Pandas - Get Group Size after GroupBy
• Use .size() to count all rows per group including NaN values, while .count() excludes NaN values and returns counts per column
• Use .size() to count all rows per group including NaN values, while .count() excludes NaN values and returns counts per column
Getting sample size wrong is one of the most expensive mistakes in applied statistics. Too small, and you lack the statistical power to detect real effects—your experiment fails to show significance…
Read more →Running a study with too few participants wastes everyone’s time. You’ll likely fail to detect effects that actually exist, leaving you with inconclusive results and nothing to show for your effort….
Read more →Figure size directly impacts the readability and professionalism of your visualizations. A plot that looks perfect on your laptop screen might become illegible when inserted into a presentation or…
Read more →Statistical significance has a credibility problem. With a large enough sample, you can achieve a p-value below 0.05 for differences so small they’re meaningless in practice. This is where effect…
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