multiple Elseif conditions in Pandas Dataframe over multiple columns

Question

I am facing a problem in applying multiple Elif condition over a large dataset. The sample data is as below:

Id = ['AM12','AM21','AM31','AM41','AM66','AM81','AM77','AM87','AM27','AM69']
Exec = ['Athreyu','Megan','','Omar','Michael','','Oliver','','Jesus','']
AD_Executive= ['','Fer','Virat','John','','John','','','pandya','John']
Ex_FiscId= ['John','Sonal','','Ram','','Anthony','','','Sriju','']
full_nm = ['pulari','','Burgers','Saheb','Bhavya','Borah','Dutta','Upinder','Ruhaan','Rochan']
df_ex = pd.DataFrame(list(zip(Id, full_nm,Exec,AD_Executive,Ex_FiscId)), 
           columns =['Id', 'full_nm','Exec','AD_Executive','Ex_FiscId'])

I want to create a new column for Final Name. The condition which I am applying is -

def final(df_ex):
   if df_ex['Ex_FiscId'] != np.NaN:
    return df_ex['Ex_FiscId']
  elif (df_ex['Ex_FiscId'] == np.NaN) & (df_ex['AD_Executive'] != np.NaN):
    return df_ex['AD_Executive']
  elif (df_ex['Ex_FiscId'] == np.NaN) & (df_ex['AD_Executive'] == np.NaN) & (df_ex['Exec'] !=np.NaN):
    return df['Exec']
  elif (df_ex['Ex_FiscId'] == np.NaN) & (df_ex['AD_Executive'] == np.NaN) & (df_ex['Exec'] ==np.NaN):
    return df_ex['full_nm']

df_ex['Final'] = df_ex.apply(final, axis = 1)

But it is not yielding the desired output. The code only seems to read the first if condition ignoring the other conditions.

I am also attaching the input and desired output table for reference-

Id	full_nm	Exec	AD_Executive	Ex_FiscId
AM12	pulari	Athreyu		John
AM21		Megan	Fer	Sonal
AM31	Burgers		Virat
AM41	Saheb	Omar	John	Ram
AM66	Bhavya	Michael
AM81	Borah		John	Anthony
AM77	Dutta	Oliver
AM87	Upinder
AM27	Ruhaan	Jesus	pandya	Sriju
AM69	Rochan		John

Desired Output -

Id	full_nm	Exec	AD_Executive	Ex_FiscId	Final
AM12	pulari	Athreyu		John	John
AM21		Megan	Fer	Sonal	Sonal
AM31	Burgers		Virat		Virat
AM41	Saheb	Omar	John	Ram	Ram
AM66	Bhavya	Michael			Michael
AM81	Borah		John	Anthony	Anthony
AM77	Dutta	Oliver			Oliver
AM87	Upinder				Upinder
AM27	Ruhaan	Jesus	pandya	Sriju	Sriju
AM69	Rochan		John		John

Everything, including `np.NaN`, is `!= np.NaN`, so you'd never get past that first block. You should use `pd.isna()` to check if something is null. But really you should implement all of this logic with `np.select`: See https://stackoverflow.com/questions/18194404/create-column-with-elif-in-pandas — ALollz, Mar 18 '21 at 16:29

score 0 · Answer 1 · answered Mar 18 '21 at 17:23

let's try this:

df['final'] = df_ex.apply(lambda x: x[x[::-1].notnull().idxmax()], axis=1)

Output:

    Id  full_nm     Exec    AD_Executive    Ex_FiscId   final
0   AM12    pulari  Athreyu     NaN            John     John
1   AM21    NaN     Megan       Fer            Sonal    Sonal
2   AM31    Burgers NaN         Virat          NaN      Virat
3   AM41    Saheb   Omar        John           Ram      Ram
4   AM66    Bhavya  Michael     NaN            NaN      Michael
5   AM81    Borah   NaN         John           Anthony  Anthony
6   AM77    Dutta   Oliver      NaN            NaN      Oliver
7   AM87    Upinder NaN         NaN            NaN      Upinder
8   AM27    Ruhaan  Jesus       pandya         Sriju    Sriju
9   AM69    Rochan  NaN         John           NaN      John

score 0 · Answer 2 · answered Mar 19 '21 at 10:54

def final(df_ex):
if df_ex['Ex_FiscId'] !="":
    return df_ex['Ex_FiscId']
elif (df_ex['Ex_FiscId']=="") & (df_ex['AD_Executive'] !=""):
    return df_ex['AD_Executive']
elif (df_ex['Ex_FiscId']=="") & (df_ex['AD_Executive'] =="") & (df_ex['Exec'] !=""):
    return df_ex['Exec']
elif (df_ex['Ex_FiscId']=="") & (df_ex['AD_Executive'] =="") & (df_ex['Exec'] ==""):
    return df_ex['full_nm']


df_ex['Final'] = df_ex.apply(final, axis = 1)

multiple Elseif conditions in Pandas Dataframe over multiple columns

2 Answers2