I have a series s
s = pd.Series([1, 2])
What is an efficient way to make s look like
0 [1]
1 [2]
dtype: object
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4 Answers
Here's one approach that extracts into array and extends to 2D by introducing a new axis with None/np.newaxis -
pd.Series(s.values[:,None].tolist())
Here's a similar one, but extends to 2D by reshaping -
pd.Series(s.values.reshape(-1,1).tolist())
Runtime test using @P-robot's setup -
In [43]: s = pd.Series(np.random.randint(1,10,1000))
In [44]: %timeit pd.Series(np.vstack(s.values).tolist()) # @Nickil Maveli's soln
100 loops, best of 3: 5.77 ms per loop
In [45]: %timeit pd.Series([[a] for a in s]) # @P-robot's soln
1000 loops, best of 3: 412 µs per loop
In [46]: %timeit s.apply(lambda x: [x]) # @mgc's soln
1000 loops, best of 3: 551 µs per loop
In [47]: %timeit pd.Series(s.values[:,None].tolist()) # Approach1
1000 loops, best of 3: 307 µs per loop
In [48]: %timeit pd.Series(s.values.reshape(-1,1).tolist()) # Approach2
1000 loops, best of 3: 306 µs per loop
5 Comments
Nickil Maveli
Much better approach compared to mine.
mgc
@Divakar For the sake of completeness you could add an "Approach3" with
pd.Series(memoryview(s.values.reshape(-1,1)).tolist()) as it should even be faster (as least with my current config. it is) to build the Series on the memoryview (probably more visible with a larger sample).
Divakar
@mgc Is it only for python3? At my end with python 2.7, I am getting this error :
"NotImplementedError: tolist() only supports byte views", when trying to do : memoryview(s.values.reshape(-1,1)).tolist().mgc
@Divakar you're right it's only python 3. My bad, i don't use python 2.7 that much. I added "with my config" (but i should have define it : python 3.5.2 / numpy 1.11.1) because I can not guarantee that to be the case across all versions (so finally it might not be such a good candidate as an "Approach 3" for your answer!)
Divakar
@mgc Still very much appreciate the feedback and for someone with python3 could try it out too!
If you want the result to still be a pandas Series you can use the apply method :
In [1]: import pandas as pd
In [2]: s = pd.Series([1, 2])
In [3]: s.apply(lambda x: [x])
Out[3]:
0 [1]
1 [2]
dtype: object
Comments
This does it:
import numpy as np
np.array([[a] for a in s],dtype=object)
array([[1],
[2]], dtype=object)
Comments
Adjusting atomh33ls' answer, here's a series of lists:
output = pd.Series([[a] for a in s])
type(output)
>> pandas.core.series.Series
type(output[0])
>> list
Timings for a selection of the suggestions:
import numpy as np, pandas as pd
s = pd.Series(np.random.randint(1,10,1000))
>> %timeit pd.Series(np.vstack(s.values).tolist())
100 loops, best of 3: 3.2 ms per loop
>> %timeit pd.Series([[a] for a in s])
1000 loops, best of 3: 393 µs per loop
>> %timeit s.apply(lambda x: [x])
1000 loops, best of 3: 473 µs per loop
