Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory556.6 KiB
Average record size in memory57.0 B

Variable types

Categorical4
Text2

Dataset

Description생활 SOC 복합화 시설 주기능시설인 체육시설을 중심으로 연계 시설의 수요를 분석하여 후보지를 도출생활SOC 복합시설 조성을 위한 기초자료 및 적절성 검토자료로 활용유인력과 인구밀도로 시설물별 가중치 부여 후 수요도 중첩수요가 높고 공급이 낮은 지역을 입지 우선지역으로 도출함인구밀도(수요)와 유인력(공급)을 재분류하여 복합화 시설물별 수요도 산출
Author국토교통부
URLhttps://www.data.go.kr/data/15123092/fileData.do

Alerts

대분류 has constant value ""Constant
시설분류 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 시설분류High correlation
구분 is highly imbalanced (54.8%)Imbalance

Reproduction

Analysis started2023-12-12 04:07:56.774294
Analysis finished2023-12-12 04:07:58.105288
Duration1.33 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
체육시설군
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
체육시설군 10000
100.0%

Length

2023-12-12T13:07:58.170115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:07:58.262884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육시설군 10000
100.0%

시설분류
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
오산시 체육시설
4277 
오산시 도서관
3642 
서초구 주차장
477 
서초구 도서관
469 
서초구 국민체육센터
 
387
Other values (5)
748 

Length

Max length10
Median length8
Mean length7.6448
Min length7

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row오산시 체육시설
2nd row오산시 체육시설
3rd row오산시 도서관
4th row오산시 도서관
5th row오산시 도서관

Common Values

ValueCountFrequency (%)
오산시 체육시설 4277
42.8%
오산시 도서관 3642
36.4%
서초구 주차장 477
 
4.8%
서초구 도서관 469
 
4.7%
서초구 국민체육센터 387
 
3.9%
청양군 국민체육센터 297
 
3.0%
청양군 도서관 167
 
1.7%
고양시 주차장 167
 
1.7%
청양군 돌봄센터 116
 
1.2%
고양시 국민체육센터 1
 
< 0.1%

Length

2023-12-12T13:07:58.358573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:07:58.484055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오산시 7919
39.6%
도서관 4278
21.4%
체육시설 4277
21.4%
서초구 1333
 
6.7%
국민체육센터 685
 
3.4%
주차장 644
 
3.2%
청양군 580
 
2.9%
고양시 168
 
0.8%
돌봄센터 116
 
0.6%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
유인력
9054 
수요도
946 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유인력 9054
90.5%
수요도 946
 
9.5%

Length

2023-12-12T13:07:58.612961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:07:58.733707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유인력 9054
90.5%
수요도 946
 
9.5%

등급코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
2136 
3
2088 
1
2062 
4
1936 
5
1778 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
2 2136
21.4%
3 2088
20.9%
1 2062
20.6%
4 1936
19.4%
5 1778
17.8%

Length

2023-12-12T13:07:58.857944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:07:58.967078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2136
21.4%
3 2088
20.9%
1 2062
20.6%
4 1936
19.4%
5 1778
17.8%

격자번호
Text

Distinct9469
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:07:59.323735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8943 ?
Unique (%)89.4%

Sample

1st row다마325956
2nd row다마345945
3rd row다마297917
4th row다마360852
5th row다마191946
ValueCountFrequency (%)
다사575456 3
 
< 0.1%
다사570458 3
 
< 0.1%
다사579429 3
 
< 0.1%
다사606389 3
 
< 0.1%
다사589394 3
 
< 0.1%
다사569427 2
 
< 0.1%
다마316877 2
 
< 0.1%
다마274817 2
 
< 0.1%
다마301915 2
 
< 0.1%
다마217850 2
 
< 0.1%
Other values (9459) 9975
99.8%
2023-12-12T13:07:59.834913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
다 10000
12.5%
9 8283
10.4%
마 7919
9.9%
2 7631
9.5%
3 7554
9.4%
8 7245
9.1%
4 5797
7.2%
5 5404
6.8%
1 5237
6.5%
6 4798
6.0%
Other values (4) 10132
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
75.0%
Other Letter 20000
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 8283
13.8%
2 7631
12.7%
3 7554
12.6%
8 7245
12.1%
4 5797
9.7%
5 5404
9.0%
1 5237
8.7%
6 4798
8.0%
7 4191
7.0%
0 3860
6.4%
Other Letter
ValueCountFrequency (%)
다 10000
50.0%
마 7919
39.6%
사 1501
 
7.5%
바 580
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
75.0%
Hangul 20000
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 8283
13.8%
2 7631
12.7%
3 7554
12.6%
8 7245
12.1%
4 5797
9.7%
5 5404
9.0%
1 5237
8.7%
6 4798
8.0%
7 4191
7.0%
0 3860
6.4%
Hangul
ValueCountFrequency (%)
다 10000
50.0%
마 7919
39.6%
사 1501
 
7.5%
바 580
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
75.0%
Hangul 20000
 
25.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
다 10000
50.0%
마 7919
39.6%
사 1501
 
7.5%
바 580
 
2.9%
ASCII
ValueCountFrequency (%)
9 8283
13.8%
2 7631
12.7%
3 7554
12.6%
8 7245
12.1%
4 5797
9.7%
5 5404
9.0%
1 5237
8.7%
6 4798
8.0%
7 4191
7.0%
0 3860
6.4%

공간정보
Text

Distinct9469
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:08:00.123880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length188
Median length187
Mean length186.896
Min length176

Characters and Unicode

Total characters1868960
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8943 ?
Unique (%)89.4%

Sample

1st rowMULTIPOLYGON (((126.749596207752 36.1551242676355,126.750707815751 36.1551312246151,126.750699182391 36.1560326983016,126.749587490572 36.1560257832315,126.749596207752 36.1551242676355)))
2nd rowMULTIPOLYGON (((126.771920317212 36.1453450592912,126.773031757573 36.1453517648137,126.773023459489 36.1462532804097,126.771912019128 36.1462465329777,126.771920317212 36.1453450592912)))
3rd rowMULTIPOLYGON (((126.718819368034 36.1197670950181,126.719930473119 36.1197743034548,126.719921672121 36.1206757771413,126.718810567036 36.1206685687046,126.718819368034 36.1197670950181)))
4th rowMULTIPOLYGON (((126.789348305571 36.0616048593361,126.790458572465 36.0616114391301,126.790450442019 36.0625129547261,126.789340175125 36.0625063749321,126.789348305571 36.0616048593361)))
5th rowMULTIPOLYGON (((126.600745090563 36.1450835858216,126.601856530924 36.1450919258153,126.601846305002 36.1459933995018,126.600734864641 36.1459850175986,126.600745090563 36.1450835858216)))
ValueCountFrequency (%)
multipolygon 10000
 
14.3%
37.5113796943386,127.013453490573 3
 
< 0.1%
36.1482236562987 3
 
< 0.1%
127.019122339328 3
 
< 0.1%
37.4484465212787 3
 
< 0.1%
37.4493478273271,127.054555745355 3
 
< 0.1%
37.4493521440073,127.054550380937 3
 
< 0.1%
37.4484465212787,127.055686212636 3
 
< 0.1%
127.054555745355 3
 
< 0.1%
37.4843811232058 3
 
< 0.1%
Other values (56799) 59973
85.7%
2023-12-12T13:08:00.627543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 219040
11.7%
1 186903
10.0%
3 166125
8.9%
2 165363
8.8%
7 146696
 
7.8%
0 124823
 
6.7%
4 122281
 
6.5%
5 121899
 
6.5%
8 120129
 
6.4%
9 115701
 
6.2%
Other values (15) 380000
20.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1488960
79.7%
Other Punctuation 140000
 
7.5%
Uppercase Letter 120000
 
6.4%
Space Separator 60000
 
3.2%
Close Punctuation 30000
 
1.6%
Open Punctuation 30000
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 219040
14.7%
1 186903
12.6%
3 166125
11.2%
2 165363
11.1%
7 146696
9.9%
0 124823
8.4%
4 122281
8.2%
5 121899
8.2%
8 120129
8.1%
9 115701
7.8%
Uppercase Letter
ValueCountFrequency (%)
O 20000
16.7%
L 20000
16.7%
U 10000
8.3%
N 10000
8.3%
G 10000
8.3%
Y 10000
8.3%
P 10000
8.3%
I 10000
8.3%
T 10000
8.3%
M 10000
8.3%
Other Punctuation
ValueCountFrequency (%)
. 100000
71.4%
, 40000
 
28.6%
Space Separator
ValueCountFrequency (%)
60000
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30000
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1748960
93.6%
Latin 120000
 
6.4%

Most frequent character per script

Common
ValueCountFrequency (%)
6 219040
12.5%
1 186903
10.7%
3 166125
9.5%
2 165363
9.5%
7 146696
8.4%
0 124823
7.1%
4 122281
7.0%
5 121899
7.0%
8 120129
6.9%
9 115701
6.6%
Other values (5) 260000
14.9%
Latin
ValueCountFrequency (%)
O 20000
16.7%
L 20000
16.7%
U 10000
8.3%
N 10000
8.3%
G 10000
8.3%
Y 10000
8.3%
P 10000
8.3%
I 10000
8.3%
T 10000
8.3%
M 10000
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1868960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 219040
11.7%
1 186903
10.0%
3 166125
8.9%
2 165363
8.8%
7 146696
 
7.8%
0 124823
 
6.7%
4 122281
 
6.5%
5 121899
 
6.5%
8 120129
 
6.4%
9 115701
 
6.2%
Other values (15) 380000
20.3%

Correlations

2023-12-12T13:08:00.759593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설분류구분등급코드
시설분류1.0000.9820.283
구분0.9821.0000.067
등급코드0.2830.0671.000
2023-12-12T13:08:00.858543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등급코드시설분류구분
등급코드1.0000.1210.082
시설분류0.1211.0000.884
구분0.0820.8841.000
2023-12-12T13:08:00.978479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설분류구분등급코드
시설분류1.0000.8840.121
구분0.8841.0000.082
등급코드0.1210.0821.000

Missing values

2023-12-12T13:07:57.932917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:07:58.048420image/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

대분류시설분류구분등급코드격자번호공간정보
28287체육시설군오산시 체육시설유인력2다마325956MULTIPOLYGON (((126.749596207752 36.1551242676355,126.750707815751 36.1551312246151,126.750699182391 36.1560326983016,126.749587490572 36.1560257832315,126.749596207752 36.1551242676355)))
14255체육시설군오산시 체육시설유인력2다마345945MULTIPOLYGON (((126.771920317212 36.1453450592912,126.773031757573 36.1453517648137,126.773023459489 36.1462532804097,126.771912019128 36.1462465329777,126.771920317212 36.1453450592912)))
34276체육시설군오산시 도서관유인력2다마297917MULTIPOLYGON (((126.718819368034 36.1197670950181,126.719930473119 36.1197743034548,126.719921672121 36.1206757771413,126.718810567036 36.1206685687046,126.718819368034 36.1197670950181)))
38969체육시설군오산시 도서관유인력4다마360852MULTIPOLYGON (((126.789348305571 36.0616048593361,126.790458572465 36.0616114391301,126.790450442019 36.0625129547261,126.789340175125 36.0625063749321,126.789348305571 36.0616048593361)))
30380체육시설군오산시 도서관유인력1다마191946MULTIPOLYGON (((126.600745090563 36.1450835858216,126.601856530924 36.1450919258153,126.601846305002 36.1459933995018,126.600734864641 36.1459850175986,126.600745090563 36.1450835858216)))
25930체육시설군오산시 도서관유인력3다마398844MULTIPOLYGON (((126.831601479478 36.0546354325743,126.832711830192 36.0546415932731,126.83270420266 36.0555430669596,126.831593851946 36.0555369481703,126.831601479478 36.0546354325743)))
9475체육시설군서초구 주차장유인력4다사551422MULTIPOLYGON (((126.992175442629 37.4779399239821,126.993306329005 37.477944785486,126.993300377854 37.4788460915344,126.992169323839 37.4788412719401,126.992175442629 37.4779399239821)))
33085체육시설군오산시 체육시설유인력2다마234938MULTIPOLYGON (((126.648615480703 36.1382211540538,126.649726837245 36.1382290330428,126.649717114237 36.1391305067293,126.648605757695 36.1391225858308,126.648615480703 36.1382211540538)))
39009체육시설군서초구 주차장유인력4다사559437MULTIPOLYGON (((127.001133266371 37.4914984486492,127.002264571843 37.491503226334,127.002258536873 37.4924046162015,127.00112731522 37.4923998385167,127.001133266371 37.4914984486492)))
4874체육시설군오산시 체육시설유인력4다마356894MULTIPOLYGON (((126.784563999057 36.099441985646,126.785674936504 36.09944856544,126.785666806058 36.100350081036,126.784555868611 36.1003434593325,126.784563999057 36.099441985646)))
대분류시설분류구분등급코드격자번호공간정보
8976체육시설군오산시 도서관유인력2다마337894MULTIPOLYGON (((126.763457947766 36.099314035894,126.764568801394 36.0993208671451,126.76456050331 36.1002224246507,126.763449649682 36.1002155933996,126.763457947766 36.099314035894)))
13928체육시설군서초구 주차장유인력1다사631405MULTIPOLYGON (((127.082739140411 37.4629712279248,127.083869942969 37.4629752093288,127.083864997646 37.4638765991963,127.082734111269 37.4638726177923,127.082739140411 37.4629712279248)))
35506체육시설군오산시 도서관유인력1다마333836MULTIPOLYGON (((126.759505042229 36.0469992254148,126.760615057666 36.0470060566659,126.760606675763 36.0479076141715,126.759496492687 36.0479007410109,126.759505042229 36.0469992254148)))
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10021체육시설군오산시 도서관유인력5다마282882MULTIPOLYGON (((126.702471723176 36.0881050425061,126.703582409166 36.0881124185809,126.703573272891 36.0890139341769,126.702462586901 36.0890065581021,126.702471723176 36.0881050425061)))