Brewers Scorecard

This appears to be a large text file containing the results of Major League Baseball (MLB) games for the 2019 season. The text is in a comma-separated values (CSV) format, with each line representing a game.

To parse this data, you can use Python's built-in csv module or a library like Pandas to read and manipulate the CSV file. Here's an example of how you could use Pandas to load the data:

import pandas as pd

# Load the data from the CSV file
df = pd.read_csv('mlb_data.csv', delimiter='|')

# Print the first few rows of the DataFrame
print(df.head())

# Print the shape and structure of the DataFrame
print(f'Shape: {df.shape}')
print(f'Dtype: {df.dtypes}')

# Perform some basic analysis on the data (e.g., calculate the number of wins, losses, and ties)
wins = df['W'].sum()
losses = df['L'].sum()
ties = df['T'.replace('.', '')].sum()

print(f'Wins: {wins}')
print(f'Losses: {losses}')
print(f'Ties: {ties}')

# Group the data by team and calculate the total number of wins, losses, and ties for each team
team_results = df.groupby('Team')[['W', 'L', 'T'.replace('.', '')]].sum()

# Print the results
print(team_results)

This code assumes that the CSV file is named mlb_data.csv and contains a header row with column names. The data should be in the following format:

The W, L, and T columns represent the number of wins, losses, and ties for each game. The data can be used to analyze team performance, calculate statistics like win-loss percentage, and identify trends in the season.

Note that this is just a basic example, and you may need to modify it to fit your specific use case. Additionally, the code assumes that the CSV file is in the correct format; if the file has an incorrect structure or missing data, the code may not work as expected. Updated: June 4, 2025 at 1:37 AM