Polars integration¶
This page describes the polars integration of Energy Quantified’s Python client. We chose to integrate with polars because it is quite popular and it works great with time series data. For full documentation on polars, refer to their documentation.
Polars is not required to use the energyquantified package. You must
therefore install polars separately to use the following operations.
We also support pandas for data frames. See the pandas integration page for more information.
Convert data series to data frames¶
You can convert any of these types to a polars.DataFrame:
They all have a method called to_polars_dataframe(). Periodseries differs from
the two others in which you must supply a frequency parameter.
There also exists an alias of to_polars_dataframe() called to_pl_df(). The term
df is a commonly used shorthand and variable name for DataFrame’s.
Note: There also exist a method called to_pandas_dataframe() and to_pd_df() for
converting to pandas data frames. See the
pandas integration page for more information.
Convert time series¶
Converting a time series is simple:
>>> df = timeseries.to_polars_dataframe()
>>> df
shape: (5, 2)
┌─────────────────────────┬─────────────────────────────────┐
│ date ┆ DE Wind Power Production MWh/h… │
│ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 │
╞═════════════════════════╪═════════════════════════════════╡
│ 2020-01-01 00:00:00 CET ┆ 9071.91 │
│ 2020-01-02 00:00:00 CET ┆ 16347.9 │
│ 2020-01-03 00:00:00 CET ┆ 32408.07 │
│ 2020-01-04 00:00:00 CET ┆ 33438.74 │
│ 2020-01-05 00:00:00 CET ┆ 13230.72 │
└─────────────────────────┴─────────────────────────────────┘
You can also set a custom name if you think the default one is a little too verbose:
>>> df = timeseries.to_polars_dataframe(name='de wind')
>>> df
shape: (5, 2)
┌─────────────────────────┬──────────┐
│ date ┆ de wind │
│ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 │
╞═════════════════════════╪══════════╡
│ 2020-01-01 00:00:00 CET ┆ 9071.91 │
│ 2020-01-02 00:00:00 CET ┆ 16347.9 │
│ 2020-01-03 00:00:00 CET ┆ 32408.07 │
│ 2020-01-04 00:00:00 CET ┆ 33438.74 │
│ 2020-01-05 00:00:00 CET ┆ 13230.72 │
└─────────────────────────┴──────────┘
If you have a scenario-based time series (such as a time series loaded for an ensemble forecast), you will get one column per ensemble.
Notice that each column is made from three components. The first is the curve, the second is the instance, and the third is the scenarios.
>>> forecast.instance
<Instance: issued="2020-10-26 00:00:00+00:00", tag="ec-ens", scenarios=51>
>>> df = forecast.to_polars_dataframe(name='de wind')
>>> df
shape: (14, 53)
┌─────────────────────────┬──────────┬─────────────┬─────────────┬───┬─────────────┬─────────────┬─────────────┬─────────────┐
│ date ┆ de wind ┆ de wind e00 ┆ de wind e01 ┆ … ┆ de wind e47 ┆ de wind e48 ┆ de wind e49 ┆ de wind e50 │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════╪══════════╪═════════════╪═════════════╪═══╪═════════════╪═════════════╪═════════════╪═════════════╡
│ 2020-10-27 00:00:00 CET ┆ 26575.48 ┆ 26733.56 ┆ 27500.18 ┆ … ┆ 24269.32 ┆ 24301.24 ┆ 30265.62 ┆ 24280.31 │
│ 2020-10-28 00:00:00 CET ┆ 30657.37 ┆ 30446.78 ┆ 28420.88 ┆ … ┆ 28426.01 ┆ 27353.77 ┆ 32797.71 ┆ 28044.18 │
│ 2020-10-29 00:00:00 CET ┆ 27776.44 ┆ 27720.31 ┆ 30748.11 ┆ … ┆ 30731.12 ┆ 25900.96 ┆ 29088.77 ┆ 28441.85 │
│ 2020-10-30 00:00:00 CET ┆ 26984.86 ┆ 23955.59 ┆ 32940.16 ┆ … ┆ 38920.07 ┆ 34470.99 ┆ 26831.95 ┆ 30003.82 │
│ 2020-10-31 00:00:00 CET ┆ 15179.69 ┆ 14326.49 ┆ 16155.63 ┆ … ┆ 16874.91 ┆ 10602.34 ┆ 8203.1 ┆ 27192.68 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2020-11-05 00:00:00 CET ┆ 19212.78 ┆ 41683.26 ┆ 29293.03 ┆ … ┆ 25730.43 ┆ 33394.54 ┆ 29395.27 ┆ 13270.89 │
│ 2020-11-06 00:00:00 CET ┆ 18814.47 ┆ 38715.68 ┆ 8994.4 ┆ … ┆ 21424.98 ┆ 36817.54 ┆ 39952.28 ┆ 6765.39 │
│ 2020-11-07 00:00:00 CET ┆ 16637.23 ┆ 20419.86 ┆ 3061.14 ┆ … ┆ 11021.58 ┆ 25435.09 ┆ 30738.6 ┆ 4298.48 │
│ 2020-11-08 00:00:00 CET ┆ 13431.43 ┆ 7185.19 ┆ 3304.81 ┆ … ┆ 30308.28 ┆ 14438.97 ┆ 21285.35 ┆ 12204.36 │
│ 2020-11-09 00:00:00 CET ┆ 13936.37 ┆ 16653.86 ┆ 7325.08 ┆ … ┆ 33969.75 ┆ 23434.31 ┆ 24121.93 ┆ 10273.44 │
└─────────────────────────┴──────────┴─────────────┴─────────────┴───┴─────────────┴─────────────┴─────────────┴─────────────┘
There are 52 columns with data here. The first one, the one without a scenario identifier, is the mean of all the other scenarios (also known as ensembles).
You can extract a single ensemble like so (here we extract scenario e48
from the 2020-10-26 00:00 ec-ens instance:
>>> df.select(pl.col('date'), pl.col('de wind e48'))
shape: (14, 2)
┌─────────────────────────┬─────────────┐
│ date ┆ de wind e48 │
│ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 │
╞═════════════════════════╪═════════════╡
│ 2020-10-27 00:00:00 CET ┆ 24301.24 │
│ 2020-10-28 00:00:00 CET ┆ 27353.77 │
│ 2020-10-29 00:00:00 CET ┆ 25900.96 │
│ 2020-10-30 00:00:00 CET ┆ 34470.99 │
│ 2020-10-31 00:00:00 CET ┆ 10602.34 │
│ … ┆ … │
│ 2020-11-05 00:00:00 CET ┆ 33394.54 │
│ 2020-11-06 00:00:00 CET ┆ 36817.54 │
│ 2020-11-07 00:00:00 CET ┆ 25435.09 │
│ 2020-11-08 00:00:00 CET ┆ 14438.97 │
│ 2020-11-09 00:00:00 CET ┆ 23434.31 │
└─────────────────────────┴─────────────┘
Convert period-based series¶
Period-based series are converted almost the same as time series. The only
difference is that you must supply a frequency parameter to the
to_polars_dataframe(frequency) method. You should read the above section before
continuing.
Here we convert a REMIT series for German nuclear available capacity to a daily
average capacity polars.DataFrame:
>>> from energyquantified.time import Frequency
>>> periodseries.instance
<Instance: issued="2020-10-24 14:10:40+00:00", tag="a-PvMRn_EpOJtngkh4D06Q">
>>> df = periodseries.to_polars_dataframe(
>>> frequency=Frequency.P1D,
>>> name='de nuclear remit'
>>> )
>>> df
shape: (7, 2)
┌──────────────────────────┬──────────────────┐
│ date ┆ de nuclear remit │
│ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 │
╞══════════════════════════╪══════════════════╡
│ 2020-10-23 00:00:00 CEST ┆ 7145.6 │
│ 2020-10-24 00:00:00 CEST ┆ 7958.7 │
│ 2020-10-25 00:00:00 CEST ┆ 8124.0 │
│ 2020-10-26 00:00:00 CET ┆ 8124.0 │
│ 2020-10-27 00:00:00 CET ┆ 8124.0 │
│ 2020-10-28 00:00:00 CET ┆ 8124.0 │
│ 2020-10-29 00:00:00 CET ┆ 8124.0 │
└──────────────────────────┴──────────────────┘
Convert OHLC data¶
When you have an OHLCList, which is the
response type from eq.ohlc.load(), you can do this:
>>> df = ohlc_list.to_polars_dataframe()
>>> df
shape: (37, 11)
┌────────────┬────────┬───────┬────────────┬──────┬──────┬──────┬───────┬────────────┬────────┬───────────────┐
│ traded ┆ period ┆ front ┆ delivery ┆ open ┆ high ┆ low ┆ close ┆ settlement ┆ volume ┆ open_interest │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ date ┆ str ┆ i32 ┆ date ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════════╪════════╪═══════╪════════════╪══════╪══════╪══════╪═══════╪════════════╪════════╪═══════════════╡
│ 2020-10-15 ┆ day ┆ 1 ┆ 2020-10-16 ┆ null ┆ null ┆ null ┆ null ┆ 23.24 ┆ 0.0 ┆ 0.0 │
│ 2020-10-15 ┆ day ┆ 2 ┆ 2020-10-17 ┆ null ┆ null ┆ null ┆ null ┆ 19.0 ┆ 0.0 ┆ 0.0 │
│ 2020-10-15 ┆ day ┆ 3 ┆ 2020-10-18 ┆ null ┆ null ┆ null ┆ null ┆ 16.0 ┆ 0.0 ┆ 0.0 │
│ 2020-10-15 ┆ day ┆ 4 ┆ 2020-10-19 ┆ null ┆ null ┆ null ┆ null ┆ 20.0 ┆ 0.0 ┆ 0.0 │
│ 2020-10-15 ┆ day ┆ 5 ┆ 2020-10-20 ┆ null ┆ null ┆ null ┆ null ┆ 20.0 ┆ 0.0 ┆ 0.0 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2020-10-15 ┆ year ┆ 6 ┆ 2026-01-01 ┆ null ┆ null ┆ null ┆ null ┆ 27.78 ┆ 0.0 ┆ 92.0 │
│ 2020-10-15 ┆ year ┆ 7 ┆ 2027-01-01 ┆ null ┆ null ┆ null ┆ null ┆ 28.49 ┆ 0.0 ┆ 111.0 │
│ 2020-10-15 ┆ year ┆ 8 ┆ 2028-01-01 ┆ null ┆ null ┆ null ┆ null ┆ 28.75 ┆ 0.0 ┆ 75.0 │
│ 2020-10-15 ┆ year ┆ 9 ┆ 2029-01-01 ┆ null ┆ null ┆ null ┆ null ┆ 30.15 ┆ 0.0 ┆ 10.0 │
│ 2020-10-15 ┆ year ┆ 10 ┆ 2030-01-01 ┆ null ┆ null ┆ null ┆ null ┆ 30.3 ┆ 0.0 ┆ 10.0 │
└────────────┴────────┴───────┴────────────┴──────┴──────┴──────┴───────┴────────────┴────────┴───────────────┘
You can filter down further the contracts you want. Say that you only wish to work on front contracts, then do this:
>>> df.filter(pl.col('front') == 1)
shape: (5, 11)
┌────────────┬─────────┬───────┬────────────┬──────┬──────┬──────┬───────┬────────────┬────────┬───────────────┐
│ traded ┆ period ┆ front ┆ delivery ┆ open ┆ high ┆ low ┆ close ┆ settlement ┆ volume ┆ open_interest │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ date ┆ str ┆ i32 ┆ date ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════════╪═════════╪═══════╪════════════╪══════╪══════╪══════╪═══════╪════════════╪════════╪═══════════════╡
│ 2020-10-15 ┆ day ┆ 1 ┆ 2020-10-16 ┆ null ┆ null ┆ null ┆ null ┆ 23.24 ┆ 0.0 ┆ 0.0 │
│ 2020-10-15 ┆ month ┆ 1 ┆ 2020-11-01 ┆ 23.5 ┆ 23.5 ┆ 22.3 ┆ 22.3 ┆ 22.35 ┆ 343.0 ┆ 10104.0 │
│ 2020-10-15 ┆ quarter ┆ 1 ┆ 2021-01-01 ┆ 28.1 ┆ 28.1 ┆ 27.1 ┆ 27.15 ┆ 27.1 ┆ 251.0 ┆ 5731.0 │
│ 2020-10-15 ┆ week ┆ 1 ┆ 2020-10-19 ┆ 21.5 ┆ 21.5 ┆ 20.0 ┆ 20.0 ┆ 20.0 ┆ 310.0 ┆ 200.0 │
│ 2020-10-15 ┆ year ┆ 1 ┆ 2021-01-01 ┆ 23.5 ┆ 23.5 ┆ 22.9 ┆ 23.0 ┆ 22.95 ┆ 89.0 ┆ 9790.0 │
└────────────┴─────────┴───────┴────────────┴──────┴──────┴──────┴───────┴────────────┴────────┴───────────────┘
For more details on filtering, see the polars documentation.
Convert a list of series to a data frame¶
Responses from eq.instances.load() and eq.period_instances.load()
respectively return a TimeseriesList and a
PeriodseriesList.
Both list implementations subclasses Python’s built-in list, so you can call
append(), extend(), pop(), remove() and more on them. They
also have utility methods for converting all series contained in them to a
single polars.DataFrame.
Convert a time series list¶
Say that you have loaded three wind power forecasts in daily resolution
using eq.instances.load(), then you can convert them to a
single polars.DataFrame like this:
>>> df = timeseries_list.to_polars_dataframe()
>>> df
shape: (10, 4)
┌──────────────────────────┬─────────────────────────────────┬─────────────────────────────────┬─────────────────────────────────┐
│ date ┆ DE Wind Power Production MWh/h… ┆ DE Wind Power Production MWh/h… ┆ DE Wind Power Production MWh/h… │
│ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════════╪═════════════════════════════════╪═════════════════════════════════╪═════════════════════════════════╡
│ 2020-10-25 00:00:00 CEST ┆ null ┆ null ┆ 23719.96 │
│ 2020-10-26 00:00:00 CET ┆ 14148.87 ┆ 15312.22 ┆ 15718.38 │
│ 2020-10-27 00:00:00 CET ┆ 22220.05 ┆ 22581.1 ┆ 22822.31 │
│ 2020-10-28 00:00:00 CET ┆ 27906.2 ┆ 29214.3 ┆ 29885.93 │
│ 2020-10-29 00:00:00 CET ┆ 28905.48 ┆ 26575.11 ┆ 35468.61 │
│ 2020-10-30 00:00:00 CET ┆ 23433.83 ┆ 13625.3 ┆ 36959.33 │
│ 2020-10-31 00:00:00 CET ┆ 9089.62 ┆ 13267.83 ┆ 22327.69 │
│ 2020-11-01 00:00:00 CET ┆ 20825.92 ┆ 23891.26 ┆ 24770.07 │
│ 2020-11-02 00:00:00 CET ┆ 26066.78 ┆ 38314.79 ┆ 21068.64 │
│ 2020-11-03 00:00:00 CET ┆ 26569.44 ┆ 25767.19 ┆ null │
└──────────────────────────┴─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────┘
You can also add more time series to timeseries_list using the built-in
list methods. There is only one requirement: They must have the same frequency.
>>> timeseries_list.insert(0, wind_actual) # Add actual first
>>> timeseries_list.insert(1, wind_normal) # Add normal second
>>> df = timeseries_list.to_polars_dataframe()
>>> df
shape: (12, 6)
┌──────────────────────────┬──────────────────────────┬──────────────────────────┬───────────────────────────────────┬───────────────────────────────────┬───────────────────────────────────┐
│ date ┆ DE Wind Power Production ┆ DE Wind Power Production ┆ DE Wind Power Production MWh/h… ┆ DE Wind Power Production MWh/h… ┆ DE Wind Power Production MWh/h… │
│ --- ┆ MWh/h… ┆ MWh/h… ┆ --- ┆ --- ┆ --- │
│ datetime[μs, CET] ┆ --- ┆ --- ┆ f64 ┆ f64 ┆ f64 │
│ ┆ f64 ┆ f64 ┆ ┆ ┆ │
╞══════════════════════════╪══════════════════════════╪══════════════════════════╪═══════════════════════════════════╪═══════════════════════════════════╪═══════════════════════════════════╡
│ 2020-10-23 00:00:00 CEST ┆ null ┆ null ┆ null ┆ 13213.79 ┆ 15756.45 │
│ 2020-10-24 00:00:00 CEST ┆ null ┆ null ┆ null ┆ 22506.26 ┆ 16053.49 │
│ 2020-10-25 00:00:00 CEST ┆ null ┆ null ┆ 23719.96 ┆ 24920.88 ┆ 16398.96 │
│ 2020-10-26 00:00:00 CET ┆ 14148.87 ┆ 15312.22 ┆ 15718.38 ┆ null ┆ 16713.83 │
│ 2020-10-27 00:00:00 CET ┆ 22220.05 ┆ 22581.1 ┆ 22822.31 ┆ null ┆ 17030.37 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2020-10-30 00:00:00 CET ┆ 23433.83 ┆ 13625.3 ┆ 36959.33 ┆ null ┆ 17740.51 │
│ 2020-10-31 00:00:00 CET ┆ 9089.62 ┆ 13267.83 ┆ 22327.69 ┆ null ┆ 17838.79 │
│ 2020-11-01 00:00:00 CET ┆ 20825.92 ┆ 23891.26 ┆ 24770.07 ┆ null ┆ 17844.98 │
│ 2020-11-02 00:00:00 CET ┆ 26066.78 ┆ 38314.79 ┆ 21068.64 ┆ null ┆ 17773.66 │
│ 2020-11-03 00:00:00 CET ┆ 26569.44 ┆ 25767.19 ┆ null ┆ null ┆ 17646.08 │
└──────────────────────────┴──────────────────────────┴──────────────────────────┴───────────────────────────────────┴───────────────────────────────────┴───────────────────────────────────┘
To get all instances for the forecast curve from the polars.DataFrame, use polars’
built-in filtering capabilities:
>>> df.select(pl.col('date'), pl.col('^.*Forecast.*$'))
shape: (12, 4)
┌──────────────────────────┬─────────────────────────────────┬─────────────────────────────────┬─────────────────────────────────┐
│ date ┆ DE Wind Power Production MWh/h… ┆ DE Wind Power Production MWh/h… ┆ DE Wind Power Production MWh/h… │
│ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════════╪═════════════════════════════════╪═════════════════════════════════╪═════════════════════════════════╡
│ 2020-10-23 00:00:00 CEST ┆ null ┆ null ┆ null │
│ 2020-10-24 00:00:00 CEST ┆ null ┆ null ┆ null │
│ 2020-10-25 00:00:00 CEST ┆ null ┆ null ┆ 23719.96 │
│ 2020-10-26 00:00:00 CET ┆ 14148.87 ┆ 15312.22 ┆ 15718.38 │
│ 2020-10-27 00:00:00 CET ┆ 22220.05 ┆ 22581.1 ┆ 22822.31 │
│ … ┆ … ┆ … ┆ … │
│ 2020-10-30 00:00:00 CET ┆ 23433.83 ┆ 13625.3 ┆ 36959.33 │
│ 2020-10-31 00:00:00 CET ┆ 9089.62 ┆ 13267.83 ┆ 22327.69 │
│ 2020-11-01 00:00:00 CET ┆ 20825.92 ┆ 23891.26 ┆ 24770.07 │
│ 2020-11-02 00:00:00 CET ┆ 26066.78 ┆ 38314.79 ┆ 21068.64 │
│ 2020-11-03 00:00:00 CET ┆ 26569.44 ┆ 25767.19 ┆ null │
└──────────────────────────┴─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────┘
Convert a period-based series list¶
Just like with Periodseries, specify a
frequency to first convert to a fixed-interval time series in your
preferred resolution in a polars.DataFrame. Using the German nuclear REMIT
capacity example as before, we can see how the available nuclear capacity was
at different times:
>>> from energyquantified.time import Frequency
>>> df = periodseries_list.to_polars_dataframe(frequency=Frequency.P1D)
>>> df
shape: (9, 5)
┌──────────────────────────┬─────────────────────────────────┬─────────────────────────────────┬─────────────────────────────────┬─────────────────────────────────┐
│ date ┆ DE Nuclear Capacity Available … ┆ DE Nuclear Capacity Available … ┆ DE Nuclear Capacity Available … ┆ DE Nuclear Capacity Available … │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════════╪═════════════════════════════════╪═════════════════════════════════╪═════════════════════════════════╪═════════════════════════════════╡
│ 2020-10-21 00:00:00 CEST ┆ null ┆ null ┆ null ┆ null │
│ 2020-10-22 00:00:00 CEST ┆ null ┆ null ┆ null ┆ null │
│ 2020-10-23 00:00:00 CEST ┆ null ┆ null ┆ null ┆ null │
│ 2020-10-24 00:00:00 CEST ┆ null ┆ null ┆ 8118.020833 ┆ 8124.0 │
│ 2020-10-25 00:00:00 CEST ┆ 8124.0 ┆ null ┆ 8124.0 ┆ 8124.0 │
│ 2020-10-26 00:00:00 CET ┆ 8124.0 ┆ null ┆ 8124.0 ┆ 8124.0 │
│ 2020-10-27 00:00:00 CET ┆ 8124.0 ┆ null ┆ 8124.0 ┆ 8124.0 │
│ 2020-10-28 00:00:00 CET ┆ 8124.0 ┆ null ┆ 8124.0 ┆ 8124.0 │
│ 2020-10-29 00:00:00 CET ┆ null ┆ null ┆ null ┆ null │
└──────────────────────────┴─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────┘
Column headers for time series data¶
The data frames created from time series data has columns made of three components:
Curve name
Instance or contract
Scenario
Curve name is set the timeseries.curve.name by default. If there is
no curve attribute on the Timeseries object,
it defaults to be blank. The user can override this name by setting a custom
name (see below).
Instance or contract is set (defaults to blank) when the time series is an instance (forecast) or when the response is an OHLC series converted to a time series:
For instances, this column header is set to
<curve.name> <issued> <tag>, like so:DE Wind Power Production MWh/h 15min Forecast 2020-10-16 00:00 ec.
Scenario is the scenario or ensemble ID. This header is blank unless you
load ensemble data or scenario time series. For ensembles, it is normally
named eNN where NN is the zero-padded ensemble ID. ECMWF ensemble
forecasts, for example, have 51 scenarios, named from e00, e01, …,
e49, e50. Climate series uses underlying weather years. These column
headers are named after the weather year they are based on: y1980,
y1981, …, y2018, y2019.
Set custom time series name¶
Energy Quantified’s curve names are made to be easy to understand but can be
quite long. So we made a set_name
method for Timeseries: and
Periodseries.
Use it to set your own custom name before converting to a polars.DataFrame:
>>> timeseries.name
'DE Wind Power Production MWh/h 15min Actual'
>>> timeseries.set_name('de wind actual')
>>> timeseries.name
'de wind actual'
The custom name is reflected in the polars.DataFrame column header:
>>> timeseries.to_polars_dataframe()
shape: (5, 2)
┌─────────────────────────┬─────────────────┐
│ date ┆ de wind actual │
│ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 │
╞═════════════════════════╪═════════════════╡
│ 2020-01-01 00:00:00 CET ┆ 9071.91 │
│ 2020-01-02 00:00:00 CET ┆ 16347.9 │
│ 2020-01-03 00:00:00 CET ┆ 32408.07 │
│ 2020-01-04 00:00:00 CET ┆ 33438.74 │
│ 2020-01-05 00:00:00 CET ┆ 13230.72 │
└─────────────────────────┴─────────────────┘
You can also specify a name when invoking the to_polars_dataframe() method on
time series objects:
>>> timeseries.to_polars_dataframe(name='my awesome name')
shape: (5, 2)
┌─────────────────────────┬──────────────────┐
│ date ┆ my awesome name │
│ --- ┆ --- │
│ datetime[μs, CET] ┆ f64 │
╞═════════════════════════╪══════════════════╡
│ 2020-01-01 00:00:00 CET ┆ 9071.91 │
│ 2020-01-02 00:00:00 CET ┆ 16347.9 │
│ 2020-01-03 00:00:00 CET ┆ 32408.07 │
│ 2020-01-04 00:00:00 CET ┆ 33438.74 │
│ 2020-01-05 00:00:00 CET ┆ 13230.72 │
└─────────────────────────┴──────────────────┘
