Below is:
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Both the financial and academic worlds are increasingly adopting Python for the same reasons.
IPython Notebook is universally used and loved.
matplotlib
, pandas
, numpy
, bokeh
, sympy
, ...No clear future for visualisations in Python.
matplotlib
is universally used for publication-quality charts. API is difficult to use but powerful and well engineered.matplotlib
magic incantation to draw charts, no interactivity, no JavaScript.matplotlib
of course work just as well: ggplot
, seaborn
, prettyplotlib
ggplot
in R because of the Grammar of Graphics, and people love Python because it's a one-stop stopmatplotlib
is fantastic work and stood the test of time.Cython is almost universally used, but more agile methods are being sought
Everyone uses scikit-learn
scikit-learn
and nltk
.MapReduce/clusters have less hype and traction than you'd expect
scikit-learn
core contributors strongly prefer shared-memory parallelism to clusters, and are actively creating OpenMP-style abstractions (with better debugging and NumPy array performance).
from IPython.core.display import HTML
def css_styling():
styles = open("styles/custom.css", "r").read()
return HTML(styles)
css_styling()
%autosave 10
Autosaving every 10 seconds