, and call to action.

Draft and refine prompts with clear task definitions and output formats, using iterative feedback to guide ChatGPT-4. Balance creativity with specificity, test prompts, and align context, outcome, length, and format.

Explore prompting techniques for data analysis with Python, pandas, and ChatGPT, including zero-shot, few-shot, and fine-tuning, and learn when to apply each approach.

Explain how the priming prompt sets baseline context and voice for ChatGPT. Show how styling, macros, formatting, and audience context tailor outputs for data analysis tasks.

download and install the anaconda distribution to set up a ready-to-use python data analysis environment with preinstalled libraries, conda package manager, and the jupyter notebook for writing and testing code.

Install and launch Anaconda on Mac, follow prompts to add to path, install dependencies and Jupyter, then open Anaconda Navigator via the terminal.

Access conda from the macOS terminal to manage Anaconda environments, update conda, and create and activate a dedicated Python data analysis environment for project isolation.

Learn to manage conda environments, switch between bases and projects, install Jupyter Notebook and key libraries like pandas, bottleneck, numexpr, and matplotlib, and keep them updated.

Open the terminal, activate your environment, and launch Jupyter Notebook. Create a new Python 3 notebook, run cells with shift+enter, and manage environments by closing terminals or deleting them.

Close and halt the Jupyter notebook from the file menu or terminal, then deactivate to return to the base environment and manage conda environments by listing, activating, and deleting them.

Open and save new Python scripts using Jupyter Notebooks, navigate directories, and manage file paths across Mac and Windows, including saving in a designated Pandas data analysis folder.

Master Jupyter notebook basics by using keyboard shortcuts to run cells with shift+enter or ctrl+enter, switch between command and edit modes, and manage cells with insert, cut, copy, paste.

Explore the header options in Jupyter Notebook and learn core actions like saving, running cells, and restarting the kernel, with pandas documentation and ChatGPT prompts for data analysis.

Explore how Jupyter cell outputs work, showing that only the last command prints by default and how to reveal all results with the print command.

Learn how to import and alias libraries in a Jupyter notebook, focusing on pandas, numpy, and matplotlib for data loading, manipulation, and visualization.

Explore Google CoLab as a cloud python console to start coding without installing software, compare it to Jupyter, and learn to create notebooks while skipping the Anaconda setup.

Explore Python, a high-level, interpreted language prized for readability and versatility. Leverage extensive libraries and frameworks like pandas, Django, and TensorFlow for web development, data science, and automation.

Master fundamental coding principles by learning syntax and data types, control structures, functions, and modules, while practicing problem solving, testing, debugging, and building a strong portfolio.

Learn how to use comments in a Jupyter notebook to document Python code, add hash-based comments, apply best practices, and rely on version control rather than disabling code.

Explore Python's basic data types, including integers, floats, strings, booleans, lists, tuples, dictionaries, and sets, with type checks and practical examples.

Master Python operators, including arithmetic (+ - * / // %), exponentiation, comparisons (== != > < >=), logical (and or not), and membership (in not in) with lists.

Explore Python's operator precedence with PEMDAS, see how variables are created and manipulated, and learn case sensitivity and snake_case naming, plus avoiding reserved keywords.

Explore the difference between local and global variables in Python, learn how to use the global keyword, see real-world examples, and discover best practices for clean, bug-free code.

Explore built-in functions in Python, core language tools like print, type, len, and conversions with str, int, and float, without importing modules.

Show how string methods return a new string, not the original; assign results to a new variable or back to the original to apply a permanent change.

Explore Python's in and not in functions, including starts with, ends with, and contains, to return booleans when checking strings, lists, and dictionaries.

Master Python type casting by converting between int, float, and string, with practical examples in Google Colab that cover user input, data processing, and formatted output.

Explore index positioning and slicing in Python strings, learn zero-based indexing, inclusive/exclusive substrings, and negative indexing, and see how these concepts transfer to series and dataframes.

Explore the dictionary data type in Python, including key-value pairs and updating or adding items in a sample menu. Learn len, in, not in, and if else for dictionaries.

Explore tuples in Python, an immutable core data structure, including creation, indexing, concatenation, repetition, and unpacking, with nested tuples and practical examples such as coordinates and RGB color values.

Learn match case statements in Python 3.10 and explore pattern matching with a basic example. See advanced patterns with conditions and shapes, and understand how ChatGPT guides these demonstrations.

Learn how to use for loops in Python to iterate a dictionary, find items priced at 8.99, and print the matching item names.

Master Python while loops by repeating code while a condition remains true, with examples in input validation, prime finding, and a guessing game, aided by ChatGPT demonstrations.

Learn how break and continue statements control Python loops with for and while, using live examples guided by ChatGPT, and explore combining them with best practices.

Explore Python modules and manage dependencies with pip, import built-in modules like math, install pandas, and create a simple data frame to illustrate practical use.

Explore taking user input in Python with the input function, including prompts and string handling. Convert input to int or float and manage errors with try and except.

Learn to combine text and calculations in Python using strings and numbers, concatenation with plus, str conversion, and f-strings to create dynamic outputs such as bills and reports.

Learn to craft flexible prompts for large language models using variables to create reusable templates, enabling personalized greetings, dynamic weather reports, and adaptive recommendations.

Explore guiding AI decision making using rule-based, machine learning, and reinforcement learning approaches, set inputs and constraints, and evaluate outcomes with accuracy, precision, recall, and feedback.

Explore recursion in Python with base and recursive cases, using ChatGPT to illustrate factorial and Fibonacci examples, while noting stack frames, stack overflow risks, and iterative or memoized alternatives.

Explore Python's exception handling with try, except, else, and finally blocks to prevent crashes, support graceful recovery, and enable debugging, logging, and maintainability; learn to create custom exceptions.

Learn to create and activate Python virtual environments to isolate project dependencies, prevent package conflicts, and enable reproducible setups using pip and a requirements.txt file.

Explore how Python's import statement brings modules and packages into your code, using dot notation, from and as aliases, and understand the module search path and runtime initialization.

Explore the OS module in Python to interact with the operating system, perform file and directory operations, retrieve and set environment variables, and execute system commands with live coding examples.

Explore Python lambda functions, anonymous one-expression tools, and their use with map, filter, reduce, and sorting to create concise, reusable code for data analysis.

Master regular expressions in Python by using the re module to search, find all, replace, and split text with patterns and literals, metacharacters, classes, quantifiers, anchors, escaping, and ChatGPT examples.

discover how asynchronous input output in python enables concurrent task execution with an event loop, coroutines, and await, improving efficiency and scalability for io-bound operations.

Explore Python multithreading and multiprocessing, including how the global interpreter lock affects concurrency, when to use threads for io-bound tasks, and when to use processes for cpu-bound workloads.

Master the pandas library by studying series and dataframes after recapping Anaconda setup, Jupyter, Colab, and essential Python data analysis and visualization skills.

Convert Python lists and dictionaries into pandas series with pd.Series, inspect index and dtype (int64 or object), and compare series to lists and dictionaries.

Explore how pd.series converts dictionaries and lists into a series, detailing the data, index, name, and dtype parameters, with practical examples.

Update a pandas series value by index, changing cold steel to Udemy. Retrieve it with the get method using a not found default; also cover filtering and case sensitivity.

Learn to append 'Udemy instructor' to every value in a pandas series by defining a custom function and applying it with df.apply, handling non-string values by converting to strings.

Explore a pandas series by inspecting attributes such as dtype, size, shape, values, and index, and differentiate them from methods like head and mean.

Identify and quantify nan values in a pandas series, then clean data by dropping, filling, or using forward fill, backfill, or interpolate to handle missing values.

Explore working with data frames: import datasets, set indices, clean missing values, normalize data types, filter by one or multiple columns, apply calculations, parse durations, and prepare for visualization.

Import the Udemy courses data from a csv into a pandas data frame, set the course id as the index for easy filtering, and parse the created date column.

Learn to detect and clean missing values in a pandas data frame using has nans, isnull, fill, and drop, including subset parameter drops for specific columns.

Convert the duration column from string to numeric by extracting the first number with a regular expression, then cast it to float to enable filtering by course length.

Identify and remove duplicate rows in a dataframe using the duplicated method and drop duplicates. Count duplicates with sum and prepare for further filtering in pandas.

Identify and remove duplicate rows in a data frame with the duplicated method and sum, then apply pandas filtering to extract courses by rating, year, or duration.

Learn to filter dataframes with multiple pandas conditions using boolean masks, combining duration, reviews, rating, and year to isolate precise courses by instructor.

Learn to identify top five instructors by course count using instructor ids, then filter those courses with ratings above 4.5 and summarize results using group by and pivot table concepts.