Overview

Dataset statistics

Number of variables7
Number of observations81
Missing cells77
Missing cells (%)13.6%
Duplicate rows2
Duplicate rows (%)2.5%
Total size in memory4.6 KiB
Average record size in memory57.6 B

Variable types

Unsupported5
Text1
Categorical1

Dataset

Description지역축제관광객현황공개자료2015
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202317

Alerts

Dataset has 2 (2.5%) duplicate rowsDuplicates
Unnamed: 6 is highly imbalanced (61.8%)Imbalance
2015년 지역축제 관광객 현황 has 41 (50.6%) missing valuesMissing
Unnamed: 1 has 6 (7.4%) missing valuesMissing
Unnamed: 2 has 9 (11.1%) missing valuesMissing
Unnamed: 3 has 7 (8.6%) missing valuesMissing
Unnamed: 4 has 8 (9.9%) missing valuesMissing
Unnamed: 5 has 6 (7.4%) missing valuesMissing
2015년 지역축제 관광객 현황 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 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: 4 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

Reproduction

Analysis started2024-03-14 02:25:25.451009
Analysis finished2024-03-14 02:25:25.870256
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2015년 지역축제 관광객 현황
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing41
Missing (%)50.6%
Memory size780.0 B

Unnamed: 1
Text

MISSING 

Distinct61
Distinct (%)81.3%
Missing6
Missing (%)7.4%
Memory size780.0 B
2024-03-14T11:25:26.023200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.9333333
Min length3

Characters and Unicode

Total characters445
Distinct characters160
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)80.0%

Sample

1st row축제 수
2nd row56개
3rd row47개
4th row55개
5th row축제개요
ValueCountFrequency (%)
소 15
 
16.3%
계 15
 
16.3%
부안마실축제 1
 
1.1%
필봉마을굿축제 1
 
1.1%
하소백련축제 1
 
1.1%
삼례딸기축제 1
 
1.1%
와일드푸드축제 1
 
1.1%
홍삼&마이문화제 1
 
1.1%
마을축제 1
 
1.1%
운장산고로쇠축제 1
 
1.1%
Other values (54) 54
58.7%
2024-03-14T11:25:26.444576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 55
 
12.4%
축 38
 
8.5%
소 20
 
4.5%
17
 
3.8%
계 17
 
3.8%
산 9
 
2.0%
화 8
 
1.8%
장 6
 
1.3%
봉 6
 
1.3%
사 6
 
1.3%
Other values (150) 263
59.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 417
93.7%
Space Separator 17
 
3.8%
Decimal Number 8
 
1.8%
Other Punctuation 2
 
0.4%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 55
 
13.2%
축 38
 
9.1%
소 20
 
4.8%
계 17
 
4.1%
산 9
 
2.2%
화 8
 
1.9%
장 6
 
1.4%
봉 6
 
1.4%
사 6
 
1.4%
개 6
 
1.4%
Other values (142) 246
59.0%
Decimal Number
ValueCountFrequency (%)
5 4
50.0%
6 1
 
12.5%
4 1
 
12.5%
7 1
 
12.5%
2 1
 
12.5%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 417
93.7%
Common 27
 
6.1%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 55
 
13.2%
축 38
 
9.1%
소 20
 
4.8%
계 17
 
4.1%
산 9
 
2.2%
화 8
 
1.9%
장 6
 
1.4%
봉 6
 
1.4%
사 6
 
1.4%
개 6
 
1.4%
Other values (142) 246
59.0%
Common
ValueCountFrequency (%)
17
63.0%
5 4
 
14.8%
& 2
 
7.4%
6 1
 
3.7%
4 1
 
3.7%
7 1
 
3.7%
2 1
 
3.7%
Latin
ValueCountFrequency (%)
N 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 417
93.7%
ASCII 28
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 55
 
13.2%
축 38
 
9.1%
소 20
 
4.8%
계 17
 
4.1%
산 9
 
2.2%
화 8
 
1.9%
장 6
 
1.4%
봉 6
 
1.4%
사 6
 
1.4%
개 6
 
1.4%
Other values (142) 246
59.0%
ASCII
ValueCountFrequency (%)
17
60.7%
5 4
 
14.3%
& 2
 
7.1%
N 1
 
3.6%
6 1
 
3.6%
4 1
 
3.6%
7 1
 
3.6%
2 1
 
3.6%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9
Missing (%)11.1%
Memory size780.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7
Missing (%)8.6%
Memory size780.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)9.9%
Memory size780.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6
Missing (%)7.4%
Memory size780.0 B

Unnamed: 6
Categorical

IMBALANCE 

Distinct7
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size780.0 B
 
65 
<NA>
10 
공동 개최
 
2
(단위 : 명)
 
1
전년대비
 
1
Other values (2)
 
2

Length

Max length9
Median length2
Mean length2.5555556
Min length2

Unique

Unique4 ?
Unique (%)4.9%

Sample

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

Common Values

ValueCountFrequency (%)
  65
80.2%
<NA> 10
 
12.3%
공동 개최 2
 
2.5%
(단위 : 명) 1
 
1.2%
전년대비 1
 
1.2%
증감율(%) 1
 
1.2%
수산물축제와 통합 1
 
1.2%

Length

2024-03-14T11:25:26.581091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:25:26.677433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 10
47.6%
공동 2
 
9.5%
개최 2
 
9.5%
단위 1
 
4.8%
1
 
4.8%
명 1
 
4.8%
전년대비 1
 
4.8%
증감율 1
 
4.8%
수산물축제와 1
 
4.8%
통합 1
 
4.8%

Correlations

2024-03-14T11:25:26.742988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 6
Unnamed: 11.0001.000
Unnamed: 61.0001.000

Missing values

2024-03-14T11:25:25.597006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:25:25.693513image/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.
2024-03-14T11:25:25.798385image/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

2015년 지역축제 관광객 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
0NaN<NA>NaNNaNNaNNaN<NA>
1□ 연도별 현황<NA>NaNNaNNaN(단위 : 명, %)<NA>
2구분축제 수방문객 수NaNNaN증감률<NA>
3NaN<NA>계외국인내국인(전년 동기 대비)<NA>
4201556개6184966618046123162△0.09%<NA>
5201447개6190450726976117753△31.8%<NA>
6201355개90761601230528953108△9.7%<NA>
7NaN<NA>NaNNaNNaNNaN<NA>
8□ 시군별 현황<NA>NaNNaNNaNNaN<NA>
9* 1일 이상 지역축제 포함<NA>NaNNaNNaNNaN(단위 : 명)
2015년 지역축제 관광객 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
71소 계664591664591
72고창군청보리밭축제4.18~5.10413000413000
73(5)동학농민혁명무장기포4.2515001500
74NaN기념제와무장읍성축제NaNNaNNaNNaN<NA>
75NaN고창해풍고추축제8.29~302000020000
76NaN모양성제10.20~26125091125091
77NaN고창갯벌체험축제5.23~25105000105000수산물축제와 통합
78부안군소 계182795700182095
79(2)부안마실축제5.1~5.3122795550122245
80NaN곰소젓갈축제10.9~10.116000015059850

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 6# duplicates
0소 계15
1<NA><NA>5