Python Essentials: Learn Python Basics with Coding Exercises

Open and use the Jupyter Notebook to write and run your first Python code, printing hello world with the print function, and begin exploring Python basics for beginners.

Discover variables in Python, learn to assign data with the equals sign, and follow naming conventions: valid characters, underscores for spaces, case sensitivity, and descriptive names.

Explore python data types—integer, float, string or object, and boolean—and learn to check types with type(), distinguish whole numbers from decimals, and use quotes for strings.

Explore data type conversion and casting in Python by using int, float, and str to convert values, illustrate type changes in calculations, and ensure correct data types for data analysis.

Master Python arithmetic operators such as plus, minus, multiply, divide, modulus, and exponent through practical examples that show how these operators compute results in short, hands-on coding exercises.

Explore Python comparison operators such as >, <, >=, <=, ==, and != to compare values and yield booleans, with examples.

Explore how to use Python logical operators and, or, not to combine comparisons and produce boolean results through practical examples.

Learn how to create lists in Python using brackets, including strings, numbers, and mixed data, and how to index, slice, and modify them with append, insert, and remove.

Explore Python sets and their operations, including union, intersection, and difference; learn to add or remove elements and preserve unique elements while applying element-wise transformations.

Explore Python dictionaries by learning key-value pairs, items, and dictionary methods to modify, access, and extend data, and compare dictionaries with sets using practical examples.

Explore conditional statements in python using if, elif, and else to control output based on comparison and logical conditions, test conditions, and print results.

Explore how to use logical operators with conditional statements in Python to categorize ages, assess temperatures, and determine eligibility using if, elif, else, and not.

Master Python looping structures by using for loops to iterate over lists and while loops to run until a condition is met, including applying conditions, appending results, and using break.

Define, create, and call custom Python functions using def, then build a percentage function that computes category percentages from the total number of respondents, demonstrated with male and female examples.

Install essential Python packages in a Jupyter notebook, including Pandas, Matplotlib, Seaborn, NumPy, SciPy, and scikit-learn, using pip before restarting the kernel and analyzing data.

Learn to handle missing values in a dataframe with Python code generated by ChatGPT, using pandas and NumPy, including drop, fill with zero or mean, and forward or backward fill.

Inspect and fix data types in a pandas dataset by using df.dtypes, convert columns to numeric, datetime, categorical, or string, and validate results with ChatGPT-assisted code.

Learn to detect and handle outliers in Python using z-score and IQR, remove or replace them with the mean, optionally apply log transformation, and save cleaned data.

Learn to handle duplicate values in a Python dataframe with ChatGPT guided code, check duplicates across the full dataframe or specific columns, and keep the first or last occurrence.

Learn how to extract day, month, and year from date variables in python with pandas, using code generated via chatgpt and addressing common copy warnings for clean results.

Identify the most-sold country by analyzing frequency data and counting category values in Python, then visualize with a bar chart, highlighting Portugal, France, and Sweden.

Answer data analysis questions using python and chatgpt to perform cross-tabulation between two categorical variables and visualize with a heatmap, identify top sales representatives by category.

Calculate the average cost by category in Python using the groupby method and visualize the results with a line chart to identify the most and least costly product categories.

Analyze the relationship between cost and order value using a Python correlation test. Plot a scatter with a trend line to illustrate a strong, significant, positive relationship.

Learn how to diagnose and fix module not found errors in Python, illustrated by importing pandas, correcting misspellings, and ensuring the module is installed and available in your environment.

Identify and fix syntax errors in Python by recognizing grammar mistakes, missing parentheses, quotes, and indentation issues, with examples like missing closing parentheses and invalid syntax in if else.

Master key errors in Python by examining dictionary access, recognizing when a non-existent key like gender triggers an error, and correcting code to read existing keys such as name.

Learn how Python raises an index error when accessing a list beyond its bounds and fix it by using a valid index, such as two to get orange.

Discover how attribute errors occur in Python when accessing a non-existent attribute, like a list's length, and fix them by using the correct approach, such as the len() function.

Learn how value errors arise in Python when converting non-numeric strings to integers. Explore why 'hello' triggers a ValueError, and how numeric strings can be safely converted.

Understand how type errors arise in Python when mixing strings and integers. Practice fixing them by converting integers before concatenation to run code successfully.

Learn to diagnose root causes, debug effectively, and use ChatGPT to resolve Python errors within seconds while practicing with data frames, imputation, and visualization.

Discover how to use ChatGPT to diagnose and fix Python coding errors, from debugging to improving code, by framing a three-part command block with code, errors, and instructions.

Use ChatGPT to diagnose Python code errors in a Jupyter notebook, reshape education data to a 2D array for the simple imputer, and fix data imputation and visualization workflows.

Discover how to use chatgpt to debug python code, revise scripts, and generate correct imputation and interactive visualizations of age distribution by education level in Jupyter.

Learn to use ChatGPT to shorten and improve Python code, impute missing education values with mode via the dot field method, and create Plotly interactive visuals.