[FIXED] Combining dataframes to replace missing values

Issue

I am trying to merge two dataframes together to replace possible missing values. For example:

df1:
Fruit     Price
Apple        5
Banana       7
Orange       4

df2: 
Fruit     Price
Pear         3

result:
Fruit     Price
Apple        5
Banana       7
Orange       4
Pear         3

I've used the concat method to do this and it works, however when both dataframes have the same column instead of having both duplicate values I want to use the data from the first df and ignore the second df. For example:

df1:    
Fruit     Price
Apple        5
Banana       7
Orange       4
Pear         6

df2: 
Fruit     Price
Pear         3

result:
Fruit     Price
Apple        5
Banana       7
Orange       4
Pear         6

How can I do this so it works on both examples?

Solution

You could use concat and chain drop_duplicates on your Fruit column. Just always make sure to put the dataframe you want the values to keep in case of duplicated as the first parameter in concat:

# Case 1
>>> pd.concat([df1b,df2]).drop_duplicates('Fruit')

    Fruit  Price
0   Apple      5
1  Banana      7
2  Orange      4
0    Pear      3

# Case 2
>>> pd.concat([df1,df2]).drop_duplicates('Fruit')

   Fruit  Price
0   Apple      5
1  Banana      7
2  Orange      4
3    Pear      6

---

Setup

df1 = pd.DataFrame({'Fruit':['Apple','Banana','Orange','Pear'],
                   'Price':[5,7,4,6]})
df1b = pd.DataFrame({'Fruit':['Apple','Banana','Orange'],
                   'Price':[5,7,4]})
df2 = pd.DataFrame({'Fruit':['Pear'],
                   'Price':[3]})

Answered By - sophocles