def test_init_groupby_data_kwarg(self):
from bokeh.sampledata.autompg import autompg as df
group = df.groupby(('origin', 'cyl'))
ds = ColumnDataSource(data=group)
s = group.describe()
self.assertTrue(len(ds.column_names)) == 41
self.assertIsInstance(ds.data['origin_cyl'], np.ndarray)
for key in s.columns.values:
k2 = "_".join(key)
self.assertIsInstance(ds.data[k2], np.ndarray)
self.assertEquals(list(s[key]), list(ds.data[k2]))
def test_init_groupby_with_None_subindex_name(self):
df = pd.DataFrame({"A": [1, 2, 3, 4] * 2, "B": [10, 20, 30, 40] * 2, "C": range(8)})
group = df.groupby(['A', [10, 20, 30, 40] * 2])
ds = ColumnDataSource(data=group)
s = group.describe()
self.assertTrue(len(ds.column_names)) == 41
self.assertIsInstance(ds.data['index'], np.ndarray)
for key in s.columns.values:
k2 = "_".join(key)
self.assertIsInstance(ds.data[k2], np.ndarray)
self.assertEquals(list(s[key]), list(ds.data[k2]))
def test_init_groupby_data_kwarg(self, pd):
from bokeh.sampledata.autompg import autompg as df
group = df.groupby(by=['origin', 'cyl'])
ds = ColumnDataSource(data=group)
s = group.describe()
assert len(ds.column_names) == 49
assert isinstance(ds.data['origin_cyl'], np.ndarray)
for key in s.columns.values:
k2 = "_".join(key)
assert isinstance(ds.data[k2], np.ndarray)
assert list(s[key]) == list(ds.data[k2])
def test_init_groupby_arg(self, pd):
from bokeh.sampledata.autompg import autompg as df
group = df.groupby(by=['origin', 'cyl'])
ds = bms.ColumnDataSource(group)
s = group.describe()
assert len(ds.column_names) == 49
assert isinstance(ds.data['origin_cyl'], np.ndarray)
for key in s.columns.values:
k2 = "_".join(key)
assert isinstance(ds.data[k2], np.ndarray)
assert list(s[key]) == list(ds.data[k2])
def test_data_accepts_groupby_with_None_subindex_name(self, pd) -> None:
df = pd.DataFrame({"A": [1, 2, 3, 4] * 2, "B": [10, 20, 30, 40] * 2, "C": range(8)})
group = df.groupby(['A', [10, 20, 30, 40] * 2])
ds = bms.ColumnDataSource()
assert ds.data == {}
ds.data = group
s = group.describe()
assert len(ds.column_names) == 17
assert isinstance(ds.data['index'], np.ndarray)
for key in s.columns.values:
k2 = "_".join(key)
assert isinstance(ds.data[k2], np.ndarray)
assert list(s[key]) == list(ds.data[k2])
def test_init_groupby_with_None_subindex_name(self):
df = pd.DataFrame({
"A": [1, 2, 3, 4] * 2,
"B": [10, 20, 30, 40] * 2,
"C": range(8)
})
group = df.groupby(['A', [10, 20, 30, 40] * 2])
ds = ColumnDataSource(data=group)
s = group.describe()
self.assertTrue(len(ds.column_names)) == 41
self.assertIsInstance(ds.data['index'], np.ndarray)
for key in s.columns.values:
k2 = "_".join(key)
self.assertIsInstance(ds.data[k2], np.ndarray)
self.assertEquals(list(s[key]), list(ds.data[k2]))
"""
Link
https://bokeh.pydata.org/en/latest/docs/gallery/bar_pandas_groupby_colormapped.html
"""
#Libraries
from bokeh.io import show, output_file
from bokeh.palettes import Spectral5
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg as df
from bokeh.transform import factor_cmap
output_file("bar_pandas_groupby_colormapped.html")
df.cyl = df.cyl.astype(str)
group = df.groupby('cyl')
cyl_cmap = factor_cmap('cyl',
palette=Spectral5,
factors=sorted(df.cyl.unique()))
p = figure(plot_height=350,
x_range=group,
title="MPG by #Cylinders",
toolbar_location=None,
tools="")
p.vbar(x='cyl',
top='mpg_mean',
width=1,
source=group,
import pandas as pd
# simple examples with inferred meaning
# implied index
d1 = Donut([2, 4, 5, 2, 8])
# explicit index
d2 = Donut(pd.Series([2, 4, 5, 2, 8], index=['a', 'b', 'c', 'd', 'e']))
# given a categorical series of data with no aggregation
d3 = Donut(autompg.cyl.astype(str))
# given a categorical series of data with no aggregation
d4 = Donut(autompg.groupby('cyl').displ.mean())
# given a categorical series of data with no aggregation
d5 = Donut(autompg.groupby(['cyl', 'origin']).displ.mean(),
hover_text='mean')
# no values specified
d6 = Donut(autompg, label='cyl', agg='count')
# explicit examples
d7 = Donut(autompg, label='cyl',
values='displ', agg='mean')
# nested donut chart for the provided labels, with colors assigned
# by the first level
d8 = Donut(autompg, label=['cyl', 'origin'],
from bokeh.io import show, output_file
from bokeh.palettes import Spectral5
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg as df
from bokeh.transform import factor_cmap
output_file("bar_pandas_groupby_colormapped.html")
df.cyl = df.cyl.astype(str)
group = df.groupby('cyl')
cyl_cmap = factor_cmap('cyl', palette=Spectral5, factors=sorted(df.cyl.unique()))
p = figure(plot_height=350, x_range=group, title="MPG by # Cylinders",
toolbar_location=None, tools="")
p.vbar(x='cyl', top='mpg_mean', width=1, source=group,
line_color=cyl_cmap, fill_color=cyl_cmap)
p.y_range.start = 0
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "some stuff"
p.xaxis.major_label_orientation = 1.2
p.outline_line_color = None
show(p)
import pandas as pd
# simple examples with inferred meaning
# implied index
d1 = Donut([2, 4, 5, 2, 8])
# explicit index
d2 = Donut(pd.Series([2, 4, 5, 2, 8], index=['a', 'b', 'c', 'd', 'e']))
# given a categorical series of data with no aggregation
d3 = Donut(autompg.cyl.astype(str))
# given a categorical series of data with no aggregation
d4 = Donut(autompg.groupby('cyl').displ.mean())
# given a categorical series of data with no aggregation
d5 = Donut(autompg.groupby(['cyl', 'origin']).displ.mean(), hover_text='mean')
# no values specified
d6 = Donut(autompg, label='cyl', agg='count')
# explicit examples
d7 = Donut(autompg, label='cyl', values='displ', agg='mean')
# nested donut chart for the provided labels, with colors assigned
# by the first level
d8 = Donut(autompg, label=['cyl', 'origin'], values='displ', agg='mean')
# show altering the spacing in levels
import pandas as pd
# simple examples with inferred meaning
# implied index
d1 = Donut([2, 4, 5, 2, 8])
# explicit index
d2 = Donut(pd.Series([2, 4, 5, 2, 8], index=['a', 'b', 'c', 'd', 'e']))
# given a categorical series of data with no aggregation
d3 = Donut(autompg.cyl.astype(str))
# given a categorical series of data with no aggregation
d4 = Donut(autompg.groupby('cyl').displ.mean())
# given a categorical series of data with no aggregation
d5 = Donut(autompg.groupby(['cyl', 'origin']).displ.mean(),
hover_text='mean')
# no values specified
d6 = Donut(autompg, label='cyl', agg='count')
# explicit examples
d7 = Donut(autompg, label='cyl',
values='displ', agg='mean')
# nested donut chart for the provided labels, with colors assigned
# by the first level
d8 = Donut(autompg, label=['cyl', 'origin'],
p.xgrid.grid_line_color = None
show(p)
## Pandas
from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource
from bokeh.palettes import Spectral5
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg as df
from bokeh.transform import factor_cmap
output_file("groupby.html")
df.cyl = df.cyl.astype(str)
group = df.groupby('cyl')
source = ColumnDataSource(group)
cyl_cmap = factor_cmap('cyl', palette=Spectral5, factors=sorted(df.cyl.unique()))
p = figure(plot_height=350, x_range=group, title="MPG by # Cylinders",
toolbar_location=None, tools="")
p.vbar(x='cyl', top='mpg_mean', width=1, source=source,
line_color=cyl_cmap, fill_color=cyl_cmap)
p.y_range.start = 0
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "some stuff"
p.xaxis.major_label_orientation = 1.2
return y
df["more_labels"] = df["cyl"].map(new_labels)
# Specifying your labels
p1 = Bar(df, label='labels', values='mpg',
title="Total MPG by CYL, remapped labels, p1",
width=400, height=400, legend="top_right")
p2 = Bar(df, label=['cyl', 'more_labels'], values='mpg',
title="Total MPG by CYL, multiple labels, p2", width=400, height=400,
legend="top_right")
# Plot with "intermediate-level" bokeh.plotting interface
new_df = DataFrame(df.groupby(['cyl'])['mpg'].sum())
factors = ["three", "four", "five", "six", "eight"]
ordinate = new_df['mpg'].tolist()
mpg = [x * 0.5 for x in ordinate]
p3 = figure(x_range=factors, width=400, height=400,
title="Total MPG by CYL, using 'rect' instead of 'bar', p3")
p3.rect(factors, y=mpg, width=0.75, height=ordinate)
p3.y_range = Range1d(0, 6000)
p3.xaxis.axis_label = "x axis name"
p3.yaxis.axis_label = "Sum(Mpg)"
# With HoverTool, using 'quad' instead of 'rect'
top = [int(x) for x in ordinate]
bottom = [0] * len(top)
import pandas as pd
# simple examples with inferred meaning
# implied index
d1 = Donut([2, 4, 5, 2, 8])
# explicit index
d2 = Donut(pd.Series([2, 4, 5, 2, 8], index=["a", "b", "c", "d", "e"]))
# given a categorical series of data with no aggregation
d3 = Donut(autompg.cyl.astype(str))
# given a categorical series of data with no aggregation
d4 = Donut(autompg.groupby("cyl").displ.mean())
# given a categorical series of data with no aggregation
d5 = Donut(autompg.groupby(["cyl", "origin"]).displ.mean(), hover_text="mean")
# no values specified
d6 = Donut(autompg, label="cyl", agg="count")
# explicit examples
d7 = Donut(autompg, label="cyl", values="displ", agg="mean")
# nested donut chart for the provided labels, with colors assigned
# by the first level
d8 = Donut(autompg, label=["cyl", "origin"], values="displ", agg="mean")
# show altering the spacing in levels
