CODEWITHSUNDEEP
pythonpandasdataframe
user3084006
user3084006

Reputation: 5584

Transforming Pandas Dataframe

Is there a pandas function to transform this data so it show the columns as a,b,c,d,e or whatever is inside the data field and the rows count how many of the letters there are.

import pandas as pd

trans = pd.read_table('output.txt', header=None, index_col=0)

print trans
>>> 
        1  2    3    4
0                     
11      a  b    c  NaN
666     a  d    e  NaN
10101   b  c    d  NaN
1010    a  b    c    d
414147  b  c  NaN  NaN
10101   a  b    d  NaN
1242    d  e  NaN  NaN
101     a  b    c    d
411     c  d    e  NaN
444     a  b    c  NaN

instead I want the output to be like this:

        a  b    c     d   e
0                     
11      1  1    1   NaN  NaN
666     1  NaN  NaN   1    1

The function .stack() almost gets it done but in the wrong format.

Upvotes: 5

Views: 1599

Answers (2)

Rutger Kassies
Rutger Kassies

Reputation: 64493

You could also use Pandas get_dummies()

pd.get_dummies(df.unstack().dropna()).groupby(level=1).sum()

results in:

        a  b  c  d  e
0                    
11      1  1  1  0  0
666     1  0  0  1  1
10101   0  1  1  1  0
1010    1  1  1  1  0
414147  0  1  1  0  0
10101   1  1  0  1  0
1242    0  0  0  1  1
101     1  1  1  1  0
411     0  0  1  1  1
444     1  1  1  0  0

You could replace the zeros with NaN's in you want to.

Its a bit obscure in one line. df.unstack().dropna() basically flattens your DataFrame to a series and drops al NaN's. The get_dummies gives a table of all the occurrences of the letters, but for each level in the unstack DataFrame. The grouping and sum then combine the index to the original shape.

Upvotes: 5

roman
roman

Reputation: 117606

Something like this may be:

>>> st = pd.DataFrame(trans.stack()).reset_index(level=0)
>>> st.columns = ['i','c']
>>> st.pivot_table(rows='i', cols='c', aggfunc=len)
c        a   b   c   d   e
i                         
11       1   1   1 NaN NaN
101      1   1   1   1 NaN
411    NaN NaN   1   1   1
444      1   1   1 NaN NaN
666      1 NaN NaN   1   1
1010     1   1   1   1 NaN
1242   NaN NaN NaN   1   1
10101    1   2   1   2 NaN
414147 NaN   1   1 NaN NaN

Upvotes: 2

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