Sustainable Development Goals

This website was designed to provide insights on the 17 Sustainable Development Goals (SDG) defined by United Nations. The Sustainable Development Goals (SDGs) are a universal call to action to end poverty, protect the planet and improve the lives and prospects of everyone, everywhere. The 17 Goals were adopted by all UN Member States in 2015, as part of the 2030 Agenda for Sustainable Development.

Rankings

World Index Score

import pandas as pd
import altair as alt

source = pd.read_csv('rankings/average.csv')

alt.Chart(source).mark_line(point=True).encode(
   alt.X('Year', scale=alt.Scale(zero=False)),
   alt.Y('Average', scale=alt.Scale(zero=False)),
   order='Year',
   tooltip=['Average']
)

Index Score versus Spillover Score

20212020

Overall performance

All 193 UN Member States are ranked by their overall score. The overall score measures a country’s total progress towards achieving all 17 SDGs. The score can be interpreted as a percentage of SDG achievement. A score of 100 indicates that all SDGs have been achieved.

202120202019201820172016

All regionsEast and South AsiaEastern Europe and Central AsiaLatin America and the CaribbeanMiddle East and North AfricaOECD membersOceaniaSub-Saharan Africa

All regions

Rank	Flag	Country	Score
1		Finland	85.90
2		Sweden	85.61
3		Denmark	84.86
4		Germany	82.48
5		Belgium	82.19
6		Austria	82.08
7		Norway	81.98
8		France	81.67
9		Slovenia	81.60
10		Estonia	81.58
11		Netherlands	81.56
12		Czech Republic	81.39
13		Ireland	80.96
14		Croatia	80.38
15		Poland	80.22
16		Switzerland	80.10
17		United Kingdom	79.97
18		Japan	79.85
19		Slovak Republic	79.57
20		Spain	79.46
21		Canada	79.16
22		Latvia	79.15
23		New Zealand	79.13
24		Belarus	78.82
25		Hungary	78.78
26		Italy	78.76
27		Portugal	78.64
28		Korea, Rep.	78.59
29		Iceland	78.17
30		Chile	77.13
31		Lithuania	76.70
32		United States	76.01
33		Malta	75.75
34		Serbia	75.59
35		Australia	75.58
36		Ukraine	75.51
37		Greece	75.41
38		Israel	75.04
39		Romania	74.97
40		Cyprus	74.87
41		Uruguay	74.55
42		Luxembourg	74.21
43		Thailand	74.19
44		Kyrgyz Republic	74.00
45		Bulgaria	73.81
46		Russian Federation	73.75
47		Bosnia and Herzegovina	73.70
48		Moldova	73.68
49		Cuba	73.65
50		Costa Rica	73.55
51		Vietnam	72.85
52		Argentina	72.80
53		Ecuador	72.54
54		North Macedonia	72.53
55		Azerbaijan	72.41
56		Georgia	72.23
57		China	72.06
58		Armenia	71.79
59		Kazakhstan	71.64
60		Tunisia	71.44
61		Brazil	71.34
62		Fiji	71.24
63		Peru	71.09
64		Albania	71.02
65		Malaysia	70.88
66		Algeria	70.86
67		Dominican Republic	70.76
68		Colombia	70.56
69		Morocco	70.53
70		Turkey	70.38
71		United Arab Emirates	70.17
72		Jordan	70.14
73		Oman	70.13
74		Iran, Islamic Rep.	70.01
75		Bhutan	69.98
76		Singapore	69.89
77		Uzbekistan	69.84
78		Tajikistan	69.76
79		Maldives	69.27
80		Mexico	69.13
81		Jamaica	68.97
82		Egypt, Arab Rep.	68.65
83		Barbados	68.45
84		Brunei Darussalam	68.27
85		Montenegro	68.21
86		Cabo Verde	68.14
87		Sri Lanka	68.10
88		Panama	67.98
89		El Salvador	67.93
90		Bolivia	67.60
91		Suriname	66.98
92		Paraguay	66.94
93		Lebanon	66.84
94		Qatar	66.73
95		Mauritius	66.71
96		Nepal	66.52
97		Indonesia	66.34
98		Saudi Arabia	66.30
99		Nicaragua	66.29
100		Bahrain	66.06
101		Myanmar	64.95
102		Cambodia	64.54
103		Philippines	64.51
104		Belize	64.43
105		Iraq	63.82
106		Mongolia	63.79
107		South Africa	63.74
108		Trinidad and Tobago	63.50
109		Bangladesh	63.45
110		Lao PDR	63.01
111		Gabon	62.82
112		Honduras	62.77
113		Kuwait	62.54
114		Ghana	62.49
115		Botswana	61.92
116		Namibia	61.77
117		Turkmenistan	61.14
118		Kenya	60.60
119		Vanuatu	60.52
120		India	60.07
121		Guatemala	59.91
122		Venezuela, RB	59.32
123		Gambia, The	59.26
124		Sao Tome and Principe	58.79
125		Zimbabwe	58.66
126		Senegal	58.43
127		Syrian Arab Republic	58.01
128		Guyana	57.89
129		Pakistan	57.72
130		Rwanda	57.58
131		Cote d’Ivoire	57.56
132		Tanzania	56.43
133		Mauritania	55.51
134		Cameroon	55.26
135		Lesotho	54.59
136		Ethiopia	54.52
137		Afghanistan	53.93
138		Djibouti	53.76
139		Burkina Faso	53.51
140		Uganda	53.46
141		Zambia	53.39
142		Eswatini	53.33
143		Togo	53.23
144		Congo, Rep.	52.95
145		Yemen, Rep.	52.86
146		Mali	52.16
147		Burundi	51.80
148		Sierra Leone	51.69
149		Malawi	51.37
150		Haiti	51.35
151		Papua New Guinea	51.33
152		Mozambique	51.05
153		Guinea	50.96
154		Angola	50.30
155		Benin	49.87
156		Niger	49.53
157		Sudan	49.48
158		Congo, Dem. Rep.	49.30
159		Madagascar	49.01
160		Nigeria	48.93
161		Liberia	48.65
162		Somalia	45.61
163		Chad	40.90
164		South Sudan	38.90
165		Central African Republic	38.27
nan		Andorra	nan
nan		Antigua and Barbuda	nan
nan		Bahamas, The	nan
nan		Comoros	nan
nan		Dominica	nan
nan		Eritrea	nan
nan		Micronesia, Fed. Sts.	nan
nan		Guinea-Bissau	nan
nan		Equatorial Guinea	nan
nan		Grenada	nan
nan		Kiribati	nan
nan		St. Kitts and Nevis	nan
nan		Libya	nan
nan		St. Lucia	nan
nan		Liechtenstein	nan
nan		Monaco	nan
nan		Marshall Islands	nan
nan		Nauru	nan
nan		Palau	nan
nan		Korea, Dem. Rep.	nan
nan		Solomon Islands	nan
nan		San Marino	nan
nan		Seychelles	nan
nan		Timor-Leste	nan
nan		Tonga	nan
nan		Tuvalu	nan
nan		St. Vincent and the Grenadines	nan
nan		Samoa	nan

Spillover performance

Countries are ranked by their spillover score. Each country’s actions can have positive or negative effects on other countries’ abilities to achieve the SDGs. The Spillover Index assesses such spillovers along three dimensions: environmental & social impacts embodied into trade, economy & finance, and security. A higher score means that a country causes more positive and fewer negative spillover effects.

20212020201920182017

All regionsEast and South AsiaEastern Europe and Central AsiaLatin America and the CaribbeanMiddle East and North AfricaOECD membersOceaniaSub-Saharan Africa

All regions

Rank	Flag	Country	Score
1		Myanmar	99.99
2		Sudan	99.99
3		Somalia	99.96
4		South Sudan	99.94
5		Chad	99.82
6		Burundi	99.77
7		Moldova	99.74
8		Ethiopia	99.72
9		Pakistan	99.68
10		Central African Republic	99.65
11		Haiti	99.63
12		Mali	99.57
13		Cameroon	99.56
14		Mozambique	99.54
15		Sierra Leone	99.54
16		Guinea	99.53
17		Madagascar	99.52
18		Cote d’Ivoire	99.49
19		Congo, Dem. Rep.	99.40
20		Burkina Faso	99.37
21		Afghanistan	99.34
22		Bangladesh	99.33
23		Niger	99.30
24		Lao PDR	99.22
25		Uganda	99.21
26		Nepal	99.15
27		Senegal	99.15
28		Togo	99.14
29		Malawi	99.08
30		Yemen, Rep.	99.02
31		Nigeria	98.93
32		India	98.90
33		Rwanda	98.87
34		Cambodia	98.85
35		Benin	98.82
36		Egypt, Arab Rep.	98.72
37		Tanzania	98.50
38		Iraq	98.35
39		Zambia	98.33
40		Syrian Arab Republic	98.33
41		Morocco	98.26
42		Djibouti	98.24
43		Papua New Guinea	98.19
44		Uzbekistan	98.09
45		Zimbabwe	98.08
46		Nicaragua	98.05
47		Liberia	98.05
48		Mauritania	97.95
49		Philippines	97.92
50		Gambia, The	97.90
51		Bolivia	97.89
52		Congo, Rep.	97.74
53		Azerbaijan	97.64
54		Brazil	97.57
55		Tajikistan	97.50
56		Algeria	97.49
57		Ghana	97.45
58		Ecuador	97.14
59		Cuba	97.10
60		Angola	97.02
61		Peru	96.88
62		Indonesia	96.87
63		Armenia	96.68
64		Belarus	96.66
65		Venezuela, RB	96.45
66		Sri Lanka	96.37
67		Vietnam	96.37
68		Kyrgyz Republic	96.03
69		Iran, Islamic Rep.	95.93
70		Bosnia and Herzegovina	95.84
71		Dominican Republic	95.59
72		Sao Tome and Principe	95.57
73		Mexico	95.46
74		Cabo Verde	95.43
75		Colombia	95.19
76		Lesotho	95.18
77		Mongolia	94.89
78		Kenya	94.89
79		Honduras	94.82
80		Kazakhstan	94.65
81		China	94.62
82		Argentina	94.54
83		Vanuatu	94.49
84		Albania	94.34
85		Tunisia	94.24
86		Ukraine	93.98
87		Bhutan	93.90
88		North Macedonia	93.87
89		Turkey	93.70
90		Paraguay	93.69
91		Jamaica	93.64
92		Belize	93.55
93		Chile	93.24
94		Fiji	93.19
95		Gabon	93.04
96		Uruguay	90.95
97		El Salvador	90.82
98		Georgia	90.73
99		Suriname	90.59
100		Turkmenistan	90.47
101		Jordan	90.39
102		Thailand	88.68
103		South Africa	88.56
104		Maldives	88.26
105		Trinidad and Tobago	88.12
106		Namibia	87.54
107		Guatemala	87.28
108		Bulgaria	86.87
109		Croatia	84.83
110		Eswatini	84.81
111		Romania	83.99
112		Bahrain	83.92
113		Poland	83.56
114		Costa Rica	83.34
115		Serbia	82.57
116		Oman	81.85
117		Lebanon	81.16
118		Botswana	80.63
119		Russian Federation	80.51
120		Hungary	80.03
121		Barbados	78.83
122		Malaysia	77.77
123		Panama	76.34
124		Slovak Republic	75.04
125		Qatar	73.00
126		Greece	72.27
127		Estonia	72.08
128		Latvia	72.06
129		Korea, Rep.	71.63
130		Italy	71.19
131		Saudi Arabia	71.09
132		Brunei Darussalam	70.94
133		New Zealand	70.82
134		Portugal	69.92
135		Finland	69.84
136		Israel	69.63
137		Montenegro	69.31
138		Japan	68.78
139		Lithuania	68.38
140		Czech Republic	67.80
141		Sweden	67.09
142		Spain	64.75
143		Canada	64.07
144		Australia	63.92
145		Cyprus	63.84
146		United States	62.14
147		Belgium	61.97
148		Malta	61.38
149		Slovenia	60.76
150		Denmark	60.51
151		Germany	60.37
152		Iceland	60.27
153		Ireland	60.04
154		Austria	59.52
155		Norway	58.71
156		France	55.89
157		United Kingdom	54.12
158		Mauritius	48.37
159		Netherlands	47.02
160		Kuwait	43.26
161		Switzerland	41.66
162		United Arab Emirates	38.82
163		Luxembourg	31.03
164		Guyana	30.00
165		Singapore	20.55
166		Andorra	nan
167		Antigua and Barbuda	nan
168		Bahamas, The	nan
169		Comoros	nan
170		Dominica	nan
171		Eritrea	nan
172		Micronesia, Fed. Sts.	nan
173		Guinea-Bissau	nan
174		Equatorial Guinea	nan
175		Grenada	nan
176		Kiribati	nan
177		St. Kitts and Nevis	nan
178		Libya	nan
179		St. Lucia	nan
180		Liechtenstein	nan
181		Monaco	nan
182		Marshall Islands	nan
183		Nauru	nan
184		Palau	nan
185		Korea, Dem. Rep.	nan
186		Solomon Islands	nan
187		San Marino	nan
188		Seychelles	nan
189		Timor-Leste	nan
190		Tonga	nan
191		Tuvalu	nan
192		St. Vincent and the Grenadines	nan
193		Samoa	nan

Interactive map

Basic Statistical Visualization

(This tutorial is adapted from Vega-Lite’s documentation)

This tutorial will guide you through the basic process of creating visualizations in Altair. First, you will need to make sure you have the Altair package and its dependencies installed (see installation) and make sure you understand how altair plots are displayed (see displaying-charts). This tutorial will assume you are working within a Jupyter notebook user interface, so that plots are automatically rendered.

Here is the outline of this basic tutorial:

• The Data

• Encodings and Marks

• Data Transformation: Aggregation

• Customizing your Visualization

• Publishing your Visualization

The Data

Data in Altair is built around the Pandas Dataframe. One of the defining characteristics of statistical visualization is that it begins with tidy Dataframes. For the purposes of this tutorial, we’ll start by importing Pandas and creating a simple DataFrame to visualize, with a categorical variable in column a and a numerical variable in column b:

import pandas as pd
data = pd.DataFrame({'a': list('CCCDDDEEE'),
                     'b': [2, 7, 4, 1, 2, 6, 8, 4, 7]})

When using Altair, datasets are most commonly provided as a Dataframe. As we will see, the labeled columns of the dataframe are an essential piece of plotting with Altair.

The Chart Object

The fundamental object in Altair is the Chart, which takes a dataframe as a single argument:

import altair as alt
chart = alt.Chart(data)

So far, we have defined the Chart object, but we have not yet told the chart to do anything with the data. That will come next.

Encodings and Marks

With this chart object in hand, we can now specify how we would like the data to be visualized. This is done via the mark attribute of the chart object, which is most conveniently accessed via the Chart.mark_* methods. For example, we can show the data as a point using mark_point():

alt.Chart(data).mark_point()

Here the rendering consists of one point per row in the dataset, all plotted on top of each other, since we have not yet specified positions for these points.

To visually separate the points, we can map various encoding channels, or channels for short, to columns in the dataset. For example, we could encode the variable a of the data with the x channel, which represents the x-axis position of the points. This can be done straightforwardly via the Chart.encode() method:

alt.Chart(data).mark_point().encode(
    x='a',
)

The encode() method builds a key-value mapping between encoding channels (such as x, y, color, shape, size, etc.) to columns in the dataset, accessed by column name.

For pandas dataframes, Altair automatically determines the appropriate data type for the mapped column, which in this case is a nominal value, or an unordered categorical.

Though we’ve now separated the data by one attribute, we still have multiple points overlapping within each category. Let’s further separate these by adding a y encoding channel, mapped to the "b" column:

alt.Chart(data).mark_point().encode(
    x='a',
    y='b'
)

The type of the data in the "b" column is again automatically-inferred by Altair, and this time is treated as a quantitative type (i.e. real-valued). Additionally, we see that grid lines and appropriate axis titles are automatically added as well.

Data Transformation: Aggregation

To allow for more flexibility in how data are visualized, Altair has a built-in syntax for aggregation of data. For example, we can compute the average of all values by specifying this aggregate within the column identifier:

alt.Chart(data).mark_point().encode(
    x='a',
    y='average(b)'
)

Now within each x-axis category, we see a single point reflecting the average of the values within that category.

Typically, aggregated values are not represented by point markings, but by bar markings. We can do this by replacing mark_point() with mark_bar():

alt.Chart(data).mark_bar().encode(
    x='a',
    y='average(b)'
)

Because the categorical feature is mapped to the x-axis, the result is a vertical bar chart. To get a horizontal bar chart, all we need is to swap the x and y keywords:

alt.Chart(data).mark_bar().encode(
    y='a',
    x='average(b)'
)

Aside: Examining the JSON Output

Recall that Altair’s main purpose is to convert plot specifications to a JSON string that conforms to the Vega-Lite schema. It is instructive here to use the to_json() method to inspect the JSON specification that Altair is exporting and sending as JSON to Vega-Lite:

chart = alt.Chart(data).mark_bar().encode(
    x='a',
    y='average(b)',
)
print(chart.to_json())

{
  "$schema": "https://vega.github.io/schema/vega-lite/v4.17.0.json",
  "config": {
    "view": {
      "continuousHeight": 300,
      "continuousWidth": 400
    }
  },
  "data": {
    "name": "data-347f1284ea3247c0f55cb966abbdd2d8"
  },
  "datasets": {
    "data-347f1284ea3247c0f55cb966abbdd2d8": [
      {
        "a": "C",
        "b": 2
      },
      {
        "a": "C",
        "b": 7
      },
      {
        "a": "C",
        "b": 4
      },
      {
        "a": "D",
        "b": 1
      },
      {
        "a": "D",
        "b": 2
      },
      {
        "a": "D",
        "b": 6
      },
      {
        "a": "E",
        "b": 8
      },
      {
        "a": "E",
        "b": 4
      },
      {
        "a": "E",
        "b": 7
      }
    ]
  },
  "encoding": {
    "x": {
      "field": "a",
      "type": "nominal"
    },
    "y": {
      "aggregate": "average",
      "field": "b",
      "type": "quantitative"
    }
  },
  "mark": "bar"
}

Notice here that encode(x='a') has been expanded to a JSON structure with a field name, and a type for the data. The encode(y='b') has been expanded similarly and includes an aggregate field.

Altair’s full shorthand syntax includes a way to specify the type of the column as well:

y = alt.Y('average(b):Q')
print(y.to_json())

{
  "aggregate": "average",
  "field": "b",
  "type": "quantitative"
}

This short-hand is equivalent to spelling-out the attributes by name:

y = alt.Y(field='b', type='quantitative', aggregate='average')
print(y.to_json())

This more verbose means of specifying channels can be used directly in Altair chart specifications, a fact that becomes useful when using some of the more advanced field configurations:

alt.Chart(data).mark_bar().encode(
    alt.Y('a', type='nominal'),
    alt.X('b', type='quantitative', aggregate='average')
)

Customizing your Visualization

By default, Altair via Vega-Lite makes some choices about default properties of the visualization. Altair also provides an API to customize the look of the visualization. For example, we can specify the axis titles using the axis attribute of channel classes, and we can specify the color of the marking by setting the color keyword of the Chart.mark_* methods to any valid HTML color string:

alt.Chart(data).mark_bar(color='firebrick').encode(
    alt.Y('a', title='category'),
    alt.X('average(b)', title='avg(b) by category')
)

Publishing your Visualization

Once you have visualized your data, perhaps you would like to publish it somewhere on the web. This can be done straightforwardly using the Vega-Embed Javascript package. A simple example of a stand-alone HTML document can be generated for any chart using the Chart.save() method:

chart = alt.Chart(data).mark_bar().encode(
    x='a',
    y='average(b)',
)
chart.save('chart.html')

The basic HTML template produces output that looks like this, where the JSON specification for your plot produced by Chart.to_json() should be stored in the spec Javascript variable:

<!DOCTYPE html>
<html>
  <head>
    <script src="https://cdn.jsdelivr.net/npm/vega@3"></script>
    <script src="https://cdn.jsdelivr.net/npm/vega-lite@2"></script>
    <script src="https://cdn.jsdelivr.net/npm/vega-embed@3"></script>
  </head>
  <body>
    <div id="vis"></div>
    <script type="text/javascript">
      var spec = {};  /* JSON dump of your chart's spec */
      var opt = {"renderer": "canvas", "actions": false};  /* Options for the embedding */
      vegaEmbed("#vis", spec, opt);
    </script>
  </body>
</html>

The save() method provides a convenient way to save such HTML output to file. For more information on embedding Altair/Vega-Lite, see the documentation of the Vega-Embed project.

Basic Statistical Visualization

(This tutorial is adapted from Vega-Lite’s documentation)

This tutorial will guide you through the basic process of creating visualizations in Altair. First, you will need to make sure you have the Altair package and its dependencies installed (see installation) and make sure you understand how altair plots are displayed (see displaying-charts). This tutorial will assume you are working within a Jupyter notebook user interface, so that plots are automatically rendered.

Here is the outline of this basic tutorial:

• The Data

• Encodings and Marks

• Data Transformation: Aggregation

• Customizing your Visualization

• Publishing your Visualization

The Data

Data in Altair is built around the Pandas Dataframe. One of the defining characteristics of statistical visualization is that it begins with tidy Dataframes. For the purposes of this tutorial, we’ll start by importing Pandas and creating a simple DataFrame to visualize, with a categorical variable in column a and a numerical variable in column b:

import pandas as pd
data = pd.DataFrame({'a': list('CCCDDDEEE'),
                     'b': [2, 7, 4, 1, 2, 6, 8, 4, 7]})

When using Altair, datasets are most commonly provided as a Dataframe. As we will see, the labeled columns of the dataframe are an essential piece of plotting with Altair.

The Chart Object

The fundamental object in Altair is the Chart, which takes a dataframe as a single argument:

import altair as alt
chart = alt.Chart(data)

So far, we have defined the Chart object, but we have not yet told the chart to do anything with the data. That will come next.

Encodings and Marks

With this chart object in hand, we can now specify how we would like the data to be visualized. This is done via the mark attribute of the chart object, which is most conveniently accessed via the Chart.mark_* methods. For example, we can show the data as a point using mark_point():

alt.Chart(data).mark_point()

Here the rendering consists of one point per row in the dataset, all plotted on top of each other, since we have not yet specified positions for these points.

To visually separate the points, we can map various encoding channels, or channels for short, to columns in the dataset. For example, we could encode the variable a of the data with the x channel, which represents the x-axis position of the points. This can be done straightforwardly via the Chart.encode() method:

alt.Chart(data).mark_point().encode(
    x='a',
)

The encode() method builds a key-value mapping between encoding channels (such as x, y, color, shape, size, etc.) to columns in the dataset, accessed by column name.

For pandas dataframes, Altair automatically determines the appropriate data type for the mapped column, which in this case is a nominal value, or an unordered categorical.

Though we’ve now separated the data by one attribute, we still have multiple points overlapping within each category. Let’s further separate these by adding a y encoding channel, mapped to the "b" column:

alt.Chart(data).mark_point().encode(
    x='a',
    y='b'
)

The type of the data in the "b" column is again automatically-inferred by Altair, and this time is treated as a quantitative type (i.e. real-valued). Additionally, we see that grid lines and appropriate axis titles are automatically added as well.

Data Transformation: Aggregation

To allow for more flexibility in how data are visualized, Altair has a built-in syntax for aggregation of data. For example, we can compute the average of all values by specifying this aggregate within the column identifier:

alt.Chart(data).mark_point().encode(
    x='a',
    y='average(b)'
)

Now within each x-axis category, we see a single point reflecting the average of the values within that category.

Typically, aggregated values are not represented by point markings, but by bar markings. We can do this by replacing mark_point() with mark_bar():

alt.Chart(data).mark_bar().encode(
    x='a',
    y='average(b)'
)

Because the categorical feature is mapped to the x-axis, the result is a vertical bar chart. To get a horizontal bar chart, all we need is to swap the x and y keywords:

alt.Chart(data).mark_bar().encode(
    y='a',
    x='average(b)'
)

Aside: Examining the JSON Output

Recall that Altair’s main purpose is to convert plot specifications to a JSON string that conforms to the Vega-Lite schema. It is instructive here to use the to_json() method to inspect the JSON specification that Altair is exporting and sending as JSON to Vega-Lite:

chart = alt.Chart(data).mark_bar().encode(
    x='a',
    y='average(b)',
)
print(chart.to_json())

{
  "$schema": "https://vega.github.io/schema/vega-lite/v4.17.0.json",
  "config": {
    "view": {
      "continuousHeight": 300,
      "continuousWidth": 400
    }
  },
  "data": {
    "name": "data-347f1284ea3247c0f55cb966abbdd2d8"
  },
  "datasets": {
    "data-347f1284ea3247c0f55cb966abbdd2d8": [
      {
        "a": "C",
        "b": 2
      },
      {
        "a": "C",
        "b": 7
      },
      {
        "a": "C",
        "b": 4
      },
      {
        "a": "D",
        "b": 1
      },
      {
        "a": "D",
        "b": 2
      },
      {
        "a": "D",
        "b": 6
      },
      {
        "a": "E",
        "b": 8
      },
      {
        "a": "E",
        "b": 4
      },
      {
        "a": "E",
        "b": 7
      }
    ]
  },
  "encoding": {
    "x": {
      "field": "a",
      "type": "nominal"
    },
    "y": {
      "aggregate": "average",
      "field": "b",
      "type": "quantitative"
    }
  },
  "mark": "bar"
}

Notice here that encode(x='a') has been expanded to a JSON structure with a field name, and a type for the data. The encode(y='b') has been expanded similarly and includes an aggregate field.

Altair’s full shorthand syntax includes a way to specify the type of the column as well:

y = alt.Y('average(b):Q')
print(y.to_json())

{
  "aggregate": "average",
  "field": "b",
  "type": "quantitative"
}

This short-hand is equivalent to spelling-out the attributes by name:

y = alt.Y(field='b', type='quantitative', aggregate='average')
print(y.to_json())

This more verbose means of specifying channels can be used directly in Altair chart specifications, a fact that becomes useful when using some of the more advanced field configurations:

alt.Chart(data).mark_bar().encode(
    alt.Y('a', type='nominal'),
    alt.X('b', type='quantitative', aggregate='average')
)

Customizing your Visualization

By default, Altair via Vega-Lite makes some choices about default properties of the visualization. Altair also provides an API to customize the look of the visualization. For example, we can specify the axis titles using the axis attribute of channel classes, and we can specify the color of the marking by setting the color keyword of the Chart.mark_* methods to any valid HTML color string:

alt.Chart(data).mark_bar(color='firebrick').encode(
    alt.Y('a', title='category'),
    alt.X('average(b)', title='avg(b) by category')
)

Publishing your Visualization

Once you have visualized your data, perhaps you would like to publish it somewhere on the web. This can be done straightforwardly using the Vega-Embed Javascript package. A simple example of a stand-alone HTML document can be generated for any chart using the Chart.save() method:

chart = alt.Chart(data).mark_bar().encode(
    x='a',
    y='average(b)',
)
chart.save('chart.html')

The basic HTML template produces output that looks like this, where the JSON specification for your plot produced by Chart.to_json() should be stored in the spec Javascript variable:

<!DOCTYPE html>
<html>
  <head>
    <script src="https://cdn.jsdelivr.net/npm/vega@3"></script>
    <script src="https://cdn.jsdelivr.net/npm/vega-lite@2"></script>
    <script src="https://cdn.jsdelivr.net/npm/vega-embed@3"></script>
  </head>
  <body>
    <div id="vis"></div>
    <script type="text/javascript">
      var spec = {};  /* JSON dump of your chart's spec */
      var opt = {"renderer": "canvas", "actions": false};  /* Options for the embedding */
      vegaEmbed("#vis", spec, opt);
    </script>
  </body>
</html>

The save() method provides a convenient way to save such HTML output to file. For more information on embedding Altair/Vega-Lite, see the documentation of the Vega-Embed project.

Bug Reports & Questions

SDG is Apache-licensed and the source is available on GitHub. If any questions or issues come up as you use Altair, please get in touch via Git Issues.

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