Static and Interactive Data Visualizations in Python

Explore the pandas data analysis toolkit in Python, learn to create series and data frames, use labels and iloc indexing, and read data from external sources for wrangling.

Learn to read csv data in python with pandas, handle standard comma separated values and non-standard separators like semicolon or tab. Inspect and adapt separators for non-standard files.

Learn to read excel files with pandas, load a workbook from your working directory, and access multiple sheets by name or index into a dataframe.

Learn to read multiple tables from a web page with pd.read_html, then index by position to extract the tentative list and other tables.

Learn basic data exploration with pandas: drop columns, inspect null values, and fill missing values using age by gender means, and cabin or embarked substitutions.

Explore conditional data selection with pandas to filter languages by speaker counts and endangerment, using a real Kaggle dataset to prep for indexing and pre processing.

Learn to drop rows and columns from a data frame using pandas, employing drop with index and axis to remove rows or the latitude column.

Explore merging and joining data in Python by combining global firepower and GDP datasets on country, using inner, left, right, and outer joins, with missing values handling.

Explore how data visualization reveals patterns, trends, and correlations in data, using charts like bar plots, histograms, and line charts for exploratory data analysis.

Explore theoretical principles behind data visualization and how data types—categorical, numerical, and ordinal—shape the choice of histograms, bar charts, line charts, and scatter plots.

Visualize the distribution of continuous numerical data with histograms in Python. Explore bin customization, frequency, rug plots, and box plots through iris and GDP per capita examples.

Visualize the distribution of continuous numerical variables with box plots, detailing min, first quartile, median, third quartile, and max, using iris and tips datasets across species and sex.

Explore relationships between two or more quantitative variables with scatter plots and scatter matrix, using pandas data frames, and color by category to reveal patterns across datasets.

Learn to visualize discrete data with bar plots and stacked bars using matplotlib and seaborn, comparing country scores, university influence, happiness factors, and Titanic survival.

Explore how pie charts visualize country-level aggregate influence, using categorical data and percentages to compare USA, China, Japan, and others, with color schemes and auto percentage labels.

Learn to create line charts in Python to track changes over time and across categories, using happiness scores, family metrics, and rainfall time series across countries and subdivisions.

Plot multiple lines in a single chart to track stock performance. It uses Apple, Google, and IBM from 2006 to 2018, plotting opening values with a legend.

Explore how to visualize real Nobel Prize data with intermediate plots, data wrangling, and pre processing, using bar plots, joint plots, histograms, and scatter plots to reveal trends.

Continue exploring data visualization in Python with Seaborn line plots, new data processing techniques, and merging datasets to visualize Nobel Prize trends by year and category.

Learn the grammar of graphics and how to apply it in Python with a ggplot2-style approach, focusing on data, aesthetics, geoms, coordinates, and color.

Explore static data visualizations using the grammar of graphics with a ggplot-like Python package; build bar plots for categorical data, layer aesthetics, and flip coordinates for clarity.

Create a simple bivariate scatter plot in python using ggplot to explore the relationship between x and y variables, coloring by manufacturer.

Explore core spatial data concepts, including geographic coordinate systems and latitude-longitude, learn about Mercator projection, and differentiate raster and vector data with shapefiles and x y data.

Learn to read and visualize a shapefile in Python, color by eco code or realm, handle missing values by replacing them with unknown, and display a legend for geographic regions.

Filter a shapefile with geopandas by area to select equal regions between 20 and 5000 square kilometres, then visualize the filtered regions on a map using matplotlib.

Explore in-built GIS datasets with geopandas and geoplot to visualize country shapefiles, and customize continent and country maps with figure size and color settings for clearer geographic visuals.

Discover choropleth mapping to visualize country-level data such as urban population and GDP per person through shading. Learn how map classify uses equal interval and quintile schemes for world maps.

Combine shape file data with GDP data by country code to create a choropleth map, converting to a geo data frame and visualizing GDP by country with a legend.

Explore interactive visualizations and how they reveal underlying data from a Tesla stock time-series chart. Click points to see open and close values, guiding decisions, using Blackley in Python.

Learn to build interactive visualizations with plotly express in Python, using World Bank data to plot GDP per capita vs birth rate, colored by region and sized by population.

Explore World Bank data with plot express to create interactive visuals, including scatter plots sized by population and colored by income for East Asia and Pacific, with income-based facets.

Learn to create animated visualizations with express animations in Python, using animated scatter and bar plots to track changes in neonatal mortality and GDP per capita over time.

Create interactive geographic visualizations in Python: a scatter plot colored by income animated by year with iso country codes, and an interactive map of neonatal mortality and GDP per capita.

Discover Posit, a browser-based platform to deploy, share, teach, and learn data science from RStudio or Jupyter notebooks, enabling Shiny, Streamlit, and Dash apps without installing software.