I Visualization of statistical relationships (“relplot”)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%%javascript
IPython.OutputArea.prototype._should_scroll = function(lines) {
    return false; // disable auto scrolling
}

Based on https://seaborn.pydata.org/tutorial/relational.html

Simple scatter plot

tips = sns.load_dataset("tips")
tips.head()
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4 
sns.relplot(data=tips,
            x="total_bill",
            y="tip")

Simple scatter plot: different colors for different categorical value

sns.relplot(data=tips,
            x="total_bill",
            y="tip",
            hue="time")

Different colors/markers based on categorical values

sns.relplot(
    data=tips,
    x="total_bill",
    y="tip",
    hue="smoker",
    style="time",
)

Add information about a third variable with color

sns.relplot(
    data=tips,
    x="total_bill",
    y="tip",
    hue="size",
)

Add information about a third variable with size

sns.relplot(data=tips,
            x="total_bill",
            y="tip",
            hue='time',
            size="size")

sns.relplot(data=tips,
            x="total_bill",
            y="tip",
            hue='time',  # colors
            style='day', # markers 
            size="size", # size of the points
            col='sex',   # subplots in columns
            kind='scatter', # lineplot or scatterplot
            row='smoker')  # subplots in the rows

sns.relplot(data=tips,
            x="total_bill",
            y="tip",
            hue='time',
            style='sex',
            size="size")

Stock prices (emphasize continuity

dowjones = sns.load_dataset("dowjones")
dowjones.head()
Date Price
0 1914-12-01 55.00
1 1915-01-01 56.55
2 1915-02-01 56.00
3 1915-03-01 58.30
4 1915-04-01 66.45 
sns.relplot(data=dowjones,
            x="Date",
            y="Price",
            kind="line")

fMRI measurements (x-axis is time), several signals for each value of x

fmri = sns.load_dataset("fmri")
fmri.head()
subject timepoint event region signal
0 s13 18 stim parietal -0.017552
1 s5 14 stim parietal -0.080883
2 s12 18 stim parietal -0.081033
3 s11 18 stim parietal -0.046134
4 s10 18 stim parietal -0.037970 
sns.relplot(data=fmri,
            x="timepoint",
            y="signal",
            kind="line")

Plotting samples from different categories with different colors and styles

sns.relplot(
    data=fmri,
    kind="line",
    x="timepoint",
    y="signal",
    hue="event",
)

sns.relplot(
    data=fmri,
    kind="line",
    x="timepoint",
    y="signal",
    hue="region",
    style="event",
)

Plotting samples from different categories on different subplots

sns.relplot(
    data=fmri,
    kind="line",
    x="timepoint",
    y="signal",
    hue="region",
    col="event",
    row='subject'
)

sns.relplot(
    data=fmri,
    kind="line",
    x="timepoint",
    y="signal",
    row="region",
    col="event",
)