Overview

Dataset statistics

Number of variables10
Number of observations327
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.0 KiB
Average record size in memory81.4 B

Variable types

Categorical6
Text4

Dataset

Description전북특별자치도 김제시 야외운동기구 현황입니다.연도, 엽변동, 설치기구 종류, 주소, 소유권자, 설치품목 및 설치수량, 담당부서등을 제공합니다.
Author전북특별자치도 김제시
URLhttps://www.data.go.kr/data/15037946/fileData.do

Alerts

연도 is highly overall correlated with 시공업체High correlation
시공업체 is highly overall correlated with 연도High correlation
소요예산(천원) is highly imbalanced (54.1%)Imbalance

Reproduction

Analysis started2024-03-14 18:01:18.290566
Analysis finished2024-03-14 18:01:20.105768
Duration1.82 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2018
71 
2016
67 
2019
67 
2015
62 
2017
60 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2018 71
21.7%
2016 67
20.5%
2019 67
20.5%
2015 62
19.0%
2017 60
18.3%

Length

2024-03-15T03:01:20.308731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:01:20.581770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 71
21.7%
2016 67
20.5%
2019 67
20.5%
2015 62
19.0%
2017 60
18.3%

읍면동
Categorical

Distinct19
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
금산면
 
22
봉남면
 
21
청하면
 
20
교월동
 
19
죽산면
 
19
Other values (14)
226 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row만경읍
2nd row만경읍
3rd row만경읍
4th row만경읍
5th row죽산면

Common Values

ValueCountFrequency (%)
금산면 22
 
6.7%
봉남면 21
 
6.4%
청하면 20
 
6.1%
교월동 19
 
5.8%
죽산면 19
 
5.8%
백산면 18
 
5.5%
요촌동 18
 
5.5%
황산면 18
 
5.5%
백구면 17
 
5.2%
진봉면 17
 
5.2%
Other values (9) 138
42.2%

Length

2024-03-15T03:01:20.977150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금산면 22
 
6.7%
봉남면 21
 
6.4%
청하면 20
 
6.1%
교월동 19
 
5.8%
죽산면 19
 
5.8%
백산면 18
 
5.5%
요촌동 18
 
5.5%
황산면 18
 
5.5%
공덕면 17
 
5.2%
광활면 17
 
5.2%
Other values (9) 138
42.2%

구분
Text

Distinct278
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-15T03:01:22.324963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.5015291
Min length2

Characters and Unicode

Total characters818
Distinct characters178
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

Unique244 ?
Unique (%)74.6%

Sample

1st row익수당
2nd row장산
3rd row옥산
4th row창자
5th row해창
ValueCountFrequency (%)
데이터 12
 
3.5%
미집계 12
 
3.5%
수각 3
 
0.9%
신기 3
 
0.9%
화동 3
 
0.9%
대율 3
 
0.9%
농원 3
 
0.9%
몽포 2
 
0.6%
농장 2
 
0.6%
상목 2
 
0.6%
Other values (271) 296
86.8%
2024-03-15T03:01:24.133460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 31
 
3.8%
동 31
 
3.8%
산 26
 
3.2%
대 20
 
2.4%
정 16
 
2.0%
당 15
 
1.8%
이 15
 
1.8%
수 15
 
1.8%
14
 
1.7%
계 13
 
1.6%
Other values (168) 622
76.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 787
96.2%
Space Separator 14
 
1.7%
Decimal Number 7
 
0.9%
Lowercase Letter 3
 
0.4%
Uppercase Letter 2
 
0.2%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 31
 
3.9%
동 31
 
3.9%
산 26
 
3.3%
대 20
 
2.5%
정 16
 
2.0%
당 15
 
1.9%
이 15
 
1.9%
수 15
 
1.9%
계 13
 
1.7%
용 13
 
1.7%
Other values (156) 592
75.2%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 2
28.6%
3 1
 
14.3%
9 1
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
a 1
33.3%
p 1
33.3%
t 1
33.3%
Space Separator
ValueCountFrequency (%)
14
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 787
96.2%
Common 26
 
3.2%
Latin 5
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 31
 
3.9%
동 31
 
3.9%
산 26
 
3.3%
대 20
 
2.5%
정 16
 
2.0%
당 15
 
1.9%
이 15
 
1.9%
수 15
 
1.9%
계 13
 
1.7%
용 13
 
1.7%
Other values (156) 592
75.2%
Common
ValueCountFrequency (%)
14
53.8%
1 3
 
11.5%
2 2
 
7.7%
) 2
 
7.7%
( 2
 
7.7%
3 1
 
3.8%
- 1
 
3.8%
9 1
 
3.8%
Latin
ValueCountFrequency (%)
A 2
40.0%
a 1
20.0%
p 1
20.0%
t 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 787
96.2%
ASCII 31
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 31
 
3.9%
동 31
 
3.9%
산 26
 
3.3%
대 20
 
2.5%
정 16
 
2.0%
당 15
 
1.9%
이 15
 
1.9%
수 15
 
1.9%
계 13
 
1.7%
용 13
 
1.7%
Other values (156) 592
75.2%
ASCII
ValueCountFrequency (%)
14
45.2%
1 3
 
9.7%
2 2
 
6.5%
A 2
 
6.5%
) 2
 
6.5%
( 2
 
6.5%
3 1
 
3.2%
- 1
 
3.2%
a 1
 
3.2%
p 1
 
3.2%
Other values (2) 2
 
6.5%

설치장소 지번
Text

Distinct304
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-15T03:01:25.531869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length7.8990826
Min length5

Characters and Unicode

Total characters2583
Distinct characters136
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

Unique294 ?
Unique (%)89.9%

Sample

1st row만경로7699-21
2nd row능제3길35-49
3rd row지평선로2145-14
4th row지평선로2145-14
5th row대창리115-1
ValueCountFrequency (%)
데이터 14
 
2.9%
미집계 14
 
2.9%
창제리 6
 
1.2%
용산리 4
 
0.8%
대율1길157 3
 
0.6%
백학동 3
 
0.6%
2길 3
 
0.6%
은파리 3
 
0.6%
옥성리 3
 
0.6%
옥포리 2
 
0.4%
Other values (402) 434
88.8%
2024-03-15T03:01:27.554881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 254
 
9.8%
- 213
 
8.2%
리 174
 
6.7%
162
 
6.3%
2 157
 
6.1%
3 127
 
4.9%
5 111
 
4.3%
4 110
 
4.3%
6 108
 
4.2%
7 86
 
3.3%
Other values (126) 1081
41.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1192
46.1%
Other Letter 1012
39.2%
Dash Punctuation 213
 
8.2%
Space Separator 162
 
6.3%
Other Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 174
 
17.2%
길 79
 
7.8%
동 58
 
5.7%
산 35
 
3.5%
대 25
 
2.5%
월 20
 
2.0%
로 19
 
1.9%
상 18
 
1.8%
계 18
 
1.8%
신 18
 
1.8%
Other values (110) 548
54.2%
Decimal Number
ValueCountFrequency (%)
1 254
21.3%
2 157
13.2%
3 127
10.7%
5 111
9.3%
4 110
9.2%
6 108
9.1%
7 86
 
7.2%
8 83
 
7.0%
9 79
 
6.6%
0 77
 
6.5%
Dash Punctuation
ValueCountFrequency (%)
- 213
100.0%
Space Separator
ValueCountFrequency (%)
162
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1570
60.8%
Hangul 1012
39.2%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 174
 
17.2%
길 79
 
7.8%
동 58
 
5.7%
산 35
 
3.5%
대 25
 
2.5%
월 20
 
2.0%
로 19
 
1.9%
상 18
 
1.8%
계 18
 
1.8%
신 18
 
1.8%
Other values (110) 548
54.2%
Common
ValueCountFrequency (%)
1 254
16.2%
- 213
13.6%
162
10.3%
2 157
10.0%
3 127
8.1%
5 111
7.1%
4 110
7.0%
6 108
6.9%
7 86
 
5.5%
8 83
 
5.3%
Other values (5) 159
10.1%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1571
60.8%
Hangul 1012
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 254
16.2%
- 213
13.6%
162
10.3%
2 157
10.0%
3 127
8.1%
5 111
7.1%
4 110
7.0%
6 108
6.9%
7 86
 
5.5%
8 83
 
5.3%
Other values (6) 160
10.2%
Hangul
ValueCountFrequency (%)
리 174
 
17.2%
길 79
 
7.8%
동 58
 
5.7%
산 35
 
3.5%
대 25
 
2.5%
월 20
 
2.0%
로 19
 
1.9%
상 18
 
1.8%
계 18
 
1.8%
신 18
 
1.8%
Other values (110) 548
54.2%

설치장소 면적(제곱미터)
Text

Distinct237
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-15T03:01:29.252695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.1437309
Min length1

Characters and Unicode

Total characters1355
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)59.6%

Sample

1st row7296
2nd row829
3rd row9924
4th row9924
5th row12020
ValueCountFrequency (%)
데이터 41
 
11.1%
미집계 41
 
11.1%
250㎡ 4
 
1.1%
330㎡ 4
 
1.1%
192㎡ 3
 
0.8%
327㎡ 3
 
0.8%
291㎡ 3
 
0.8%
397㎡ 3
 
0.8%
284㎡ 3
 
0.8%
236㎡ 2
 
0.5%
Other values (228) 261
70.9%
2024-03-15T03:01:31.319638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㎡ 181
13.4%
1 121
 
8.9%
2 119
 
8.8%
3 112
 
8.3%
0 81
 
6.0%
4 80
 
5.9%
9 75
 
5.5%
5 72
 
5.3%
8 71
 
5.2%
7 69
 
5.1%
Other values (10) 374
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 860
63.5%
Other Letter 247
 
18.2%
Other Symbol 181
 
13.4%
Space Separator 41
 
3.0%
Other Punctuation 26
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 121
14.1%
2 119
13.8%
3 112
13.0%
0 81
9.4%
4 80
9.3%
9 75
8.7%
5 72
8.4%
8 71
8.3%
7 69
8.0%
6 60
7.0%
Other Letter
ValueCountFrequency (%)
데 41
16.6%
이 41
16.6%
계 41
16.6%
집 41
16.6%
미 41
16.6%
터 41
16.6%
평 1
 
0.4%
Other Symbol
ValueCountFrequency (%)
㎡ 181
100.0%
Space Separator
ValueCountFrequency (%)
41
100.0%
Other Punctuation
ValueCountFrequency (%)
. 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1108
81.8%
Hangul 247
 
18.2%

Most frequent character per script

Common
ValueCountFrequency (%)
㎡ 181
16.3%
1 121
10.9%
2 119
10.7%
3 112
10.1%
0 81
7.3%
4 80
7.2%
9 75
6.8%
5 72
 
6.5%
8 71
 
6.4%
7 69
 
6.2%
Other values (3) 127
11.5%
Hangul
ValueCountFrequency (%)
데 41
16.6%
이 41
16.6%
계 41
16.6%
집 41
16.6%
미 41
16.6%
터 41
16.6%
평 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 927
68.4%
Hangul 247
 
18.2%
CJK Compat 181
 
13.4%

Most frequent character per block

CJK Compat
ValueCountFrequency (%)
㎡ 181
100.0%
ASCII
ValueCountFrequency (%)
1 121
13.1%
2 119
12.8%
3 112
12.1%
0 81
8.7%
4 80
8.6%
9 75
8.1%
5 72
7.8%
8 71
7.7%
7 69
7.4%
6 60
6.5%
Other values (2) 67
7.2%
Hangul
ValueCountFrequency (%)
데 41
16.6%
이 41
16.6%
계 41
16.6%
집 41
16.6%
미 41
16.6%
터 41
16.6%
평 1
 
0.4%

소유권자
Categorical

Distinct36
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
마을
99 
경로당
78 
데이터 미집계
29 
마을회관
25 
경로
24 
Other values (31)
72 

Length

Max length7
Median length6
Mean length3.0703364
Min length2

Unique

Unique23 ?
Unique (%)7.0%

Sample

1st row김제시
2nd row경로당
3rd row경로당
4th row경로당
5th row한산이씨종중

Common Values

ValueCountFrequency (%)
마을 99
30.3%
경로당 78
23.9%
데이터 미집계 29
 
8.9%
마을회관 25
 
7.6%
경로 24
 
7.3%
모정 13
 
4.0%
김제시 10
 
3.1%
마을회 9
 
2.8%
마을공터 6
 
1.8%
경로회 3
 
0.9%
Other values (26) 31
 
9.5%

Length

2024-03-15T03:01:31.761574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
마을 99
27.8%
경로당 78
21.9%
데이터 29
 
8.1%
미집계 29
 
8.1%
마을회관 25
 
7.0%
경로 24
 
6.7%
모정 13
 
3.7%
김제시 10
 
2.8%
마을회 9
 
2.5%
마을공터 6
 
1.7%
Other values (27) 34
 
9.6%

설치품목
Text

Distinct125
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-15T03:01:32.705743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length6.5137615
Min length2

Characters and Unicode

Total characters2130
Distinct characters99
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)24.2%

Sample

1st row하늘걷기
2nd row하늘걷기
3rd row허리돌리기
4th row허리돌리기
5th row하늘걷기
ValueCountFrequency (%)
허리돌리기 41
 
10.8%
허리돌리기외1 24
 
6.3%
1 20
 
5.3%
데이터 19
 
5.0%
미집계 19
 
5.0%
공중걷기 15
 
4.0%
하늘걷기 13
 
3.4%
큰활차외 10
 
2.6%
파도타기외1 9
 
2.4%
하늘걷기외1 8
 
2.1%
Other values (117) 200
52.9%
2024-03-15T03:01:34.061524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 295
 
13.8%
리 250
 
11.7%
외 121
 
5.7%
허 119
 
5.6%
돌 104
 
4.9%
걷 88
 
4.1%
1 83
 
3.9%
+ 59
 
2.8%
55
 
2.6%
공 47
 
2.2%
Other values (89) 909
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1862
87.4%
Decimal Number 122
 
5.7%
Math Symbol 59
 
2.8%
Space Separator 55
 
2.6%
Other Punctuation 32
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 295
15.8%
리 250
 
13.4%
외 121
 
6.5%
허 119
 
6.4%
돌 104
 
5.6%
걷 88
 
4.7%
공 47
 
2.5%
중 47
 
2.5%
하 44
 
2.4%
늘 43
 
2.3%
Other values (80) 704
37.8%
Decimal Number
ValueCountFrequency (%)
1 83
68.0%
2 19
 
15.6%
3 16
 
13.1%
5 3
 
2.5%
4 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
/ 22
68.8%
. 10
31.2%
Math Symbol
ValueCountFrequency (%)
+ 59
100.0%
Space Separator
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1862
87.4%
Common 268
 
12.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 295
15.8%
리 250
 
13.4%
외 121
 
6.5%
허 119
 
6.4%
돌 104
 
5.6%
걷 88
 
4.7%
공 47
 
2.5%
중 47
 
2.5%
하 44
 
2.4%
늘 43
 
2.3%
Other values (80) 704
37.8%
Common
ValueCountFrequency (%)
1 83
31.0%
+ 59
22.0%
55
20.5%
/ 22
 
8.2%
2 19
 
7.1%
3 16
 
6.0%
. 10
 
3.7%
5 3
 
1.1%
4 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1862
87.4%
ASCII 268
 
12.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 295
15.8%
리 250
 
13.4%
외 121
 
6.5%
허 119
 
6.4%
돌 104
 
5.6%
걷 88
 
4.7%
공 47
 
2.5%
중 47
 
2.5%
하 44
 
2.4%
늘 43
 
2.3%
Other values (80) 704
37.8%
ASCII
ValueCountFrequency (%)
1 83
31.0%
+ 59
22.0%
55
20.5%
/ 22
 
8.2%
2 19
 
7.1%
3 16
 
6.0%
. 10
 
3.7%
5 3
 
1.1%
4 1
 
0.4%

설치수량
Categorical

Distinct7
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
1
199 
2
66 
데이터 미집계
38 
3
 
14
4
 
7
Other values (2)
 
3

Length

Max length7
Median length1
Mean length1.7003058
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
1 199
60.9%
2 66
 
20.2%
데이터 미집계 38
 
11.6%
3 14
 
4.3%
4 7
 
2.1%
5 2
 
0.6%
10 1
 
0.3%

Length

2024-03-15T03:01:34.426066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:01:34.679716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 199
54.5%
2 66
 
18.1%
데이터 38
 
10.4%
미집계 38
 
10.4%
3 14
 
3.8%
4 7
 
1.9%
5 2
 
0.5%
10 1
 
0.3%

소요예산(천원)
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
데이터 미집계
232 
10000
93 
15000
 
1
13000
 
1

Length

Max length7
Median length7
Mean length6.4189602
Min length5

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row10000
2nd row데이터 미집계
3rd row데이터 미집계
4th row데이터 미집계
5th row10000

Common Values

ValueCountFrequency (%)
데이터 미집계 232
70.9%
10000 93
28.4%
15000 1
 
0.3%
13000 1
 
0.3%

Length

2024-03-15T03:01:34.914538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:01:35.151714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
데이터 232
41.5%
미집계 232
41.5%
10000 93
16.6%
15000 1
 
0.2%
13000 1
 
0.2%

시공업체
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
다영
81 
리드디자인
60 
서울체육사
58 
서율체육사
39 
서울체육
19 
Other values (12)
70 

Length

Max length10
Median length5
Mean length4.2232416
Min length2

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st row리드디자인
2nd row리드디자인
3rd row리드디자인
4th row리드디자인
5th row승리체육사

Common Values

ValueCountFrequency (%)
다영 81
24.8%
리드디자인 60
18.3%
서울체육사 58
17.7%
서율체육사 39
11.9%
서울체육 19
 
5.8%
승리체육사 16
 
4.9%
서율체육 12
 
3.7%
대한유통 11
 
3.4%
대한 유통 6
 
1.8%
지스포텍 6
 
1.8%
Other values (7) 19
 
5.8%

Length

2024-03-15T03:01:35.489681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
다영 81
24.3%
리드디자인 60
18.0%
서울체육사 58
17.4%
서율체육사 39
11.7%
서울체육 19
 
5.7%
승리체육사 16
 
4.8%
서율체육 12
 
3.6%
대한유통 11
 
3.3%
지스포텍 6
 
1.8%
유통 6
 
1.8%
Other values (9) 26
 
7.8%

Correlations

2024-03-15T03:01:35.757951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도읍면동소유권자설치수량소요예산(천원)시공업체
연도1.0000.0000.7240.4530.0000.818
읍면동0.0001.0000.6120.5270.0000.761
소유권자0.7240.6121.0000.6520.0000.438
설치수량0.4530.5270.6521.0000.3150.413
소요예산(천원)0.0000.0000.0000.3151.0000.000
시공업체0.8180.7610.4380.4130.0001.000
2024-03-15T03:01:36.039622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동소요예산(천원)시공업체연도설치수량소유권자
읍면동1.0000.0000.3470.0000.2610.195
소요예산(천원)0.0001.0000.0000.0000.2200.000
시공업체0.3470.0001.0000.5880.1970.128
연도0.0000.0000.5881.0000.3100.416
설치수량0.2610.2200.1970.3101.0000.316
소유권자0.1950.0000.1280.4160.3161.000
2024-03-15T03:01:36.245329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도읍면동소유권자설치수량소요예산(천원)시공업체
연도1.0000.0000.4160.3100.0000.588
읍면동0.0001.0000.1950.2610.0000.347
소유권자0.4160.1951.0000.3160.0000.128
설치수량0.3100.2610.3161.0000.2200.197
소요예산(천원)0.0000.0000.0000.2201.0000.000
시공업체0.5880.3470.1280.1970.0001.000

Missing values

2024-03-15T03:01:19.434655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:01:19.911365image/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

연도읍면동구분설치장소 지번설치장소 면적(제곱미터)소유권자설치품목설치수량소요예산(천원)시공업체
02015만경읍익수당만경로7699-217296김제시하늘걷기110000리드디자인
12015만경읍장산능제3길35-49829경로당하늘걷기1데이터 미집계리드디자인
22015만경읍옥산지평선로2145-149924경로당허리돌리기1데이터 미집계리드디자인
32015만경읍창자지평선로2145-149924경로당허리돌리기1데이터 미집계리드디자인
42015죽산면해창대창리115-112020한산이씨종중하늘걷기410000승리체육사
52015죽산면종남종신리155327마을회관상체근육풀기데이터 미집계데이터 미집계승리체육사
62015죽산면외리흥산리586-11524김제시상체근육풀기데이터 미집계데이터 미집계리드디자인
72015죽산면영구서포리368-4490개인상체근육풀기데이터 미집계데이터 미집계리드디자인
82015백산면하건하건1길20데이터 미집계경로당공중걷기410000서울체육사
92015백산면옥석옥석길39데이터 미집계마을회관허리돌리기데이터 미집계데이터 미집계서울체육사
연도읍면동구분설치장소 지번설치장소 면적(제곱미터)소유권자설치품목설치수량소요예산(천원)시공업체
3172019신풍동길보아파신풍동 6138449㎡마을어깨+허리돌리기210000대한유통
3182019신풍동용호용동 51-2192㎡경로당하늘걷기+달리기어깨+허리돌리기하늘걷기+달리기1데이터 미집계대한유통
3192019검산동검산주공2대리데이터 미집계21㎡마을공터등허리지압기+오금펴기강화기+허리돌리기310000(유)서우벤딩
3202019검산동포내도작4길 55200㎡경로당오금펴기강화기2데이터 미집계(유)서우벤딩
3212019검산동데이터 미집계순동 27-6129㎡마을회관평행봉1데이터 미집계(유)서우벤딩
3222019검산동데이터 미집계백학동 28-1데이터 미집계데이터 미집계데이터 미집계데이터 미집계데이터 미집계(유)서우벤딩
3232019교월동죽절복죽동 365-1397㎡경로당허리돌리기+하늘걷기110000서우밴딩
3242019교월동이문복죽동 153-11015㎡경로당옆파도타기1데이터 미집계서울체육
3252019교월동가작장화동 54-1793㎡마을공터허리돌리기+하늘걷기어께+상체근육풀기1데이터 미집계서우밴딩
3262019교월동율교명덕동474-59306㎡경로당데이터 미집계1데이터 미집계리드디자