Python A-Z™: Python For Data Science With Real Exercises!

Create and manipulate variables in Python, perform arithmetic, and print results using the print function in Python 3. Explore using modules like math for sqrt and strings with concatenation.

Master the Python while loop, which uses a condition and a colon with indentation to define the body. See how indexing starts at zero and how that differs from R.

Explore the for loop in Python, compare it with the while loop, and learn to use range and lists with an iteration variable to print values zero to four.

Master python conditional logic with if, elif, and else. Explore nested and chained statements housing normally distributed numbers (mean zero, std one) using numpy's randn and classify them.

Master how Python uses indentation to define code blocks in an if-else chain, and see how misindentation changes which statements run, including print outputs.

Discover how Python lists act as ordered sequences, are able to hold mixed data types, and are accessed via zero-based indexing to extract specific elements.

Explore creating lists in Python, including mixed data types and nested lists, and learn to generate sequences with range and list(range(n)) for practical for loops and matrices.

Learn how Python packages group related modules to extend functionality, install with conda or pip, and import whole packages or specific functions for data processing, web scraping, and visualization.

Compute monthly profit, profit after tax (30% tax), and profit margins from revenue and expenses in Python, then identify good and bad months, plus best and worst months, all as lists.

Analyze basketball trends with Python by exploring the top 10 paid players' ten-year NBA dataset, learning to work with matrices and derive games played, mpg, fg%, and points per game.

Compute field goals per game and minutes per game by performing element-wise division of matrices in python, handling zero divisions and rounding with numpy. Access players quickly via dictionaries.

Expand your data visualization skills in Python by building multi-line charts with pyplot, customizing line styles and markers, and adding legends to compare players' salaries and games.

Create your own Python function to automate plots by passing a player list, mapping names to indices via a dictionary, and looping to generate labeled visualizations.

Build matrices for free throws and attempts, analyze free throw accuracy, and plot trends while excluding free throws to reveal playing style and two versus three points preferences.

Explore your dataset in Python by inspecting rows, columns, and basic statistics with dataframe methods like head, tail, info, and describe to gain quick insights.

Rename dataframe columns with a simple approach, turning messy headers into consistent names like BirthRate and InternetUsers, using stats.columns and a list of new labels.

Master basic data frame operations in python, including subsetting, element-wise column math, and adding or dropping columns, while comparing python to r's vectorization and recycling concepts.

Learn to access single elements of a dataframe using .at and .iat, distinguishing label-based versus integer-location indexing, with practical examples and caveats.

Explore seaborn in Python, building on matplotlib to map distributions with distplot and boxplots from a data frame. Adjust bins, figure size, and warnings while comparing InternetUsers, BirthRate by IncomeGroup.

Visualize internet users versus birth rate with seaborn lmplot, colored by income group in a facet grid, noting the absence of a linear relation and exploring fit_reg and size adjustments.

Master keyword arguments in Python through an advanced tutorial on Seaborn lmplot, use dictionaries via scatter_kws to pass through to Matplotlib’s scatter and control marker size.

Explore how to handle categorical variables in Python by converting columns to category types, inspecting unique levels, and treating year as a category for accurate analysis.

Explore visualizing relationships with Seaborn's jointplot in Python by plotting critic rating versus audience rating, using marginal distributions and hex plots in Jupyter notebooks.

Explore histograms using distplot to compare audience and critic ratings, contrast Seaborn styling and grid with PiPlot, and illustrate normally distributed audience data versus non-normally distributed critics with bin 15.

Test the law of large numbers by generating N normal numbers (mean zero, std dev one), counting between -1 and 1, and observing convergence toward 68.2% as N grows.

Create a seaborn plot of domestic gross percentage by genre using boxplots and stripplots, customize style, colors, labels, and legend placement for a polished visualization.