Seaborn

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
%matplotlib inline

# global config settings to control things like style, font size, color palette etc.
sb.set(context="notebook", style="darkgrid", palette="dark")

# seaborn has some nice built-in color palette features
sb.palplot(sb.color_palette())
sb.palplot(sb.color_palette("husl", 8))
sb.palplot(sb.color_palette("hls", 8))

# matplotlib colormap of evenly spaced colors
sb.palplot(sb.color_palette("coolwarm", 7))

# sequential palette with linear increase in brightness
sb.palplot(sb.cubehelix_palette(8))

# palettes are used in a plot via the color paramter
gammas = sb.load_dataset("gammas")
sb.tsplot(gammas, "timepoint", "subject", "ROI", "BOLD signal", color="muted")

# facetting histograms by subsets of data
sb.set(style="darkgrid")

tips = sb.load_dataset("tips")
g = sb.FacetGrid(tips, row="sex", col="time", margin_titles=True)
bins = np.linspace(0, 60, 13)
g.map(plt.hist, "total_bill", color="steelblue", bins=bins, lw=0)

# several distribution plot examples
sb.set(style="white", palette="muted")
f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=True)
sb.despine(left=True)

rs = np.random.RandomState(10)

b, g, r, p = sb.color_palette("muted", 4)

d = rs.normal(size=100)

sb.distplot(d, kde=False, color=b, ax=axes[0, 0])
sb.distplot(d, hist=False, rug=True, color=r, ax=axes[0, 1])
sb.distplot(d, hist=False, color=g, kde_kws={"shade": True}, ax=axes[1, 0])
sb.distplot(d, color=p, ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()

# hexbin plot with marginal distributions
from scipy.stats import kendalltau
sb.set(style="ticks")

rs = np.random.RandomState(11)
x = rs.gamma(2, size=1000)
y = -.5 * x + rs.normal(size=1000)
sb.jointplot(x, y, kind="hex", stat_func=kendalltau, color="#4CB391")

# faceted logistic regression
sb.set(style="darkgrid")
df = sb.load_dataset("titanic")

pal = dict(male="#6495ED", female="#F08080")
g = sb.lmplot("age", "survived", col="sex", hue="sex", data=df,
               palette=pal, y_jitter=.02, logistic=True)
g.set(xlim=(0, 80), ylim=(-.05, 1.05))

# linear regression with marginal distributions
sb.set(style="darkgrid")
tips = sb.load_dataset("tips")
color = sb.color_palette()[2]
g = sb.jointplot("total_bill", "tip", data=tips, kind="reg",
                  xlim=(0, 60), ylim=(0, 12), color=color, size=7)

# correlation matrix
sb.set(style="darkgrid")

rs = np.random.RandomState(33)
d = rs.normal(size=(100, 30))

f, ax = plt.subplots(figsize=(9, 9))
cmap = sb.blend_palette(["#00008B", "#6A5ACD", "#F0F8FF",
                          "#FFE6F8", "#C71585", "#8B0000"], as_cmap=True)
sb.heatmap(d, annot=False, cmap=cmap, ax=ax)
f.tight_layout()

# pair plot example
sb.set(style="darkgrid")
df = sb.load_dataset("iris")
sb.pairplot(df, hue="species", size=2.5)