Overview

Dataset statistics

Number of variables15
Number of observations471
Missing cells1868
Missing cells (%)26.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.5 KiB
Average record size in memory127.3 B

Variable types

Text8
Numeric7

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21236/F/1/datasetView.do

Alerts

휘발유(유연) is highly overall correlated with 하이브리드 (경유+전기) and 4 other fieldsHigh correlation
CNG is highly overall correlated with 하이브리드 (CNG+전기)High correlation
하이브리드 (경유+전기) is highly overall correlated with 휘발유(유연) and 3 other fieldsHigh correlation
하이브리드 (LPG+전기) is highly overall correlated with 휘발유(유연) and 4 other fieldsHigh correlation
하이브리드 (CNG+전기) is highly overall correlated with 휘발유(유연) and 3 other fieldsHigh correlation
수소 is highly overall correlated with 휘발유(유연) and 3 other fieldsHigh correlation
기타연료 is highly overall correlated with 휘발유(유연) and 4 other fieldsHigh correlation
엘피지 has 23 (4.9%) missing valuesMissing
전기 has 69 (14.6%) missing valuesMissing
휘발유(유연) has 251 (53.3%) missing valuesMissing
휘발유(무연) has 5 (1.1%) missing valuesMissing
CNG has 266 (56.5%) missing valuesMissing
하이브리드 (휘발유+전기) has 20 (4.2%) missing valuesMissing
하이브리드 (경유+전기) has 220 (46.7%) missing valuesMissing
하이브리드 (LPG+전기) has 226 (48.0%) missing valuesMissing
하이브리드 (CNG+전기) has 464 (98.5%) missing valuesMissing
수소 has 189 (40.1%) missing valuesMissing
기타연료 has 128 (27.2%) missing valuesMissing
사용본거지법정동명 has unique valuesUnique

Reproduction

Analysis started2024-05-11 06:16:03.780470
Analysis finished2024-05-11 06:16:14.224747
Duration10.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사용본거지법정동명
Text

UNIQUE 

Distinct471
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2024-05-11T15:16:14.392354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length13.583864
Min length11

Characters and Unicode

Total characters6398
Distinct characters220
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique471 ?
Unique (%)100.0%

Sample

1st row서울특별시 종로구 청운동
2nd row서울특별시 종로구 신교동
3rd row서울특별시 종로구 궁정동
4th row서울특별시 종로구 효자동
5th row서울특별시 종로구 창성동
ValueCountFrequency (%)
서울특별시 471
33.3%
종로구 87
 
6.2%
중구 74
 
5.2%
성북구 39
 
2.8%
용산구 37
 
2.6%
영등포구 34
 
2.4%
마포구 26
 
1.8%
서대문구 20
 
1.4%
성동구 17
 
1.2%
강남구 16
 
1.1%
Other values (485) 592
41.9%
2024-05-11T15:16:14.928448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
942
14.7%
서 522
 
8.2%
동 489
 
7.6%
구 487
 
7.6%
시 472
 
7.4%
특 471
 
7.4%
별 471
 
7.4%
울 471
 
7.4%
가 145
 
2.3%
로 138
 
2.2%
Other values (210) 1790
28.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5317
83.1%
Space Separator 942
 
14.7%
Decimal Number 139
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 522
 
9.8%
동 489
 
9.2%
구 487
 
9.2%
시 472
 
8.9%
특 471
 
8.9%
별 471
 
8.9%
울 471
 
8.9%
가 145
 
2.7%
로 138
 
2.6%
종 95
 
1.8%
Other values (201) 1556
29.3%
Decimal Number
ValueCountFrequency (%)
1 37
26.6%
2 34
24.5%
3 23
16.5%
4 17
12.2%
5 14
 
10.1%
6 9
 
6.5%
7 4
 
2.9%
8 1
 
0.7%
Space Separator
ValueCountFrequency (%)
942
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5317
83.1%
Common 1081
 
16.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 522
 
9.8%
동 489
 
9.2%
구 487
 
9.2%
시 472
 
8.9%
특 471
 
8.9%
별 471
 
8.9%
울 471
 
8.9%
가 145
 
2.7%
로 138
 
2.6%
종 95
 
1.8%
Other values (201) 1556
29.3%
Common
ValueCountFrequency (%)
942
87.1%
1 37
 
3.4%
2 34
 
3.1%
3 23
 
2.1%
4 17
 
1.6%
5 14
 
1.3%
6 9
 
0.8%
7 4
 
0.4%
8 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5317
83.1%
ASCII 1081
 
16.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
942
87.1%
1 37
 
3.4%
2 34
 
3.1%
3 23
 
2.1%
4 17
 
1.6%
5 14
 
1.3%
6 9
 
0.8%
7 4
 
0.4%
8 1
 
0.1%
Hangul
ValueCountFrequency (%)
서 522
 
9.8%
동 489
 
9.2%
구 487
 
9.2%
시 472
 
8.9%
특 471
 
8.9%
별 471
 
8.9%
울 471
 
8.9%
가 145
 
2.7%
로 138
 
2.6%
종 95
 
1.8%
Other values (201) 1556
29.3%

동별 총 대수
Text

Distinct443
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2024-05-11T15:16:15.573942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.2314225
Min length2

Characters and Unicode

Total characters2464
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)88.7%

Sample

1st row768
2nd row362
3rd row40
4th row185
5th row162
ValueCountFrequency (%)
50 4
 
0.8%
232 3
 
0.6%
754 2
 
0.4%
499 2
 
0.4%
2,789 2
 
0.4%
231 2
 
0.4%
3 2
 
0.4%
155 2
 
0.4%
196 2
 
0.4%
271 2
 
0.4%
Other values (433) 448
95.1%
2024-05-11T15:16:16.539992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
471
19.1%
1 269
10.9%
, 260
10.6%
2 221
9.0%
4 194
7.9%
3 177
 
7.2%
6 171
 
6.9%
5 149
 
6.0%
9 146
 
5.9%
0 143
 
5.8%
Other values (2) 263
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1733
70.3%
Space Separator 471
 
19.1%
Other Punctuation 260
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 269
15.5%
2 221
12.8%
4 194
11.2%
3 177
10.2%
6 171
9.9%
5 149
8.6%
9 146
8.4%
0 143
8.3%
7 137
7.9%
8 126
7.3%
Space Separator
ValueCountFrequency (%)
471
100.0%
Other Punctuation
ValueCountFrequency (%)
, 260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2464
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
471
19.1%
1 269
10.9%
, 260
10.6%
2 221
9.0%
4 194
7.9%
3 177
 
7.2%
6 171
 
6.9%
5 149
 
6.0%
9 146
 
5.9%
0 143
 
5.8%
Other values (2) 263
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
471
19.1%
1 269
10.9%
, 260
10.6%
2 221
9.0%
4 194
7.9%
3 177
 
7.2%
6 171
 
6.9%
5 149
 
6.0%
9 146
 
5.9%
0 143
 
5.8%
Other values (2) 263
10.7%

휘발유
Text

Distinct370
Distinct (%)79.2%
Missing4
Missing (%)0.8%
Memory size3.8 KiB
2024-05-11T15:16:17.337025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.3404711
Min length2

Characters and Unicode

Total characters2027
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)65.7%

Sample

1st row212
2nd row92
3rd row6
4th row60
5th row39
ValueCountFrequency (%)
42 5
 
1.1%
6 4
 
0.9%
2 4
 
0.9%
15 4
 
0.9%
3 4
 
0.9%
61 4
 
0.9%
48 4
 
0.9%
19 4
 
0.9%
53 4
 
0.9%
65 4
 
0.9%
Other values (360) 426
91.2%
2024-05-11T15:16:18.703718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
467
23.0%
1 244
12.0%
2 194
9.6%
, 160
 
7.9%
3 146
 
7.2%
4 132
 
6.5%
5 127
 
6.3%
7 124
 
6.1%
6 117
 
5.8%
8 114
 
5.6%
Other values (2) 202
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
69.1%
Space Separator 467
 
23.0%
Other Punctuation 160
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 244
17.4%
2 194
13.9%
3 146
10.4%
4 132
9.4%
5 127
9.1%
7 124
8.9%
6 117
8.4%
8 114
8.1%
0 106
7.6%
9 96
 
6.9%
Space Separator
ValueCountFrequency (%)
467
100.0%
Other Punctuation
ValueCountFrequency (%)
, 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
467
23.0%
1 244
12.0%
2 194
9.6%
, 160
 
7.9%
3 146
 
7.2%
4 132
 
6.5%
5 127
 
6.3%
7 124
 
6.1%
6 117
 
5.8%
8 114
 
5.6%
Other values (2) 202
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
467
23.0%
1 244
12.0%
2 194
9.6%
, 160
 
7.9%
3 146
 
7.2%
4 132
 
6.5%
5 127
 
6.3%
7 124
 
6.1%
6 117
 
5.8%
8 114
 
5.6%
Other values (2) 202
10.0%

경유
Text

Distinct406
Distinct (%)86.8%
Missing3
Missing (%)0.6%
Memory size3.8 KiB
2024-05-11T15:16:19.943469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.6410256
Min length2

Characters and Unicode

Total characters2172
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique358 ?
Unique (%)76.5%

Sample

1st row241
2nd row116
3rd row12
4th row63
5th row62
ValueCountFrequency (%)
34 4
 
0.9%
1 4
 
0.9%
96 4
 
0.9%
73 3
 
0.6%
88 3
 
0.6%
229 3
 
0.6%
19 3
 
0.6%
86 3
 
0.6%
146 3
 
0.6%
2 3
 
0.6%
Other values (396) 435
92.9%
2024-05-11T15:16:20.820285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
468
21.5%
1 240
11.0%
, 196
9.0%
4 163
 
7.5%
2 163
 
7.5%
3 150
 
6.9%
6 148
 
6.8%
7 134
 
6.2%
9 133
 
6.1%
5 133
 
6.1%
Other values (2) 244
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1508
69.4%
Space Separator 468
 
21.5%
Other Punctuation 196
 
9.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 240
15.9%
4 163
10.8%
2 163
10.8%
3 150
9.9%
6 148
9.8%
7 134
8.9%
9 133
8.8%
5 133
8.8%
8 124
8.2%
0 120
8.0%
Space Separator
ValueCountFrequency (%)
468
100.0%
Other Punctuation
ValueCountFrequency (%)
, 196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2172
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
468
21.5%
1 240
11.0%
, 196
9.0%
4 163
 
7.5%
2 163
 
7.5%
3 150
 
6.9%
6 148
 
6.8%
7 134
 
6.2%
9 133
 
6.1%
5 133
 
6.1%
Other values (2) 244
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
468
21.5%
1 240
11.0%
, 196
9.0%
4 163
 
7.5%
2 163
 
7.5%
3 150
 
6.9%
6 148
 
6.8%
7 134
 
6.2%
9 133
 
6.1%
5 133
 
6.1%
Other values (2) 244
11.2%

엘피지
Text

MISSING 

Distinct280
Distinct (%)62.5%
Missing23
Missing (%)4.9%
Memory size3.8 KiB
2024-05-11T15:16:21.531430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.703125
Min length2

Characters and Unicode

Total characters1659
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique224 ?
Unique (%)50.0%

Sample

1st row22
2nd row25
3rd row2
4th row4
5th row2
ValueCountFrequency (%)
1 14
 
3.1%
3 12
 
2.7%
5 9
 
2.0%
11 8
 
1.8%
7 8
 
1.8%
12 7
 
1.6%
22 7
 
1.6%
9 7
 
1.6%
13 7
 
1.6%
2 7
 
1.6%
Other values (270) 362
80.8%
2024-05-11T15:16:22.503012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
448
27.0%
1 225
13.6%
2 158
 
9.5%
3 115
 
6.9%
4 112
 
6.8%
5 109
 
6.6%
6 100
 
6.0%
7 99
 
6.0%
, 85
 
5.1%
9 75
 
4.5%
Other values (2) 133
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1126
67.9%
Space Separator 448
 
27.0%
Other Punctuation 85
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 225
20.0%
2 158
14.0%
3 115
10.2%
4 112
9.9%
5 109
9.7%
6 100
8.9%
7 99
8.8%
9 75
 
6.7%
0 67
 
6.0%
8 66
 
5.9%
Space Separator
ValueCountFrequency (%)
448
100.0%
Other Punctuation
ValueCountFrequency (%)
, 85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1659
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
448
27.0%
1 225
13.6%
2 158
 
9.5%
3 115
 
6.9%
4 112
 
6.8%
5 109
 
6.6%
6 100
 
6.0%
7 99
 
6.0%
, 85
 
5.1%
9 75
 
4.5%
Other values (2) 133
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
448
27.0%
1 225
13.6%
2 158
 
9.5%
3 115
 
6.9%
4 112
 
6.8%
5 109
 
6.6%
6 100
 
6.0%
7 99
 
6.0%
, 85
 
5.1%
9 75
 
4.5%
Other values (2) 133
 
8.0%

전기
Text

MISSING 

Distinct139
Distinct (%)34.6%
Missing69
Missing (%)14.6%
Memory size3.8 KiB
2024-05-11T15:16:23.014092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length2.8034826
Min length2

Characters and Unicode

Total characters1127
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)18.4%

Sample

1st row9
2nd row3
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1 44
 
10.9%
2 23
 
5.7%
4 19
 
4.7%
3 19
 
4.7%
5 17
 
4.2%
6 10
 
2.5%
14 10
 
2.5%
7 9
 
2.2%
11 9
 
2.2%
8 8
 
2.0%
Other values (129) 234
58.2%
2024-05-11T15:16:23.885861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
402
35.7%
1 185
16.4%
2 96
 
8.5%
3 76
 
6.7%
4 71
 
6.3%
5 68
 
6.0%
6 63
 
5.6%
7 53
 
4.7%
8 44
 
3.9%
9 35
 
3.1%
Other values (2) 34
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 721
64.0%
Space Separator 402
35.7%
Other Punctuation 4
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 185
25.7%
2 96
13.3%
3 76
10.5%
4 71
 
9.8%
5 68
 
9.4%
6 63
 
8.7%
7 53
 
7.4%
8 44
 
6.1%
9 35
 
4.9%
0 30
 
4.2%
Space Separator
ValueCountFrequency (%)
402
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
402
35.7%
1 185
16.4%
2 96
 
8.5%
3 76
 
6.7%
4 71
 
6.3%
5 68
 
6.0%
6 63
 
5.6%
7 53
 
4.7%
8 44
 
3.9%
9 35
 
3.1%
Other values (2) 34
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
402
35.7%
1 185
16.4%
2 96
 
8.5%
3 76
 
6.7%
4 71
 
6.3%
5 68
 
6.0%
6 63
 
5.6%
7 53
 
4.7%
8 44
 
3.9%
9 35
 
3.1%
Other values (2) 34
 
3.0%

휘발유(유연)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)9.5%
Missing251
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean4.9045455
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-05-11T15:16:24.137656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile14
Maximum29
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7843903
Coefficient of variation (CV)0.97550126
Kurtosis5.4077032
Mean4.9045455
Median Absolute Deviation (MAD)2
Skewness2.0534607
Sum1079
Variance22.89039
MonotonicityNot monotonic
2024-05-11T15:16:24.406528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 59
 
12.5%
2 32
 
6.8%
3 24
 
5.1%
4 19
 
4.0%
5 16
 
3.4%
9 14
 
3.0%
8 11
 
2.3%
7 10
 
2.1%
6 9
 
1.9%
10 6
 
1.3%
Other values (11) 20
 
4.2%
(Missing) 251
53.3%
ValueCountFrequency (%)
1 59
12.5%
2 32
6.8%
3 24
5.1%
4 19
 
4.0%
5 16
 
3.4%
6 9
 
1.9%
7 10
 
2.1%
8 11
 
2.3%
9 14
 
3.0%
10 6
 
1.3%
ValueCountFrequency (%)
29 1
 
0.2%
25 1
 
0.2%
23 2
0.4%
20 2
0.4%
17 1
 
0.2%
16 1
 
0.2%
15 2
0.4%
14 4
0.8%
13 1
 
0.2%
12 3
0.6%

휘발유(무연)
Text

MISSING 

Distinct390
Distinct (%)83.7%
Missing5
Missing (%)1.1%
Memory size3.8 KiB
2024-05-11T15:16:25.163254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.5193133
Min length2

Characters and Unicode

Total characters2106
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique338 ?
Unique (%)72.5%

Sample

1st row238
2nd row108
3rd row15
4th row47
5th row51
ValueCountFrequency (%)
29 4
 
0.9%
87 4
 
0.9%
7 4
 
0.9%
56 4
 
0.9%
80 4
 
0.9%
1 3
 
0.6%
20 3
 
0.6%
32 3
 
0.6%
135 3
 
0.6%
19 3
 
0.6%
Other values (380) 431
92.5%
2024-05-11T15:16:26.553375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
466
22.1%
1 231
11.0%
, 183
 
8.7%
2 173
 
8.2%
5 151
 
7.2%
3 150
 
7.1%
4 143
 
6.8%
9 131
 
6.2%
7 130
 
6.2%
6 124
 
5.9%
Other values (2) 224
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1457
69.2%
Space Separator 466
 
22.1%
Other Punctuation 183
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 231
15.9%
2 173
11.9%
5 151
10.4%
3 150
10.3%
4 143
9.8%
9 131
9.0%
7 130
8.9%
6 124
8.5%
8 116
8.0%
0 108
7.4%
Space Separator
ValueCountFrequency (%)
466
100.0%
Other Punctuation
ValueCountFrequency (%)
, 183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2106
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
466
22.1%
1 231
11.0%
, 183
 
8.7%
2 173
 
8.2%
5 151
 
7.2%
3 150
 
7.1%
4 143
 
6.8%
9 131
 
6.2%
7 130
 
6.2%
6 124
 
5.9%
Other values (2) 224
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
466
22.1%
1 231
11.0%
, 183
 
8.7%
2 173
 
8.2%
5 151
 
7.2%
3 150
 
7.1%
4 143
 
6.8%
9 131
 
6.2%
7 130
 
6.2%
6 124
 
5.9%
Other values (2) 224
10.6%

CNG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)32.7%
Missing266
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean44
Minimum1
Maximum661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-05-11T15:16:26.850192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q319
95-th percentile247
Maximum661
Range660
Interquartile range (IQR)17

Descriptive statistics

Standard deviation95.79973
Coefficient of variation (CV)2.1772666
Kurtosis14.837642
Mean44
Median Absolute Deviation (MAD)5
Skewness3.468506
Sum9020
Variance9177.5882
MonotonicityNot monotonic
2024-05-11T15:16:27.154085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 41
 
8.7%
2 28
 
5.9%
11 10
 
2.1%
5 10
 
2.1%
3 10
 
2.1%
6 9
 
1.9%
10 7
 
1.5%
15 7
 
1.5%
4 6
 
1.3%
14 5
 
1.1%
Other values (57) 72
 
15.3%
(Missing) 266
56.5%
ValueCountFrequency (%)
1 41
8.7%
2 28
5.9%
3 10
 
2.1%
4 6
 
1.3%
5 10
 
2.1%
6 9
 
1.9%
7 5
 
1.1%
8 3
 
0.6%
9 4
 
0.8%
10 7
 
1.5%
ValueCountFrequency (%)
661 1
0.2%
607 1
0.2%
383 1
0.2%
318 1
0.2%
317 1
0.2%
302 1
0.2%
295 1
0.2%
294 1
0.2%
289 1
0.2%
282 1
0.2%

하이브리드 (휘발유+전기)
Text

MISSING 

Distinct263
Distinct (%)58.3%
Missing20
Missing (%)4.2%
Memory size3.8 KiB
2024-05-11T15:16:27.936885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.3968958
Min length2

Characters and Unicode

Total characters1532
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)44.3%

Sample

1st row42
2nd row17
3rd row5
4th row10
5th row7
ValueCountFrequency (%)
3 15
 
3.3%
10 13
 
2.9%
5 12
 
2.7%
4 12
 
2.7%
11 11
 
2.4%
6 10
 
2.2%
2 10
 
2.2%
1 9
 
2.0%
9 8
 
1.8%
8 8
 
1.8%
Other values (253) 343
76.1%
2024-05-11T15:16:28.992821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
451
29.4%
1 209
13.6%
2 144
 
9.4%
4 105
 
6.9%
5 101
 
6.6%
3 98
 
6.4%
6 94
 
6.1%
8 81
 
5.3%
9 78
 
5.1%
7 73
 
4.8%
Other values (2) 98
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1050
68.5%
Space Separator 451
29.4%
Other Punctuation 31
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 209
19.9%
2 144
13.7%
4 105
10.0%
5 101
9.6%
3 98
9.3%
6 94
9.0%
8 81
 
7.7%
9 78
 
7.4%
7 73
 
7.0%
0 67
 
6.4%
Space Separator
ValueCountFrequency (%)
451
100.0%
Other Punctuation
ValueCountFrequency (%)
, 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1532
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
451
29.4%
1 209
13.6%
2 144
 
9.4%
4 105
 
6.9%
5 101
 
6.6%
3 98
 
6.4%
6 94
 
6.1%
8 81
 
5.3%
9 78
 
5.1%
7 73
 
4.8%
Other values (2) 98
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
451
29.4%
1 209
13.6%
2 144
 
9.4%
4 105
 
6.9%
5 101
 
6.6%
3 98
 
6.4%
6 94
 
6.1%
8 81
 
5.3%
9 78
 
5.1%
7 73
 
4.8%
Other values (2) 98
 
6.4%

하이브리드 (경유+전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)11.6%
Missing220
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean6.9442231
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-05-11T15:16:29.273174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median4
Q39
95-th percentile21
Maximum53
Range52
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation8.4652747
Coefficient of variation (CV)1.2190384
Kurtosis9.1057764
Mean6.9442231
Median Absolute Deviation (MAD)3
Skewness2.6606908
Sum1743
Variance71.660876
MonotonicityNot monotonic
2024-05-11T15:16:29.508106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 63
 
13.4%
2 35
 
7.4%
3 25
 
5.3%
6 17
 
3.6%
4 16
 
3.4%
8 12
 
2.5%
5 11
 
2.3%
12 9
 
1.9%
7 7
 
1.5%
11 6
 
1.3%
Other values (19) 50
 
10.6%
(Missing) 220
46.7%
ValueCountFrequency (%)
1 63
13.4%
2 35
7.4%
3 25
 
5.3%
4 16
 
3.4%
5 11
 
2.3%
6 17
 
3.6%
7 7
 
1.5%
8 12
 
2.5%
9 5
 
1.1%
10 4
 
0.8%
ValueCountFrequency (%)
53 2
0.4%
44 1
0.2%
39 1
0.2%
37 2
0.4%
32 2
0.4%
28 1
0.2%
24 1
0.2%
22 2
0.4%
21 2
0.4%
20 2
0.4%

하이브리드 (LPG+전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)12.7%
Missing226
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean6.9755102
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-05-11T15:16:29.750058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q39
95-th percentile24.6
Maximum46
Range45
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.3420358
Coefficient of variation (CV)1.1959033
Kurtosis7.2549429
Mean6.9755102
Median Absolute Deviation (MAD)3
Skewness2.5428065
Sum1709
Variance69.589562
MonotonicityNot monotonic
2024-05-11T15:16:30.077953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 56
 
11.9%
2 34
 
7.2%
3 21
 
4.5%
4 20
 
4.2%
5 17
 
3.6%
10 12
 
2.5%
6 10
 
2.1%
8 10
 
2.1%
7 9
 
1.9%
9 8
 
1.7%
Other values (21) 48
 
10.2%
(Missing) 226
48.0%
ValueCountFrequency (%)
1 56
11.9%
2 34
7.2%
3 21
 
4.5%
4 20
 
4.2%
5 17
 
3.6%
6 10
 
2.1%
7 9
 
1.9%
8 10
 
2.1%
9 8
 
1.7%
10 12
 
2.5%
ValueCountFrequency (%)
46 1
0.2%
45 1
0.2%
43 1
0.2%
39 2
0.4%
36 1
0.2%
35 2
0.4%
34 1
0.2%
31 1
0.2%
30 1
0.2%
27 1
0.2%

하이브리드 (CNG+전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)85.7%
Missing464
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean7.2857143
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-05-11T15:16:30.340398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3
Q12
median3
Q36
95-th percentile24.1
Maximum31
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation10.703804
Coefficient of variation (CV)1.4691496
Kurtosis5.9704579
Mean7.2857143
Median Absolute Deviation (MAD)1
Skewness2.412315
Sum51
Variance114.57143
MonotonicityNot monotonic
2024-05-11T15:16:30.623460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 2
 
0.4%
3 1
 
0.2%
8 1
 
0.2%
31 1
 
0.2%
1 1
 
0.2%
4 1
 
0.2%
(Missing) 464
98.5%
ValueCountFrequency (%)
1 1
0.2%
2 2
0.4%
3 1
0.2%
4 1
0.2%
8 1
0.2%
31 1
0.2%
ValueCountFrequency (%)
31 1
0.2%
8 1
0.2%
4 1
0.2%
3 1
0.2%
2 2
0.4%
1 1
0.2%

수소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)12.1%
Missing189
Missing (%)40.1%
Infinite0
Infinite (%)0.0%
Mean7.4787234
Minimum1
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-05-11T15:16:30.877416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q39.75
95-th percentile24.9
Maximum108
Range107
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation10.830031
Coefficient of variation (CV)1.4481123
Kurtosis38.516454
Mean7.4787234
Median Absolute Deviation (MAD)3
Skewness5.1039439
Sum2109
Variance117.28958
MonotonicityNot monotonic
2024-05-11T15:16:31.148196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 76
16.1%
2 33
 
7.0%
6 24
 
5.1%
3 19
 
4.0%
4 15
 
3.2%
7 14
 
3.0%
5 13
 
2.8%
10 12
 
2.5%
8 9
 
1.9%
9 8
 
1.7%
Other values (24) 59
 
12.5%
(Missing) 189
40.1%
ValueCountFrequency (%)
1 76
16.1%
2 33
7.0%
3 19
 
4.0%
4 15
 
3.2%
5 13
 
2.8%
6 24
 
5.1%
7 14
 
3.0%
8 9
 
1.9%
9 8
 
1.7%
10 12
 
2.5%
ValueCountFrequency (%)
108 1
0.2%
91 1
0.2%
46 1
0.2%
41 1
0.2%
34 1
0.2%
31 2
0.4%
30 2
0.4%
28 1
0.2%
27 1
0.2%
26 2
0.4%

기타연료
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct78
Distinct (%)22.7%
Missing128
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean19.918367
Minimum1
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-05-11T15:16:31.404168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q328
95-th percentile77.9
Maximum144
Range143
Interquartile range (IQR)26

Descriptive statistics

Standard deviation25.790752
Coefficient of variation (CV)1.2948226
Kurtosis4.4276977
Mean19.918367
Median Absolute Deviation (MAD)8
Skewness2.0305132
Sum6832
Variance665.16291
MonotonicityNot monotonic
2024-05-11T15:16:31.640168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 65
13.8%
2 35
 
7.4%
5 14
 
3.0%
4 14
 
3.0%
7 13
 
2.8%
3 13
 
2.8%
6 11
 
2.3%
18 8
 
1.7%
9 8
 
1.7%
11 7
 
1.5%
Other values (68) 155
32.9%
(Missing) 128
27.2%
ValueCountFrequency (%)
1 65
13.8%
2 35
7.4%
3 13
 
2.8%
4 14
 
3.0%
5 14
 
3.0%
6 11
 
2.3%
7 13
 
2.8%
8 6
 
1.3%
9 8
 
1.7%
10 5
 
1.1%
ValueCountFrequency (%)
144 1
 
0.2%
130 1
 
0.2%
123 1
 
0.2%
119 1
 
0.2%
114 1
 
0.2%
106 1
 
0.2%
102 1
 
0.2%
101 1
 
0.2%
97 1
 
0.2%
89 3
0.6%

Interactions

2024-05-11T15:16:11.327464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:04.596068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:05.913074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:06.990943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:08.100990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:09.148877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:10.217445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:11.515443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:04.877568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:06.060166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:07.194905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:08.242616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:09.332990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:10.360919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:11.758512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:05.053697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:06.185895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:07.349375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:08.371633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:09.490790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:10.508487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:11.930102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:05.231005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:06.335051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:07.502086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:08.505919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:09.632577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:10.666861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:12.077755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:05.409862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:06.464845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:07.654598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:08.653830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:09.784279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:10.834014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:12.214468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:05.569354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:06.609509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:07.818516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:08.814924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:09.924435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:11.004635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:12.384689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:05.750020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:06.769313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:07.958343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:08.940386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:10.053598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:11.159827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:16:31.850765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
휘발유(유연)CNG하이브리드 (경유+전기)하이브리드 (LPG+전기)하이브리드 (CNG+전기)수소기타연료
휘발유(유연)1.0000.5310.7770.8930.9250.5260.815
CNG0.5311.0000.1900.6550.7670.2760.479
하이브리드\n(경유+전기)0.7770.1901.0000.5740.9400.7140.839
하이브리드\n(LPG+전기)0.8930.6550.5741.0000.7130.5810.792
하이브리드\n(CNG+전기)0.9250.7670.9400.7131.0000.4240.749
수소0.5260.2760.7140.5810.4241.0000.699
기타연료0.8150.4790.8390.7920.7490.6991.000
2024-05-11T15:16:32.092799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
휘발유(유연)CNG하이브리드 (경유+전기)하이브리드 (LPG+전기)하이브리드 (CNG+전기)수소기타연료
휘발유(유연)1.0000.3740.6510.7030.8120.6790.759
CNG0.3741.0000.3510.4750.6490.3600.406
하이브리드\n(경유+전기)0.6510.3511.0000.5580.4640.6460.684
하이브리드\n(LPG+전기)0.7030.4750.5581.0000.9270.6600.757
하이브리드\n(CNG+전기)0.8120.6490.4640.9271.0000.1440.700
수소0.6790.3600.6460.6600.1441.0000.745
기타연료0.7590.4060.6840.7570.7000.7451.000

Missing values

2024-05-11T15:16:12.657797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:16:13.512458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-11T15:16:13.910278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

사용본거지법정동명동별 총 대수휘발유경유엘피지전기휘발유(유연)휘발유(무연)CNG하이브리드 (휘발유+전기)하이브리드 (경유+전기)하이브리드 (LPG+전기)하이브리드 (CNG+전기)수소기타연료
0서울특별시 종로구 청운동7682122412291238<NA>42<NA>1<NA><NA>2
1서울특별시 종로구 신교동36292116253<NA>108<NA>17<NA><NA><NA>1<NA>
2서울특별시 종로구 궁정동406122<NA><NA>15<NA>5<NA><NA><NA><NA><NA>
3서울특별시 종로구 효자동185606341<NA>47<NA>10<NA><NA><NA><NA><NA>
4서울특별시 종로구 창성동162396221<NA>51<NA>7<NA><NA><NA><NA><NA>
5서울특별시 종로구 통의동12625447<NA><NA>41<NA>8<NA><NA><NA>1<NA>
6서울특별시 종로구 적선동168407751<NA>39<NA>6<NA><NA><NA><NA><NA>
7서울특별시 종로구 통인동190485513<NA><NA>64<NA>10<NA><NA><NA><NA><NA>
8서울특별시 종로구 누상동790179254636<NA>250<NA>35<NA><NA><NA>12
9서울특별시 종로구 누하동2194294151<NA>57<NA>10<NA><NA><NA><NA><NA>
사용본거지법정동명동별 총 대수휘발유경유엘피지전기휘발유(유연)휘발유(무연)CNG하이브리드 (휘발유+전기)하이브리드 (경유+전기)하이브리드 (LPG+전기)하이브리드 (CNG+전기)수소기타연료
461서울특별시 송파구 마천동11,8091,8974,5401,5796943,28810429129<NA>620
462서울특별시 강동구 명일동14,1743,1264,3299608334,923869066<NA>1327
463서울특별시 강동구 고덕동16,6873,7965,50295914355,275291986<NA>2547
464서울특별시 강동구 상일동16,8753,7165,3261,066150115,54269561810<NA>3440
465서울특별시 강동구 길동15,8962,8125,3732,55612484,4612507610<NA>928
466서울특별시 강동구 둔촌동8,6641,6983,03083540<NA>2,696233723<NA>615
467서울특별시 강동구 암사동21,6864,3627,3361,88410596,97059311610<NA>1642
468서울특별시 강동구 성내동22,1864,0578,0472,17115596,8841576067<NA>2847
469서울특별시 강동구 천호동25,8794,8469,6682,553131127,72858381113<NA>2351
470서울특별시 강동구 강일동10,5281,8623,5571,2919813,1071853724921723