I will apologize now as I am sure the format of my question and the information I provide will not be up to par with what is expected on this site. I have been coding SQL and VBA for several years and I am trying to pick up a 3rd language to enhance my skill set. I will get better with time.
Now to my issue...
I am trying to scrape a series of tables with code that has worked on BasketballReference.com however on NBA.com the code brings back nothing. When digging further the make_soup does not contain the tr and td tags that I see when inspecting the table in my browser. Below is the code I am using and for reference a picture of what my csv file looks like.
import urllib
import urllib.request
from bs4 import BeautifulSoup
import os
import csv
import time
def make_soup(url):
thepage = urllib.request.urlopen(url)
soupdata = BeautifulSoup(thepage, "html.parser")
return soupdata
with open('PlayTypeKey.csv', 'r') as PlaytypeData:
csv_reader = csv.reader(PlaytypeData)
a = []
b = []
c = []
d = []
next(csv_reader)
for row in csv_reader:
a1 = row[0]
b1 = row[1]
c1 = row[2]
d1 = row[3]
a.append(a1)
b.append(b1)
c.append(c1)
d.append(d1)
playerdatasaved = ""
i = 0
while i < 5:
soup = make_soup("http://stats.nba.com/players/"+a[i]+"/?Season="+b[i]+"&SeasonType=Regular%20Season&PerMode="+c[i]+"&OD="+d[i])
for record in soup.findAll('tr'):
playerdata = b[i]+ a[i] + ","
for data in record.findAll('td'):
playerdata=playerdata+","+data.text
playerdatasaved = playerdatasaved + "\n" + playerdata[1:]
i=i+1
header = "Season,PlayType,PLAYER,TEAM,GP,POSS,FREQ,PPP,PTS,FGM,FGA,FG%,EFG%,FT-Freq,TO-Freq,SF-Freq,AND ONE-Freq,SCORE-Freq,PERCENTILE"
file = open(os.path.expanduser("BasketballPlayTypeData.csv"), "wb")
file.write(bytes(header, encoding="ascii", errors='ignore'))
file.write(bytes(playerdatasaved, encoding="ascii", errors='ignore'))
PlayTypeKey.csv data:
PlayType Season Mode OffDef
isolation 2015-16 Totals offensive
isolation 2016-17 Totals offensive
isolation 2017-18 Totals offensive
transition 2015-16 Totals offensive
transition 2016-17 Totals offensive
transition 2017-18 Totals offensive
My limited ability to trouble shoot tells me that the table data is not coming back when I make the soup out of the URL. When printing the text of the soup I get this...
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NBA.com/Stats | Players Isolation
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Scores
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Advanced Box Scores Advanced
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{{ team.city }} {{ team.name }}
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{{ t.city }} {{ t.name }}
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Shooting
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{{ t.city }} {{ t.name }}
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{{ t.city }} {{ t.name }}
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Combine Non-Stationary Shooting
Combine Strength & Agility
Combine Anthro
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Search For A Player or Team
No Results Found
{{ ::item.city }} {{ ::item.name }}
{{ ::item.fn }} {{ ::item.ln }}
{{ ::item.fn }} {{ ::item.ln }}
See More Results
Stats Home
/
Players
/
Playtype
/
Isolation
{{ alpha }}
Sortable Player Stats
Sortable Team Stats
{{ betaText }}
Official Leaders
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{{ gammaText }}
Traditional
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Opponent
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{{ gammaText }}
Traditional
Advanced
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Scoring
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{{ gammaText }}
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Isolation
Pick & Roll Ball Handler
Pick & Roll Roll Man
Post Up
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Handoff
Cut
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Misc
{{ gammaText }}
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Touches
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{{ gammaText }}
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3 Pointers
2 Pointers
< 6ft.
< 10ft.
> 15ft.
{{ gammaText }}
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{{ gammaText }}
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And One Freq
And One Frequency
Score Freq
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Minimum of 10 min/game and 10 possessions per play type to qualify.
provided by Synergy
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Towards the bottom of what is pasted above are the headers of the table but no corresponding text or code for the table itself. I won't clutter this post with what I get when printing the HTML but I get no hits when searching for the tr or td tags.
Thanks in advance for anyone that spends time looking into this and I just want to say this site has already been immensely valuable to me.
The webpage does not contain any tables with TR and TD. It is displayed by the page first requesting all of the data via a separate call and then rendering this using Javascript. Normally you would need to use something like selenium to allow this to be done, but a much faster approach is to use your browser to monitor the network requests and recreate them using Python.
In this case the request results in all of the data being return in JSON format which is much easier to parse than needing BeautifulSoup. Simply load it using Python's JSON library and you will have all the player data as one big Python data structure.
When reading your CSV file, it would be easier just to use the information a row at a time rather than trying to build multiple lists and then index them in a separate loop.
The JSON request you need takes slightly different parameters (as seen by monitoring network activity in a browser) to the ones for the HTML page you were trying to get, so the CSV file would need to be updated:
PlayType,Season,Mode,OffDef
isolation,2015,Totals,offensive
isolation,2016,Totals,offensive
isolation,2017,Totals,offensive
transition,2015,Totals,offensive
transition,2016,Totals,offensive
transition,2017,Totals,offensive
The script to get the data is as follows:
import urllib
import urllib.request
import os
import csv
import json
from operator import itemgetter
fieldnames = ["PlayerFirstName", "PlayerLastName", "TeamNameAbbreviation", "GP", "Poss"]
req_fields = itemgetter(*fieldnames)
with open("PlayTypeKey.csv", "r", newline="") as f_input, \
open(os.path.expanduser("BasketballPlayTypeData.csv"), "w", newline="") as f_output:
csv_input = csv.reader(f_input)
next(csv_input)
csv_output = csv.writer(f_output)
csv_output.writerow(fieldnames)
for play_type, season, mode, off_def in csv_input:
url = f"http://stats-prod.nba.com/wp-json/statscms/v1/synergy/player/?category={play_type}&limit=500&names={off_def}&season={season}&seasonType=Reg"
print(url)
json_data = urllib.request.urlopen(url).read()
data = json.loads(json_data)
for player in data['results']:
row = [season, play_type] + list(req_fields(player))
csv_output.writerow(row)
If you were to print json_data you would see all the possible data that is available for each player. I have shown how to extract the first few columns. itemgetter() is used as a shortcut to extracting the required bits from each player entry.
This script will give you an output CSV file starting:
PlayerFirstName,PlayerLastName,TeamNameAbbreviation,GP,Poss
2015,isolation,Aaron,Gordon,ORL,78,30
2015,isolation,Norman,Powell,TOR,49,9
2015,isolation,Al,Jefferson,CHA,47,9
You could obviously modify the output to make it identical to your other site. The big advantage to this approach is that you will get all the player data in a single call, no need to cycle through multiple pages.