Day 1 Recap

Recap of Day 1

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• Running and Quitting
  • Start Jupyter Lab from the “Anaconda Prompt” by typing jupyter lab
  • Alternatively you can start Jupyter Lab from the Anaconda Navigator
  • Jupyter Lab will start in your default web browser
  • Jupyter Lab will open in the directory where you started it
• Variables
  • Variable Names
    • can only contain letters, digits, and underscore _ (typically used to separate words in long variable names)
    • cannot start with a digit
    • are case sensitive (age, Age and AGE are three different variables)
    • should be descriptive and meaningful
  • Variables must be assigned a value before they can be used
  • We can use indexing to access individual elements of a string
    • hello[0] will return h
  • We can use slicing to access a range of elements in a string
    • hello[0:3] will return hel
• Types and Type Conversions
  • Every value in Python has a type
  • We can use the type() function to find out the type of a value
  • Common types are int, float, str
  • We can convert between types using the int(), float(), and str() functions
  • We can use the + operator to concatenate strings or add numbers
  • Not all operators work with all types
• Libraries
  • Libraries are collections of functions that extend the capabilities of Python
  • There are a number of useful libraries that come with python
  • We can import a library using the import statement
  • We can use help() to get information about a library or a function
  • We can use import ... as ... to give a library a shorter alias
• Reading Tabular Data
  • We can use the pandas library to read tabular data
  • We can use the read_csv() function to read a CSV file
• Dataframes
  • We can access specific elements of a DataFrame using the iloc method
    • data.iloc[0, 0] will return the element in the first row and first column
    • data.iloc[0, :] will return the first row
    • data.iloc[:, 0] will return the first column
    • data.iloc[0:2, 0:2] will return the first two rows and columns
• Plotting
  • We can use the matplotlib library to create plots
    • plt.plot() will create a line plot
    • plt.scatter() will create a scatter plot
  • We can use the xlabel(), ylabel(), and title() functions to add labels to the plot
  • We can use the show() function to display the plot