Overview

Dataset statistics

Number of variables12
Number of observations267
Missing cells67
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.9 KiB
Average record size in memory99.5 B

Variable types

Text6
Categorical3
Numeric3

Dataset

Description경기도 의정부시의 오피스텔 현황을 제공합니다. 제공하는 내용은 건물명, 시군구명, 소재지도로명주소, 소재지지번주소, 대지면적, 건축면적, 지상층수, 지하층수, 세대수, 주용도, 기타용도, 비고 입니다.
Author경기도 의정부시
URLhttps://www.data.go.kr/data/15127236/fileData.do

Alerts

시군구명 has constant value ""Constant
지상층수 is highly overall correlated with 지하층수High correlation
지하층수 is highly overall correlated with 지상층수High correlation
주용도 is highly overall correlated with 비고High correlation
비고 is highly overall correlated with 주용도High correlation
주용도 is highly imbalanced (50.0%)Imbalance
비고 is highly imbalanced (77.8%)Imbalance
건물명 has 20 (7.5%) missing valuesMissing
대지면적 has 17 (6.4%) missing valuesMissing
지하층수 has 4 (1.5%) missing valuesMissing
세대 has 24 (9.0%) missing valuesMissing
지하층수 has 161 (60.3%) zerosZeros
세대 has 5 (1.9%) zerosZeros

Reproduction

Analysis started2024-03-30 06:57:13.730503
Analysis finished2024-03-30 06:57:19.431556
Duration5.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건물명
Text

MISSING 

Distinct179
Distinct (%)72.5%
Missing20
Missing (%)7.5%
Memory size2.2 KiB
2024-03-30T06:57:19.977480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length21
Mean length5.8097166
Min length2

Characters and Unicode

Total characters1435
Distinct characters240
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)59.9%

Sample

1st row영빈오피스텔
2nd row동원오피스텔
3rd row삼정팰리스
4th row영빈오피스텔
5th row천리교 본부
ValueCountFrequency (%)
한솔엠파트 9
 
3.1%
델프라임 8
 
2.7%
현대아트홈 6
 
2.0%
담린 6
 
2.0%
정은스카이 5
 
1.7%
아리띠애 5
 
1.7%
한솔그랑빌 4
 
1.4%
정은리치빌 4
 
1.4%
드림팰리스 4
 
1.4%
정은펠리체 4
 
1.4%
Other values (201) 240
81.4%
2024-03-30T06:57:21.568413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 74
 
5.2%
빌 74
 
5.2%
리 72
 
5.0%
48
 
3.3%
트 44
 
3.1%
이 32
 
2.2%
정 32
 
2.2%
아 31
 
2.2%
라 31
 
2.2%
파 27
 
1.9%
Other values (230) 970
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1314
91.6%
Space Separator 48
 
3.3%
Decimal Number 46
 
3.2%
Uppercase Letter 13
 
0.9%
Dash Punctuation 5
 
0.3%
Close Punctuation 4
 
0.3%
Open Punctuation 4
 
0.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 74
 
5.6%
빌 74
 
5.6%
리 72
 
5.5%
트 44
 
3.3%
이 32
 
2.4%
정 32
 
2.4%
아 31
 
2.4%
라 31
 
2.4%
파 27
 
2.1%
한 22
 
1.7%
Other values (207) 875
66.6%
Decimal Number
ValueCountFrequency (%)
1 13
28.3%
3 9
19.6%
6 5
 
10.9%
0 4
 
8.7%
5 4
 
8.7%
2 4
 
8.7%
7 3
 
6.5%
8 2
 
4.3%
9 1
 
2.2%
4 1
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
A 3
23.1%
I 2
15.4%
J 2
15.4%
B 2
15.4%
E 1
 
7.7%
U 1
 
7.7%
S 1
 
7.7%
M 1
 
7.7%
Space Separator
ValueCountFrequency (%)
48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1314
91.6%
Common 108
 
7.5%
Latin 13
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 74
 
5.6%
빌 74
 
5.6%
리 72
 
5.5%
트 44
 
3.3%
이 32
 
2.4%
정 32
 
2.4%
아 31
 
2.4%
라 31
 
2.4%
파 27
 
2.1%
한 22
 
1.7%
Other values (207) 875
66.6%
Common
ValueCountFrequency (%)
48
44.4%
1 13
 
12.0%
3 9
 
8.3%
- 5
 
4.6%
6 5
 
4.6%
0 4
 
3.7%
) 4
 
3.7%
( 4
 
3.7%
5 4
 
3.7%
2 4
 
3.7%
Other values (5) 8
 
7.4%
Latin
ValueCountFrequency (%)
A 3
23.1%
I 2
15.4%
J 2
15.4%
B 2
15.4%
E 1
 
7.7%
U 1
 
7.7%
S 1
 
7.7%
M 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1314
91.6%
ASCII 121
 
8.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 74
 
5.6%
빌 74
 
5.6%
리 72
 
5.5%
트 44
 
3.3%
이 32
 
2.4%
정 32
 
2.4%
아 31
 
2.4%
라 31
 
2.4%
파 27
 
2.1%
한 22
 
1.7%
Other values (207) 875
66.6%
ASCII
ValueCountFrequency (%)
48
39.7%
1 13
 
10.7%
3 9
 
7.4%
- 5
 
4.1%
6 5
 
4.1%
0 4
 
3.3%
) 4
 
3.3%
( 4
 
3.3%
5 4
 
3.3%
2 4
 
3.3%
Other values (13) 21
17.4%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
경기의정부시
267 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기의정부시
2nd row경기의정부시
3rd row경기의정부시
4th row경기의정부시
5th row경기의정부시

Common Values

ValueCountFrequency (%)
경기의정부시 267
100.0%

Length

2024-03-30T06:57:22.137962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T06:57:22.578811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기의정부시 267
100.0%

소재지도로명주소
Text

Distinct244
Distinct (%)92.1%
Missing2
Missing (%)0.7%
Memory size2.2 KiB
2024-03-30T06:57:23.285599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length18.988679
Min length14

Characters and Unicode

Total characters5032
Distinct characters78
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique226 ?
Unique (%)85.3%

Sample

1st row경기도 의정부시 범골로86번길 59
2nd row경기도 의정부시 호국로1276번길 25
3rd row경기도 의정부시 흥선로164번길 8-1
4th row경기도 의정부시 범골로86번길 59
5th row경기도 의정부시 호암로 145
ValueCountFrequency (%)
경기도 265
25.0%
의정부시 265
25.0%
평화로 15
 
1.4%
호암로 13
 
1.2%
22 8
 
0.8%
시민로156번길 7
 
0.7%
호국로1336번길 7
 
0.7%
회룡로117번길 7
 
0.7%
18 6
 
0.6%
24 6
 
0.6%
Other values (292) 461
43.5%
2024-03-30T06:57:24.548537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
795
15.8%
의 283
 
5.6%
시 280
 
5.6%
경 278
 
5.5%
정 270
 
5.4%
부 266
 
5.3%
도 265
 
5.3%
기 265
 
5.3%
로 265
 
5.3%
1 265
 
5.3%
Other values (68) 1800
35.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3015
59.9%
Decimal Number 1139
 
22.6%
Space Separator 795
 
15.8%
Dash Punctuation 83
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 283
9.4%
시 280
9.3%
경 278
9.2%
정 270
9.0%
부 266
8.8%
도 265
8.8%
기 265
8.8%
로 265
8.8%
번 182
 
6.0%
길 182
 
6.0%
Other values (56) 479
15.9%
Decimal Number
ValueCountFrequency (%)
1 265
23.3%
2 140
12.3%
3 140
12.3%
5 119
10.4%
9 89
 
7.8%
4 89
 
7.8%
6 88
 
7.7%
8 80
 
7.0%
7 74
 
6.5%
0 55
 
4.8%
Space Separator
ValueCountFrequency (%)
795
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3015
59.9%
Common 2017
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 283
9.4%
시 280
9.3%
경 278
9.2%
정 270
9.0%
부 266
8.8%
도 265
8.8%
기 265
8.8%
로 265
8.8%
번 182
 
6.0%
길 182
 
6.0%
Other values (56) 479
15.9%
Common
ValueCountFrequency (%)
795
39.4%
1 265
 
13.1%
2 140
 
6.9%
3 140
 
6.9%
5 119
 
5.9%
9 89
 
4.4%
4 89
 
4.4%
6 88
 
4.4%
- 83
 
4.1%
8 80
 
4.0%
Other values (2) 129
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3015
59.9%
ASCII 2017
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
795
39.4%
1 265
 
13.1%
2 140
 
6.9%
3 140
 
6.9%
5 119
 
5.9%
9 89
 
4.4%
4 89
 
4.4%
6 88
 
4.4%
- 83
 
4.1%
8 80
 
4.0%
Other values (2) 129
 
6.4%
Hangul
ValueCountFrequency (%)
의 283
9.4%
시 280
9.3%
경 278
9.2%
정 270
9.0%
부 266
8.8%
도 265
8.8%
기 265
8.8%
로 265
8.8%
번 182
 
6.0%
길 182
 
6.0%
Other values (56) 479
15.9%

소재지지번주소
Text

Distinct247
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-03-30T06:57:25.421787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length22.247191
Min length17

Characters and Unicode

Total characters5940
Distinct characters37
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique230 ?
Unique (%)86.1%

Sample

1st row경기도 의정부시 의정부동 0599-0003
2nd row경기도 의정부시 의정부동 0209-0010
3rd row경기도 의정부시 의정부동 0435-0026
4th row경기도 의정부시 의정부동 0599-0004
5th row경기도 의정부시 호원동 0247-0056
ValueCountFrequency (%)
경기도 267
25.0%
의정부시 267
25.0%
의정부동 116
10.9%
호원동 70
 
6.6%
가능동 51
 
4.8%
금오동 16
 
1.5%
민락동 6
 
0.6%
신곡동 4
 
0.4%
0717-0008 3
 
0.3%
0226-0002 3
 
0.3%
Other values (248) 265
24.8%
2024-03-30T06:57:26.898141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1004
16.9%
801
13.5%
의 383
 
6.4%
정 383
 
6.4%
부 383
 
6.4%
동 267
 
4.5%
기 267
 
4.5%
경 267
 
4.5%
시 267
 
4.5%
도 267
 
4.5%
Other values (27) 1651
27.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2786
46.9%
Decimal Number 2094
35.3%
Space Separator 801
 
13.5%
Dash Punctuation 258
 
4.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 383
13.7%
정 383
13.7%
부 383
13.7%
동 267
9.6%
기 267
9.6%
경 267
9.6%
시 267
9.6%
도 267
9.6%
호 70
 
2.5%
원 70
 
2.5%
Other values (14) 162
5.8%
Decimal Number
ValueCountFrequency (%)
0 1004
47.9%
2 239
 
11.4%
1 156
 
7.4%
6 132
 
6.3%
4 114
 
5.4%
3 107
 
5.1%
8 100
 
4.8%
7 86
 
4.1%
5 81
 
3.9%
9 75
 
3.6%
Space Separator
ValueCountFrequency (%)
801
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 258
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3153
53.1%
Hangul 2786
46.9%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 383
13.7%
정 383
13.7%
부 383
13.7%
동 267
9.6%
기 267
9.6%
경 267
9.6%
시 267
9.6%
도 267
9.6%
호 70
 
2.5%
원 70
 
2.5%
Other values (14) 162
5.8%
Common
ValueCountFrequency (%)
0 1004
31.8%
801
25.4%
- 258
 
8.2%
2 239
 
7.6%
1 156
 
4.9%
6 132
 
4.2%
4 114
 
3.6%
3 107
 
3.4%
8 100
 
3.2%
7 86
 
2.7%
Other values (2) 156
 
4.9%
Latin
ValueCountFrequency (%)
C 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3154
53.1%
Hangul 2786
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1004
31.8%
801
25.4%
- 258
 
8.2%
2 239
 
7.6%
1 156
 
4.9%
6 132
 
4.2%
4 114
 
3.6%
3 107
 
3.4%
8 100
 
3.2%
7 86
 
2.7%
Other values (3) 157
 
5.0%
Hangul
ValueCountFrequency (%)
의 383
13.7%
정 383
13.7%
부 383
13.7%
동 267
9.6%
기 267
9.6%
경 267
9.6%
시 267
9.6%
도 267
9.6%
호 70
 
2.5%
원 70
 
2.5%
Other values (14) 162
5.8%

대지면적
Text

MISSING 

Distinct228
Distinct (%)91.2%
Missing17
Missing (%)6.4%
Memory size2.2 KiB
2024-03-30T06:57:28.000645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.412
Min length6

Characters and Unicode

Total characters1603
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)84.0%

Sample

1st row286.90
2nd row306.00
3rd row329.30
4th row301.80
5th row4,529.00
ValueCountFrequency (%)
872.00 3
 
1.2%
1,318.00 3
 
1.2%
1,866.70 3
 
1.2%
204.10 3
 
1.2%
1,165.90 2
 
0.8%
537.00 2
 
0.8%
890.70 2
 
0.8%
808.70 2
 
0.8%
1,572.20 2
 
0.8%
609.30 2
 
0.8%
Other values (218) 226
90.4%
2024-03-30T06:57:29.735316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 411
25.6%
. 250
15.6%
3 113
 
7.0%
2 111
 
6.9%
1 110
 
6.9%
4 108
 
6.7%
5 97
 
6.1%
9 95
 
5.9%
8 90
 
5.6%
7 88
 
5.5%
Other values (2) 130
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1303
81.3%
Other Punctuation 300
 
18.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 411
31.5%
3 113
 
8.7%
2 111
 
8.5%
1 110
 
8.4%
4 108
 
8.3%
5 97
 
7.4%
9 95
 
7.3%
8 90
 
6.9%
7 88
 
6.8%
6 80
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 250
83.3%
, 50
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 411
25.6%
. 250
15.6%
3 113
 
7.0%
2 111
 
6.9%
1 110
 
6.9%
4 108
 
6.7%
5 97
 
6.1%
9 95
 
5.9%
8 90
 
5.6%
7 88
 
5.5%
Other values (2) 130
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 411
25.6%
. 250
15.6%
3 113
 
7.0%
2 111
 
6.9%
1 110
 
6.9%
4 108
 
6.7%
5 97
 
6.1%
9 95
 
5.9%
8 90
 
5.6%
7 88
 
5.5%
Other values (2) 130
 
8.1%

건축면적
Text

Distinct250
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-03-30T06:57:30.740135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.0299625
Min length4

Characters and Unicode

Total characters1610
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)88.4%

Sample

1st row127.42
2nd row207.26
3rd row181.26
4th row129.72
5th row156.32
ValueCountFrequency (%)
185.84 4
 
1.5%
219.94 3
 
1.1%
263.28 2
 
0.7%
243.18 2
 
0.7%
259.59 2
 
0.7%
408.20 2
 
0.7%
237.48 2
 
0.7%
198.64 2
 
0.7%
209.31 2
 
0.7%
252.72 2
 
0.7%
Other values (240) 244
91.4%
2024-03-30T06:57:32.498374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 267
16.6%
1 210
13.0%
2 206
12.8%
0 145
9.0%
8 125
7.8%
4 119
7.4%
6 113
7.0%
3 107
6.6%
9 106
 
6.6%
7 104
 
6.5%
Other values (2) 108
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1336
83.0%
Other Punctuation 274
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 210
15.7%
2 206
15.4%
0 145
10.9%
8 125
9.4%
4 119
8.9%
6 113
8.5%
3 107
8.0%
9 106
7.9%
7 104
7.8%
5 101
7.6%
Other Punctuation
ValueCountFrequency (%)
. 267
97.4%
, 7
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1610
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 267
16.6%
1 210
13.0%
2 206
12.8%
0 145
9.0%
8 125
7.8%
4 119
7.4%
6 113
7.0%
3 107
6.6%
9 106
 
6.6%
7 104
 
6.5%
Other values (2) 108
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 267
16.6%
1 210
13.0%
2 206
12.8%
0 145
9.0%
8 125
7.8%
4 119
7.4%
6 113
7.0%
3 107
6.6%
9 106
 
6.6%
7 104
 
6.5%
Other values (2) 108
6.7%

지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3033708
Minimum0
Maximum49
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-03-30T06:57:32.945642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median8
Q310
95-th percentile19.7
Maximum49
Range49
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.8621706
Coefficient of variation (CV)0.52262462
Kurtosis18.895596
Mean9.3033708
Median Absolute Deviation (MAD)1
Skewness3.480296
Sum2484
Variance23.640703
MonotonicityNot monotonic
2024-03-30T06:57:33.341843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7 67
25.1%
9 45
16.9%
8 34
12.7%
10 30
11.2%
6 29
10.9%
5 15
 
5.6%
11 10
 
3.7%
15 6
 
2.2%
16 4
 
1.5%
20 4
 
1.5%
Other values (12) 23
 
8.6%
ValueCountFrequency (%)
0 1
 
0.4%
4 1
 
0.4%
5 15
 
5.6%
6 29
10.9%
7 67
25.1%
8 34
12.7%
9 45
16.9%
10 30
11.2%
11 10
 
3.7%
12 3
 
1.1%
ValueCountFrequency (%)
49 1
 
0.4%
27 3
1.1%
25 3
1.1%
24 1
 
0.4%
23 1
 
0.4%
22 1
 
0.4%
20 4
1.5%
19 3
1.1%
18 1
 
0.4%
16 4
1.5%

지하층수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)2.3%
Missing4
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.53612167
Minimum0
Maximum5
Zeros161
Zeros (%)60.3%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-03-30T06:57:33.743707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8547283
Coefficient of variation (CV)1.5942805
Kurtosis6.1960446
Mean0.53612167
Median Absolute Deviation (MAD)0
Skewness2.2334817
Sum141
Variance0.73056047
MonotonicityNot monotonic
2024-03-30T06:57:34.136111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 161
60.3%
1 80
30.0%
2 11
 
4.1%
3 6
 
2.2%
4 4
 
1.5%
5 1
 
0.4%
(Missing) 4
 
1.5%
ValueCountFrequency (%)
0 161
60.3%
1 80
30.0%
2 11
 
4.1%
3 6
 
2.2%
4 4
 
1.5%
5 1
 
0.4%
ValueCountFrequency (%)
5 1
 
0.4%
4 4
 
1.5%
3 6
 
2.2%
2 11
 
4.1%
1 80
30.0%
0 161
60.3%

세대
Real number (ℝ)

MISSING  ZEROS 

Distinct40
Distinct (%)16.5%
Missing24
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean24.962963
Minimum0
Maximum298
Zeros5
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-03-30T06:57:34.507888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q18
median9
Q316
95-th percentile126.6
Maximum298
Range298
Interquartile range (IQR)8

Descriptive statistics

Standard deviation53.733835
Coefficient of variation (CV)2.1525424
Kurtosis16.291343
Mean24.962963
Median Absolute Deviation (MAD)1
Skewness4.0818898
Sum6066
Variance2887.3251
MonotonicityNot monotonic
2024-03-30T06:57:35.138359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
8 86
32.2%
9 36
13.5%
12 26
 
9.7%
16 19
 
7.1%
6 12
 
4.5%
24 9
 
3.4%
10 5
 
1.9%
0 5
 
1.9%
28 4
 
1.5%
298 3
 
1.1%
Other values (30) 38
14.2%
(Missing) 24
 
9.0%
ValueCountFrequency (%)
0 5
 
1.9%
2 2
 
0.7%
4 2
 
0.7%
6 12
 
4.5%
7 1
 
0.4%
8 86
32.2%
9 36
13.5%
10 5
 
1.9%
12 26
 
9.7%
14 1
 
0.4%
ValueCountFrequency (%)
298 3
1.1%
297 1
 
0.4%
288 2
0.7%
231 2
0.7%
198 1
 
0.4%
196 1
 
0.4%
172 1
 
0.4%
156 1
 
0.4%
130 1
 
0.4%
96 1
 
0.4%

주용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
공동주택
142 
업무시설
119 
숙박시설
 
3
제2종근린생활시설
 
2
단독주택
 
1

Length

Max length9
Median length4
Mean length4.0374532
Min length4

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row업무시설
2nd row업무시설
3rd row숙박시설
4th row업무시설
5th row업무시설

Common Values

ValueCountFrequency (%)
공동주택 142
53.2%
업무시설 119
44.6%
숙박시설 3
 
1.1%
제2종근린생활시설 2
 
0.7%
단독주택 1
 
0.4%

Length

2024-03-30T06:57:35.679573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T06:57:36.116512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동주택 142
53.2%
업무시설 119
44.6%
숙박시설 3
 
1.1%
제2종근린생활시설 2
 
0.7%
단독주택 1
 
0.4%

기타용도
Text

Distinct124
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-03-30T06:57:36.529101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length16.393258
Min length4

Characters and Unicode

Total characters4377
Distinct characters59
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)33.0%

Sample

1st row업무시설,오피스텔
2nd row업무시설(오피스텔)
3rd row생활숙박시설,오피스텔
4th row업무시설,오피스텔/근린생활
5th row업무시설(오피스텔),단독주택(다가구주택)
ValueCountFrequency (%)
오피스텔 87
19.2%
및 66
14.6%
다세대주택 52
 
11.5%
업무시설(오피스텔 47
 
10.4%
단지형다세대주택,오피스텔 20
 
4.4%
공동주택(다세대주택 20
 
4.4%
공동주택 7
 
1.5%
다세대주택및오피스텔 7
 
1.5%
다세대주택,오피스텔 6
 
1.3%
오피스텔,다세대주택 5
 
1.1%
Other values (94) 135
29.9%
2024-03-30T06:57:37.693403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 294
 
6.7%
택 293
 
6.7%
오 267
 
6.1%
피 267
 
6.1%
스 267
 
6.1%
텔 267
 
6.1%
) 208
 
4.8%
( 208
 
4.8%
시 189
 
4.3%
185
 
4.2%
Other values (49) 1932
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3544
81.0%
Close Punctuation 209
 
4.8%
Open Punctuation 209
 
4.8%
Other Punctuation 198
 
4.5%
Space Separator 185
 
4.2%
Decimal Number 19
 
0.4%
Dash Punctuation 13
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 294
 
8.3%
택 293
 
8.3%
오 267
 
7.5%
피 267
 
7.5%
스 267
 
7.5%
텔 267
 
7.5%
시 189
 
5.3%
다 170
 
4.8%
세 168
 
4.7%
대 168
 
4.7%
Other values (36) 1194
33.7%
Decimal Number
ValueCountFrequency (%)
1 10
52.6%
2 7
36.8%
6 1
 
5.3%
5 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 178
89.9%
/ 16
 
8.1%
. 4
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 208
99.5%
] 1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 208
99.5%
[ 1
 
0.5%
Space Separator
ValueCountFrequency (%)
185
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3544
81.0%
Common 833
 
19.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 294
 
8.3%
택 293
 
8.3%
오 267
 
7.5%
피 267
 
7.5%
스 267
 
7.5%
텔 267
 
7.5%
시 189
 
5.3%
다 170
 
4.8%
세 168
 
4.7%
대 168
 
4.7%
Other values (36) 1194
33.7%
Common
ValueCountFrequency (%)
) 208
25.0%
( 208
25.0%
185
22.2%
, 178
21.4%
/ 16
 
1.9%
- 13
 
1.6%
1 10
 
1.2%
2 7
 
0.8%
. 4
 
0.5%
[ 1
 
0.1%
Other values (3) 3
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3544
81.0%
ASCII 833
 
19.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 294
 
8.3%
택 293
 
8.3%
오 267
 
7.5%
피 267
 
7.5%
스 267
 
7.5%
텔 267
 
7.5%
시 189
 
5.3%
다 170
 
4.8%
세 168
 
4.7%
대 168
 
4.7%
Other values (36) 1194
33.7%
ASCII
ValueCountFrequency (%)
) 208
25.0%
( 208
25.0%
185
22.2%
, 178
21.4%
/ 16
 
1.9%
- 13
 
1.6%
1 10
 
1.2%
2 7
 
0.8%
. 4
 
0.5%
[ 1
 
0.1%
Other values (3) 3
 
0.4%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
집합건축물
252 
일반건축물
 
12
<NA>
 
3

Length

Max length5
Median length5
Mean length4.988764
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반건축물
2nd row일반건축물
3rd row일반건축물
4th row일반건축물
5th row일반건축물

Common Values

ValueCountFrequency (%)
집합건축물 252
94.4%
일반건축물 12
 
4.5%
<NA> 3
 
1.1%

Length

2024-03-30T06:57:38.277618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T06:57:38.708403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
집합건축물 252
94.4%
일반건축물 12
 
4.5%
na 3
 
1.1%

Interactions

2024-03-30T06:57:16.729820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:57:14.928078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:57:15.788821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:57:17.044338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:57:15.253339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:57:16.035378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:57:17.407976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:57:15.510625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:57:16.303007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T06:57:38.910547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지상층수지하층수세대주용도비고
지상층수1.0000.5870.8300.5190.138
지하층수0.5871.0000.8360.2250.000
세대0.8300.8361.0000.5130.000
주용도0.5190.2250.5131.0000.503
비고0.1380.0000.0000.5031.000
2024-03-30T06:57:39.243262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주용도비고
주용도1.0000.607
비고0.6071.000
2024-03-30T06:57:39.569718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지상층수지하층수세대주용도비고
지상층수1.0000.5420.4020.3650.146
지하층수0.5421.0000.3120.1540.000
세대0.4020.3121.0000.2600.000
주용도0.3650.1540.2601.0000.607
비고0.1460.0000.0000.6071.000

Missing values

2024-03-30T06:57:17.902034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T06:57:18.610519image/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.
2024-03-30T06:57:19.136970image/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

건물명시군구명소재지도로명주소소재지지번주소대지면적건축면적지상층수지하층수세대주용도기타용도비고
0영빈오피스텔경기의정부시경기도 의정부시 범골로86번길 59경기도 의정부시 의정부동 0599-0003286.90127.42500업무시설업무시설,오피스텔일반건축물
1동원오피스텔경기의정부시경기도 의정부시 호국로1276번길 25경기도 의정부시 의정부동 0209-0010306.00207.26600업무시설업무시설(오피스텔)일반건축물
2삼정팰리스경기의정부시경기도 의정부시 흥선로164번길 8-1경기도 의정부시 의정부동 0435-0026329.30181.269<NA><NA>숙박시설생활숙박시설,오피스텔일반건축물
3영빈오피스텔경기의정부시경기도 의정부시 범골로86번길 59경기도 의정부시 의정부동 0599-0004301.80129.72500업무시설업무시설,오피스텔/근린생활일반건축물
4천리교 본부경기의정부시<NA>경기도 의정부시 호원동 0247-00564,529.00156.3251<NA>업무시설업무시설(오피스텔),단독주택(다가구주택)일반건축물
5<NA>경기의정부시경기도 의정부시 호암로 145경기도 의정부시 호원동 0085-0018489.00293.0360<NA>업무시설업무시설(오피스텔)일반건축물
6드림빌15차경기의정부시경기도 의정부시 시민로146번길 28경기도 의정부시 의정부동 0134-0027223.40155.4290<NA>숙박시설생활숙박시설,오피스텔일반건축물
7<NA>경기의정부시경기도 의정부시 평화로243번길 56-4경기도 의정부시 호원동 0256-0006517.00299.6540<NA>단독주택단독주택(다가구주택), 업무시설(오피스텔)일반건축물
8드림빌17차경기의정부시경기도 의정부시 시민로132번길 32경기도 의정부시 의정부동 0135-0014190.70131.80100<NA>숙박시설생활숙박시설 및 오피스텔일반건축물
9<NA>경기의정부시경기도 의정부시 호국로1346번길 102경기도 의정부시 의정부동 0123-0015181.70124.325<NA><NA>업무시설오피스텔및다가구주택일반건축물
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