Seaborn

• A statistical data visualization library based on Matplotlib.

• Provides a high-level interface for drawing attractive statistical graphics.

• website

Use matplotlib objects if you need to modify aspects of the plot.

import pathlib

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

Proteomics data example

dir_data = pathlib.Path("data")
df = pd.read_csv(dir_data / "proteins" / "proteins.csv", index_col=0).T
df

Reference	DMSO_rep1	DMSO_rep2	DMSO_rep3	DMSO_rep4	Suf_rep1	Suf_rep2	Suf_rep3	Suf_rep4
A5A613	27.180209	NaN	NaN	NaN	NaN	NaN	NaN	NaN
P00350	28.151576	27.926204	27.653250	27.151643	27.441837	27.031610	27.814631	27.587217
P00363	30.247131	30.261665	29.969625	29.470663	30.004725	30.085997	29.904057	29.575194
P00370	27.459171	26.873349	26.599971	26.438623	27.399691	27.189188	27.139030	27.223715
P00393	26.823758	26.756617	25.442346	25.798954	26.671118	26.885970	26.711192	26.320866
...	...	...	...	...	...	...	...	...
Q57261	28.410859	27.940694	27.070328	26.679649	27.995432	27.055135	27.313219	26.643479
Q59385-2	23.554913	25.240354	NaN	22.524292	NaN	NaN	NaN	NaN
Q59385	27.640279	27.243650	27.525020	27.403753	27.498873	27.666957	27.708407	27.847610
Q7DFV3	28.512794	27.620780	27.678892	27.255831	28.090220	27.525537	27.814369	27.605449
Q93K97	27.223010	25.291110	24.358694	25.767196	25.956190	25.230565	26.103059	26.177716

2269 rows × 8 columns

Horizontal Bar Plot

• Using the orient parameter to switch to horizontal orientation.

• calculates the mean and standard deviation of the mean automatically

ax = sns.barplot(
    data=df,
    orient="h",
    errorbar=("pi", 50),
    capsize=0.4,
    err_kws={"color": ".5", "linewidth": 2.5},
    linewidth=2.5,
    edgecolor=".5",
    facecolor=(0, 0, 0, 0),
)

Customizing the Plot

• You can customize the plot further using Matplotlib functions.

_ = ax.set_xticklabels(
    ax.get_xticklabels(),
    rotation=45,
    horizontalalignment="right",
)
ax.get_figure().tight_layout()

/tmp/ipykernel_2365/3272659636.py:1: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  _ = ax.set_xticklabels(

Style the plot as in ggplot2

with plt.style.context("ggplot"):
    ax = sns.barplot(data=df)

The end.