Re-structure a pandas dataframe

Question

I've the following pandas dataframe:

>>>    df = pd.DataFrame([
                    [np.nan, 2, 'x', 0], 
                    [3, 4, 'y', 0],
                    [9, 6, 'x', 1],
                    [np.nan, np.nan, 'y', 1]],
                   columns=['ignore', 'value', 'col', 'row'])


>>> df
   ignore  value col  row
0     NaN    2.0   x    0
1     3.0    4.0   y    0
2     9.0    6.0   x    1
3     NaN    NaN   y    1

I want to be able to convert it to something like the following:

   x      y 
0  2.0    4.0
1  6.0    NaN

Is it possible using pivot or multi-index or anything else? Or the only possible solution is looping through individual values?

Or `pd.crosstab(df.row, df.col, df.value, aggfunc=sum)` or `df.groupby(['row', 'col'])['value'].sum().unstack()` — Zero, Jan 17 '18 at 11:13

score 0 · Answer 1 · answered Jan 17 '18 at 11:16

0

You can probably use pd.crosstab:

pd.crosstab(columns=df.col,values=df.value,index=df.row, aggfunc=np.min)
Out[29]: 
col    x    y
row          
0    2.0  4.0
1    6.0  NaN

answered Jan 17 '18 at 11:16

Allen Qin

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Well whats the use of this, when the question is exact dupe . – Bharath Jan 17 '18 at 11:18

Re-structure a pandas dataframe

1 Answers1