Firstly your approach is inefficient because the appending to the list on a row by basis will be slow as it has to periodically grow the list when there is insufficient space for the new entry, list comprehensions are better in this respect as the size is determined up front and allocated once.

However, I think fundamentally your approach is a little wasteful as you have a dataframe already so why create a new one for each of these users?

I would sort the dataframe by column 'name', set the index to be this and if required not drop the column.

Then generate a list of all the unique entries and then you can perform a lookup using these entries and crucially if you only querying the data, use the selection criteria to return a view on the dataframe without incurring a costly data copy.

Use pandas.DataFrame.sort_values and pandas.DataFrame.set_index:

# sort the dataframe
df.sort_values(by='name', axis=1, inplace=True)

# set the index to be this and don't drop
df.set_index(keys=['name'], drop=False,inplace=True)

# get a list of names
names=df['name'].unique().tolist()

# now we can perform a lookup on a 'view' of the dataframe
joe = df.loc[df.name=='joe']

# now you can query all 'joes'

use np.split(..., axis=1):

Demo:

In [255]: df = pd.DataFrame(np.random.rand(5, 6), columns=list('abcdef'))

In [256]: df
Out[256]:
          a         b         c         d         e         f
0  0.823638  0.767999  0.460358  0.034578  0.592420  0.776803
1  0.344320  0.754412  0.274944  0.545039  0.031752  0.784564
2  0.238826  0.610893  0.861127  0.189441  0.294646  0.557034
3  0.478562  0.571750  0.116209  0.534039  0.869545  0.855520
4  0.130601  0.678583  0.157052  0.899672  0.093976  0.268974

In [257]: dfs = np.split(df, [4], axis=1)

In [258]: dfs[0]
Out[258]:
          a         b         c         d
0  0.823638  0.767999  0.460358  0.034578
1  0.344320  0.754412  0.274944  0.545039
2  0.238826  0.610893  0.861127  0.189441
3  0.478562  0.571750  0.116209  0.534039
4  0.130601  0.678583  0.157052  0.899672

In [259]: dfs[1]
Out[259]:
          e         f
0  0.592420  0.776803
1  0.031752  0.784564
2  0.294646  0.557034
3  0.869545  0.855520
4  0.093976  0.268974

np.split() is pretty flexible - let's split an original DF into 3 DFs at columns with indexes [2,3]:

In [260]: dfs = np.split(df, [2,3], axis=1)

In [261]: dfs[0]
Out[261]:
          a         b
0  0.823638  0.767999
1  0.344320  0.754412
2  0.238826  0.610893
3  0.478562  0.571750
4  0.130601  0.678583

In [262]: dfs[1]
Out[262]:
          c
0  0.460358
1  0.274944
2  0.861127
3  0.116209
4  0.157052

In [263]: dfs[2]
Out[263]:
          d         e         f
0  0.034578  0.592420  0.776803
1  0.545039  0.031752  0.784564
2  0.189441  0.294646  0.557034
3  0.534039  0.869545  0.855520
4  0.899672  0.093976  0.268974