Prompt Engineering for Data Analysis Python, Pandas, ChatGPT

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.

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.

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.

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.

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

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 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.

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.

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

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 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.

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.

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.