I have a DataFrame, let's say:
#d = {'col1': [1, 2, 3], 'col2': [3, 4, 5]} // that's what the data might look like
df = pd.DataFrame(data=d)
and I have a np array with [0, 2].
Now I want to add a column to the DataFrame, where there is a 1, when the index of the row is in the np array, otherwise a 0.
Does anyone have an idea?
Use Index.isin with cast mask to integers:
d = {'col1': [1, 2, 3], 'col2': [3, 4, 5]}
df = pd.DataFrame(data=d)
a = np.array([0, 2])
df['new'] = df.index.isin(a).astype(int)
#alternative
#df['new'] = np.in1d(df.index, a).astype(int)
Or use numpy.where:
df['new'] = np.where(df.index.isin(a), 1, 0)
#alternative
#df['new'] = np.where(np.in1d(df.index, a), 1, 0)
print (df)
col1 col2 new
0 1 3 1
1 2 4 0
2 3 5 1
