Creating a dictionary from a using a list and dataframe-CodePudding

I have a list that is created using two columns of a dataframe. I need to create a dictionary where keys will be the elements of the list and the values will be the elements of a column in the dataframe. Below is an Example that I just created. The dataframe I am using is large and so is the list.

data={'init':[1,2,1], 'term':[2,3,3], 'cost':[10,20,30]}
df=pd.DataFrame.from_dict(data)
link=[(1,2),(1,3),(2,3) ]

I need to create the following dictionary using the dataframe and list.

link_cost={(1,2): 10,(1,3):30,(2,3):20,}

Could anyone help me with this? Any comments or instruction would be appreciated.

CodePudding user response：

Let's try set_index reindex then Series.to_dict:

d = df.set_index(['init', 'term'])['cost'].reindex(index=link).to_dict()

d:

{(1, 2): 10, (1, 3): 30, (2, 3): 20}

set_index with multiple columns will create a MultiIndex which can be indexed with tuples. Selecting a specific column and then reindexing will allow the list link to reorder/select specific values from the Series. Series.to_dict will create the dictionary output.

Setup used:

import pandas as pd

df = pd.DataFrame({
    'init': [1, 2, 1], 
    'term': [2, 3, 3],
    'cost': [10, 20, 30]
})
link = [(1, 2), (1, 3), (2, 3)]

CodePudding user response：

Why are you even using pandas for this? You have the dict right there:

link_cost = dict(zip(link, data['cost']}}
# or if you must use the dataframe it's the same
link_cost = dict(zip(link, df['cost']}}

{(1,2): 10, (2,3):20, (1,3):30}

CodePudding user response：

One approach is to use DataFrame.set_index, Index.isin and DataFrame.itertuples:

import pandas as pd

data = {'init': [1, 2, 1], 'term': [2, 3, 3], 'cost': [10, 20, 30]}
df = pd.DataFrame.from_dict(data)
link = [(1, 2), (2, 3), (1, 3)]

cols = ["init", "term"]
new = df.set_index(cols)
res = dict(new[new.index.isin(link)].itertuples(name=None))
print(res)

Output

{(1, 2): 10, (2, 3): 20, (1, 3): 30}