• variables are names for values
• in python the "=" symbol assigns the value on the right to the name on the left, e.g.,               my_variable = "my variable's value"
• the variable is created when a value is assigned to it
• variable names
  • can only contain letters, digits, and underscores
  • cannot start with a digit
  • should be descriptive

variables

• int: an integer, e.g., 52 or -52
• string: a set of characters; specified within single or double quotes, e.g., 'i am a string' or "my_string"
• float: a floating-point number; specified with a decimal point, e.g., 52.001 or -52.001

... plus more we'll get to later

data types

• functions (also called methods) are predefined actions
• like variables, each function has a name
• unlike variables, functions can take arguments (also called parameters)
• you can call a function by invoking it by name and passing it zero or more arguments in parentheses, e.g., len('my_string')

functions

• python libraries are self-contained groups of python modules that provide additional functionality to the main python language.
• many libraries are open source, meaning you can freely use, look at, and repurpose them. 
• our anaconda environment will help us import python libraries into our python programs, allowing us to use the libraries' classes, functions, and variables in our notebooks transparently

libraries

• tabular data is information in table form
• proprietary programs like excel and numbers create table-like data, but they frequently include styling and extra metadata that can confuse and bog down programs
• it is best to use open, plaintext formats for your data, including tabular data.
• the best examples are CSV and TSV files, which stand for Comma-Separated Value and Tab-Separated Value, respectively.

tabular data

• use DataFrame.iloc() to select data by index position
• use DataFrame.loc() to select data by (column/row) label
• when in doubt, use help(data.loc) and help(data.iloc) to check

selecting data in data frames