Create new column in Pandas DataFrame based on other columns

Question

I have Dataframe like:

Now I would like to add column V based on conditions on I1, I2 and I3. Conditions are like:

v = 1 if I1>23 and I2.str.contains('abc')
v = 2 if I3 == 20
v == ...............
...................

One row can satisfy multiple condition what I want is to multiply such rows and filter out the rows which is not satisfying any condition such as suppose N1 will satisfy for V=1,2 and 3. While N2 does not satisfy any and N3 will satisfy v=2. I want the final dataframe to look like:

Could someone please help me with this? Thanks.

Answer 1

If I understood your question correctly, suppose you have a dataframe like the below:

df = pd.DataFrame({
    "NAME": [ "N1", "N2", "N3" ],
    "I1": [ 1, 4, 4 ],
    "I2": [ 2, 5, 2 ],
    "I3": [ 3, 6, 6 ]
})

ie:

>>> df
  NAME  I1  I2  I3
0   N1   1   2   3
1   N2   4   5   6
2   N3   4   2   6

To reproduce your example, I will assume that the conditions are I1 = 1 , I2 = 2 , and I3 = 3 :

cond1 = df["I1"] == 1
cond2 = df["I2"] == 2
cond3 = df["I3"] == 3

To build the expected dataframe, you can do:

result = pd.concat([
    df[cond1].assign(V=1),
    df[cond2].assign(V=2),
    df[cond3].assign(V=3)
])

Result:

>>> result
  NAME  I1  I2  I3  V
0   N1   1   2   3  1
0   N1   1   2   3  2
2   N3   4   2   6  2
0   N1   1   2   3  3