def main(file_path):
df = FileLoader.load(file_path)
FileLoader.display(df, 10)
network = create_netwok(df)
network.summary_in_file(f"{file_path}_net_summary")
client = openrouteservice.Client(key=APIKEY)
output = network.calc_network(client)
network.tours[0].create_map()
# #todo : faire un df qui fait un calcul sur le df et mettre en 2eme sheet
output.to_excel("output.xlsx")
from FileLoader import FileLoader
loader = FileLoader()
data = loader.load("../resources/athlete_events.csv")
#print(data)
loader.display(data, -2)
from FileLoader import FileLoader loader = FileLoader() df = FileLoader.load(loader, "athlete_events.csv") loader.display(df, -2)
#!/usr/bin/env python3
from FileLoader import FileLoader
loader = FileLoader()
data = loader.load('../resources/athlete_events.csv')
loader.display(data, 12)
from FileLoader import FileLoader
fl = FileLoader()
df = fl.load("../resources/athlete_events.csv")
fl.display(df, -5)
fl.display(df, 5)
#!/usr/bin/env python
import importlib.util
import sys
sys.path.append('../ex00')
from FileLoader import FileLoader
def proportionBySport(df, year, sport, sex):
p = df[(df.Year == year) & (df.Sex == sex)]\
.drop_duplicates(subset=['Name', 'ID']).shape[0]
p_s = df[(df.Year == year) & (df.Sex == sex) & (df.Sport == sport)]\
.drop_duplicates(subset=['Name', 'ID']).shape[0]
return p_s / p
if __name__ == '__main__':
ld = FileLoader()
df = ld.load('../resources/athlete_events.csv')
ld.display(df, 3)
print(proportionBySport(df, 2004, 'Tennis', 'F'))
from FileLoader import FileLoader
fl = FileLoader()
data = fl.load("../athlete_events.csv")
fl.display(data, 10)
fl.display(data, -10)
from FileLoader import FileLoader
file = FileLoader()
df = file.load('../resources/athlete_events.csv')
print('5 first rows\n')
file.display(df, 5)
print('\n\n5 last rows')
file.display(df, -5)
# **************************************************************************** #
# #
# ::: :::::::: #
# test.py :+: :+: :+: #
# +:+ +:+ +:+ #
# By: lboukrou <*****@*****.**> +#+ +:+ +#+ #
# +#+#+#+#+#+ +#+ #
# Created: 2020/04/24 23:43:58 by lamia #+# #+# #
# Updated: 2020/06/20 20:02:38 by lboukrou ### ########.fr #
# #
# **************************************************************************** #
import pandas as pd
from FileLoader import FileLoader
df = FileLoader()
ds_path = df.load("../resources/athlete_events.csv")
print(type(ds_path))
n = -15
df.display(ds_path, n)
# open(ds_path
# print(ds_path)
from FileLoader import FileLoader
def youngestFellah(df, year):
df = df[df['Year'] == year]
m = df[df['Sex'] == 'M'].nsmallest(1, 'Age')
f = df[df['Sex'] == 'F'].nsmallest(1, 'Age')
return {'m': m.iloc[0]['Age'], 'f': f.iloc[0]['Age']}
if __name__ == "__main__":
df = FileLoader.load("../data/athlete_events.csv")
FileLoader.display(df)
dic = youngestFellah(df, 2004)
print(dic)
from FileLoader import FileLoader
def youngestFellah(df, year):
ol_year = df[df['Year'].eq(year)]
ol_year_female = ol_year[ol_year['Sex'].eq('F')]
ol_year_male = ol_year[ol_year['Sex'].eq('M')]
youngest_ol = {
'Female' : ol_year_female.Age.min(),
'Male' : ol_year_male.Age.min(),
}
# OR :
#youngest_ol = {'f': df['Age'][(df['Sex'] == 'F') & (df['Year'] == year)].min(),
# 'm': df['Age'][(df['Sex'] == 'M') & (df['Year'] == year)].min()}
print (youngest_ol)
# age, sex, year
loader = FileLoader()
data = loader.load("../resources/athlete_events.csv")
loader.display(data, 10)
youngestFellah(data, 2004)
from FileLoader import FileLoader
import numpy as np
import pandas as pd
path = "./athlete_events.csv"
n = 10
print(path, n)
MY_DataFrame = FileLoader()
open = MY_DataFrame.load(path)
MY_DataFrame.display(open, n)
print(open.columns)
def YoungestFellah(open):
youngest = {}
youngest['m'] = open.sort_values(['Sex','Age'], ascending=[False, True])['Age'].iloc[0]
youngest['f'] = open.sort_values(['Sex','Age'], ascending=[True, True])['Age'].iloc[0]
print(youngest)
return youngest
def ProportionBySport(df, year, sport, gender = 'M'):
good_year = df[df.Year == year]
# print(good_year)
good_gender = good_year[good_year.Sex == gender]
# print(good_gender)
no_duplicate = good_gender.drop_duplicates('Name')
# print(no_duplicate)
proportion = no_duplicate['Sport'].value_counts(normalize = True)
value = proportion[sport]
print(value)
return value
import pandas as pd from FileLoader import FileLoader fl = FileLoader() path2 = r"C:\Users\Gabriel\Desktop\Mes documents - Google Drive\DATA\19\day04\athlete_events.csv" df = fl.load(path2) # csv2 = pd.read_csv(r"C:\Users\Gabriel\Desktop\Mes documents - Google Drive\DATA\19\day04\athlete_events.csv") fl.display(df, -10)
