循环遍历DataFrame并按event类型填充对应xCordAdjusted、yCordAdjusted数据至字典的实现问题

Let's break down what's going wrong with your current code, then walk through a corrected implementation that will populate your league_data dictionary as expected.

Key Issues in Your Original Code #

• Wrong DataFrame iteration: for data in season_df loops through your DataFrame's column names (like season, event), not the actual rows of data. So your checks against event_types are comparing column names to event values—this will never match, hence your empty lists.
• Incorrect event check: Even if you were looping rows, if data in event_types is checking the entire row object against your event list, not the value stored in the event column of that row.
• Nonsensical x-range condition: if 'x' in range(0,100) compares the string "x" to integers 0-99, which will always be False. This line is blocking all your append operations entirely.
• Adding strings instead of values: append(['xCordAdjusted']) adds the literal string "xCordAdjusted" (wrapped in a list) to your x/y lists, not the numeric coordinate value from the row.

Corrected Implementation (Row-by-Row Iteration) #

This approach uses iterrows() to loop through each row of your DataFrame, which is straightforward for smaller datasets:

# Initialize the target dictionary with empty lists
league_data = {
    'SHOT': {'x': [], 'y': []},
    'GOAL': {'x': [], 'y': []},
    'MISS': {'x': [], 'y': []}
}

target_events = ['SHOT', 'GOAL', 'MISS']

# Iterate over each row (ignore the index with "_")
for _, row in season_df.iterrows():
    current_event = row['event']
    # Only process events we care about
    if current_event in target_events:
        # Extract the adjusted coordinate values
        x_adjusted = row['xCordAdjusted']
        y_adjusted = row['yCordAdjusted']
        # Append to the corresponding lists in the dictionary
        league_data[current_event]['x'].append(x_adjusted)
        league_data[current_event]['y'].append(y_adjusted)

More Efficient Version (Using groupby) #

For larger datasets, iterrows() can be slow. Using pandas' groupby is a more optimized approach:

league_data = {}
target_events = ['SHOT', 'GOAL', 'MISS']

# First filter the DataFrame to only include our target events
filtered_season_df = season_df[season_df['event'].isin(target_events)]

# Group by event type and extract coordinate lists
for event, group in filtered_season_df.groupby('event'):
    league_data[event] = {
        'x': group['xCordAdjusted'].tolist(),
        'y': group['yCordAdjusted'].tolist()
    }

What This Does #

• Both versions will take your sample rows (like the MISS event with xCordAdjusted=61 and yCordAdjusted=29) and add those values to the correct lists in league_data.
• After running, your league_data['MISS']['x'] will contain [61], league_data['SHOT']['x'] will contain [54, 40], and so on.

内容的提问来源于stack exchange，提问作者jhh19