移動與累計聚合
使用 Polars 進行資料轉換
Liam Brannigan
Data Scientist & Polars Contributor
為什麼要用移動統計？
為什麼要用移動統計？
對欄位計算移動平均
prices.with_columns(
)
對欄位計算移動平均
prices.with_columns(
pl.col("price").rolling_mean( )
)
對欄位計算移動平均
prices.with_columns(
pl.col("price").rolling_mean(window_size=3)
)
對欄位計算移動平均
prices.with_columns(
pl.col("price").rolling_mean(window_size=3).alias("smoothed")
)
對欄位計算移動平均
prices.with_columns(
pl.col("price").rolling_mean(window_size=3).alias("smoothed")
)
| time | price | solar | smoothed |
| --- | --- | --- | --- |
| datetime[µs] | f64 | f64 | f64 |
|---------------------|-------|-------|----------|
| 2025-07-05 00:00:00 | 34.2 | 0.0 | null |
| 2025-07-05 01:00:00 | 28.7 | 0.0 | null |
對欄位計算移動平均
prices.with_columns(
pl.col("price").rolling_mean(window_size=3).alias("smoothed")
)
| time | price | solar | smoothed |
| --- | --- | --- | --- |
| datetime[µs] | f64 | f64 | f64 |
|---------------------|-------|-------|----------|
| 2025-07-05 00:00:00 | 34.2 | 0.0 | null |
| 2025-07-05 01:00:00 | 28.7 | 0.0 | null |
| 2025-07-05 02:00:00 | 27.5 | 0.0 | 30.1 |
視窗置中
prices.with_columns(
pl.col("price").rolling_mean(window_size=3, )
)
視窗置中
prices.with_columns(
pl.col("price").rolling_mean(window_size=3, center=True).alias("centered")
)
| time | price | solar | centered |
| --- | --- | --- | --- |
| datetime[µs] | f64 | f64 | f64 |
|---------------------|-------|-------|----------|
| 2025-07-05 00:00:00 | 34.2 | 0.0 | null |
| 2025-07-05 01:00:00 | 28.7 | 0.0 | 30.1 |
| 2025-07-05 02:00:00 | 27.5 | 0.0 | 27.1 |
其他移動運算式
.rolling_sum()－移動總和.rolling_std()－移動標準差.rolling_max()／.rolling_min()
對 DataFrame 做移動計算
prices.rolling( )
對 DataFrame 做移動計算
prices.rolling(index_column="time", )
對 DataFrame 做移動計算
prices.rolling(index_column="time", period="3h")
對 DataFrame 做移動計算
prices.rolling(index_column="time", period="3h").agg(
pl.all().mean()
)
對 DataFrame 做移動計算
prices.rolling(index_column="time", period="3h").agg(
pl.all().mean()
)
| time | price | solar |
| --- | --- | --- |
| datetime[µs] | f64 | f64 |
|---------------------|-------|------- |
| 2025-07-05 10:00:00 | 1.8 | 247.3 |
| 2025-07-05 11:00:00 | 1.4 | 347.7 |
| 2025-07-05 12:00:00 | 0.9 | 420.7 |
為什麼要用累計統計？
- 任務：計算累計值
為什麼要用累計統計？
累計統計
prices.with_columns(
pl.col("solar")
)
累計統計
prices.with_columns(
pl.col("solar").cum_max()
)
累計統計
prices.with_columns(
pl.col("solar").cum_max().alias("cumulative_solar")
)
累計統計
prices.with_columns(
pl.col("solar").cum_max().alias("cumulative_solar")
)
| time | price | solar | cumulative_solar |
| --- | --- | --- | --- |
| datetime[µs] | f64 | f64 | f64 |
|---------------------|-------|-------|------------------|
| ... | ... | ... | ... |
| 2025-07-05 10:00:00 | 2.3 | 419.0 | 419.0 |
| 2025-07-05 11:00:00 | 0.5 | 481.0 | 481.0 |
| 2025-07-05 12:00:00 | 0.0 | 462.0 | 481.0 |
| ... | ... | ... | ... |
用累計和計算即時總量
prices.with_columns(
pl.col("solar").cum_sum().alias("total_energy")
)
| time | price | solar | total_energy |
| --- | --- | --- | --- |
| datetime[µs] | f64 | f64 | f64 |
|---------------------|-------|-------|--------------|
| 2025-07-05 06:00:00 | 22.1 | 62.0 | 62.0 |
| 2025-07-05 07:00:00 | 4.5 | 100.0 | 162.0 |
| 2025-07-05 08:00:00 | 1.7 | 180.0 | 342.0 |
一起來練習吧！
使用 Polars 進行資料轉換
