With the below sample-supplied code, I can’t export a chart (which renders correctly in Jupyter) to a static file
py.image.save_as(fig, 'MyChart.png', scale=3)
Error Dump
---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
//anaconda/envs/OSMNX/lib/python3.6/site-packages/plotly/api/v2/utils.py in validate_response(response)
65 try:
---> 66 parsed_content = response.json()
67 except ValueError:
//anaconda/envs/OSMNX/lib/python3.6/site-packages/requests/models.py in json(self, **kwargs)
891 pass
--> 892 return complexjson.loads(self.text, **kwargs)
893
//anaconda/envs/OSMNX/lib/python3.6/json/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
353 parse_constant is None and object_pairs_hook is None and not kw):
--> 354 return _default_decoder.decode(s)
355 if cls is None:
//anaconda/envs/OSMNX/lib/python3.6/json/decoder.py in decode(self, s, _w)
338 """
--> 339 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
340 end = _w(s, end).end()
//anaconda/envs/OSMNX/lib/python3.6/json/decoder.py in raw_decode(self, s, idx)
356 except StopIteration as err:
--> 357 raise JSONDecodeError("Expecting value", s, err.value) from None
358 return obj, end
JSONDecodeError: Expecting value: line 2 column 1 (char 1)
During handling of the above exception, another exception occurred:
PlotlyRequestError Traceback (most recent call last)
<ipython-input-195-f01521e13baf> in <module>()
112 # profit_df= pd.read_pickle(url)
113 # profit_df.columns
--> 114 f(af_df, start_year=2013, end_year=2016, client_count=30, mytitle='Sales over Median Income')
<ipython-input-195-f01521e13baf> in f(df, start_year, end_year, client_count, mytitle)
104
105 #py.image.save_as(fig,'./charts/{}.png'.format(mytitle.title().replace(" ","")))
--> 106 py.image.save_as(fig, 'my_image.png', scale=3)
107
108 return py.iplot(fig, filename='Affinity Sales')
//anaconda/envs/OSMNX/lib/python3.6/site-packages/plotly/plotly/plotly.py in save_as(cls, figure_or_data, filename, format, width, height, scale)
830 filename += '.' + format
831
--> 832 img = cls.get(figure_or_data, format, width, height, scale)
833
834 f = open(filename, 'wb')
//anaconda/envs/OSMNX/lib/python3.6/site-packages/plotly/plotly/plotly.py in get(figure_or_data, format, width, height, scale)
743 payload['scale'] = scale
744
--> 745 response = v2.images.create(payload)
746
747 headers = response.headers
//anaconda/envs/OSMNX/lib/python3.6/site-packages/plotly/api/v2/images.py in create(body)
16 """
17 url = build_url(RESOURCE)
---> 18 return request('post', url, json=body)
//anaconda/envs/OSMNX/lib/python3.6/site-packages/plotly/api/v2/utils.py in request(method, url, **kwargs)
151 content = response.content if response else 'No content'
152 raise exceptions.PlotlyRequestError(message, status_code, content)
--> 153 validate_response(response)
154 return response
//anaconda/envs/OSMNX/lib/python3.6/site-packages/plotly/api/v2/utils.py in validate_response(response)
67 except ValueError:
68 message = content if content else 'No Content'
---> 69 raise exceptions.PlotlyRequestError(message, status_code, content)
70
71 message = ''
PlotlyRequestError:
<html><head>
<meta http-equiv="content-type" content="text/html;charset=utf-8">
<title>502 Server Error</title>
</head>
<body text=#000000 bgcolor=#ffffff>
<h1>Error: Server Error</h1>
<h2>The server encountered a temporary error and could not complete your request.<p>Please try again in 30 seconds.</h2>
<h2></h2>
</body></html>
My Code (which renders correctly within Jupyter)
import plotly.plotly as py
from plotly.graph_objs import *
import pandas as pd
def popup_text(row):
val = '''{0}<br>\
Total Profit: ${1:,.0f}<br>\
{2}, {3}<br>\
ShipToZip: {4}<br>\
CityPop: {5:,.0f}<br>\
CityGrowth: {6:.0%}<br>\
Density: {7:,.0f}<br>\
County Density: {8:,.0f}<br>\
Households: {9:,.0f}<br>\
AvgIncome: ${10:,.0f}<br>\
CountyAvgIncome: ${11:,.0f}<br>\
White: {12:.0%}<br>\
FEMA Disaster Count: {13:,.0f}<br>\
Bldg Valuation Growth: {14:.0%}'''.format(
row['Company'],
pd.to_numeric(row['Profit_sum'],errors='coerce'),
row['City'], row['State'], row['ShipToLocZip'],
row['CityPop2016'], row['CityPopGrowth'], row['Density'],
row['County_PopDensity'], row['Households'], row['AvgIncome'],
row['County_MedianHouseoldIncome'], row['PopulationRaceWhite'],
row['DisasterCount'], row['ValuationGrowth'])
return val.replace('\\','').strip()
def f(df, start_year, end_year, client_count, mytitle):
# Set range
df = df[df.OrderDate.dt.year.between(start_year, end_year)]
# Add Profit Total for Every Client
df['Profit_sum'] = df.groupby('Company')['Profit'].transform(sum)
df = df.sort_values(['Profit_sum', 'Company'], ascending=[False, False])
# Entire Sales History
plotly_df = df[[
'Company', 'ShipToLocZip', 'City', 'State', 'Population', 'Density',
'CityPop2016', 'CityPopGrowth', 'AvgIncome',
'County_MedianHouseoldIncome', 'County_PopDensity', 'DisasterCount',
'ValuationGrowth', 'PopulationRaceWhite', 'Households', 'Latitude',
'Longitude', 'Profit_sum'
]]
# plotly_df.head(1)
plotly_df['Text'] = plotly_df.apply(lambda row: popup_text(row), axis=1)
# 20 best clients
bestclient_df = (plotly_df.groupby('Company').agg({
'Profit_sum':
'min',
'Latitude':
lambda x: x.value_counts().index[0],
'Longitude':
lambda x: x.value_counts().index[0],
'Text':
'min'
}).reset_index().sort_values('Profit_sum',
ascending=False)[0:client_count])
# bestclient_df.head(1)
data = graph_objs.Data([
Scattermapbox(
lat=list(plotly_df.Latitude),
lon=list(plotly_df.Longitude),
mode='markers',
marker=Marker(
size=4,
#color=colors,
opacity=0.7),
text=list(plotly_df.Text),
hoverinfo='text',
showlegend=False),
Scattermapbox(
lat=list(bestclient_df.Latitude),
lon=list(bestclient_df.Longitude),
mode='markers',
marker=Marker(size=12, color='rgb(50, 255, 100)', opacity=0.8),
text=list(bestclient_df.Text),
hoverinfo='text',
showlegend=False,
)
])
layout = graph_objs.Layout(
title= '{0} and {1} Best Clients ({2}-{3})'.format(
mytitle, client_count, start_year, end_year),
autosize=True,
hovermode='closest',
mapbox=dict(
layers=[
dict(sourcetype = 'geojson',
source =sources[k],
below="water",
type = 'fill',
#color = sources[k],#the list of colors for each shape/layer in choropleth
color = sources[k]['features'][0]['properties']['Color'],
opacity=0.5,
) for k in range(len(sources))
],
accesstoken=MAPBOX_API_KEY,
bearing=0,
center=dict(lat=38, lon=-94),
pitch=0,
zoom=3,
style='light'
)
)
fig = dict(data=data, layout=layout)
#py.image.save_as(fig,'./charts/{}.png'.format(mytitle.title().replace(" ","")),scale=3)
py.image.save_as(fig, 'MyChart.png', scale=3)
return py.iplot(fig, filename='Affinity Sales')
f(af_df, start_year=2013, end_year=2016, client_count=30, mytitle='Sales over Median Income')
Much appreciation in advance as the chart (which renders in Jupyter) is too small for presentation purposes.