marathon-python | Python client library for Mesos Marathon 's REST API | Job Orchestrator library

int(marathon=round(1, members[loop].distance/26.22))

marathon = int(round(1, members[loop].distance / 26.22))

# A marathon is 42.195 kilometers, or 26 miles, 385 yards:
MILES_PER_MARATHON =

city = ['City', '________________', 'Boston', 'Montreal', 'Toronto', 'Detroit', 'Mackinaw City', 'Sault Ste. Marie', 'Wawa', 'Marathon', 'Thunder Bay', 'Duluth', 'Minneapolis', 'Fargo', 'Winnipeg', 'Regina', 'Swift Current', 'Medicine Hat'

cols = ['5K', '10K', '15K', '20K', 'Half']
df[cols] = df[cols].interpolate(limit=2, limit_direction='backward', axis=1)

#       Age M/F Country      5K     10K     15K     20K    Half  Official Time
# 2323   38   M     CHI  1300.0  2568.0

import sys
import math


def runner_normalisation(t):
    new_times = []
    total_times = []
    for x in t:
        new_times.append(x.split(":"))
    #print(new_times)
    for x in range(len(new_times)):
        for i in range(len(new_t

class Status(aenum.IntEnum):
    FINISHED = 0
    DID_NOT_FINISH = -1
    DID_NOT_ATTEND = -2
    DISQUALIFIED = -3

class Placement(aenum.NamedTuple):
    race = 0, 'name of race'
    status = 1, 'finished race?'
    placement = 2, 'posit

def normalize(d):
    return pd.json_normalize(d, 'sites', ['sport_key', 'sport_nice', 'home_team'])\
           .assign(teams=[d['teams']]*len(d['sites']))


df = pd.concat([normalize(d) for d in data], ignore_index=True)

df

|   Index | xaxis   |   yaxis1 |   xayis2 |
|--------:|:--------|---------:|---------:|
|       0 | A       |       30 |     1985 |
|       1 | A       |       29 |     2002 |
|       2 | B       |       21 |   

import ast

f = lambda x: '|'.join(y['name'] for y in ast.literal_eval(x))
df['Activities'] = df['Activities'].apply(f)
print (df)
                Activities
0    Football|Rugby|Hockey
1            Chess|Fencing
2  Rugby|Marathon|Baseball