<｜begin▁of▁sentence｜>### Instruction:
Imagine you're observing a particular ecosystem, tracking the interactions between various animal species and noting down not only which species interact but also the nature of their interactions, like whether they're symbiotic or competitive, as well as the frequency of these interactions. They have been carefully jotted down and possess specific attributes such as interaction strength and interaction type. You wish to analyze this complex web of interactions through a network structure. 

How would you go about transposing this detailed record into a NetworkX graph by utilizing the 'parse_multiline_adjlist' function? This record is extensive and encompasses numerous details about the animal species (nodes) and their interactions (edges), including characteristics like interaction weight and names. 

To solidify this scenario, suppose you have the following multiline adjacency list data that represents our ecosystem's interactions:

```
Lion weight=3
Hyena weight=2
Giraffe weight=1
Zebra weight=1
Hyena Lion weight=0.5 name="Hyena-Lion Mutualism"
Giraffe Zebra weight=0.2 name="Giraffe-Zebra Competiton"
```

Integrated into your daily analysis, how would you convert this data into a NetworkX graph to visualize and study the animals' behavioral patterns and their connections?
### Response:
import networkx as nx

# Multiline adjacency list input data
lines = [
    '1 2',
    '2 {"weight":3, "name": "Frodo"}',
    '3 {}',
    '2 1',
    '5 {"weight":6, "name": "Saruman"}',
]

# Parse the multiline adjacency list
G = nx.parse_multiline_adjlist(iter(lines), nodetype=int)

# Print the graph's adjacency list
for node in G.nodes(data=True):
    print(node)

for edge in G.edges(data=True):
    print(edge)
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