Overview

Dataset statistics

Number of variables8
Number of observations36
Missing cells137
Missing cells (%)47.6%
Duplicate rows2
Duplicate rows (%)5.6%
Total size in memory2.4 KiB
Average record size in memory67.7 B

Variable types

Unsupported6
Text1
Categorical1

Alerts

Dataset has 2 (5.6%) duplicate rowsDuplicates
도내 승마장 현황 has 24 (66.7%) missing valuesMissing
Unnamed: 1 has 17 (47.2%) missing valuesMissing
Unnamed: 2 has 19 (52.8%) missing valuesMissing
Unnamed: 3 has 19 (52.8%) missing valuesMissing
Unnamed: 5 has 19 (52.8%) missing valuesMissing
Unnamed: 6 has 19 (52.8%) missing valuesMissing
Unnamed: 7 has 20 (55.6%) missing valuesMissing
도내 승마장 현황 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 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: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-13 23:53:15.728024
Analysis finished2024-03-13 23:53:16.156065
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도내 승마장 현황
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing24
Missing (%)66.7%
Memory size420.0 B

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17
Missing (%)47.2%
Memory size420.0 B

Unnamed: 2
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing19
Missing (%)52.8%
Memory size420.0 B
2024-03-14T08:53:16.341355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length15.764706
Min length3

Characters and Unicode

Total characters268
Distinct characters75
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

Unique17 ?
Unique (%)100.0%

Sample

1st row주 소
2nd row덕진구 호성동 호성로 19
3rd row익산시 금마면 갈산리 242-20
4th row익산시 삼기면 용연리 산21-1
5th row정읍시 송산1길 142-101
ValueCountFrequency (%)
김제시 3
 
4.7%
장수군 3
 
4.7%
남원시 3
 
4.7%
익산시 2
 
3.1%
천천면 2
 
3.1%
월곡리 2
 
3.1%
주 1
 
1.6%
665 1
 
1.6%
288-2 1
 
1.6%
용지면 1
 
1.6%
Other values (45) 45
70.3%
2024-03-14T08:53:16.696402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
 
17.5%
리 13
 
4.9%
- 13
 
4.9%
1 13
 
4.9%
2 12
 
4.5%
면 11
 
4.1%
시 9
 
3.4%
4 8
 
3.0%
산 8
 
3.0%
8 7
 
2.6%
Other values (65) 127
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148
55.2%
Decimal Number 60
22.4%
Space Separator 47
 
17.5%
Dash Punctuation 13
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 13
 
8.8%
면 11
 
7.4%
시 9
 
6.1%
산 8
 
5.4%
군 6
 
4.1%
장 5
 
3.4%
천 5
 
3.4%
읍 4
 
2.7%
수 4
 
2.7%
월 3
 
2.0%
Other values (53) 80
54.1%
Decimal Number
ValueCountFrequency (%)
1 13
21.7%
2 12
20.0%
4 8
13.3%
8 7
11.7%
7 5
 
8.3%
0 3
 
5.0%
5 3
 
5.0%
3 3
 
5.0%
6 3
 
5.0%
9 3
 
5.0%
Space Separator
ValueCountFrequency (%)
47
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 148
55.2%
Common 120
44.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 13
 
8.8%
면 11
 
7.4%
시 9
 
6.1%
산 8
 
5.4%
군 6
 
4.1%
장 5
 
3.4%
천 5
 
3.4%
읍 4
 
2.7%
수 4
 
2.7%
월 3
 
2.0%
Other values (53) 80
54.1%
Common
ValueCountFrequency (%)
47
39.2%
- 13
 
10.8%
1 13
 
10.8%
2 12
 
10.0%
4 8
 
6.7%
8 7
 
5.8%
7 5
 
4.2%
0 3
 
2.5%
5 3
 
2.5%
3 3
 
2.5%
Other values (2) 6
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 148
55.2%
ASCII 120
44.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47
39.2%
- 13
 
10.8%
1 13
 
10.8%
2 12
 
10.0%
4 8
 
6.7%
8 7
 
5.8%
7 5
 
4.2%
0 3
 
2.5%
5 3
 
2.5%
3 3
 
2.5%
Other values (2) 6
 
5.0%
Hangul
ValueCountFrequency (%)
리 13
 
8.8%
면 11
 
7.4%
시 9
 
6.1%
산 8
 
5.4%
군 6
 
4.1%
장 5
 
3.4%
천 5
 
3.4%
읍 4
 
2.7%
수 4
 
2.7%
월 3
 
2.0%
Other values (53) 80
54.1%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19
Missing (%)52.8%
Memory size420.0 B

Unnamed: 4
Categorical

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
체육
13 
시설업
13 
농어촌형
3 
승마시설
3 
신고
 
1
Other values (3)
3 

Length

Max length4
Median length3
Mean length2.7777778
Min length2

Unique

Unique4 ?
Unique (%)11.1%

Sample

1st row신고
2nd row구분
3rd row체:13
4th row농:3
5th row체육

Common Values

ValueCountFrequency (%)
체육 13
36.1%
시설업 13
36.1%
농어촌형 3
 
8.3%
승마시설 3
 
8.3%
신고 1
 
2.8%
구분 1
 
2.8%
체:13 1
 
2.8%
농:3 1
 
2.8%

Length

2024-03-14T08:53:16.809014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:53:16.929331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육 13
36.1%
시설업 13
36.1%
농어촌형 3
 
8.3%
승마시설 3
 
8.3%
신고 1
 
2.8%
구분 1
 
2.8%
체:13 1
 
2.8%
농:3 1
 
2.8%

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19
Missing (%)52.8%
Memory size420.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19
Missing (%)52.8%
Memory size420.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing20
Missing (%)55.6%
Memory size420.0 B

Correlations

2024-03-14T08:53:17.000610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 2Unnamed: 4
Unnamed: 21.0001.000
Unnamed: 41.0001.000

Missing values

2024-03-14T08:53:15.847054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T08:53:15.949376image/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.
2024-03-14T08:53:16.046967image/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

도내 승마장 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
0시군승마장명주 소개설신고시설현항NaNNaN
1NaNNaN<NA>년도구분총면적마필수마방수
2계16<NA>NaN체:13NaN220536
3-9(운영 12, 미운영 4)<NA>NaN농:3NaNNaNNaN
4전주시전주승마장덕진구 호성동 호성로 192010체육23,484㎡1968
5NaNNaN<NA>NaN시설업NaNNaNNaN
6익산시풀잎승마장익산시 금마면 갈산리 242-202012체육1,130㎡1112
7NaNNaN<NA>NaN시설업NaNNaNNaN
8NaN익산승마장익산시 삼기면 용연리 산21-12013체육4,500㎡517
9NaNNaN<NA>NaN시설업NaNNaNNaN
도내 승마장 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
26NaN장수승마장장수군 천천면 월곡리 6652008체육165,314㎡9184
27NaNNaN<NA>NaN시설업NaNNaNNaN
28NaN장수힐스리조트승마장장수군 천천면 월곡리 산92-12013농어촌형26,110㎡330
29NaNNaN<NA>NaN승마시설NaNNaNNaN
30순창군금과승마장순창군 금과면 방축리 산33-82009체육174.4㎡-7
31NaNNaN<NA>NaN시설업NaNNaNNaN
32고창군해변승마클럽고창군 상하면 명사십리로 282-92013농어촌형512㎡44
33NaNNaN<NA>NaN승마시설NaNNaNNaN
34부안군아리울승마장부안군 상서면 가오리 734-42008체육18,060㎡1618
35NaNNaN<NA>NaN시설업NaNNaNNaN

Duplicate rows

Most frequently occurring

Unnamed: 2Unnamed: 4# duplicates
1<NA>시설업13
0<NA>승마시설3