vectorize conditional assignment in pandas dataframe

One simple method would be to assign the default value first and then perform 2 loc calls:

In [66]:df = pd.DataFrame({'x':[0,-3,5,-1,1]})dfOut[66]:   x0  01 -32  53 -14  1In [69]:df['y'] = 0df.loc[df['x'] < -2, 'y'] = 1df.loc[df['x'] > 2, 'y'] = -1dfOut[69]:   x  y0  0  01 -3  12  5 -13 -1  04  1  0

If you wanted to use np.where then you could do it with a nested np.where:

In [77]:df['y'] = np.where(df['x'] < -2 , 1, np.where(df['x'] > 2, -1, 0))dfOut[77]:   x  y0  0  01 -3  12  5 -13 -1  04  1  0

So here we define the first condition as where x is less than -2, return 1, then we have another np.where which tests the other condition where x is greater than 2 and returns -1, otherwise return 0

timings

In [79]:%timeit df['y'] = np.where(df['x'] < -2 , 1, np.where(df['x'] > 2, -1, 0))1000 loops, best of 3: 1.79 ms per loopIn [81]:%%timeitdf['y'] = 0df.loc[df['x'] < -2, 'y'] = 1df.loc[df['x'] > 2, 'y'] = -1100 loops, best of 3: 3.27 ms per loop

So for this sample dataset the np.where method is twice as fast

This is a good use case for pd.cut where you define ranges and based on those ranges you can assign labels:

df['y'] = pd.cut(df['x'], [-np.inf, -2, 2, np.inf], labels=[1, 0, -1], right=False)

Output

   x  y0  0  01 -3  12  5 -13 -1  04  1  0