英文:
Combining multiple groups in Polars
问题
我有一个像这样的数据框：
| category | year | count |
|---|---|---|
| apple | 2022 | 5 |
| apple | 2021 | 8 |
| banana | 2022 | 1 |
| cold | 2022 | 9 |
| cold | 2021 | 2 |
| warm | 2022 | 1 |
| warm | 2021 | 3 |
我需要根据预设的分组列表（'fruit'，'temperature'）对行进行分组，然后按年份进行聚合。最终的数据框将如下所示：
| category | year | count |
|---|---|---|
| fruit | 2022 | 6 |
| fruit | 2021 | 8 |
| temp | 2022 | 10 |
| temp | 2021 | 5 |
Category的值是字符串。我正在寻找任何可以使此工作的解决方案。实际的数据框要长得多，所以我希望使用类似带有分组的字典来进行聚合。
英文:
I have a dataframe like this:
| category | year | count |
|---|---|---|
| apple | 2022 | 5 |
| apple | 2021 | 8 |
| banana | 2022 | 1 |
| cold | 2022 | 9 |
| cold | 2021 | 2 |
| warm | 2022 | 1 |
| warm | 2021 | 3 |
I need to group the rows based on a pre-set list of groupings ('fruit', 'temperature') and then aggregate by year. The final DF would look like this:
| category | year | count |
|---|---|---|
| fruit | 2022 | 6 |
| fruit | 2021 | 8 |
| temp | 2022 | 10 |
| temp | 2021 | 5 |
The Category values are strings. I'm looking for any solution to make this work. The actual dataframe is quite a bit longer, so I'm hoping to use something like a dict with the groupings to aggregate.
答案1
得分: 1
我会将 category 列映射为标准化为 fruit/temperature，然后进行分组：
md = {"apple": "fruit", "banana": "fruit", "cold": "temp", "warm": "temp"}
df.with_columns(pl.col("category").map_dict(md)).groupby("category", "year").sum()
shape: (4, 3)
┌──────────┬──────┬───────┐
│ category ┆ year ┆ count │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞══════════╪══════╪═══════╡
│ fruit ┆ 2022 ┆ 6 │
│ temp ┆ 2022 ┆ 10 │
│ fruit ┆ 2021 ┆ 8 │
│ temp ┆ 2021 ┆ 5 │
└──────────┴──────┴───────┘
你也可以使用 when/then 链来标准化 category 列，但在更复杂的示例中，map_dict 会是更简洁的代码。
英文:
I would map_dict the category column to standardize that to fruit/temperature, then a groupby:
md = {"apple": "fruit", "banana": "fruit", "cold": "temp", "warm": "temp"}
df.with_columns(pl.col("category").map_dict(md)).groupby("category", "year").sum()
shape: (4, 3)
┌──────────┬──────┬───────┐
│ category ┆ year ┆ count │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞══════════╪══════╪═══════╡
│ fruit ┆ 2022 ┆ 6 │
│ temp ┆ 2022 ┆ 10 │
│ fruit ┆ 2021 ┆ 8 │
│ temp ┆ 2021 ┆ 5 │
└──────────┴──────┴───────┘
You could also do a when/then chain to standardize the category column, but in more complicated examples map_dict will be more concise code.
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