Chartit is a Django app that can be used to easily create charts from the data in your database. The charts are rendered using Highcharts and jQuery JavaScript libraries. Data in your database can be plotted as simple line charts, column charts, area charts, scatter plots, and many more chart types. Data can also be plotted as Pivot Charts where the data is grouped and/or pivoted by specific column(s).
- Plot charts from models.
- Plot data from multiple models on the same axis on a chart.
- Plot pivot charts from models. Data can be pivoted by across multiple columns.
- Legend pivot charts by multiple columns.
- Combine data from multiple models to plot on same pivot charts.
- Plot a pareto chart, paretoed by a specific column.
- Plot only a top few items per category in a pivot chart.
You can install Django-Chartit from PyPI. Just do :
$ pip install django_chartitYou also need supporting JavaScript libraries. See the Required JavaScript Libraries section for more details.
Plotting a chart or pivot chart on a webpage involves the following steps.
- Create a
DataPoolorPivotDataPoolobject that specifies what data you need to retrieve and from where. - Create a
ChartorPivotChartobject to plot the data in theDataPoolorPivotDataPoolrespectively. - Return the
Chart/PivotChartobject from a djangoviewfunction to the django template. - Use the
load_chartstemplate tag to load the charts to HTML tags with specific ids.
It is easier to explain the steps above with examples. So read on.
Here is a short example of how to create a line chart. Let's say we have a simple model with 3 fields - one for month and two for temperatures of Boston and Houston. :
class MonthlyWeatherByCity(models.Model):
month = models.IntegerField()
boston_temp = models.DecimalField(max_digits=5, decimal_places=1)
houston_temp = models.DecimalField(max_digits=5, decimal_places=1)And let's say we want to create a simple line chart of month on the x-axis and the temperatures of the two cities on the y-axis. :
from chartit import DataPool, Chart
def weather_chart_view(request):
#Step 1: Create a DataPool with the data we want to retrieve.
weatherdata = \
DataPool(
series=
[{'options': {
'source': MonthlyWeatherByCity.objects.all()},
'terms': [
'month',
'houston_temp',
'boston_temp']}
])
#Step 2: Create the Chart object
cht = Chart(
datasource = weatherdata,
series_options =
[{'options':{
'type': 'line',
'stacking': False},
'terms':{
'month': [
'boston_temp',
'houston_temp']
}}],
chart_options =
{'title': {
'text': 'Weather Data of Boston and Houston'},
'xAxis': {
'title': {
'text': 'Month number'}}})
#Step 3: Send the chart object to the template.
return render_to_response({'weatherchart': cht})And you can use the load_charts filter in the django template to render the chart. :
<head>
<!-- code to include the highcharts and jQuery libraries goes here -->
<!-- load_charts filter takes a comma-separated list of id's where -->
<!-- the charts need to be rendered to -->
{% load chartit %}
{{ weatherchart|load_charts:"container" }}
</head>
<body>
<div id='container'> Chart will be rendered here </div>
</body>Here is an example of how to create a pivot chart. Let's say we have the following model. :
class DailyWeather(models.Model):
month = models.IntegerField()
day = models.IntegerField()
temperature = models.DecimalField(max_digits=5, decimal_places=1)
rainfall = models.DecimalField(max_digits=5, decimal_places=1)
city = models.CharField(max_length=50)
state = models.CharField(max_length=2)We want to plot a pivot chart of month (along the x-axis) versus the average rainfall (along the y-axis) of the top 3 cities with highest average rainfall in each month. :
from chartit import PivotDataPool, PivotChart
def rainfall_pivot_chart_view(request):
#Step 1: Create a PivotDataPool with the data we want to retrieve.
rainpivotdata = \
PivotDataPool(
series =
[{'options': {
'source': DailyWeather.objects.all(),
'categories': ['month']},
'terms': {
'avg_rain': Avg('rainfall'),
'legend_by': ['city'],
'top_n_per_cat': 3}}
])
#Step 2: Create the PivotChart object
rainpivcht = \
PivotChart(
datasource = rainpivotdata,
series_options =
[{'options':{
'type': 'column',
'stacking': True},
'terms':[
'avg_rain']}],
chart_options =
{'title': {
'text': 'Rain by Month in top 3 cities'},
'xAxis': {
'title': {
'text': 'Month'}}})
#Step 3: Send the PivotChart object to the template.
return render_to_response({'rainpivchart': rainpivcht})And you can use the load_charts filter in the django template to render the chart. :
<head>
<!-- code to include the highcharts and jQuery libraries goes here -->
<!-- load_charts filter takes a comma-separated list of id's where -->
<!-- the charts need to be rendered to -->
{% load chartit %}
{{ rainpivchart|load_charts:"container" }}
</head>
<body>
<div id='container'> Chart will be rendered here </div>
</body>The above examples are just a brief taste of what you can do with Django-Chartit. For more examples and to look at the charts in actions, check out the demo website.
Full documentation is available here .
The following JavaScript Libraries are required for using Django-Chartit.
Note
While Django-Chartit itself is licensed under the BSD license, Highcharts is licensed under the Highcharts license and jQuery is licensed under both MIT License and GNU General Public License (GPL) Version 2. It is your own responsibility to abide by respective licenses when downloading and using the supporting JavaScript libraries.
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