Lec 11 Data

• Containers (list, tuples)
  • Store references to objects
    • More space, slower
    • Can have varying types
• Flat sequences
  • Raw values in consecutive memory locations
  • Elements same type
  • Fast iteration
    • Good for math, data processing
  • Efficient multidimensional data

Arrays:

from array import array
a = array("l", [1, 2, 3])

print(type(a))
print(a[0])

• Numpy arrays: - Container format for passing arrays between libraries - Can be reshaped with shape() function
  • When added together, actually adds instead of concatenating

import numpy as np

v1 = np.array([
    [1, 2, 3],
    [4, 5, 6]
])

print(v1)
print("v1 Python type:", type(v1))
print("NumPy array type:", v1.dtype)

• Get the sum of n number of dice rolls:

  • Hint: generate 20 random ints between a lower and upper bound

• Pandas:

  • For data analysis and visualization
  • Data types:
    • Series: 1d arrays of any data type
    • DataFrame: 2d labelled array of any data type