I have the below dataset and would like to rename 1 to Sold, 2 to Sold, and nan to Unsold

DF

Desired DF

I am currently using the replace function but doing each condition separately. Is there a way to do it all at the same time?

df['Status'] = df['Status'].str.replace['1', 'Sold']

CodePudding user response：

use map with a dictionary:

df['Status'] = df['Status'].map({'1': 'Solde', '2': 'Solde', np.nan: 'Unsold'})

NB. If NaNs are strings:

df['Status'] = df['Status'].map({'1': 'Solde', '2': 'Solde', 'nan': 'Unsold'})

Or, if you only have 1/2 -> Sold ; nan -> Unsold:

# if real NaN
df['Status'] = np.where(df['Status'].notna(), 'Sold', 'Unsold')

# if string
df['Status'] = np.where(df['Status'].ne('nan'), 'Sold', 'Unsold')