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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]: