🎉 Flexmonster Pivot Table & Charts v2.8 has arrived!Read the blog post
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  1. API reference
  2. Welcome
    1. Component overview
    2. Quick start
    3. System requirements
    4. Troubleshooting
    5. Managing license keys
    6. Migrating from WebDataRocks to Flexmonster
  3. Connecting to Data Source
    1. JSON
      1. Connecting to JSON
      2. Data types in JSON
    2. CSV
      1. Connecting to CSV
      2. Data types in CSV
    3. Database
      1. Connecting to SQL databases
      2. Connecting to other databases
      3. Connecting to a database with Node.js
      4. Connecting to a database with .NET
      5. Connecting to a database with .NET Core
      6. Connecting to a database with Java
      7. Connecting to a database with PHP
    4. MongoDB
      1. Introduction to the Flexmonster MongoDB Connector
      2. Getting started with the MongoDB Connector
      3. Embedding the MongoDB Connector into the server
    5. Microsoft Analysis Services
      1. Connecting to Microsoft Analysis Services
      2. Getting started with the Accelerator
      3. Installing the Accelerator as a Windows service
      4. Referring the Accelerator as a DLL
      5. Configuring the authentication process
      6. Configuring a secure HTTPS connection
      7. Troubleshooting
    6. Pentaho Mondrian
      1. Connecting to Pentaho Mondrian
      2. Getting started with the Accelerator
      3. Configuring Mondrian roles
      4. Configuring username/password protection
      5. Configuring a secure HTTPS connection
      6. Troubleshooting
    7. Elasticsearch
      1. Connecting to Elasticsearch
      2. Configuring the mapping
    8. Custom data source API
      1. Introduction to the custom data source API
      2. A quick overview of a sample Node.js server
      3. A quick overview of a sample .NET Core server
      4. Implementing the custom data source API server
      5. Implementing filters
      6. Returning data for the drill-through view
      7. Supporting more aggregation functions
  4. Security
    1. Security in Flexmonster
    2. Security aspects of connecting to an OLAP cube
      1. Ways of connecting to an OLAP cube
      2. The data transfer process
      3. Data security
      4. Data access management
    3. Security aspects when connecting to a database
      1. Ways of connecting to a database
      2. The data transfer process
      3. Data access management
  5. Configuring report
    1. What is a report
    2. Data source
    3. Slice
    4. Options
    5. Mapping
    6. Number formatting
    7. Conditional formatting
    8. Set the report for the component
    9. Get the report from the component
    10. Date and time formatting
    11. Configuring global options
    12. Export and print
    13. Calculated values
    14. Custom sorting
  6. Integration with frameworks
    1. Available tutorials
    2. Integration with AngularJS (v1.x)
    3. Integration with Angular
    4. Integration with React
    5. Integration with React Native
    6. Integration with Vue
    7. Integration with Python
      1. Integration with Django
      2. Integration with Jupyter Notebook
    8. Integration with R Shiny
    9. Integration with Webpack
    10. Integration with ASP.NET
    11. Integration with jQuery
    12. Integration with JSP
    13. Integration with TypeScript
    14. Integration with RequireJS
    15. Integration with PhoneGap
  7. Integration with charts
    1. Integration with Highcharts
    2. Integration with Google Charts
    3. Integration with FusionCharts
    4. Integration with any charting library
  8. Customizing
    1. Customizing the Toolbar
    2. Customizing appearance
    3. Customizing the context menu
    4. Localizing the component
  9. Updating to the latest version
    1. Updating to the latest version
    2. Release notes
    3. Migration guide from 2.7 to 2.8
    4. Migration guide from 2.6 to 2.7
    5. Migration guide from 2.5 to 2.6
    6. Migration guide from 2.4 to 2.5
    7. Migration guide from 2.3 to 2.4
    8. Migration guide from 2.2 to 2.3
    9. Documentation for older versions
Table of contents

Integration with Jupyter Notebook

This tutorial will help you integrate Flexmonster with Jupyter Notebook applications. Follow these steps to set up a simple project.

Prerequisites

To run a simple application, you will need Jupyter Notebook. For simplicity, you can use the web version of it, which doesn’t require anything to be installed on your computer. You can also follow this guide and install Jupyter yourself.

After that, choose one of the following options:

  1. Integrate Flexmonster into an existing/new Jupyter Notebook application
  2. Run a simple Jupyter Notebook and Flexmonster sample from GitHub

Integrate Flexmonster into an existing/new Jupyter Notebook application

To integrate Flexmonster into a Jupyter Notebook app, follow these steps:

Step 1. If you don’t have an existing Jupyter Python 3 notebook, open Jupyter Notebook and create a new Python 3 file.

Step 2. Import libraries to work with HTML and JSON in Python, as well as Pandas for data manipulations:

from IPython.display import HTML
import json
import pandas as pd

Step 3. Define a function to display Flexmonster in HTML:

def pivot(flexmonster_json_object):
#the format function is needed to insert Flexmonster object into the script
    code = '''
      <script src="https://cdn.flexmonster.com/flexmonster.js"></script>
      <h1>Flexmonster Integration with Jupyter Notebook</h1>
      <div id="pivot-container"></div>
      <script>
       new Flexmonster({});
     </script>
     '''.format(flexmonster_json_object)
#convert the code string to HTML
   return HTML(code)

Step 4. Define some data with Pandas and convert it to JSON using the orient="records" parameter to present the data in a way so Flexmonster could process it:

data = pd.DataFrame([["Lemon cake", 30, 4.99],["Apple pie", 45, 6.99], ["Raspberry jam", 70, 3.99]],index=['row 1', 'row 2', 'row 3'], columns=['Product', 'Quantity', 'Price per Item'])
json_data = data.to_json(orient="records")

Step 5. Define Flexmonster and add your license or trial key. If you don’t have a license key, get a trial key here.

flexmonster = {
    "container":"#pivot-container",
    "componentFolder":"https://cdn.flexmonster.com/",
    "report": {
        "dataSource": "json",
        "data": json.loads(json_data)
    },
    "licenseKey": "XXXX-XXXX-XXXX-XXXX-XXXX"
}

Step 6. Convert the flexmonster object to a JSON-formatted string:

flexmonster_json_object = json.dumps(flexmonster)

Step 7. Display the component using the previously defined pivot function:

pivot(flexmonster_json_object)

Step 8. Run the notebook by selecting the Cell section in the navigation bar and clicking on the Run all command.

The component will appear in an output cell right after the one containing the pivot function call.

Run a simple Jupyter Notebook and Flexmonster sample from GitHub

Step 1. Download the .zip archive with the sample or clone it from GitHub with the following command:

git clone https://github.com/flexmonster/pivot-jupyter-notebook

Step 2a. If you use the desktop version of Jupyter Notebook, run it with the following command:

jupyter notebook

Jupyter Notebook will be automatically launched in your browser.

Step 2b. Then, upload the Flexmonster_in_Jupyter_Notebook.ipynb file to the Jupyter Notebook. The file will appear in the Files tab.

Step 3. Open the Flexmonster_in_Jupyter_Notebook.ipynb file. Add your license or trial key to the flexmonster object in both examples. If you don’t have a license key, get a trial key here.

flexmonster = {
"container": "#pivot-container1",
"componentFolder": "https://cdn.flexmonster.com/",
"width": "100%",
"height": 430,
"toolbar": True,
"report": {
"dataSource": {
"type": "json",
"data": json.loads(json_data)
},
"slice": {
"rows": [
{
"uniqueName": "Product"
}
],
"columns": [
{
"uniqueName": "[Measures]"
}
],
"measures": [
{
"uniqueName": "Quantity",
"aggregation": "sum"
}
]
}
},
"licenseKey": "XXXX-XXXX-XXXX-XXXX-XXXX"
}

Step 4. Run the sample project by selecting the Cell section in the navigation bar and clicking on the Run all command.

What’s next?

You may be interested in the following articles: