Interactive python dashboards | Plotly Dash 2022| 3 Projects

Explore numpy basics for working with numerical data in Python, creating arrays with arange, zeros, and ones, indexing and slicing; learn mean, min, max, sum, and std.

Master pandas basics: create lists, series, and dictionaries; build data frames and read data from github or excel; use describe and info; index, select, update, and filter with loc/iloc.

Learn to use for and while loops to iterate over lists, strings, and ranges in Python, control flow with break and continue, and generate simple outputs like multiplication tables.

Learn how the Python range function works: default start at zero, end is exclusive, and how to use one, two, or three arguments with a specified step to generate sequences.

Create and compare bar charts of GDP per capita across years using plotly go and plotly express, focusing on India and the United States with customized layout and bar mode.

Build and compare two-line life-expectancy charts for India and the United States using plotly go dot scatter on the Gapminder dataset, with hover text, color, width, and axis titles.

Create scatter plots and line charts to compare life expectancy by year for India and the United States. Use fig.add_trace with go.scatter and adjust mode between marker and line.

Create bubble charts in Plotly Express using Gapminder data to visualize GDP per capita, life expectancy, and population through bubble size, with continent color and year filtering.

Explore building choropleth maps with Plotly Express, using GeoJSON and ISO alpha codes to visualize Montreal election districts and global life expectancy data with interactive color scales.

Create data cards in Dash Plotly using card header, card body with title and text, and three cards in a container layout with color options and inverse or outline styles.

Explore dash html components by building a live app layout with html div, h1, h2, p, a, img, and marquee, and apply style for centered content and responsive updates.

Build interactive widgets using dash core components such as dropdowns, sliders, inputs, checklists, and date pickers. Configure options, default values, placeholders, and markdown formatting to create rich dashboards.

Explore Plotly Dash by building a pandas dataframe from a csv URL and styling a data table with fixed headers, height, and odd/even row formatting.

Build an interactive dash app by wiring a dropdown to a callback that updates the output's children text using input and output components and a defined layout.

Learn to implement a callback using a stimulus button in a dash app, updating a click counter via a button_update function and displaying the current count from zero.

Learn to implement a callback with states in a dash app by capturing two text box inputs with default values Montreal and Canada, and updating a live component after submit.

Design a covid-19 tracker dashboard with a navigation bar, data cards for confirmed and recovered cases, and a country dropdown, search for specific countries, and 24-hour updates.

Create a navigation bar by reusing the existing code, swap in a coronavirus image and the covid-19 title, and wire links to routes and a fundraiser.

Learn to read Covid-19 API data, parse global and country stats, and build three data cards showing total and last 24 hours for confirmed, recovered, and deaths.

Create a world globe visualization in dash that shows total confirmed cases by country, colored by case totals and including deaths and recoveries, using alpha-2 and alpha-3 country code mappings.

Build a Dash table with a country dropdown to filter and display covid-19 data by country, with sortable columns and styling.

Learn to deploy a Dash dashboard to Heroku, a cloud-based platform with a free tier, by preparing a virtual environment, managing dependencies, and pushing to production.

Learn to build a dashboard that compares current and reference periods in retail sales using dropdowns. Visualize differences by month, week, stores, and top departments.

Create store-level sales data for two months, aggregate weekly sales per store, and build department-level differences between two months for dashboard visuals.

Build a navigation bar for the dashboard using a Bootstrap theme and blue styling, then adjust text color and prepare for adding dashboard cards in the next lecture.

Create interactive python dashboards with Plotly Dash by building dynamic cards that show total monthly sales and the delta versus a reference, updated via callbacks from month dropdowns.

Build a dynamic sales card in a Plotly Dash dashboard by showing total current sales and the sales difference, format numbers as dollars in millions, and style for clarity.

Create a total stores card in a Python dash dashboard by counting distinct stores and computing the current period value and its difference from a prior period, and update variables.

Learn to build a Plotly Dash dashboard that compares weekly sales across two user-selected months, using a prepared weekly sales dataset and a responsive line chart with clear formatting.

Build a dataset from store sales to show the top 10 stores by weekly sales for the current month and a reference period, using two side-by-side bar charts.

Learn to rapidly prototype machine learning dashboards by building an under-30-minute input-driven app that generates context-based answers from a paragraph and a question.

Build an interface with two input boxes and a text area for context, a question field, a button to generate an answer based on the context, and a loading indicator.

Build an interactive question-answering feature in a Dash app by wiring a generate button callback to pass context and question to a transformers pipeline that returns an answer.