Yes, Python is easy for game development due to its simple syntax and rich libraries like Pygame, which simplify the game creation process, making it accessible for beginners.
Key takeaways:
Game play analysis helps in understanding player engagement and preferences by examining data related to their interactions and behaviors in the game.
Key metrics to look at include player retention, average playtime, and the popularity of different game features.
Python, along with the pandas library, is a powerful tool for manipulating and analyzing game play data effectively.
Common analysis tasks involve calculating average, total, and maximum playtime per player to gauge their engagement levels.
Retention rates show how many players return to the game over time, which is crucial for understanding player loyalty.
Identifying popular game features can guide future development and updates, ensuring the game meets player expectations.
In Python, Gameplay analysis involves examining data related to player interactions, behaviors, and events within a game. This analysis can provide insights into player engagement, preferences, and areas for improvement. A common task is to explore metrics such as player retention, average playtime, popular game features, and more.
Let’s consider several hypothetical gameplay analysis problems. We’ll use Python with pandas for data manipulation.
Suppose we have a dataset with columns player_id and playtime_min, where each row represents a gaming session for a player. In this example, we calculate the average playtime for each player based on their gaming sessions.
import pandas as pd# Sample datasetdata = {'player_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],'playtime_min': [30, 45, 20, 40, 60, 25, 35, 50, 15],}df = pd.DataFrame(data)# Calculate average playtime per playeraverage_playtime = df.groupby('player_id')['playtime_min'].mean()# Display the resultsprint("Average Playtime per Player:")print(average_playtime)
This code does the following:
Line 1: Imports pandas for data manipulation.
Lines 4–7: Creates a sample dataset (replace it with any actual data).
Line 9: Creates a pandas DataFrame from the dataset.
Line 12: Uses the groupby function to group the data by player_id, and applies the mean function to calculate the average playtime for each player.
Lines 15–16: Displays the results.
In this problem, we aim to calculate the total playtime for each player across multiple gaming sessions. This metric is valuable for understanding player engagement and how much time each player invests in the game.
import pandas as pd# Sample datasetdata = {'player_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],'playtime_min': [30, 45, 20, 40, 60, 25, 35, 50, 15],}df = pd.DataFrame(data)# Calculate total playtime per playertotal_playtime = df.groupby('player_id')['playtime_min'].sum()# Display the resultsprint("Total Playtime per Player:")print(total_playtime)
This code does the following:
Line 1: Imports pandas for data manipulation.
Lines 4–7: Creates a sample dataset (replace it with any actual data).
Line 9: Creates a pandas DataFrame from the dataset.
Line 12: Uses the groupby function to group the data by player_id, and applies the sum function to calculate the total playtime for each player.
Lines 15–16: Displays the results.
The code example below identifies the maximum playtime recorded for each player. This metric can help game developers understand the most intense or extended gaming sessions, revealing which players are engaging with the game for longer periods during a single session.
import pandas as pd# Sample datasetdata = {'player_id': [1, 2, 3, 1, 2, 3, 1, 2, 3],'playtime_min': [30, 45, 20, 40, 60, 25, 35, 50, 15],}df = pd.DataFrame(data)# Identify the maximum playtime per playermax_playtime = df.groupby('player_id')['playtime_min'].max()# Display the resultsprint("Maximum Playtime per Player:")print(max_playtime)
This code does the following:
Line 1: Imports pandas for data manipulation.
Lines 4–7: Create a sample dataset (replace it with any actual data).
Line 9: Creates a pandas DataFrame from the dataset.
Line 12: Uses the groupby function to group the data by player_id, and applies the max function to calculate the maximum playtime for each player.
Lines 15–16: Displays the results.
Retention rate is a key metric in gameplay analysis that measures the percentage of players who return to the game after a certain period. This metric helps in understanding player loyalty and the effectiveness of game features in retaining players.
import pandas as pddata = {'player_id': [1, 2, 3, 4, 5],'first_play_date': ['2023-01-01', '2023-01-02', '2023-01-01', '2023-01-03', '2023-01-04'],'last_play_date': ['2023-01-10', '2023-01-05', '2023-01-07', '2023-01-12', '2023-01-04']}df = pd.DataFrame(data)df['first_play_date'] = pd.to_datetime(df['first_play_date'])df['last_play_date'] = pd.to_datetime(df['last_play_date'])df['retention_days'] = (df['last_play_date'] - df['first_play_date']).dt.daysretained_players = df[df['retention_days'] > 7]retention_rate = len(retained_players) / len(df) * 100print(f"Retention Rate: {retention_rate:.2f}%")
Line 1: Imports the necessary library.
Lines 2–7: A dataset named data is created containing player IDs, their first play dates, and their last play dates. This data is then converted into a pandas DataFrame called df.
Lines 8–9: The first_play_date and last_play_date columns are converted from strings to datetime objects using pd.to_datetime(). This conversion allows for date arithmetic, which is essential for calculating retention.
Line 10: Calculates the retention period for each player.
This line calculates the difference between the last play date and the first play date for each player.
The result is a Timedelta object representing the duration between the two dates. Using .dt.days extracts the total number of days from this timedelta.
For example, if a player’s first play date is 2023-01-01 and their last play date is 2023-01-10, the difference is 9 days, which would be stored in the retention_days column.
Line 11: Filters the DataFrame to include only those players whose retention days are greater than 7. This indicates they played the game consistently over a week.
Line 12: len(retained_players) gives the number of players who retained (i.e., played for more than 7 days).
len(df) gives the total number of players in the dataset.
The division provides the proportion of retained players, which is then multiplied by 100 to convert it into a percentage.
Line 14: Prints the retention rate.
Understanding which game features are most popular among players can guide future development and updates. This analysis involves counting the number of times specific features are used across different sessions.
import pandas as pddata = {'player_id': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],'session_id': [101, 102, 103, 104, 105, 106, 107, 108, 109, 110],'features_used': ['A,B,C', 'A,C', 'B,C', 'A,B', 'C', 'A,B', 'B', 'C', 'A,C', 'B']}df = pd.DataFrame(data)features_df = df['features_used'].str.split(',', expand=True).stack().reset_index(level=1, drop=True)features_count = features_df.value_counts()print("Feature Usage Count:\n", features_count)
Lines 2–7: Imports the necessary library and creates a sample dataset.
Line 8: Takes the features_used column.
Splits each string entry at commas into multiple columns
Transforms this wide format into a long format using stack(). This allows each feature to appear as a separate row.
Finally, reset_index(level=1, drop=True) removes the extra index created when stacking the data, making it simpler to read and work with. This way, the data is organized neatly, allowing you to easily count how many times each feature is used in the game sessions.
Line 9: Counts the occurrences of each feature across all sessions.
Line 10: Displays the count of each feature, helping to identify the most popular features.
In a real-world scenario, we might have a more extensive dataset and conduct a more in-depth analysis. We could explore additional metrics, visualize trends, or employ machine learning techniques for predictive analysis.
Remember, the specific gameplay analysis problem and the code we write will depend on the goals of our analysis and the nature of our game data.
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