Categorical Data Type#

Categoricals can only take on a limited number of possible values (categories) and
can be sorted according to the custom order of the categories.

To harness Categorical data type in Lets-Plot you can either add a pandas.Categotical variable to
your pandas.DataFrame or annotate any variable in your dataset as Categorical using
Lets-Plot as_discrete() function and the levels parameter.

import pandas as pd

from lets_plot import *

LetsPlot.setup_html()

mpg_df = pd.read_csv ("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv")
mpg_df.head(4)

	Unnamed: 0	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	1	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	2	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	3	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
3	4	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact

1. Data Type of the “manufacturer” is `Unordered Discrete` by Default .#

ggplot(mpg_df) + geom_bar(aes(x='manufacturer')) + coord_flip()

# 
# Create a list of categories sorted according to a num. of vehicles in the dataset.
# 

brands_by_count = mpg_df['manufacturer'].value_counts().index.tolist()
brands_by_count

['dodge',
 'toyota',
 'volkswagen',
 'ford',
 'chevrolet',
 'audi',
 'hyundai',
 'subaru',
 'nissan',
 'honda',
 'jeep',
 'pontiac',
 'land rover',
 'mercury',
 'lincoln']

2. First Option: Add a `pandas.Categorical` Variable#

manufacturer_cat = pd.Categorical(mpg_df['manufacturer'], categories=brands_by_count, ordered=True)
mpg_df['manufacturer_cat'] = manufacturer_cat

ggplot(mpg_df) + \
    geom_bar(aes(x='manufacturer_cat'),
             labels=layer_labels(['..count..']),
             tooltips='none') + \
    coord_flip()

3. Second Option: Annotate “manufacturer” as a `Categorical` Using `as_discrete(levels=..)`#

ggplot(mpg_df) + \
    geom_bar(aes(x=as_discrete('manufacturer', levels=brands_by_count)),
             labels=layer_labels(['..count..']),
             tooltips='none') + \
    coord_flip()

Categorical Data Type#

1. Data Type of the “manufacturer” is Unordered Discrete by Default .#

2. First Option: Add a pandas.Categorical Variable#

3. Second Option: Annotate “manufacturer” as a Categorical Using as_discrete(levels=..)#

4. Faceted Plot with a Categorical as a Facet Variable#

1. Data Type of the “manufacturer” is `Unordered Discrete` by Default .#

2. First Option: Add a `pandas.Categorical` Variable#

3. Second Option: Annotate “manufacturer” as a `Categorical` Using `as_discrete(levels=..)`#

4. Faceted Plot with a `Categorical` as a Facet Variable#