Lists

  • create lists
  • concatenating lists
  • edit lists using list methods (e.g. pop(), append(), insert(), etc.)
  • determine list length (i.e., how many elements are in a list)
  • access items in a list using indexes
  • storing items in a list in a variable
  • creating a slice of a list
  • converting a string into a list using .split()
  • the difference between a tuple and a list

Files

  • open a file to read or write from it (and close it when finished)
  • read a file one line at a time with a for loop or .readline()
  • write to a file
  • use the newline character, "\n", correctly
  • read a csv file (and handling the header)
  • edit a csv file
  • create and writing a csv file

Dictionaries

  • create an empty dictionary
  • add a key-value pair to a dictionary
  • modify the value in a key-value pair
  • determine dictionary length (i.e., how many key-value pairs are in a dictionary)
  • use dictionaries to analyze files
  • retrieve all keys or values at one time
  • retrieve keys or values one at a time
  • retrieve all key-value pairs at once
  • use dictionaries to solve data science problems

NumPy

  • create an array from a list
  • create an array from a range
  • create a array of all zeros of any length
  • create an array of random integers of any length
  • access items in an array using indexes
  • create multi-dimensional arrays
  • modify the shape of multi-dimensional arrays
  • make a slice of an array (1D and 2D)
  • use broadcasting in arrays

Matplotlib

  • create visualizations of data (e.g., line plots, histograms, and bar charts)
  • edit the color, style, and shape of the visualizations
  • add/edit the title, xlabel, ylabel, figure name, xticks, yticks, figure size
  • display multiple visualizations on a single figure
  • read data from a file and create a visualization of the data (or a subset of the data)

Pandas

  • create dataframes from lists
  • create dataframes from existing dataframes
  • create dataframes from csv files (and edit column names)
  • write a dataframe to a csv file
  • sort dataframes by column
  • make a slice of a dataframe
  • use .head() and .tail() correctly
  • use .unique() correctly
  • access and edit data points using indexes and columns (and dataframe.loc[])
  • create visualizations of data in dataframes

Putting it all together

  • Create Data (if it doesn’t already exist)
  • Access Data (that has already been created)
  • Manipulate or Munge Data (that has already been input)
  • Analyze Data (that has already been munged)
  • Present Data (to help people understand it)

Study Problems

  1. Using data from ebird_year.csv, we can create a list of data that groups the total number of checklists submitted each month of 2022: 
    checklists_per_month = [('Jan', 56), ('Feb', 10), ('Mar', 33), ('Apr', 37), ('May', 34), ('June', 21), ('July', 4), ('Aug', 4), ('Sept', 4), ('Oct', 22), ('Nov', 7)]

    Use this list to create this bar chart: Figure 1