docs: add Polars example to readme (#68)

* Add Polars example to readme

* Update README.md

README.md

@@ -2,7 +2,7 @@
 
 This ia an API client to get weather data from the [Open-Meteo Weather API](https://open-meteo.com) based on the Python library `requests`.
 
-Instead of using JSON, the API client uses FlatBuffers to transfer data. Encoding data in FlatBuffers is more efficient for long time-series data. Data can be transferred to `numpy` or `pandas` using [Zero-Copy](https://en.wikipedia.org/wiki/Zero-copy) to analyze large amount of data quickly. The schema definition files can be found on [GitHub open-meteo/sdk](https://github.com/open-meteo/sdk).
+Instead of using JSON, the API client uses FlatBuffers to transfer data. Encoding data in FlatBuffers is more efficient for long time-series data. Data can be transferred to `numpy`, `pandas`, or `polars` using [Zero-Copy](https://en.wikipedia.org/wiki/Zero-copy) to analyze large amount of data quickly. The schema definition files can be found on [GitHub open-meteo/sdk](https://github.com/open-meteo/sdk).
 
 This library is primarily designed for data-scientists to process weather data. In combination with the [Open-Meteo Historical Weather API](https://open-meteo.com/en/docs/historical-weather-api) data from 1940 onwards can be analyzed quickly.
 
@@ -64,8 +64,7 @@ hourly_wind_speed_10m = next(filter(lambda x: x.Variable() == Variable.wind_spee
 
 ### Pandas
 
-For `Pandas` you can prepare a data-frame from hourly data like follows:
-
+After using `NumPy` to create arrays for hourly data, you can use `Pandas` to create a DataFrame from hourly data like follows:
 
 ```python
 import pandas as pd
@@ -80,13 +79,43 @@ hourly_data["temperature_2m"] = hourly_temperature_2m
 hourly_data["precipitation"] = hourly_precipitation
 hourly_data["wind_speed_10m"] = hourly_wind_speed_10m
 
-hourly_dataframe = pd.DataFrame(data = hourly_data)
-print(hourly_dataframe)
-#date temperature_2m precipitation
-#0 2023-08-01 00:00:00 16.945999 1.7
-#1 2023-08-01 01:00:00 16.996000 2.1
-#2 2023-08-01 02:00:00 16.996000 1.0
-#3 2023-08-01 03:00:00 16.846001 0.2
+hourly_dataframe_pd = pd.DataFrame(data = hourly_data)
+print(hourly_dataframe_pd)
+# date temperature_2m precipitation wind_speed_10m
+# 0 2024-06-21 00:00:00 17.437000 0.0 6.569383
+# 1 2024-06-21 01:00:00 17.087000 0.0 6.151683
+# 2 2024-06-21 02:00:00 16.786999 0.0 7.421590
+# 3 2024-06-21 03:00:00 16.337000 0.0 5.154416
+```
+
+### Polars
+
+Additionally, `Polars` can also be used to create a DataFrame from hourly data using the `NumPy` arrays created previously:
+
+```python
+import polars as pl
+from datetime import datetime, timedelta, timezone
+
+start = datetime.fromtimestamp(hourly.Time(), timezone.utc)
+end = datetime.fromtimestamp(hourly.TimeEnd(), timezone.utc)
+freq = timedelta(seconds = hourly.Interval())
+
+hourly_dataframe_pl = pl.select(
+ date = pl.datetime_range(start, end, freq, closed = "left"),
+ temperature_2m = hourly_temperature_2m,
+ precipitation = hourly_precipitation,
+ wind_speed_10m = hourly_wind_speed_10m
+)
+print(hourly_dataframe_pl)
+# ┌─────────────────────────┬────────────────┬───────────────┬────────────────┐
+# │ date ┆ temperature_2m ┆ precipitation ┆ wind_speed_10m │
+# │ --- ┆ --- ┆ --- ┆ --- │
+# │ datetime[μs, UTC] ┆ f32 ┆ f32 ┆ f32 │
+# ╞═════════════════════════╪════════════════╪═══════════════╪════════════════╡
+# │ 2024-06-21 00:00:00 UTC ┆ 17.437 ┆ 0.0 ┆ 6.569383 │
+# │ 2024-06-21 01:00:00 UTC ┆ 17.087 ┆ 0.0 ┆ 6.151683 │
+# │ 2024-06-21 02:00:00 UTC ┆ 16.786999 ┆ 0.0 ┆ 7.42159 │
+# │ 2024-06-21 03:00:00 UTC ┆ 16.337 ┆ 0.0 ┆ 5.154416 │
 ```
 
 ### Caching Data
@@ -114,6 +143,7 @@ om = openmeteo_requests.Client(session=retry_session)
 ```
 
 # TODO
+
 - Document multi location/timeinterval usage
 - Document FlatBuffers data structure
 - Document time start/end/interval
@@ -123,4 +153,5 @@ om = openmeteo_requests.Client(session=retry_session)
 - Consider dedicated pandas library to convert responses quickly
 
 # License
+
 MIT