How can I rearrange df as the nodes index in pytorch geometric manner?

I'd like to rearrange my dataframe (data) as the node index to the original name in pytorch geometric manner for extracting node embedding.

import pandas as pd


data = {'Source': ['Rainfall', 'SP2', 'SP2', 'Inflow','Rainfall','Inflow', 'Inflow', 'Inflow','SWT','SP1','SP1','SWD'],
       'Target': ['SP1', 'Evp', 'Outflow', 'SP2','SWD','SWD', 'SP2','SP1','SP1','SP2','Evp','Loss']}  
df = pd.DataFrame(data)
nodes = pd.concat([df['Source'], df['Target']]).unique()
node_indices = {node: i for i, node in enumerate(nodes)}
df['Source'] = df['Source'].map(node_indices)
df['Target'] = df['Target'].map(node_indices)

This is my expected outputs

Expected outputs:

Appreciate any though or suggestions.

I'm assuming the goal is to get an intermediate encoding of some parameters and make a visual representation of the relationships between source and target values (which seem to be all from the same pool, i.e. nodes in this case).

data = {
    'Source': ['Rainfall', 'SP2', 'SP2', 'Inflow','Rainfall','Inflow', 'Inflow', 'Inflow','SWT','SP1','SP1','SWD'],
    'Target': ['SP1', 'Evp', 'Outflow', 'SP2','SWD','SWD', 'SP2','SP1','SP1','SP2','Evp','Loss']
}  
df = pd.DataFrame(data)

# get all unique values of data
nodes = {name: code for code, name in enumerate({*df.values.flat})}

# replace values with their codes
df_coded = df.replace(nodes)

# connect original and encoded data in one DataFrame
# use multilevel headers to ease separate columns by names and codes
df_repr = pd.concat([df, df_coded], axis=1, keys=['Name','Code'])

# rearange columns like (source|name, source|code, target|code, target|name)
df_repr = df_repr.iloc[:, [0,2,3,1]]

# display transposed representation with hidden original indexes
print(df_repr.T.to_string(header=False))

With this we obtain the following output:

Update

As of the line df_repr = df_repr.iloc[:, [0,2,3,1]], this is reordering of columns by their indexes. Here [0,2,3,1] means put the second column (which is indexed by 1) at the very end. We can do it with .loc as well by passing names of columns in a desired order. It's just gonna be somewhat longer in this case:

df_repr = df_repr.loc[:, [('Name','Source'), 
                          ('Code','Source'), 
                          ('Code','Target'), 
                          ('Name','Target')]]

But if readability is a priority, then of course it's better to use loc instead of iloc.

P.S. Because we are manipulating here only with columns, the following also works:

reordered_columns = [
    ('Name','Source'), 
    ('Code','Source'), 
    ('Code','Target'), 
    ('Name','Target')
]
df_repr = df_repr[reordered_columns]