3 Silent Pandas Mistakes You Should Be Aware Of

<p>Not knowing the mistakes we make in programming does not necessarily make us a fool. However, it may result in undesired consequences.</p> <p>Some mistakes shine like a diamond and can be recognized from miles away. Even if you don&rsquo;t notice them, compilers (or interpreters) inform us about them by raising errors.</p> <p>On the other hand, there exist some &ldquo;silent&rdquo; mistakes that are hard to notice but have the potential to cause serious issues.</p> <p>They don&rsquo;t result in any errors but make the function or operation to execute things in a different way than you think it would. Hence, the outcome changes without you noticing.</p> <p>We&rsquo;ll learn about three of such issues.</p> <p>You&rsquo;re a data analyst working at a retail company. You&rsquo;ve been asked to analyze the results of a recently run series of promotions. One of the tasks in this analysis is calculating the total sales quantities for each promotion and the grand total.</p> <p>Let&rsquo;s say the promotion data is stored in a DataFrame that looks like the following (definitely not this small in real life)</p> <p><a href="https://towardsdatascience.com/3-silent-pandas-mistakes-you-should-be-aware-of-80d0112de6b5">Click Here</a></p> <p>&nbsp;</p>