Overview

Dataset statistics

Number of variables10
Number of observations22
Missing cells43
Missing cells (%)19.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory87.0 B

Variable types

Unsupported5
Text4
Categorical1

Dataset

Description전라북도승마장현황2016년
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202849

Alerts

Unnamed: 0 has 22 (100.0%) missing valuesMissing
전라북도 승마장 현황(2016. 12월) has 1 (4.5%) missing valuesMissing
Unnamed: 2 has 12 (54.5%) missing valuesMissing
Unnamed: 3 has 1 (4.5%) missing valuesMissing
Unnamed: 4 has 2 (9.1%) missing valuesMissing
Unnamed: 5 has 2 (9.1%) missing valuesMissing
Unnamed: 7 has 1 (4.5%) missing valuesMissing
Unnamed: 8 has 1 (4.5%) missing valuesMissing
Unnamed: 9 has 1 (4.5%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전라북도 승마장 현황(2016. 12월) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 00:26:01.625636
Analysis finished2024-03-14 00:26:02.186404
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing22
Missing (%)100.0%
Memory size330.0 B

전라북도 승마장 현황(2016. 12월)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.5%
Memory size308.0 B

Unnamed: 2
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing12
Missing (%)54.5%
Memory size308.0 B
2024-03-14T09:26:02.274060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9
Min length2

Characters and Unicode

Total characters29
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row시군
2nd row전주시
3rd row군산시
4th row익산시
5th row정읍시
ValueCountFrequency (%)
시군 1
10.0%
전주시 1
10.0%
군산시 1
10.0%
익산시 1
10.0%
정읍시 1
10.0%
남원시 1
10.0%
김제시 1
10.0%
장수군 1
10.0%
고창군 1
10.0%
부안군 1
10.0%
2024-03-14T09:26:02.516845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 7
24.1%
군 5
17.2%
산 2
 
6.9%
김 1
 
3.4%
부 1
 
3.4%
창 1
 
3.4%
고 1
 
3.4%
수 1
 
3.4%
장 1
 
3.4%
제 1
 
3.4%
Other values (8) 8
27.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7
24.1%
군 5
17.2%
산 2
 
6.9%
김 1
 
3.4%
부 1
 
3.4%
창 1
 
3.4%
고 1
 
3.4%
수 1
 
3.4%
장 1
 
3.4%
제 1
 
3.4%
Other values (8) 8
27.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7
24.1%
군 5
17.2%
산 2
 
6.9%
김 1
 
3.4%
부 1
 
3.4%
창 1
 
3.4%
고 1
 
3.4%
수 1
 
3.4%
장 1
 
3.4%
제 1
 
3.4%
Other values (8) 8
27.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 7
24.1%
군 5
17.2%
산 2
 
6.9%
김 1
 
3.4%
부 1
 
3.4%
창 1
 
3.4%
고 1
 
3.4%
수 1
 
3.4%
장 1
 
3.4%
제 1
 
3.4%
Other values (8) 8
27.6%

Unnamed: 3
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing1
Missing (%)4.5%
Memory size308.0 B
2024-03-14T09:26:02.673953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.3809524
Min length4

Characters and Unicode

Total characters134
Distinct characters68
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

Unique21 ?
Unique (%)100.0%

Sample

1st row승마장명
2nd row19개소
3rd row전주승마장
4th row피터팬승마장
5th row에벤에셀승마장
ValueCountFrequency (%)
승마장명 1
 
4.2%
19개소 1
 
4.2%
아리울 1
 
4.2%
해변승마클럽 1
 
4.2%
나봄리조트승마장 1
 
4.2%
장수승마장 1
 
4.2%
장수승마체험장 1
 
4.2%
승마공원 1
 
4.2%
인디안 1
 
4.2%
전북말산업복합센터 1
 
4.2%
Other values (14) 14
58.3%
2024-03-14T09:26:02.933581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 18
 
13.4%
승 17
 
12.7%
장 15
 
11.2%
에 3
 
2.2%
스 3
 
2.2%
3
 
2.2%
남 3
 
2.2%
산 3
 
2.2%
인 2
 
1.5%
호 2
 
1.5%
Other values (58) 65
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 129
96.3%
Space Separator 3
 
2.2%
Decimal Number 2
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 18
 
14.0%
승 17
 
13.2%
장 15
 
11.6%
에 3
 
2.3%
스 3
 
2.3%
남 3
 
2.3%
산 3
 
2.3%
인 2
 
1.6%
호 2
 
1.6%
수 2
 
1.6%
Other values (55) 61
47.3%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
9 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 129
96.3%
Common 5
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 18
 
14.0%
승 17
 
13.2%
장 15
 
11.6%
에 3
 
2.3%
스 3
 
2.3%
남 3
 
2.3%
산 3
 
2.3%
인 2
 
1.6%
호 2
 
1.6%
수 2
 
1.6%
Other values (55) 61
47.3%
Common
ValueCountFrequency (%)
3
60.0%
1 1
 
20.0%
9 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 129
96.3%
ASCII 5
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 18
 
14.0%
승 17
 
13.2%
장 15
 
11.6%
에 3
 
2.3%
스 3
 
2.3%
남 3
 
2.3%
산 3
 
2.3%
인 2
 
1.6%
호 2
 
1.6%
수 2
 
1.6%
Other values (55) 61
47.3%
ASCII
ValueCountFrequency (%)
3
60.0%
1 1
 
20.0%
9 1
 
20.0%

Unnamed: 4
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing2
Missing (%)9.1%
Memory size308.0 B
2024-03-14T09:26:03.130822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length15.6
Min length2

Characters and Unicode

Total characters312
Distinct characters79
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

Unique20 ?
Unique (%)100.0%

Sample

1st row주소
2nd row전주시 덕진구 호성로 19
3rd row군산시 성산면 송호로 128
4th row익산시 금마면 갈산길 40
5th row익산시 삼기면 용연리 산21-1
ValueCountFrequency (%)
정읍시 4
 
5.4%
익산시 4
 
5.4%
장수군 3
 
4.1%
천천면 2
 
2.7%
김제시 2
 
2.7%
남원시 2
 
2.7%
77 1
 
1.4%
용지면 1
 
1.4%
부교리 1
 
1.4%
84-4 1
 
1.4%
Other values (53) 53
71.6%
2024-03-14T09:26:03.481764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54
 
17.3%
1 15
 
4.8%
시 14
 
4.5%
산 13
 
4.2%
2 13
 
4.2%
면 12
 
3.8%
- 11
 
3.5%
4 10
 
3.2%
8 9
 
2.9%
리 8
 
2.6%
Other values (69) 153
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 177
56.7%
Decimal Number 70
 
22.4%
Space Separator 54
 
17.3%
Dash Punctuation 11
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 14
 
7.9%
산 13
 
7.3%
면 12
 
6.8%
리 8
 
4.5%
읍 8
 
4.5%
로 8
 
4.5%
정 6
 
3.4%
천 6
 
3.4%
군 6
 
3.4%
장 5
 
2.8%
Other values (57) 91
51.4%
Decimal Number
ValueCountFrequency (%)
1 15
21.4%
2 13
18.6%
4 10
14.3%
8 9
12.9%
0 5
 
7.1%
3 5
 
7.1%
9 4
 
5.7%
7 4
 
5.7%
5 3
 
4.3%
6 2
 
2.9%
Space Separator
ValueCountFrequency (%)
54
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 177
56.7%
Common 135
43.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 14
 
7.9%
산 13
 
7.3%
면 12
 
6.8%
리 8
 
4.5%
읍 8
 
4.5%
로 8
 
4.5%
정 6
 
3.4%
천 6
 
3.4%
군 6
 
3.4%
장 5
 
2.8%
Other values (57) 91
51.4%
Common
ValueCountFrequency (%)
54
40.0%
1 15
 
11.1%
2 13
 
9.6%
- 11
 
8.1%
4 10
 
7.4%
8 9
 
6.7%
0 5
 
3.7%
3 5
 
3.7%
9 4
 
3.0%
7 4
 
3.0%
Other values (2) 5
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 177
56.7%
ASCII 135
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54
40.0%
1 15
 
11.1%
2 13
 
9.6%
- 11
 
8.1%
4 10
 
7.4%
8 9
 
6.7%
0 5
 
3.7%
3 5
 
3.7%
9 4
 
3.0%
7 4
 
3.0%
Other values (2) 5
 
3.7%
Hangul
ValueCountFrequency (%)
시 14
 
7.9%
산 13
 
7.3%
면 12
 
6.8%
리 8
 
4.5%
읍 8
 
4.5%
로 8
 
4.5%
정 6
 
3.4%
천 6
 
3.4%
군 6
 
3.4%
장 5
 
2.8%
Other values (57) 91
51.4%

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)9.1%
Memory size308.0 B

Unnamed: 6
Categorical

Distinct4
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size308.0 B
체육시설업
12 
농어촌형승마시설
7 
<NA>
2 
신고구분
 
1

Length

Max length8
Median length5
Mean length5.8181818
Min length4

Unique

Unique1 ?
Unique (%)4.5%

Sample

1st row신고구분
2nd row<NA>
3rd row<NA>
4th row체육시설업
5th row체육시설업

Common Values

ValueCountFrequency (%)
체육시설업 12
54.5%
농어촌형승마시설 7
31.8%
<NA> 2
 
9.1%
신고구분 1
 
4.5%

Length

2024-03-14T09:26:03.608290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:26:03.712573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육시설업 12
54.5%
농어촌형승마시설 7
31.8%
na 2
 
9.1%
신고구분 1
 
4.5%

Unnamed: 7
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing1
Missing (%)4.5%
Memory size308.0 B
2024-03-14T09:26:03.900247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.2380952
Min length3

Characters and Unicode

Total characters131
Distinct characters23
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

Unique21 ?
Unique (%)100.0%

Sample

1st row시설 및 마필내역
2nd row총면적
3rd row23,478㎡
4th row1,041㎡
5th row1,600㎡
ValueCountFrequency (%)
시설 1
 
4.3%
659㎡ 1
 
4.3%
512㎡ 1
 
4.3%
37,927㎡ 1
 
4.3%
166,649㎡ 1
 
4.3%
31,361㎡ 1
 
4.3%
11,570㎡ 1
 
4.3%
22,532㎡ 1
 
4.3%
1,642㎡ 1
 
4.3%
114,582㎡ 1
 
4.3%
Other values (13) 13
56.5%
2024-03-14T09:26:04.215536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㎡ 19
14.5%
, 16
12.2%
1 15
11.5%
2 14
10.7%
6 11
8.4%
5 9
6.9%
4 8
 
6.1%
3 7
 
5.3%
7 6
 
4.6%
8 5
 
3.8%
Other values (13) 21
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84
64.1%
Other Symbol 19
 
14.5%
Other Punctuation 16
 
12.2%
Other Letter 10
 
7.6%
Space Separator 2
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
17.9%
2 14
16.7%
6 11
13.1%
5 9
10.7%
4 8
9.5%
3 7
8.3%
7 6
 
7.1%
8 5
 
6.0%
9 5
 
6.0%
0 4
 
4.8%
Other Letter
ValueCountFrequency (%)
설 1
10.0%
적 1
10.0%
면 1
10.0%
총 1
10.0%
역 1
10.0%
내 1
10.0%
필 1
10.0%
마 1
10.0%
및 1
10.0%
시 1
10.0%
Other Symbol
ValueCountFrequency (%)
㎡ 19
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
92.4%
Hangul 10
 
7.6%

Most frequent character per script

Common
ValueCountFrequency (%)
㎡ 19
15.7%
, 16
13.2%
1 15
12.4%
2 14
11.6%
6 11
9.1%
5 9
7.4%
4 8
6.6%
3 7
 
5.8%
7 6
 
5.0%
8 5
 
4.1%
Other values (3) 11
9.1%
Hangul
ValueCountFrequency (%)
설 1
10.0%
적 1
10.0%
면 1
10.0%
총 1
10.0%
역 1
10.0%
내 1
10.0%
필 1
10.0%
마 1
10.0%
및 1
10.0%
시 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102
77.9%
CJK Compat 19
 
14.5%
Hangul 10
 
7.6%

Most frequent character per block

CJK Compat
ValueCountFrequency (%)
㎡ 19
100.0%
ASCII
ValueCountFrequency (%)
, 16
15.7%
1 15
14.7%
2 14
13.7%
6 11
10.8%
5 9
8.8%
4 8
7.8%
3 7
6.9%
7 6
 
5.9%
8 5
 
4.9%
9 5
 
4.9%
Other values (2) 6
 
5.9%
Hangul
ValueCountFrequency (%)
설 1
10.0%
적 1
10.0%
면 1
10.0%
총 1
10.0%
역 1
10.0%
내 1
10.0%
필 1
10.0%
마 1
10.0%
및 1
10.0%
시 1
10.0%

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.5%
Memory size308.0 B

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.5%
Memory size308.0 B

Correlations

2024-03-14T09:26:04.295160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 6Unnamed: 7
Unnamed: 21.0001.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.0001.000
Unnamed: 41.0001.0001.0001.0001.000
Unnamed: 61.0001.0001.0001.0001.000
Unnamed: 71.0001.0001.0001.0001.000

Missing values

2024-03-14T09:26:01.876700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:26:01.999511image/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.
2024-03-14T09:26:02.108039image/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

Unnamed: 0전라북도 승마장 현황(2016. 12월)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
0<NA>구분시군승마장명주소개설년도신고구분시설 및 마필내역NaNNaN
1<NA>NaN<NA><NA><NA>NaN<NA>총면적마필수마방수
2<NA>계<NA>19개소<NA>NaN<NA><NA>379690
3<NA>1전주시전주승마장전주시 덕진구 호성로 192010체육시설업23,478㎡4168
4<NA>2군산시피터팬승마장군산시 성산면 송호로 1282015체육시설업1,041㎡1310
5<NA>3익산시에벤에셀승마장익산시 금마면 갈산길 402011체육시설업1,600㎡1412
6<NA>4<NA>익산승마장익산시 삼기면 용연리 산21-12010체육시설업5,685㎡1010
7<NA>5<NA>호남승마장익산시 낭산면 용기리 303-12015농어촌형승마시설2,119㎡1010
8<NA>6<NA>호남제일승마장익산시 황등면 죽촌리 3-812015체육시설업6,542㎡1515
9<NA>7정읍시웨스턴스프링스정읍시 송산1길 142-1012012체육시설업52,337㎡1538
Unnamed: 0전라북도 승마장 현황(2016. 12월)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
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13<NA>11남원시한국경마축산고남원시 운봉읍 황산로 9522010체육시설업114,582㎡6250
14<NA>12<NA>남원 에덴승마랜드남원시 주천면 중송길 772013체육시설업1,642㎡010
15<NA>13김제시전북말산업복합센터김제시 용지면 부교리 84-42014체육시설업22,532㎡8480
16<NA>14<NA>인디안 승마공원김제시 만경읍 장산리 288-22008체육시설업11,570㎡748
17<NA>15장수군장수승마체험장장수군 장수읍 노하리 284-142012농어촌형승마시설31,361㎡2220
18<NA>16<NA>장수승마장장수군 천천면 월곡리 6652008체육시설업166,649㎡20244
19<NA>17<NA>나봄리조트승마장장수군 천천면 승마로 1005-242013농어촌형승마시설37,927㎡1520
20<NA>18고창군해변승마클럽고창군 상하면 명사십리로 282-72013농어촌형승마시설512㎡98
21<NA>19부안군아리울 승마장부안군 상서면 부안로 18872014농어촌형승마시설16,648㎡1419