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March 4, 2020 27 min
In episode 26, we interviewed Bhavani Ravi about the Python data analysis library pandas. After a brief introduction about her use of machine leaning models for pharmaceutical research, we talked extensively about pandas. She told us how much pandas is important for her everyday tasks and the strict quality standards of the project. We talked about the features provided by pandas and its compatibility with other Python libraries. We then discussed the importance of FLOSS in her industry and how they are contributing back to important projects. She share with us her experience as a first time contributor to pandas and how to find good first time issues for newcomers. We finished the interview with out usual quick questions. 00:00:17 Introduction 00:00:26 Introducing Bhavani Ravi 00:00:49 Using machine learning models for pharmaceutical research 00:02:46 How she got involed in the pandas project 00:04:29 Her elevator pitch for pandas 00:04:43 How she use pandas in her everyday job 00:05:24 What does pandas bring that is lacking in basic Python 00:06:53 Preparing data for machine learning algorithms 00:08:12 The performance of pandas 00:09:21 Data formats supported by pandas 00:11:03 Data structures provided by pandas 00:11:42 Data analysis tools provided by pandas 00:12:32 Using pandas data structures with scikit-learn 00:12:55 Plotting data from pandas 00:13:39 Transition to Python version 2 00:14:51 Commercial usage of pandas 00:15:16 Companies contributing back to pandas 00:16:02 Exposition of students to pandas 00:16:42 Tutorials to start with pandas 00:18:26 Python libraries dependencies of pandas 00:18:55 Main communication channels 00:19:44 Her experience contributing to pandas 00:21:14 Skills to contribute to the project 00:21:49 List of good first issues 00:22:21 Tasks for non-programmers 00:23:12 FLOSS and the industry 00:24:16 The most notable scientific discovery in recent years 00:24:33 Her favourite text processing tool 00:25:06 Anything else? 00:25:38 How to contact Bhavani 00:25:57 Outro
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