Imputing nulls in a row with other row if one column is same-CodePudding

I have a dataframe

data = [[1000, 'x', 'A'], [2000,'y', 'A'], ['NaN','NaN', 'A'], ['NaN','NaN','B'], [1700,'z', 'B']]

df = pd.DataFrame(data, columns=['Price', 'Attribute', 'Model' ])
df = df.replace('NaN',np.nan)

Now i want to impute the nulls in such a way that if Model is same, copy the content of rows having least Price to the row having nulls.

The output should look like

data = [[1000, 'x', 'A'], [2000,'y', 'A'], [1000, 'x', 'A'], [1700,'z','B'], [1700,'z', 'B']]
  
df = pd.DataFrame(data, columns=['Price', 'Attribute', 'Model' ])

I have tried groupby and followed Merge two duplicate rows with imputing values from each other

but it did not work. can someone help

CodePudding user response：

If there is multiple columns use DataFrame.fillna with minimal values per groups to new columns by GroupBy.transform:

cols = ['Price','Col1']
df[cols] = df[cols].fillna(df.groupby('Model')[cols].transform('min'))
print(df)
    Price Attribute Model
0  1000.0         x     A
1  2000.0         y     A
2  1000.0       NaN     A
3  1700.0       NaN     B
4  1700.0         z     B

EDIT: If need replace all missing values by rows with no NaNs use:

data = [[1000, 'x', 'A'], [2000,'y', 'A'], [np.nan,np.nan, 'A'], 
        [np.nan,np.nan,'B'], [1700,'z', 'B']]

df = pd.DataFrame(data, columns=['Price', 'Attribute', 'Model' ])

df1 = df.loc[df.dropna().groupby('Model')['Price'].idxmin()]
print (df1)
    Price Attribute Model
0  1000.0         x     A
4  1700.0         z     B

df = df.set_index('Model').fillna(df1.set_index('Model')).reset_index()
print (df)
  Model   Price Attribute
0     A  1000.0         x
1     A  2000.0         y
2     A  1000.0         x
3     B  1700.0         z
4     B  1700.0         z