from pyjstat import pyjstat
from collections import OrderedDict
import urllib2
import json

dataset_url_1 = 'http://data.ssb.no/api/v0/dataset/1108.json?lang=en'

# Important: JSON data must be retrieved in order; 
# this can be accomplished using the object_pairs_hook parameter
# of the json.load method. 
population_json_data = json.load(urllib2.urlopen(dataset_url_1),
                      object_pairs_hook=OrderedDict)
population_results = pyjstat.from_json_stat(population_json_data, naming="id")

# Get the first result, since we're using only one input dataset
population_dataset = population_results[0]

# Filter the variable to plot
population_data = population_dataset[population_dataset['ContentsCode'] == 
                  'Folketallet11']
population_data.head()

import vincent
from vincent.values import ValueRef
from IPython.display import display

vincent.core.initialize_notebook()

# Get the map data
norway_topo = r'http://d1jbhcb2qvxl2c.cloudfront.net/norway_id.topo.json'
geo_data = [{'name': 'norway',
             'url': norway_topo,
             'feature': 'norway'}]

# Create the chart and bind the data to the map
vis_map = vincent.Map(data=population_data, geo_data=geo_data, scale=1000, 
                  projection = 'mercator', center = [17.81, 63.4],
                  data_bind='value', data_key='Region',
                  map_key={'norway': 'properties.KOMM'})
vis_map.marks[0].properties.enter.stroke_opacity = ValueRef(value=0.5)

# Get the categories for labeling the chart 
categories = pyjstat.get_dim_label(population_json_data['dataset'],'ContentsCode')

# Plot the chart
vis_map.legend(title= str(categories.loc['Folketallet11', 'label']))
display(vis_map)

# Filter dataset by region (Oslo, for example )
oslo = population_dataset.loc[population_dataset['Region'] == 
                  301, ['ContentsCode', 'value']]
# Reindex the dataset for a better x-axis legend
oslo = oslo.set_index('ContentsCode')

# Remove non-comparable variables
bar = vincent.Bar(oslo.iloc[1:10])

# Plot the chart
bar.axis_titles(x='Variable', y='Population')
display(bar)

import datetime as dt
import time

dataset_url_2 = 'http://data.ssb.no/api/v0/dataset/1086.json?lang=en'

# Important: JSON data must be retrieved in order; 
# this can be accomplished using the object_pairs_hook parameter
# of the json.load method. 
cpi_json_data = json.load(urllib2.urlopen(dataset_url_2),
                      object_pairs_hook=OrderedDict)
cpi_results = pyjstat.from_json_stat(cpi_json_data)

# Get the first result, since we're using only one input dataset
cpi_dataset = cpi_results[0]

# Filter the variable to plot
cpi_ts = cpi_dataset[cpi_dataset['contents'] == 
                  '12-month rate (per cent)']

# split time column into year and month and generate datetime column
year, month = zip(*(s.split("M") for s in list(cpi_ts['time'])))
dates = [dt.datetime(int(y) , int(m), 1) for m,y in zip(month,year)]

# filter unwanted columns and add datetime column
cpi_data = cpi_ts.loc[:,['consumption group','value']]
cpi_data['dates'] = dates

# pivot dataframe in order to get a column by category
cpi_data = cpi_data.pivot(index='dates',columns='consumption group', values='value')

# plot a line chart
line = vincent.Line(cpi_data)
line.scales[0].type = 'time'
line.axis_titles(x='Time', y='Value')
line.legend(title='Consumer Price Index - 12-month rate (per cent)')
display(line)

import numpy as np

dataset_url_3 = 'http://data.ssb.no/api/v0/dataset/29843.json?lang=en'

# Important: JSON data must be retrieved in order; 
# this can be accomplished using the object_pairs_hook parameter
# of the json.load method. 
iop_json_data = json.load(urllib2.urlopen(dataset_url_3),
                      object_pairs_hook=OrderedDict)
iop_results = pyjstat.from_json_stat(iop_json_data)

# Get the first result, since we're using only one input dataset
iop_dataset = iop_results[0]

# Filter the variable to plot
iop_ts = iop_dataset[iop_dataset['contents'] == 
                  'Seasonally adjusted']

# split time column into year and month and generate datetime column
year, month = zip(*(s.split("M") for s in list(iop_ts['time'])))
dates = [dt.datetime(int(y) , int(m), 1) for m,y in zip(month,year)]

# filter unwanted columns and add datetime column
iop_data = iop_ts.loc[:,['industry/main industrial grouping','value']]
iop_data['dates'] = dates

# remove null-valued rows
iop_data = iop_data[np.isfinite(iop_data['value'])]

# pivot dataframe in order to get a column by category
iop_data = iop_data.pivot(index='dates',columns='industry/main industrial grouping', values='value')

# plot a multi-line chart
multi = vincent.Line(iop_data)
multi.scales[0].type = 'time'
multi.axis_titles(x='Time', y='Value')
multi.legend(title='Index of Production (2005=100) Seasonally adjusted')
display(multi)