RoadMap Module 4

[hfboyce]

Organized by exercise (will have practice problems inbetween numbers )

1. Python data types (float/int/bool/string/NoneType)

   • Talk about how we've seen numbers (int and floats) and how we put words and characters in quotations (single, double, 3 single quotes) which are strings. Also talk about True/False = boolean
   • NaN in dataframe-> What are these?
   • Introduce type() function to identify them
   • Introduce .lower() , len()
   • casting

2. Python Data Structures: Lists and Tuples and Sets

   • Explain str.split() segue into lists (from output)
   • append(), values in a list ( lists can have lists as entries etc)
   • Explain what immutable means
   • Compare and contrast
   • Create a dataframe from list

3. Python Data Structures: Dictionaries

   • Define dictionary
   • Use actual dictionary as analogy (word- definition == Key- value)
   • Features, how to obtain key and value
   • Create a dataframe from dictionary (the json idea is in the assignment here)

4. Dataframe/column type and dtypes

   • Start by taking type of a data frame
   • Take type of a column = panda series
   • double square brackets vs signle square brackets
   • Dataframe = made up of panda series
   • Introduce column dtypes
   • dtypes function
   • What's in each cell for column dtypes.
   • Diagram

5. Operations with different data types

   • Different operators
   • Comparison Operators with booleans
   • What happens when we add strings?
   • What happens when we divide 2 ints?
   • Can we add different types together?
   • What operations work on each?
   • Can this logic be carried over to operations with columns types? segue to next section

6. Operations with Columns

   • Splitting up a column of dtype object
   • default column dtype
   • .astype()
   • taking mean, sum, max, min of column dtypes (what happens?) (.mean(axis=1) vs .mean(axis=0))
   • changing column dtypes
   • dtype argument in pd.read_csv()