Overview

Dataset statistics

Number of variables5
Number of observations2404
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory96.4 KiB
Average record size in memory41.1 B

Variable types

Categorical2
Text2
Numeric1

Dataset

Description경남개발공사가 보유한 미분양 사업지구에 대한 자료입니다.
Author경남개발공사
URLhttps://www.data.go.kr/data/15091416/fileData.do

Reproduction

Analysis started2023-12-12 15:30:23.042398
Analysis finished2023-12-12 15:30:23.743977
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업지구명
Categorical

Distinct19
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size18.9 KiB
경남혁신도시개발사업
604 
정촌일반산업단지조성사업1
343 
부산/진해경제자유구역
305 
마산중리택지개발사업
183 
삼계지구 택지조성
163 
Other values (14)
806 

Length

Max length19
Median length17
Mean length12.139351
Min length7

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
경남혁신도시개발사업 604
25.1%
정촌일반산업단지조성사업1 343
14.3%
부산/진해경제자유구역 305
12.7%
마산중리택지개발사업 183
 
7.6%
삼계지구 택지조성 163
 
6.8%
금산지구 택지조성 154
 
6.4%
가호국민임대아파트 임대(가좌) 116
 
4.8%
사천제2일반산업단지 111
 
4.6%
창원현동보금자리주택지구조성사업 100
 
4.2%
서김해일반산업단지 90
 
3.7%
Other values (9) 235
 
9.8%

Length

2023-12-13T00:30:23.840385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경남혁신도시개발사업 604
19.8%
정촌일반산업단지조성사업1 343
11.2%
택지조성 317
10.4%
부산/진해경제자유구역 305
10.0%
마산중리택지개발사업 183
 
6.0%
삼계지구 163
 
5.3%
금산지구 154
 
5.1%
가호국민임대아파트 116
 
3.8%
임대(가좌 116
 
3.8%
사천제2일반산업단지 111
 
3.6%
Other values (17) 637
20.9%

예정지번
Text

Distinct1992
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Memory size18.9 KiB
2023-12-13T00:30:24.616207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length5.405574
Min length1

Characters and Unicode

Total characters12995
Distinct characters62
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1692 ?
Unique (%)70.4%

Sample

1st row44562
2nd row44563
3rd row44835
4th row44844
5th row44847
ValueCountFrequency (%)
1l 14
 
0.6%
2l 12
 
0.5%
34b 10
 
0.4%
5l 10
 
0.4%
3l 9
 
0.4%
4l 9
 
0.4%
33b 9
 
0.4%
13b 7
 
0.3%
9b 6
 
0.2%
32b 6
 
0.2%
Other values (1941) 2388
96.3%
2023-12-13T00:30:25.230859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2606
20.1%
1 1638
12.6%
4 1564
12.0%
2 1091
8.4%
3 699
 
5.4%
6 598
 
4.6%
5 593
 
4.6%
B 580
 
4.5%
D 504
 
3.9%
8 503
 
3.9%
Other values (52) 2619
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7884
60.7%
Dash Punctuation 2606
 
20.1%
Uppercase Letter 1496
 
11.5%
Other Number 532
 
4.1%
Other Letter 331
 
2.5%
Space Separator 76
 
0.6%
Other Punctuation 38
 
0.3%
Lowercase Letter 23
 
0.2%
Math Symbol 9
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 79
23.9%
산 64
19.3%
클 39
11.8%
지 25
 
7.6%
상 24
 
7.3%
원 24
 
7.3%
주 16
 
4.8%
무 15
 
4.5%
근 8
 
2.4%
생 8
 
2.4%
Other values (14) 29
 
8.8%
Decimal Number
ValueCountFrequency (%)
1 1638
20.8%
4 1564
19.8%
2 1091
13.8%
3 699
8.9%
6 598
 
7.6%
5 593
 
7.5%
8 503
 
6.4%
7 478
 
6.1%
0 367
 
4.7%
9 353
 
4.5%
Other Number
ValueCountFrequency (%)
③ 124
23.3%
④ 90
16.9%
① 87
16.4%
② 87
16.4%
⑦ 36
 
6.8%
⑤ 35
 
6.6%
⑧ 32
 
6.0%
⑥ 24
 
4.5%
⑨ 12
 
2.3%
⑩ 5
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
B 580
38.8%
D 504
33.7%
R 207
 
13.8%
L 79
 
5.3%
C 43
 
2.9%
A 34
 
2.3%
E 21
 
1.4%
T 19
 
1.3%
J 9
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
a 19
82.6%
u 3
 
13.0%
o 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 37
97.4%
. 1
 
2.6%
Math Symbol
ValueCountFrequency (%)
~ 8
88.9%
+ 1
 
11.1%
Dash Punctuation
ValueCountFrequency (%)
- 2606
100.0%
Space Separator
ValueCountFrequency (%)
76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11145
85.8%
Latin 1519
 
11.7%
Hangul 331
 
2.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2606
23.4%
1 1638
14.7%
4 1564
14.0%
2 1091
9.8%
3 699
 
6.3%
6 598
 
5.4%
5 593
 
5.3%
8 503
 
4.5%
7 478
 
4.3%
0 367
 
3.3%
Other values (16) 1008
 
9.0%
Hangul
ValueCountFrequency (%)
업 79
23.9%
산 64
19.3%
클 39
11.8%
지 25
 
7.6%
상 24
 
7.3%
원 24
 
7.3%
주 16
 
4.8%
무 15
 
4.5%
근 8
 
2.4%
생 8
 
2.4%
Other values (14) 29
 
8.8%
Latin
ValueCountFrequency (%)
B 580
38.2%
D 504
33.2%
R 207
 
13.6%
L 79
 
5.2%
C 43
 
2.8%
A 34
 
2.2%
E 21
 
1.4%
a 19
 
1.3%
T 19
 
1.3%
J 9
 
0.6%
Other values (2) 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12132
93.4%
Enclosed Alphanum 532
 
4.1%
Hangul 331
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2606
21.5%
1 1638
13.5%
4 1564
12.9%
2 1091
9.0%
3 699
 
5.8%
6 598
 
4.9%
5 593
 
4.9%
B 580
 
4.8%
D 504
 
4.2%
8 503
 
4.1%
Other values (18) 1756
14.5%
Enclosed Alphanum
ValueCountFrequency (%)
③ 124
23.3%
④ 90
16.9%
① 87
16.4%
② 87
16.4%
⑦ 36
 
6.8%
⑤ 35
 
6.6%
⑧ 32
 
6.0%
⑥ 24
 
4.5%
⑨ 12
 
2.3%
⑩ 5
 
0.9%
Hangul
ValueCountFrequency (%)
업 79
23.9%
산 64
19.3%
클 39
11.8%
지 25
 
7.6%
상 24
 
7.3%
원 24
 
7.3%
주 16
 
4.8%
무 15
 
4.5%
근 8
 
2.4%
생 8
 
2.4%
Other values (14) 29
 
8.8%

확정지번
Text

Distinct2242
Distinct (%)93.4%
Missing3
Missing (%)0.1%
Memory size18.9 KiB
2023-12-13T00:30:25.658991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length5.4102457
Min length1

Characters and Unicode

Total characters12990
Distinct characters29
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2089 ?
Unique (%)87.0%

Sample

1st row1036
2nd row1038
3rd row1047-1
4th row1047-10,11 1048-1
5th row1047-12
ValueCountFrequency (%)
신안동 26
 
1.1%
제덕동 12
 
0.5%
1246-1 3
 
0.1%
1244-2 3
 
0.1%
1247-3 3
 
0.1%
11475 3
 
0.1%
11355 3
 
0.1%
1245 3
 
0.1%
44685 2
 
0.1%
12875 2
 
0.1%
Other values (2239) 2384
97.5%
2023-12-13T00:30:26.272536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2783
21.4%
- 1638
12.6%
2 1600
12.3%
4 1208
9.3%
0 944
 
7.3%
7 830
 
6.4%
3 791
 
6.1%
8 780
 
6.0%
5 768
 
5.9%
9 721
 
5.6%
Other values (19) 927
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11132
85.7%
Dash Punctuation 1638
 
12.6%
Other Letter 121
 
0.9%
Space Separator 43
 
0.3%
Other Punctuation 37
 
0.3%
Math Symbol 8
 
0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2783
25.0%
2 1600
14.4%
4 1208
10.9%
0 944
 
8.5%
7 830
 
7.5%
3 791
 
7.1%
8 780
 
7.0%
5 768
 
6.9%
9 721
 
6.5%
6 707
 
6.4%
Other Letter
ValueCountFrequency (%)
동 39
32.2%
안 27
22.3%
신 27
22.3%
덕 12
 
9.9%
제 12
 
9.9%
외 2
 
1.7%
록 1
 
0.8%
블 1
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
B 1
33.3%
L 1
33.3%
E 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 36
97.3%
. 1
 
2.7%
Math Symbol
ValueCountFrequency (%)
~ 7
87.5%
+ 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 1638
100.0%
Space Separator
ValueCountFrequency (%)
43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12866
99.0%
Hangul 121
 
0.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2783
21.6%
- 1638
12.7%
2 1600
12.4%
4 1208
9.4%
0 944
 
7.3%
7 830
 
6.5%
3 791
 
6.1%
8 780
 
6.1%
5 768
 
6.0%
9 721
 
5.6%
Other values (8) 803
 
6.2%
Hangul
ValueCountFrequency (%)
동 39
32.2%
안 27
22.3%
신 27
22.3%
덕 12
 
9.9%
제 12
 
9.9%
외 2
 
1.7%
록 1
 
0.8%
블 1
 
0.8%
Latin
ValueCountFrequency (%)
B 1
33.3%
L 1
33.3%
E 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12869
99.1%
Hangul 121
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2783
21.6%
- 1638
12.7%
2 1600
12.4%
4 1208
9.4%
0 944
 
7.3%
7 830
 
6.4%
3 791
 
6.1%
8 780
 
6.1%
5 768
 
6.0%
9 721
 
5.6%
Other values (11) 806
 
6.3%
Hangul
ValueCountFrequency (%)
동 39
32.2%
안 27
22.3%
신 27
22.3%
덕 12
 
9.9%
제 12
 
9.9%
외 2
 
1.7%
록 1
 
0.8%
블 1
 
0.8%

용도
Categorical

Distinct23
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size18.9 KiB
단독주택(일반)
1291 
산업시설용지
249 
지원시설용지
228 
상업용지
185 
유통업무설비
133 
Other values (18)
318 

Length

Max length8
Median length8
Mean length6.8331947
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row공동주택용지
2nd row공동주택용지
3rd row단독주택(일반)
4th row공공청사
5th row단독주택(일반)

Common Values

ValueCountFrequency (%)
단독주택(일반) 1291
53.7%
산업시설용지 249
 
10.4%
지원시설용지 228
 
9.5%
상업용지 185
 
7.7%
유통업무설비 133
 
5.5%
근린생활시설 62
 
2.6%
공동주택용지 49
 
2.0%
클러스터용지 39
 
1.6%
준주거용지 27
 
1.1%
자동차시설 26
 
1.1%
Other values (13) 115
 
4.8%

Length

2023-12-13T00:30:26.447948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단독주택(일반 1291
53.7%
산업시설용지 249
 
10.4%
지원시설용지 228
 
9.5%
상업용지 185
 
7.7%
유통업무설비 133
 
5.5%
근린생활시설 62
 
2.6%
공동주택용지 49
 
2.0%
클러스터용지 39
 
1.6%
준주거용지 27
 
1.1%
자동차시설 26
 
1.1%
Other values (13) 115
 
4.8%

면적
Real number (ℝ)

Distinct1770
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2600.861
Minimum0
Maximum291003
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size21.3 KiB
2023-12-13T00:30:26.612600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200.9
Q1254.175
median321.05
Q3944.45
95-th percentile11862.15
Maximum291003
Range291003
Interquartile range (IQR)690.275

Descriptive statistics

Standard deviation11254.476
Coefficient of variation (CV)4.3272117
Kurtosis282.64849
Mean2600.861
Median Absolute Deviation (MAD)110.8
Skewness14.371474
Sum6252469.8
Variance1.2666323 × 108
MonotonicityNot monotonic
2023-12-13T00:30:26.752004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210.5 15
 
0.6%
268.0 11
 
0.5%
210.0 8
 
0.3%
329.8 8
 
0.3%
290.5 8
 
0.3%
240.0 7
 
0.3%
256.0 7
 
0.3%
255.8 7
 
0.3%
323.8 6
 
0.2%
331.8 6
 
0.2%
Other values (1760) 2321
96.5%
ValueCountFrequency (%)
0.0 3
0.1%
8.1 1
 
< 0.1%
26.1 1
 
< 0.1%
33.1 1
 
< 0.1%
36.0 1
 
< 0.1%
126.5 1
 
< 0.1%
153.1 1
 
< 0.1%
155.3 1
 
< 0.1%
157.1 1
 
< 0.1%
158.5 1
 
< 0.1%
ValueCountFrequency (%)
291003.0 1
< 0.1%
206387.5 1
< 0.1%
185799.3 1
< 0.1%
171649.2 1
< 0.1%
103319.5 1
< 0.1%
89576.4 1
< 0.1%
88565.0 1
< 0.1%
80142.4 1
< 0.1%
76581.0 1
< 0.1%
67994.1 1
< 0.1%

Interactions

2023-12-13T00:30:23.397933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:30:26.855743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업지구명용도면적
사업지구명1.0000.8570.304
용도0.8571.0000.304
면적0.3040.3041.000
2023-12-13T00:30:26.956448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도사업지구명
용도1.0000.436
사업지구명0.4361.000
2023-12-13T00:30:27.042948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적사업지구명용도
면적1.0000.1320.126
사업지구명0.1321.0000.436
용도0.1260.4361.000

Missing values

2023-12-13T00:30:23.553077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:30:23.685778image/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.

Sample

사업지구명예정지번확정지번용도면적
0마산중리택지개발사업445621036공동주택용지24775.4
1마산중리택지개발사업445631038공동주택용지24880.8
2마산중리택지개발사업448351047-1단독주택(일반)314.8
3마산중리택지개발사업448441047-10,11 1048-1공공청사1914.1
4마산중리택지개발사업448471047-12단독주택(일반)245.1
5마산중리택지개발사업448481047-13단독주택(일반)241.0
6마산중리택지개발사업448491047-14단독주택(일반)262.7
7마산중리택지개발사업448501047-15단독주택(일반)322.3
8마산중리택지개발사업448511047-16단독주택(일반)319.9
9마산중리택지개발사업448521047-17단독주택(일반)329.2
사업지구명예정지번확정지번용도면적
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