英文:
Group by month, sum one column and average another
问题
我有一个数据框，看起来像这样：
| 日期 | 游戏次数 | 评分 |
|---|---|---|
| 2019-05-23 | 8 | 22 |
| 2023-01-29 | 10 | 32 |
实际表格要长得多。我想按月份对表格进行分组（日期列是一个 DateTime 格式的列），并在这样做的同时，将游戏次数列相加，但将评分列求平均。基本上，每一行都会有一个月份，该月的游戏总次数和该月的平均评分。在日期列上分组的同时，如何执行这些不同的汇总操作呢？
英文:
I have the a dataframe that looks like the following:
| Date | Games Played | Rating |
|---|---|---|
| 2019-05-23 | 8 | 22 |
| 2023-01-29 | 10 | 32 |
The actual table is much longer. I want to group the table by month (the date column is a DateTime format column), and in doing so, sum together the games played column but average the rating column. Essentially, every row will have a month, total games played that month, and average rating for that month. How can I do these separate aggregations while still grouping by month in the date column.
答案1
得分: 1
尝试：
x = df.groupby(df['Date'].dt.month).agg({'Games Played': 'sum', 'Rating': 'mean'})print(x)
打印出：
Games Played RatingDate1 13 18.55 11 21.0
使用的数据框：
Date Games Played Rating0 2019-05-23 8 221 2019-05-24 1 212 2019-05-25 2 203 2023-01-28 3 54 2023-01-29 10 32
如果要按年和月分组：
x = df.groupby([df['Date'].dt.year, df['Date'].dt.month]).agg({'Games Played': 'sum', 'Rating': 'mean'})print(x)
英文:
Try:
x = df.groupby(df['Date'].dt.month).agg({'Games Played': 'sum', 'Rating': 'mean'})print(x)
Prints:
Games Played RatingDate1 13 18.55 11 21.0
DataFrame used:
Date Games Played Rating0 2019-05-23 8 221 2019-05-24 1 212 2019-05-25 2 203 2023-01-28 3 54 2023-01-29 10 32
If you want to group by year and month:
x = df.groupby([df['Date'].dt.year, df['Date'].dt.month]).agg({'Games Played': 'sum', 'Rating': 'mean'})print(x)
答案2
得分: 1
使用 aggregate 方法：
df.groupby(df.Date.dt.month).aggregate({'Games Played': 'sum', 'Rating': 'mean'})
英文:
Use aggregate
df.groupby(df.Date.dt.month).aggregate({'Games Played': 'sum', 'Rating': 'mean'})
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