Using Pandas for Team Data Analysis
Pandas is a powerful Python library that makes it easy to work with tabular data—data organized in rows and columns, much like a spreadsheet. As a coach, you often handle team data such as player names, goals, assists, and minutes played. Pandas introduces the DataFrame, a flexible structure for storing and manipulating this kind of information. With a DataFrame, you can quickly organize, filter, and analyze your athletes' stats, allowing you to make informed coaching decisions with less effort and more accuracy.
123456789101112import pandas as pd # Create a DataFrame with team data data = { "Name": ["Alex", "Jordan", "Taylor", "Morgan", "Casey"], "Goals": [7, 3, 9, 2, 6], "Assists": [4, 5, 2, 6, 3], "Minutes Played": [540, 480, 600, 390, 510] } team_df = pd.DataFrame(data) print(team_df)
Once you have your team data in a DataFrame, you can perform a range of operations to gain insights. To select a specific column, such as "Goals", use square brackets: team_df["Goals"]. This gives you a list of goals for each athlete. You can filter rows to focus on athletes who meet certain criteria. For example, to see only those who have scored more than 5 goals, you can use a condition: team_df[team_df["Goals"] > 5]. Pandas also makes it easy to calculate statistics, such as the average, sum, or maximum of any column. For instance, team_df["Minutes Played"].mean() returns the average minutes played by all athletes. These operations help you quickly identify leaders, trends, and areas for improvement within your team.
12345678# Filter athletes with more than 5 goals top_scorers = team_df[team_df["Goals"] > 5] # Calculate their average minutes played average_minutes = top_scorers["Minutes Played"].mean() print("Athletes with more than 5 goals:") print(top_scorers) print("Average minutes played by these athletes:", average_minutes)
1. What is a pandas DataFrame?
2. How can you select a specific column in a DataFrame?
3. Why might a coach filter athletes based on performance metrics?
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Pandas is a powerful Python library that makes it easy to work with tabular data—data organized in rows and columns, much like a spreadsheet. As a coach, you often handle team data such as player names, goals, assists, and minutes played. Pandas introduces the DataFrame, a flexible structure for storing and manipulating this kind of information. With a DataFrame, you can quickly organize, filter, and analyze your athletes' stats, allowing you to make informed coaching decisions with less effort and more accuracy.
123456789101112import pandas as pd # Create a DataFrame with team data data = { "Name": ["Alex", "Jordan", "Taylor", "Morgan", "Casey"], "Goals": [7, 3, 9, 2, 6], "Assists": [4, 5, 2, 6, 3], "Minutes Played": [540, 480, 600, 390, 510] } team_df = pd.DataFrame(data) print(team_df)
Once you have your team data in a DataFrame, you can perform a range of operations to gain insights. To select a specific column, such as "Goals", use square brackets: team_df["Goals"]. This gives you a list of goals for each athlete. You can filter rows to focus on athletes who meet certain criteria. For example, to see only those who have scored more than 5 goals, you can use a condition: team_df[team_df["Goals"] > 5]. Pandas also makes it easy to calculate statistics, such as the average, sum, or maximum of any column. For instance, team_df["Minutes Played"].mean() returns the average minutes played by all athletes. These operations help you quickly identify leaders, trends, and areas for improvement within your team.
12345678# Filter athletes with more than 5 goals top_scorers = team_df[team_df["Goals"] > 5] # Calculate their average minutes played average_minutes = top_scorers["Minutes Played"].mean() print("Athletes with more than 5 goals:") print(top_scorers) print("Average minutes played by these athletes:", average_minutes)
1. What is a pandas DataFrame?
2. How can you select a specific column in a DataFrame?
3. Why might a coach filter athletes based on performance metrics?
Tak for dine kommentarer!
