Learn Python for Data Analysis & Visualize Data with Plotly

Open PyCharm, start a new project with a virtual environment, name it, and create files like text, Python, and HTML; run code from the editor or terminal.

Learn to install Python libraries using the pip install command in the PyCharm terminal, with examples like pandas, and understand where the terminal appears across PyCharm versions.

Install plotly, import plotly express, create x and y lists, assign a title, and display an interactive scatter plot to taste how Python visuals with Plotly come together.

Explore variables as building blocks in Python, storing integers, strings, and booleans in memory with dynamic typing and assignment via the equals sign. Learn naming conventions, snake_case, and reserved words.

Explore basic arithmetic in Python by performing addition, subtraction, multiplication, division, and exponentiation. Learn to compute squares, roots, and remainders, and run code from editors or the terminal.

Explore Python strings: creation with quotes, immutability, slicing, concatenation, repetition, membership checks, and common methods like upper, lower, strip, split, for web scraping, data parsing, text analysis, and file handling.

Explore Python's top ten string methods, including type, len, lower, upper, strip, replace, split, join, find, startswith, and endswith, with practical data analysis examples.

Explore how Python lists store different data points, using a shopping list to illustrate adding, removing, searching, counting, sorting, copying, reversing, and merging lists with append, extend, and insert.

Learn how to create lists with square bracket notation in Python, index elements with zero-based and negative indices, and slice subsets, highlighting mutability and heterogeneous data.

Learn how Python dictionaries store data as key–value pairs, compare them to real dictionaries, and perform add, access, update, delete, membership tests, and iteration with keys, items, and values.

Explore Python dictionaries by building key-value mappings, using keys and values, and retrieving items with get or indexing, including handling missing keys gracefully with None.

Explore Python conditionals and control flow to model real-world decisions, using if, if else, elif, and nested statements, then master loops with for and while, including break, continue, and pass.

Learn how the python if statement uses a condition to evaluate to true or false, with age checks, greater than and greater than or equal to, and indentation rules.

Explore the if else statement in Python, including its basic structure, true/false conditions, and how to handle two options or more with examples like age and country comparisons.

Learn how if, elif, and else control flow in Python, why order matters, and how top-to-bottom evaluation chooses the first true condition.

Explore python for loops and how to iterate over strings and lists, using descriptive loop variables, print results, compute lengths with len, and format outputs.

Explore how to use while loops in Python, compare for and while loops, avoid infinite loops, and follow a practical age example that increments until a condition becomes false.

Learn how Python functions create reusable blocks for code reusability, organize code, accept parameters, return results, and use built-in functions like print, len, type, and abs.

Define Python functions with def, parameters, and indentation to create reusable code and return results. Use docstrings and help to explain parameters and return values for future users.

Master pandas for data manipulation, wrangling, and analysis using series and dataframes. Learn to load data, clean missing values, perform groupby, indexing, time series, and basic visualizations with other libraries.

Learn to use pandas to load and explore csv data, handle common file location errors, and inspect data with shape, columns, head, tail, and sample.

Learn to create interactive data visualizations with Plotly, including histograms, bar charts, scatter plots, and line charts, and explore Plotly Express for visuals and dashboard integration with pandas and Streamlit.

Explore how Plotly for Python creates interactive data visuals, from scatter plots and bubble charts to dashboards, with zoom and other interactive features.

Explore how visualization charts reveal relationships using scatter plots and line plots, and learn when to use each to display two values or time-based trends.

Learn to build scatter plots with Plotly Express in pandas, using iris data to map sepal length vs petal length, color by species, and size by sepal width; explore facets.

Learn to create a basic line chart in Plotly Express by providing x and y lists and a title, then calling show.

Create an interactive line chart of Apple stock open price with dates on the x axis, using Plotly Express and pandas, converting the date column to datetime.

Visualize multiple line charts in a single figure by passing a list of column names like high, open, low, and close, revealing a legend and enabling simultaneous data comparison.

Explore advanced line chart techniques by preparing data with date-time types, filtering to Apple, Google, and Microsoft using pandas, and using faceting to compare multiple series.

Learn to create pie charts and bar charts with appropriate parameters, compare two or more categories, and decide when a bar chart better represents iris dataset length measurements.

Create pie charts with Plotly Express using the tips dataset to compare tips by gender and smoker status, and learn when to use donut charts and facets for multi-category data.

Aggregate data by species to compute average petal width, then visualize it with a Plotly Express bar chart of species on x axis and petal width on y axis.

Learn to visualize data distributions with histograms, violin plots, and box plots, comparing a continuous variable across categories and interpreting distribution, percentile spread, and trends.

Learn to create histograms with Plotly Express by passing a data frame (iris) and selecting an axis, adjusting bins, and adding color by species to explore distribution of sepal lengths.

Learn to create box plots and violin plots with Plotly Express from a data frame, visualize sepal width by species, and customize orientation and coloring for clear distribution insights.

Visualize hierarchical data using sunburst and treemap charts, exploring their differences and interactive features. Learn to build these charts and leverage animation to reveal data structures.

Learn to build a sunburst chart with Plotly Express, using pandas to load tips.csv, identify discrete and continuous variables, and define path and values for interactive visualization.

Create treemap charts in Python using Plotly and pandas by selecting a numeric, continuous value and a discrete path of categories such as dinner vs lunch and smoker status.

Learn to create scatter Mapbox charts in Plotly Express by mapping city latitude and longitude, sizing points by population, and adjusting zoom and map style for clarity.

Learn to build a choropleth map in Python with Plotly to visualize internet usage by country, filtered to 2016 with pandas and location mode as country names.