Python Pandas replace multiple columns zero to Nan

Question

List with attributes of persons loaded into pandas dataframe df2. For cleanup I want to replace value zero (0 or '0') by np.nan.

df2.dtypes

ID                   object
Name                 object
Weight              float64
Height              float64
BootSize             object
SuitSize             object
Type                 object
dtype: object

Working code to set value zero to np.nan:

df2.loc[df2['Weight'] == 0,'Weight'] = np.nan
df2.loc[df2['Height'] == 0,'Height'] = np.nan
df2.loc[df2['BootSize'] == '0','BootSize'] = np.nan
df2.loc[df2['SuitSize'] == '0','SuitSize'] = np.nan

Believe this can be done in a similar/shorter way:

df2[["Weight","Height","BootSize","SuitSize"]].astype(str).replace('0',np.nan)

However the above does not work. The zero's remain in df2. How to tackle this?

Answer 1

I think you need replace by dict:

cols = ["Weight","Height","BootSize","SuitSize","Type"]
df2[cols] = df2[cols].replace({'0':np.nan, 0:np.nan})

Answer 2

data['amount']=data['amount'].replace(0, np.nan)
data['duration']=data['duration'].replace(0, np.nan)