CODEWITHSUNDEEP
pythonpandasnumpy
msh855
msh855

Reputation: 1571

Create a new column based on conditions from other columns in Python

I want to do in Python something very similar as this question from this one R users. My intention is to create a new column that its values are created based on conditions from other columns

For example: 

d = {'year': [2010, 2011,2013, 2014], 'PD': [0.5, 0.8, 0.9, np.nan], 'PD_thresh': [0.7, 0.8, 0.9, 0.7]}
df_temp = pd.DataFrame(data=d)

Now I want to create a condition that says:

pseudo-code:

if for year X the value of PD is greater or equal to the value of PD_thresh
then set 0 in a new column y_pseudo 

otherwise set 1

My expected outcome is this: 

df_temp 
Out[57]: 
   year   PD  PD_thresh  y_pseudo
0  2010  0.5        0.7       0.0
1  2011  0.6        0.7       0.0
2  2013  0.9        0.8       1.0
3  2014  NaN        0.7       NaN

Upvotes: 2

Views: 610

Answers (2)

tasha
tasha

Reputation: 86

Your data d is different from your outcome, and I think you meant 1 if greater than the threshold, not the other way around, so I have this:

y = [a if np.isnan(a) else 1 if a>=b else 0 for a,b in zip(df_temp.PD,df_temp.PD_thresh)]
df_temp['y_pseudo'] = y

Output:

   year   PD  PD_thresh  y_pseudo
0  2010  0.5        0.7       0.0
1  2011  0.8        0.8       1.0
2  2013  0.9        0.9       1.0
3  2014  NaN        0.7       NaN

Upvotes: 1

jezrael
jezrael

Reputation: 863531

Use numpy.select with isna and ge:

m1 = df_temp['PD'].isna()
m2 = df_temp['PD'].ge(df_temp['PD_thresh'])

df_temp['y_pseudo'] = np.select([m1, m2], [np.nan, 1], default=0)
print (df_temp)
   year   PD  PD_thresh  y_pseudo
0  2010  0.5        0.7       0.0
1  2011  0.6        0.8       0.0
2  2013  0.9        0.9       1.0
3  2014  NaN        0.7       NaN

Another solution is convert mask to integer for True/False to 1/0 mapping and set only non missing rows by notna:

m2 = df_temp['PD'].ge(df_temp['PD_thresh'])
m3 = df_temp['PD'].notna()

df_temp.loc[m3, 'y_pseudo'] = m2[m3].astype(int)
print (df_temp)
   year   PD  PD_thresh  y_pseudo
0  2010  0.5        0.7       0.0
1  2011  0.6        0.8       0.0
2  2013  0.9        0.9       1.0
3  2014  NaN        0.7       NaN

Upvotes: 4

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