How to convert multiple list of dictionaries plus a list into a dataframe

Question

can anyone help with the following: I have a set of data output structured below

list = [0.2, 0.5]
dict1 = [{'a':13, 'b':16}, {'a':25, 'b':70}]
dict2 = [{'a':0.15, 'b':0.02}, {'a':0.30, 'b':0.24}]

The dictionary elements of the list of dictionary in dict1 and dict2 are the corresponding output of the values in the list. This means that 13 and 16 in dict 1 is as a result of the value 0.2 in the list variable.

I want to convert these values in a dataframe such that I would have something that looks like this:

index list dict1 dict2
a     0.2  13    0.15
b     0.2  16    0.02
a     0.5  25    0.30
b     0.5  70    0.24

Is there a way to go about this. The above is just an imaginative representation of how I think the dataframe could be. A better approach on how to present this in a dataframe is also appreciated

Asked By: Babz

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Source

Answer 1

Not the cleanest solution, but this would work:

import pandas as pd

lst = [0.2, 0.5]
dict1 = [{'a':13, 'b':16}, {'a':25, 'b':70}]
dict2 = [{'a':0.15, 'b':0.02}, {'a':0.30, 'b':0.24}]

df = pd.DataFrame()
for l, d1, d2 in zip(lst, dict1, dict2):
    for d1i, d2i in zip(d1.items(), d2.items()):
        df = df.append({'index': d1i[0], 'list': l, 'dict1': d1i[1], 'dict2': d2i[1]}, ignore_index=True)

df.set_index('index', inplace=True)

print(df.to_string())

Output:

       list  dict1  dict2
index                    
a       0.2     13   0.15
b       0.2     16   0.02
a       0.5     25   0.30
b       0.5     70   0.24

I’m positive this could be done as a oneliner, but I’m not too familiar with Pandas myself.

Answered By: B Remmelzwaal

Answer 2

Alternative solution (list renamed to lst):

df = pd.concat(
    pd.DataFrame({"list": v, "dict1": pd.Series(d1), "dict2": pd.Series(d2)})
    for v, d1, d2 in zip(lst, dict1, dict2)
)

Answered By: Timus

How to convert multiple list of dictionaries plus a list into a dataframe

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