<jupyter_start><jupyter_text>More exploration<jupyter_code>a = ts[[param,'time']].groupby('time').count().rename({param:'count'})
b = ts[[param, 'time']].groupby('time').mean()
import xarray as xr
xr.merge([a,b])
da = ts[['t2m','time']]
#da.groupby("time.dayofyear") - 
#da.groupby("time.dayofyear").mean(["time","stacked_forecast_time_step"])
da.groupby("time.dayofyear").count() #"time.dayofyear")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.24475524475524477, 'fraction_numerical': 0.002331002331002331, 'mean_word_length': 10.31578947368421, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '8726', 'license': 'Apache-2.0', 'n_tokens_mistral': 174, 'n_tokens_neox': 173, 'n_words': 17, 'path': 'time-convention/convention-2-rt.ipynb', 'repo_name': 'ecmwf-lab/playground', 'size': '429'}