This talk:
Research with Jupyter
Python is clear, powerful and popular
# Set up the matplotlib figure
f, axes = plt.subplots(3, 3, figsize=(9, 9), sharex=True, sharey=True)
# Rotate the starting point around the cubehelix hue circle
for ax, s in zip(axes.flat, np.linspace(0, 3, 10)):
# Create a cubehelix colormap to use with kdeplot
cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)
# Generate and plot a random bivariate dataset
x, y = rs.randn(2, 50)
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=ax)
ax.set(xlim=(-3, 3), ylim=(-3, 3))
Unlike more specialised computational languages like R, Matlab, Julia, and others, Python does not have numerical and statistical tools built-in to the language.
The Scipy stack provides a standalone, versatile and powerful scientific working environment, including: NumPy, SciPy, IPython (Jupyter), Matplotlib, Pandas, and many others...