<｜begin▁of▁sentence｜>### Instruction:
Imagine you're an industrial designer working on the conceptual framework for a new logistics system to streamline the workflow between three primary departments—let's call them A, B, and C. As part of your design process, you've constructed a tabular representation that outlines the intensity of communication and collaboration necessary between these departments. This table is meticulously detailed with numerical weights indicating the strength of the connections.

You've got this data structured in a way that mirrors an adjacency matrix, utilizing a tool you're familiar with: a pandas DataFrame. Each department is symbolically represented along the rows and columns with 'A', 'B', and 'C' heading their respective lines. The integers within this matrix serve as a quantifiable measure of the interaction volumes between the departments.

To better visualize this dense data and translate it into actionable insights for optimizing the design of the logistics system, you plan to leverage NetworkX, a powerful library known for its adeptness in graph theory and network analysis.

In the spirit of design-centric problem-solving, could you craft a scenario where this DataFrame is transmuted into a NetworkX graph? Furthermore, how might one extract the basic characteristics of this network graph—characteristics that will undoubtedly inform the nuances of your logistics system design?
### Response:
import pandas as pd
import networkx as nx

# Create a pandas DataFrame to represent the adjacency matrix
# The values represent the weights of the edges
adjacency_df = pd.DataFrame(
    [
        [0, 1, 1],
        [1, 0, 0],
        [1, 0, 0]
    ],
    columns=['A', 'B', 'C'],
    index=['A', 'B', 'C']
)

# Create a graph from the pandas adjacency matrix
G = nx.from_pandas_adjacency(adjacency_df)

# Output the graph information
print(nx.info(G))

# If you need a directed graph you can use:
# DiG = nx.from_pandas_adjacency(adjacency_df, create_using=nx.DiGraph)
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