Pandas: parsing values in structured non tabular text

You can use read_csv with some separator which is not in data like | or ¥:

import pandas as pd
from pandas.compat import StringIO

temp=u"""
k1[a-token]
v1
v2
k2[a-token]
v1'
k3[a-token]
v1"
v2"
v3"
"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp), sep="|", names=['B'])
print (df)
             B
0  k1[a-token]
1           v1
2           v2
3  k2[a-token]
4          v1'
5  k3[a-token]
6          v1"
7          v2"
8          v3"

Then insert new column A with extract values with [a-token] and last use boolean indexing with mask by duplicated for remove rows with keys in values column:

df.insert(0, 'A', df['B'].str.extract('(.*)\[a-token\]', expand=False).ffill())
df = df[df['A'].duplicated()].reset_index(drop=True)
print (df)
    A    B
0  k1   v1
1  k1   v2
2  k2  v1'
3  k3  v1"
4  k3  v2"
5  k3  v3"

But if file have duplicated keys:

print (df)
              B
0   k1[a-token]
1            v1
2            v2
3   k2[a-token]
4           v1'
5   k3[a-token]
6           v1"
7           v2"
8           v3"
9   k2[a-token]
10          v1'

df.insert(0, 'A', df['B'].str.extract('(.*)\[a-token\]', expand=False).ffill())
df = df[df['A'].duplicated()].reset_index(drop=True)
print (df)
    A            B
0  k1           v1
1  k1           v2
2  k2          v1'
3  k3          v1"
4  k3          v2"
5  k3          v3"
6  k2  k2[a-token]
7  k2          v1'

Then is necessary change mask to:

df.insert(0, 'A', df['B'].str.extract('(.*)\[a-token\]', expand=False).ffill())
df = df[~df['B'].str.contains('\[a-token]')].reset_index(drop=True)
print (df)
    A    B
0  k1   v1
1  k1   v2
2  k2  v1'
3  k3  v1"
4  k3  v2"
5  k3  v3"
6  k2  v1'

With your file as 'temp.txt'...

df = pd.read_csv('temp.txt',
                 header=None,
                 delim_whitespace=True,
                 names=['data'])

bins = df.data.str.endswith('[a-token]')

idx_bins = df[bins][:]
idx_bins.data = idx_bins.data.str.rstrip(to_strip='[a-token]')
idx_vals = df[~bins][:]

a = pd.DataFrame(idx_bins.index.values, columns=['a'])
b = pd.DataFrame(idx_vals.index.values, columns=['b'])

merge_df = pd.merge_asof(b, a, left_on='b', right_on='a')
new_df = pd.DataFrame({'A': idx_bins.data.loc[list(merge_df.a)].values, 
                       'B': idx_vals.data.values})