I'm trying to get the unique available value for each site. The original pandas dataframe is with three columns:
Site | Available | Capacity |
---|---|---|
A | 7 | 20 |
A | 7 | 20 |
A | 8 | 20 |
B | 15 | 35 |
B | 15 | 35 |
C | 12 | 25 |
C | 12 | 25 |
C | 11 | 25 |
and I want to get the unique available of each site. The desired table is like below:
Site | Unique Available |
---|---|
A | 7 |
8 | |
B | 15 |
C | 12 |
11 |
You can get the lists of unique available per site with GroupBy.unique()
>>> df.groupby('Site')['Available'].unique()
Site
A [7, 8]
B [15]
C [12, 11]
Name: Available, dtype: object
Then with explode()
you can expand these lists and with reset_index()
get the index back to a column:
>>> df.groupby('Site')['Available'].unique().explode().reset_index()
Site Available
0 A 7
1 A 8
2 B 15
3 C 12
4 C 11
Otherwise simply get both columns and remove duplicates:
>>> df[['Site', 'Available']].drop_duplicates()
Site Available
0 A 7
2 A 8
3 B 15
5 C 12
7 C 11
Approach with: GroupBy.apply()
+ Series.drop_duplicates()
(df.groupby('Site')['Available']
.apply(lambda s: s.drop_duplicates())
.reset_index(level=1, drop=True)
.reset_index(name='Unique Available')
)
Result:
Site Unique Available
0 A 7
1 A 8
2 B 15
3 C 12
4 C 11
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