How to subset one column in pandas with condition on another column

Question

I have a dataframe like below..

     dish_quant_bought    dish_name
              20              A 
              18              B
              20              A
              18              B
              20              A
              18              B
              10              C
              18              B
              10              C
              18              B
              20              D
              20              D
              20              D
              20              D

I want to extract corresponding dish_quant_bought for given dish name.. So for dish A it should return only 20, for B is 18 ,C is 10 & for D is 20

I am trying with below code but it doesn't work..

df_['dish_quant_bought']['dish_name'] == 'A'

Please help..

Answer 1

Unclear why you have duplicates but you can use a boolean condition to mask the df and return the col of interest:

In [256]:
df = df.drop_duplicates()
df

Out[256]:
    dish_quant_bought dish_name
0                  20         A
1                  18         B
6                  10         C
10                 20         D

In [257]:    
df.loc[df['dish_name']=='A', 'dish_quant_bought']

Out[257]:
0    20
Name: dish_quant_bought, dtype: int64

The mask on your original df:

In [259]:
df['dish_name']=='A'

Out[259]:
0      True
1     False
2      True
3     False
4      True
5     False
6     False
7     False
8     False
9     False
10    False
11    False
12    False
13    False
Name: dish_name, dtype: bool

You can get the unique value by calling unique after the filtering without calling drop_duplicates first:

In [297]:
df.loc[df['dish_name']=='A', 'dish_quant_bought'].unique()

Out[297]:
array([20], dtype=int64)

Answer 2

Well, if they have the same quantity value, you could get by using groupby and then mean on the selected column, it will produce the same value

df.groupby(["dish_name"])["dish_quant_bought"].mean()

then just select whatever name you want