In [1]:

from lets_plot import *
from lets_plot.mapping import *

LetsPlot.setup_html() 

In [2]:

blank_theme = theme(line=element_blank(), axis=element_blank())

In [3]:

w,h = 400,250

tooltip_content = (
    layer_tooltips()
        .line('count|@{..count..} (@{..prop..})')
        .line('total|@{..sum..}')
        .format('..prop..', '.0%')
        .format('..count..', '.1f')
        .format('..sum..', '.1f')
)

def bar_and_pie(bar_plot, pie_plot):
    bunch = GGBunch()
    bunch.add_plot(bar_plot, 0, 0)
    bunch.add_plot(pie_plot + blank_theme, w, 0)
    return bunch.show()

In [4]:

df = {
  'name':  ['a', 'b', 'c', 'd', 'b'],
  'value': [40,   90, 10,  50,  20 ],
}

p = ggplot(df) + ggsize(w,h)

In [5]:

# data as is - stat identity

bar_and_pie(
    p + geom_bar(aes(x='name', y='value', fill='name'), stat='identity'),
    p + geom_pie(aes(slice='value', fill='name'), size=10, stat='identity', stroke=1)
)

In [6]:

# count 

bar_and_pie(
    p + geom_bar(aes(x='name', fill='name')),
    p + geom_pie(aes(fill='name'), size=10, stroke=1, tooltips=tooltip_content)
)

In [7]:

# sum

bar_and_pie(
    p + geom_bar(aes(x='name', fill='name', weight='value')),
    p + geom_pie(aes(fill='name', weight='value'), stroke=1, size=10, tooltips=tooltip_content)
)

In [8]:

# sum + ordering

bar_and_pie(
    p + geom_bar(aes(x='name', fill=as_discrete('name', order_by='..count..'), weight='value')),
    p + geom_pie(
        aes(fill=as_discrete('name', order_by='..count..'), weight='value'),
        stroke=1, size=10,
        tooltips=tooltip_content
    )
)

In [ ]:

In [9]:

df2 = {
    'x': [1, 1, 1, 1, 1, 1.5, 1.5,   2,   2,   2 ],
    'y': [1, 1, 1, 1, 1,   2,   2, 1.5, 1.5, 1.5],
    's': [3, 1, 2, 1, 4,   1,   3,   3,   3,  1],
    'n': ['a', 'b', 'a', 'c', 'a',  'a', 'b', 'c', 'a',  'b']
}

p2 = ggplot(df2) + xlim(0.5,2.5) + ylim(0.5,2.5)
 
p2 + geom_pie(aes('x', 'y', slice='s', fill='n'), size=10, hole=0.3, stat='identity') 

Out[9]:

In [10]:

p2 + geom_pie(aes('x', 'y', fill=as_discrete('n', order_by='..count..', order=-1), weight='s'), 
              size=10, hole=0.3) 

Out[10]:

In [11]:

# Map total count in each point to size

p2 + geom_pie(aes('x', 'y', fill=as_discrete('n', order_by='..count..', order=-1), weight='s', size='..sum..'),
              hole=0.3, tooltips=tooltip_content) 

Out[11]:

In [12]:

# Mapping 'fill' and 'size' to the same variable

ggplot({'n': ["a", "b", "c"], 's': [1, 2, 3]}) + blank_theme + \
    geom_pie(aes(fill='n', slice='s', size='n'), stat="identity") 

Out[12]:

In [ ]:

In [13]:

import pandas as pd

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

Out[13]:

	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
4	5	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact

In [14]:

p3 = ggplot(mpg_df) + ggsize(400,300)

In [15]:

ordered_class = as_discrete('class', order_by='..count..')

bar_and_pie(
    p3 + geom_bar(aes(x='class', fill=ordered_class)), 
    p3 + geom_pie(aes(fill=ordered_class), size=10, hole=0.3)
)

In [16]:

bar_and_pie(
    p3 + geom_bar(aes(x='class', fill=ordered_class, weight ='displ')), 
    p3 + geom_pie(aes(fill=ordered_class,  weight='displ'), size=10, hole=0.3, tooltips=tooltip_content)
)

In [ ]:

In [17]:

ggplot(mpg_df) + blank_theme + \
    geom_pie(aes(fill=ordered_class,  weight='displ', size='..sum..'), hole=0.3, tooltips=tooltip_content) + \
    facet_grid(x='year') + \
    scale_size(range=[5, 10]) + \
    guides(size='none') 

Out[17]:

In [18]:

ggplot(mpg_df) + blank_theme + \
    geom_pie(aes(fill=as_discrete('drv', order=1), size='..sum..'), tooltips=tooltip_content) + \
    facet_wrap(facets='trans', ncol=5) + \
    scale_size(range=[2, 10]) + \
    guides(size='none') 

Out[18]:

In [ ]:

In [19]:

length = {
  'name' : ['20-50 km', '50-75 km', '10-20 km', '75-100 km', '3-5 km', '7-10 km', '5-7 km', '>100 km', '2-3 km'],
  'count': [1109, 696, 353, 192, 168, 86, 74, 65, 53],
  'explode': [0, 0, 0, 0.1, 0.1, 0.2, 0.3, 0.4, 0.6]
}

ggplot(length) + blank_theme + \
    geom_pie(aes(fill='name', slice='count', explode='explode'), stat='identity',
            stroke=1, stroke_color='black', size=20) + \
    scale_fill_gradient(low='dark_blue', high='light_green') 

Out[19]:

In [20]:

calories = {
    'slice': [35, 25, 25, 15],
    'label': ["Apples", "Bananas", "Cherries", "Dates"],
    'explode': [0.1, 0, 0, 0]
}

p4 = ggplot(calories) + \
    blank_theme + \
    scale_fill_brewer(palette='Set1') + \
    ggsize(w, h)

p_pie = p4 + \
    geom_pie(aes(fill='label', slice='slice', explode='explode'), stat='identity', size=18)

p_donut = p4 + \
    geom_pie(aes(fill='label', slice='slice', explode='explode'), stat='identity', hole=0.8, size=18)

bunch = GGBunch()
bunch.add_plot(p_pie + theme(legend_position='none'), 0, 0)
bunch.add_plot(p_donut, w, 0)
bunch

Out[20]:

In [ ]:

In [21]:

import pandas as pd

referendum_df = pd.read_csv("data/montenegrin_referendum_2006.csv")
referendum_df.head()

Out[21]:

	Municipality	Yes	Yes%	No	No%	Registered	Voted	Voted%
0	Andrijevica	1084	27.60	2824	71.89	4369	3928	89.91
1	Bar	16640	63.07	9496	35.99	32255	26382	81.79
2	Berane	11268	46.85	12618	52.46	28342	24051	84.86
3	Bijelo Polje	19405	55.36	15437	44.04	40110	35051	87.39
4	Budva	5908	52.75	5180	46.25	12797	11200	87.52

In [22]:

from lets_plot.geo_data import *

country = 'Montenegro'
geocoder = geocode_states(names=referendum_df['Municipality']).scope(country)

boundaries = geocoder.get_boundaries(resolution=15)

The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).

/Users/olarionova/miniconda3/lib/python3.7/site-packages/geopandas/array.py:93: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  aout[:] = out
/Users/olarionova/miniconda3/lib/python3.7/site-packages/geopandas/array.py:93: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
  aout[:] = out

In [23]:

boundaries = pd.merge(boundaries, referendum_df[['Municipality','Yes%']], left_on='state', right_on='Municipality')
boundaries.head()

Out[23]:

	state	found name	geometry	Municipality	Yes%
0	Andrijevica	Opština Andrijevica	MULTIPOLYGON (((19.61901 42.80456, 19.63347 42...	Andrijevica	27.60
1	Bar	Opština Bar	MULTIPOLYGON (((18.98948 42.16385, 18.98910 42...	Bar	63.07
2	Berane	Opština Berane	MULTIPOLYGON (((19.61729 42.88021, 19.62012 42...	Berane	46.85
3	Bijelo Polje	Opština Bijelo Polje	MULTIPOLYGON (((19.41061 43.07992, 19.41992 43...	Bijelo Polje	55.36
4	Budva	Opština Budva	MULTIPOLYGON (((18.80014 42.28039, 18.79997 42...	Budva	52.75

In [24]:

plot_title = ggtitle("Results of the Montenegrin independence referendum, 2006")

fill_colors = ['#a50026', '#d73027', '#66bd63', '#4daf4a','#006837']
    
map_layer = geom_map(aes(fill="Yes%"), 
                     data=boundaries,
                     tooltips=layer_tooltips()
                                 .title('@state')
                                 .line('\'Yes\' votes|@{Yes%}')
                                 .format('Yes%', '{} %')) + \
            scale_fill_gradientn(fill_colors)

ggplot() + map_layer + plot_title + blank_theme + ggsize(800, 700) 

Out[24]:

In [25]:

mref_df = referendum_df

# Blank or invalid votes:
mref_df["Blank"] = mref_df["Voted"] - mref_df["Yes"] - mref_df["No"]

mref_df = mref_df[["Municipality", "Registered", "No", "Yes", "Blank"]]

id_vars = ["Municipality", "Registered"]
mref_df = pd.melt(frame=mref_df, id_vars=id_vars, var_name="Vote", value_name="Number")
mref_df

Out[25]:

	Municipality	Registered	Vote	Number
0	Andrijevica	4369	No	2824
1	Bar	32255	No	9496
2	Berane	28342	No	12618
3	Bijelo Polje	40110	No	15437
4	Budva	12797	No	5180
...	...	...	...	...
58	Rožaje	19646	Blank	90
59	Šavnik	2306	Blank	20
60	Tivat	10776	Blank	91
61	Ulcinj	17117	Blank	137
62	Žabljak	3407	Blank	24

63 rows × 4 columns

In [26]:

ggplot() + \
    map_layer  + \
    geom_pie(aes(color='Vote', slice='Number'), 
             data=mref_df, stat='identity', 
             map=geocoder, map_join=['Municipality','state'],
             fill_by='color', size=5, stroke=1.5) + \
    scale_color_manual(values=[ "#e41a1c","#4daf4a", "#999999"]) + \
    plot_title + \
    blank_theme + \
    ggsize(800, 700)

Out[26]:

In [27]:

pie_layer = geom_pie(aes('Municipality', color='Vote', weight='Number', size='..sum..', group='Municipality'),
                     data=mref_df,
                     map=geocoder, map_join=['Municipality','state'],
                     fill_by='color', hole=0.2, stroke=1.5,
                     tooltips=layer_tooltips()
                              .title('@Municipality')
                              .line('Vote|@Vote')
                              .line('Number|@{..count..}')
                              .line('Percent|@{..prop..}')
                              .line('Total voted|@{..sum..}')
                              .line('Registered|@Registered')
                              .format('..prop..', '.2%')
                              .format('Registered', ',d')) + \
            scale_color_manual(values=['#e41a1c','#4daf4a', '#999999']) + \
            scale_size(name='Total voted', range=[3, 8])

ggplot() + \
    map_layer + \
    pie_layer + \
    plot_title + \
    blank_theme + \
    ggtitle("Results of the Montenegrin independence referendum, 2006") + \
    ggsize(800, 700)

Out[27]:

In [28]:

# on livemap

ggplot() + \
    geom_livemap() + \
    map_layer + \
    pie_layer + \
    plot_title + \
    theme(legend_position='none')

Out[28]: