I'm downloading football data with pandas read_html function, but not struggling to clean the player names with all the accented characters. This is what I have so far:
import pandas as pd
from unidecode import unidecode
shooting = pd.read_html("https://widgets.sports-reference.com/wg.fcgi?css=1&site=fb&url=%2Fen%2Fcomps%2F9%2Fshooting%2FPremier-League-Stats&div=div_stats_shooting")
for idx,table in enumerate(shooting):
print("***************************")
print(idx)
print(table)
shooting = table
for col in [('Unnamed: 1_level_0', 'Player')]:
shooting[col] = shooting[col].apply(unidecode)
shooting
shooting = table
#print(shooting.droplevel(1))
table.to_csv (r'C:\Users\khabs\OneDrive\Documents\Python Testing\shooting.csv', index = False, header=True)
print (shooting)
I think the issue is that the coding is messed before I even do the cleaning, but really not sure.
Any help would be greatly appreciated!!
Just use the encoding parameter in pandas.
import pandas as pd
url = "https://widgets.sports-reference.com/wg.fcgi?css=1&site=fb&url=%2Fen%2Fcomps%2F9%2Fshooting%2FPremier-League-Stats&div=div_stats_shooting"
shooting = pd.read_html(url, header=1, encoding='utf8')[0]
However, that (and I'm assuming) will not get you what you want, as there are extra html characters in the response returned from that widget.
Just go after the actual html. The table is within the comments.
import requests
import pandas as pd
url = 'https://fbref.com/en/comps/9/shooting/Premier-League-Stats'
html = requests.get(url).text.replace('<!--', '').replace('-->', '')
shooting = pd.read_html(html, header=1)[-1]
shooting = shooting[shooting['Rk'].ne('Rk')]
Output:
print(shooting.head(10))
Rk Player Nation Pos ... npxG/Sh G-xG np:G-xG Matches
0 1 Brenden Aaronson us USA FW,MF ... 0.03 -0.1 -0.1 Matches
1 2 Che Adams sct SCO FW ... 0.09 +1.6 +1.6 Matches
2 3 Tyler Adams us USA MF ... 0.01 0.0 0.0 Matches
3 4 Tosin Adarabioyo eng ENG DF ... NaN 0.0 0.0 Matches
4 5 Rayan Aït Nouri fr FRA DF ... 0.08 -0.1 -0.1 Matches
5 6 Nathan Aké nl NED DF ... 0.05 -0.2 -0.2 Matches
6 7 Thiago Alcántara es ESP MF ... NaN 0.0 0.0 Matches
7 8 Trent Alexander-Arnold eng ENG DF ... 0.05 -0.2 -0.2 Matches
8 9 Alisson br BRA GK ... NaN 0.0 0.0 Matches
9 10 Dele Alli eng ENG FW,MF ... NaN 0.0 0.0 Matches
I have a very specific issue which I have not been able to find a solution to.
Recently, I began a project for which I am monitoring about 100 ETFs and Mutual funds based on specific data acquired from Morningstar. The current solution works great - but I later found out that I need more data from another "Tab" within the website. Specifically, I am trying to get data from the 1st table from the following website: https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000Z1MC&tab=1
Right now, I have the code below for scraping data from a table from the tab "Indhold" on the website, and exporting it to Excel. My question is therefore: How do I adjust the code to scrape data from another part of the website?.
To briefly explain the code and reiterate: The code below scrapes data from another tab from the same websites. The many, many IDs are for each website representing each mutual fund/ETF. The setup works very well so I am hoping to simply adjust it (If that is possible) to extract the table from the link above. I have very limited knowledge of the topic so any help is much, much appreciated.
import requests
import re
import pandas as pd
from openpyxl import load_workbook
auth = 'https://www.morningstar.dk/Common/funds/snapshot/PortfolioSAL.aspx'
# Create a Pandas Excel writer using XlsxWriter as the engine.
path= r'/Users/karlemilthulstrup/Downloads/data2.xlsm'
book = load_workbook(path ,read_only = False, keep_vba=True)
writer = pd.ExcelWriter(path, engine='openpyxl')
writer.book = book
ids = ['F00000VA2N','F0GBR064OO','F00000YKC2','F000015MVX','F0000020YA','0P00015YTR','0P00015YTT','F0GBR05V8D','F0GBR06XKI','F000013CKH','F00000MG6K','F000014G49',
'F00000WC0Z','F00000QSD2','F000016551','F0000146QH','F0000146QI','F0GBR04KZS','F0GBR064VU','F00000VXLM','F0000119R1','F0GBR04L4T','F000015CS3','F000015CS5','F000015CS6',
'F000015CS4','F000013BZE','F0GBR05W0Q','F000016M1C','F0GBR04L68','F00000Z9T9','F0GBR04JI8','F00000Z9TG','F0GBR04L2P','F000014CU8','F00000ZG2G','F00000MLEW',
'F000013ZOY','F000016614','F00000WUI9','F000015KRL','F0GBR04LCR','F000010ES9','F00000P780','F0GBR04HC3','F000015CV6','F00000YWCK','F00000YWCJ','F00000NAI5',
'F0GBR04L81','F0GBR05KNU','F0GBR06XKB','F00000NAI3','F0GBR06XKF','F000016UA9','F000013FC2','F000014NRE','0P0000CNVT','0P0000CNVX','F000015KRI',
'F000015KRG','F00000XLK7','F0GBR04IDG','F00000XLK6','F00000073J','F00000XLK4','F000013CKG','F000013CKJ','F000013CKK','F000016P8R','F000016P8S','F000011JG6',
'F000014UZQ','F0000159PE','F0GBR04KZG','F0000002OY','F00000TW9K','F0000175CC','F00000NBEL','F000016054','F000016056','F00000TEYP','F0000025UI','F0GBR04FV7',
'F00000WP01','F000011SQ4','F0GBR04KZO','F000010E19','F000013ZOX','F0GBR04HD7','F00000YKC1','F0GBR064UG','F00000JSDD','F000010ROF','F0000100CA','F0000100CD',
'FOGBR05KQ0','F0GBR04LBB','F0GBR04LBZ','F0GBR04LCN','F00000WLA7','F0000147D7','F00000ZB5E','F00000WC0Y']
headers = {'User-Agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Mobile Safari/537.36'}
payload = {
'languageId': 'da-DK',
'locale': 'da-DK',
'clientId': 'MDC_intl',
'benchmarkId': 'category',
'component': 'sal-components-mip-factor-profile',
'version': '3.40.1'}
for api_id in ids:
payload = {
'Site': 'dk',
'FC': '%s' %api_id,
'IT': 'FO',
'LANG': 'da-DK',}
response = requests.get(auth, params=payload)
search = re.search('(tokenMaaS:[\w\s]*\")(.*)(\")', response.text, re.IGNORECASE)
bearer = 'Bearer ' + search.group(2)
headers.update({'Authorization': bearer})
url = 'https://www.us-api.morningstar.com/sal/sal-service/fund/factorProfile/%s/data' %api_id
jsonData = requests.get(url, headers=headers, params=payload).json()
rows = []
for k, v in jsonData['factors'].items():
row = {}
row['factor'] = k
historicRange = v.pop('historicRange')
row.update(v)
for each in historicRange:
row.update(each)
rows.append(row.copy())
df = pd.DataFrame(rows)
sheetName = jsonData['id']
df.to_excel(writer, sheet_name=sheetName, index=False)
print('Finished: %s' %sheetName)
writer.save()
writer.close()
If I understand you correctly, you want to get first table of that URL in the form of pandas dataframe:
import requests
import pandas as pd
from bs4 import BeautifulSoup
# load the page into soup:
url = "https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000Z1MC&tab=1"
soup = BeautifulSoup(requests.get(url).content, "html.parser")
# find correct table:
tbl = soup.select_one(".returnsCalenderYearTable")
# remove the first row (it's not header):
tbl.tr.extract()
# convert the html to pandas DF:
df = pd.read_html(str(tbl))[0]
# move the first row to header:
df.columns = map(str, df.loc[0])
df = df.loc[1:].reset_index(drop=True).rename(columns={"nan": "Name"})
print(df)
Prints:
Name 2014* 2015* 2016* 2017* 2018 2019 2020 31-08
0 Samlet afkast % 2627 1490 1432 584 -589 2648 -482 1841
1 +/- Kategori 1130 583 808 -255 164 22 -910 -080
2 +/- Indeks 788 591 363 -320 -127 -262 -1106 -162
3 Rank i kategori 2 9 4 80 38 54 92 63
EDIT: To load from multiple URLs:
import requests
import pandas as pd
from bs4 import BeautifulSoup
urls = [
"https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000VA2N&tab=1",
"https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F0GBR064OO&tab=1",
"https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000YKC2&tab=1",
"https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F000015MVX&tab=1",
]
all_data = []
for url in urls:
print("Loading URL {}".format(url))
# load the page into soup:
soup = BeautifulSoup(requests.get(url).content, "html.parser")
# find correct table:
tbl = soup.select_one(".returnsCalenderYearTable")
# remove the first row (it's not header):
tbl.tr.extract()
# convert the html to pandas DF:
df = pd.read_html(str(tbl))[0]
# move the first row to header:
df.columns = map(lambda x: str(x).replace("*", "").strip(), df.loc[0])
df = df.loc[1:].reset_index(drop=True).rename(columns={"nan": "Name"})
df["Company"] = soup.h1.text.split("\n")[0].strip()
df["URL"] = url
all_data.append(df.loc[:, ~df.isna().all()])
df = pd.concat(all_data, ignore_index=True)
print(df)
Prints:
Name 2016 2017 2018 2019 2020 31-08 Company URL
0 Samlet afkast % 1755.0 942.0 -1317.0 1757.0 -189.0 3018 Great Dane Globale Aktier https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000VA2N&tab=1
1 +/- Kategori 966.0 -54.0 -186.0 -662.0 -967.0 1152 Great Dane Globale Aktier https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000VA2N&tab=1
2 +/- Indeks 686.0 38.0 -854.0 -1153.0 -813.0 1015 Great Dane Globale Aktier https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000VA2N&tab=1
3 Rank i kategori 10.0 24.0 85.0 84.0 77.0 4 Great Dane Globale Aktier https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000VA2N&tab=1
4 Samlet afkast % NaN 1016.0 -940.0 1899.0 767.0 2238 Independent Generations ESG https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F0GBR064OO&tab=1
5 +/- Kategori NaN 20.0 190.0 -520.0 -12.0 373 Independent Generations ESG https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F0GBR064OO&tab=1
6 +/- Indeks NaN 112.0 -478.0 -1011.0 143.0 235 Independent Generations ESG https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F0GBR064OO&tab=1
7 Rank i kategori NaN 26.0 69.0 92.0 43.0 25 Independent Generations ESG https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F0GBR064OO&tab=1
8 Samlet afkast % NaN NaN -939.0 1898.0 766.0 2239 Independent Generations ESG Akk https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000YKC2&tab=1
9 +/- Kategori NaN NaN 191.0 -521.0 -12.0 373 Independent Generations ESG Akk https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000YKC2&tab=1
10 +/- Indeks NaN NaN -477.0 -1012.0 142.0 236 Independent Generations ESG Akk https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000YKC2&tab=1
11 Rank i kategori NaN NaN 68.0 92.0 44.0 24 Independent Generations ESG Akk https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F00000YKC2&tab=1
12 Samlet afkast % NaN NaN NaN NaN NaN 2384 Investin Sustainable World https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F000015MVX&tab=1
13 +/- Kategori NaN NaN NaN NaN NaN 518 Investin Sustainable World https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F000015MVX&tab=1
14 +/- Indeks NaN NaN NaN NaN NaN 381 Investin Sustainable World https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F000015MVX&tab=1
15 Rank i kategori NaN NaN NaN NaN NaN 18 Investin Sustainable World https://www.morningstar.dk/dk/funds/snapshot/snapshot.aspx?id=F000015MVX&tab=1
I'm learning on how to webscrape using Python since I'm a novice. Right now, I attempted to webscrape Euros 2020 stats from this website https://theanalyst.com/na/2021/06/euro-2020-player-stats. After running my initial code (see below) to gather the html from the webpage, I cannot locate the table tag and its data-table class. I can see the table and its data-table when I inspected the website, but it is not shown when I print out the page_soup.
from urllib.request import urlopen as uReq # Web client
from bs4 import BeautifulSoup as soup # HTML data structure
url_page = 'https://theanalyst.com/na/2021/06/euro-2020-player-stats'
# Open connection & download the html from the url
uClient = uReq(url_page)
# Parses html into a soup data structure
page_soup = soup(uClient.read(), "html.parser")
uClient.close()
print(page_soup)
The table is loaded dynamically in JSON format via sending a GET request to:
https://dataviz.theanalyst.com/euro-2020-hub/player_stats_3_2020.json
Since we're dealing with JSON data, it's easier to use the requests library to get the data.
Here is an example using the pandas library to print the table into a DataFrame (you don't have to use the pandas library).
import pandas as pd
import requests
url = "https://dataviz.theanalyst.com/euro-2020-hub/player_stats_3_2020.json"
response = requests.get(url).json()
print(pd.json_normalize(response["data"]).to_string())
Output (truncated):
player_id team_id team_name player_first_name player_last_name player age position detailed_position mins_played np_shots np_sot np_goals np_xG op_chances_created op_assists op_xA op_passes op_pass_completion_rate tackles_won interceptions recoveries avg_carry_distance avg_carry_progress carry_w_shot carry_w_goal carry_w_chance_created carry_w_assist take_ons take_ons_success_rate goal_ending total_xG shot_ending team_badge
0 103955 114 England Raheem Sterling Raheem Sterling 26 Forward Second Striker 641 14 8 3 3.82 2 1 1.18 193 0.85 5 4 23 12.98 6.73 3 0 3 1 38 52.63 6 7.08 24 https://omo.akamai.opta.net/image.php?secure=true&h=omo.akamai.opta.net&sport=football&entity=team&description=badges&dimensions=150&id=114
1 56979 114 England Jordan Henderson Jordan Henderson 31 Midfielder Central Midfielder 150 1 1 1 0.32 0 0 0.06 111 0.88 0 1 11 7.83 0.49 0 0 0 0 3 66.67 0 0.00 0 https://omo.akamai.opta.net/image.php?secure=true&h=omo.akamai.opta.net&sport=football&entity=team&description=badges&dimensions=150&id=114
2 78830 114 England Harry Kane Harry Kane 27 Forward Striker 649 15 7 4 3.57 5 0 0.39 159 0.70 0 3 8 10.52 3.06 2 0 2 0 15 53.33 7 6.38 21 https://omo.akamai.opta.net/image.php?secure=true&h=omo.akamai.opta.net&sport=football&entity=team&description=badges&dimensions=150&id=114
3 58621 114 England Kyle Walker Kyle Walker 31 Defender Full Back 599 0 0 0 0.00 2 0 0.18 352 0.87 0 8 37 11.66 5.09 0 0 0 0 1 100.00 3 2.54 10 https://omo.akamai.opta.net/image.php?secure=true&h=omo.akamai.opta.net&sport=football&entity=team&description=badges&dimensions=150&id=114
The variable response is now a dictionary (dict) which you can access the keys/values. To view and prettify the data:
from pprint import pprint
print(type(response))
pprint(response)
Output (truncated):
<class 'dict'>
{'data': [{'age': 26,
'avg_carry_distance': 12.98,
'avg_carry_progress': 6.73,
'carry_w_assist': 1,
'carry_w_chance_created': 3,
'carry_w_goal': 0,
'carry_w_shot': 3,
'detailed_position': 'Second Striker',
I have a pandas data frame that looks like:
High Low ... Volume OpenInterest
2018-01-02 983.25 975.50 ... 8387 67556
2018-01-03 986.75 981.00 ... 7447 67525
2018-01-04 985.25 977.00 ... 8725 67687
2018-01-05 990.75 984.00 ... 7948 67975
I calculate the Average True Range and save it into a series:
i = 0
TR_l = [0]
while i < (df.shape[0]-1):
#TR = max(df.loc[i + 1, 'High'], df.loc[i, 'Close']) - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
TR = max(df['High'][i+1], df['Close'][i]) - min(df['Low'][i+1], df['Close'][i])
TR_l.append(TR)
i = i + 1
TR_s = pd.Series(TR_l)
ATR = pd.Series(TR_s.ewm(span=n, min_periods=n).mean(), name='ATR_' + str(n))
With a 14-period rolling window ATR looks like:
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
10 NaN
11 NaN
12 NaN
13 8.096064
14 7.968324
15 8.455205
16 9.046418
17 8.895405
18 9.088769
19 9.641879
20 9.516764
But when I do:
df = df.join(ATR)
The ATR column in df is all NaN. It's because the indexes are different between the data frame and ATR. Is there any way to add the ATR column into the data frame?
Consider shift to avoid the while loop across rows and list building. Below uses Union Pacific (UNP) railroad stock data to demonstrate:
import pandas as pd
import pandas_datareader as pdr
stock_df = pdr.get_data_yahoo('UNP').loc['2019-01-01':'2019-03-29']
# SHIFT DATA ONE DAY BACK AND JOIN TO ORIGINAL DATA
stock_df = stock_df.join(stock_df.shift(-1), rsuffix='_future')
# CALCULATE TR DIFFERENCE BY ROW
stock_df['TR'] = stock_df.apply(lambda x: max(x['High_future'], x['Close']) - min(x['Low_future'], x['Close']), axis=1)
# CALCULATE EWM MEAN
n = 14
stock_df['ATR'] = stock_df['TR'].ewm(span=n, min_periods=n).mean()
Output
print(stock_df.head(20))
# High Low Open Close Volume Adj Close High_future Low_future Open_future Close_future Volume_future Adj Close_future TR ATR
# Date
# 2019-01-02 138.320007 134.770004 135.649994 137.779999 3606300.0 137.067413 136.750000 132.169998 136.039993 132.679993 5684500.0 131.993790 5.610001 NaN
# 2019-01-03 136.750000 132.169998 136.039993 132.679993 5684500.0 131.993790 138.580002 134.520004 134.820007 137.789993 5649900.0 137.077362 5.900009 NaN
# 2019-01-04 138.580002 134.520004 134.820007 137.789993 5649900.0 137.077362 139.229996 136.259995 137.330002 138.649994 4034200.0 137.932907 2.970001 NaN
# 2019-01-07 139.229996 136.259995 137.330002 138.649994 4034200.0 137.932907 152.889999 149.039993 151.059998 150.750000 10558800.0 149.970337 14.240005 NaN
# 2019-01-08 152.889999 149.039993 151.059998 150.750000 10558800.0 149.970337 151.059998 148.610001 150.289993 150.360001 4284600.0 149.582352 2.449997 NaN
# 2019-01-09 151.059998 148.610001 150.289993 150.360001 4284600.0 149.582352 155.289993 149.009995 149.899994 154.660004 6444600.0 153.860123 6.279999 NaN
# 2019-01-10 155.289993 149.009995 149.899994 154.660004 6444600.0 153.860123 155.029999 153.089996 153.639999 153.210007 3845200.0 152.417618 1.940002 NaN
# 2019-01-11 155.029999 153.089996 153.639999 153.210007 3845200.0 152.417618 154.240005 151.649994 152.229996 153.889999 3507100.0 153.094101 2.590012 NaN
# 2019-01-14 154.240005 151.649994 152.229996 153.889999 3507100.0 153.094101 154.360001 151.740005 153.789993 152.479996 4685100.0 151.691391 2.619995 NaN
# 2019-01-15 154.360001 151.740005 153.789993 152.479996 4685100.0 151.691391 153.729996 150.910004 152.910004 151.970001 4053200.0 151.184021 2.819992 NaN
# 2019-01-16 153.729996 150.910004 152.910004 151.970001 4053200.0 151.184021 154.919998 150.929993 151.110001 154.639999 4075400.0 153.840210 3.990005 NaN
# 2019-01-17 154.919998 150.929993 151.110001 154.639999 4075400.0 153.840210 158.800003 155.009995 155.539993 158.339996 5003900.0 157.521072 4.160004 NaN
# 2019-01-18 158.800003 155.009995 155.539993 158.339996 5003900.0 157.521072 157.199997 154.410004 156.929993 155.020004 6052900.0 154.218262 3.929993 NaN
# 2019-01-22 157.199997 154.410004 156.929993 155.020004 6052900.0 154.218262 156.020004 152.429993 155.449997 154.330002 4858000.0 153.531830 3.590012 4.011254
# 2019-01-23 156.020004 152.429993 155.449997 154.330002 4858000.0 153.531830 160.759995 156.009995 160.039993 160.339996 9222400.0 159.510742 6.429993 4.376440
# 2019-01-24 160.759995 156.009995 160.039993 160.339996 9222400.0 159.510742 162.000000 160.220001 161.460007 160.949997 7770700.0 160.117584 1.779999 3.991223
# 2019-01-25 162.000000 160.220001 161.460007 160.949997 7770700.0 160.117584 160.789993 159.339996 160.000000 159.899994 3733800.0 159.073013 1.610001 3.643168
# 2019-01-28 160.789993 159.339996 160.000000 159.899994 3733800.0 159.073013 160.929993 158.750000 160.039993 160.169998 3436900.0 159.341614 2.179993 3.432011
# 2019-01-29 160.929993 158.750000 160.039993 160.169998 3436900.0 159.341614 161.889999 159.440002 161.089996 160.820007 4112200.0 159.988266 2.449997 3.291831
# 2019-01-30 161.889999 159.440002 161.089996 160.820007 4112200.0 159.988266 160.990005 157.020004 160.750000 159.070007 7438600.0 158.247314 3.970001 3.387735
How do I turn a table like this--batting gamelogs table--into a CSV file using Python and BeautifulSoup?
I want the first header where it says Rk, Gcar, Gtm, etc. and not any of the other headers within the table (the ones for each month of the season).
Here is the code I have so far:
from bs4 import BeautifulSoup
from urllib2 import urlopen
import csv
def stir_the_soup():
player_links = open('player_links.txt', 'r')
player_ID_nums = open('player_ID_nums.txt', 'r')
id_nums = [x.rstrip('\n') for x in player_ID_nums]
idx = 0
for url in player_links:
print url
soup = BeautifulSoup(urlopen(url), "lxml")
p_type = ""
if url[-12] == 'p':
p_type = "pitching"
elif url[-12] == 'b':
p_type = "batting"
table = soup.find(lambda tag: tag.name=='table' and tag.has_attr('id') and tag['id']== (p_type + "_gamelogs"))
header = [[val.text.encode('utf8') for val in table.find_all('thead')]]
rows = []
for row in table.find_all('tr'):
rows.append([val.text.encode('utf8') for val in row.find_all('th')])
rows.append([val.text.encode('utf8') for val in row.find_all('td')])
with open("%s.csv" % id_nums[idx], 'wb') as f:
writer = csv.writer(f)
writer.writerow(header)
writer.writerows(row for row in rows if row)
idx += 1
player_links.close()
if __name__ == "__main__":
stir_the_soup()
The id_nums list contains all of the id numbers for each player to use as the names for the separate CSV files.
For each row, the leftmost cell is a tag and the rest of the row is tags. In addition to the header how do I put that into one row?
this code gets you the big table of stats, which is what I think you want.
Make sure you have lxml, beautifulsoup4 and pandas installed.
df = pd.read_html(r'https://www.baseball-reference.com/players/gl.fcgi?id=abreuto01&t=b&year=2010')
print(df[4])
Here is the output of first 5 rows. You may need to clean it slightly as I don't know what your exact endgoal is:
df[4].head(5)
Rk Gcar Gtm Date Tm Unnamed: 5 Opp Rslt Inngs PA ... CS BA OBP SLG OPS BOP aLI WPA RE24 Pos
0 1 66 2 (1) Apr 6 ARI NaN SDP L,3-6 7-8 1 ... 0 1.000 1.000 1.000 2.000 9 .94 0.041 0.51 PH
1 2 67 3 Apr 7 ARI NaN SDP W,5-3 7-8 1 ... 0 .500 .500 .500 1.000 9 1.16 -0.062 -0.79 PH
2 3 68 4 Apr 9 ARI NaN PIT W,9-1 8-GF 1 ... 0 .667 .667 .667 1.333 2 .00 0.000 0.13 PH SS
3 4 69 5 Apr 10 ARI NaN PIT L,3-6 CG 4 ... 0 .500 .429 .500 .929 2 1.30 -0.040 -0.37 SS
4 5 70 7 (1) Apr 13 ARI # LAD L,5-9 6-6 1 ... 0 .429 .375 .429 .804 9 1.52 -0.034 -0.46 PH
to select certain columns within this DataFrame: df[4]['COLUMN_NAME_HERE'].head(5)
Example: df[4]['Gcar']
Also, if doing df[4] is getting annoying you could always just switch to another dataframe df2=df[4]
import pandas as pd
from bs4 import BeautifulSoup
import urllib2
url = 'https://www.baseball-reference.com/players/gl.fcgi?id=abreuto01&t=b&year=2010'
html=urllib2.urlopen(url)
bs = BeautifulSoup(html,'lxml')
table = str(bs.find('table',{'id':'batting_gamelogs'}))
dfs = pd.read_html(table)
This uses Pandas, which is pretty useful for stuff like this. It also puts it in a pretty reasonable format to do other operations on.
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_html.html