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?
7 Answers
I think you need replace by dict:
cols = ["Weight","Height","BootSize","SuitSize","Type"] df2[cols] = df2[cols].replace({'0':np.nan, 0:np.nan}) You could use the 'replace' method and pass the values that you want to replace in a list as the first parameter along with the desired one as the second parameter:
cols = ["Weight","Height","BootSize","SuitSize","Type"] df2[cols] = df2[cols].replace(['0', 0], np.nan) data['amount']=data['amount'].replace(0, np.nan) data['duration']=data['duration'].replace(0, np.nan) Try:
df2.replace(to_replace={ 'Weight':{0:np.nan}, 'Height':{0:np.nan}, 'BootSize':{'0':np.nan}, 'SuitSize':{'0':np.nan}, }) in column "age", replace zero with blanks
df['age'].replace(['0', 0'], '', inplace=True) Replace zero with nan for single column
df['age'] = df['age'].replace(0, np.nan) Replace zero with nan for multiple columns
cols = ["Glucose", "BloodPressure", "SkinThickness", "Insulin", "BMI"] df[cols] = df[cols].replace(['0', 0], np.nan) Replace zero with nan for dataframe
df.replace(0, np.nan, inplace=True) Another alternative way:
cols = ["Weight","Height","BootSize","SuitSize","Type"] df2[cols] = df2[cols].mask(df2[cols].eq(0) | df2[cols].eq('0')) If you just want to o replace the zeros in whole dataframe, you can directly replace them without specifying any columns:
df = df.replace({0:pd.NA}) 
