[python] Pandas DataFrame column to list

I'd like to clarify a few things:

1. As other answers have pointed out, the simplest thing to do is use pandas.Series.tolist(). I'm not sure why the top voted answer leads off with using pandas.Series.values.tolist() since as far as I can tell, it adds syntax/confusion with no added benefit.
2. tst[lookupValue][['SomeCol']] is a dataframe (as stated in the question), not a series (as stated in a comment to the question). This is because tst[lookupValue] is a dataframe, and slicing it with [['SomeCol']] asks for a list of columns (that list that happens to have a length of 1), resulting in a dataframe being returned. If you remove the extra set of brackets, as in tst[lookupValue]['SomeCol'], then you are asking for just that one column rather than a list of columns, and thus you get a series back.
3. You need a series to use pandas.Series.tolist(), so you should definitely skip the second set of brackets in this case. FYI, if you ever end up with a one-column dataframe that isn't easily avoidable like this, you can use pandas.DataFrame.squeeze() to convert it to a series.
4. tst[lookupValue]['SomeCol'] is getting a subset of a particular column via chained slicing. It slices once to get a dataframe with only certain rows left, and then it slices again to get a certain column. You can get away with it here since you are just reading, not writing, but the proper way to do it is tst.loc[lookupValue, 'SomeCol'] (which returns a series).
5. Using the syntax from #4, you could reasonably do everything in one line: ID = tst.loc[tst['SomeCol'] == 'SomeValue', 'SomeCol'].tolist()

Demo Code:

import pandas as pd
df = pd.DataFrame({'colA':[1,2,1],
                   'colB':[4,5,6]})
filter_value = 1

print "df"
print df
print type(df)

rows_to_keep = df['colA'] == filter_value
print "\ndf['colA'] == filter_value"
print rows_to_keep
print type(rows_to_keep)

result = df[rows_to_keep]['colB']
print "\ndf[rows_to_keep]['colB']"
print result
print type(result)

result = df[rows_to_keep][['colB']]
print "\ndf[rows_to_keep][['colB']]"
print result
print type(result)

result = df[rows_to_keep][['colB']].squeeze()
print "\ndf[rows_to_keep][['colB']].squeeze()"
print result
print type(result)

result = df.loc[rows_to_keep, 'colB']
print "\ndf.loc[rows_to_keep, 'colB']"
print result
print type(result)

result = df.loc[df['colA'] == filter_value, 'colB']
print "\ndf.loc[df['colA'] == filter_value, 'colB']"
print result
print type(result)

ID = df.loc[rows_to_keep, 'colB'].tolist()
print "\ndf.loc[rows_to_keep, 'colB'].tolist()"
print ID
print type(ID)

ID = df.loc[df['colA'] == filter_value, 'colB'].tolist()
print "\ndf.loc[df['colA'] == filter_value, 'colB'].tolist()"
print ID
print type(ID)

Result:

df
   colA  colB
0     1     4
1     2     5
2     1     6
<class 'pandas.core.frame.DataFrame'>

df['colA'] == filter_value
0     True
1    False
2     True
Name: colA, dtype: bool
<class 'pandas.core.series.Series'>

df[rows_to_keep]['colB']
0    4
2    6
Name: colB, dtype: int64
<class 'pandas.core.series.Series'>

df[rows_to_keep][['colB']]
   colB
0     4
2     6
<class 'pandas.core.frame.DataFrame'>

df[rows_to_keep][['colB']].squeeze()
0    4
2    6
Name: colB, dtype: int64
<class 'pandas.core.series.Series'>

df.loc[rows_to_keep, 'colB']
0    4
2    6
Name: colB, dtype: int64
<class 'pandas.core.series.Series'>

df.loc[df['colA'] == filter_value, 'colB']
0    4
2    6
Name: colB, dtype: int64
<class 'pandas.core.series.Series'>

df.loc[rows_to_keep, 'colB'].tolist()
[4, 6]
<type 'list'>

df.loc[df['colA'] == filter_value, 'colB'].tolist()
[4, 6]
<type 'list'>