Zelazny7
2020-11-11 20:29:19
To formalize some of the approaches laid out above:
Create a function that operates on the rows of your dataframe like so:
def f(row):
if row['A'] == row['B']:
val = 0
elif row['A'] > row['B']:
val = 1
else:
val = -1
return val
Then apply it to your dataframe passing in the axis=1 option:
In [1]: df['C'] = df.apply(f, axis=1)
In [2]: df
Out[2]:
A B C
a 2 2 0
b 3 1 1
c 1 3 -1
Of course, this is not vectorized so performance may not be as good when scaled to a large number of records. Still, I think it is much more readable. Especially coming from a SAS background.
Edit
Here is the vectorized version
df['C'] = np.where(
df['A'] == df['B'], 0, np.where(
df['A'] > df['B'], 1, -1))
Thank you, I am beginning with pandas and this was very helpful +1
What if I want to pass another parameter along with row in the function? If I do, it says row not defined..
You have to use the
argsparameter of the.applyfunction: pandas.pydata.org/pandas-docs/stable/generated/…I'm an old SAS user learning Python, and there's definitely a learning curve! :-) For example, the above code could be written in SAS as:
data df; set df; if A=B then C=0; else if A>B then C=1; else C=-1; run;Very elegant and simple.A well defined answer