Learning iPython Notebook

Install Anaconda, a free python distribution from continuum analytics, with science, engineering, mathematics, and data analysis packages, and the python notebook for our lessons.

Launch the Anaconda Python notebook from the start menu, then open a browser to access notebooks; understand OS-specific default directories and how to copy notebooks to your home folder.

Install the Pandoc document converter using Anaconda's package manager, and follow cross-platform installation steps from John McFarland's Pandorica installing guide; we will use Pandoc in the final lesson.

Explore the Python notebook interface, create and manage notebooks, work with code and markdown cells, run and organize code with shortcuts, and document data analysis workflows.

Master using the IPython notebook display function to show multiple results, embed rich media with I-frame and images, and learn practical cell workflow and state management in notebooks.

Explore common tasks in the Python notebook, including shell commands, magic commands such as time and pre-run profiling, and inline plotting with matplotlib.

Open the Python notebook labeled project password generator, describe the problem, and implement valid and random_string using random and string modules, cover basic types, if clauses, and loops.

Create a function that generates an eight character, random password with at least one uppercase, one lowercase, one punctuation mark, and one digit, and explore Python arithmetic and boolean operations.

Explore basic Python data types, focusing on strings and lists; learn string quotes, indexing and slicing, common methods (upper, lower, title, replace, split, join), and mutability of lists and booleans.

Learn basic control flow in Python notebooks, including if, else, and elif, while and for loops, truthiness of data types, and iterating with slicing.

Master writing and reusing basic Python functions, from a simple double function to a quadratic formula for roots, and explore modules like math, random, and string for practical utilities.

Compare stochastic password generation with a deterministic approach in IPython notebook that builds and shuffles a password using upper, lower, digits, and punctuation, and validates it with set operations.

Open your python notebook project visualizing stock ticker data and follow along to build a stock ticker visualization. Explore Quando data service, json basics, pandas, and plotting for MSFC data.

Explore special cells in Python books that demonstrate scripting the creation of Python content and automatically generating and linking section headings from the agenda.

Visualize stock ticker data in an IPython notebook by downloading data with requests, using pandas for manipulation, and plotting simple charts similar to Google Finance, sourcing data from Quandl.

Explore Python comparison operators across numbers, strings, and tuples, demonstrate operator chaining, and show functions with positional and keyword arguments, defaults, and flexible argument ordering.

Explore Python dictionaries by mapping English to Spanish, indexing by keys to retrieve values, and using keys, values, and items to inspect, add, replace, or delete entries.

Learn how to create and inspect date and datetime objects, perform date arithmetic with timedelta, and manipulate data with pandas DataFrame.

Learn to work with data frames in iPython notebook by indexing columns, creating new columns with transformations, and plotting multiple functions using pandas and matplotlib.

Pull and visualize Microsoft stock data from Quandl, parse dates into a date time index, and compute daily log returns with a cumulative sum in Pandas from 1986 to 2014.

Explore parsing release dates, building human readable datetimes, and plotting stock returns around Windows 95, 98, and 2000, and around iPhone and iPad releases, using iPython notebook and pandas.

Review the iPython notebook concepts and build a slideshow of results, using the i-frame utility, future imports, and essential modules to control display and format data.

Learn to structure IPython notebooks as Reveal.js powered slides, using slides, fragments, and skip or notes markers, and export notebooks to standalone HTML slides viewable in a browser.

generate a slide show from a notebook by extracting headings as an agenda and hyperlinking it to the headings; split into top-level and sub slides using markdown.

Explore IPython notebook basics, including creating sections and slides, using keyboard shortcuts and toolbar commands, running cells, and leveraging shell and magic commands for inline plots.

Learn Python data types in IPython notebook, comparing mutable types (list, dict, set) with immutable ones (strings, numbers, date-time), and explore mutability through code examples and markdown slides.

Learn to manipulate strings in Python by building multi-line strings, cleaning text, and renaming files with artist, album, and song metadata using split, replace, strip, join, and enumerate.

Learn to work with Python types by generating 1000 random numbers from 0 to 99, then compute mean, median, and mode with lists and dictionaries, including date parsing for holidays.

Explore calculating the worst day of the year relative to upcoming holidays in a iPython Notebook, using date arithmetic and time deltas, then identify three-day weekends.

Review arithmetic operations in Python, including addition, subtraction, multiplication, division, modulo, and exponentiation, and explore boolean logic with and, or, not and truthy values like empty strings and lists.

Define simple functions and explore calling conventions using keyword arguments in a Python notebook. Embed an i-frame with a frame function that defaults width and height.

Compare methods and functions in Python, noting the implicit object argument in methods. Use help and math module to learn about functions, constants, and list methods like append and reverse.

Learn to access module functions with direct import or module prefix, using math.sqrt and ascii lowercase constants; import random.choice, and explore submodules used for plotting in Anaconda.

Learn how the string module provides ASCII lowercase, uppercase, digits, and punctuation constants, join and print them to build a password, and use the random module’s functions like choice.

Fetch data from the web using the requests library, inspect JSON responses, and use BeautifulSoup to extract unique Python standard library module names from a webpage.

Explore NumPy's numeric and scientific computation tools, including linspace for evenly spaced samples, broadcasting on arrays, and plotting sin, cos, and tan with matplotlib.

Visualize computations with matplotlib in IPython notebooks, plotting factorial versus a linear function, then customize graphs with markers, colors, and axis limits to explore XOR neural network outputs.

Explore using pandas and dataframes in a Python notebook to fetch weather data, build a datetime index, slice by time, and convert Kelvin to Celsius and Fahrenheit for plotting.

Convert the notebook into slides with nbconvert and post serve, then review Python basics, functions, scope, and key libraries like matplotlib and pandas.