I have a pandas data frame like so.
| fruit | year | price |
|---|---|---|
| apple | 2018 | 4 |
| apple | 2019 | 3 |
| apple | 2020 | 5 |
| plum | 2019 | 3 |
| plum | 2020 | 2 |
and I want to add column [last_year_price]
please help......
CodePudding user response：
For this, you can use groupby and shift:
df['last_year_price'] = df.groupby('fruit').shift(1).price
CodePudding user response：
Use:
df = df.merge(df.loc[df.groupby('fruit')['year'].idxmax(), ['fruit','price']].rename(columns={'price':'last_year_price'}), on='fruit', how='left')
print (df)
fruit year price last_year_price
0 apple 2018 4 5
1 apple 2019 3 5
2 apple 2020 5 5
3 plum 2019 3 2
4 plum 2020 2 2
CodePudding user response：
Firstly, create a list containing all the values that you want to have in your "last_year_price" column.
Secondly, simply type: df['last_year_price] = list_of_values and that should do the trick (replace df for whatever you named your dataframe).