Flow Python for nodes and edges

This article is one of the Integration features supported by Flow. See also: Using Flow Datasets Integrations 

 Flow Immersive Python Client – a convenient solution for pushing data from pandas to Flow. With this library, you can easily upload datasets and identify them using a unique title. If you upload a new dataset with the same title, it will create a new version of the same dataset. 

 Our python library is available on PyPi . Start by installing it with pip: 

 pip install flowgl 

 Here's a short script pushing an example pandas DataFrame to a dataset titled 'My Dataset'. 

 # Import the necessary libraries:

import pandas as pd

from flowgl import Client

# Create a sample pandas dataframe:

df = pd.DataFrame({

 'name': ['John', 'Jane', 'Joe'],

 'age': [30, 25, 40],

 'city': ['New York', 'San Francisco', 'Los Angeles']

})

# Create an instance of the client with your Flow credentials:

client = Client(

 username="flow immersive username",

 password="flow immersive password",

)

# Push the dataframe to Flow by defining a title:

client.push_data(

 df,

 dataset_title='Test',

)

 

 If the title doesn't yet exist for your user, a dataset will be created. If the title exists, a new version will be created. 

 If you have a dictionary of nodes and edges, you can use the push_nodes_and_edges_dict method. This method requires you to specify the nodes and edges lists in the provided dictionary using jsonpath. Here's an example: 

 my_dict = {

 'nested_object': {

 'nodes': [

 {'key': 1, 'name': 'John'},

 {'key': 2, 'name': 'Jane'},

 {'key': 3, 'name': 'Joe'},

 ],

 'edges': [

 {'src': 1, 'dest': 2},

 {'src': 2, 'dest': 3},

 ]

 }

}

client.push_nodes_and_edges_dict(

 my_dict,

 nodes_jsonpath='$.nested_object.nodes',

 edges_jsonpath='$.nested_object.edges',

 node_id_key='key',

 edge_source_key='src',

 edge_target_key='dest',

 dataset_title='My Dataset',

) 

 

 With the Flow Immersive Python Client, it's easy to push data from pandas to Flow. The code examples provided in this blog post give you a good starting point for using the Flow Immersive Python Client in your own projects. 

 See also: Using Flow Datasets Integrations