列的交互式选择

本例展示了如何通过访问选择器小部件的值来交互式选择列，然后计算所选列的差值。它还说明了如何使用indexof根据活动选择值过滤列。

方法语法

import pandas as pd
import numpy as np
import altair as alt


# Create timeseries data
rng = np.random.default_rng(905)
ex_ts = pd.DataFrame(
    rng.random((10, 4)),
    columns=['a', 'b', 'c', 'd'],
).assign(
    date=pd.date_range(
        start=pd.to_datetime('2022-02-22')-pd.Timedelta(9, unit='D'),
        end=pd.to_datetime('2022-02-22')).strftime('%Y-%m-%d'),
)

# Create heatmap with selection
select_x = alt.selection_point(fields=['level_0'], name='select_x', value='b')
select_y = alt.selection_point(fields=['level_1'], name='select_y', value='d')
heatmap = alt.Chart(
    ex_ts.drop(columns='date').corr().stack().reset_index().rename(columns={0: 'correlation'}),
    title='Click a tile to compare timeseries',
    height=250,
    width=250,
).mark_rect().encode(
    alt.X('level_0').title(None),
    alt.Y('level_1').title(None),
    alt.Color('correlation').scale(domain=[-1, 1], scheme='blueorange'),
    opacity=alt.when(select_x, select_y).then(alt.value(1)).otherwise(alt.value(0.4)),
).add_params(
    select_x, select_y
)

# Create chart with individual lines/timeseries
base = alt.Chart(
    ex_ts.melt(
        id_vars='date',
        var_name='category',
        value_name='value',
    ),
    height=100,
    width=300,
    title='Individual timeseries',
)
lines = base.transform_filter(
    # If the category is not in the selected values, the returned index is -1
    'indexof(datum.category, select_x.level_0) !== -1'
   '| indexof(datum.category, select_y.level_1) !== -1'
).mark_line().encode(
    alt.X('date:T').axis(labels=False).title(None),
    alt.Y('value').scale(domain=(0, 1)),
    alt.Color('category').legend(orient='top', offset=-20).title(None)
)

# Create chart with difference between lines/timeseries
dynamic_title = alt.Title(alt.expr(f'"Difference " + {select_x.name}.level_0 + " - " + {select_y.name}.level_1'))
# We pivot transform to get each category as a column
lines_diff = base.transform_pivot(
    'category', 'value', groupby=['date']
# In the calculate transform we use the values from the selection to subset the columns to substract
).transform_calculate(
    difference = f'datum[{select_x.name}.level_0] - datum[{select_y.name}.level_1]'
).mark_line(color='grey').encode(
    alt.X('date:T').axis(format='%Y-%m-%d').title(None),
    alt.Y('difference:Q').scale(domain=(-1, 1)),
).properties(
    title=dynamic_title
)

# Layout the charts
(lines & lines_diff) | heatmap

属性语法

import pandas as pd
import numpy as np
import altair as alt


# Create timeseries data
rng = np.random.default_rng(905)
ex_ts = pd.DataFrame(
    rng.random((10, 4)),
    columns=['a', 'b', 'c', 'd'],
).assign(
    date=pd.date_range(
        start=pd.to_datetime('2022-02-22')-pd.Timedelta(9, unit='D'),
        end=pd.to_datetime('2022-02-22')).strftime('%Y-%m-%d'),
)

# Create heatmap with selection
select_x = alt.selection_point(fields=['level_0'], name='select_x', value='b')
select_y = alt.selection_point(fields=['level_1'], name='select_y', value='d')
heatmap = alt.Chart(
    ex_ts.drop(columns='date').corr().stack().reset_index().rename(columns={0: 'correlation'}),
    title='Click a tile to compare timeseries',
    height=250,
    width=250,
).mark_rect().encode(
    alt.X('level_0', title=None),
    alt.Y('level_1', title=None),
    alt.Color('correlation', scale=alt.Scale(domain=[-1, 1], scheme='blueorange')),
    opacity=alt.when(select_x, select_y).then(alt.value(1)).otherwise(alt.value(0.4)),
).add_params(
    select_x, select_y
)

# Create chart with individual lines/timeseries
base = alt.Chart(
    ex_ts.melt(
        id_vars='date',
        var_name='category',
        value_name='value',
    ),
    height=100,
    width=300,
    title='Individual timeseries',
)
lines = base.transform_filter(
    # If the category is not in the selected values, the returned index is -1
    'indexof(datum.category, select_x.level_0) !== -1'
   '| indexof(datum.category, select_y.level_1) !== -1'
).mark_line().encode(
    alt.X('date:T', axis=alt.Axis(labels=False), title=None),
    alt.Y('value', scale=alt.Scale(domain=(0, 1))),
    alt.Color('category', legend=alt.Legend(orient='top', offset=-20), title=None)
)

# Create chart with difference between lines/timeseries
dynamic_title = alt.Title(alt.expr(f'"Difference " + {select_x.name}.level_0 + " - " + {select_y.name}.level_1'))
# We pivot transform to get each category as a column
lines_diff = base.transform_pivot(
    'category', 'value', groupby=['date']
# In the calculate transform we use the values from the selection to subset the columns to substract
).transform_calculate(
    difference = f'datum[{select_x.name}.level_0] - datum[{select_y.name}.level_1]'
).mark_line(color='grey').encode(
    alt.X('date:T', axis=alt.Axis(format='%Y-%m-%d'), title=None),
    alt.Y('difference:Q', scale=alt.Scale(domain=(-1, 1))),
).properties(
    title=dynamic_title
)

# Layout the charts
(lines & lines_diff) | heatmap