Cfrgtkky

I have a weird data structure (don't know if it's a list or tuple) as a results of using Networkx. I need to convert it into a dataframe.

The 'list' I have, has the following structure:

[('a', 'a', {'weight': 2}),

 ('a', '!', {'weight': 0}),

 ('a', 'c', {'weight': 2}),

 ('a', 'b', {'weight': 1}),

 ('a', 'q', {'weight': 1}),

 ('a', 's', {'weight': 2}),... ]

and I need a dataframe as follows:

Inf   Prov  Weight

a        a       2

a        !       0

a        c       2

a        b       1

a        q       1

a        s       2

Can somebody give me hand, please?

It is probably easiest to simplify the data a bit first before creating the dataframe.
Depending on the operation that gave you the initial data (a list of tuples, each one containing 2 strings and a dict), it may be possible to have that operation give you simplified data. But in the case that this is not possible -- or more generally, when you don't have control over data structures generated by a given library, you can do some simple manipulations, e.g. like this:

import pandas as pd

# initial data from qu

raw_data = [('a', 'a', {'weight': 2}),

 ('a', '!', {'weight': 0}),

 ('a', 'c', {'weight': 2}),

 ('a', 'b', {'weight': 1}),

 ('a', 'q', {'weight': 1}),

 ('a', 's', {'weight': 2}),]



# transform data to extract the value of each weight

data = [(elem1, elem2, d_elem.get('weight', 0)) for (elem1, elem2, d_elem) in raw_data]



# put together the dataframe from the list of records

df = pd.DataFrame.from_records(data, columns=['Inf', 'Prov', 'Weight'])

print(df)

gives the result as desired:

  Inf Prov  Weight

0   a    a       2

1   a    !       0

2   a    c       2

3   a    b       1

4   a    q       1

5   a    s       2

using dict.get allows us to specify a default value if it is not defined, rather than raising a KeyError.

import pandas as pd

x=[('a', 'a', {'weight': 2}),

 ('a', '!', {'weight': 0}),

 ('a', 'c', {'weight': 2}),

 ('a', 'b', {'weight': 1}),

 ('a', 'q', {'weight': 1}),

 ('a', 's', {'weight': 2})]



inf_list=list()

prov_list=list()

weight_list=list()



for tuple in x:

    inf_list.append(tuple[0])

    prov_list.append(tuple[1])

    weight_list.append(tuple[2])



df=pd.DataFrame()

df['inf']=inf_list

df['prov']=prov_list

df['weight']=weight_list



df['weight']=df['weight'].map(lambda x:x['weight'])



print(df)