Using conditional to generate new column in pandas dataframe

Question

I have a pandas dataframe that looks like this:

   portion  used
0        1   1.0
1        2   0.3
2        3   0.0
3        4   0.8

I\'d

Answer 1

Use np.where, is usually fast

In [845]: df['alert'] = np.where(df.used == 1, 'Full', 
                                 np.where(df.used == 0, 'Empty', 'Partial'))

In [846]: df
Out[846]:
   portion  used    alert
0        1   1.0     Full
1        2   0.3  Partial
2        3   0.0    Empty
3        4   0.8  Partial

---

_Timings

In [848]: df.shape
Out[848]: (100000, 3)

In [849]: %timeit df['alert'] = np.where(df.used == 1, 'Full', np.where(df.used == 0, 'Empty', 'Partial'))
100 loops, best of 3: 6.17 ms per loop

In [850]: %%timeit
     ...: df.loc[df['used'] == 1.0, 'alert'] = 'Full'
     ...: df.loc[df['used'] == 0.0, 'alert'] = 'Empty'
     ...: df.loc[(df['used'] >0.0) & (df['used'] < 1.0), 'alert'] = 'Partial'
     ...:
10 loops, best of 3: 21.9 ms per loop

In [851]: %timeit df['alert'] = df.apply(alert, axis=1)
1 loop, best of 3: 2.79 s per loop