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O 그룹별로 집계하기
# 실습을 위해 데이터를 불러옵니다. !git clone https://github.com/taehojo/data.git # 15장의 주택 가격 예측 데이터를 불러옵니다. = pd.read_csv("../data/house_train.csv")
실행 결과
|
|
Id |
MSSubClass |
LotFrontage |
LotArea |
... |
YrSold |
SaleCondition |
SalePrice |
|
0 |
1 |
60 |
65.0 |
8450 |
... |
2008 |
Normal |
208500 |
|
1 |
2 |
20 |
80.0 |
9600 |
... |
2007 |
Normal |
181500 |
|
2 |
3 |
60 |
68.0 |
11250 |
... |
2008 |
Normal |
223500 |
|
3 |
4 |
70 |
60.0 |
9550 |
... |
2006 |
Abnormal |
140000 |
|
4 |
5 |
60 |
84.0 |
14260 |
... |
2008 |
Normal |
250000 |
|
... |
... |
... |
... |
... |
... |
... |
... |
... |
|
1455 |
1456 |
60 |
62.0 |
7917 |
... |
2007 |
Normal |
175000 |
|
1456 |
1457 |
20 |
85.0 |
13175 |
... |
2010 |
Normal |
210000 |
|
1457 |
1458 |
70 |
66.0 |
9042 |
... |
2010 |
Normal |
266500 |
|
1458 |
1459 |
20 |
68.0 |
9717 |
... |
2010 |
Normal |
142125 |
|
1459 |
1460 |
20 |
75.0 |
9937 |
... |
2008 |
Normal |
147500 |
1460 rows × 81 columns