How to remove characters in pandas data frame when web scraping with Python?

Question

I am trying to web scrape, by using Python 3, a table off of this website into a.csv file: 2011-2012 NBA National Schedule

The table starts out like:

                Revised Schedule                    Original Schedule

Date            Time      Game                Net   Time      Game                  Net
Sun., 12/25/11  12 PM     BOS (1) at NY (1)   TNT   12 PM     BOS (7) at NY (7)     ESPN
Sun., 12/25/11  2:30 PM   MIA (1) at DAL (1)  ABC   2:30 PM   MIA (8) at DAL (5)    ABC
Sun., 12/25/11  5 PM      CHI (1) at LAL (1)  ABC   5 PM      CHI (6) at LAL (9)    ABC
Sun., 12/25/11  8 PM      ORL (1) at OKC (1)  ESPN  no game   no game               no game
Sun., 12/25/11  10:30 PM  LAC (1) at GS (1)   ESPN  no game   no game               no game
Tue., 12/27/11  8 PM      BOS (2) at MIA (2)  TNT   no game   no game               no game
Tue., 12/27/11  10:30 PM  UTA (1) at LAL (2)  TNT   no game   no game               no game

I am only interested in the revised schedule which is the first 4 columns. The output I want in a.csv file looks like this:

I am using these packages:

import re
import requests
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
from itertools import groupby

This is the code I have done to match the format I want:

df = pd.read_html("https://www.sportsmediawatch.com/2011/12/revised-2011-12-nba-national-tv-schedule/", header=0)[0]

revisedCols = ['Date'] + [ col for col in df.columns if 'Revised' in col ]
df = df[revisedCols]

df.columns = df.iloc[0,:]

df = df.iloc[1:,:].reset_index(drop=True)


# Format Date to m/d/y
df['Date'] = np.where(df.Date.str.startswith(('10/', '11/', '12/')), df.Date + ' 11', df.Date + ' 12')
df['Date']=pd.to_datetime(df['Date'])
df['Date']=df['Date'].dt.strftime('%m/%d/%Y')

# Split the Game column
df[['Away','Home']] = df.Game.str.split('at',expand=True)   


# Final dataframe with desired columns
df = df[['Date','Time','Away','Home','Net']]

df.columns = ['Date', 'Time', 'Away', 'Home', 'Network']

print(df)

Output:

           Date      Time      Away        Home Network
0    12/25/2011     12 PM   BOS (1)      NY (1)     TNT
1    12/25/2011   2:30 PM   MIA (1)     DAL (1)     ABC
2    12/25/2011      5 PM   CHI (1)     LAL (1)     ABC
3    12/25/2011      8 PM   ORL (1)     OKC (1)    ESPN
4    12/25/2011  10:30 PM   LAC (1)      GS (1)    ESPN
5    12/27/2011      8 PM   BOS (2)     MIA (2)     TNT
6    12/27/2011  10:30 PM   UTA (1)     LAL (2)     TNT

I noticed there is (1), (2), etc. next to each team name in the Away and Home columns. How do I implement the scraper to removed the (1), (2), etc. next to each team name in the Away and Home columns?

Answer 1

you can use str.replace with the parenthesis and the number(s) and also str.strip at it seems there are some whitespaces at the beginning or the end:

df['Away'] = df['Away'].str.replace('\(\d*\)', '').str.strip()
df['Home'] = df['Home'].str.replace('\(\d*\)', '').str.strip()
print (df.head())
         Date      Time Away Home Network
0  12/25/2011     12 PM  BOS   NY     TNT
1  12/25/2011   2:30 PM  MIA  DAL     ABC
2  12/25/2011      5 PM  CHI  LAL     ABC
3  12/25/2011      8 PM  ORL  OKC    ESPN
4  12/25/2011  10:30 PM  LAC   GS    ESPN

Answer 2

import re
import numpy as np
import pandas as pd

dataset = pd.read_csv("Dataset.csv")
dataset.rename(columns={'Country(or dependent territory)': 'Country'}, inplace = True)
dataset.rename(columns={'% of worldpopulation': 'Percentage of World Population'}, inplace = True)
dataset.rename(columns={'Total Area': 'Total Area (km2)'}, inplace = True)

Answer 3

You can add this code after split the Game column

df['Away']=df['Away'].astype(str).str[0:-4]
df['Home']=df['Home'].astype(str).str[0:-4]

Answer 4

Instead of spliting the Game column at 'at ', don't specifically state a delimiter. .split() will split at every white space, and then you just want the 0 index, and 3rd index values there. So really just change 1 line of code:

from df[['Away','Home']] = df.Game.str.split('at',expand=True) to df[['Away','Home']] = df.Game.str.split(expand=True)[[0,3]]

import pandas as pd
import numpy as np

df = pd.read_html("https://www.sportsmediawatch.com/2011/12/revised-2011-12-nba-national-tv-schedule/", header=0)[0]

revisedCols = ['Date'] + [ col for col in df.columns if 'Revised' in col ]
df = df[revisedCols]

df.columns = df.iloc[0,:]

df = df.iloc[1:,:].reset_index(drop=True)


# Format Date to m/d/y
df['Date'] = np.where(df.Date.str.startswith(('10/', '11/', '12/')), df.Date + ' 11', df.Date + ' 12')
df['Date']=pd.to_datetime(df['Date'])
df['Date']=df['Date'].dt.strftime('%m/%d/%Y')

# Split the Game column
df[['Away','Home']] = df.Game.str.split(expand=True)[[0,3]]   


# Final dataframe with desired columns
df = df[['Date','Time','Away','Home','Net']]

df.columns = ['Date', 'Time', 'Away', 'Home', 'Network']

print(df)