Develop Reference

How to scrape this football page?

https://fbref.com/en/partidas/25d5b9bd/Coritiba-Cuiaba-2022Julho25-Serie-A
I wanna scrape the Team Stats, such as Possession and Shots on Target, also whats below like Fouls, Corners...
What I have now is very over complicated code, basically stripping and splitting multiple times this string to grab the values I want.
#getting a general info dataframe with all matches
championship_url = 'https://fbref.com/en/comps/24/1495/schedule/2016-Serie-A-Scores-and-Fixtures'
data = requests.get(URL)
time.sleep(3)
matches = pd.read_html(data.text, match="Resultados e Calendários")[0]
#putting stats info in each match entry (this is an example match to test)
match_url = 'https://fbref.com/en/partidas/25d5b9bd/Coritiba-Cuiaba-2022Julho25-Serie-A'
data = requests.get(match_url)
time.sleep(3)
soup = BeautifulSoup(data.text, features='lxml')
# ID the match to merge later on
home_team = soup.find("h1").text.split()[0]
round_week = float(soup.find("div", {'id': 'content'}).text.split()[18].strip(')'))
# collecting stats
stats = soup.find("div", {"id": "team_stats"}).text.split()[5:] #first part of stats with the progress bars
stats_extra = soup.find("div", {"id": "team_stats_extra"}).text.split()[2:] #second part
all_stats = {'posse_casa':[], 'posse_fora':[], 'chutestotais_casa':[], 'chutestotais_fora':[],
'acertopasses_casa':[], 'acertopasses_fora':[], 'chutesgol_casa':[], 'chutesgol_fora':[],
'faltas_casa':[], 'faltas_fora':[], 'escanteios_casa':[], 'escanteios_fora':[],
'cruzamentos_casa':[], 'cruzamentos_fora':[], 'contatos_casa':[], 'contatos_fora':[],
'botedef_casa':[], 'botedef_fora':[], 'aereo_casa':[], 'aereo_fora':[],
'defesas_casa':[], 'defesas_fora':[], 'impedimento_casa':[], 'impedimento_fora':[],
'tirometa_casa':[], 'tirometa_fora':[], 'lateral_casa':[], 'lateral_fora':[],
'bolalonga_casa':[], 'bolalonga_fora':[], 'Em casa':[home_team], 'Sem':[round_week]}
#not gonna copy everything but is kinda like this for each stat
#stats = '\nEstatísticas do time\n\n\nCoritiba \n\n\n\t\n\n\n\n\n\n\n\n\n\n Cuiabá\n\nPosse\n\n\n\n42%\n\n\n\n\n\n58%\n\n\n\n\nChutes ao gol\n\n\n\n2 of 4\xa0—\xa050%\n\n\n\n\n\n0%\xa0—\xa00 of 8\n\n\n\n\nDefesas\n\n\n\n0 of 0\xa0—\xa0%\n\n\n\n\n\n50%\xa0—\xa01 of 2\n\n\n\n\nCartões\n\n\n\n\n\n\n\n\n\n\n\n\n\n'
#first grabbing 42% possession
all_stats['posse_casa']=stats.replace('\n','').replace('\t','')[20:].split('Posse')[1][:5].split('%')[0]
#grabbing 58% possession
all_stats['posse_fora']=stats.replace('\n','').replace('\t','')[20:].split('Posse')[1][:5].split('%')[1]
all_stats_df = pd.DataFrame.from_dict(all_stats)
championship_data = matches.merge(all_stats_df, on=['Em casa','Sem'])
There are a lot of stats in that dic bc in previous championship years, FBref has all those stats, only in the current year championship there is only 12 of them to fill. I do intend to run the code in 5-6 different years, so I made a version with all stats, and in current year games I intend to fill with nothing when there's no stat in the page to scrap.

You can get Fouls, Corners and Offsides and 7 tables worth of data from that page with the following code:
from bs4 import BeautifulSoup
import requests
import pandas as pd
url = 'https://fbref.com/en/partidas/25d5b9bd/Coritiba-Cuiaba-2022Julho25-Serie-A'
r = requests.get(url)
soup = BeautifulSoup(r.text, 'html.parser')
coritiba_fouls = soup.find('div', string='Fouls').previous_sibling.text.strip()
cuiaba_fouls = soup.find('div', string='Fouls').next_sibling.text.strip()
coritiba_corners = soup.find('div', string='Corners').previous_sibling.text.strip()
cuiaba_corners = soup.find('div', string='Corners').next_sibling.text.strip()
coritiba_offsides = soup.find('div', string='Offsides').previous_sibling.text.strip()
cuiaba_offsides = soup.find('div', string='Offsides').next_sibling.text.strip()
print('Coritiba Fouls: ' + coritiba_fouls, 'Cuiaba Fouls: ' + cuiaba_fouls)
print('Coritiba Corners: ' + coritiba_corners, 'Cuiaba Corners: ' + cuiaba_corners)
print('Coritiba Offsides: ' + coritiba_offsides, 'Cuiaba Offsides: ' + cuiaba_offsides)
dfs = pd.read_html(r.text)
print('Number of tables: ' + str(len(dfs)))
for df in dfs:
print(df)
print('___________')
This will print in the terminal:
Coritiba Fouls: 16 Cuiaba Fouls: 12
Coritiba Corners: 4 Cuiaba Corners: 4
Coritiba Offsides: 0 Cuiaba Offsides: 1
Number of tables: 7
Coritiba (4-2-3-1) Coritiba (4-2-3-1).1
0 23 Alex Muralha
1 2 Matheus Alexandre
2 3 Henrique
3 4 Luciano Castán
4 6 Egídio Pereira Júnior
5 9 Léo Gamalho
6 11 Alef Manga
7 25 Bernanrdo Lemes
8 78 Régis
9 97 Valdemir
10 98 Igor Paixão
11 Bench Bench
12 21 Rafael William
13 5 Guillermo de los Santos
14 15 Matías Galarza
15 16 Natanael
16 18 Guilherme Biro
17 19 Thonny Anderson
18 28 Pablo Javier García
19 32 Bruno Gomes
20 44 Márcio Silva
21 52 Adrián Martínez
22 75 Luiz Gabriel
23 88 Hugo
___________
Cuiabá (4-1-4-1) Cuiabá (4-1-4-1).1
0 1 Walter
1 2 João Lucas
2 3 Joaquim
3 4 Marllon Borges
4 5 Camilo
5 6 Igor Cariús
6 7 Alesson
7 8 João Pedro Pepê
8 9 Valdívia
9 10 Rodriguinho Marinho
10 11 Rafael Gava
11 Bench Bench
12 12 João Carlos
13 13 Daniel Guedes
14 14 Paulão
15 15 Marcão Silva
16 16 Cristian Rivas
17 17 Gabriel Pirani
18 18 Jenison
19 19 André
20 20 Kelvin Osorio
21 21 Jonathan Cafu
22 22 André Luis
23 23 Felipe Marques
___________
Coritiba Cuiabá
Possession Possession
0 42% 58%
1 Shots on Target Shots on Target
2 2 of 4 — 50% 0% — 0 of 8
3 Saves Saves
4 0 of 0 — % 50% — 1 of 2
5 Cards Cards
6 NaN NaN
_____________
[....]

How do i increase an element value from column in Pandas?

Hello I have this Pandas code (look below) but turn out it give me this error: TypeError: can only concatenate str (not "int") to str
import pandas as pd
import numpy as np
import os
_data0 = pd.read_excel("C:\\Users\\HP\\Documents\\DataScience task\\Gender_Age.xlsx")
_data0['Age' + 1]
I wanted to change the element values from column 'Age', imagine if I wanted to increase the column elements from 'Age' by 1, how do i do that? (With Number of Children as well)
The output I wanted:
First Name Last Name Age Number of Children
0 Kimberly Watson 36 2
1 Victor Wilson 35 6
2 Adrian Elliott 35 2
3 Richard Bailey 36 5
4 Blake Roberts 35 6
Original output:
First Name Last Name Age Number of Children
0 Kimberly Watson 24 1
1 Victor Wilson 23 5
2 Adrian Elliott 23 1
3 Richard Bailey 24 4
4 Blake Roberts 23 5

Try:
df['Age'] = df['Age'] - 12
df['Number of Children'] = df['Number of Children'] - 1

pandas Plot area by specifiying index column

My data looks like:
Club Count
0 AC Milan 2
1 Ajax 1
2 FC Barcelona 4
3 Bayern Munich 2
4 Chelsea 1
5 Dortmund 1
6 FC Porto 1
7 Inter Milan 1
8 Juventus 1
9 Liverpool 2
10 Man U 2
11 Real Madrid 7
I'm trying to plot an Area plot using Club as the X Axis, when plotting all data, it looks correct but the X axis displayed is the index and not the Clubs.
When specifying the index as Club(index=x), it shows correct, but the scale of the y axis is set from 0 to 0.05, assuming that's why nothing is displayed since the count is from 1 to 7 any suggestions ?
Code used:
data.columns = ['Club', 'Count']
x=data.Club
y=data.Count
print(data)
ax.margins(0, 10)
data.plot.area()
df = pd.DataFrame(y,index=x)
df.plot.area()
results:

Change to
df = pd.Series(y,index=x)
df.plot.area()

Populate a dataframe column based on a column of other dataframe

I have a dataframe with the population of a region and i want to populate a column of other dataframe with the same distribution.
The first dataframe looks like this:
Municipio Population Population5000
0 Lisboa 3184984 1291
1 Porto 2597191 1053
2 Braga 924351 375
3 Setúbal 880765 357
4 Aveiro 814456 330
5 Faro 569714 231
6 Leiria 560484 227
7 Coimbra 541166 219
8 Santarém 454947 184
9 Viseu 378784 154
10 Viana do Castelo 252952 103
11 Vila Real 214490 87
12 Castelo Branco 196989 80
13 Évora 174490 71
14 Guarda 167359 68
15 Beja 158702 64
16 Bragança 140385 57
17 Portalegre 120585 49
18 Total 12332794 5000
Basically, the second dataframe has 5000 rows and i want to create a column with a name corresponding to the Municipios from the first df.
My problem is that i dont know how to populate the column with same occurence distribution from the first dataframe.
The final result would be something like this:
Municipio
0 Porto
1 Porto
2 Lisboa
3 Évora
4 Lisboa
5 Aveiro
...
4996 Viseu
4997 Lisboa
4998 Porto
4999 Guarda
5000 Beja
Can someone help me?

I would use a simple comprehension to build a list of size 5000 with as many elements with a town name as the value of Population5000, and optionally shuffle it if you want a random order:
lst = [m for m,n in df.loc[:len(df)-2,
['Municipio', 'Population5000']].to_numpy()
for i in range(n)]
random.shuffle(lst)
result = pd.Series(1, index=lst, name='Municipio')
Initialized with random.seed(0), it gives:
Setúbal 1
Santarém 1
Lisboa 1
Setúbal 1
Aveiro 1
..
Santarém 1
Porto 1
Lisboa 1
Faro 1
Aveiro 1
Name: Municipio, Length: 5000, dtype: int64

You could just do a simple map if you do;
map = dict(zip(DF1['Population5000'], DF1['Municipio']))
DF2['Municipo'] = DF2['Population5000'].map(map)
or just change the population 5000 column name in the map (DF2) to whatever the column containing your population values is called.

map = dict(zip(municipios['Population5000'], municipios['Municipio']))
df['Municipio'] = municipios['Population5000'].map(map)
I tried this as suggested by Amen_90 and the column Municipio from the second dataframe it only gets populated with 1 instance of every Municipio, when i wanted to have the same value_counts as in the column "Population5000" in my first dataframe.
df["Municipio"].value_counts()
Beja 1
Aveiro 1
Bragança 1
Vila Real 1
Porto 1
Santarém 1
Coimbra 1
Guarda 1
Leiria 1
Castelo Branco 1
Viseu 1
Total 1
Faro 1
Portalegre 1
Braga 1
Évora 1
Setúbal 1
Viana do Castelo 1
Lisboa 1
Name: Municipio, dtype: int64

Simplifying categorical variables with python/pandas

I'm working with an airbnb dataset on Kaggle:
https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings
and want to simplify the values for the language column into 2 groupings - english and non-english.
For instance:
users.language.value_counts()
en 15011
zh 101
fr 99
de 53
es 53
ko 43
ru 21
it 20
ja 19
pt 14
sv 11
no 6
da 5
nl 4
el 2
pl 2
tr 2
cs 1
fi 1
is 1
hu 1
Name: language, dtype: int64
And the result I want it is:
users.language.value_counts()
english 15011
non-english 459
Name: language, dtype: int64
This is sort of the solution I want:
def language_groupings():
for i in users:
if users.language !='en':
replace(users.language.str, 'non-english')
else:
replace(users.language.str, 'english')
return users
users['language'] = users.apply(lambda row: language_groupings)
Except there's obviously something wrong with this as it returns an empty series when I run value_counts on the column.

Try this:
users.language = np.where( users.language !='en', 'non-english', 'english' )

is that what you want?
In [181]: x
Out[181]:
val
en 15011
zh 101
fr 99
de 53
es 53
ko 43
ru 21
it 20
ja 19
pt 14
sv 11
no 6
da 5
nl 4
el 2
pl 2
tr 2
cs 1
fi 1
is 1
hu 1
In [182]: x.groupby(x.index == 'en').sum()
Out[182]:
val
False 459
True 15011