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https://github.com/fx-kirin/kani_highcharts
Folked from python-highcharts https://github.com/kyper-data/python-highcharts
https://github.com/fx-kirin/kani_highcharts
Last synced: about 2 months ago
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Folked from python-highcharts https://github.com/kyper-data/python-highcharts
- Host: GitHub
- URL: https://github.com/fx-kirin/kani_highcharts
- Owner: fx-kirin
- License: mit
- Created: 2019-09-09T11:55:57.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-06-28T03:20:59.000Z (over 2 years ago)
- Last Synced: 2024-11-13T15:31:56.725Z (2 months ago)
- Language: Python
- Size: 1.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# python-highcharts
## License
The python-highcharts wrapper is licensed under the [MIT license](http://opensource.org/licenses/MIT).
However, please be aware that the Highcharts project itself, as well as Highmaps and Highstock, are only free for non-commercial use under the Creative Commons Attribution-NonCommercial license. Commercial use requires the purchase of a separate license. Pop over to [Highcharts](http://shop.highsoft.com/) for more information.
## Overview
python-highcharts is a simple translation layer between Python and Javascript for Highcharts projects (highcharts, highmaps, and highstocks).
In addition, python-highcharts integrates with [Jupyter notebook](https://github.com/jupyter/notebook), which enables you to render Highcharts, Highmaps, and Highstock visualizations directly in notebooks. See examples [here](https://github.com/kyper-data/python-highcharts/tree/developer/examples/ipynb).
The original framework was inspired by [python-nvd3](https://github.com/areski/python-nvd3) and [PyHighcharts](https://github.com/fidyeates/PyHighcharts).
## Installation
python-highcharts supports Python 2.7/3.4+ and is available on PyPI. To install:
```
pip install python-highcharts
```
---------------------------------------------------------------------------------------------------------------
# Highcharts/Highstock
## Basic Usage
Usage of python-highcharts is very similar to usage of the original Javascript library.
The main input is a python dictionary similar to Highcharts's 'options' object. The dictionary supports most options listed in the official [Highcharts documentation](http://api.highcharts.com/highcharts).
However, the data_set(s) need to be input by a separate function.
```python
from highcharts import Highchart
# A chart is the container that your data will be rendered in, it can (obviously) support multiple data series within it.
chart = Highchart()
# Adding a series requires at minimum an array of data points.
# You can also change the series type, the name, or other series options as kwargs.
data = range(1,20)
chart.add_data_set(data, series_type='line', name='Example Series')
# This will generate and save a .html file at the location you assign
chart.save_file()
```
You can add chart options using set_options. Ex:
```python
chart.set_options('chart', {'resetZoomButton': {'relativeTo': 'plot', 'position': {'x': 0,'y': -30}}})
chart.set_options('xAxis', {'events': {'afterBreaks': 'function(e){return}'}})
chart.set_options('tooltip', {'formatter': 'default_tooltip'})
```
The set_options function can update the options automatically if you input the same option_type. Ex:
```python
chart.set_options('chart', {'style': {"fontSize": '22px'}})
chart.set_options('chart', {'resetZoomButton': {'position': {'x': 10}}})
chart.set_options('chart', {'resetZoomButton': {'relativeTo': 'chart'}})
chart.set_options('xAxis', {'plotBands': {'color': '#FCFFC5', 'from': 2, 'to': 4}})
chart.set_options('xAxis', {'plotBands': {'color': '#FCFFC5', 'from': 6, 'to': 8}})
chart.set_options('xAxis', {'plotBands': {'color': '#FCFFC5', 'from': 10, 'to': 12}})
```
However, the better practice is to construct chart options by a dictionary (as Highcharts suggests: http://www.highcharts.com/docs/getting-started/your-first-chart) and then input by the set_dict_options function. Ex:
```python
options = {
'title': {
'text': 'Atmosphere Temperature by Altitude'
},
'subtitle': {
'text': 'According to the Standard Atmosphere Model'
},
'xAxis': {
'reversed': False,
'title': {
'enabled': True,
'text': 'Altitude'
},
'labels': {
'formatter': 'function () {\
return this.value + "km";\
}'
},
'maxPadding': 0.05,
'showLastLabel': True
},
'yAxis': {
'title': {
'text': 'Temperature'
},
'labels': {
'formatter': "function () {\
return this.value + '°';\
}"
},
'lineWidth': 2
},
'legend': {
'enabled': False
},
'tooltip': {
'headerFormat': '{series.name}
',
'pointFormat': '{point.x} km: {point.y}°C'
}
}
chart.set_dict_options(options)
```
Unlike Javascript Highcharts, the series option can't be included in the options dictionary. It needs to input by the add_data_set (and/or add_drilldown_data_set) function, Ex:
```python
chart.add_data_set(data, 'scatter', 'Outlier',
marker={
'fillColor': 'white',
'lineWidth': 1,
'lineColor': 'Highcharts.getOptions().colors[0]'
},
tooltip={'pointFormat': 'Observation: {point.y}'}
)
chart.add_drilldown_data_set(data_2, 'column', 'Chrome', name='Chrome')
```
## Example Usage
```python
from highcharts import Highchart
chart = Highchart()
chart.set_options('chart', {'inverted': True})
options = {
'title': {
'text': 'Atmosphere Temperature by Altitude'
},
'subtitle': {
'text': 'According to the Standard Atmosphere Model'
},
'xAxis': {
'reversed': False,
'title': {
'enabled': True,
'text': 'Altitude'
},
'labels': {
'formatter': 'function () {\
return this.value + "km";\
}'
},
'maxPadding': 0.05,
'showLastLabel': True
},
'yAxis': {
'title': {
'text': 'Temperature'
},
'labels': {
'formatter': "function () {\
return this.value + '°';\
}"
},
'lineWidth': 2
},
'legend': {
'enabled': False
},
'tooltip': {
'headerFormat': '{series.name}
',
'pointFormat': '{point.x} km: {point.y}°C'
}
}
chart.set_dict_options(options)
data = [[0, 15], [10, -50], [20, -56.5], [30, -46.5], [40, -22.1],
[50, -2.5], [60, -27.7], [70, -55.7], [80, -76.5]]
chart.add_data_set(data, 'spline', 'Temperature', marker={'enabled': False})
chart.save_file()
```
## Jupyter/IPython notebook
To render charts in Jupyter notebooks, simply put the chart object on the last line of a cell:
```python
chart.set_dict_options(options)
data = [[0, 15], [10, -50], [20, -56.5], [30, -46.5], [40, -22.1],
[50, -2.5], [60, -27.7], [70, -55.7], [80, -76.5]]
chart.add_data_set(data, 'spline', 'Temperature', marker={'enabled': False})
chart
```
### Example notebooks:
* [Highcharts](http://nbviewer.ipython.org/github/kyper-data/python-highcharts/blob/master/examples/ipynb/highcharts/Example1.ipynb)
* [Highmaps](http://nbviewer.ipython.org/github/kyper-data/python-highcharts/blob/master/examples/ipynb/highmaps/Example1.ipynb)
* [Highstock](http://nbviewer.ipython.org/github/kyper-data/python-highcharts/blob/master/examples/ipynb/highstock/Example1-basic-line.ipynb)
## Todo:
* More charts support
* Clean code and put more explanation
Reference: [Highcharts API](http://api.highcharts.com/highcharts)
---------------------------------------------------------------------------------------------------------------
# Highmaps
## Basic Usage
Usage of python-highcharts is very similar to usage of the original Javascript library.
The main input is a python dictionary similar to Highmaps's 'options' object. The dictionary supports most options listed in the official [Highmaps documentation](http://api.highcharts.com/highmaps).
However, the data_set(s) need to be input by a separate function.
```python
from highcharts import Highmap
# A chart is the container that your data will be rendered in, it can (obviously) support multiple data series within it.
chart = Highmap()
# Adding a series requires a minimum of one argument, an array of data points
chart.add_data_set(data, series_type='map', name='Example Series')
# This will generate and save a .html file at the location you assign
chart.save_file()
```
Although you can add chart option using set_options, but
a better practice is to construct chart options by a dictionary (as highcharts suggests: http://www.highcharts.com/docs/getting-started/your-first-chart) and then input by set_dict_optoins function. Ex.
```python
options = {
'chart': {
'borderWidth': 1,
'marginRight': 50
},
'title': {
'text': 'US Counties unemployment rates, April 2015'
},
'legend': {
'title': {
'text': 'Unemployment
rate',
'style': {
'color': "(Highcharts.theme && Highcharts.theme.textColor) || 'black'"
}
},
'layout': 'vertical',
'align': 'right',
'floating': True,
'valueDecimals': 0,
'valueSuffix': '%',
'backgroundColor': "(Highcharts.theme && Highcharts.theme.legendBackgroundColor) || 'rgba(255, 255, 255, 0.85)'",
'symbolRadius': 0,
'symbolHeight': 14
},
'mapNavigation': {
'enabled': True
},
'colorAxis': {
'dataClasses': [{
'from': 0,
'to': 2,
'color': "#F1EEF6"
}, {
'from': 2,
'to': 4,
'color': "#D4B9DA"
}, {
'from': 4,
'to': 6,
'color': "#C994C7"
}, {
'from': 6,
'to': 8,
'color': "#DF65B0"
}, {
'from': 8,
'to': 10,
'color': "#DD1C77"
}, {
'from': 10,
'color': "#980043"
}]
},
'plotOptions': {
'map':{
'mapData': 'geojson'
},
'mapline': {
'showInLegend': False,
'enableMouseTracking': False
}
},
}
chart.set_dict_options(options)
```
The map data is set by set_map_source function. It is recommended to use the map collection on highcharts: http://code.highcharts.com/mapdata/
For the map properties visit: http://www.highcharts.com/docs/maps/map-collection
The default setting is to use the Highchart Javascript map.
```python
# set_map_source requires a least one argument: the map data url
chart.set_map_source('http://code.highcharts.com/mapdata/countries/us/us-all-all.js', jsonp_map = False)
```
However, the better practice is to load map data using function in highmap_helper library
and convert it in preparation to be added directly by the add_map or add_data_set functions.
```python
from highmap_helper import jsonp_loader, js_map_loader, geojson_handler
map_url = 'http://code.highcharts.com/mapdata/countries/us/us-all-all.js'
# Load .js format map data from the source and convert to GeoJSON object
geojson = js_map_loader(map_url)
# Similarly, json format (jsonp) map data can be loaded using:
geojson = jsonp_loader("a_jsonp_map_url")
# Reconstruct a GeoJSON object in preparation to be read directly.
# geojson_handler function is similar to Highcharts.geojson in Highcharts: http://api.highcharts.com/highmaps#Highcharts.geojson
mapdata = geojson_handler(geojson)
chart.add_map_data(mapdata)
```
The series option in Highmaps needs to be input separately using add_data_set (and/or add_drilldown_data_set) function, Ex.
```python
chart.add_data_set(data, 'map', 'Unemployment rate', joinBy=['hc-key', 'code'],
tooltip={
'valueSuffix': '%'
},
borderWidth = 0.5,
states={
'hover': {
'color': '#bada55'
}
}
)
chart.add_drilldown_data_set(sub_data, 'map', id=mapkey, name=item['name'],
dataLabels={
'enabled': True,
'format': '{point.name}'
}
)
```
The data set can be loaded directly from the url (jsonp format), but it is not recommended:
```python
data_url = 'http://www.highcharts.com/samples/data/jsonp.php?filename=us-counties-unemployment.json&callback=?'
chart.add_data_from_jsonp(data_url, 'json_data', 'map', 'Unemployment rate', joinBy=['hc-key', 'code'],
tooltip={
'valueSuffix': '%'
},
borderWidth = 0.5,
states={
'hover': {
'color': '#bada55'
}
}
)
```
Furthermore, python-highcharts has a function to add Javascript in the beginning or the end of JQuery body: $(function(){},
but, again, it is not recommended unless it is really necessary.
```python
# function requires at least two arguments: script (javascript) and location ('head' or 'end')
chart.add_JSscript("var lines = Highcharts.geojson(Highcharts.maps['countries/us/us-all-all'], 'mapline');", 'head')
```
## Examples
Bad practice:
1) load data directly and handle it in Javascript 2) insert javascript into thea head 3) use unquote function RawJavaScriptText to prepare Javascript:
```python
from highcharts import Highmap
from common import RawJavaScriptText
chart = Highmap()
options = {
'chart': {
'borderWidth': 1,
'marginRight': 50
},
'title': {
'text': 'US Counties unemployment rates, April 2015'
},
'legend': {
'title': {
'text': 'Unemployment
rate',
'style': {
'color': "(Highcharts.theme && Highcharts.theme.textColor) || 'black'"
}
},
'layout': 'vertical',
'align': 'right',
'floating': True,
'valueDecimals': 0,
'valueSuffix': '%',
'backgroundColor': "(Highcharts.theme && Highcharts.theme.legendBackgroundColor) || 'rgba(255, 255, 255, 0.85)'",
'symbolRadius': 0,
'symbolHeight': 14
},
'mapNavigation': {
'enabled': True
},
'colorAxis': {
'dataClasses': [{
'from': 0,
'to': 2,
'color': "#F1EEF6"
}, {
'from': 2,
'to': 4,
'color': "#D4B9DA"
}, {
'from': 4,
'to': 6,
'color': "#C994C7"
}, {
'from': 6,
'to': 8,
'color': "#DF65B0"
}, {
'from': 8,
'to': 10,
'color': "#DD1C77"
}, {
'from': 10,
'color': "#980043"
}]
},
'plotOptions': {
'mapline': {
'showInLegend': False,
'enableMouseTracking': False
}
}
}
chart.set_dict_options(options)
data_url = 'http://www.highcharts.com/samples/data/jsonp.php?filename=us-counties-unemployment.json&callback=?'
chart.add_data_from_jsonp(data_url, 'json_data', 'map', 'Unemployment rate',
joinBy=['hc-key', 'code'],
tooltip={'valueSuffix': '%'},
borderWidth=0.5,
states={'hover': {'color': '#bada55'}}
)
chart.add_data_set(RawJavaScriptText('[lines[0]]'), 'mapline', 'State borders', color = 'white')
chart.add_data_set(RawJavaScriptText('[lines[1]]'), 'mapline', 'Separator', color = 'gray')
chart.set_map_source('http://code.highcharts.com/mapdata/countries/us/us-all-all.js', jsonp_map = False)
chart.add_JSscript("var lines = Highcharts.geojson(Highcharts.maps['countries/us/us-all-all'], 'mapline');", 'head')
chart.add_JSscript("Highcharts.each(geojson, function (mapPoint) {\
mapPoint.name = mapPoint.name + ', ' + mapPoint.properties['hc-key'].substr(3, 2);\
});", 'head')
chart.save_file()
```
Better practice:
```python
from highcharts import Highmap
from highmap_helper import jsonp_loader, js_map_loader, geojson_handler
chart = Highmap()
options = {
'chart': {
'borderWidth': 1,
'marginRight': 50
},
'title': {
'text': 'US Counties unemployment rates, April 2015'
},
'legend': {
'title': {
'text': 'Unemployment
rate',
'style': {
'color': "(Highcharts.theme && Highcharts.theme.textColor) || 'black'"
}
},
'layout': 'vertical',
'align': 'right',
'floating': True,
'valueDecimals': 0,
'valueSuffix': '%',
'backgroundColor': "(Highcharts.theme && Highcharts.theme.legendBackgroundColor) || 'rgba(255, 255, 255, 0.85)'",
'symbolRadius': 0,
'symbolHeight': 14
},
'mapNavigation': {
'enabled': True
},
'colorAxis': {
'dataClasses': [{
'from': 0,
'to': 2,
'color': "#F1EEF6"
}, {
'from': 2,
'to': 4,
'color': "#D4B9DA"
}, {
'from': 4,
'to': 6,
'color': "#C994C7"
}, {
'from': 6,
'to': 8,
'color': "#DF65B0"
}, {
'from': 8,
'to': 10,
'color': "#DD1C77"
}, {
'from': 10,
'color': "#980043"
}]
},
'plotOptions': {
'map':{
'mapData': 'geojson'
},
'mapline': {
'showInLegend': False,
'enableMouseTracking': False
}
}
}
chart.set_dict_options(options)
# read data and map directly from url
data_url = 'http://www.highcharts.com/samples/data/jsonp.php?filename=us-counties-unemployment.json&callback=?'
map_url = 'http://code.highcharts.com/mapdata/countries/us/us-all-all.js'
data = jsonp_loader(data_url)
geojson = js_map_loader(map_url)
mapdata = geojson_handler(geojson)
lines = geojson_handler(geojson, 'mapline')
for x in mapdata:
x.update({'name':x['name']+', '+x['properties']['hc-key'].split('-')[1].upper()})
#map(lambda x: x['properties'].update({'name':x['properties']['name']+', '+x['properties']['hc-key'].split('-')[1]}), geojson['features'])
chart.add_data_set(data, 'map', 'Unemployment rate', joinBy = ['hc-key', 'code'],
tooltip={'valueSuffix': '%'},
borderWidth=0.5,
states={
'hover': {
'color': '#bada55'
}
}
)
chart.add_data_set([lines[0]], 'mapline', 'State borders', color = 'white')
chart.add_data_set([lines[3]], 'mapline', 'Separator', color = 'gray')
chart.add_map_data(mapdata)
chart.save_file()
```
## Todo:
* More examples
* Clean code and put more explanation
Reference: [Highcharts API](http://api.highcharts.com/highcharts)