<jupyter_start><jupyter_text>Visualization of the Training Set<jupyter_code>df = dd.read_parquet(TRAINING_GOLD_PATH)
df.head()
len(df)
def plot_bars(kdim="day_of_year", vdim="temperature", plot_type=hv.Bars):
    plot = plot_type(df, kdims=[kdim], vdims=vdim).aggregate(function=np.mean)
    plot.opts(height=500, responsive=True)
    
    return plot
plot_bars(plot_type=hv.Curve)
kdims = ["year", "quarter", "day_of_year", "month", "day", "hour", "day_of_year",]
vdims = ["temperature", "dewpoint", "pressure", "wind_direction", "wind_speed", "sky_condition", "precipitation_one_hour", "precipitation_six_hour"]
plots_bars = {}
for k in kdims[0:3]:
    for v in vdims[0:2]:
        plots_bars[(k,v)]=plot_bars(k,v, hv.Curve)
hv.NdLayout(plots_bars, kdims=["key", "value"]).cols(1)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17610837438423646, 'fraction_numerical': 0.009852216748768473, 'mean_word_length': 7.381443298969073, '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': '448246', 'license': 'MIT', 'n_tokens_mistral': 337, 'n_tokens_neox': 331, 'n_words': 52, 'path': 'notebooks/viz_training_dataset.ipynb', 'repo_name': 'MarcSkovMadsen/load-forecasts', 'size': '812'}