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[3 Advaned Visualizations using Seaborn](https://anandology.com/python-for-bioinformatics/seaborn.html) [Python for Bioinformatics](https://anandology.com/python-for-bioinformatics/) - [Preface](https://anandology.com/python-for-bioinformatics/index.html) - [1 A Taste of Python](https://anandology.com/python-for-bioinformatics/taste-of-python.html) - [2 Introduction to Data Analysis and Visualization](https://anandology.com/python-for-bioinformatics/introduction-to-data-analysis.html) - [3 Advaned Visualizations using Seaborn](https://anandology.com/python-for-bioinformatics/seaborn.html) - [4 Interactive Visualizations using Plotly](https://anandology.com/python-for-bioinformatics/plotly.html) - [5 Interfacing R from Python](https://anandology.com/python-for-bioinformatics/rpy2.html) - [References](https://anandology.com/python-for-bioinformatics/references.html) - [Cookbook](https://anandology.com/python-for-bioinformatics/cookbook/index.html) - [6 Making Pie Charts](https://anandology.com/python-for-bioinformatics/cookbook/piechart.html) - [7 Extract GSE data from NCBI database](https://anandology.com/python-for-bioinformatics/cookbook/geoparse.html) - [8 Regular Expressions](https://anandology.com/python-for-bioinformatics/cookbook/regular-expressions.html) ## Table of contents - [3\.1 Seaborn](https://anandology.com/python-for-bioinformatics/seaborn.html#seaborn) - [3\.1.1 Palmer Penguins](https://anandology.com/python-for-bioinformatics/seaborn.html#palmer-penguins) - [3\.1.2 Scatterplot](https://anandology.com/python-for-bioinformatics/seaborn.html#scatterplot) - [3\.2 Distributions](https://anandology.com/python-for-bioinformatics/seaborn.html#distributions) - [3\.3 Categorical plots](https://anandology.com/python-for-bioinformatics/seaborn.html#categorical-plots) - [3\.4 Comparing Distributions](https://anandology.com/python-for-bioinformatics/seaborn.html#comparing-distributions) - [3\.4.1 Combining multiple views on the data](https://anandology.com/python-for-bioinformatics/seaborn.html#combining-multiple-views-on-the-data) - [3\.4.2 Showing multiple charts](https://anandology.com/python-for-bioinformatics/seaborn.html#showing-multiple-charts) - [3\.4.3 Multiple Charts in a grid](https://anandology.com/python-for-bioinformatics/seaborn.html#multiple-charts-in-a-grid) [Edit this page](https://github.dev/anandology/python-for-bioinformatics/blob/main/seaborn.ipynb) [Report an issue](https://github.com/anandology/python-for-bioinformatics/issues/new) # 3 Advaned Visualizations using Seaborn While matplotlib provides simple visualiation charts that are easy to generate, the `seaborn` library provide more sophiticated charts often handly to present complex data from the bioinformatics. ## 3\.1 Seaborn Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn has three categories of charts - *relplot* (relational), *displot* (distributions) and *catplot* (categories).  Searborn organization From [Seaborn Tutorial](https://seaborn.pydata.org/tutorial/function_overview.html). ### 3\.1.1 Palmer Penguins  Palmer Penguins In this section, we’ll use [Palmer Penguins dataset](https://github.com/allisonhorst/palmerpenguins), which is comes packaged with seaborn. The dataset includes measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex. ``` import seaborn as sns ``` ``` df = sns.load_dataset("penguins") ``` ``` df.head() ``` | | species | island | bill\_length\_mm | bill\_depth\_mm | flipper\_length\_mm | body\_mass\_g | sex | |---|---|---|---|---|---|---|---| | 0 | Adelie | Torgersen | 39\.1 | 18\.7 | 181\.0 | 3750\.0 | Male | | 1 | Adelie | Torgersen | 39\.5 | 17\.4 | 186\.0 | 3800\.0 | Female | | 2 | Adelie | Torgersen | 40\.3 | 18\.0 | 195\.0 | 3250\.0 | Female | | 3 | Adelie | Torgersen | NaN | NaN | NaN | NaN | NaN | | 4 | Adelie | Torgersen | 36\.7 | 19\.3 | 193\.0 | 3450\.0 | Female | ``` df.shape ``` ``` (344, 7) ``` While seaborn can ignore missing values when plotting, it may be good idea in general to clean the data to remove missing values before starting any exploration. ``` ``` ``` species 0 island 0 bill_length_mm 2 bill_depth_mm 2 flipper_length_mm 2 body_mass_g 2 sex 11 dtype: int64 ``` ``` ``` ``` ``` ``` (333, 7) ``` ### 3\.1.2 Scatterplot ``` df.head() ``` | | species | island | bill\_length\_mm | bill\_depth\_mm | flipper\_length\_mm | body\_mass\_g | sex | |---|---|---|---|---|---|---|---| | 0 | Adelie | Torgersen | 39\.1 | 18\.7 | 181\.0 | 3750\.0 | Male | | 1 | Adelie | Torgersen | 39\.5 | 17\.4 | 186\.0 | 3800\.0 | Female | | 2 | Adelie | Torgersen | 40\.3 | 18\.0 | 195\.0 | 3250\.0 | Female | | 4 | Adelie | Torgersen | 36\.7 | 19\.3 | 193\.0 | 3450\.0 | Female | | 5 | Adelie | Torgersen | 39\.3 | 20\.6 | 190\.0 | 3650\.0 | Male | The scatter plot allows visualizing two dimentions. More dimentions can be added a scatter plot to control color, size and style. ``` ``` ``` <Axes: xlabel='bill_length_mm', ylabel='bill_depth_mm'> ```  The *scatterplot* is a special kind of *relplot*. We can get the same output using the following ways as well. When we specify both `x` and `y` arguments to ``` sns.relplot(df, x="bill_length_mm", y="bill_depth_mm") ``` ``` ``` ``` <Axes: xlabel='bill_length_mm', ylabel='bill_depth_mm'> ```  ``` ``` ``` <Axes: xlabel='bill_length_mm', ylabel='bill_depth_mm'> ```  ``` ``` ``` <Axes: xlabel='bill_length_mm', ylabel='bill_depth_mm'> ```  Seaborn allows drawing lines on the graphs using matplotlib primitives. ``` ``` ``` <matplotlib.lines._AxLine at 0x7f6f9e001f30> ```  ## 3\.2 Distributions ``` ```  ``` ```  The distplots allow grouping by color. ``` sns.displot(df, x="flipper_length_mm", kind="kde", hue="species") ```  We can stack multiple distributions on top of each other. ``` ```  We could do the same with histograms. ``` ```  ## 3\.3 Categorical plots Catplots allows visualizaing categorical data. The default view is a scatter plot with a small jitter added to make the points visible. ``` sns.catplot(df, x="species", y="bill_length_mm") ```  A slightly better looking version of that is a swarm plot. ``` sns.catplot(df, x="species", y="bill_length_mm", kind="swarm") ```  we can add another dimension usng hue. ``` sns.catplot(df, x="species", y="bill_length_mm", kind="swarm", hue="sex") ```  We could even flip the axes, if we want. ``` sns.catplot(df, x="bill_length_mm", y="species", kind="swarm", hue="sex") ```  ## 3\.4 Comparing Distributions The *boxplot* and *voilinplot*, kinds of catplots, allows comparing distributions. ``` sns.boxplot(df, y='bill_length_mm') ``` ``` <Axes: ylabel='bill_length_mm'> ```  ``` sns.violinplot(df, y='bill_length_mm') ``` ``` <Axes: ylabel='bill_length_mm'> ```  Both these plots allow splitting the distribution by a categorical column. ``` sns.violinplot(df, y='bill_length_mm', x="species") ``` ``` <Axes: xlabel='species', ylabel='bill_length_mm'> ```  We could add another dimension using hue. ``` sns.violinplot(df, y='bill_length_mm', x="species", hue="sex") ``` ``` <Axes: xlabel='species', ylabel='bill_length_mm'> ```  We could use the space better by splitting the violin when there are only two categories. ``` sns.violinplot(df, y='bill_length_mm', x="species", hue="sex", split=True) ``` ``` <Axes: xlabel='species', ylabel='bill_length_mm'> ```  ### 3\.4.1 Combining multiple views on the data The `jointplot` and `pairplot` plots both relationships and distubutions in a single graph. ``` sns.jointplot(df, x="bill_length_mm", y="bill_depth_mm", height=3) ```  ``` sns.jointplot(df, x="bill_length_mm", y="bill_depth_mm", hue="species", height=4) ```  The pairplot show relations between all the numerical columns in a single grid. ``` sns.pairplot(data=df, hue="species") ```  ### 3\.4.2 Showing multiple charts Seaborn allows showing a grip of charts for displaying more information. ``` sns.relplot(df, x="bill_length_mm", y="bill_depth_mm", col="species", height=3) ```  ``` ```  ``` ```  When there are too many categories, we can even specify `col_wrap`. ``` ```  This functionality is similar to Facetwrap in R. ### 3\.4.3 Multiple Charts in a grid ``` import matplotlib.pyplot as plt ``` ``` ``` ``` <Axes: xlabel='species', ylabel='Count'> ```  [2 Introduction to Data Analysis and Visualization](https://anandology.com/python-for-bioinformatics/introduction-to-data-analysis.html) [4 Interactive Visualizations using Plotly](https://anandology.com/python-for-bioinformatics/plotly.html) Python for Bioinformatics was written by [Anand Chitipothu](https://anandology.com/). This book was built with [Quarto](https://quarto.org/).