hydrogen

vectorized methods like `df.column + df.other_column`. This is a huge improvement over having half of your analysis in database queries and half in a programming language. > Matplotlib is unintuitive and poorly documented Try https://seaborn.pydata.org/ for statistical graphics. > Pandas also implements it's own versions of standard python objects! You need to know, and go back and forth between two, ways of doing things. This sucks but is unavoidable, because Python does not have fast data types with support for missing values built in, so all your columns would have to be of mixed type (the actual type + None) and everything would slow down and simple things like computing the mean of a column with missing values would not work. Note that you don't actually "need to go back and forth" because Pandas will happily convert plain Python objects to their Numpy equivalents for you. > 3. All these libraries separate logically grouped concepts. It's not functional, you're just going to have to deal with that. But split-apply-combine and similar patterns are quite elegant in Pandas: http://pandas.pydata.org/pandas-docs/stable/groupby.html > 4. Because everything is meaningless lists of numbers there are no ways to reuse code. A lot of data analysis is throw-away code. Some of it can be abstracted into reusable code, some of it can't. Lastly, don't forget that Python does have a lot of things going for it when it comes to data analysis, from geospatial tools (http://toblerity.org/shapely/) to Bayesian modeling (http://pymc-devs.github.io/pymc3/index.html), as well as interactive coding with Jupyter and Hydrogen for the Atom editor (https://github.com/nteract/hydrogen).