Split lists within DataFrames into multiple DataFrames

Question

I have this pandas DataFrame filled with lists filled with strings, that I wish to split into two frames:

Input:

df = pd.DataFrame({
  'A': {'a': ['NaN'],'b': ['1.11', '0.00']},
  'B': {'a': ['3.33', '0.22'],'b': ['NaN']},
  })

Desired output:

df1 = pd.DataFrame({
  'A': {'a': ['NaN'],'b': ['1.11']},
  'B': {'a': ['3.33'],'b': ['NaN']},
  })
df2 = pd.DataFrame({
  'A': {'a': ['NaN'],'b': ['0.00']},
  'B': {'a': ['0.22'],'b': ['NaN']},
   })

I tried to use the apply function, which works for Series, and was wondering if there is an easy way to apply an operation that achieves this on the entire df.

Are there always either 1 or 2 items in each list?

jpp
– jpp

2018-03-21 15:16:41 +00:00
Commented Mar 21, 2018 at 15:16 — jpp
– jpp, Commented Mar 21, 2018 at 15:16
yes, a mean and a standard deviation, or neither

Michiel Karrenbelt
– Michiel Karrenbelt

2018-03-21 15:20:22 +00:00
Commented Mar 21, 2018 at 15:20 — Michiel Karrenbelt
– Michiel Karrenbelt, Commented Mar 21, 2018 at 15:20

BENY · Accepted Answer · 2018-03-21 15:20:52Z

4

You can stack and apply(pd.Series)

s=df.stack().apply(pd.Series)
s[0].unstack()
Out[508]: 
      A     B
a   NaN  3.33
b  1.11   NaN
s[1].unstack()
Out[509]: 
      A     B
a   NaN  0.22
b  0.00   NaN

If you do need object for single cell

s[0].unstack().applymap(lambda x : [x])
Out[512]: 
        A       B
a   [NaN]  [3.33]
b  [1.11]   [NaN]
s[1].unstack().applymap(lambda x : [x])
Out[513]: 
        A       B
a   [nan]  [0.22]
b  [0.00]   [nan]

answered Mar 21, 2018 at 15:20

BENY

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