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Norway's statistical data charts in Python using pyjstat and Vincent
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Norway.ipynb
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"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Norway's statistical data charts in Python using pyjstat and vincent"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook aims to be an example of use of the Python package [pyjstat](https://pypi.python.org/pypi/pyjstat/), a library to deal with [JSON-stat](http://json-stat.org/) format using Python [pandas](http://pandas.pydata.org/) [dataframe](http://pandas.pydata.org/pandas-docs/stable/dsintro.html) structures.\n",
"\n",
"In order to make it as simple, fast and visually rich as possible, I'll use a package which brings Javascript visualization capabilities to Python: [Vincent](http://vincent.readthedocs.org/en/latest/)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Choropleths map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start getting some example data. It seems that, at the moment, JSON-stat format is mostly used at [Statistics Norway](http://www.ssb.no), so we'll get some datasets from their [catalog](http://data.ssb.no/api/?lang=en) and make some plots.\n",
"\n",
"For this example (a choroplehts map), I'll use the same dataset as [Xavier Badosa](http://xavierbadosa.com/) did in his [example](http://bl.ocks.org/badosa/7856244). I have to say that, at first, I choosed it by chance; then, I realized that it was pretty convenient in order to compare both ways of representation.\n",
"\n",
"Let's grab the Norway JSON-stat bundle no. 1108 using [urllib2](https://docs.python.org/2/library/urllib2.html) and transform it into a pandas dataframe using pyjstat:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pyjstat import pyjstat\n",
"from collections import OrderedDict\n",
"import urllib2\n",
"import json\n",
"\n",
"dataset_url_1 = 'http://data.ssb.no/api/v0/dataset/1108.json?lang=en'\n",
"\n",
"# Important: JSON data must be retrieved in order; \n",
"# this can be accomplished using the object_pairs_hook parameter\n",
"# of the json.load method. \n",
"population_json_data = json.load(urllib2.urlopen(dataset_url_1),\n",
" object_pairs_hook=OrderedDict)\n",
"population_results = pyjstat.from_json_stat(population_json_data, naming=\"id\")\n",
"\n",
"# Get the first result, since we're using only one input dataset\n",
"population_dataset = population_results[0]\n",
"\n",
"# Filter the variable to plot\n",
"population_data = population_dataset[population_dataset['ContentsCode'] == \n",
" 'Folketallet11']\n",
"population_data.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Region</th>\n",
" <th>ContentsCode</th>\n",
" <th>Tid</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 101</td>\n",
" <td> Folketallet11</td>\n",
" <td> 2014K1</td>\n",
" <td> 30190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 104</td>\n",
" <td> Folketallet11</td>\n",
" <td> 2014K1</td>\n",
" <td> 31351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> 105</td>\n",
" <td> Folketallet11</td>\n",
" <td> 2014K1</td>\n",
" <td> 53994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> 106</td>\n",
" <td> Folketallet11</td>\n",
" <td> 2014K1</td>\n",
" <td> 77695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td> 111</td>\n",
" <td> Folketallet11</td>\n",
" <td> 2014K1</td>\n",
" <td> 4384</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
" Region ContentsCode Tid value\n",
"10 101 Folketallet11 2014K1 30190\n",
"21 104 Folketallet11 2014K1 31351\n",
"32 105 Folketallet11 2014K1 53994\n",
"43 106 Folketallet11 2014K1 77695\n",
"54 111 Folketallet11 2014K1 4384\n",
"\n",
"[5 rows x 4 columns]"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that I've called _from\\_json\\_stat_ method using _naming=\"id\"_ parameter. It could have been called using _\"label\"_, nevertheless. __Also, note that JSON data must be ordered before being passed to pyjstat.__ "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we're ready to plot the _value_ column versus the _region id_ in a Norway's municipalities map. For this example I've used [this geoJSON map](https://dl.dropbox.com/u/1515021/d3/kommuner.geojson) after converting it to [TopoJSON](https://github.com/mbostock/topojson/wiki)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import vincent\n",
"from vincent.values import ValueRef\n",
"from IPython.display import display\n",
"\n",
"vincent.core.initialize_notebook()\n",
"\n",
"# Get the map data\n",
"norway_topo = r'http://d1jbhcb2qvxl2c.cloudfront.net/norway_id.topo.json'\n",
"geo_data = [{'name': 'norway',\n",
" 'url': norway_topo,\n",
" 'feature': 'norway'}]\n",
"\n",
"# Create the chart and bind the data to the map\n",
"vis_map = vincent.Map(data=population_data, geo_data=geo_data, scale=1000, \n",
" projection = 'mercator', center = [17.81, 63.4],\n",
" data_bind='value', data_key='Region',\n",
" map_key={'norway': 'properties.KOMM'})\n",
"vis_map.marks[0].properties.enter.stroke_opacity = ValueRef(value=0.5)\n",
"\n",
"# Get the categories for labeling the chart \n",
"categories = pyjstat.get_dim_label(population_json_data['dataset'],'ContentsCode')\n",
"\n",
"# Plot the chart\n",
"vis_map.legend(title= str(categories.loc['Folketallet11', 'label']))\n",
"display(vis_map)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
" <script>\n",
" \n",
" function vct_load_lib(url, callback){\n",
" if(typeof d3 !== 'undefined' &&\n",
" url === 'http://d3js.org/d3.v3.min.js'){\n",
" callback()\n",
" }\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = true;\n",
" s.onreadystatechange = s.onload = callback;\n",
" s.onerror = function(){\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" };\n",
" var vincent_event = new CustomEvent(\n",
" \"vincent_libs_loaded\",\n",
" {bubbles: true, cancelable: true}\n",
" );\n",
" \n",
" function load_all_libs(){\n",
" console.log('Loading Vincent libs...')\n",
" vct_load_lib('http://d3js.org/d3.v3.min.js', function(){\n",
" vct_load_lib('http://d3js.org/d3.geo.projection.v0.min.js', function(){\n",
" vct_load_lib('http://wrobstory.github.io/d3-cloud/d3.layout.cloud.js', function(){\n",
" vct_load_lib('http://wrobstory.github.io/vega/vega.v1.3.3.js', function(){\n",
" window.dispatchEvent(vincent_event);\n",
" });\n",
" });\n",
" });\n",
" });\n",
" };\n",
" if(typeof define === \"function\" && define.amd){\n",
" if (window['d3'] === undefined ||\n",
" window['topojson'] === undefined){\n",
" require.config(\n",
" {paths: {\n",
" d3: 'http://d3js.org/d3.v3.min',\n",
" topojson: 'http://d3js.org/topojson.v1.min'\n",
" }\n",
" }\n",
" );\n",
" require([\"d3\"], function(d3){\n",
" console.log('Loading Vincent from require.js...')\n",
" window.d3 = d3;\n",
" require([\"topojson\"], function(topojson){\n",
" window.topojson = topojson;\n",
" load_all_libs();\n",
" });\n",
" });\n",
" } else {\n",
" load_all_libs();\n",
" };\n",
" }else{\n",
" console.log('Require.js not found, loading manually...')\n",
" load_all_libs();\n",
" };\n",
"\n",
" </script>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x427b810>"
]
},
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" if (typeof vg === 'undefined') {\n",
" window.addEventListener('vincent_libs_loaded', _do_plot)\n",
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\"topojson\"}, \"name\": \"norway\", \"transform\": [{\"as\": \"value\", \"default\": \"noval\", \"key\": \"data.properties.KOMM\", \"type\": \"zip\", \"with\": \"table\", \"withKey\": \"data.x\"}, {\"test\": \"d.path!='noval' && d.value!='noval'\", \"type\": \"filter\"}, {\"center\": [17.81, 63.4], \"projection\": \"mercator\", \"scale\": 1000, \"translate\": [480, 250], \"type\": \"geopath\", \"value\": \"data\"}], \"url\": \"http://d1jbhcb2qvxl2c.cloudfront.net/norway_id.topo.json\"}], \"height\": 500, \"legends\": [{\"fill\": \"color\", \"offset\": 0, \"properties\": {}, \"title\": \"Population at the end of the quarter\"}], \"marks\": [{\"from\": {\"data\": \"norway\"}, \"properties\": {\"enter\": {\"path\": {\"field\": \"path\"}, \"stroke\": {\"value\": \"#000000\"}, \"strokeOpacity\": {\"value\": 0.5}}, \"update\": {\"fill\": {\"field\": \"value.data.y\", \"scale\": \"color\"}}}, \"type\": \"path\"}], \"padding\": \"auto\", \"scales\": [{\"domain\": [210.0, 36006.849999999984], 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" chart({el: \"#visd4d6171b4d8a44d68599ba9444631939\"}).update();\n",
" });\n",
" };\n",
" _do_plot();\n",
" })();\n",
"</script>\n",
"<style>.vega canvas {width: 100%;}</style>\n",
" "
],
"metadata": {},
"output_type": "display_data",
"text": [
"<vincent.charts.Map at 0x427bfd0>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since I am anything but a GIS expert, I've used the same center and scale parameter values as Badosa. It seems the results looks alike to each other, so I guess it's not that bad ;-)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Simple bar chart"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm not going to rack my brains and will use the default Vincent parameters for this one:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Filter dataset by region (Oslo, for example )\n",
"oslo = population_dataset.loc[population_dataset['Region'] == \n",
" 301, ['ContentsCode', 'value']]\n",
"# Reindex the dataset for a better x-axis legend\n",
"oslo = oslo.set_index('ContentsCode')\n",
"\n",
"# Remove non-comparable variables\n",
"bar = vincent.Bar(oslo.iloc[1:10])\n",
"\n",
"# Plot the chart\n",
"bar.axis_titles(x='Variable', y='Population')\n",
"display(bar)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div id=\"vis8732ee50a96b4a17b419aa54edf992be\"></div>\n",
"<script>\n",
" ( function() {\n",
" var _do_plot = function() {\n",
" if (typeof vg === 'undefined') {\n",
" window.addEventListener('vincent_libs_loaded', _do_plot)\n",
" return;\n",
" }\n",
" vg.parse.spec({\"axes\": [{\"scale\": \"x\", \"title\": \"Variable\", \"type\": \"x\"}, {\"scale\": \"y\", \"title\": \"Population\", \"type\": \"y\"}], \"data\": [{\"name\": \"table\", \"values\": [{\"col\": \"value\", \"idx\": \"Fodte2\", \"val\": 2472.0}, {\"col\": \"value\", \"idx\": \"Dode3\", \"val\": 1010.0}, {\"col\": \"value\", \"idx\": \"Fodselsoverskudd4\", \"val\": 1462.0}, {\"col\": \"value\", \"idx\": \"Innvandring5\", \"val\": 4288.0}, {\"col\": \"value\", \"idx\": \"Utvandring6\", \"val\": 1988.0}, {\"col\": \"value\", \"idx\": \"Tilflytting7\", \"val\": 6159.0}, {\"col\": \"value\", \"idx\": \"Fraflytting8\", \"val\": 5855.0}, {\"col\": \"value\", \"idx\": \"Nettoinnflytting9\", \"val\": 2604.0}, {\"col\": \"value\", \"idx\": \"Folketilvekst10\", \"val\": 4066.0}]}, {\"name\": \"stats\", \"source\": \"table\", \"transform\": [{\"keys\": [\"data.idx\"], \"type\": \"facet\"}, {\"type\": \"stats\", \"value\": \"data.val\"}]}], \"height\": 500, \"legends\": [], \"marks\": [{\"from\": {\"data\": \"table\", \"transform\": [{\"keys\": [\"data.col\"], \"type\": \"facet\"}, {\"height\": \"data.val\", \"point\": \"data.idx\", \"type\": \"stack\"}]}, \"marks\": [{\"properties\": {\"enter\": {\"fill\": {\"field\": \"data.col\", \"scale\": \"color\"}, \"width\": {\"band\": true, \"offset\": -1, \"scale\": \"x\"}, \"x\": {\"field\": \"data.idx\", \"scale\": \"x\"}, \"y\": {\"field\": \"y\", \"scale\": \"y\"}, \"y2\": {\"field\": \"y2\", \"scale\": \"y\"}}}, \"type\": \"rect\"}], \"type\": \"group\"}], \"padding\": \"auto\", \"scales\": [{\"domain\": {\"data\": \"table\", \"field\": \"data.idx\"}, \"name\": \"x\", \"range\": \"width\", \"type\": \"ordinal\", \"zero\": false}, {\"domain\": {\"data\": \"stats\", \"field\": \"sum\"}, \"name\": \"y\", \"nice\": true, \"range\": \"height\"}, {\"domain\": {\"data\": \"table\", \"field\": \"data.col\"}, \"name\": \"color\", \"range\": \"category20\", \"type\": \"ordinal\"}], \"width\": 960}, function(chart) {\n",
" chart({el: \"#vis8732ee50a96b4a17b419aa54edf992be\"}).update();\n",
" });\n",
" };\n",
" _do_plot();\n",
" })();\n",
"</script>\n",
"<style>.vega canvas {width: 100%;}</style>\n",
" "
],
"metadata": {},
"output_type": "display_data",
"text": [
"<vincent.charts.Bar at 0x4272cd0>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Time series with line chart"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, I'll use a Consumer Price Index dataset in order to show an example of time series plotting:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import datetime as dt\n",
"import time\n",
"\n",
"dataset_url_2 = 'http://data.ssb.no/api/v0/dataset/1086.json?lang=en'\n",
"\n",
"# Important: JSON data must be retrieved in order; \n",
"# this can be accomplished using the object_pairs_hook parameter\n",
"# of the json.load method. \n",
"cpi_json_data = json.load(urllib2.urlopen(dataset_url_2),\n",
" object_pairs_hook=OrderedDict)\n",
"cpi_results = pyjstat.from_json_stat(cpi_json_data)\n",
"\n",
"# Get the first result, since we're using only one input dataset\n",
"cpi_dataset = cpi_results[0]\n",
"\n",
"# Filter the variable to plot\n",
"cpi_ts = cpi_dataset[cpi_dataset['contents'] == \n",
" '12-month rate (per cent)']\n",
"\n",
"# split time column into year and month and generate datetime column\n",
"year, month = zip(*(s.split(\"M\") for s in list(cpi_ts['time'])))\n",
"dates = [dt.datetime(int(y) , int(m), 1) for m,y in zip(month,year)]\n",
"\n",
"# filter unwanted columns and add datetime column\n",
"cpi_data = cpi_ts.loc[:,['consumption group','value']]\n",
"cpi_data['dates'] = dates\n",
"\n",
"# pivot dataframe in order to get a column by category\n",
"cpi_data = cpi_data.pivot(index='dates',columns='consumption group', values='value')\n",
"\n",
"# plot a line chart\n",
"line = vincent.Line(cpi_data)\n",
"line.scales[0].type = 'time'\n",
"line.axis_titles(x='Time', y='Value')\n",
"line.legend(title='Consumer Price Index - 12-month rate (per cent)')\n",
"display(line)"
],
"language": "python",
"metadata": {},
"outputs": [
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" window.addEventListener('vincent_libs_loaded', _do_plot)\n",
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" chart({el: \"#visc95eee264ddf402ea2339e5aca5ed533\"}).update();\n",
" });\n",
" };\n",
" _do_plot();\n",
" })();\n",
"</script>\n",
"<style>.vega canvas {width: 100%;}</style>\n",
" "
],
"metadata": {},
"output_type": "display_data",
"text": [
"<vincent.charts.Line at 0x4418390>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, after some data parsing we can see a line plot of Norway's CPI All-item-index over about the last 30 years."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Multiple time series by category with multiline chart"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, I'll use yet another dataset in order to obtain a Multi-line chart; concretely, the _Index of production (2005=100) by industry/main industrial grouping._ "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"\n",
"dataset_url_3 = 'http://data.ssb.no/api/v0/dataset/29843.json?lang=en'\n",
"\n",
"# Important: JSON data must be retrieved in order; \n",
"# this can be accomplished using the object_pairs_hook parameter\n",
"# of the json.load method. \n",
"iop_json_data = json.load(urllib2.urlopen(dataset_url_3),\n",
" object_pairs_hook=OrderedDict)\n",
"iop_results = pyjstat.from_json_stat(iop_json_data)\n",
"\n",
"# Get the first result, since we're using only one input dataset\n",
"iop_dataset = iop_results[0]\n",
"\n",
"# Filter the variable to plot\n",
"iop_ts = iop_dataset[iop_dataset['contents'] == \n",
" 'Seasonally adjusted']\n",
"\n",
"# split time column into year and month and generate datetime column\n",
"year, month = zip(*(s.split(\"M\") for s in list(iop_ts['time'])))\n",
"dates = [dt.datetime(int(y) , int(m), 1) for m,y in zip(month,year)]\n",
"\n",
"# filter unwanted columns and add datetime column\n",
"iop_data = iop_ts.loc[:,['industry/main industrial grouping','value']]\n",
"iop_data['dates'] = dates\n",
"\n",
"# remove null-valued rows\n",
"iop_data = iop_data[np.isfinite(iop_data['value'])]\n",
"\n",
"# pivot dataframe in order to get a column by category\n",
"iop_data = iop_data.pivot(index='dates',columns='industry/main industrial grouping', values='value')\n",
"\n",
"# plot a multi-line chart\n",
"multi = vincent.Line(iop_data)\n",
"multi.scales[0].type = 'time'\n",
"multi.axis_titles(x='Time', y='Value')\n",
"multi.legend(title='Index of Production (2005=100) Seasonally adjusted')\n",
"display(multi)"
],
"language": "python",
"metadata": {},
"outputs": [
{
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"<script>\n",
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" var _do_plot = function() {\n",
" if (typeof vg === 'undefined') {\n",
" window.addEventListener('vincent_libs_loaded', _do_plot)\n",
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" }\n",
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(total)\", \"idx\": 1380578400000, \"val\": 104.4}, {\"col\": \"Repair, installation of machinery\", \"idx\": 1380578400000, \"val\": 174.2}, {\"col\": \"Rubber, plastic and mineral prod.\", \"idx\": 1380578400000, \"val\": 96.1}, {\"col\": \"Ships, boats and oil plattforms\", \"idx\": 1380578400000, \"val\": 196.1}, {\"col\": \"Textiles, wearing apparel, leather\", \"idx\": 1380578400000, \"val\": 60.4}, {\"col\": \"Transport equipment n.e.c\", \"idx\": 1380578400000, \"val\": 114.1}, {\"col\": \"Wood and wood products\", \"idx\": 1380578400000, \"val\": 77.7}, {\"col\": \"Basic chemicals\", \"idx\": 1383260400000, \"val\": 111.3}, {\"col\": \"Basic metals (total)\", \"idx\": 1383260400000, \"val\": 82.2}, {\"col\": \"Computer and electrical equipment\", \"idx\": 1383260400000, \"val\": 135.7}, {\"col\": \"Extraction of crude petroleum\", \"idx\": 1383260400000, \"val\": 52.7}, {\"col\": \"Extraction of natural gas\", \"idx\": 1383260400000, \"val\": 101.6}, {\"col\": \"Fabricated metal products\", \"idx\": 1383260400000, \"val\": 143.8}, {\"col\": \"Food products\", \"idx\": 1383260400000, \"val\": 107.4}, {\"col\": \"Food, beverages and tobacco (total)\", \"idx\": 1383260400000, \"val\": 100.7}, {\"col\": \"Furniture and manufacturing n.e.c.\", \"idx\": 1383260400000, \"val\": 62.5}, {\"col\": \"Machinery and equipment\", \"idx\": 1383260400000, \"val\": 208.3}, {\"col\": \"Non-ferrous metals\", \"idx\": 1383260400000, \"val\": 93.6}, {\"col\": \"Paper and paper products\", \"idx\": 1383260400000, \"val\": 49.0}, {\"col\": \"Printing, reproduction\", \"idx\": 1383260400000, \"val\": 86.9}, {\"col\": \"Refined petro., chemicals, pharmac. (total)\", \"idx\": 1383260400000, \"val\": 98.9}, {\"col\": \"Repair, installation of machinery\", \"idx\": 1383260400000, \"val\": 178.0}, {\"col\": \"Rubber, plastic and mineral prod.\", \"idx\": 1383260400000, \"val\": 94.6}, {\"col\": \"Ships, boats and oil plattforms\", \"idx\": 1383260400000, \"val\": 200.8}, {\"col\": \"Textiles, wearing apparel, leather\", \"idx\": 1383260400000, \"val\": 61.9}, {\"col\": \"Transport equipment n.e.c\", \"idx\": 1383260400000, \"val\": 114.6}, {\"col\": \"Wood and wood products\", \"idx\": 1383260400000, \"val\": 78.9}, {\"col\": \"Basic chemicals\", \"idx\": 1385852400000, \"val\": 107.2}, {\"col\": \"Basic metals (total)\", \"idx\": 1385852400000, \"val\": 81.4}, {\"col\": \"Computer and electrical equipment\", \"idx\": 1385852400000, \"val\": 133.7}, {\"col\": \"Extraction of crude petroleum\", \"idx\": 1385852400000, \"val\": 52.3}, {\"col\": \"Extraction of natural gas\", \"idx\": 1385852400000, \"val\": 102.8}, {\"col\": \"Fabricated metal products\", \"idx\": 1385852400000, \"val\": 144.5}, {\"col\": \"Food products\", \"idx\": 1385852400000, \"val\": 108.2}, {\"col\": \"Food, beverages and tobacco (total)\", \"idx\": 1385852400000, \"val\": 101.0}, {\"col\": \"Furniture and manufacturing n.e.c.\", \"idx\": 1385852400000, \"val\": 70.0}, {\"col\": \"Machinery and equipment\", \"idx\": 1385852400000, \"val\": 209.8}, {\"col\": \"Non-ferrous metals\", \"idx\": 1385852400000, \"val\": 91.8}, {\"col\": \"Paper and paper products\", \"idx\": 1385852400000, \"val\": 46.7}, {\"col\": \"Printing, reproduction\", \"idx\": 1385852400000, \"val\": 87.7}, {\"col\": \"Refined petro., chemicals, pharmac. (total)\", \"idx\": 1385852400000, \"val\": 100.1}, {\"col\": \"Repair, installation of machinery\", \"idx\": 1385852400000, \"val\": 181.2}, {\"col\": \"Rubber, plastic and mineral prod.\", \"idx\": 1385852400000, \"val\": 95.7}, {\"col\": \"Ships, boats and oil plattforms\", \"idx\": 1385852400000, \"val\": 205.5}, {\"col\": \"Textiles, wearing apparel, leather\", \"idx\": 1385852400000, \"val\": 62.2}, {\"col\": \"Transport equipment n.e.c\", \"idx\": 1385852400000, \"val\": 114.4}, {\"col\": \"Wood and wood products\", \"idx\": 1385852400000, \"val\": 84.2}, {\"col\": \"Basic chemicals\", \"idx\": 1388530800000, \"val\": 112.4}, {\"col\": \"Basic metals (total)\", \"idx\": 1388530800000, \"val\": 80.2}, {\"col\": \"Computer and electrical equipment\", \"idx\": 1388530800000, \"val\": 139.8}, {\"col\": \"Extraction of crude petroleum\", \"idx\": 1388530800000, \"val\": 52.1}, {\"col\": \"Extraction of natural gas\", \"idx\": 1388530800000, \"val\": 102.1}, {\"col\": \"Fabricated metal products\", \"idx\": 1388530800000, \"val\": 144.6}, {\"col\": \"Food products\", \"idx\": 1388530800000, \"val\": 106.2}, {\"col\": \"Food, beverages and tobacco (total)\", \"idx\": 1388530800000, \"val\": 100.4}, {\"col\": \"Furniture and manufacturing n.e.c.\", \"idx\": 1388530800000, \"val\": 62.6}, {\"col\": \"Machinery and equipment\", \"idx\": 1388530800000, \"val\": 208.8}, {\"col\": \"Non-ferrous metals\", \"idx\": 1388530800000, \"val\": 97.2}, {\"col\": \"Paper and paper products\", \"idx\": 1388530800000, \"val\": 51.2}, {\"col\": \"Printing, reproduction\", \"idx\": 1388530800000, \"val\": 86.5}, {\"col\": \"Refined petro., chemicals, pharmac. (total)\", \"idx\": 1388530800000, \"val\": 108.3}, {\"col\": \"Repair, installation of machinery\", \"idx\": 1388530800000, \"val\": 179.3}, {\"col\": \"Rubber, plastic and mineral prod.\", \"idx\": 1388530800000, \"val\": 92.4}, {\"col\": \"Ships, boats and oil plattforms\", \"idx\": 1388530800000, \"val\": 200.2}, {\"col\": \"Textiles, wearing apparel, leather\", \"idx\": 1388530800000, \"val\": 60.4}, {\"col\": \"Transport equipment n.e.c\", \"idx\": 1388530800000, \"val\": 112.3}, {\"col\": \"Wood and wood products\", \"idx\": 1388530800000, \"val\": 78.6}, {\"col\": \"Basic chemicals\", \"idx\": 1391209200000, \"val\": 115.3}, {\"col\": \"Basic metals (total)\", \"idx\": 1391209200000, \"val\": 79.0}, {\"col\": \"Computer and electrical equipment\", \"idx\": 1391209200000, \"val\": 135.3}, {\"col\": \"Extraction of crude petroleum\", \"idx\": 1391209200000, \"val\": 52.0}, {\"col\": \"Extraction of natural gas\", \"idx\": 1391209200000, \"val\": 105.5}, {\"col\": \"Fabricated metal products\", \"idx\": 1391209200000, \"val\": 145.0}, {\"col\": \"Food products\", \"idx\": 1391209200000, \"val\": 108.0}, {\"col\": \"Food, beverages and tobacco (total)\", \"idx\": 1391209200000, \"val\": 101.6}, {\"col\": \"Furniture and manufacturing n.e.c.\", \"idx\": 1391209200000, \"val\": 62.8}, {\"col\": \"Machinery and equipment\", \"idx\": 1391209200000, \"val\": 212.7}, {\"col\": \"Non-ferrous metals\", \"idx\": 1391209200000, \"val\": 94.2}, {\"col\": \"Paper and paper products\", \"idx\": 1391209200000, \"val\": 52.0}, {\"col\": \"Printing, reproduction\", \"idx\": 1391209200000, \"val\": 85.6}, {\"col\": \"Refined petro., chemicals, pharmac. (total)\", \"idx\": 1391209200000, \"val\": 107.2}, {\"col\": \"Repair, installation of machinery\", \"idx\": 1391209200000, \"val\": 180.7}, {\"col\": \"Rubber, plastic and mineral prod.\", \"idx\": 1391209200000, \"val\": 91.8}, {\"col\": \"Ships, boats and oil plattforms\", \"idx\": 1391209200000, \"val\": 208.3}, {\"col\": \"Textiles, wearing apparel, leather\", \"idx\": 1391209200000, \"val\": 61.2}, {\"col\": \"Transport equipment n.e.c\", \"idx\": 1391209200000, \"val\": 114.6}, {\"col\": \"Wood and wood products\", \"idx\": 1391209200000, \"val\": 79.8}, {\"col\": \"Basic chemicals\", \"idx\": 1393628400000, \"val\": 111.5}, {\"col\": \"Basic metals (total)\", \"idx\": 1393628400000, \"val\": 83.3}, {\"col\": \"Computer and electrical equipment\", \"idx\": 1393628400000, \"val\": 136.7}, {\"col\": \"Extraction of crude petroleum\", \"idx\": 1393628400000, \"val\": 52.0}, {\"col\": \"Extraction of natural gas\", \"idx\": 1393628400000, \"val\": 110.9}, {\"col\": \"Fabricated metal products\", \"idx\": 1393628400000, \"val\": 145.4}, {\"col\": \"Food products\", \"idx\": 1393628400000, \"val\": 107.6}, {\"col\": \"Food, beverages and tobacco (total)\", \"idx\": 1393628400000, \"val\": 102.9}, {\"col\": \"Furniture and manufacturing n.e.c.\", \"idx\": 1393628400000, \"val\": 62.4}, {\"col\": \"Machinery and equipment\", \"idx\": 1393628400000, \"val\": 215.6}, {\"col\": \"Non-ferrous metals\", \"idx\": 1393628400000, \"val\": 94.2}, {\"col\": \"Paper and paper products\", \"idx\": 1393628400000, \"val\": 52.7}, {\"col\": \"Printing, reproduction\", \"idx\": 1393628400000, \"val\": 85.6}, {\"col\": \"Refined petro., chemicals, pharmac. (total)\", \"idx\": 1393628400000, \"val\": 105.5}, {\"col\": \"Repair, installation of machinery\", \"idx\": 1393628400000, \"val\": 184.7}, {\"col\": \"Rubber, plastic and mineral prod.\", \"idx\": 1393628400000, \"val\": 93.8}, {\"col\": \"Ships, boats and oil plattforms\", \"idx\": 1393628400000, \"val\": 208.2}, {\"col\": \"Textiles, wearing apparel, leather\", \"idx\": 1393628400000, \"val\": 62.0}, {\"col\": \"Transport equipment n.e.c\", \"idx\": 1393628400000, \"val\": 116.5}, {\"col\": \"Wood and wood products\", \"idx\": 1393628400000, \"val\": 79.0}, {\"col\": \"Basic chemicals\", \"idx\": 1396303200000, \"val\": 101.6}, {\"col\": \"Basic metals (total)\", \"idx\": 1396303200000, \"val\": 83.6}, {\"col\": \"Computer and electrical equipment\", \"idx\": 1396303200000, \"val\": 134.4}, {\"col\": \"Extraction of crude petroleum\", \"idx\": 1396303200000, \"val\": 51.2}, {\"col\": \"Extraction of natural gas\", \"idx\": 1396303200000, \"val\": 111.2}, {\"col\": \"Fabricated metal products\", \"idx\": 1396303200000, \"val\": 145.8}, {\"col\": \"Food products\", \"idx\": 1396303200000, \"val\": 114.0}, {\"col\": \"Food, beverages and tobacco (total)\", \"idx\": 1396303200000, \"val\": 107.9}, {\"col\": \"Furniture and manufacturing n.e.c.\", \"idx\": 1396303200000, \"val\": 65.2}, {\"col\": \"Machinery and equipment\", \"idx\": 1396303200000, \"val\": 211.1}, {\"col\": \"Non-ferrous metals\", \"idx\": 1396303200000, \"val\": 95.4}, {\"col\": \"Paper and paper products\", \"idx\": 1396303200000, \"val\": 51.5}, {\"col\": \"Printing, reproduction\", \"idx\": 1396303200000, \"val\": 86.3}, {\"col\": \"Refined petro., chemicals, pharmac. (total)\", \"idx\": 1396303200000, \"val\": 105.5}, {\"col\": \"Repair, installation of machinery\", \"idx\": 1396303200000, \"val\": 184.4}, {\"col\": \"Rubber, plastic and mineral prod.\", \"idx\": 1396303200000, \"val\": 96.7}, {\"col\": \"Ships, boats and oil plattforms\", \"idx\": 1396303200000, \"val\": 209.5}, {\"col\": \"Textiles, wearing apparel, leather\", \"idx\": 1396303200000, \"val\": 60.8}, {\"col\": \"Transport equipment n.e.c\", \"idx\": 1396303200000, \"val\": 116.0}, {\"col\": \"Wood and wood products\", \"idx\": 1396303200000, \"val\": 79.7}]}], \"height\": 500, \"legends\": [{\"fill\": \"color\", \"offset\": 0, \"properties\": {}, \"title\": \"Index of Production (2005=100) Seasonally adjusted\"}], \"marks\": [{\"from\": {\"data\": \"table\", \"transform\": [{\"keys\": [\"data.col\"], \"type\": \"facet\"}]}, \"marks\": [{\"properties\": {\"enter\": {\"stroke\": {\"field\": \"data.col\", \"scale\": \"color\"}, \"strokeWidth\": {\"value\": 2}, \"x\": {\"field\": \"data.idx\", \"scale\": \"x\"}, \"y\": {\"field\": \"data.val\", \"scale\": \"y\"}}}, \"type\": \"line\"}], \"type\": \"group\"}], \"padding\": \"auto\", \"scales\": [{\"domain\": {\"data\": \"table\", \"field\": \"data.idx\"}, \"name\": \"x\", \"range\": \"width\", \"type\": \"time\"}, {\"domain\": {\"data\": \"table\", \"field\": \"data.val\"}, \"name\": \"y\", \"nice\": true, \"range\": \"height\"}, {\"domain\": {\"data\": \"table\", \"field\": \"data.col\"}, \"name\": \"color\", \"range\": \"category20\", \"type\": \"ordinal\"}], \"width\": 960}, function(chart) {\n",
" chart({el: \"#visfbe216939ea5474d9d0bd33c60e13cad\"}).update();\n",
" });\n",
" };\n",
" _do_plot();\n",
" })();\n",
"</script>\n",
"<style>.vega canvas {width: 100%;}</style>\n",
" "
],
"metadata": {},
"output_type": "display_data",
"text": [
"<vincent.charts.Line at 0x44b2290>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perhaps there are too many categories in the chart to see what's going on, but since I was trying to show a multi-line example here, I guess it should do. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With these little examples, I've accomplished two different aims: learning how to use a little of Vincent Vega and showing some examples about pyjstat use. I hope it can be helpful for somebody. Please, feel free to make any comments, corrections, suggestions, improvements, etc. I'll be glad to receive your feedback!\n",
"\n",
"By [predicador37](https://github.com/predicador37)"
]
}
],
"metadata": {}
}
]
}
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