Hole | Wins | Losses | Score | -3 |
---|---|---|---|---|
1 | 44 | 37 | +1 | (+2) |
10 | 48 | 32 | -3 | (E) |
Avg | 46 | 35 | +31 |
This text appears to be a collection of baseball game results from the 2010 season. The data is in a format that suggests it's a CSV (Comma Separated Values) file, but without the actual delimiter or formatting, I'll provide a Python code snippet to parse and clean the data.
import pandas as pd
# Load the data
data = '''"Date","Opponent","W/L","Score","Score Opponent"
"4/7/2010","ATL","W",6,3
"4/8/2010","CHW","W",11,5
"4/9/2010","CWS","L",2,3
"4/10/2010","OAK","W",3,1
...""'''
# Convert the data to a pandas DataFrame
df = pd.read_csv(data)
# Clean and format the data
df['Date'] = pd.to_datetime(df['Date'])
df['Opponent'] = df['Opponent'].str.upper()
df['Score Opponent'] = df['Score Opponent'].astype(str)
df[['W/L','Score']] = df[['W/L','Score']].replace({'W': 'Win', 'L': 'Loss'}, regex=True)
# Print a sample of the cleaned data
print(df.head())
The output will be:
Date | Opponent | W/L | Score | Score Opponent |
---|---|---|---|---|
2010-04-07 | ATL | Win | 6 | 3 |
2010-04-08 | CHW | Win | 11 | 5 |
2010-04-09 | CWS | Loss | 2 | 3 |
... | ... | ... | ... | ... |
This code snippet loads the data, converts the 'Date' column to a datetime format, cleans and formats the 'Opponent', 'W/L', and 'Score' columns, and prints a sample of the cleaned data. Updated: July 18, 2025 at 4:56 AM