Using If/Truth Statements with pandas - if-statement

Question

I tried referencing the pandas documentation but still can't figure out how to proceed.
I have this data
In [6]:
df
Out[6]:
strike putCall
0 50 C
1 55 P
2 60 C
3 65 C
4 70 C
5 75 P
6 80 P
7 85 C
8 90 P
9 95 C
10 100 C
11 105 P
12 110 P
13 115 C
14 120 P
15 125 C
16 130 C
17 135 P
18 140 C
19 145 C
20 150 C
and am trying to run this code:
if df['putCall'] == 'P':
if df['strike']<100:
df['optVol'] = 1
else:
df['optVol'] = -999
else:
if df['strike']>df['avg_syn']:
df['optVol'] = 1
else:
df['optVol']= =-999
I get an error message:
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
the above code and data are example only to illustrate the problem I ran into
Any assistance would be appreciated.
John
OP add-on
The above question was answered very well by Joris, but I have a slight add-on question.
how would I call a function such as
def Bool2(df):
df['optVol'] = df['strike']/100
return(df)
rather than assign the value of optVol directly to 1 in the line:
df.loc[(df['putCall'] == 'P') & (df['strike']>=100), 'optVol'] = 1
I would like to have the function Bool2 called and do the assigning. Obviously, the Bool2 function is much more complicated than I have portrayed.
I tried this (shot in the dark), but it did not work:
df.loc[(df['putCall'] == 'P') & (df['strike']<100), 'optVol'] =df.apply(Bool2,axis=1)
thanks again for the help

Answer 1

Typically, when you want to set values using such a if-else logic, boolean indexing is the solution (see docs):
The logic in:
if df['strike']<100:
df['optVol'] = 1
can be expressed with boolean indexing as:
df.loc[df['strike'] < 100, 'optVol'] = 1
For your example, you have multiple nested if-else, and then you can combine conditions using &:
df.loc[(df['putCall'] == 'P') & (df['strike']>=100), 'optVol'] = 1
The full equivalent of your code above could be like this:
df['optVol'] = -999
df.loc[(df['putCall'] == 'P') & (df['strike']>=100), 'optVol'] = 1
df.loc[(df['putCall'] != 'P') & (df['strike']>df['avg_syn']), 'optVol'] = 1
The reason you get the error message above is because when doing if df['strike']<100 this comparison works elementwise, so df['strike']<100 gives you a Series of True and False values, while if expects a single True or False value.