<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>baron_harkonnen</author><text>I used to very strongly agree with you re: matplotlib, but I&amp;#x27;ve recently switched from using almost exclusively ggplot2 to almost exlusively Matplotlib and my realization is that they are very different tools serving very different purposes.&lt;p&gt;ggplot2 is obviously fantastic and makes beautiful plots, and very easily at that. However it is definitely a &amp;quot;convention over configuration&amp;quot; tool. For 99% of the typical plot you might want to create, ggplot is going to be easier and look nicer.&lt;p&gt;However matplotlib lib really shines when you want to make very custom plots. If you have a plot in your mind that you want to see on paper, matplotlib will be the better tool for helping you create exactly what you are looking for.&lt;p&gt;For certain projects I&amp;#x27;ve done, where I want to do a bunch of non-standard visualizations, especially ones that tend to be fairly dense, I prefer matplotlib. For day to day analytics ggplot2 is so much better it&amp;#x27;s ridiculous. The real issue is that Python doesn&amp;#x27;t really offer anything in the same league as ggplot2 for &amp;quot;convention over configuration&amp;quot; type plotting.&lt;p&gt;Fully agree on Pandas. R&amp;#x27;s native data frame + tidyverse is world&amp;#x27;s easier. Pandas&amp;#x27; overly complex indexing system is a persistent source of annoyance no matter how much I use that library.</text><parent_chain><item><author>disgruntledphd2</author><text>I&amp;#x27;m with you on sklearn, the DL libraries and Numpy, but Pandas and Matplotlib are poor, poor relations of the tools available in the R ecosystem (dplyr&amp;#x2F;ggplot etc).</text></item><item><author>lysecret</author><text>Excellent library for train_test_split. 
Jokes aside. This next to Numpy, Pandas Jupyter and Matplotlib + the DL libraries are the reason Python is the powerhouse it is for Data Science.</text></item></parent_chain></comment><story><title>Scikit-Learn Version 1.0</title><url>https://scikit-learn.org/dev/whats_new/v1.0.html</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>boringg</author><text>Wait how many companies are actually using R in the wild?  As I understand it, R is born of academia, great for statistics&amp;#x2F;analysis but breaks down on data manipulation and isn&amp;#x27;t used in production&amp;#x2F;data engineering.  Maybe my understanding is dated though?</text><parent_chain><item><author>disgruntledphd2</author><text>I&amp;#x27;m with you on sklearn, the DL libraries and Numpy, but Pandas and Matplotlib are poor, poor relations of the tools available in the R ecosystem (dplyr&amp;#x2F;ggplot etc).</text></item><item><author>lysecret</author><text>Excellent library for train_test_split. 
Jokes aside. This next to Numpy, Pandas Jupyter and Matplotlib + the DL libraries are the reason Python is the powerhouse it is for Data Science.</text></item></parent_chain></comment><story><title>Scikit-Learn Version 1.0</title><url>https://scikit-learn.org/dev/whats_new/v1.0.html</url></story>