Exploratory data analysis with Altair

This notebook collects explorations of Altair’s most interesting features. Originally published on Kaggle.

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


ames_data = "https://github.com/eaisi/discover-projects/blob/main/ames-housing/AmesHousing.csv?raw=true"
train = pd.read_csv(ames_data).rename(columns=lambda s: s.replace(" ",""))

train

	Order	PID	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	...	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	526301100	20	RL	141.0	31770	Pave	NaN	IR1	Lvl	...	0	NaN	NaN	NaN	0	5	2010	WD	Normal	215000
1	2	526350040	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	...	0	NaN	MnPrv	NaN	0	6	2010	WD	Normal	105000
2	3	526351010	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	...	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal	172000
3	4	526353030	20	RL	93.0	11160	Pave	NaN	Reg	Lvl	...	0	NaN	NaN	NaN	0	4	2010	WD	Normal	244000
4	5	527105010	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	...	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal	189900
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2925	2926	923275080	80	RL	37.0	7937	Pave	NaN	IR1	Lvl	...	0	NaN	GdPrv	NaN	0	3	2006	WD	Normal	142500
2926	2927	923276100	20	RL	NaN	8885	Pave	NaN	IR1	Low	...	0	NaN	MnPrv	NaN	0	6	2006	WD	Normal	131000
2927	2928	923400125	85	RL	62.0	10441	Pave	NaN	Reg	Lvl	...	0	NaN	MnPrv	Shed	700	7	2006	WD	Normal	132000
2928	2929	924100070	20	RL	77.0	10010	Pave	NaN	Reg	Lvl	...	0	NaN	NaN	NaN	0	4	2006	WD	Normal	170000
2929	2930	924151050	60	RL	74.0	9627	Pave	NaN	Reg	Lvl	...	0	NaN	NaN	NaN	0	11	2006	WD	Normal	188000

2930 rows × 82 columns

Bar Chart with Highlighted Bar

Basic bar chart with a bars highlighted based on the percentage of missing values.

missing = train.isnull().sum()*100/train.isnull().sum().sum()
missing = missing[missing > 0].reset_index()
missing.columns = ['Column', 'Count missing']
missing.head()

	Column	Count missing
0	LotFrontage	3.111309
1	Alley	17.347133
2	MasVnrType	11.270557
3	MasVnrArea	0.146041
4	BsmtQual	0.507969

alt.Chart(missing).mark_bar().encode(
    x=alt.X('Column', sort='-y'),
    y='Count missing',
    color=alt.condition(
        alt.datum['Count missing'] >10,  # If count missing is > 10%, returns True,
        alt.value('orange'),             # which sets the bar orange.
        alt.value('steelblue')           # And if it's not true it sets the bar steelblue.
    ),
    tooltip=['Count missing']
).properties(
    width=500,
    height=300
).configure_axis(
    grid=False
)

Boxplot

Creation of a basic boxplot using .mark_boxplot() method

alt.Chart(train).mark_boxplot().encode(
    x='OverallQual:O',
    y='SalePrice:Q',
    color='OverallQual:N'
).properties(
    width=500,
    height=300
)

Heatmaps

Creation of a basic heatmap using .mark_rect() method.

alt.Chart(train).mark_rect().encode(
    x='MSZoning',
    y='ExterQual',
    color='average(SalePrice)',
    tooltip=['MSZoning', 'ExterQual', 'average(SalePrice)']
).properties(
    width=500,
    height=300
)

Bindings, Selections & Conditions

Here you can select the KitchenQual feature from a dropdown menu and see how the graph changes color!

input_dropdown = alt.binding_select(options=list(train['KitchenQual'].unique()), name='Lot Shape')
selection = alt.selection_point(fields=['KitchenQual'], bind=input_dropdown)
color = alt.condition(selection,
                      alt.Color('KitchenQual:N', legend=None),
                      alt.value('lightgray'))

alt.Chart(train).mark_point().encode(
    x='GrLivArea',
    y='SalePrice',
    color=color,
    tooltip=['GrLivArea', 'SalePrice']
).properties(
    width=500,
    height=300
).add_params(
    selection
).configure_axis(
    grid=False
)

Interactive Chart with Cross-Highlight

In this more advanced example, I use the ExterQual feature as a filter for a binned heatmap.

Click on the bar chart bars to change the heatmap!

pts = alt.selection_point(encodings=['x'])

rect = alt.Chart(train).mark_rect().encode(
    x=alt.X('GrLivArea', bin=alt.Bin(maxbins=40)),
    y=alt.Y('GarageArea', bin=alt.Bin(maxbins=40)),
    color='average(SalePrice)'
).properties(
    width=500,
    height=300
).transform_filter(
    pts
)

bar = alt.Chart(train).mark_bar().encode(
    x='ExterQual:N',
    y='count()',
    color=alt.condition(pts, alt.ColorValue("steelblue"), alt.ColorValue("grey")),
    tooltip=['ExterQual', 'count()']
).properties(
    width=550,
    height=200
).add_params(
    pts
)


alt.vconcat(
    rect,
    bar
).resolve_legend(
    color="independent",
    size="independent"
).configure_axis(
    grid=False
)

Dot Dash Plot

A Dot Dash Plot is basically a scatter plot with both axis removed and replaced with barcode plots (aka strip plots), which allow you to see the distribution of values of each measure used in the scatter plot.

# Configure the options common to all layers
brush = alt.selection_interval()
base = alt.Chart(train).add_params(brush)

# Configure the points
points = base.mark_point().encode(
    x=alt.X('GrLivArea', title=''),
    y=alt.Y('SalePrice', title=''),
    color=alt.condition(brush, 'KitchenQual', alt.value('grey'))
)

# Configure the ticks
tick_axis = alt.Axis(labels=False, domain=False, ticks=False)

x_ticks = base.mark_tick().encode(
    alt.X('GrLivArea', axis=tick_axis),
    alt.Y('KitchenQual', title='', axis=tick_axis),
    color=alt.condition(brush, 'KitchenQual', alt.value('lightgrey'))
)

y_ticks = base.mark_tick().encode(
    alt.X('KitchenQual', title='', axis=tick_axis),
    alt.Y('SalePrice', axis=tick_axis),
    color=alt.condition(brush, 'KitchenQual', alt.value('lightgrey'))
)

# Build the chart
(
    y_ticks | (points & x_ticks)
).configure_axis(
    grid=False
)

Multifeature Scatter Plot

Let’s create a scatter plot with multiple feature encodings.

With .interactive() you can zoom in. You can also click on legend to select specific KitchenQual values.

selection = alt.selection_point(fields=['KitchenQual'], bind='legend')

alt.Chart(train).mark_circle().encode(
    alt.X('GrLivArea', scale=alt.Scale(zero=False)),
    alt.Y('GarageArea', scale=alt.Scale(zero=False, padding=1)),
    color='KitchenQual',
    size=alt.Size('SalePrice', bin=alt.Bin(maxbins=10), title='SalePrice'),
    opacity=alt.condition(selection, alt.value(1), alt.value(0.2)),
    tooltip=['GrLivArea', 'SalePrice']
).properties(
    width=500,
    height=500
).add_params(
    selection
).configure_axis(
    grid=False
).interactive()

Scatter Matrix

Scatter matrix are one of the most common graph you’ll see on Kaggle. It consists of several pair-wise scatter plots of variables presented in a matrix format, useful to visualize multiple relationships between a pair of variables.

In Altair this can be achieved using a RepeatChart, let’s see how!

alt.Chart(train).mark_circle().encode(
    alt.X(alt.repeat("column"), type='quantitative'),
    alt.Y(alt.repeat("row"), type='quantitative'),
    color='KitchenQual'
).properties(
    width=300,
    height=300
).repeat(
    # Here we tell Altair we want to repeat out scatter plots for each row-column pair
    row=['GrLivArea', 'GarageArea', 'TotalBsmtSF'],
    column=['TotalBsmtSF', 'GarageArea', 'GrLivArea']
).configure_axis(
    grid=False
)

Layered Histogram

Using Altair, we can make overlapping histograms or layers histograms from data:

alt.Chart(train).mark_bar(
    opacity=0.5,
    binSpacing=0
).encode(
    alt.X('SalePrice:Q', bin=alt.Bin(maxbins=50)),
    alt.Y('count()', stack=None),
    alt.Color('MSZoning:N')
)