Create a new column evaluating values in previous column

Question

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.

Asked By: user19613662

||

Source

Answer 1

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

Answered By: Corralien

Create a new column evaluating values in previous column

Question:

Answers: