def test_irregular_subplots():
df = cf.datagen.bubble(10, 50, mode='stocks')
figs = cf.figures(df, [
dict(kind='histogram', keys='x', color='blue'),
dict(kind='scatter', mode='markers', x='x', y='y', size=5),
dict(
kind='scatter', mode='markers', x='x', y='y', size=5, color='teal')
],
asList=True)
figs.append(
cf.datagen.lines(1).figure(bestfit=False,
colors=['blue'],
bestfit_colors=['pink']))
base_layout = cf.tools.get_base_layout(figs)
sp = cf.subplots(
figs,
shape=(3, 2),
base_layout=base_layout,
vertical_spacing=.15,
horizontal_spacing=.03,
specs=[[{
'rowspan': 2
}, {}], [None, {}], [{
'colspan': 2
}, None]],
subplot_titles=['Histogram', 'Scatter 1', 'Scatter 2', 'Bestfit Line'])
sp['layout'].update(showlegend=False)
return sp
def test_irregular_subplots():
df = cf.datagen.bubble(10, 50, mode='stocks')
figs = cf.figures(df, [
dict(kind='histogram', keys='x', color='blue'),
dict(kind='scatter', mode='markers', x='x', y='y', size=5),
dict(kind='scatter', mode='markers', x='x', y='y',
size=5, color='teal')],asList=True)
figs.append(cf.datagen.lines(1).figure(bestfit=False, colors=['blue'],
bestfit_colors=['pink']))
base_layout = cf.tools.get_base_layout(figs)
sp = cf.subplots(figs, shape=(3, 2), base_layout=base_layout,
vertical_spacing=.15, horizontal_spacing=.03,
specs=[[{'rowspan': 2}, {}], [None, {}],
[{'colspan': 2}, None]],
subplot_titles=['Histogram', 'Scatter 1',
'Scatter 2', 'Bestfit Line'])
sp['layout'].update(showlegend=False)
return sp
def test_irregular_subplots():
df = cf.datagen.bubble(10, 50, mode="stocks")
figs = cf.figures(
df,
[
dict(kind="histogram", keys="x", color="blue"),
dict(kind="scatter", mode="markers", x="x", y="y", size=5),
dict(kind="scatter", mode="markers", x="x", y="y", size=5, color="teal"),
],
asList=True,
)
figs.append(cf.datagen.lines(1).figure(bestfit=False, colors=["blue"], bestfit_colors=["pink"]))
base_layout = cf.tools.get_base_layout(figs)
sp = cf.subplots(
figs,
shape=(3, 2),
base_layout=base_layout,
vertical_spacing=0.15,
horizontal_spacing=0.03,
specs=[[{"rowspan": 2}, {}], [None, {}], [{"colspan": 2}, None]],
subplot_titles=["Histogram", "Scatter 1", "Scatter 2", "Bestfit Line"],
)
sp["layout"].update(showlegend=False)
return sp
df = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])
df.scatter_matrix()
df = cf.datagen.lines(4)
df.iplot(subplots=True,
shape=(4, 1),
shared_xaxes=True,
vertical_spacing=.02,
fill=True)
df.iplot(subplots=True, subplot_titles=True, legend=False)
df = cf.datagen.bubble(10, 50, mode='stocks')
figs = cf.figures(df, [
dict(kind='histogram', keys='x', color='blue'),
dict(kind='scatter', mode='markers', x='x', y='y', size=5),
dict(kind='scatter', mode='markers', x='x', y='y', size=5, color='teal')
],
asList=True)
figs.append(
cf.datagen.lines(1).figure(bestfit=True,
colors=['blue'],
bestfit_colors=['pink']))
base_layout = cf.tools.get_base_layout(figs)
sp = cf.subplots(
figs,
shape=(3, 2),
base_layout=base_layout,
vertical_spacing=.15,
horizontal_spacing=.03,
specs=[[{
'rowspan': 2
title='up视频质量走势参考',
xTitle='时间',
yTitle='比率')
return df3.figure(
kind='lines',
orientation='v',
)
def month_video_play(self):
df1 = self.df_data[[
'created', 'stat#favorite', 'stat#coin', 'stat#like'
]]
df2 = df1.groupby([df1.created.dt.year,
df1.created.dt.month]).agg('sum')
# df2.iplot(kind='bar', barmode='stack', orientation='v')
df2.iplot(kind='histogram', bins=10)
return df2
if __name__ == '__main__':
upid = 176037767
analysis_video = AnalysisVideo(upid)
figs = cf.figures(analysis_video.month_video_play(),
[dict(kind='bar', barmode='stack')],
asList=True)
figs.append(analysis_video.month_video_play_rate())
layout = cf.tools.get_base_layout(figs)
sp = cf.subplots(figs, shape=(1, 2), base_layout=layout)
sp['layout'].update(showlegend=False)
cf.iplot(sp)