How To Make Stripplot with Jitter in Altair Python?

#import libraries
import seaborn
import altair as alt
# Getting data
tip = seaborn.load_dataset('tips')
# plotting the stripplot
stripplot = alt.Chart(tip).mark_circle(size=14).encode(
    # X-axis jitter Vertical
    x=alt.X(
        'jitter:Q',
        title=None,
        axis=alt.Axis(ticks=True, grid=False, labels=False),
        scale=alt.Scale(),
    ),
    y=alt.Y('tip:Q',
            scale=alt.Scale()),
    color=alt.Color('time:N', legend=None),
    column=alt.Column(
        'time:N',
    ),
).transform_calculate(
    # Generate Gaussian jitter with a Box-Muller transform
    jitter='sqrt(-2*log(random()))*cos(2*PI*random())')
stripplot

#import libraries
import altair as alt
from vega_datasets import data
# Getting data
weather = data.seattle_weather()
# plotting the stripplot
stripplot = alt.Chart(weather).mark_circle(size=14).encode(
    x=alt.X(
        'jitter:Q',
        title=None,
        axis=alt.Axis(ticks=True, grid=False, labels=False),
        scale=alt.Scale(),
    ),
    y=alt.Y('temp_max:Q',
            scale=alt.Scale(
                domain=(-1, 40))),
    color=alt.Color('weather:N', legend=None),
    column=alt.Column(
        'weather:N',
        header=alt.Header(
            labelFontSize=16,
            labelAngle=0,
            titleOrient='top',
            labelOrient='bottom',
            labelAlign='center',
            labelPadding=25,
        ),
    ),
).transform_calculate(
    # Generate Gaussian jitter with a Box-Muller transform
    jitter='sqrt(-2*log(random()))*cos(2*PI*random())'
).configure_facet(
    spacing=0
).configure_view(
    stroke=None
).configure_axis(
    labelFontSize=16,
    titleFontSize=16
).properties(height=400, width=100)
stripplot

#import libraries
import seaborn
import altair as alt
import pandas as pd
# Creating our own data
data = [['Tom', 10, 'Male'], ['Nick', 25, 'Male'], ['Juli', 14, 'Female'],
        ['Sarah', 30, 'Male'], ['Pulkit', 20, 'Male'], ['Ritika', 20, 'Female'],
        ['Sayantan', 60, 'Male'], ['Pam', 39, 'Female'], ['Peter', 42, 'Male'],
        ['Jenefer', 24, 'Female'], ['Tony', 29, 'Female'], ['Myler', 22, 'Female']]
df = pd.DataFrame(data, columns=['Name', 'Age', 'Gender'])
# plotting the stripplot Horizontal
horizontal_stripplot = alt.Chart(df, width=600, height=100).mark_circle(size=40).encode(
    y=alt.Y(
        'jitter:Q',
        title=None,
        axis=alt.Axis(ticks=True, grid=False, labels=False),
        scale=alt.Scale(),
    ),
    x=alt.X('Age:Q', scale=alt.Scale()),
    color=alt.Color('Gender:N', legend=None),
    row=alt.Row(
        'Gender:N',
        header=alt.Header(
            labelAngle=0,
            labelFontSize=16,
            titleOrient='top',
            labelOrient='left',
            labelAlign='left',
        ),
    ),
).transform_calculate(
    # Generate Gaussian jitter with a Box-Muller transform
    jitter='sqrt(-2*log(random()))*cos(2*PI*random())'
).configure_facet(
    spacing=0
).configure_view(
    stroke=None
).configure_axis(
    labelFontSize=16,
    titleFontSize=16
)
horizontal_stripplot