<｜begin▁of▁sentence｜>### Instruction:
Imagine stepping into the salon and instead of seeing the usual buzz of activity, you find me, perched by my styling station with something quite different at hand. I've got a chart laid out before me, sort of like the color charts we use, but this one's a bit more complex. This chart, let’s call it the "Style Connect Matrix," has all the popular hairstyles we offer along the rows and columns, and the numbers on it tell us how closely related these styles are—sort of like which cuts often go with which color techniques.

Now, I'm looking to jazz up the way we keep track of these style combinations, aiming to create a visual web—a network, if you will—of interconnected hairstyles, showing which ones complement each other the best. To do this, I need to transform this "Style Connect Matrix," from its grid form into something more like a model or a structure that shows all the interconnections, like the patterns we see when we're layering a cut or weaving highlights through hair.

The crux of it is, how can I take our intricate chart here and rework it into a snazzy network design that illustrates the relationship between these hairdos in a way that's easy for all our stylists to follow? The goal is to have a visual map that's as useful to us as our shears and combs, something that'll help us cross-reference and create the most flattering combinations of cuts and colors for our clients.
### Response:
import pandas as pd
import networkx as nx

# Create a pandas DataFrame as an adjacency matrix
adjacency_df = pd.DataFrame([[0, 1], [1, 0]], columns=['A', 'B'], index=['A', 'B'])

# Convert DataFrame to a NetworkX Graph
G = nx.from_pandas_adjacency(adjacency_df)

# Print the created Graph
print(G.edges())
print(G.nodes())
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