How to use the altair.X function in altair

rng = np.random.RandomState(1)
x = rng.rand(40) ** 2
y = 10 - 1. / (x + 0.1) + rng.randn(40)
df = pd.DataFrame({'x': x, 'y': y})

# Define the degree of the polynomial fit
degree_list = [1, 3, 5]

# Build a dataframe with the fitted data
poly_data = pd.DataFrame({'xfit': np.linspace(df['x'].min(), df['x'].max(), 500)})
for degree in degree_list:
    poly_data[str(degree)] = np.poly1d(np.polyfit(df['x'], df['y'], degree))(poly_data['xfit'])

# Plot the data points on an interactive axis
points = alt.Chart(df).mark_circle(color='black').encode(
    x=alt.X('x', title='x'),
    y=alt.Y('y', title='y')
).interactive()

# Plot the best fit polynomials
polynomial_fit = alt.Chart(poly_data).transform_fold(
    ['1', '3', '5'],
    as_=['degree', 'yfit']
).mark_line().encode(
    x='xfit:Q',
    y='yfit:Q',
    color='degree:N'
)

points + polynomial_fit

).transform_calculate(
    year="year(datum.Date)"
).transform_calculate(
    decade="floor(datum.year / 10)"
).transform_calculate(
    scaled_date="(datum.year % 10) + (month(datum.Date)/12)"
).transform_window(
    first_date='first_value(scaled_date)',
    last_date='last_value(scaled_date)',
    sort=[{"field": "scaled_date", "order": "ascending"}],
    groupby=['decade'],
    frame=[None, None]
).transform_calculate(
  end="datum.first_date === datum.scaled_date ? 'first' : datum.last_date === datum.scaled_date ? 'last' : null"
).encode(
    x=alt.X(
        "scaled_date:Q",
        axis=alt.Axis(title="Year into Decade", tickCount=11)
    ),
    y=alt.Y(
        "CO2:Q",
        title="CO2 concentration in ppm",
        scale=alt.Scale(zero=False)
    )
)

line = base.mark_line().encode(
    color=alt.Color(
        "decade:O",
        scale=alt.Scale(scheme="magma"),
        legend=None
    )

def filtered_hist(field, label, filter):
  """Creates a layered chart of histograms.
  The first layer (light gray) contains the histogram of the full data, and the
  second contains the histogram of the filtered data.
  Args:
    field: the field for which to generate the histogram.
    label: String label of the histogram.
    filter: an alt.Selection object to be used to filter the data.
  """
  base = alt.Chart().mark_bar().encode(
      x=alt.X(field, bin=alt.Bin(maxbins=10), title=label),
      y="count()",
  ).properties(
      width=300,
  )
  return alt.layer(
      base.transform_filter(filter),
      base.encode(color=alt.value('lightgray'), opacity=alt.value(.7)),
  ).resolve_scale(y='independent')

interval
            )
    #TODO get best score not mock https://altair-viz.github.io/user_guide/transform.html
    
    bottom_charts = alt.layer(
        base_bottom,
        highlight_bottom,
        best_score,
        data=data
        ).repeat(
            column=param_cols,
        )
    
    metric_line = alt.Chart(data, width=width).mark_area(filled=False).encode(
            y=alt.Y("{}:Q".format(metric_col),bin=alt.Bin(maxbins=metric_bins), title=None),
            x=alt.X('count()', title=metric_col),
            color=alt.Color('mean({}):Q'.format(metric_col), scale=alt.Scale(scheme='yelloworangered'), legend=None),
            ).add_selection(
                interval
            )
    metric_bar = alt.Chart(data, width=width).mark_bar().encode(
            y=alt.Y("{}:Q".format(metric_col),bin=alt.Bin(maxbins=metric_bins), title=None),
            x=alt.X('count()', title=metric_col),
            color=alt.Color('mean({}):Q'.format(metric_col), scale=alt.Scale(scheme='yelloworangered'), legend=None),
            )
    
    metric_chart = alt.layer(metric_bar, metric_line)
    
    combined_chart = alt.vconcat(top_charts,
                                 alt.hconcat(bottom_charts, metric_chart))
    
    return combined_chart

Natural Disasters
-----------------
This example shows a visualization of global deaths from natural disasters.
"""
# category: case studies
import altair as alt
from vega_datasets import data

source = data.disasters.url

alt.Chart(source).mark_circle(
    opacity=0.8,
    stroke='black',
    strokeWidth=1
).encode(
    alt.X('Year:O', axis=alt.Axis(labelAngle=0)),
    alt.Y('Entity:N'),
    alt.Size('Deaths:Q',
        scale=alt.Scale(range=[0, 4000]),
        legend=alt.Legend(title='Annual Global Deaths')
    ),
    alt.Color('Entity:N', legend=None)
).properties(
    width=450,
    height=320
).transform_filter(
    alt.datum.Entity != 'All natural disasters'
)

"""
Layered Plot with Dual-Axis
---------------------------
This example shows how to combine two plots and keep their axes.
"""
# category: bar charts
import altair as alt
from vega_datasets import data

source = data.seattle_weather()

base = alt.Chart(source).encode(
    alt.X('month(date):O',
        axis=alt.Axis(format='%b'),
        scale=alt.Scale(zero=False)
    )
)

bar = base.mark_bar().encode(
    y='mean(precipitation)'
)


line =  base.mark_line(color='red').encode(
    y='mean(temp_max)',
)

alt.layer(
    bar,

"""
Binned Heatmap
--------------
This example shows how to make a heatmap from binned quantitative data.
"""
# category: other charts
import altair as alt
from vega_datasets import data

source = data.movies()

alt.Chart(source).mark_rect().encode(
    alt.X('IMDB_Rating:Q', bin=alt.Bin(maxbins=60)),
    alt.Y('Rotten_Tomatoes_Rating:Q', bin=alt.Bin(maxbins=40)),
    alt.Color('count(IMDB_Rating):Q', scale=alt.Scale(scheme='greenblue'))
)

"""
Horizontal Grouped Bar Chart
----------------------------
This example shows a grouped bar chart achieved by making cosmetic changes to a trellis plot.
"""
# category: bar charts
import altair as alt
from vega_datasets import data

source = data.barley()

alt.Chart(source).mark_bar().encode(
    # The field used to define the subelements of each group
    x=alt.X(
        'year:N',
        axis=alt.Axis(title="")
    ),
    # The length of the bars
    y=alt.Y(
        'yield:Q',
        axis=alt.Axis(grid=False)
    ),
    color='year:N',
    # The field used to group the subelements
    column='variety:N'
).configure_view(
    stroke='transparent'  # Remove the trellis frames so multiple charts appear as one.
).transform_filter(
    alt.datum.site == 'Morris'
)

def _build_depth_histogram(data):
  """Build histogram with depth (DP)."""
  width = 200
  height = 200
  title = 'Depth'
  depth_data = _integer_counts_to_histogram(data)
  depth_histogram = _placeholder_for_empty_chart(
      'No entries in VCF with DP', width=width, height=height, title=title)
  if not depth_data.empty:
    # s = bin_start, e = bin_end, c = count
    depth_histogram = alt.Chart(depth_data).mark_bar(color=BAR_COLOR_DEPTH) \
        .encode(x=alt.X('s', title='Depth'),
                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 depth_histogram