For today's warmup, you will be working with National Basketball Association data. This data was acquired using the nba_api package. You will not have to use the API, rather, you will work accessing data stored in dictionaries created from the data returned from the API calls.

from nba_api.stats.endpoints import leaguestandings
import pickle

with open('data/nba_data.p', 'rb') as read_file:
    team_dicts = pickle.load(read_file)

The team_dicts object is a list, each element of which is a dictionary associated with a different NBA team's stats from the 2019-2020 season.

# Type of the outer object
type(team_dicts)

list

# Type of the first inner object
type(team_dicts[0])

dict

The first team listed, i.e. the team dictionary associated with index 0 of team_dicts, is the LA Lakers.

lakers = team_dicts[0]

# Take a look at the content of the lakers dictionary
lakers

{'LeagueID': '00',
 'SeasonID': '22019',
 'TeamID': 1610612747,
 'TeamCity': 'Los Angeles',
 'TeamName': 'Lakers',
 'Conference': 'West',
 'ConferenceRecord': '36-10',
 'PlayoffRank': 1,
 'ClinchIndicator': ' - w',
 'Division': 'Pacific',
 'DivisionRecord': '10-3 ',
 'DivisionRank': 1,
 'WINS': 52,
 'LOSSES': 19,
 'WinPCT': 0.732,
 'LeagueRank': 0,
 'Record': '52-19',
 'HOME': '25-10',
 'ROAD': '27-9 ',
 'L10': '4-6  ',
 'Last10Home': '7-3  ',
 'Last10Road': '6-4  ',
 'OT': '2-0  ',
 'ThreePTSOrLess': '7-3  ',
 'TenPTSOrMore': '25-11',
 'LongHomeStreak': 6,
 'strLongHomeStreak': 'W 6',
 'LongRoadStreak': 14,
 'strLongRoadStreak': 'W 14',
 'LongWinStreak': 10,
 'LongLossStreak': 4,
 'CurrentHomeStreak': -1,
 'strCurrentHomeStreak': 'L 1',
 'CurrentRoadStreak': -2,
 'strCurrentRoadStreak': 'L 2',
 'CurrentStreak': -1,
 'strCurrentStreak': 'L 1',
 'ConferenceGamesBack': 0.0,
 'DivisionGamesBack': 0.0,
 'ClinchedConferenceTitle': 1,
 'ClinchedDivisionTitle': 1,
 'ClinchedPlayoffBirth': 1,
 'EliminatedConference': 0,
 'EliminatedDivision': 0,
 'AheadAtHalf': '39-8 ',
 'BehindAtHalf': '11-10',
 'TiedAtHalf': '2-1  ',
 'AheadAtThird': '43-0 ',
 'BehindAtThird': '7-16 ',
 'TiedAtThird': '2-3  ',
 'Score100PTS': '48-14',
 'OppScore100PTS': '36-19',
 'OppOver500': '22-14',
 'LeadInFGPCT': '42-4 ',
 'LeadInReb': '38-7 ',
 'FewerTurnovers': '33-5 ',
 'PointsPG': 113.4,
 'OppPointsPG': 107.6,
 'DiffPointsPG': 5.8,
 'vsEast': '16-9',
 'vsAtlantic': '5-5',
 'vsCentral': '4-3',
 'vsSoutheast': '7-1',
 'vsWest': '36-10',
 'vsNorthwest': '12-3',
 'vsPacific': '10-3',
 'vsSouthwest': '14-4',
 'Jan': '10-4',
 'Feb': '9-2',
 'Mar': '4-1',
 'Apr': None,
 'May': None,
 'Jun': None,
 'Jul': '1-0',
 'Aug': '2-5',
 'Sep': None,
 'Oct': '3-1',
 'Nov': '14-1',
 'Dec': '9-5',
 'PreAS': '41-12',
 'PostAS': '11-7'}

Dictionaries are made of key:value pairs. You can see the keys of a dictionary using the .keys() method.

# List all the keys of the lakers dictionary
# your code here

You can see the values of a dictionary using the .values() method.

# List all the values of the lakers dictionary
# your code here

Task 1

# Assign the Laker's Division Rank to the variable division rank

division_rank = None

Task 2

Now using the team_dict list, loop through each team dictionary and print out the team name and the division rank for the 2019-20. The output of the first round of the loop should look like:

Lakers 1

# Your code here

Task 3

Next, let's create a set of dictionaries nested within a dictionary.

Each dictionary will represent a division, and each value will be another dictionary.

The second level dictionaries will have team names as keys, and division rank as values.

So, one key:value pair of the outer dictionary would look like so:

'Pacific': {'Lakers': 1, 'Clippers': 2, 'Suns': 3, 'Kings': 4, 'Warriors': 5}

# First step, get a list of all the division names
division_names = [team['Division'] for team in team_dicts]

# What you see above is a list comprehension.  It does the same thing as below.
division_names = []

for team in team_dicts:
    division_names.append(team['Division'])

Once you get the hang of them, list comprehensions are amazing. They are visually concise and faster than explicit for loops.

# We can use set to get the unique values
division_names = list(set(division_names))
division_names

['Central', 'Southwest', 'Southeast', 'Atlantic', 'Northwest', 'Pacific']

Now your turn. Populate a dictionary called division_dictionary that has the division ranks of each team in each of the 6 divisions.

Hint: you may need two for loops, one nested within the other. A dictionary for each division should be created between the two for loops.

# Your code here
division_dictionary = None

Bonus

The original API call is sort of messy. With the .get_dict() method associated with the nba_api package, we are left with a nested series of lists and dictionary. To get the data, we need to parse it with a series of keys and list indices.

The bonus task asks you to recreate the exercise from task 3 above, starting with the new_response object below. I.E., create a division dictionary for the 2018-19 season.

If you are feeling adventurous, code a function that takes the response as an argument, and returns the dicitonary.

The first key: value pair should be:

'Central': {'Bucks': 1, 'Pacers': 2, 'Pistons': 3, 'Bulls': 4, 'Cavaliers': 5},

new_response= leaguestandings.LeagueStandings(season='2018-19').get_dict()

# Your code here