Pandas：如何在 lambda 公式中使用 (df.groupby)

Question

The example below:

import pandas as pd
list1 = ['a','a','a','b','b','b','b','c','c','c']
list2 = range(len(list1))
df = pd.DataFrame(zip(list1, list2), columns=  ['Item','Value'])
df

gives:

required: GroupFirstValue column as shown below.

The idea is to use a lambda formula to get the 'first' value for each group..for example "a"'s first value is 0, "b"'s first value is 3, "c"'s first value is 7. That's why those numbers appear in the GroupFirstValue column.

Note: I know that I can do this on 2 steps...one is the original df and the second is a grouped by df and then merge them together. The idea is to see if this can be done more efficiently in a single step. Many thanks in advance!

Answer 1

groupby and use first

df.groupby('Item')['Value'].first()

or you can use transform and assign to a new column in your frame

df['new_col'] = df.groupby('Item')['Value'].transform('first')

Answer 2

Use mask and duplicated

df['GroupFirstValue'] = df.Value.mask(df.Item.duplicated())

Out[109]:
  Item  Value  GroupFirstValue
0    a      0              0.0
1    a      1              NaN
2    a      2              NaN
3    b      3              3.0
4    b      4              NaN
5    b      5              NaN
6    b      6              NaN
7    c      7              7.0
8    c      8              NaN
9    c      9              NaN