Mastering Data Visualization with Python

Learn to source plotting data from csv files, seaborn's built-in datasets (tips, Titanic, mpg, iris), and the pie dataset by importing numpy, pandas, and seaborn, and loading datasets.

Install pydataset with pip, import data from the package, and explore datasets like diamond, housing, Nile, chick weights, warpbreaks, and Titanic for visualization with pandas, matplotlib, and seaborn.

Identify the figure and axes components, add a title, x axis label, y axis label, legends, markers, spines, and grids, and learn plotting with pandas, matplotlib, and seaborn.

Import numpy and pandas to create the first plot from the housing dataset, visualizing price and lot size, while noting axis issues and overlap to refine later.

Refine a Pandas plot of housing data by plotting price with a title and x and y labels, add lot size, and switch to subplots for clearer comparison.

Create side-by-side line plots using pandas and matplotlib, manage layout with tight layout, and explore Nile river flow by plotting Nile versus time with proper x and y labels.

Import libraries and the housing data, group by bedrooms, count occurrences, and create a bar plot showing the frequency of bedroom counts (1–6) with labeled axes and a title.

Explore box and whisker plots to compare house price distributions across bedroom counts, highlighting median, quartiles, interquartile range, and outliers using pandas, matplotlib, and seaborn.

Visualize housing prices with a histogram to see distribution across bins, and use a KDE (kernel density estimate) plot for a smooth density curve.

Explore the relationship between two continuous variables with a scatterplot, plotting lot size (X) against price (Y) to reveal an increasing but imperfect trend influenced by multiple factors.

Create a pie chart from housing data using pandas value_counts to show the distribution of 1–4 story houses, and note pandas' limitations versus bar charts and matplotlib.

Master data visualization with Python by selecting appropriate plots, including line, bar, pie, histogram, KDE density, scatter, box and whisker, and violin plots for single and paired variables using pandas.

Explore line plotting with matplotlib using the Austres dataset to visualize Australia’s population over time, compare with pandas plots, and learn figure sizing, styling, and labeling.

Refine a matplotlib line plot by adding axis labels, a legend, and multiple lines, with grid options, alpha, and line style, and explore pandas-based plotting.

Explore bar plots with matplotlib to visualize bedroom counts, group by bedrooms, and create a pareto chart with a cumulative sum for insights in data visualization.

Create and customize a box and whisker plot in matplotlib using the housing_df price data, with options for notch, showmean, orientation, and axis labels; discover why Seaborn simplifies multiple plots.

Learn to create and customize histograms with matplotlib's plt.hist: set bins, choose hist types (bar, step, stepfilled), and explore cumulative options with grid for data analysis.

Explore scatter plots in Python by plotting price against lot size with Matplotlib, and see how Seaborn adds color for air conditioner and dot size for bedrooms to visualize variables.

Visualize house counts by story level with a pie plot using the housing_df stories column and value_counts. Title, labels, and legends enhance clarity, and next we explore subplot interfaces.

Explore Matplotlib's subplot interface to create multiple axes on a figure, plot population and percent change with Pandas pct_change, and compare two plots for clearer visualization.

Explore how to create a single plot with the subplots approach in Matplotlib, define fig and ax, customize size, labels, and title, and save the figure.

Learn to create a figure with two axes using subplots, plotting population and percent change on separate axes with a shared x-axis and a super title.

Learn to annotate a two-panel plot by adding a mean horizontal line on the percent change chart, labeling it, and pointing to the lowest change with an arrow.

Create side-by-side bar and pie plots using matplotlib subplots to visualize house stories counts; customize ticks, labels, grids, legends, and percentages for clear data storytelling.

Create and customize a scatter plot of lot size vs price and histograms with matplotlib subplots, including two-by-two layouts and axis labeling, then explore seaborn pair plots for correlograms.

Master Seaborn for plotting and learn automatic axis labels, optimized bins, and built-in statistics while creating histograms, joint plots, and pair plots from Iris and housing data.

Explore Seaborn's plot types, including histplot, displot, rel plot, and catplot, and learn how figure level versus axes level controls sizing with height and aspect while leveraging Pandas dataframes.

Use seaborn relplot to create a scatterplot of lot size vs price from housing_df, then refine it with matplotlib features like title, axis labels, annotate text, horizontal lines, and grids.

Explore creating Seaborn relplot scatterplots to compare houses with and without driveway. Learn to use hue, column, and row for facets, and size and color to encode bedrooms and stories.

Explore line plots in Seaborn using relplot with kind='line' to visualize average salary by years of service, including confidence intervals, and compare by sex or discipline.

Explore displot in Seaborn to create histogram, KDE, ECDF, and rug plots, including combining them and understanding the default histogram behavior, with a focus on future histogram customization.

Explore displot visualizations for histogram, KDE, ECDF, and rug plots, and learn to adjust hue by bedrooms, color blind palette, and stacking or dodging.

Explore two dimensional histograms and kde plots to visualize price and lot size distributions, using color bars and heat map style density in seaborn displot.

Explore seaborn's cat plots, covering six categorical plots—strip, swarm, box, violin, point, and bar—classified by info level into categorical scatter, distribution, and estimate plots; next, examine strip and swarm plots.

Explore strip plots and swarm plots to visualize price distributions with seaborn, comparing by bedrooms and driveway, and learn when to adjust aspect ratio for clarity.

Explore box plots and violin plots in Seaborn to summarize distributions, interpret medians and quartiles, and split data by factors like bedrooms and driveway for clear comparisons.

Explore categorical plots in Seaborn, focusing on point plots and bar plots to visualize house prices by bedrooms, including how bars show averages and 95 percent confidence intervals.

Master seaborn joint plots by combining scatter plots with histograms or KDEs for sepal length and petal length, using iris data and species hue to reveal class patterns.

Explore seaborn pair plots to examine relationships among iris sepal and petal measurements across species, with diagonal histograms or kde and options like vars and diag_kind.

Explore linear model plots in Seaborn using lmplot to reveal relationships between sepal length and petal length, including per-species lines, and extend to joint and pair plots with regression lines.

Learn to set plot styles with Seaborn by choosing from five built-in options (dark grid, white grid, dark, white, ticks) and adjusting context and color palettes for publication formats.

Set the plot context in Seaborn to tailor size and emphasis using paper, notebook, talk, and poster. Switch context with sns.set_context to suit PowerPoint slides.

Apply seaborn palettes by data type: quantitative for categories, sequential for ordered values, and diverging for clear boundaries; experiment with color blind, pastel, and flare for readability.

Learn to craft themed plots by combining style, context, and color palettes in seaborn, using set_theme to tailor plots for notebook, talk, or poster contexts.

Install and configure Anaconda on Windows to run Python 3.8, launch Jupyter Notebook via Anaconda Navigator, and build and run Python notebooks with interactive cells.

Launch and navigate Jupyter notebook via Anaconda Navigator, create Python folders and notebooks, and switch between code and markdown cells using common shortcuts such as Shift+Enter and Y/M.

Get started with Python in a Jupyter notebook, running basic calculations (modulo, addition, multiplication) and exploring the math module, including pow, sqrt, factorial, and logs.

Define variables and assign values in Python, print results, and inspect types using type(); distinguish integers, floats, and strings, avoid reserved names, and manage quotes; introduces lists.

Master the basics of Python lists, including creating single lists and lists of lists, indexing and slicing, modifying values, and using append, pop, and count for data manipulation.

Explore Python lists by creating, reading, and counting items, using range to build sequences, and checking membership with in; note upcoming topics dictionary, tuple, and set.

Explore dictionaries in Python: define with curly braces, map keys to values, access values by key, and use keys, items, and pop to manage entries, contrasting with lists.

Explore how tuples use round brackets, differ from lists, and provide immutable sequences in Python. Remember tuples cannot be assigned, appended, or deleted, guiding use of them instead of lists.

Explore the set data structure in Python, highlighting its unordered nature and unique elements, with comparisons to lists and dictionaries and examples of curly braces.

Explore how Python uses logical operators to compare values, producing booleans with equality, not equal to, and relational checks like less than, greater than, and less than or equal to.

Learn to build and manipulate NumPy arrays, including 1d vectors and 2d matrices, using np.array, np.arange, np.linspace, zeros, ones, and reshape.

Learn how to select values from NumPy arrays, including one-dimensional and multi-dimensional arrays, using indexing, slicing, and boolean masking, with examples of 3 by 10 matrices and stepwise arange.

Create and manipulate numpy arrays using arange, perform element-wise operations, and understand slicing and the relationship between a selection and the original array. Changes propagate unless you copy.

Learn how Pandas handles structured data like Excel sheets by introducing series and dataframes, built on NumPy and Matplotlib, with multi-type columns, indexing, and reading values.

Learn to create a pandas DataFrame from a numpy array with custom data, index, and columns, or build one quickly in a single line, and preview importing Excel data.

Learn to import csv data into a pandas DataFrame with pd.read_csv, set the index, and inspect data using head, tail, info, and describe.

Select and view pandas dataframe columns, compute a volume from length, width, and height, round to two decimals, and drop or save columns using axis and inplace.

Mastering data visualization with Python: learn to work with rows in a pandas dataframe by using .loc and .iloc, applying boolean masks, and combining conditions for operator and length filters.