III Categorical data

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
}

# III Visualization of distributional data (“displot”)

penguins = sns.load_dataset("penguins")
penguins.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

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

Categorical scatterplots

sns.catplot(data=tips,
            x="day",
            y="tip",
           # kind='strip' # default is 'strip'
           jitter=False # default is True
           )

sns.catplot(data=tips,
            x="day",
            y="tip",
            kind="swarm")

/home/pierro/mambaforge/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning:

8.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.

sns.catplot(data=tips,
            x="day",
            y="tip",
            hue="time",
            kind="swarm")

/home/pierro/mambaforge/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning:

8.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.

sns.catplot(data=tips,
            x="day",
            y="total_bill",
            hue="size",
            col='sex')

# Specify the ordering if the categorical values

sns.catplot(data=tips,
            x="day",
            y="tip",
            order=["Thur", "Fri","Sat","Sun"])

# Swapping orientation

sns.catplot(data=tips,
            x="total_bill",
            y="day",
            hue="time",
            col='sex')

Comparing distributions along categoritcal variables (mean, median, quantiles)

Boxplot using `kind='box'`

sns.catplot(data=tips,
            x="day",
            y="total_bill",
            kind="box")

sns.catplot(data=tips,
            x="day",
            y="total_bill",
            hue="smoker",
            kind="box")

Violin plots with `kind='violin'`

sns.catplot(
    data=tips,
    x="day",
    y="total_bill",
    hue="sex"
)

sns.catplot(
    data=tips,
    x="day",
    y="total_bill",
    hue="sex",
    kind='violin'
)

# have the two populations’ violin “stick” to each other with split=True'

sns.catplot(
    data=tips,
    x="day",
    y="total_bill",
    hue="sex",
    col='time',
    kind="violin",
    split=True,
)

sns.catplot(
    data=tips,
    x="day",
    y="total_bill",
    hue="sex",
    kind="violin",
    inner='stick',
    split=True,
)

# Showing additional dimensions with col=(...) or row=(...)

sns.catplot(
    data=tips,
    x="day",
    y="total_bill",
    col="sex",
    kind="violin",
    inner='stick',
    split=True,
)

Bar plots

sns.catplot(data=tips,
            x="day",
            y="total_bill",
            hue="sex",
            kind="bar")

sns.catplot(data=tips,
            x="day",
            y="total_bill",
            hue="sex",
            kind="bar",
           errorbar="ci") # standard confidence interval

sns.catplot(data=tips,
            x="day",
            y="total_bill",
            hue="sex",
            kind="bar",
           errorbar='sd') # standard deviation 'sd'

Counting data with `kind='count'`

sns.catplot(
    data=tips,
    x="day",
    hue="sex",
    kind="count"
)

Point plots to emphasize continuity

sns.catplot(data=tips,
            x="day",
            y="tip",
            col="sex",
            kind="point",
            markers='<')