sleep: that gpa though
A university in Italy was doing research on the effect of sleep on programming performance. The question is, where do we draw the line? When is the difference in performance big enough that you can't say that it is due to chance? Also, can't we explain the effect with gpa?
Here's the code we wrote to prepare for the
agg = (df .assign(gpa=lambda d: d['gpa'] < d['gpa'].mean()) .groupby('gpa') .agg(mean_unit_tests=('passed_unit_tests', np.mean), mean_asserts=('passed_asserts', np.mean), mean_user_stories=('tackled_user_stories', np.mean))).T effect_dict = dict(agg[False] - agg[True]) effect_dict
This is where we calculate the numbers exactly.
df_diff.assign(limit=lambda d: d['diff_unit_tests'] < 0.1948)['limit'].mean() df_diff.assign(limit=lambda d: d['diff_asserts'] < 0.2922)['limit'].mean() df_diff.assign(limit=lambda d: d['diff_user_stories'] < 0.0584)['limit'].mean()
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