Say Goodbye to Pandas: Why You Should Switch to Polars for Fast Data Manipulation in Python.
<p><strong>Pandas</strong> is being used widely in most data analysis works and projects. Despite being a versatile and powerful library for data manipulation and analysis in Python, there are limitations and caveats of the Pandas library that cannot be ignored by most of its users. A few of them to highlight are Intense Memory Usage with large data sets, Null Handling (<strong>NaN</strong>), Evaluations, and limitations on multi-threading. (<a href="https://wesmckinney.com/blog/apache-arrow-pandas-internals/" rel="noopener ugc nofollow" target="_blank">10 things I hate about Pandas — By Python for Data Analysis Author</a>)</p>
<p>Well, Say Hello to <a href="https://pypi.org/project/polars/" rel="noopener ugc nofollow" target="_blank"><strong>POLARS</strong></a><strong> !<br />
—</strong> One of the fastest data manipulation libraries.</p>
<p><a href="https://medium.com/@viv1kv/say-goodbye-to-pandas-why-you-should-switch-to-polars-for-fast-data-manipulation-in-python-df00dcab788f"><strong>Read More</strong></a></p>