Display minimum value excluding zero along with adjacent column value from each year + Python 3+, dataframe

Question

I have a dataframe with three columns as Year, Product, Price. I wanted to calculate minimum value excluding zero from Price from each year. Also wanted to populate adjacent value from column Product to the minimum value.

Data:

Year  Product       Price
2000   Grapes       0
2000   Apple        220
2000   pear         185
2000   Watermelon   172
2001   Orange       0 
2001   Muskmelon    90
2001   Pear         165
2001   Watermelon   99

Desirable output in new dataframe:

Year  Minimum Price  Product
2000  172            Watermelon  
2001  90             Muskmelon   

Answer 1

First filter out 0 rows by boolean indexing:

df1 = df[df['Price'] != 0]

And then use DataFrameGroupBy.idxmin for indices for minimal Price per groups with selecting by loc:

df2 = df1.loc[df1.groupby('Year')['Price'].idxmin()]

Alternative is use sort_values with drop_duplicates:

df2 = df1.sort_values(['Year', 'Price']).drop_duplicates('Year')

print (df2)
   Year     Product  Price
3  2000  Watermelon    172
5  2001   Muskmelon     90

If possible multiple minimal values and need all of them per groups:

print (df)
   Year     Product  Price
0  2000      Grapes      0
1  2000       Apple    220
2  2000        pear    172
3  2000  Watermelon    172
4  2001      Orange      0
5  2001   Muskmelon     90
6  2001        Pear    165
7  2001  Watermelon     99

df1 = df[df['Price'] != 0]
df = df1[df1['Price'].eq(df1.groupby('Year')['Price'].transform('min'))]
print (df)
   Year     Product  Price
2  2000        pear    172
3  2000  Watermelon    172
5  2001   Muskmelon     90

EDIT:

print (df)
   Year     Product  Price
0  2000      Grapes      0
1  2000       Apple    220
2  2000        pear    185
3  2000  Watermelon    172
4  2001      Orange      0
5  2001   Muskmelon     90
6  2002        Pear      0
7  2002  Watermelon      0

df['Price'] = df['Price'].replace(0, np.nan)
df2 = df.sort_values(['Year', 'Price']).drop_duplicates('Year')
df2['Product'] = df2['Product'].mask(df2['Price'].isnull(), 'No data')
print (df2)
   Year     Product  Price
3  2000  Watermelon  172.0
5  2001   Muskmelon   90.0
6  2002     No data    NaN