Overview

Dataset statistics

Number of variables14
Number of observations574
Missing cells315
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.2 KiB
Average record size in memory116.2 B

Variable types

Text5
Categorical6
Numeric3

Dataset

Description조서관리코드,프로젝트코드,지자체,조서유형(구분),대분류,중분류,소분류,위치명,지역명,면적기정,면적증감코드,면적변경,면적변경후,결정고시관리코드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-20286/S/1/datasetView.do

Alerts

대분류 has constant value ""Constant
중분류 has constant value ""Constant
면적기정 is highly overall correlated with 면적변경후 and 1 other fieldsHigh correlation
면적변경후 is highly overall correlated with 면적기정 and 1 other fieldsHigh correlation
조서유형(구분) is highly overall correlated with 면적증감코드High correlation
소분류 is highly overall correlated with 면적기정 and 1 other fieldsHigh correlation
면적증감코드 is highly overall correlated with 조서유형(구분)High correlation
지자체 is highly imbalanced (71.8%)Imbalance
조서유형(구분) is highly imbalanced (53.2%)Imbalance
위치명 has 36 (6.3%) missing valuesMissing
면적기정 has 112 (19.5%) missing valuesMissing
면적변경 has 134 (23.3%) missing valuesMissing
면적변경후 has 25 (4.4%) missing valuesMissing
조서관리코드 has unique valuesUnique
면적변경후 has 10 (1.7%) zerosZeros

Reproduction

Analysis started2024-05-11 05:51:02.878858
Analysis finished2024-05-11 05:51:09.616770
Duration6.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조서관리코드
Text

UNIQUE 

Distinct574
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-05-11T05:51:10.160912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters11480
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique574 ?
Unique (%)100.0%

Sample

1st row11000AGZ201908130008
2nd row11230AGZ202110250001
3rd row11500AGZ202301200002
4th row11000AGZ202301300001
5th row11000AGZ202302210001
ValueCountFrequency (%)
11000agz201908130008 1
 
0.2%
11000agz201311146618 1
 
0.2%
11000agz200811216395 1
 
0.2%
11000agz200912156443 1
 
0.2%
11000agz200912156444 1
 
0.2%
11000agz201311126615 1
 
0.2%
11000agz201311136616 1
 
0.2%
11000agz201311136617 1
 
0.2%
11000agz201511246861 1
 
0.2%
11000agz201511246860 1
 
0.2%
Other values (564) 564
98.3%
2024-05-11T05:51:12.047386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3520
30.7%
1 2238
19.5%
2 1242
 
10.8%
6 684
 
6.0%
A 574
 
5.0%
G 574
 
5.0%
Z 574
 
5.0%
8 379
 
3.3%
9 365
 
3.2%
7 347
 
3.0%
Other values (3) 983
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9758
85.0%
Uppercase Letter 1722
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3520
36.1%
1 2238
22.9%
2 1242
 
12.7%
6 684
 
7.0%
8 379
 
3.9%
9 365
 
3.7%
7 347
 
3.6%
5 344
 
3.5%
3 339
 
3.5%
4 300
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 574
33.3%
G 574
33.3%
Z 574
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9758
85.0%
Latin 1722
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3520
36.1%
1 2238
22.9%
2 1242
 
12.7%
6 684
 
7.0%
8 379
 
3.9%
9 365
 
3.7%
7 347
 
3.6%
5 344
 
3.5%
3 339
 
3.5%
4 300
 
3.1%
Latin
ValueCountFrequency (%)
A 574
33.3%
G 574
33.3%
Z 574
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3520
30.7%
1 2238
19.5%
2 1242
 
10.8%
6 684
 
6.0%
A 574
 
5.0%
G 574
 
5.0%
Z 574
 
5.0%
8 379
 
3.3%
9 365
 
3.2%
7 347
 
3.0%
Other values (3) 983
 
8.6%

프로젝트코드
Text

Distinct401
Distinct (%)70.1%
Missing2
Missing (%)0.3%
Memory size4.6 KiB
2024-05-11T05:51:12.834361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters11440
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique349 ?
Unique (%)61.0%

Sample

1st row11000PPL201908130004
2nd row11230PPL202110250004
3rd row11500PPL202301200001
4th row11000PPL202301300003
5th row11000PPL202302210004
ValueCountFrequency (%)
11000ppl200812262559 33
 
5.8%
11000ppl200908133713 25
 
4.4%
11000ppl201705258123 23
 
4.0%
11000ppl200610190441 16
 
2.8%
11000ppl200910013919 7
 
1.2%
11000ppl201509247599 7
 
1.2%
11000ppl201704278101 6
 
1.0%
11000ppl201702028005 6
 
1.0%
11260ppl202111250003 4
 
0.7%
11000ppl200612210620 4
 
0.7%
Other values (391) 441
77.1%
2024-05-11T05:51:13.806415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3618
31.6%
1 2248
19.7%
2 1342
 
11.7%
P 1144
 
10.0%
L 572
 
5.0%
3 397
 
3.5%
9 382
 
3.3%
6 370
 
3.2%
5 369
 
3.2%
8 359
 
3.1%
Other values (2) 639
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9724
85.0%
Uppercase Letter 1716
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3618
37.2%
1 2248
23.1%
2 1342
 
13.8%
3 397
 
4.1%
9 382
 
3.9%
6 370
 
3.8%
5 369
 
3.8%
8 359
 
3.7%
7 351
 
3.6%
4 288
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
P 1144
66.7%
L 572
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9724
85.0%
Latin 1716
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3618
37.2%
1 2248
23.1%
2 1342
 
13.8%
3 397
 
4.1%
9 382
 
3.9%
6 370
 
3.8%
5 369
 
3.8%
8 359
 
3.7%
7 351
 
3.6%
4 288
 
3.0%
Latin
ValueCountFrequency (%)
P 1144
66.7%
L 572
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3618
31.6%
1 2248
19.7%
2 1342
 
11.7%
P 1144
 
10.0%
L 572
 
5.0%
3 397
 
3.5%
9 382
 
3.3%
6 370
 
3.2%
5 369
 
3.2%
8 359
 
3.1%
Other values (2) 639
 
5.6%

지자체
Categorical

IMBALANCE 

Distinct17
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
서울특별시
490 
동작구
 
10
은평구
 
9
마포구
 
9
영등포구
 
8
Other values (12)
 
48

Length

Max length5
Median length5
Mean length4.7421603
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row성북구
2nd row동대문구
3rd row강서구
4th row서울특별시
5th row성북구

Common Values

ValueCountFrequency (%)
서울특별시 490
85.4%
동작구 10
 
1.7%
은평구 9
 
1.6%
마포구 9
 
1.6%
영등포구 8
 
1.4%
성북구 8
 
1.4%
서대문구 7
 
1.2%
중랑구 6
 
1.0%
강북구 6
 
1.0%
동대문구 5
 
0.9%
Other values (7) 16
 
2.8%

Length

2024-05-11T05:51:14.287368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 490
85.4%
동작구 10
 
1.7%
은평구 9
 
1.6%
마포구 9
 
1.6%
영등포구 8
 
1.4%
성북구 8
 
1.4%
서대문구 7
 
1.2%
강북구 6
 
1.0%
중랑구 6
 
1.0%
동대문구 5
 
0.9%
Other values (7) 16
 
2.8%

조서유형(구분)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
변경
428 
신설
105 
폐지
 
35
기정
 
5
정정
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
변경 428
74.6%
신설 105
 
18.3%
폐지 35
 
6.1%
기정 5
 
0.9%
정정 1
 
0.2%

Length

2024-05-11T05:51:14.697507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:51:15.020377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
변경 428
74.6%
신설 105
 
18.3%
폐지 35
 
6.1%
기정 5
 
0.9%
정정 1
 
0.2%

대분류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
의제처리구역
574 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의제처리구역 574
100.0%

Length

2024-05-11T05:51:15.344657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:51:15.577091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의제처리구역 574
100.0%

중분류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
재정비촉진지구
574 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재정비촉진지구 574
100.0%

Length

2024-05-11T05:51:15.874228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:51:16.145100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재정비촉진지구 574
100.0%

소분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
주거지형재정비촉진지구
334 
존치관리구역
122 
중심지형재정비촉진지구
82 
존치정비구역
36 

Length

Max length11
Median length11
Mean length9.6236934
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row주거지형재정비촉진지구
2nd row주거지형재정비촉진지구
3rd row존치관리구역
4th row주거지형재정비촉진지구
5th row주거지형재정비촉진지구

Common Values

ValueCountFrequency (%)
주거지형재정비촉진지구 334
58.2%
존치관리구역 122
 
21.3%
중심지형재정비촉진지구 82
 
14.3%
존치정비구역 36
 
6.3%

Length

2024-05-11T05:51:16.414021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:51:16.702363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거지형재정비촉진지구 334
58.2%
존치관리구역 122
 
21.3%
중심지형재정비촉진지구 82
 
14.3%
존치정비구역 36
 
6.3%

위치명
Text

MISSING 

Distinct296
Distinct (%)55.0%
Missing36
Missing (%)6.3%
Memory size4.6 KiB
2024-05-11T05:51:17.421611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length26
Mean length15.710037
Min length4

Characters and Unicode

Total characters8452
Distinct characters133
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198 ?
Unique (%)36.8%

Sample

1st row성북구 장위동 일원
2nd row동대문구 전농 1,4동, 답십리동 1,3,5동
3rd row동작구 노량진동 270-2번지 일대
4th row성북구 장위동일원
5th row보광동 151번지 일대
ValueCountFrequency (%)
일대 321
 
16.4%
일원 126
 
6.4%
중랑구 73
 
3.7%
상봉동 51
 
2.6%
동작구 48
 
2.4%
강동구 44
 
2.2%
성북구 42
 
2.1%
동대문구 40
 
2.0%
천호동 34
 
1.7%
영등포구 33
 
1.7%
Other values (327) 1150
58.6%
2024-05-11T05:51:18.859782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1466
 
17.3%
동 772
 
9.1%
일 511
 
6.0%
구 493
 
5.8%
대 438
 
5.2%
1 257
 
3.0%
지 196
 
2.3%
번 194
 
2.3%
2 185
 
2.2%
- 176
 
2.1%
Other values (123) 3764
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5104
60.4%
Decimal Number 1482
 
17.5%
Space Separator 1466
 
17.3%
Other Punctuation 216
 
2.6%
Dash Punctuation 176
 
2.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 772
 
15.1%
일 511
 
10.0%
구 493
 
9.7%
대 438
 
8.6%
지 196
 
3.8%
번 194
 
3.8%
원 174
 
3.4%
문 95
 
1.9%
중 86
 
1.7%
북 82
 
1.6%
Other values (104) 2063
40.4%
Decimal Number
ValueCountFrequency (%)
1 257
17.3%
2 185
12.5%
3 174
11.7%
6 166
11.2%
4 165
11.1%
0 148
10.0%
8 135
9.1%
5 101
 
6.8%
7 80
 
5.4%
9 71
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 156
72.2%
? 43
 
19.9%
. 15
 
6.9%
？ 2
 
0.9%
Space Separator
ValueCountFrequency (%)
1466
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5104
60.4%
Common 3348
39.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 772
 
15.1%
일 511
 
10.0%
구 493
 
9.7%
대 438
 
8.6%
지 196
 
3.8%
번 194
 
3.8%
원 174
 
3.4%
문 95
 
1.9%
중 86
 
1.7%
북 82
 
1.6%
Other values (104) 2063
40.4%
Common
ValueCountFrequency (%)
1466
43.8%
1 257
 
7.7%
2 185
 
5.5%
- 176
 
5.3%
3 174
 
5.2%
6 166
 
5.0%
4 165
 
4.9%
, 156
 
4.7%
0 148
 
4.4%
8 135
 
4.0%
Other values (9) 320
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5101
60.4%
ASCII 3346
39.6%
Compat Jamo 3
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1466
43.8%
1 257
 
7.7%
2 185
 
5.5%
- 176
 
5.3%
3 174
 
5.2%
6 166
 
5.0%
4 165
 
4.9%
, 156
 
4.7%
0 148
 
4.4%
8 135
 
4.0%
Other values (8) 318
 
9.5%
Hangul
ValueCountFrequency (%)
동 772
 
15.1%
일 511
 
10.0%
구 493
 
9.7%
대 438
 
8.6%
지 196
 
3.8%
번 194
 
3.8%
원 174
 
3.4%
문 95
 
1.9%
중 86
 
1.7%
북 82
 
1.6%
Other values (103) 2060
40.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 3
100.0%
None
ValueCountFrequency (%)
？ 2
100.0%

지역명
Text

Distinct257
Distinct (%)44.9%
Missing2
Missing (%)0.3%
Memory size4.6 KiB
2024-05-11T05:51:19.482419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length10.096154
Min length4

Characters and Unicode

Total characters5775
Distinct characters112
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)27.8%

Sample

1st row장위재정비촉진지구
2nd row전농?답십리 재정비촉진지구
3rd row존치관리구역
4th row노량진 재정비촉진지구
5th row장위 재정비촉진지구
ValueCountFrequency (%)
재정비촉진지구 89
 
12.2%
신길재정비촉진지구 23
 
3.2%
존치관리구역 22
 
3.0%
장위재정비촉진지구 22
 
3.0%
촉진지구 19
 
2.6%
흑석재정비촉진지구 17
 
2.3%
아현재정비촉진지구 13
 
1.8%
재정비 13
 
1.8%
길음재정비촉진지구 12
 
1.7%
신정재정비촉진지구 12
 
1.7%
Other values (236) 485
66.7%
2024-05-11T05:51:20.818477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 576
 
10.0%
정 465
 
8.1%
비 443
 
7.7%
진 438
 
7.6%
재 424
 
7.3%
지 416
 
7.2%
촉 411
 
7.1%
187
 
3.2%
역 183
 
3.2%
존 144
 
2.5%
Other values (102) 2088
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5279
91.4%
Decimal Number 211
 
3.7%
Space Separator 187
 
3.2%
Other Punctuation 68
 
1.2%
Dash Punctuation 10
 
0.2%
Close Punctuation 7
 
0.1%
Open Punctuation 7
 
0.1%
Connector Punctuation 4
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 576
 
10.9%
정 465
 
8.8%
비 443
 
8.4%
진 438
 
8.3%
재 424
 
8.0%
지 416
 
7.9%
촉 411
 
7.8%
역 183
 
3.5%
존 144
 
2.7%
치 144
 
2.7%
Other values (83) 1635
31.0%
Decimal Number
ValueCountFrequency (%)
1 68
32.2%
3 30
14.2%
2 29
13.7%
4 16
 
7.6%
5 15
 
7.1%
6 14
 
6.6%
7 13
 
6.2%
9 10
 
4.7%
8 10
 
4.7%
0 6
 
2.8%
Other Punctuation
ValueCountFrequency (%)
? 38
55.9%
. 26
38.2%
, 4
 
5.9%
Space Separator
ValueCountFrequency (%)
187
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5279
91.4%
Common 496
 
8.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 576
 
10.9%
정 465
 
8.8%
비 443
 
8.4%
진 438
 
8.3%
재 424
 
8.0%
지 416
 
7.9%
촉 411
 
7.8%
역 183
 
3.5%
존 144
 
2.7%
치 144
 
2.7%
Other values (83) 1635
31.0%
Common
ValueCountFrequency (%)
187
37.7%
1 68
 
13.7%
? 38
 
7.7%
3 30
 
6.0%
2 29
 
5.8%
. 26
 
5.2%
4 16
 
3.2%
5 15
 
3.0%
6 14
 
2.8%
7 13
 
2.6%
Other values (9) 60
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5279
91.4%
ASCII 496
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 576
 
10.9%
정 465
 
8.8%
비 443
 
8.4%
진 438
 
8.3%
재 424
 
8.0%
지 416
 
7.9%
촉 411
 
7.8%
역 183
 
3.5%
존 144
 
2.7%
치 144
 
2.7%
Other values (83) 1635
31.0%
ASCII
ValueCountFrequency (%)
187
37.7%
1 68
 
13.7%
? 38
 
7.7%
3 30
 
6.0%
2 29
 
5.8%
. 26
 
5.2%
4 16
 
3.2%
5 15
 
3.0%
6 14
 
2.8%
7 13
 
2.6%
Other values (9) 60
 
12.1%

면적기정
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct401
Distinct (%)86.8%
Missing112
Missing (%)19.5%
Infinite0
Infinite (%)0.0%
Mean713141.34
Minimum1536.2
Maximum3495248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-05-11T05:51:21.319942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1536.2
5-th percentile8525.74
Q1226480.45
median737366.15
Q3920555.95
95-th percentile1872420.5
Maximum3495248
Range3493711.8
Interquartile range (IQR)694075.5

Descriptive statistics

Standard deviation594840.25
Coefficient of variation (CV)0.8341127
Kurtosis5.5604145
Mean713141.34
Median Absolute Deviation (MAD)340403.74
Skewness1.6827948
Sum3.294713 × 108
Variance3.5383492 × 1011
MonotonicityNot monotonic
2024-05-11T05:51:22.044885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
510517.0 4
 
0.7%
1468936.7 3
 
0.5%
3492421.0 3
 
0.5%
1012315.0 3
 
0.5%
897090.0 3
 
0.5%
412777.7 3
 
0.5%
226476.7 3
 
0.5%
688976.9 3
 
0.5%
1044137.0 2
 
0.3%
721416.0 2
 
0.3%
Other values (391) 433
75.4%
(Missing) 112
 
19.5%
ValueCountFrequency (%)
1536.2 1
0.2%
1807.0 1
0.2%
1903.7 1
0.2%
2735.6 1
0.2%
2869.1 1
0.2%
3031.9 1
0.2%
3047.0 1
0.2%
3055.1 1
0.2%
3294.1 1
0.2%
3705.4 2
0.3%
ValueCountFrequency (%)
3495248.0 1
 
0.2%
3492638.3 1
 
0.2%
3492567.8 1
 
0.2%
3492556.2 1
 
0.2%
3492421.0 3
0.5%
1874375.6 1
 
0.2%
1874164.6 1
 
0.2%
1873422.8 1
 
0.2%
1873390.1 1
 
0.2%
1873380.1 2
0.3%

면적증감코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
1
245 
2
192 
<NA>
137 

Length

Max length4
Median length1
Mean length1.7160279
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 245
42.7%
2 192
33.4%
<NA> 137
23.9%

Length

2024-05-11T05:51:22.882321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:51:23.372491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 245
42.7%
2 192
33.4%
na 137
23.9%

면적변경
Real number (ℝ)

MISSING 

Distinct381
Distinct (%)86.6%
Missing134
Missing (%)23.3%
Infinite0
Infinite (%)0.0%
Mean69429.664
Minimum0
Maximum3495248
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-05-11T05:51:23.955565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q180
median908.65
Q311729.1
95-th percentile427904.31
Maximum3495248
Range3495248
Interquartile range (IQR)11649.1

Descriptive statistics

Standard deviation263904.37
Coefficient of variation (CV)3.801032
Kurtosis73.265572
Mean69429.664
Median Absolute Deviation (MAD)904.9
Skewness7.2500408
Sum30549052
Variance6.9645518 × 1010
MonotonicityNot monotonic
2024-05-11T05:51:24.712887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 5
 
0.9%
29609.9 4
 
0.7%
3705.4 4
 
0.7%
11832.7 4
 
0.7%
126.0 3
 
0.5%
27429.1 3
 
0.5%
1.0 3
 
0.5%
1.8 3
 
0.5%
37.0 3
 
0.5%
2735.6 2
 
0.3%
Other values (371) 406
70.7%
(Missing) 134
 
23.3%
ValueCountFrequency (%)
0.0 2
 
0.3%
0.1 5
0.9%
0.2 1
 
0.2%
0.3 1
 
0.2%
0.4 2
 
0.3%
0.5 2
 
0.3%
1.0 3
0.5%
1.3 2
 
0.3%
1.6 1
 
0.2%
1.7 1
 
0.2%
ValueCountFrequency (%)
3495248.0 1
0.2%
1851020.0 1
0.2%
1469910.0 1
0.2%
1249793.0 1
0.2%
1095800.0 1
0.2%
1073000.0 1
0.2%
1001473.0 1
0.2%
953171.0 1
0.2%
898610.0 1
0.2%
877400.0 1
0.2%

면적변경후
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct484
Distinct (%)88.2%
Missing25
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean627992.29
Minimum0
Maximum3495248
Zeros10
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2024-05-11T05:51:25.505424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2629.6
Q156788
median639854.4
Q3905595.5
95-th percentile1867851
Maximum3495248
Range3495248
Interquartile range (IQR)848807.5

Descriptive statistics

Standard deviation607517.2
Coefficient of variation (CV)0.96739595
Kurtosis5.4973962
Mean627992.29
Median Absolute Deviation (MAD)413362.7
Skewness1.7261583
Sum3.4476777 × 108
Variance3.6907714 × 1011
MonotonicityNot monotonic
2024-05-11T05:51:26.213851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
1.7%
3492421.0 4
 
0.7%
1012315.0 3
 
0.5%
801242.3 3
 
0.5%
897090.0 3
 
0.5%
688976.9 3
 
0.5%
1468936.7 3
 
0.5%
510517.0 3
 
0.5%
27429.1 2
 
0.3%
1872420.5 2
 
0.3%
Other values (474) 513
89.4%
(Missing) 25
 
4.4%
ValueCountFrequency (%)
0.0 10
1.7%
212.0 1
 
0.2%
438.56 1
 
0.2%
815.0 1
 
0.2%
1020.0 1
 
0.2%
1108.0 1
 
0.2%
1164.0 1
 
0.2%
1262.0 1
 
0.2%
1390.0 1
 
0.2%
1534.9 1
 
0.2%
ValueCountFrequency (%)
3495248.0 1
 
0.2%
3492650.9 1
 
0.2%
3492638.3 1
 
0.2%
3492556.2 1
 
0.2%
3492421.0 4
0.7%
1874375.6 1
 
0.2%
1874164.6 1
 
0.2%
1873422.8 1
 
0.2%
1873390.1 1
 
0.2%
1873380.1 1
 
0.2%

결정고시관리코드
Text

Distinct401
Distinct (%)70.4%
Missing4
Missing (%)0.7%
Memory size4.6 KiB
2024-05-11T05:51:27.076180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters11400
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique348 ?
Unique (%)61.1%

Sample

1st row11290NTC201908130004
2nd row11230NTC202110250004
3rd row11500NTC202301200001
4th row11000NTC202301300004
5th row11380NTC202302210002
ValueCountFrequency (%)
11000ntc200812262559 33
 
5.8%
11000ntc200908133713 25
 
4.4%
11000ntc201705258123 23
 
4.0%
11000ntc200610190441 16
 
2.8%
11000ntc201509247599 7
 
1.2%
11000ntc201702028005 6
 
1.1%
11000ntc201704278101 6
 
1.1%
11000ntc200612210620 4
 
0.7%
11000ntc200910013919 4
 
0.7%
11260ntc202309260001 4
 
0.7%
Other values (391) 442
77.5%
2024-05-11T05:51:28.447669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3563
31.3%
1 2259
19.8%
2 1365
 
12.0%
N 570
 
5.0%
T 570
 
5.0%
C 570
 
5.0%
3 399
 
3.5%
6 377
 
3.3%
9 374
 
3.3%
7 361
 
3.2%
Other values (3) 992
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9690
85.0%
Uppercase Letter 1710
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3563
36.8%
1 2259
23.3%
2 1365
 
14.1%
3 399
 
4.1%
6 377
 
3.9%
9 374
 
3.9%
7 361
 
3.7%
8 360
 
3.7%
5 357
 
3.7%
4 275
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
N 570
33.3%
T 570
33.3%
C 570
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9690
85.0%
Latin 1710
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3563
36.8%
1 2259
23.3%
2 1365
 
14.1%
3 399
 
4.1%
6 377
 
3.9%
9 374
 
3.9%
7 361
 
3.7%
8 360
 
3.7%
5 357
 
3.7%
4 275
 
2.8%
Latin
ValueCountFrequency (%)
N 570
33.3%
T 570
33.3%
C 570
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3563
31.3%
1 2259
19.8%
2 1365
 
12.0%
N 570
 
5.0%
T 570
 
5.0%
C 570
 
5.0%
3 399
 
3.5%
6 377
 
3.3%
9 374
 
3.3%
7 361
 
3.2%
Other values (3) 992
 
8.7%

Interactions

2024-05-11T05:51:06.394984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:51:04.311626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:51:05.359117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:51:06.980582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:51:04.768710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:51:05.641665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:51:07.357413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:51:05.057750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:51:06.005183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T05:51:28.771828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체조서유형(구분)소분류면적기정면적증감코드면적변경면적변경후
지자체1.0000.3080.2290.2590.1310.0000.256
조서유형(구분)0.3081.0000.4040.3120.7420.1280.366
소분류0.2290.4041.0000.6440.3520.0000.664
면적기정0.2590.3120.6441.0000.1050.0461.000
면적증감코드0.1310.7420.3520.1051.0000.0000.280
면적변경0.0000.1280.0000.0460.0001.0000.630
면적변경후0.2560.3660.6641.0000.2800.6301.000
2024-05-11T05:51:29.086123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체면적증감코드조서유형(구분)소분류
지자체1.0000.1170.1620.128
면적증감코드0.1171.0000.5340.235
조서유형(구분)0.1620.5341.0000.339
소분류0.1280.2350.3391.000
2024-05-11T05:51:29.416025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적기정면적변경면적변경후지자체조서유형(구분)소분류면적증감코드
면적기정1.000-0.1950.9840.1180.2170.5020.112
면적변경-0.1951.000-0.2990.0000.0820.0000.000
면적변경후0.984-0.2991.0000.1170.2440.5240.297
지자체0.1180.0000.1171.0000.1620.1280.117
조서유형(구분)0.2170.0820.2440.1621.0000.3390.534
소분류0.5020.0000.5240.1280.3391.0000.235
면적증감코드0.1120.0000.2970.1170.5340.2351.000

Missing values

2024-05-11T05:51:07.997696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T05:51:08.660104image/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.
2024-05-11T05:51:09.245920image/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

조서관리코드프로젝트코드지자체조서유형(구분)대분류중분류소분류위치명지역명면적기정면적증감코드면적변경면적변경후결정고시관리코드
011000AGZ20190813000811000PPL201908130004성북구변경의제처리구역재정비촉진지구주거지형재정비촉진지구성북구 장위동 일원장위재정비촉진지구1872072.52953171.0918901.511290NTC201908130004
111230AGZ20211025000111230PPL202110250004동대문구변경의제처리구역재정비촉진지구주거지형재정비촉진지구동대문구 전농 1,4동, 답십리동 1,3,5동전농?답십리 재정비촉진지구812867.220.1812867.111230NTC202110250004
211500AGZ20230120000211500PPL202301200001강서구변경의제처리구역재정비촉진지구존치관리구역<NA>존치관리구역71934.0137.071971.011500NTC202301200001
311000AGZ20230130000111000PPL202301300003서울특별시변경의제처리구역재정비촉진지구주거지형재정비촉진지구동작구 노량진동 270-2번지 일대노량진 재정비촉진지구739033.52100.0738933.511000NTC202301300004
411000AGZ20230221000111000PPL202302210004성북구변경의제처리구역재정비촉진지구주거지형재정비촉진지구성북구 장위동일원장위 재정비촉진지구920562.7227.0920535.711380NTC202302210002
511170AGZ20221216000111170PPL202212160001용산구변경의제처리구역재정비촉진지구존치관리구역보광동 151번지 일대한남1 존치관리구역56788.012007.058795.011170NTC202302230011
611000AGZ20230322000111000PPL202303220003서울특별시변경의제처리구역재정비촉진지구중심지형재정비촉진지구동대문구 용두ㆍ전농동 일원청량리 재정비촉진지구370774.3240.6370733.711000NTC202303220003
711000AGZ20230405000111000PPL202304050003은평구변경의제처리구역재정비촉진지구주거지형재정비촉진지구수색동 증산동수색.증산 재정비촉진지구791310.8228.0791282.811380NTC202304050001
811000AGZ20230405000311000PPL202304050005은평구변경의제처리구역재정비촉진지구주거지형재정비촉진지구수색동 증산동수색.증산 재정비촉진지구791282.8129.5791312.311380NTC202304050003
911000AGZ20230405000511000PPL202304050006은평구변경의제처리구역재정비촉진지구주거지형재정비촉진지구수색동 증산동수색?증산 재정비촉진지구791312.32169.6791142.711380NTC202304050004
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56511000AGZ20171120624911000PPL201704278101서울특별시폐지의제처리구역재정비촉진지구존치정비구역광진구 구의동 246-15 일대구의1존치정비구역3887.023887.0<NA>11000NTC201704278101
56611260AGZ20211125000411260PPL202111250003중랑구폐지의제처리구역재정비촉진지구존치정비구역중화동 296-78일대중화3 존치정비구역87633.0287633.00.011260NTC202309260001
56711230AGZ20230602000211230PPL202309190009동대문구변경의제처리구역재정비촉진지구중심지형재정비촉진지구중심지형청량리 재정비촉진지구358324.3231.6358292.711230NTC202309260002
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56911440AGZ20230710000111440PPL202307100001마포구변경의제처리구역재정비촉진지구존치정비구역합정동 380-23일대합정5, 6 존치정비구역8930.92757.98173.011440NTC202307100001
57011470AGZ20240307000111470PPL202403070010양천구변경의제처리구역재정비촉진지구주거지형재정비촉진지구양천구 신정동 1162번지 일대신정재정비 촉진지구638847.611000.0639847.611470NTC202403070007
57111440AGZ20230710000211440PPL202307100001마포구변경의제처리구역재정비촉진지구존치관리구역합정동 380-23일대존치관리구역81743.71757.982501.611440NTC202307100001
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57311260AGZ20211125000111260PPL202111250003중랑구변경의제처리구역재정비촉진지구주거지형재정비촉진지구서울특별시 중랑구 중화동 일원중화재정비촉진지구510711.52459108.251603.311260NTC202309260001