Grabbing Team Tables

Now I need to explore those links. When I started this, I wanted to learn some Python along the way. I found the following Python code that worked for the task. For completeness, I leave it here.

import pandas as pd
import bs4
import re
import urllib2
#import requests

from urllib import urlopen
from bs4 import BeautifulSoup
base_url = "http://www.spotrac.com/nfl/"

def get_page(url):
    page = urlopen(base_url)
    soup = BeautifulSoup(page, 'lxml')
    file = open("spotrac_urls.txt", 'w')
    file.write(str(soup))
    file.close()

def get_team_table(url):
    page = urlopen(url)
    soup = BeautifulSoup(page, 'lxml')

get_page(base_url)

with open("spotrac_urls.txt", 'r') as file:
    for line in file:
        line = line.strip()

from bs4 import BeautifulSoup
page = open("spotrac_urls.txt", 'r')
soup = BeautifulSoup(page, "lxml")
div = soup.find("div","subnav-posts")

links = div.find_all('a')
for link in links:
    print(link.get('href'))

len(links)

def get_team_table(url):
    page = urlopen(url)
    soup = BeautifulSoup(page, 'lxml')
    data_rows = [row for row in soup.find("table", "datatable").find_all("tr")]
    return data_rows

# create an empty list
team_data = []

for link in links:
    team_data.append(get_team_table(link.get('href')))

len(team_data)

#data_rows = [row for row in soup.find("td", "center").find_all("tr")]
table_data = []

#soup = BeautifulSoup(team_data[0], 'lxml')

#This needs to be a nested for loop because inner items of the list are BeautifulSoup Elements
for row in team_data:
    for element in row:
        #print(type(element))
        if soup.find_all("td", attrs={"class":" right xs-hide "}) is not None:
            table_data.append(element.get_text())

player_data = []
for row in table_data:
    player_data.append(row.split("\n"))
    #print(player_data)

len(player_data)

import pandas as pd
df = pd.DataFrame(player_data)
df = df.drop(14, 1)
df = df.drop(0, 1)
df = df.drop(1, 1)


df = df.drop(df.index[[0]])
df.set_index(1, inplace=True)
print(df.shape)
df.head()

players = []
for row in team_data[0]:
    if row.get_text("tr") is not None:
        players.append(row) 

column_headers = [col.get_text() for col in players[0].find_all("th") if col.get_text()]
len(column_headers)

df.columns = column_headers
df.head()

#The header repeated itself in the data.  This didn't reveal itself until the data type conversion step below
#but this fixes all occurrences of it.
rows_to_be_dropped = df.loc[df['Cap Hit'] == 'Cap %'].index
rows_to_be_kept = df.loc[df['Cap Hit'] != 'Cap %'].index
totlen = len(df)
df2a = df.drop(rows_to_be_kept)
df = df.drop(rows_to_be_dropped)
df2 = pd.Series(data=rows_to_be_dropped)

#Apply a regex to convert the 'Cap Hit' column from a string to a float.  
# df['Cap Hit'] =(df['Cap Hit'].replace('[\$,)]', "", regex=True).replace( '[(]','-',   regex=True ).astype(float))

# My fix
df['Cap.Hit'] = (df['Cap Hit'].replace('[\$,)]', "", regex=True).replace('[-,)]', "", regex=True).replace('\s\s.*', "", regex=True).astype(float))
df['Base.Salary'] = (df['Base Salary'].replace('[\$,)]', "", regex=True).replace('[-,)]', "", regex=True).astype(float))
df['Signing.Bonus'] = (df['Signing Bonus'].replace('[\$,)]', "", regex=True).replace('[-,)]', "", regex=True).astype(float))
df['Roster.Bonus'] = (df['Roster Bonus'].replace('[\$,)]', "", regex=True).replace('[-,)]', "", regex=True).astype(float))
df['Option.Bonus'] = (df['Option Bonus'].replace('[\$,)]', "", regex=True).replace('[-,)]', "", regex=True).astype(float))
df['Workout.Bonus'] = (df['Workout Bonus'].replace('[\$,)]', "", regex=True).replace('[-,)]', "", regex=True).astype(float))
df['Restruc.Bonus'] = (df['Restruc. Bonus'].replace('[\$,)]', "", regex=True).replace('[-,)]', "", regex=True).astype(float))
df['Misc'] = (df['Misc.'].replace('[\$,)]', "", regex=True).replace('[-,)]', "", regex=True).astype(float))
df['Dead.Cap'] = (df['Dead Cap'].replace('\(',"",regex=True).replace('\)',"",regex=True).replace('[\$,)]', "", regex=True).replace('[-,)]', "", regex=True).astype(float))

#Sanity check to make sure it worked.
df['Cap Hit'].sum()

There are a few wonderful things about Beautiful Soup. But it is still faster and easier for me to do things in R. Here is some R code for the same task. The R table is a messy data structure but is easier to get.

library(stringr)
data.creator <- function(link) {
read_html(link) %>% html_nodes("table") %>% html_table(header=TRUE, fill=TRUE) -> res
names(res) <- c("Active","Draft","Inactive","Team1","Team2")
return(res)
  }
team.names <- gsub("-", " ", team.names)
simpleCap <- function(x) {
  s <- strsplit(x, " ")[[1]]
  paste(toupper(substring(s, 1,1)), substring(s, 2),
      sep="", collapse=" ")
}
team.names <- sapply(team.names, simpleCap)
NFL.scrape <- sapply(team.URL, function(x) {data.creator(x)})
names(NFL.scrape) <- team.names 
# This is a hack but it works
Actives <- lapply(NFL.scrape, function(x){x$Active})
rep.res <- sapply(seq(1,32), function(x) {dim(Actives[[x]])[[1]]})
clean.me.2 <- function(data) {
data %>% str_remove_all("[\\t]") %>% str_split("\\n\\n\\n") %>% unlist() %>% matrix(data=., ncol=2, byrow=TRUE) -> dat
return(dat)
}
clean.me <- function(data) {
str_remove_all(data, "[\\t]")
}
clean.me.num <- function(data) {
str_remove_all(data, "[\\$,()\\-]")
  }
Players <- lapply(Actives, function(x){ clean.me.2(x[,1])})
Last.Name <- unlist(lapply(Players, function(x) {x[,1]}))
Player.Name <- unlist(lapply(Players, function(x) {x[,2]}))
Team <- rep(names(Actives),rep.res)
Position <- unlist(lapply(Actives, function(x){ clean.me(x[,2])}))
Base.Salary <- unlist(lapply(Actives, function(x){ clean.me.num(x[,'Base Salary'])}))
Base.Salary <- as.numeric(Base.Salary)
Signing.Bonus <- unlist(lapply(Actives, function(x){ clean.me.num(x[,'Signing Bonus'])}))
Signing.Bonus <- as.numeric(Signing.Bonus)
Roster.Bonus <- unlist(lapply(Actives, function(x){ clean.me.num(x[,'Roster Bonus'])}))
Roster.Bonus <- as.numeric(Roster.Bonus)
Option.Bonus <- unlist(lapply(Actives, function(x){ clean.me.num(x[,'Option Bonus'])}))
Option.Bonus <- as.numeric(Option.Bonus)
Workout.Bonus <- unlist(lapply(Actives, function(x){ clean.me.num(x[,'Workout Bonus'])}))
Workout.Bonus <- as.numeric(Workout.Bonus)
Cap.Pct <- unlist(lapply(Actives, function(x){ x[,'Cap %']}))
Restruc.Bonus <- unlist(lapply(Actives, function(x){ clean.me.num(x[,'Restruc. Bonus'])}))
Restruc.Bonus <- as.numeric(Restruc.Bonus)
Dead.Cap <- unlist(lapply(Actives, function(x){ clean.me.num(x[,'Dead Cap'])}))
Dead.Cap <- as.numeric(Dead.Cap)
Misc.Cap <- unlist(lapply(Actives, function(x){ clean.me.num(x[,'Misc.'])}))
Misc.Cap <- as.numeric(Misc.Cap)
Cap.Hit <- unlist(lapply(Actives, function(x){ clean.me.num(x[,'Cap Hit'])}))
Cap.Hit <- str_replace_all(Cap.Hit, pattern="\\s\\s*", "")
NFL.Salary.Data <- data.frame(Player=Player.Name, 
                       Last.Name = Last.Name,
                       Team=Team, 
                       Position=Position, 
                       Base.Salary=Base.Salary,
                       Signing.Bonus=Signing.Bonus,
                       Roster.Bonus=Roster.Bonus,
                       Option.Bonus=Option.Bonus,
                       Workout.Bonus=Workout.Bonus,
                       Cap.Hit=Cap.Hit,
                       Restruc.Bonus=Restruc.Bonus,
                       Dead.Cap=Dead.Cap,
                       Misc.Cap=Misc.Cap,
                       Cap.Pct=Cap.Pct)
save.image("~/NFL-Data.RData")

Load up a local copy of the data for now.

load(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/NFL-Data.RData"))

Summaries

library(tidyverse)
library(skimr)
skim(NFL.Salary.Data)

Variable type: factor

Variable type: numeric

NFL.Salary.Data %>% group_by(Team) %>% summarise(Total.Base.Salary=sum(Base.Salary))

## # A tibble: 32 x 2
##    Team               Total.Base.Salary
##    <fct>                          <dbl>
##  1 Arizona Cardinals          105629569
##  2 Atlanta Falcons            132367353
##  3 Baltimore Ravens           111906351
##  4 Buffalo Bills               94577460
##  5 Carolina Panthers          110158355
##  6 Chicago Bears               85128180
##  7 Cincinnati Bengals         134855807
##  8 Cleveland Browns           122139767
##  9 Dallas Cowboys             128347113
## 10 Denver Broncos             140672444
## # … with 22 more rows

NFL.Salary.Data %>% group_by(Position) %>% skim(Base.Salary)

Variable type: numeric

Now I have my salary data.

gplots::plotmeans(Base.Salary~Position, data=NFL.Salary.Data, n.label=FALSE, cex=0.6)

boxplot(Base.Salary~Position, data=NFL.Salary.Data)

Combining and Categorizing Data

NFL.Salary.Data$Pos.Char <- NFL.Salary.Data$SideOfBall <- NFL.SubTeam <- as.character(NFL.Salary.Data$Position)
O.Line <- c("LT","RT","C","G","T")
D.Line <- c("DT","DE")
LineBackers <- c("LB","ILB","OLB")
Safeties <- c("S","SS","FS")
CBs <- c("CB")
Special.Teams <- c("K","P","LS")
RBs <- c("RB","FB")
TEs <- c("TE")
WRs <- c("WR")
QBs <- c("QB")
Offense <- c(QBs,RBs,WRs,TEs,O.Line)
Defense <- c(D.Line,LineBackers,Safeties,CBs)
NFL.Salary.Data$SideOfBall[NFL.Salary.Data$Pos.Char %in% Offense] <- "Offense"
NFL.Salary.Data$SideOfBall[NFL.Salary.Data$Pos.Char %in% Special.Teams] <- "Special Teams"
NFL.Salary.Data$SideOfBall[NFL.Salary.Data$Pos.Char %in% Defense] <- "Defense"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% Special.Teams] <- "Special Teams"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% QBs] <- "Quarterbacks"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% RBs] <- "Running Backs"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% TEs] <- "Tight Ends"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% WRs] <- "Wide Receivers"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% O.Line] <- "Offensive Line"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% LineBackers] <- "Linebackers"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% CBs] <- "Cornerbacks"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% D.Line] <- "Defensive Line"
NFL.Salary.Data$SubTeam[NFL.Salary.Data$Pos.Char %in% Safeties] <- "Safeties"

That gives some positional details to the data also.