<jupyter_start><jupyter_text>Distribution of runs scored per over<jupyter_code>df = pd.read_csv(processed_file)
avoided_match_ids = df[(df.over > 20)][
    'match_id'].drop_duplicates().tolist()

df = df[~df.match_id.isin(avoided_match_ids)]
overs = df.groupby(['match_id','over']).agg(
    {'runs':'sum','extras':'sum'})
overs.reset_index(inplace=True)
overs['total_runs'] = overs['runs'] + overs['extras']
%matplotlib inline
g = sns.FacetGrid(overs, col = 'over')
g.map(plt.hist, 'total_runs')
f = sns.distplot(overs['total_runs'])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.19182948490230906, 'fraction_numerical': 0.003552397868561279, 'mean_word_length': 8.096774193548388, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, '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': '101344', 'license': 'FTL', 'n_tokens_mistral': 241, 'n_tokens_neox': 227, 'n_words': 30, 'path': 'notebooks/datadistributions.ipynb', 'repo_name': 'varunshiva/t20-mining', 'size': '563'}