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.. currentmodule:: altair
.. _user-guide-impute-transform:
Impute
~~~~~~
The impute transform allows you to fill-in missing entries in a dataset.
As an example, consider the following data, which includes missing values
that we filter-out of the long-form representation (see :ref:`data-long-vs-wide`
for more on this):
.. altair-plot::
:output: repr
import numpy as np
import pandas as pd
data = pd.DataFrame({
't': range(5),
'x': [2, np.nan, 3, 1, 3],
'y': [5, 7, 5, np.nan, 4]
}).melt('t').dropna()
data
Notice the result: the ``x`` series has no entry at ``t=1``, and the ``y``
series has a missing entry at ``t=3``. If we use Altair to visualize this
data directly, the line skips the missing entries:
.. altair-plot::
import altair as alt
raw = alt.Chart(data).mark_line(point=True).encode(
x='t:Q',
y='value:Q',
color='variable:N'
)
raw
This is not always desirable, because (particularly for a line plot with
no points) it can imply the existence of data that is not there.
Impute via Encodings
^^^^^^^^^^^^^^^^^^^^
To address this, you can use the impute method of the encoding channel.
For example, we can impute using a constant value (we'll show the raw chart
lightly in the background for reference):
.. altair-plot::
background = raw.encode(opacity=alt.value(0.2))
chart = alt.Chart(data).mark_line(point=True).encode(
x='t:Q',
y=alt.Y('value:Q').impute(value=0),
color='variable:N'
)
background + chart
Or we can impute using any supported aggregate:
.. altair-plot::
chart = alt.Chart(data).mark_line(point=True).encode(
x='t:Q',
y=alt.Y('value:Q').impute(method='mean'),
color='variable:N'
)
background + chart
Impute via Transform
^^^^^^^^^^^^^^^^^^^^
Similar to the :ref:`user-guide-bin-transform` and :ref:`user-guide-aggregate-transform`,
it is also possible to specify the impute transform outside the encoding as a
transform. For example, here is the equivalent of the above two charts:
.. altair-plot::
chart = alt.Chart(data).transform_impute(
impute='value',
key='t',
value=0,
groupby=['variable']
).mark_line(point=True).encode(
x='t:Q',
y='value:Q',
color='variable:N'
)
background + chart
.. altair-plot::
chart = alt.Chart(data).transform_impute(
impute='value',
key='t',
method='mean',
groupby=['variable']
).mark_line(point=True).encode(
x='t:Q',
y='value:Q',
color='variable:N'
)
background + chart
If you would like to use more localized imputed values, you can specify a
``frame`` parameter similar to the :ref:`user-guide-window-transform` that
will control which values are used for the imputation. For example, here
we impute missing values using the mean of the neighboring points on either
side:
.. altair-plot::
chart = alt.Chart(data).transform_impute(
impute='value',
key='t',
method='mean',
frame=[-1, 1],
groupby=['variable']
).mark_line(point=True).encode(
x='t:Q',
y='value:Q',
color='variable:N'
)
background + chart
Transform Options
^^^^^^^^^^^^^^^^^
The :meth:`~Chart.transform_impute` method is built on the :class:`~ImputeTransform`
class, which has the following options:
.. altair-object-table:: altair.ImputeTransform
|
