from datascience import *
import numpy as np

Arrays¶

a = make_array(5, 6, 7, 8)

a + 1

a * 2

a + a

(a + a).item(2)

sum(a)

len(a)

sum(a) / len(a)

np.average(a)

Columns of Tables are Arrays¶

nba = Table.read_table('nba_salaries.csv').where('season', 2020)
warriors = nba.where('team', 'Golden State Warriors')

warriors

warriors.select('salary')

warriors.column('salary')

What was the average salary among players on the warriors in the 2020 season?

np.average(warriors.column('salary'))

celtics = nba.where('team', 'Boston Celtics')

np.average(warriors.column('salary')) - np.average(celtics.column('salary'))

make_array(0, 1, 2, 3, 4, 5, 6)

np.arange(7)

np.arange(5, 11)

np.arange(0, 20, 2)

np.arange(0, 21, 2)

np.arange(0, 1, 0.1)

streets = make_array('Bancroft', 'Durant', 'Channing', 'Haste')
streets

Table()

southside = Table().with_column('Streets', streets)
southside

southside.with_column('Blocks from campus', np.arange(4))

Table().with_columns(
    'Streets', streets,
    'Blocks from campus', np.arange(4)
)

du_bois = Table.read_table('du_bois.csv')
du_bois

du_bois.column('ACTUAL AVERAGE')

du_bois.column('FOOD')

du_bois.column('ACTUAL AVERAGE') * du_bois.column('FOOD')

food_dollars = du_bois.column('ACTUAL AVERAGE') * du_bois.column('FOOD')
du_bois.with_columns('Food $', food_dollars)

du_bois

du_bois = du_bois.with_columns('Food $', food_dollars)
du_bois

du_bois.select('CLASS', 'ACTUAL AVERAGE', 'FOOD', 'Food $')

du_bois.labels

du_bois.num_rows

du_bois.num_columns