CODEWITHSUNDEEP
pythonpandasdataframe
TaxpayersMoney
TaxpayersMoney

Reputation: 689

split a string into separate columns in pandas

I have a dataframe with lots of data and 1 column that is structured like this:

index    var_1
1        a=3:b=4:c=5:d=6:e=3
2        b=3:a=4:c=5:d=6:e=3
3        e=3:a=4:c=5:d=6
4        c=3:a=4:b=5:d=6:f=3

I am trying to structure the data in that column to look like this:

index    a   b   c   d   e   f
1        3   4   5   6   3   0
2        4   3   5   6   3   0
3        4   0   5   6   3   0
4        4   5   3   6   0   3

I have done the following thus far:

df1 = df['var1'].str.split(':', expand=True)

I can then loop through the cols of df1 and do another split on '=', but then I'll just have loads of disorganised label cols and value cols.

Upvotes: 2

Views: 238

Answers (3)

kantal
kantal

Reputation: 2407

You can apply "extractall" and "pivot".
After "extractall" you get:

             0  1
index match      
1     0      a  3
      1      b  4
      2      c  5
      3      d  6
      4      e  3
2     0      b  3
      1      a  4
      2      c  5
      3      d  6
      4      e  3
3     0      e  3
      1      a  4
      2      c  5
      3      d  6
4     0      c  3
      1      a  4
      2      b  5
      3      d  6
      4      f  3

And in one step:

rslt= df.var_1.str.extractall(r"([a-z])=(\d+)") \
                .reset_index(level="match",drop=True) \
                .pivot(columns=0).fillna(0)                     


         1               
    0      a  b  c  d  e  f
    index                  
    1      3  4  5  6  3  0
    2      4  3  5  6  3  0
    3      4  0  5  6  3  0
    4      4  5  3  6  0  3

#rslt.columns= rslt.columns.levels[1].values

Upvotes: 0

yatu
yatu

Reputation: 88305

Here's one approach using str.get_dummies:

out = df.var_1.str.get_dummies(sep=':')
out = out * out.columns.str[2:].astype(int).values
out.columns = pd.MultiIndex.from_arrays([out.columns.str[0], out.columns])

print(out.max(axis=1, level=0))

       a  b  c  d  e  f
index                  
1      3  4  5  6  3  0
2      4  3  5  6  3  0
3      4  0  5  6  3  0
4      4  5  3  6  0  3

Upvotes: 1

jezrael
jezrael

Reputation: 863541

Use list comprehension with dictionaries for each value and pass to DataFrame constructor:

comp = [dict([y.split('=') for y in x.split(':')]) for x in df['var_1']]
df = pd.DataFrame(comp).fillna(0).astype(int)
print (df)
   a  b  c  d  e  f
0  3  4  5  6  3  0
1  4  3  5  6  3  0
2  4  0  5  6  3  0
3  4  5  3  6  0  3

Or use Series.str.split with expand=True for DataFrame, reshape by DataFrame.stack, again split, remove first level of MultiIndex and add new level by 0 column, last reshape by Series.unstack:

df = (df['var_1'].str.split(':', expand=True)
                 .stack()
                 .str.split('=', expand=True)
                 .reset_index(level=1, drop=True)
                 .set_index(0, append=True)[1]
                 .unstack(fill_value=0)
                 .rename_axis(None, axis=1))
print (df)
   a  b  c  d  e  f
1  3  4  5  6  3  0
2  4  3  5  6  3  0
3  4  0  5  6  3  0
4  4  5  3  6  0  3

Upvotes: 5

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