How can I make this python code more efficient?

Question

I realize this is an incredibly inefficient way to code this, so I'm hoping someone will have suggestions on a more efficient method.

Essentially I'm trying to create a column ("freq") with values of 0 for NA and "Nothing" objects and 1 otherwise. Sample df:

i   obj           freq

0.  Nothing        0
1.  Something      1
2.  NaN            0
3.  Something      1


for i in range(0,len(df)):
  if str(df["obj"].iloc[i]) == "Nothing" or str(df["obj"].iloc[i]) == NaN:
    d["freq"].iloc[i] = 0
  else:
    df["freq"].iloc[i] = 1

Answer 1

You can use np.where()

import pandas as pd 
import numpy as np

df = pd.DataFrame({'obj': {0: 'Nothing', 1: 'Something', 2: np.nan, 3: 'Something'}})

df['freq'] = np.where((df['obj'] == 'Nothing') | (df['obj'].isnull()), 0, 1)

Answer 2

Without a dataframe is hard to check if works, but it should

indexer = (df['obj'] == 'Nothing') | (df['obj'].astype(str) == 'NaN')
df.loc[indexer, 'freq'] = 0
df.loc[~indexer, 'freq'] = 1

Answer 3

In this case, it is not even necessary to use numpy:

df['freq'] = (~(df.obj.isnull() | (df.obj == 'Nothing'))) * 1

Note:

Is it useful to code with '0' and '1'? Can't we stay with the result of the boolean operation keeping the 'False' and True' values? If it is the case the answer would simply be:

df['freq'] = ~(df.obj.isnull() | (df.obj == 'Nothing'))