Pandas - transform series of dicionaries into series of dicionary values - pandas

Question

After applying pd.Series to one dataframe column, like so:
df_pos = df_matches.col.apply(pd.Series)
I ended up with:
0 {'macro': 'GOL', 'macro_position': 'Goalkeeper'}
1 {'macro': 'DEF', 'macro_position': 'Defender'}
Now I need to turn it into this dataframe:
macro macro_position
0 GOL Goalkeeper
1 LAT Defender
EDIT:
None of the answers below work. If I do:
out = list(df_pos.values)
I get a list of strings of dictionary syntax:
...
array(["{'macro': 'ATA', 'macro_posicao': 'Ataque'}"],dtype=object),
...

Answer 1

Try with
import ast
out = pd.DataFrame(df.col.apply(ast.literal_eval).tolist())
Out[71]:
macro macro_position
0 GOL Goalkeeper
1 DEF Defender

Answer 2

Two approaches:
using .apply
As mentioned by sammywemmy in the comments, by far the easiest approach is just to use .apply:
import pandas as pd
sf = pd.Series([{'macro': 'GOL', 'macro_position': 'Goalkeeper'},
{'macro': 'DEF', 'macro_position': 'Defender'}])
df = sf.apply(pd.Series)
This worked on my Python installation. Try executing the code above verbatim. Note, you do not need to write .col or anything like that. The .apply is a class method for pd.Series.
Using pd.DataFrame and dictionaries
pd.DataFrame can take a dictionary of dictionaries as an argument. So if you turn your Series into a dictionary, then you can just use pd.DataFrame, passing the dictionary as the data argument.
The one complication is that when converting a dict of dicts, it will interpret the inner dictionaries as the rows and the outer dictionaries as the columns. In your case, the rows of the series correspond to the columns, so if you just used .to_dict() naively, you would have the inner dictionaries as the columns, which is the wrong way around. The easiest way to fix this is just to transpose the DataFrame at the end, swapping rows and columns.
The result is as follows:
import pandas as pd
sf = pd.Series([{'macro': 'GOL', 'macro_position': 'Goalkeeper'},
{'macro': 'DEF', 'macro_position': 'Defender'}])
df = pd.DataFrame(sf.to_dict()).transpose()