Overview

Dataset statistics

Number of variables7
Number of observations827
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.2 KiB
Average record size in memory57.2 B

Variable types

Categorical4
Text2
Numeric1

Dataset

Description경기도 시흥시 도시계획정보시스템 시설 현황에는 시군구코드, 대,중,소분류 코드, 도면번호, 면적(도형), 데이터기준일이 있습니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15117875/fileData.do

Alerts

시군구코드 has constant value ""Constant
데이터기준일 has constant value ""Constant
중분류코드 is highly overall correlated with 대분류코드High correlation
대분류코드 is highly overall correlated with 중분류코드High correlation

Reproduction

Analysis started2024-04-20 13:39:54.743479
Analysis finished2024-04-20 13:39:56.145044
Duration1.4 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
시흥시
827 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시흥시 827
100.0%

Length

2024-04-20T22:39:56.428409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T22:39:56.791579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시흥시 827
100.0%

대분류코드
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
교통시설
344 
공공문화시설
229 
유통공급시설
141 
방재시설
59 
환경기초시설
52 

Length

Max length6
Median length6
Mean length5.025393
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
교통시설 344
41.6%
공공문화시설 229
27.7%
유통공급시설 141
17.0%
방재시설 59
 
7.1%
환경기초시설 52
 
6.3%
보건위생시설 2
 
0.2%

Length

2024-04-20T22:39:57.181418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T22:39:57.600706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교통시설 344
41.6%
공공문화시설 229
27.7%
유통공급시설 141
17.0%
방재시설 59
 
7.1%
환경기초시설 52
 
6.3%
보건위생시설 2
 
0.2%

중분류코드
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
주차장
312 
학교
118 
전기공급설비
83 
공공청사
70 
하수도
47 
Other values (23)
197 

Length

Max length13
Median length9
Mean length3.5634825
Min length2

Unique

Unique4 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
주차장 312
37.7%
학교 118
 
14.3%
전기공급설비 83
 
10.0%
공공청사 70
 
8.5%
하수도 47
 
5.7%
수도공급설비 32
 
3.9%
하천 31
 
3.7%
유수지 24
 
2.9%
철도 22
 
2.7%
사회복지시설 18
 
2.2%
Other values (18) 70
 
8.5%

Length

2024-04-20T22:39:58.046417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주차장 312
37.7%
학교 118
 
14.3%
전기공급설비 83
 
10.0%
공공청사 70
 
8.5%
하수도 47
 
5.7%
수도공급설비 32
 
3.9%
하천 31
 
3.7%
유수지 24
 
2.9%
철도 22
 
2.7%
사회복지시설 18
 
2.2%
Other values (18) 70
 
8.5%

소분류
Text

Distinct59
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
2024-04-20T22:39:58.944719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.0568319
Min length2

Characters and Unicode

Total characters5009
Distinct characters95
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

Unique13 ?
Unique (%)1.6%

Sample

1st row여객자동차터미널
2nd row여객자동차터미널
3rd row화물터미널
4th row화물터미널
5th row공영차고지
ValueCountFrequency (%)
기타주차장시설 183
22.1%
노외주차장 125
15.1%
기타전기공급설비 73
 
8.8%
기타공공청사시설 59
 
7.1%
초등학교 51
 
6.2%
중학교 26
 
3.1%
기타하수도시설 25
 
3.0%
저류시설 23
 
2.8%
소하천 21
 
2.5%
고등학교 19
 
2.3%
Other values (49) 222
26.8%
2024-04-20T22:40:00.179444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
설 503
 
10.0%
기 500
 
10.0%
타 424
 
8.5%
시 420
 
8.4%
장 317
 
6.3%
차 314
 
6.3%
주 308
 
6.1%
공 241
 
4.8%
노 125
 
2.5%
외 125
 
2.5%
Other values (85) 1732
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4984
99.5%
Close Punctuation 11
 
0.2%
Open Punctuation 11
 
0.2%
Decimal Number 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 503
 
10.1%
기 500
 
10.0%
타 424
 
8.5%
시 420
 
8.4%
장 317
 
6.4%
차 314
 
6.3%
주 308
 
6.2%
공 241
 
4.8%
노 125
 
2.5%
외 125
 
2.5%
Other values (82) 1707
34.2%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Decimal Number
ValueCountFrequency (%)
2 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4984
99.5%
Common 25
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 503
 
10.1%
기 500
 
10.0%
타 424
 
8.5%
시 420
 
8.4%
장 317
 
6.4%
차 314
 
6.3%
주 308
 
6.2%
공 241
 
4.8%
노 125
 
2.5%
외 125
 
2.5%
Other values (82) 1707
34.2%
Common
ValueCountFrequency (%)
) 11
44.0%
( 11
44.0%
2 3
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4984
99.5%
ASCII 25
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
설 503
 
10.1%
기 500
 
10.0%
타 424
 
8.5%
시 420
 
8.4%
장 317
 
6.4%
차 314
 
6.3%
주 308
 
6.2%
공 241
 
4.8%
노 125
 
2.5%
외 125
 
2.5%
Other values (82) 1707
34.2%
ASCII
ValueCountFrequency (%)
) 11
44.0%
( 11
44.0%
2 3
 
12.0%

도면번호
Text

Distinct327
Distinct (%)39.6%
Missing1
Missing (%)0.1%
Memory size6.6 KiB
2024-04-20T22:40:01.460483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5012107
Min length1

Characters and Unicode

Total characters2066
Distinct characters107
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

Unique195 ?
Unique (%)23.6%

Sample

1st row정왕1
2nd row1
3rd row2
4th row3
5th row능곡1
ValueCountFrequency (%)
1 48
 
5.8%
2 37
 
4.5%
3 26
 
3.1%
4 21
 
2.5%
5 19
 
2.3%
목감1 16
 
1.9%
6 15
 
1.8%
7 13
 
1.6%
10 13
 
1.6%
목감2 12
 
1.5%
Other values (317) 606
73.4%
2024-04-20T22:40:02.921374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 347
 
16.8%
2 182
 
8.8%
3 121
 
5.9%
4 95
 
4.6%
왕 83
 
4.0%
5 83
 
4.0%
정 82
 
4.0%
6 63
 
3.0%
7 54
 
2.6%
감 53
 
2.6%
Other values (97) 903
43.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1056
51.1%
Other Letter 1008
48.8%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
왕 83
 
8.2%
정 82
 
8.1%
감 53
 
5.3%
목 51
 
5.1%
이 42
 
4.2%
은 38
 
3.8%
시 36
 
3.6%
곡 34
 
3.4%
엠 31
 
3.1%
브 31
 
3.1%
Other values (86) 527
52.3%
Decimal Number
ValueCountFrequency (%)
1 347
32.9%
2 182
17.2%
3 121
 
11.5%
4 95
 
9.0%
5 83
 
7.9%
6 63
 
6.0%
7 54
 
5.1%
8 40
 
3.8%
9 36
 
3.4%
0 35
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1058
51.2%
Hangul 1008
48.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
왕 83
 
8.2%
정 82
 
8.1%
감 53
 
5.3%
목 51
 
5.1%
이 42
 
4.2%
은 38
 
3.8%
시 36
 
3.6%
곡 34
 
3.4%
엠 31
 
3.1%
브 31
 
3.1%
Other values (86) 527
52.3%
Common
ValueCountFrequency (%)
1 347
32.8%
2 182
17.2%
3 121
 
11.4%
4 95
 
9.0%
5 83
 
7.8%
6 63
 
6.0%
7 54
 
5.1%
8 40
 
3.8%
9 36
 
3.4%
0 35
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1058
51.2%
Hangul 1008
48.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 347
32.8%
2 182
17.2%
3 121
 
11.4%
4 95
 
9.0%
5 83
 
7.8%
6 63
 
6.0%
7 54
 
5.1%
8 40
 
3.8%
9 36
 
3.4%
0 35
 
3.3%
Hangul
ValueCountFrequency (%)
왕 83
 
8.2%
정 82
 
8.1%
감 53
 
5.3%
목 51
 
5.1%
이 42
 
4.2%
은 38
 
3.8%
시 36
 
3.6%
곡 34
 
3.4%
엠 31
 
3.1%
브 31
 
3.1%
Other values (86) 527
52.3%

면적(도형)
Real number (ℝ)

Distinct766
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12263.212
Minimum9
Maximum651583.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-04-20T22:40:03.240679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile258.2
Q1686.7
median1866
Q39720.15
95-th percentile49873.18
Maximum651583.2
Range651574.2
Interquartile range (IQR)9033.45

Descriptive statistics

Standard deviation41947.97
Coefficient of variation (CV)3.4206349
Kurtosis96.613653
Mean12263.212
Median Absolute Deviation (MAD)1485
Skewness8.6271354
Sum10141676
Variance1.7596322 × 109
MonotonicityNot monotonic
2024-04-20T22:40:03.491035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
333.0 3
 
0.4%
4051.0 3
 
0.4%
210.0 3
 
0.4%
538.0 3
 
0.4%
328.0 3
 
0.4%
100.0 3
 
0.4%
455.7 2
 
0.2%
785.0 2
 
0.2%
876.0 2
 
0.2%
792.0 2
 
0.2%
Other values (756) 801
96.9%
ValueCountFrequency (%)
9.0 1
 
0.1%
11.0 1
 
0.1%
45.0 1
 
0.1%
59.2 1
 
0.1%
59.5 1
 
0.1%
61.0 1
 
0.1%
62.0 1
 
0.1%
82.0 2
0.2%
97.0 1
 
0.1%
100.0 3
0.4%
ValueCountFrequency (%)
651583.2 1
0.1%
404160.0 1
0.1%
395329.1 1
0.1%
370000.0 1
0.1%
357100.0 1
0.1%
251180.0 1
0.1%
215789.8 1
0.1%
189464.0 1
0.1%
180306.2 1
0.1%
163728.5 1
0.1%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
2023-08-04
827 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-04
2nd row2023-08-04
3rd row2023-08-04
4th row2023-08-04
5th row2023-08-04

Common Values

ValueCountFrequency (%)
2023-08-04 827
100.0%

Length

2024-04-20T22:40:03.877963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T22:40:04.049242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-04 827
100.0%

Interactions

2024-04-20T22:39:55.228776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-20T22:40:04.182169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류코드중분류코드소분류면적(도형)
대분류코드1.0001.0001.0000.183
중분류코드1.0001.0001.0000.569
소분류1.0001.0001.0000.803
면적(도형)0.1830.5690.8031.000
2024-04-20T22:40:04.338985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중분류코드대분류코드
중분류코드1.0000.987
대분류코드0.9871.000
2024-04-20T22:40:04.476035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(도형)대분류코드중분류코드
면적(도형)1.0000.1090.248
대분류코드0.1091.0000.987
중분류코드0.2480.9871.000

Missing values

2024-04-20T22:39:55.588720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-20T22:39:55.989726image/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

시군구코드대분류코드중분류코드소분류도면번호면적(도형)데이터기준일
0시흥시교통시설자동차정류장여객자동차터미널정왕13678.02023-08-04
1시흥시교통시설자동차정류장여객자동차터미널11350.02023-08-04
2시흥시교통시설자동차정류장화물터미널267345.22023-08-04
3시흥시교통시설자동차정류장화물터미널362142.02023-08-04
4시흥시교통시설자동차정류장공영차고지능곡17817.02023-08-04
5시흥시교통시설자동차정류장공영차고지49890.02023-08-04
6시흥시교통시설자동차정류장기타자동차정류장535772.02023-08-04
7시흥시교통시설자동차정류장기타자동차정류장117012.62023-08-04
8시흥시교통시설주차장기타주차장시설11338.02023-08-04
9시흥시교통시설주차장기타주차장시설31576.02023-08-04
시군구코드대분류코드중분류코드소분류도면번호면적(도형)데이터기준일
817시흥시환경기초시설하수도공공하수도중간선하수관간선16109.02023-08-04
818시흥시환경기초시설하수도공공하수도중간선하수관간선171076.02023-08-04
819시흥시환경기초시설하수도공공하수도중간선하수관간선18416.02023-08-04
820시흥시환경기초시설하수도기타하수도시설거모1785.02023-08-04
821시흥시환경기초시설하수도하수종말처리시설거모212670.02023-08-04
822시흥시환경기초시설하수도기타하수도시설간선1326307.02023-08-04
823시흥시환경기초시설하수도기타하수도시설간선1437928.02023-08-04
824시흥시환경기초시설하수도기타하수도시설지선182.02023-08-04
825시흥시환경기초시설하수도하수종말처리시설117014.22023-08-04
826시흥시환경기초시설하수도기타하수도시설2314401.02023-08-04