Import the Seaborn Libraryfrom matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
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Seaborn is a extension to Matplotlib with more visually appealing syntax and additional Chart Types. That's why Matplotlib should also be imported.
If we want to calculate aggregates we need to import numpy aswell. Bar Plotsns.barplot(
data=df ,
x="x value column" ,
y="y value column",
# everything specified below is optional
ci="sd"
estimator=np.median | len
hue="column to compare"
)
plt.show()
# ci="sd" changes the error bar to standard deviation
# estimator is used to specifiy the aggregation and takes any argument that works on a list. (examples provided in code)
# hue adds a nested categorical variable to compare to the "y value column"
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If the specified columns need to be aggregated first, Seaborn will perform that aggregation automatically. (mean by default)
Seaborn will, by default, provide an error bar displaying the bootstrapped confidence interval(95%). | | Aggregates (with numpy)Median | np.median(df.column_name) |
KDE (Key Densitiy Estimator) Plotssns.kdeplot(dataset1, shade=True) | sns.kdeplot(dataset2, shade=True) | ... |
KDE Plots show the distribution of an univariate dataset.
univariate datasets have only one variable. (e.g.: Temperature)
shade defines if the are under the line is shaded
Boxplotssns.boxplot(
data=df,
x='label',
y='value',
# optional
width=0.45
)
plt.show()
# In Seaborn it's also possible to plot multiple Boxplots in one viz
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The box represents the interquartile range
The line in the middle of the box is the median
The end lines are the first and third quartiles
The diamonds show outliers Violin Plotssns.violinplot(
data=df,
x="label",
y="value"
)
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Two KDE plots that are symmetrical along the center line. (Just for visual effect)
A white dot represents the median.
The thick black line in the center of each violin represents the interquartile range.
The lines that extend from the center are the confidence intervals (95%) | | Seaborn Styling (Figure Style and Scale)# Themes: (called prior to plot)
sns.set_style("")
->darkgrid, whitegrid, dark, white, ticks
#Removes Plot Borders (called after plot)
sns.despine() (default: top=True, right=True)
-> bottom, left
#Adjust font- and label size
sns.set_context(context="paper", font_scale=1.4, rc={"grid.linewidth": 0.6} )
-> paper, notebook (default), talk, poster
In order of relative size
-> Arguments for rc parameter:
{
'axes.labelsize': 17.6,
'axes.titlesize': 19.0,
'font.size': 19.2,
'grid.linewidth': 1.6,
'legend.fontsize': 16.0,
'lines.linewidth': 2.8,
'lines.markeredgewidth': 0.0,
'lines.markersize': 11.2,
'patch.linewidth': 0.48,
'xtick.labelsize': 16.0,
'xtick.major.pad': 11.2,
'xtick.major.width': 1.6,
'xtick.minor.width': 0.8,
'ytick.labelsize': 16.0,
'ytick.major.pad': 11.2,
'ytick.major.width': 1.6,
'ytick.minor.width': 0.8
}
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Seaborn Styling (Color)# If you want to quickly see what a palette looks like
# Save a palette to a variable:
palette = sns.color_palette("bright")
# Use palplot and pass in the variable:
sns.palplot(palette)
# Select a palette in Seaborn:
sns.set_palette("Paired")
# Default Palettes
-> deep, muted, pastel, bright, dark, colorblind
# More Palettes using Color Brewer:
http://colorbrewer2.org
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