Cheat Sheet Seaborn Reference Guide

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Python For Data Science Cheat Sheet

Seaborn

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Statistical Data Visualization With Seaborn

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Learn Python for Data Science Interactively

Figure Aesthetics

Data

The Python visualization library Seaborn is based on

matplotlib and provides a high-level interface for drawing

aractive statistical graphics.

Make use of the following aliases to import the libraries:

The basic steps to creating plots with Seaborn are:

1. Prepare some data

2. Control figure aesthetics

3. Plot with Seaborn

4. Further customize your plot

>>> import pandas as pd

>>> import numpy as np

>>> uniform_data = np.random.rand(10, 12)

>>> data = pd.DataFrame({'x':np.arange(1,101),

'y':np.random.normal(0,4,100)})

>>> import matplotlib.pyplot as plt

>>> import seaborn as sns

Ploing With Seaborn

>>> import matplotlib.pyplot as plt

>>> import seaborn as sns

>>> tips = sns.load_dataset("tips")

>>> sns.set_style("whitegrid")

>>> g = sns.lmplot(x="tip",

y="total_bill",

data=tips,

aspect=2)

>>> g = (g.set_axis_labels("Tip","Total bill(USD)").

set(xlim=(0,10),ylim=(0,100)))

>>> plt.title("title")

>>> plt.show(g)

Step 4

Step 2

Step 1

Step 5

Step 3

1

>>> titanic = sns.load_dataset("titanic")

>>> iris = sns.load_dataset("iris")

Seaborn also offers built-in data sets:

2

3

Further Customizations

4

Show or Save Plot

>>> sns.set() (Re)set the seaborn default

>>> sns.set_style("whitegrid") Set the matplotlib parameters

>>> sns.set_style("ticks", Set the matplotlib parameters

{"xtick.major.size":8,

"ytick.major.size":8})

>>> sns.axes_style("whitegrid") Return a dict of params or use with

with to temporarily set the style

Axis Grids

>>> f, ax = plt.subplots(gsize=(5,6)) Create a figure and one subplot

>>> plt.title("A Title") Add plot title

>>> plt.ylabel("Survived") Adjust the label of the y-axis

>>> plt.xlabel("Sex") Adjust the label of the x-axis

>>> plt.ylim(0,100) Adjust the limits of the y-axis

>>> plt.xlim(0,10) Adjust the limits of the x-axis

>>> plt.setp(ax,yticks=[0,5]) Adjust a plot property

>>> plt.tight_layout() Adjust subplot params

>>> plt.show() Show the plot

>>> plt.saveg("foo.png") Save the plot as a figure

>>> plt.saveg("foo.png", Save transparent figure

transparent=True)

>>> sns.regplot(x="sepal_width", Plot data and a linear regression

y="sepal_length", model fit

data=iris,

ax=ax)

>>> g.despine(left=True) Remove le spine

>>> g.set_ylabels("Survived") Set the labels of the y-axis

>>> g.set_xticklabels(rotation=45) Set the tick labels for x

>>> g.set_axis_labels("Survived", Set the axis labels

"Sex")

>>> h.set(xlim=(0,5), Set the limit and ticks of the

ylim=(0,5), x-and y-axis

xticks=[0,2.5,5],

yticks=[0,2.5,5])

Close & Clear

>>> plt.cla() Clear an axis

>>> plt.clf() Clear an entire figure

>>> plt.close() Close a window

5

Also see Lists, NumPy & Pandas

Also see Matplotlib

Also see Matplotlib

Also see Matplotlib

Also see Matplotlib

Context Functions

>>> sns.set_context("talk") Set context to "talk"

>>> sns.set_context("notebook", Set context to "notebook",

font_scale=1.5, cale font elements and

rc={"lines.linewidth":2.5}) override param mapping

Seaborn styles

>>> sns.set_palette("husl",3) Define the color palee

>>> sns.color_palette("husl") Use with with to temporarily set palee

>>> atui = ["#9b59b6","#3498db","#95a5a6","#e74c3c","#34495e","#2ecc71"]

>>> sns.set_palette(atui) Set your own color palee

Color Palee

Plot

Axisgrid Objects

>>> g = sns.FacetGrid(titanic, Subplot grid for ploing conditional

col="survived", relationships

row="sex")

>>> g = g.map(plt.hist,"age")

>>> sns.factorplot(x="pclass", Draw a categorical plot onto a

y="survived", Facetgrid

hue="sex",

data=titanic)

>>> sns.lmplot(x="sepal_width", Plot data and regression model fits

y="sepal_length", across a FacetGrid

hue="species",

data=iris)

Regression Plots

Categorical Plots

Scaerplot

>>> sns.stripplot(x="species", Scaerplot with one

y="petal_length", categorical variable

data=iris)

>>> sns.swarmplot(x="species", Categorical scaerplot with

y="petal_length", non-overlapping points

data=iris)

Bar Chart

>>> sns.barplot(x="sex", Show point estimates and

y="survived", confidence intervals with

hue="class", scaerplot glyphs

data=titanic)

Count Plot

>>> sns.countplot(x="deck", Show count of observations

data=titanic,

palette="Greens_d")

Point Plot

>>> sns.pointplot(x="class", Show point estimates and

y="survived", confidence intervals as

hue="sex", rectangular bars

data=titanic,

palette={"male":"g",

"female":"m"},

markers=["^","o"],

linestyles=["-","--"])

Boxplot

>>> sns.boxplot(x="alive", Boxplot

y="age",

hue="adult_male",

data=titanic)

>>> sns.boxplot(data=iris,orient="h") Boxplot with wide-form data

Violinplot

>>> sns.violinplot(x="age", Violin plot

y="sex",

hue="survived",

data=titanic)

>>> plot = sns.distplot(data.y, Plot univariate distribution

kde=False,

color="b")

Distribution Plots

>>> h = sns.PairGrid(iris) Subplot grid for ploing pairwise

>>> h = h.map(plt.scatter) relationships

>>> sns.pairplot(iris) Plot pairwise bivariate distributions

>>> i = sns.JointGrid(x="x", Grid for bivariate plot with marginal

y="y", univariate plots

data=data)

>>> i = i.plot(sns.regplot,

sns.distplot)

>>> sns.jointplot("sepal_length", Plot bivariate distribution

"sepal_width",

data=iris,

kind='kde')

Matrix Plots

>>> sns.heatmap(uniform_data,vmin=0,vmax=1) Heatmap