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’t notice them, compilers (or interpreters) inform us about them by raising errors.</p>
<p>On the other hand, there exist some “silent” mistakes that are hard to notice but have the potential to cause serious issues.</p>
<p>They don’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’ll learn about three of such issues.</p>
<p>You’re a data analyst working at a retail company. You’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’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>
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