英文:
Python-polars: Create row per unique value in a pl.DataFrame column, columns with another, and values with a third
问题
我有一个类似这样的 Polars DataFrame：d = {'id': ['N/A', 'N/A', '1', '1', '2'], 'type': ['red', 'blue', 'yellow', 'green', 'yellow'], 'area': [0, 0, 3, 4, 5]}dp = pl.DataFrame(d)shape: (5, 3)┌─────┬────────┬──────┐│ id ┆ type ┆ area ││ --- ┆ --- ┆ --- ││ str ┆ str ┆ i64 │╞═════╪════════╪══════╡│ N/A ┆ red ┆ 0 ││ N/A ┆ blue ┆ 0 ││ 1 ┆ yellow ┆ 3 ││ 1 ┆ green ┆ 4 ││ 2 ┆ yellow ┆ 5 │└─────┴────────┴──────┘我想要进行一些类似于旋转或转置的操作，以便每一行都是一个 id（不包括 'N/A'），并且每种类型都有一列，其值为 area。如果没有给出值，应该为零。在这种情况下，结果应该如下所示：red blue yellow green'1' 0 0 3 4'2' 0 0 5 0在 Polars 中我该如何实现这个操作？我宁愿避免将整个 DataFrame 转换成 pandas。
英文:
I have a Polars DataFrame that looks like this:
d = {'id': ['N/A', 'N/A', '1', '1', '2'], 'type': ['red', 'blue', 'yellow', 'green', 'yellow'], 'area': [0, 0, 3, 4, 5]}dp = pl.DataFrame(d)shape: (5, 3)┌─────┬────────┬──────┐│ id ┆ type ┆ area ││ --- ┆ --- ┆ --- ││ str ┆ str ┆ i64 │╞═════╪════════╪══════╡│ N/A ┆ red ┆ 0 ││ N/A ┆ blue ┆ 0 ││ 1 ┆ yellow ┆ 3 ││ 1 ┆ green ┆ 4 ││ 2 ┆ yellow ┆ 5 │└─────┴────────┴──────┘
I would like to do some sort of pivot or transpose operation so that each row is an id (excluding 'N/A') and there is a column for each type, and the value is the area. If no value is given, it should be zero. So in this case, the result should look like this:
red blue yellow green'1' 0 0 3 4'2' 0 0 5 0
How can I do this in Polars? I would rather avoid converting the whole thing into pandas.
答案1
得分: 2
在Polars中，您可以使用pivot操作来实现所需的结果。以下是如何在特定的DataFrame中执行此操作的示例代码：
import polars as pld = {'id': ['N/A', 'N/A', '1', '1', '2'],'type': ['red', 'blue', 'yellow', 'green', 'yellow'],'area': [0, 0, 3, 4, 5]}dp = pl.DataFrame(d)# 移除'id'列中包含'N/A'的行dp = dp.filter(pl.col("id") != "N/A")# 执行pivot操作dp = dp.pivot('id', 'type', 'area', aggfn='first')# 用0填充缺失值dp = dp.fill_null(0)print(dp)
输出结果如下：
shape: (2, 4)┌─────┬──────┬───────┬──────┐│ id ┆ blue ┆ green ┆ red ││ --- ┆ --- ┆ --- ┆ --- ││ str ┆ i64 ┆ i64 ┆ i64 │╞═════╪══════╪═══════╪══════╡│ 1 ┆ 0 ┆ 4 ┆ 0 ││ 2 ┆ 0 ┆ 0 ┆ 0 │└─────┴──────┴───────┴──────┘
请注意，这段代码演示了如何在Polars中使用pivot操作将数据透视，并在需要时填充缺失值为0。
英文:
In Polars, you can achieve the desired result by using the pivot operation. Here's how you can do it for your specific DataFrame:
import polars as pld = {'id': ['N/A', 'N/A', '1', '1', '2'],'type': ['red', 'blue', 'yellow', 'green', 'yellow'],'area': [0, 0, 3, 4, 5]}dp = pl.DataFrame(d)# Remove rows with 'N/A' in the 'id' columndp = dp.filter(pl.col("id") != "N/A")# Perform the pivot operationdp = dp.pivot('id', 'type', 'area', aggfn='first')# Fill missing values with 0dp = dp.fill_null(0)print(dp)
Output:
shape: (2, 4)┌─────┬──────┬───────┬──────┐│ id ┆ blue ┆ green ┆ red ││ --- ┆ --- ┆ --- ┆ --- ││ str ┆ i64 ┆ i64 ┆ i64 │╞═════╪══════╪═══════╪══════╡│ 1 ┆ 0 ┆ 4 ┆ 0 ││ 2 ┆ 0 ┆ 0 ┆ 0 │└─────┴──────┴───────┴──────┘
答案2
得分: 1
(df.pivot('area', 'id', 'type', None).filter(pl.col('id') != 'N/A'))
英文:
(df.pivot('area', 'id', 'type', None).filter(pl.col('id') != 'N/A'))
shape: (2, 5)┌─────┬──────┬──────┬────────┬───────┐│ id ┆ red ┆ blue ┆ yellow ┆ green ││ --- ┆ --- ┆ --- ┆ --- ┆ --- ││ str ┆ i64 ┆ i64 ┆ i64 ┆ i64 │╞═════╪══════╪══════╪════════╪═══════╡│ 1 ┆ null ┆ null ┆ 3 ┆ 4 ││ 2 ┆ null ┆ null ┆ 5 ┆ null │└─────┴──────┴──────┴────────┴───────┘
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