Sportowe/1_zaj.ipynb at master

import pandas as pd
import numpy as np

scores_data = pd.read_csv('dane/scores.csv')
scores_data

	Wk	Day	Date	Time	Home	Score	Away	Attendance	Venue	Referee	Match Report	Notes
0	1.0	Fri	2020-08-21	18:00	Zagłębie Lubin	2–1	Lech Poznań	3968.0	Stadion Zagłębia Lubin	Bartosz Frankowski	Match Report	NaN
1	1.0	Sat	2020-08-22	15:00	Cracovia	2–1	Pogoń Szczecin	4053.0	Stadion Cracovii	Paweł Raczkowski	Match Report	NaN
2	1.0	Sat	2020-08-22	17:30	Śląsk Wrocław	2–0	Piast Gliwice	5259.0	Stadion Miejski	Wojciech Myć	Match Report	NaN
3	1.0	Sat	2020-08-22	20:00	RKS Raków	1–2	Legia Warsaw	1985.0	Stadion GKS-u	Jarosław Przybył	Match Report	NaN
4	1.0	Sun	2020-08-23	12:30	Wisła Płock	1–1	Stal Mielec	1318.0	Stadion im. Kazimierza Górskiego	Sebastian Jarzębak	Match Report	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
275	30.0	Sun	2021-05-16	17:30	Pogoń Szczecin	1–3	RKS Raków	NaN	Stadion Miejski im. Floriana Krygiera	Sebastian Krasny	Match Report	NaN
276	30.0	Sun	2021-05-16	17:30	Piast Gliwice	2–3	Wisła Kraków	NaN	Stadion Miejski	Mariusz Zlotek	Match Report	NaN
277	30.0	Sun	2021-05-16	17:30	Cracovia	0–1	Warta Poznań	3670.0	Stadion Cracovii	Pawel Malec	Match Report	NaN
278	30.0	Sun	2021-05-16	17:30	Śląsk Wrocław	1–1	Stal Mielec	NaN	Stadion Miejski	Tomasz Kwiatkowski	Match Report	NaN
279	30.0	Sun	2021-05-16	17:30	Wisła Płock	4–0	Zagłębie Lubin	NaN	Stadion im. Kazimierza Górskiego	Paweł Raczkowski	Match Report	NaN

280 rows × 12 columns


scores_data = scores_data[['Day', 'Date', 'Score', 'Attendance', 'Home', 'Away']]

scores_data = scores_data.dropna()
len(scores_data)

previous_won = []
win_history = {}

for idx, row in scores_data.iterrows():
    score = row["Score"].split("–")
    won = score[0] > score[1]
    previous_won.append(win_history.get(row["Home"], False))
    win_history[row["Home"]] = won
    win_history[row["Away"]] = not won

previous_won

[False,
 False,
 False,
 False,
 False,
 False,
 False,
 False,
 True,
 True,
 True,
 True,
 True,
 False,
 False,
 False,
 True,
 True,
 True,
 False,
 True,
 True,
 True,
 True,
 True,
 True,
 False,
 False,
 True,
 False,
 True,
 True,
 True,
 True,
 True,
 True,
 True,
 False,
 True,
 False,
 False,
 True,
 False,
 True,
 False]

# PIA 	Piast Gliwice Simple gold crown.svg 	1
# LEG 	Legia Warszawa 	2
# LGD 	Lechia Gdańsk Simple gold cup.svg 	3
# CRA 	Cracovia 	4
# JAG 	Jagielscores_datalonia Białystok 	5
# ZLU 	Zagłębie Lubin 	6
# POG 	Pogoń Szczecin 	7
# LPO 	Lech Poznań 	8
# WKR 	Wisła Kraków 	9
# KOR 	Korona Kielce 	10
# GZA 	Górnik Zabrze 	11
# ARK 	Arka Gdynia 	12
# ŚLĄ 	Śląsk Wrocław 	13
# WPŁ 	Wisła Płock 	14 
win_table_20 = [
    "Piast Gliwice",
    "Legia Warszawa",
    "Lechia Gdańsk",
    "Cracovia",
    "Jagiellonia Białystok",
    "Zagłębie Lubin",
    "Pogoń Szczecin",
    "Lech Poznań",
    "Wisła Kraków",
    "Korona Kielce",
    "Górnik Zabrze",
    "Arka Gdynia",
    "Śląsk Wrocław",
    "Wisła Płock",
]

win_table_4 = [
    "Piast Gliwice",
    "Legia Warszawa",
    "Lechia Gdańsk",
    "Cracovia"
]

scores_data['Date'][0]

'2020-08-21'

!pip3 install wolframalpha

Defaulting to user installation because normal site-packages is not writeable
Collecting wolframalpha
  Downloading wolframalpha-5.0.0-py3-none-any.whl (7.5 kB)
Collecting jaraco.context
  Downloading jaraco.context-4.1.2-py3-none-any.whl (4.7 kB)
Collecting more-itertools
  Downloading more_itertools-8.14.0-py3-none-any.whl (52 kB)
Collecting xmltodict
  Downloading xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)
Installing collected packages: xmltodict, more-itertools, jaraco.context, wolframalpha
Successfully installed jaraco.context-4.1.2 more-itertools-8.14.0 wolframalpha-5.0.0 xmltodict-0.13.0

WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: Ignoring invalid distribution -ip (c:\users\mrpol\appdata\roaming\python\python39\site-packages)
WARNING: You are using pip version 21.1.1; however, version 22.2.2 is available.
You should consider upgrading via the 'c:\Program Files\Python39\python.exe -m pip install --upgrade pip' command.

import wolframalpha

import time
import re
def check_weather(date: str, city: str = 'Warsaw') -> int:
    api_id = '5KAEPX-EXX246XAW7'
    question = 'Weather ' + date + ' ' + city
    client = wolframalpha.Client(api_id)
    # time.sleep(1)
    res = client.query(question)
    a = str(res)
    result = re.findall(r'average: \d+ °C', a)[0]
    temp = re.search(r'\d+', result).group()
    return int(temp)
check_weather('2020-08-22')

scores_data['Day'].unique()
days = {
    'Mon': 1,
    'Tue': 2,
    'Wed': 3,
    'Thu': 4,
    'Fri': 5,
    'Sat': 6,
    'Sun': 7,
    'nan': 0,
}


days_num = []
for d in scores_data['Day']:
    days_num.append(days[d])
is_home_top = []
is_home_top_4 = []
for d in scores_data['Home']:
    is_home_top.append(int(d in win_table_20))
    is_home_top_4.append(int(d in win_table_4))
is_away_top = []
is_away_top_4 = []
for d in scores_data['Away']:
    is_away_top.append(int(d in win_table_20))
    is_away_top_4.append(int(d in win_table_4))
    

# weather = []
# for d in scores_data['Date'][-6:]:
#     temp = check_weather(d)
#     weather.append(temp)
attendedce = [x for x in scores_data['Attendance']]
#weather

weather = ['23', '23', '23', '23', '20', '20', '20', '19', '17', '17', '18', '18', '18', '17', '17', '17', '14', '14', '16', '16', '16', '17', '17', '18', '12', '12', '12', '12', '12', '13', '13', '15', '19', '19', '19', '19', '19', '11', '14', '14', '17', '17', '18', '14', '14']

len(weather), len(scores_data['Date'])

(45, 45)

final_data = np.array([days_num, is_home_top, is_away_top, weather, attendedce])
final_data

array([['5', '6', '6', '6', '7', '7', '7', '1', '5', '5', '6', '6', '6',
        '7', '7', '7', '5', '5', '6', '6', '6', '7', '7', '1', '5', '5',
        '6', '6', '6', '7', '7', '1', '5', '5', '6', '6', '6', '1', '5',
        '5', '6', '6', '7', '7', '7'],
       ['1', '1', '1', '0', '1', '0', '1', '0', '1', '0', '1', '1', '0',
        '1', '0', '1', '0', '1', '1', '0', '1', '1', '1', '0', '0', '1',
        '1', '1', '0', '1', '1', '1', '1', '1', '1', '1', '0', '0', '0',
        '1', '0', '1', '1', '1', '1'],
       ['1', '1', '1', '0', '0', '1', '0', '1', '0', '1', '0', '1', '0',
        '1', '1', '1', '0', '0', '0', '1', '1', '1', '1', '1', '0', '1',
        '0', '1', '1', '1', '0', '0', '0', '1', '0', '0', '1', '1', '1',
        '1', '0', '1', '1', '1', '0'],
       ['23', '23', '23', '23', '20', '20', '20', '19', '17', '17', '18',
        '18', '18', '17', '17', '17', '14', '14', '16', '16', '16', '17',
        '17', '18', '12', '12', '12', '12', '12', '13', '13', '15', '19',
        '19', '19', '19', '19', '11', '14', '14', '17', '17', '18', '14',
        '14'],
       ['3968.0', '4053.0', '5259.0', '1985.0', '1318.0', '2381.0',
        '7368.0', '5070.0', '2486.0', '3420.0', '5424.0', '5044.0',
        '10000.0', '2578.0', '4308.0', '8226.0', '5065.0', '1500.0',
        '5439.0', '1872.0', '11535.0', '1877.0', '10270.0', '1675.0',
        '4397.0', '7007.0', '4507.0', '1821.0', '11000.0', '2533.0',
        '17546.0', '2649.0', '1411.0', '12492.0', '5127.0', '4877.0',
        '5881.0', '2714.0', '1631.0', '6438.0', '2987.0', '2882.0',
        '3215.0', '4890.0', '3670.0']], dtype='<U32')

import matplotlib.pyplot as plt

weather = [float(t) for t in weather]

plt.plot(weather, attendedce, 'bo')

[<matplotlib.lines.Line2D at 0x200ef599d00>]

r = np.corrcoef(np.array(weather), np.array(attendedce))
r

array([[ 1.        , -0.11323007],
       [-0.11323007,  1.        ]])

import matplotlib.pyplot as plt

plt.plot(days_num, attendedce, 'bo')

[<matplotlib.lines.Line2D at 0x200ef7433a0>]

days_num = [float(x) for x in days_num]
r = np.corrcoef(np.array(days_num), np.array(attendedce))
r

array([[1.       , 0.1964238],
       [0.1964238, 1.       ]])

import matplotlib.pyplot as plt

plt.plot(is_home_top, attendedce, 'bo')

[<matplotlib.lines.Line2D at 0x7feb0ee77a00>]

import matplotlib.pyplot as plt

plt.plot(is_home_top_4, attendedce, 'bo')

[<matplotlib.lines.Line2D at 0x200f07a6ac0>]

plt.plot(is_away_top, attendedce, 'bo')

[<matplotlib.lines.Line2D at 0x7feb0ee5edf0>]

plt.plot(is_away_top_4, attendedce, 'bo')

[<matplotlib.lines.Line2D at 0x200f07dcc10>]

is_home_and_away_top = []
for i, j in zip(is_home_top, is_away_top):
    is_home_and_away_top.append(i and j)

plt.plot(is_home_and_away_top, attendedce, 'bo')

[<matplotlib.lines.Line2D at 0x200f14b06a0>]

temp = [float(x) for x in is_home_and_away_top]
r = np.corrcoef(np.array(temp), np.array(attendedce))
r

array([[1.       , 0.1384707],
       [0.1384707, 1.       ]])

plt.plot(previous_won, attendedce, 'bo')

[<matplotlib.lines.Line2D at 0x200f58aaa60>]

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