PP06 - Pandas

<article class="post-detail"> <header> <h2 class="post-title"> <a href="https://python-podcast.de/show/pandas/">PP06 - Pandas</a> (click here to comment) </h2> <!-- link is on one line to avoid underlined whitespace --> <div class="post-card-meta"> <a href="https://python-podcast.de/show/pandas/"><time datetime="2019-03-19T01:00:00+01:00">19. März 2019</time>,</a> <span class="author">Jochen</span> </div> </header> <div class="post-body"> <section class="block-overview"> <section class="block-paragraph"> Die sechste Folge besch&auml;ftigt sich mit einer der wohl bekanntesten und meistgenutzten Python-Bibliotheken: &quot;Pandas&quot;<br /> Diesmal haben wir als Expertengast Simon dabei, der uns mehr &uuml;ber die Funktionen von Pandas erz&auml;hlt.<br /> <br /> </section> <section class="block-audio"> </section> <section class="block-paragraph"> </section> </section> <section class="block-detail"> <section class="block-paragraph"> <h2>Shownotes</h2> Unsere E-Mail f&uuml;r Fragen, Anregungen &amp; Kommentare: <a href="mailto:[email protected]">[email protected]</a> <h3>News</h3> <ul> <li><a href="https://docs.python.org/3.8/library/multiprocessing.shared_memory.html">Shared memory for multiprocessing</a>&nbsp;(<a href="https://docs.python.org/3/library/struct.html">struct</a>, wenn man das von Hand machen will)</li> <li><a href="https://www.python.org/dev/peps/pep-0584/">Operatoren f&uuml;r Dictionaries</a></li> </ul> <h3>Pandas</h3> <ul> <li>Pandas Cheatsheets&nbsp;<a href="https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PandasPythonForDataScience.pdf">Teil 1</a>,&nbsp;<a href="https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Pandas_Cheat_Sheet_2.pdf">Teil 2</a></li> <li><a href="https://github.com/ephes/data_science_tutorial/blob/master/notebooks/numpy_pandas/pandas.ipynb">Tutorialnotebook</a>&nbsp;von Jochen</li> <li><a href="https://github.com/hgrecco/pint/blob/develop/docs/pandas.rst">Ma&szlig;einheiten f&uuml;r dataframes mit pint (noch nicht released)</a>&nbsp;- verwendet die neue extension array api</li> <li>Erster Einblick in die Daten im Pandas Workflow mit df.head() df.tail() und df.describe()&nbsp;<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html">df.apply()</a></li> <li>Eher f&uuml;r Fortgeschrittene:&nbsp;<a href="https://tomaugspurger.github.io/modern-1-intro.html">Modern Pandas</a></li> <li>Artikel &uuml;ber&nbsp;<a href="https://nikgrozev.com/2015/07/01/reshaping-in-pandas-pivot-pivot-table-stack-and-unstack-explained-with-pictures/">pivot, stack und unstack</a></li> <li><a href="https://pandas-dev.github.io/pandas2/goals.html">Pandas 2.0</a></li> </ul> <h3>Podcasts und Talks</h3> <ul> <li><a href="https://www.youtube.com/watch?v=_-gJtO0XR48&amp;t=1s">Jeff Reback - What is the Future of Pandas</a></li> <li><a href="https://www.pythonpodcast.com/wes-mckinney-python-for-data-analysis-episode-203/">Wes McKinney&#39;s Career In Python For Data Analysis - Episode 203</a></li> <li><a href="https://talkpython.fm/episodes/show/200/escaping-excel-hell-with-python-and-pandas">Episode #200: Escaping Excel Hell with Python and Pandas</a></li> </ul> <h3>R</h3> <ul> <li><a href="https://www.r-project.org/">R</a>,&nbsp;<a href="https://www.rstudio.com/">R studio</a></li> <li><a href="http://shiny.rstudio.com/">Shiny</a></li> </ul> <h3>Picks</h3> <ul> <li><a href="https://djangochat.com/">Django Chat Podcast</a>&nbsp;(Jochen)</li> <li>Django-ORM-like, aber f&uuml;r flat files: &nbsp;<a href="https://github.com/kneufeld/alkali">alkali</a>&nbsp;(Jochen)</li> <li><a href="https://plot.ly/matplotlib/modifying-a-matplotlib-figure/">Matplotlib to Plotly</a>&nbsp;(Simon)</li> <li><a href="https://docs.python.org/3/library/pickle.html">pickle</a>&nbsp;(Dominik)</li> </ul> <a href="https://konektom.org/tags/67753/">&Ouml;ffentlicher Tag auf konektom</a> </section> </section> </div> </article>