Some useful seaborn snippets

seaborn_snippets.py

# import
import seaborn as sns
import matplotlib.pyplot as plt

# white background in plots
sns.set(style="whitegrid")

# df is a pandas dataframe
# plot by column by variable 'var1', in 3 columns, coloring by variable 'var2'
grid = sns.FacetGrid(df, col='var1', col_wrap=3, hue='var2', size=4, palette='Set2')
# map a scatterplot (x, y)
grid.map(plt.plot, 'x', 'y', marker="o", ms=1)
plt.subplots_adjust(top=0.85)
# add a supertitle to the plot
grid.fig.suptitle('Here goes the title');

# rotate x axis labels, given a grid facet object
g.set_xticklabels(rotation=90)

# do not share x and y axes range
grid = sns.FacetGrid(df, col='var1', col_wrap=3, hue='var2', size=4, palette='Set2', sharey = False, sharex = False)

# (re)adjust fontscale for axes labels etc
sns.set(style="whitegrid", font_scale=1.0)
# as above
grid = sns.FacetGrid(df, size=3, palette=sns.color_palette('Paired', 10), sharey=False,
                     col='var1', col_wrap=5, hue='var2')
# map a histogram of variable 'var3'
grid.map(plt.hist, 'var3', bins = 10, edgecolor='white')
# rotate the labels of x axis by 90 deg
grid.set_xticklabels(rotation=90)

# map a density plot (KDE)
g.map(sns.distplot, "var3");
# map a density plot but donfit and show 'only' a normal distribution
g.map(sns.distplot, "var3", fit=norm, kde=False);

# mix seaborn with more canonical matplotlib
# two plots that share the x axis
fig = plt.figure(figsize=(5,5))
ax1 = plt.subplot(311)
plt.plot(df.t, df.x)
plt.ylabel('x', fontsize=14)

ax2 = plt.subplot(312, sharex=ax1)
plt.plot(df.t, df.y)
plt.ylabel('y', fontsize=14)
plt.xlabel('t')

ax1.set_title('title for the plot')
plt.show()

# g facet Grid - make x ticks invisible
g.set(xticks=[])

# plot heatmaps of correlation matrices
# facet by column by variable 'var1, 3 columns
g = sns.FacetGrid(df, col='var1', size=8, col_wrap=3)
g.map_dataframe(lambda df, color: sns.heatmap(df.corr(), linewidths=0, square=True, cmap='coolwarm'));

# plot a clustermap
cm = sns.clustermap(df.corr(), square=True, linewidths=.5, cmap='coolwarm')
plt.setp(cm.ax_heatmap.yaxis.get_majorticklabels(), rotation=0);

# change matplotlib boxpolot colors
fig, ax1 = plt.subplots(figsize=(10, 8))
bp = plt.boxplot(df, patch_artist=True)
for i in range(len(bp['boxes'])):
    bp['boxes'][i].set(facecolor = sns.color_palette("Paired", 15)[i])   
# rotate the x ticks by 90 deg
xtickNames = plt.setp(ax1, xticklabels=data.columns)
plt.setp(xtickNames, rotation=90, fontsize=12);