공부 기록
ML(0805_day2) - 실습_캘리포니아 주택 가격 예측하기(데이터 전처리) 본문
캘리포니아 주택 가격 예측¶
In [1]:
import sklearn
In [2]:
sklearn.__version__
Out[2]:
'0.24.1'데이터 가져오기¶
In [3]:
import pandas as pd
housing = pd.read_csv('datasets/housing.csv')
데이터 훑어보기¶
In [4]:
housing.head()
Out[4]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -122.23 | 37.88 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 | NEAR BAY |
| 1 | -122.22 | 37.86 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 | NEAR BAY |
| 2 | -122.24 | 37.85 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 | NEAR BAY |
| 3 | -122.25 | 37.85 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 | 341300.0 | NEAR BAY |
| 4 | -122.25 | 37.85 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 | 342200.0 | NEAR BAY |
- longitude : 경도
- latitude : 위도
- housing_median_age : 주택 나이(중앙값)
- total_rooms : 전체 방 수
- total_bedrooms : 전체 침실 수
- population : 인구 수
- households : 세대 수
- median_income : 소득(중앙값)
- median_house_value : 주택 가치(중앙값)
- ocean_proximity : 바다 근접도
In [5]:
housing.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 longitude 20640 non-null float64
1 latitude 20640 non-null float64
2 housing_median_age 20640 non-null float64
3 total_rooms 20640 non-null float64
4 total_bedrooms 20433 non-null float64
5 population 20640 non-null float64
6 households 20640 non-null float64
7 median_income 20640 non-null float64
8 median_house_value 20640 non-null float64
9 ocean_proximity 20640 non-null object
dtypes: float64(9), object(1)
memory usage: 1.6+ MB
In [7]:
housing['ocean_proximity'].value_counts()
Out[7]:
<1H OCEAN 9136
INLAND 6551
NEAR OCEAN 2658
NEAR BAY 2290
ISLAND 5
Name: ocean_proximity, dtype: int64In [8]:
housing.describe()
Out[8]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | |
|---|---|---|---|---|---|---|---|---|---|
| count | 20640.000000 | 20640.000000 | 20640.000000 | 20640.000000 | 20433.000000 | 20640.000000 | 20640.000000 | 20640.000000 | 20640.000000 |
| mean | -119.569704 | 35.631861 | 28.639486 | 2635.763081 | 537.870553 | 1425.476744 | 499.539680 | 3.870671 | 206855.816909 |
| std | 2.003532 | 2.135952 | 12.585558 | 2181.615252 | 421.385070 | 1132.462122 | 382.329753 | 1.899822 | 115395.615874 |
| min | -124.350000 | 32.540000 | 1.000000 | 2.000000 | 1.000000 | 3.000000 | 1.000000 | 0.499900 | 14999.000000 |
| 25% | -121.800000 | 33.930000 | 18.000000 | 1447.750000 | 296.000000 | 787.000000 | 280.000000 | 2.563400 | 119600.000000 |
| 50% | -118.490000 | 34.260000 | 29.000000 | 2127.000000 | 435.000000 | 1166.000000 | 409.000000 | 3.534800 | 179700.000000 |
| 75% | -118.010000 | 37.710000 | 37.000000 | 3148.000000 | 647.000000 | 1725.000000 | 605.000000 | 4.743250 | 264725.000000 |
| max | -114.310000 | 41.950000 | 52.000000 | 39320.000000 | 6445.000000 | 35682.000000 | 6082.000000 | 15.000100 | 500001.000000 |
In [9]:
import matplotlib.pyplot as plt
In [10]:
housing.hist(bins=50, figsize=(20, 15))
plt.show()
테스트 세트 분리¶
In [11]:
from sklearn.model_selection import train_test_split
In [12]:
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
In [14]:
test_set.head()
Out[14]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|---|
| 20046 | -119.01 | 36.06 | 25.0 | 1505.0 | NaN | 1392.0 | 359.0 | 1.6812 | 47700.0 | INLAND |
| 3024 | -119.46 | 35.14 | 30.0 | 2943.0 | NaN | 1565.0 | 584.0 | 2.5313 | 45800.0 | INLAND |
| 15663 | -122.44 | 37.80 | 52.0 | 3830.0 | NaN | 1310.0 | 963.0 | 3.4801 | 500001.0 | NEAR BAY |
| 20484 | -118.72 | 34.28 | 17.0 | 3051.0 | NaN | 1705.0 | 495.0 | 5.7376 | 218600.0 | <1H OCEAN |
| 9814 | -121.93 | 36.62 | 34.0 | 2351.0 | NaN | 1063.0 | 428.0 | 3.7250 | 278000.0 | NEAR OCEAN |
In [15]:
housing['median_income'].hist()
Out[15]:
<AxesSubplot:>
In [19]:
import numpy as np
housing['income_cat'] = pd.cut(housing['median_income'], bins=[0., 1.5, 3., 4.5, 6., np.inf], labels=[1, 2, 3, 4, 5])
In [20]:
housing['income_cat'].value_counts()
Out[20]:
3 7236
2 6581
4 3639
5 2362
1 822
Name: income_cat, dtype: int64In [22]:
housing['income_cat'].hist()
Out[22]:
<AxesSubplot:>
In [27]:
from sklearn.model_selection import StratifiedShuffleSplit
split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
for train_index, test_index in split.split(housing, housing['income_cat']):
strat_train_set = housing.loc[train_index]
strat_test_set = housing.loc[test_index]
- income_cat의 비율¶
In [31]:
housing['income_cat'].value_counts() / len(housing)
Out[31]:
3 0.350581
2 0.318847
4 0.176308
5 0.114438
1 0.039826
Name: income_cat, dtype: float641) 계층 정보(median_income)가 반영되어 샘플링된 테스트 데이터¶
In [30]:
strat_test_set['income_cat'].value_counts() / len(strat_test_set)
Out[30]:
3 0.350533
2 0.318798
4 0.176357
5 0.114583
1 0.039729
Name: income_cat, dtype: float642) 무작위 추출된 테스트 데이터¶
In [32]:
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
test_set['income_cat'].value_counts() / len(test_set)
Out[32]:
3 0.358527
2 0.324370
4 0.167393
5 0.109496
1 0.040213
Name: income_cat, dtype: float64==> 계층 정보를 반영한 것이 무작위로 샘플링한 것보다 비율을 잘 반영해줌¶
option 2¶
In [34]:
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42, stratify= housing['income_cat'])
In [35]:
test_set['income_cat'].value_counts() / len(test_set)
Out[35]:
3 0.350533
2 0.318798
4 0.176357
5 0.114583
1 0.039729
Name: income_cat, dtype: float64- 비율에 맞게 나눈 후에는 income_cat 컬럼 사용할 일 없기 때문에 지우기¶
In [36]:
strat_train_set.drop('income_cat', axis=1, inplace=True)
strat_test_set.drop('income_cat', axis=1, inplace=True)
In [37]:
strat_train_set.head()
Out[37]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|---|
| 17606 | -121.89 | 37.29 | 38.0 | 1568.0 | 351.0 | 710.0 | 339.0 | 2.7042 | 286600.0 | <1H OCEAN |
| 18632 | -121.93 | 37.05 | 14.0 | 679.0 | 108.0 | 306.0 | 113.0 | 6.4214 | 340600.0 | <1H OCEAN |
| 14650 | -117.20 | 32.77 | 31.0 | 1952.0 | 471.0 | 936.0 | 462.0 | 2.8621 | 196900.0 | NEAR OCEAN |
| 3230 | -119.61 | 36.31 | 25.0 | 1847.0 | 371.0 | 1460.0 | 353.0 | 1.8839 | 46300.0 | INLAND |
| 3555 | -118.59 | 34.23 | 17.0 | 6592.0 | 1525.0 | 4459.0 | 1463.0 | 3.0347 | 254500.0 | <1H OCEAN |
지리적 데이터 시각화¶
In [38]:
housing.plot(kind='scatter', x='longitude', y='latitude')
Out[38]:
<AxesSubplot:xlabel='longitude', ylabel='latitude'>
In [39]:
housing.plot(kind='scatter', x='longitude', y='latitude', alpha=0.1)
Out[39]:
<AxesSubplot:xlabel='longitude', ylabel='latitude'>
- s : 점이 population에 따라 크기 다름
- c : 색이 median_house_value에 따라 다름
In [44]:
housing.plot(kind='scatter', x='longitude', y='latitude', alpha=0.4,
s=housing['population']/100, label='population', figsize=(10, 7),
c='median_house_value', cmap=plt.get_cmap('jet'), colorbar=True,
sharex=False)
Out[44]:
<AxesSubplot:xlabel='longitude', ylabel='latitude'>
==> 해안가 주택 가격이 높음, 밀집지역이 주택 가격이 높음¶
상관관계 조사¶
In [46]:
corr_matrix = housing.corr()
corr_matrix
Out[46]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | |
|---|---|---|---|---|---|---|---|---|---|
| longitude | 1.000000 | -0.924664 | -0.108197 | 0.044568 | 0.069608 | 0.099773 | 0.055310 | -0.015176 | -0.045967 |
| latitude | -0.924664 | 1.000000 | 0.011173 | -0.036100 | -0.066983 | -0.108785 | -0.071035 | -0.079809 | -0.144160 |
| housing_median_age | -0.108197 | 0.011173 | 1.000000 | -0.361262 | -0.320451 | -0.296244 | -0.302916 | -0.119034 | 0.105623 |
| total_rooms | 0.044568 | -0.036100 | -0.361262 | 1.000000 | 0.930380 | 0.857126 | 0.918484 | 0.198050 | 0.134153 |
| total_bedrooms | 0.069608 | -0.066983 | -0.320451 | 0.930380 | 1.000000 | 0.877747 | 0.979728 | -0.007723 | 0.049686 |
| population | 0.099773 | -0.108785 | -0.296244 | 0.857126 | 0.877747 | 1.000000 | 0.907222 | 0.004834 | -0.024650 |
| households | 0.055310 | -0.071035 | -0.302916 | 0.918484 | 0.979728 | 0.907222 | 1.000000 | 0.013033 | 0.065843 |
| median_income | -0.015176 | -0.079809 | -0.119034 | 0.198050 | -0.007723 | 0.004834 | 0.013033 | 1.000000 | 0.688075 |
| median_house_value | -0.045967 | -0.144160 | 0.105623 | 0.134153 | 0.049686 | -0.024650 | 0.065843 | 0.688075 | 1.000000 |
In [47]:
corr_matrix['median_house_value'].sort_values(ascending=False)
Out[47]:
median_house_value 1.000000
median_income 0.688075
total_rooms 0.134153
housing_median_age 0.105623
households 0.065843
total_bedrooms 0.049686
population -0.024650
longitude -0.045967
latitude -0.144160
Name: median_house_value, dtype: float64==> 소특이 높을수록 주택 가격이 높다
피어슨의 상관 계수(위키백과):
==> 선형관계에 있는 것들만 값이 나옴
In [45]:
from pandas.plotting import scatter_matrix
In [50]:
attributes = ['median_house_value', 'median_income', 'total_rooms', 'housing_median_age']
scatter_matrix(housing[attributes], figsize=(12, 8))
Out[50]:
array([[<AxesSubplot:xlabel='median_house_value', ylabel='median_house_value'>,
<AxesSubplot:xlabel='median_income', ylabel='median_house_value'>,
<AxesSubplot:xlabel='total_rooms', ylabel='median_house_value'>,
<AxesSubplot:xlabel='housing_median_age', ylabel='median_house_value'>],
[<AxesSubplot:xlabel='median_house_value', ylabel='median_income'>,
<AxesSubplot:xlabel='median_income', ylabel='median_income'>,
<AxesSubplot:xlabel='total_rooms', ylabel='median_income'>,
<AxesSubplot:xlabel='housing_median_age', ylabel='median_income'>],
[<AxesSubplot:xlabel='median_house_value', ylabel='total_rooms'>,
<AxesSubplot:xlabel='median_income', ylabel='total_rooms'>,
<AxesSubplot:xlabel='total_rooms', ylabel='total_rooms'>,
<AxesSubplot:xlabel='housing_median_age', ylabel='total_rooms'>],
[<AxesSubplot:xlabel='median_house_value', ylabel='housing_median_age'>,
<AxesSubplot:xlabel='median_income', ylabel='housing_median_age'>,
<AxesSubplot:xlabel='total_rooms', ylabel='housing_median_age'>,
<AxesSubplot:xlabel='housing_median_age', ylabel='housing_median_age'>]],
dtype=object)
In [54]:
housing.plot(kind='scatter', x='median_income', y='median_house_value', alpha=0.1)
plt.axis([0, 16, 0, 550000])
Out[54]:
(0.0, 16.0, 0.0, 550000.0)
특성 조합으로 실험¶
In [58]:
housing.head()
Out[58]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | income_cat | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -122.23 | 37.88 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 | NEAR BAY | 5 |
| 1 | -122.22 | 37.86 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 | NEAR BAY | 5 |
| 2 | -122.24 | 37.85 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 | NEAR BAY | 5 |
| 3 | -122.25 | 37.85 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 | 341300.0 | NEAR BAY | 4 |
| 4 | -122.25 | 37.85 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 | 342200.0 | NEAR BAY | 3 |
<특성 추출>¶
- 가구 당 전체 방 수¶
In [62]:
housing['rooms_per_household'] = housing['total_rooms'] / housing['households']
- 전체 방 당 침실의 수¶
In [63]:
housing['bedrooms_per_room'] = housing['total_bedrooms'] / housing['total_rooms']
- 가구 당 인구 수¶
In [64]:
housing['population_per_household'] = housing['population'] / housing['households']
In [65]:
corr_matrix = housing.corr()
corr_matrix['median_house_value'].sort_values(ascending=False)
Out[65]:
median_house_value 1.000000
median_income 0.688075
rooms_per_household 0.151948
total_rooms 0.134153
housing_median_age 0.105623
households 0.065843
total_bedrooms 0.049686
population_per_household -0.023737
population -0.024650
longitude -0.045967
latitude -0.144160
bedrooms_per_room -0.255880
Name: median_house_value, dtype: float64==> rooms_per_household, population_per_household는 total_rooms, population보다 median_house_value와의 상관 관계 더 높다,
데이터 전처리¶
In [68]:
print(strat_test_set.size, strat_train_set.size)
41280 165120
In [69]:
housing = strat_train_set.drop("median_house_value", axis=1)
housing_label = strat_train_set['median_house_value'].copy()
In [70]:
housing.shape
Out[70]:
(16512, 9)In [71]:
housing_label.shape
Out[71]:
(16512,)In [72]:
housing.isnull().sum()
Out[72]:
longitude 0
latitude 0
housing_median_age 0
total_rooms 0
total_bedrooms 158
population 0
households 0
median_income 0
ocean_proximity 0
dtype: int64In [75]:
sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()
In [76]:
sample_incomplete_rows
Out[76]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|
| 4629 | -118.30 | 34.07 | 18.0 | 3759.0 | NaN | 3296.0 | 1462.0 | 2.2708 | <1H OCEAN |
| 6068 | -117.86 | 34.01 | 16.0 | 4632.0 | NaN | 3038.0 | 727.0 | 5.1762 | <1H OCEAN |
| 17923 | -121.97 | 37.35 | 30.0 | 1955.0 | NaN | 999.0 | 386.0 | 4.6328 | <1H OCEAN |
| 13656 | -117.30 | 34.05 | 6.0 | 2155.0 | NaN | 1039.0 | 391.0 | 1.6675 | INLAND |
| 19252 | -122.79 | 38.48 | 7.0 | 6837.0 | NaN | 3468.0 | 1405.0 | 3.1662 | <1H OCEAN |
option 1 : 해당 샘플(row) 삭제¶
In [78]:
sample_incomplete_rows.dropna(subset=['total_bedrooms'])
Out[78]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | ocean_proximity |
|---|
option 2 : 특성 자체 삭제¶
In [86]:
sample_incomplete_rows.drop("total_bedrooms", axis=1)
Out[86]:
| longitude | latitude | housing_median_age | total_rooms | population | households | median_income | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|
| 4629 | -118.30 | 34.07 | 18.0 | 3759.0 | 3296.0 | 1462.0 | 2.2708 | <1H OCEAN |
| 6068 | -117.86 | 34.01 | 16.0 | 4632.0 | 3038.0 | 727.0 | 5.1762 | <1H OCEAN |
| 17923 | -121.97 | 37.35 | 30.0 | 1955.0 | 999.0 | 386.0 | 4.6328 | <1H OCEAN |
| 13656 | -117.30 | 34.05 | 6.0 | 2155.0 | 1039.0 | 391.0 | 1.6675 | INLAND |
| 19252 | -122.79 | 38.48 | 7.0 | 6837.0 | 3468.0 | 1405.0 | 3.1662 | <1H OCEAN |
option 3 : 특정 값으로 대체¶
In [82]:
median = housing['total_bedrooms'].median()
median
Out[82]:
433.0In [83]:
sample_incomplete_rows
Out[83]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|
| 4629 | -118.30 | 34.07 | 18.0 | 3759.0 | NaN | 3296.0 | 1462.0 | 2.2708 | <1H OCEAN |
| 6068 | -117.86 | 34.01 | 16.0 | 4632.0 | NaN | 3038.0 | 727.0 | 5.1762 | <1H OCEAN |
| 17923 | -121.97 | 37.35 | 30.0 | 1955.0 | NaN | 999.0 | 386.0 | 4.6328 | <1H OCEAN |
| 13656 | -117.30 | 34.05 | 6.0 | 2155.0 | NaN | 1039.0 | 391.0 | 1.6675 | INLAND |
| 19252 | -122.79 | 38.48 | 7.0 | 6837.0 | NaN | 3468.0 | 1405.0 | 3.1662 | <1H OCEAN |
In [85]:
sample_incomplete_rows['total_bedrooms'].fillna(median, inplace=True)
sample_incomplete_rows
Out[85]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|
| 4629 | -118.30 | 34.07 | 18.0 | 3759.0 | 433.0 | 3296.0 | 1462.0 | 2.2708 | <1H OCEAN |
| 6068 | -117.86 | 34.01 | 16.0 | 4632.0 | 433.0 | 3038.0 | 727.0 | 5.1762 | <1H OCEAN |
| 17923 | -121.97 | 37.35 | 30.0 | 1955.0 | 433.0 | 999.0 | 386.0 | 4.6328 | <1H OCEAN |
| 13656 | -117.30 | 34.05 | 6.0 | 2155.0 | 433.0 | 1039.0 | 391.0 | 1.6675 | INLAND |
| 19252 | -122.79 | 38.48 | 7.0 | 6837.0 | 433.0 | 3468.0 | 1405.0 | 3.1662 | <1H OCEAN |
- 변환기¶
In [87]:
from sklearn.impute import SimpleImputer
In [88]:
imputer = SimpleImputer(strategy='median')
- ocean_proximity는 수치값이 아니므로 잠시 제외¶
In [89]:
housing_num = housing.drop('ocean_proximity', axis=1)
housing_num
Out[89]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | |
|---|---|---|---|---|---|---|---|---|
| 17606 | -121.89 | 37.29 | 38.0 | 1568.0 | 351.0 | 710.0 | 339.0 | 2.7042 |
| 18632 | -121.93 | 37.05 | 14.0 | 679.0 | 108.0 | 306.0 | 113.0 | 6.4214 |
| 14650 | -117.20 | 32.77 | 31.0 | 1952.0 | 471.0 | 936.0 | 462.0 | 2.8621 |
| 3230 | -119.61 | 36.31 | 25.0 | 1847.0 | 371.0 | 1460.0 | 353.0 | 1.8839 |
| 3555 | -118.59 | 34.23 | 17.0 | 6592.0 | 1525.0 | 4459.0 | 1463.0 | 3.0347 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6563 | -118.13 | 34.20 | 46.0 | 1271.0 | 236.0 | 573.0 | 210.0 | 4.9312 |
| 12053 | -117.56 | 33.88 | 40.0 | 1196.0 | 294.0 | 1052.0 | 258.0 | 2.0682 |
| 13908 | -116.40 | 34.09 | 9.0 | 4855.0 | 872.0 | 2098.0 | 765.0 | 3.2723 |
| 11159 | -118.01 | 33.82 | 31.0 | 1960.0 | 380.0 | 1356.0 | 356.0 | 4.0625 |
| 15775 | -122.45 | 37.77 | 52.0 | 3095.0 | 682.0 | 1269.0 | 639.0 | 3.5750 |
16512 rows × 8 columns
- housing_num의 속성들의 중앙값을 다 구해줌¶
In [90]:
imputer.fit(housing_num)
Out[90]:
SimpleImputer(strategy='median')In [91]:
imputer.statistics_
Out[91]:
array([-118.51 , 34.26 , 29. , 2119.5 , 433. , 1164. ,
408. , 3.5409])In [92]:
housing_num.median() #pandas의 산술통계 함수
Out[92]:
longitude -118.5100
latitude 34.2600
housing_median_age 29.0000
total_rooms 2119.5000
total_bedrooms 433.0000
population 1164.0000
households 408.0000
median_income 3.5409
dtype: float64- 모든 결측치에 대해서 중앙값으로 대체시킴¶
In [93]:
X = imputer.transform(housing_num)
In [94]:
X
Out[94]:
array([[-121.89 , 37.29 , 38. , ..., 710. , 339. ,
2.7042],
[-121.93 , 37.05 , 14. , ..., 306. , 113. ,
6.4214],
[-117.2 , 32.77 , 31. , ..., 936. , 462. ,
2.8621],
...,
[-116.4 , 34.09 , 9. , ..., 2098. , 765. ,
3.2723],
[-118.01 , 33.82 , 31. , ..., 1356. , 356. ,
4.0625],
[-122.45 , 37.77 , 52. , ..., 1269. , 639. ,
3.575 ]])In [95]:
housing_num.columns
Out[95]:
Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',
'total_bedrooms', 'population', 'households', 'median_income'],
dtype='object')In [96]:
housing_tr = pd.DataFrame(X, columns=housing_num.columns, index=housing_num.index)
In [97]:
sample_incomplete_rows.index
Out[97]:
Int64Index([4629, 6068, 17923, 13656, 19252], dtype='int64')In [98]:
housing_tr.loc[sample_incomplete_rows.index]
Out[98]:
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | |
|---|---|---|---|---|---|---|---|---|
| 4629 | -118.30 | 34.07 | 18.0 | 3759.0 | 433.0 | 3296.0 | 1462.0 | 2.2708 |
| 6068 | -117.86 | 34.01 | 16.0 | 4632.0 | 433.0 | 3038.0 | 727.0 | 5.1762 |
| 17923 | -121.97 | 37.35 | 30.0 | 1955.0 | 433.0 | 999.0 | 386.0 | 4.6328 |
| 13656 | -117.30 | 34.05 | 6.0 | 2155.0 | 433.0 | 1039.0 | 391.0 | 1.6675 |
| 19252 | -122.79 | 38.48 | 7.0 | 6837.0 | 433.0 | 3468.0 | 1405.0 | 3.1662 |
In [99]:
imputer.strategy
Out[99]:
'median'
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