Replace one column of a dataframe's values with another

Question

I am working with a table like this: unique IDs might have 1 entry or they might have many entries.

ID	Start	Stop	Days
101	1/1/2021	1/31/2021	270
101	2/1/2020	3/31/2020	270
102	1/1/2021	3/29/2020	208
102	3/30/2020	3/31/2020	208
103	1/1/2020	3/31/2020	210
104	1/1/2020	1/2/2020	130
104	1/3/2020	3/31/2020	130
105	1/1/2020	3/31/2020	160

I need to replace the number in the days column based off another dataframe, which gives a new Days number for each ID. This dataframe looks like:

ID	Days
101	290
102	250
103	215
104	180
105	175

How can I replace the Days column in the first dataframe with the Days information for each ID in the second dataframe? My end result should look thus:

ID	Start	Stop	Days
101	1/1/2021	1/31/2021	290
101	2/1/2020	3/31/2020	290
102	1/1/2021	3/29/2020	250
102	3/30/2020	3/31/2020	250
103	1/1/2020	3/31/2020	215
104	1/1/2020	1/2/2020	180
104	1/3/2020	3/31/2020	180
105	1/1/2020	3/31/2020	175

I tried df.update and converting the second dataframe to a dictionary and using.map but with no success. Any help would be greatly appreciated!

Thanks,

Sean

Answer 1

You could try this:

df_1.drop(columns=Days, inplace=True)
final = pd.merge(df1, df2, on='ID')
final.head()

Answer 2

df_1["Days"] = pd.merge(df_1, df_2, on="ID")["Days_y"]
print(df_1)

Prints:

    ID      Start       Stop  Days
0  101   1/1/2021  1/31/2021   290
1  101   2/1/2020  3/31/2020   290
2  102   1/1/2021  3/29/2020   250
3  102  3/30/2020  3/31/2020   250
4  103   1/1/2020  3/31/2020   215
5  104   1/1/2020   1/2/2020   180
6  104   1/3/2020  3/31/2020   180
7  105   1/1/2020  3/31/2020   175

Answer 3

You can also map() the first df's ID with the second df's Days :

df1.Days = df1.ID.map(df2.set_index('ID').Days)

#          Start       Stop  Days
# ID                             
# 101   1/1/2021  1/31/2021   290
# 101   2/1/2020  3/31/2020   290
# 102   1/1/2021  3/29/2020   250
# 102  3/30/2020  3/31/2020   250
# 103   1/1/2020  3/31/2020   215
# 104   1/1/2020   1/2/2020   180
# 104   1/3/2020  3/31/2020   180
# 105   1/1/2020  3/31/2020   175