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How can I shift columns based on certain row?

Time:11-17

My datatable is below.

menu_nm dtl rcp
0 sandwich amazing sandwich!!! bread 10g
1 hamburger bread 20g, vegetable 10g ???
2 salad fresh salad!!! apple sauce 10g, banana 40g, cucumber 5g
3 juice sweet juice!! orange 50g, water 100ml
4 fruits strawberry 10g, grape 20g, melon 10g ???

and I want to get this datatable

menu_nm dtl rcp
0 sandwich amazing sandwich!!! bread 10g
1 hamburger bread 20g, vegetable 10g
2 salad fresh salad!!! apple sauce 10g, banana 40g, cucumber 5g
3 juice sweet juice!! orange 50g, water 100ml
4 fruits strawberry 10g, grape 20g, melon 10g

I want to shift row 1, 4 to rcp column, but I can't find method or logic that I try. I just know that shifting all row and all column, I don't know how I can shift certain row and column.

If you know hint or answer, please tell me. thanks.

CodePudding user response:

assumption: rcp column contains "???" that needs to be replaced with the a values from dtl

# create a filter where value under rcp is "???"
m=df['rcp'].eq('???')

# using loc, shift the values

df.loc[m, 'rcp'] =  df['dtl']
df.loc[m, 'dtl'] =  ""
df
    menu_nm     dtl                     rcp
0   sandwich    amazing sandwich!!!     bread 10g
1   hamburger                           bread 20g, vegetable 10g
2   salad       fresh salad!!!          apple sauce 10g, banana 40g, cucumber 5g
3   juice       sweet juice!!           orange 50g, water 100ml
4   fruits                              strawberry 10g, grape 20g, melon 10g

CodePudding user response:

You can access index location using .iloc as below:

>>> df=pd.DataFrame({"COLA":[1,2,3,4], "COLB":[100,200,300,400], "COLC":[1000,2000,3000,4000]})
>>> df
   COLA  COLB  COLC
0     1   100  1000
1     2   200  2000
2     3   300  3000
3     4   400  4000
>>> df['COLC'].iloc[1]=df['COLB'].iloc[1]
>>> df
   COLA  COLB  COLC
0     1   100  1000
1     2   200   200
2     3   300  3000
3     4   400  4000
>>> df['COLB'].iloc[1]=''
>>> df
   COLA COLB  COLC
0     1  100  1000
1     2        200
2     3  300  3000
3     4  400  4000

Follow similar steps for row 4.

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