Create a new column evaluating values in previous column

user_19613662 0
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Starting from an imported df from excel like that:

Lev. Material Text QTY
.1 X222 Model3 1
.2 4027721 Gruoup1 1
.3 4647273 Gruoup1.1 4
.3 573828 Gruoup1.2 1
.2 47883 Gruoup2 1
.3 573829 Gruoup2.1 5
.3 747458 Gruoup2.2 4

I want to add a new column reporting a specific value, obtained after an evaluation of the column Lev. I tried to use a if command like that:

def categorise_row:   
 if df ["Lev"] == ".1" or ".2"
   df ["Material"] 
 else df.iloc[-1]["Material"] 
df ['ColF'] = df.apply (categorise_row)

The result has to be, if Lev = .1 or .2 the value present in column material instead, the result has to be equal to the result present in the new column upper row.

Expected result:

Lev. Material Text QTY NewColumn
.1 X222 Model3 1 X222
.2 4027721 Gruoup1 1 4027721
.3 4647273 Gruoup1.1 4 4027721
.3 573828 Gruoup1.2 1 4027721
.2 47883 Gruoup2 1 47883
.3 573829 Gruoup2.1 5 47883
.3 747458 Gruoup2.2 4 47883.
pandas python
1 Answers

You can use where to hide values from other levels and use ffill to broadcast the last valid to bottom rows:

df['ColF'] = df['Material'].where(df['Lev.'].isin(['.1', '.2'])).ffill()
print(df)

# Output
  Lev. Material       Text  QTY     ColF
0   .1     X222     Model3    1     X222
1   .2  4027721    Gruoup1    1  4027721
2   .3  4647273  Gruoup1.1    4  4027721
3   .3   573828  Gruoup1.2    1  4027721
4   .2    47883    Gruoup2    1    47883
5   .3   573829  Gruoup2.1    5    47883
6   .3   747458  Gruoup2.2    4    47883
user_152399510
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