Rullande och kumulativa aggregeringar
Datatransformation med Polars
Liam Brannigan
Data Scientist & Polars Contributor
Varför rullande statistik?
Varför rullande statistik?
Rullande medelvärde på en kolumn
prices.with_columns(
)
Rullande medelvärde på en kolumn
prices.with_columns(
pl.col("price").rolling_mean( )
)
Rullande medelvärde på en kolumn
prices.with_columns(
pl.col("price").rolling_mean(window_size=3)
)
Rullande medelvärde på en kolumn
prices.with_columns(
pl.col("price").rolling_mean(window_size=3).alias("smoothed")
)
Rullande medelvärde på en kolumn
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 |
Rullande medelvärde på en kolumn
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 |
Centrera fönstret
prices.with_columns(
pl.col("price").rolling_mean(window_size=3, )
)
Centrera fönstret
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 |
Andra rullande uttryck
.rolling_sum()– rullande summa.rolling_std()– rullande standardavvikelse.rolling_max()/.rolling_min()
Rullande aggregering på en DataFrame
prices.rolling( )
Rullande aggregering på en DataFrame
prices.rolling(index_column="time", )
Rullande aggregering på en DataFrame
prices.rolling(index_column="time", period="3h")
Rullande aggregering på en DataFrame
prices.rolling(index_column="time", period="3h").agg(
pl.all().mean()
)
Rullande aggregering på en DataFrame
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 |
Varför kumulativ statistik?
- Uppgift: beräkna kumulativa värden
Varför kumulativ statistik?
Kumulativ statistik
prices.with_columns(
pl.col("solar")
)
Kumulativ statistik
prices.with_columns(
pl.col("solar").cum_max()
)
Kumulativ statistik
prices.with_columns(
pl.col("solar").cum_max().alias("cumulative_solar")
)
Kumulativ statistik
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 |
| ... | ... | ... | ... |
Kumulativ summa för löpande totaler
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 |
Nu kör vi en övning!
Datatransformation med Polars
