You can use the DataFrame constructor with lists created by to_list:

import pandas as pd

d1 = {'teams': [['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG'],
                ['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG'],['SF', 'NYG']]}
df2 = pd.DataFrame(d1)
print (df2)
       teams
0  [SF, NYG]
1  [SF, NYG]
2  [SF, NYG]
3  [SF, NYG]
4  [SF, NYG]
5  [SF, NYG]
6  [SF, NYG]

df2[['team1','team2']] = pd.DataFrame(df2.teams.tolist(), index= df2.index)
print (df2)
       teams team1 team2
0  [SF, NYG]    SF   NYG
1  [SF, NYG]    SF   NYG
2  [SF, NYG]    SF   NYG
3  [SF, NYG]    SF   NYG
4  [SF, NYG]    SF   NYG
5  [SF, NYG]    SF   NYG
6  [SF, NYG]    SF   NYG

And for a new DataFrame:

df3 = pd.DataFrame(df2['teams'].to_list(), columns=['team1','team2'])
print (df3)
  team1 team2
0    SF   NYG
1    SF   NYG
2    SF   NYG
3    SF   NYG
4    SF   NYG
5    SF   NYG
6    SF   NYG

A solution with apply(pd.Series) is very slow:

#7k rows
df2 = pd.concat([df2]*1000).reset_index(drop=True)

In [121]: %timeit df2['teams'].apply(pd.Series)
1.79 s ± 52.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [122]: %timeit pd.DataFrame(df2['teams'].to_list(), columns=['team1','team2'])
1.63 ms ± 54.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Use pd.DataFrame.from_records and join:

cols = ['MatchWord', 'Prox', 'MatchID']
out = df.join(pd.DataFrame.from_records(df['Ref'], index=df.index, columns=cols))
print(out)

# Output
  RefID                 Ref  MatchWord  Prox  MatchID
0  Ref1       (baby, 60, 0)       baby    60        0
1  Ref2  (something, 90, 2)  something    90        2

You can loop through the Series with apply() function and convert each list to a Series, this automatically expand the list as a series in the column direction:

df[0].apply(pd.Series)

#   0    1   2
#0  8   10  12
#1  7    9  11

Update: To keep other columns of the data frame, you can concatenate the result with the columns you want to keep:

pd.concat([df[0].apply(pd.Series), df[1]], axis = 1)

#   0    1   2  1
#0  8   10  12  A
#1  7    9  11  B

Why not just change the DataFrame in place?

for idx, row in df.iterrows():
    for hobby in row.hobbies.split(";"):
        df.loc[idx, hobby] = True

df.fillna(False, inplace=True)

Pandas functions working with missing values nice, so use Series.str.join with Series.str.get_dummies, DataFrame.pop is for extract column and last join to original data:

df = df.join(df.pop('Values').str.join('|').str.get_dummies())
print (df)
    ID  AB  BC  CD
0  ID1   1   1   0
1  ID2   0   0   0
2  ID3   1   0   1

EDIT: If values are not lists, only string representation of lists use ast.literal_eval for converting to lists:

import ast

df = (df.join(df.pop('Values')
        .apply(ast.literal_eval)
        .str.join('|')
        .str.get_dummies()))