def update_pie_plot(group):
return dict(data=[
go.Pie(labels=[
legendScatterDict[group][val] for val in dfPokemon[group].unique()
],
values=[
len(dfPokemon[dfPokemon[group] == val])
for val in dfPokemon[group].unique()
])
],
layout=go.Layout(title='Pie Chart Pokemon',
margin={
'l': 160,
'b': 40,
't': 40,
'r': 10
}))
def update_scatter_plot(hue, x, y):
return dict(data=[
go.Scatter(x=dfPokemon[dfPokemon[hue] == val][x],
y=dfPokemon[dfPokemon[hue] == val][y],
name=legendScatterDict[hue][val],
mode='markers') for val in dfPokemon[hue].unique()
],
layout=go.Layout(title='Scatter Plot Pokemon',
xaxis={'title': x},
yaxis=dict(title=y),
margin={
'l': 40,
'b': 40,
't': 40,
'r': 10
},
hovermode='closest'))
def update_category_graph(jenisplot, x, y, stats):
return dict(
layout=go.Layout(title='{} Plot Pokemon'.format(jenisplot),
xaxis={'title': x},
yaxis=dict(title=y),
boxmode='group',
violinmode='group'),
data=[
listGoFunc[jenisplot](x=generateValuePlot('True', x,
y)['x'][jenisplot],
y=generateValuePlot('True', x, y,
stats)['y'][jenisplot],
name='Legendary'),
listGoFunc[jenisplot](x=generateValuePlot('False', x,
y)['x'][jenisplot],
y=generateValuePlot('False', x, y,
stats)['y'][jenisplot],
name='Non-Legendary')
])
def update_hist_plot(x, hue, std):
std = int(std)
if (hue == 'All'):
return dict(data=[
go.Histogram(x=dfPokemon[
(dfPokemon[x] >= (dfPokemon[x].mean() -
(std * dfPokemon[x].std())))
& (dfPokemon[x] <= (dfPokemon[x].mean() +
(std * dfPokemon[x].std())))][x],
name='Normal',
marker=dict(color='green')),
go.Histogram(x=dfPokemon[(dfPokemon[x] <
(dfPokemon[x].mean() -
(std * dfPokemon[x].std())))
| (dfPokemon[x] >
(dfPokemon[x].mean() +
(std * dfPokemon[x].std())))][x],
name='Not Normal',
marker=dict(color='red'))
],
layout=go.Layout(
title='Histogram {} Stats Pokemon'.format(x),
xaxis=dict(title=x),
yaxis=dict(title='Count'),
height=450,
width=1000))
subtitles = []
for val in dfPokemon[hue].unique():
dfSub = dfPokemon[dfPokemon[hue] == val]
outlierCount = len(
dfSub[(dfSub[x] < (dfSub[x].mean() - (std * dfSub[x].std())))
| (dfSub[x] > (dfSub[x].mean() + (std * dfSub[x].std())))])
subtitles.append(
legendScatterDict[hue][val] +
" ({}% outlier)".format(round(outlierCount / len(dfSub) * 100, 2)))
fig = tools.make_subplots(rows=rowcolhist[hue]['row'],
cols=rowcolhist[hue]['col'],
subplot_titles=subtitles)
uniqueData = dfPokemon[hue].unique().reshape(rowcolhist[hue]['row'],
rowcolhist[hue]['col'])
index = 1
for r in range(1, rowcolhist[hue]['row'] + 1):
for c in range(1, rowcolhist[hue]['col'] + 1):
dfSub = dfPokemon[dfPokemon[hue] == uniqueData[r - 1, c - 1]]
fig.append_trace(
go.Histogram(
x=dfSub[(dfSub[x] >= (dfSub[x].mean() -
(std * dfSub[x].std())))
& (dfSub[x] <= (dfSub[x].mean() +
(std * dfSub[x].std())))][x],
name='Normal {} {}'.format(hue, uniqueData[r - 1, c - 1]),
marker=dict(color='green')), r, c)
fig.append_trace(
go.Histogram(
x=dfSub[(dfSub[x] <
(dfSub[x].mean() - (std * dfSub[x].std())))
| (dfSub[x] >
(dfSub[x].mean() + (std * dfSub[x].std())))][x],
name='Not Normal {} {}'.format(hue, uniqueData[r - 1,
c - 1]),
marker=dict(color='red')), r, c)
fig['layout']['xaxis' + str(index)].update(title=x.capitalize())
fig['layout']['yaxis' + str(index)].update(title='Count')
index += 1
if (hue == 'Generation'):
fig['layout'].update(height=700,
width=1000,
title='Histogram {} Stats Pokemon'.format(x))
else:
fig['layout'].update(height=450,
width=1000,
title='Histogram {} Stats Pokemon'.format(x))
return fig