How to use the altair.Axis function in altair

size=alt.Size('sum(workshop_hours):Q', legend=None),
        tooltip=['workshop_name:O', 'class_size:Q']
    ).transform_filter(
        multi
    ).add_selection(
        brush
    )
    picker = alt.Chart(df).mark_rect().encode(
        y=alt.Y('name:N'),
        color=color, 
    ).add_selection(
        multi
    )

    a = alt.Chart(resp_nwm).mark_square(size=40).encode(
        x=alt.X('variable:N', scale=alt.Scale(rangeStep=80), axis=alt.Axis(labelAngle=0)),
        y=alt.Y('value'),
        color=alt.Color('name', legend=None),
        tooltip=['name','value']
    ).transform_calculate(
        value=expr.round(expr.exp(datum.value))
    ).transform_filter(
        multi
    )

    b = a.mark_line(opacity=0.8, interpolate='monotone').encode(
        x=alt.X('variable')
    )
    chart = picker | (point & a+b) | (box & bar)
    #chart = alt.hconcat(picker, alt.vconcat(point, a+b) , alt.vconcat(box, bar))
    return chart.to_json()

for a in spec.annotation]
    else:
        annotation = [None] * len(spec.mz)
        color = [colors[None]] * len(spec.mz)
    calc_mz = [a.calc_mz if a is not None else None for a in annotation]
    fragment = [str(a) if a is not None else None for a in annotation]
    spec_df = pd.DataFrame({'exp_mz': spec.mz, 'intensity': intensity,
                            'calc_mz': calc_mz, 'fragment': fragment,
                            'color': color})

    x_axis = altair.X('exp_mz', axis=altair.Axis(title='m/z',
                                                 titleFontStyle='italic',
                                                 grid=grid),
                      scale=altair.Scale(nice=True, padding=5, zero=False))
    y_axis = altair.Y('intensity',
                      axis=altair.Axis(title='Intensity', format='%',
                                       grid=grid),
                      scale=altair.Scale(nice=True, padding=5))
    color = altair.Color('color', scale=None, legend=None)
    tooltip_not_annotated = [altair.Tooltip('exp_mz', format='.4f',
                                            title='Experimental m/z'),
                             altair.Tooltip('intensity', format='.0%',
                                            title='Intensity')]
    tooltip_annotated = [altair.Tooltip('fragment', title='Fragment'),
                         altair.Tooltip('exp_mz', format='.4f',
                                        title='Experimental m/z'),
                         altair.Tooltip('calc_mz', format='.4f',
                                        title='Theoretical m/z'),
                         altair.Tooltip('intensity', format='.0%',
                                        title='Intensity')]
    # Unannotated peaks.
    spec_plot = (altair.Chart(spec_df[spec_df['fragment'].isna()])

height = 200

  ti = titv_counts['Transition']
  tv = titv_counts['Transversion']
  # Show TiTv ratio with fallback to avoid division by 0
  titv_ratio = '%.2f' % (float(ti) / tv) if tv > 0 else '%d / 0' % (ti)
  title = 'Biallelic Ti/Tv ratio: %s' % (titv_ratio)

  tt_chart = _placeholder_for_empty_chart(
      'No biallelic SNPs', width=width, height=height, title=title)
  tt_labels = ['Transition', 'Transversion']
  if sum([titv_counts[k] for k in titv_counts]) > 0:
    tt_data = _dict_to_dataframe(titv_counts)
    bars = alt.Chart(tt_data).mark_bar().encode(
        x=alt.X(
            'label', sort=tt_labels, axis=alt.Axis(title=None, labelAngle=0)),
        y=alt.Y('value', axis=alt.Axis(title='Count', format='s')),
        tooltip=alt.Tooltip('value', format='.4s'),
        color=alt.Color(
            'label',
            legend=None,
            sort=tt_labels,
            scale=alt.Scale(scheme='teals', domain=tt_labels)))
    labels = bars.mark_text(dy=-5).encode(text=alt.Text('value', format='.4s'))
    tt_chart = (bars + labels).properties(
        title=title, width=width, height=height)
  return tt_chart

============================
This chart visualizes the age distribution of the US population over time,
using a wrapped faceting of the data by decade.
"""
# category: case studies
import altair as alt
from vega_datasets import data

source = data.population.url

alt.Chart(source).mark_area().encode(
    x='age:O',
    y=alt.Y(
        'sum(people):Q',
        title='Population',
        axis=alt.Axis(format='~s')
    ),
    facet=alt.Facet('year:O', columns=5),
).properties(
    title='US Age Distribution By Year',
    width=90,
    height=80
)

pca_vals = pd.DataFrame(pca_vals, index=df.index)
        pca_vals.rename(columns=lambda x: "PC{}".format(x + 1), inplace=True)

        # label the axes
        if xlabel is None:
            xlabel = "PC1 ({}%)".format(round(pca.explained_variance_ratio_[0] * 100, 2))
        if ylabel is None:
            ylabel = "PC2 ({}%)".format(round(pca.explained_variance_ratio_[1] * 100, 2))

        # don't send all the data to vega, just what we're plotting
        plot_data = pd.concat(
            [pca_vals.loc[:, ("PC1", "PC2")], magic_metadata], axis=1
        ).reset_index()

        alt_kwargs = dict(
            x=alt.X("PC1", axis=alt.Axis(title=xlabel)),
            y=alt.Y("PC2", axis=alt.Axis(title=ylabel)),
            tooltip=[magic_fields[t] for t in tooltip],
            href="url:N",
            url="https://app.onecodex.com/classification/" + alt.datum.classification_id,
        )

        # only add these parameters if they are in use
        if color:
            domain = magic_metadata[color].values
            color_kwargs = {
                "legend": alt.Legend(title=magic_fields[color]),
            }
            if not is_continuous(domain):
                color_range = interleave_palette(domain)
                color_kwargs["scale"] = alt.Scale(domain=domain, range=color_range)

This example demonstrates how to generate a facetted scatterplot,
with marginal facetted histograms, and how to share their respective
- x,some y-limits.
"""
# category: other charts

import altair as alt
from vega_datasets import data

iris = data.iris()

xscale = alt.Scale(domain=(4.0, 8.0))
yscale = alt.Scale(domain=(1.9, 4.55))

area_args = {'opacity': .3, 'interpolate': 'step'}
blank_axis = alt.Axis(title='')

points = alt.Chart(iris).mark_circle().encode(
    alt.X('sepalLength', scale=xscale),
    alt.Y('sepalWidth', scale=yscale),
    color='species',
)

top_hist = alt.Chart(iris).mark_area(**area_args).encode(
    alt.X('sepalLength:Q',
          # when using bins, the axis scale is set through
          # the bin extent, so we do not specify the scale here
          # (which would be ignored anyway)
          bin=alt.Bin(maxbins=20, extent=xscale.domain),
          stack=None,
          axis=blank_axis,
         ),

# gq = genotype quality, found at :GQ: in FORMAT column of VCF
  width = 200
  height = 200
  title = 'Genotype quality'
  gq_data = _integer_counts_to_histogram(data)
  gq_histogram = _placeholder_for_empty_chart(
      'No entries in VCF with GQ', width=width, height=height, title=title)
  if not gq_data.empty:
    # standardize x-axis limits across reports
    domain = [min(0, data[0][0]), max(150, data[-1][0])]
    # s = bin_start, e = bin_end, c = count
    gq_histogram = alt.Chart(gq_data).mark_bar(color=BAR_COLOR_GQ) \
        .encode(
            x=alt.X('s', title='GQ', scale=alt.Scale(domain=domain)),
            x2='e',
            y=alt.Y('c', title='Count', stack=True, axis=alt.Axis(format='s'))) \
        .properties(width=width, height=height, title=title) \
        .interactive(bind_y=False)
  return gq_histogram

from vega_datasets import data

alt.Chart(data.cars()).transform_density(
    'Miles_per_Gallon',
    as_=['Miles_per_Gallon', 'density'],
    extent=[5, 50],
    groupby=['Origin']
).mark_area(orient='horizontal').encode(
    y='Miles_per_Gallon:Q',
    color='Origin:N',
    x=alt.X(
        'density:Q',
        stack='center',
        impute=None,
        title=None,
        axis=alt.Axis(labels=False, values=[0],grid=False, ticks=True),
    ),
    column=alt.Column(
        'Origin:N',
        header=alt.Header(
            titleOrient='bottom',
            labelOrient='bottom',
            labelPadding=0,
        ),
    )
).properties(
    width=100
).configure_facet(
    spacing=0
).configure_view(
    stroke=None
)

.transform_window(
            sort=[alt.SortField(field=default_rank_col, order="ascending")],
            # We don't use an alt.WindowFieldDef here because python gets
            # confused when you use "as" as an actual argument name. So we just
            # use this syntax.
            window=[{"op": "row_number", "as": "qurro_x"}],
        )
        .encode(
            # type="ordinal" needed on the scale here to make bars adjacent;
            # see https://stackoverflow.com/a/55544817/10730311.
            x=alt.X(
                "qurro_x",
                title="Feature Rankings",
                type="ordinal",
                scale=alt.Scale(paddingOuter=1, paddingInner=0, rangeStep=1),
                axis=alt.Axis(ticks=False, labelAngle=0),
            ),
            y=alt.Y(default_rank_col, type="quantitative"),
            color=alt.Color(
                "qurro_classification",
                title="Log-Ratio Classification",
                scale=alt.Scale(
                    domain=["None", "Numerator", "Denominator", "Both"],
                    range=["#e0e0e0", "#f00", "#00f", "#949"],
                ),
            ),
            tooltip=[
                alt.Tooltip(
                    field="qurro_x",
                    title="Current Ranking",
                    type="quantitative",
                ),

"""
Grouped Bar Chart
-----------------------
This example shows a population broken out by gender and age for a specific year.
The grouping is achieved by building a trellis plot with narrow column
encoded on the age groups and x-axes encoded on gender.
"""
# category: bar charts
import altair as alt
from vega_datasets import data

source = data.population.url

alt.Chart(source).mark_bar(stroke='transparent').encode(
    alt.X('gender:N', scale=alt.Scale(rangeStep=12), axis=alt.Axis(title='')),
    alt.Y('sum(people):Q', axis=alt.Axis(title='population', grid=False)),
    color=alt.Color('gender:N', scale=alt.Scale(range=["#EA98D2", "#659CCA"])),
    column='age:O'
).configure_view(
    stroke='transparent'
).configure_axis(
    domainWidth=0.8
).transform_filter(
    alt.datum.year == 2000
).transform_calculate(
    'gender', alt.expr.if_(alt.datum.sex == 2, 'Female', 'Male')
)