Overview

Dataset statistics

Number of variables5
Number of observations52
Missing cells38
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory45.5 B

Variable types

Numeric3
Text2

Dataset

Description전북특별자치도 지역축제 관광객현황 데이터입니다. 이 데이터에서 축제명, 기간, 외국인 방문객수, 내국인 방문객수 등을 제공합니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055596/fileData.do

Alerts

외국인 is highly overall correlated with 내국인High correlation
내국인 is highly overall correlated with 외국인High correlation
기간 has 1 (1.9%) missing valuesMissing
외국인 has 36 (69.2%) missing valuesMissing
내국인 has 1 (1.9%) missing valuesMissing
번호 has unique valuesUnique
축제명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 18:59:47.891807
Analysis finished2024-03-14 18:59:51.100022
Duration3.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.5
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size596.0 B
2024-03-15T03:59:51.249180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.55
Q113.75
median26.5
Q339.25
95-th percentile49.45
Maximum52
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.57187763
Kurtosis-1.2
Mean26.5
Median Absolute Deviation (MAD)13
Skewness0
Sum1378
Variance229.66667
MonotonicityStrictly increasing
2024-03-15T03:59:51.701827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
28 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%
43 1
1.9%

축제명
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size544.0 B
2024-03-15T03:59:52.630303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.0961538
Min length3

Characters and Unicode

Total characters369
Distinct characters151
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)100.0%

Sample

1st row전주세계소리축제
2nd row한지문화축제
3rd row전주비빔밥축제
4th row전주국제영화제
5th row전주대사습놀이
ValueCountFrequency (%)
전주세계소리축제 1
 
1.9%
한지문화축제 1
 
1.9%
필봉마을굿축제 1
 
1.9%
홍삼&마이문화제 1
 
1.9%
마을축제 1
 
1.9%
운장산고로쇠축제 1
 
1.9%
동향수박축제 1
 
1.9%
무주반딧불축제 1
 
1.9%
장수한우랑사과랑축제 1
 
1.9%
의암논개축제 1
 
1.9%
Other values (42) 42
80.8%
2024-03-15T03:59:53.821220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 51
 
13.8%
축 34
 
9.2%
산 9
 
2.4%
화 8
 
2.2%
봉 6
 
1.6%
사 6
 
1.6%
장 6
 
1.6%
고 5
 
1.4%
주 5
 
1.4%
기 5
 
1.4%
Other values (141) 234
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 366
99.2%
Other Punctuation 2
 
0.5%
Uppercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 51
 
13.9%
축 34
 
9.3%
산 9
 
2.5%
화 8
 
2.2%
봉 6
 
1.6%
사 6
 
1.6%
장 6
 
1.6%
고 5
 
1.4%
주 5
 
1.4%
기 5
 
1.4%
Other values (139) 231
63.1%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 366
99.2%
Common 2
 
0.5%
Latin 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 51
 
13.9%
축 34
 
9.3%
산 9
 
2.5%
화 8
 
2.2%
봉 6
 
1.6%
사 6
 
1.6%
장 6
 
1.6%
고 5
 
1.4%
주 5
 
1.4%
기 5
 
1.4%
Other values (139) 231
63.1%
Common
ValueCountFrequency (%)
& 2
100.0%
Latin
ValueCountFrequency (%)
N 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 366
99.2%
ASCII 3
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 51
 
13.9%
축 34
 
9.3%
산 9
 
2.5%
화 8
 
2.2%
봉 6
 
1.6%
사 6
 
1.6%
장 6
 
1.6%
고 5
 
1.4%
주 5
 
1.4%
기 5
 
1.4%
Other values (139) 231
63.1%
ASCII
ValueCountFrequency (%)
& 2
66.7%
N 1
33.3%

기간
Text

MISSING 

Distinct45
Distinct (%)88.2%
Missing1
Missing (%)1.9%
Memory size544.0 B
2024-03-15T03:59:54.659277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length19.490196
Min length10

Characters and Unicode

Total characters994
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)76.5%

Sample

1st row2015-10-07~2015-10-11
2nd row2015-05-02~2015-05-05
3rd row2015-10-22~2015-10-25
4th row2015-04-30~2015-05-09
5th row2015-05-30~2015-06-01
ValueCountFrequency (%)
2015-10-07~2015-10-11 2
 
3.9%
2015-10-02~2015-10-04 2
 
3.9%
2015-10-30~2015-11-01 2
 
3.9%
2015-05-02~2015-05-05 2
 
3.9%
2015-10-09~2015-10-11 2
 
3.9%
2015-08-14~2015-08-16 2
 
3.9%
2021-10-15 1
 
2.0%
2015-07-17~2015-07-19 1
 
2.0%
2021-04-25 1
 
2.0%
2015-07-30~2015-08-03 1
 
2.0%
Other values (35) 35
68.6%
2024-03-15T03:59:55.889552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 228
22.9%
- 190
19.1%
1 186
18.7%
2 137
13.8%
5 126
12.7%
~ 44
 
4.4%
8 20
 
2.0%
3 19
 
1.9%
9 13
 
1.3%
7 12
 
1.2%
Other values (2) 19
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 760
76.5%
Dash Punctuation 190
 
19.1%
Math Symbol 44
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 228
30.0%
1 186
24.5%
2 137
18.0%
5 126
16.6%
8 20
 
2.6%
3 19
 
2.5%
9 13
 
1.7%
7 12
 
1.6%
4 12
 
1.6%
6 7
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 190
100.0%
Math Symbol
ValueCountFrequency (%)
~ 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 228
22.9%
- 190
19.1%
1 186
18.7%
2 137
13.8%
5 126
12.7%
~ 44
 
4.4%
8 20
 
2.0%
3 19
 
1.9%
9 13
 
1.3%
7 12
 
1.2%
Other values (2) 19
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 228
22.9%
- 190
19.1%
1 186
18.7%
2 137
13.8%
5 126
12.7%
~ 44
 
4.4%
8 20
 
2.0%
3 19
 
1.9%
9 13
 
1.3%
7 12
 
1.2%
Other values (2) 19
 
1.9%

외국인
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)93.8%
Missing36
Missing (%)69.2%
Infinite0
Infinite (%)0.0%
Mean3862.75
Minimum100
Maximum33061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size596.0 B
2024-03-15T03:59:56.302743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1262.5
median729
Q33125
95-th percentile15765.25
Maximum33061
Range32961
Interquartile range (IQR)2862.5

Descriptive statistics

Standard deviation8214.6602
Coefficient of variation (CV)2.1266352
Kurtosis12.256624
Mean3862.75
Median Absolute Deviation (MAD)629
Skewness3.3970435
Sum61804
Variance67480642
MonotonicityNot monotonic
2024-03-15T03:59:56.682827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100 2
 
3.8%
457 1
 
1.9%
890 1
 
1.9%
5258 1
 
1.9%
3000 1
 
1.9%
10000 1
 
1.9%
3500 1
 
1.9%
300 1
 
1.9%
33061 1
 
1.9%
113 1
 
1.9%
Other values (5) 5
 
9.6%
(Missing) 36
69.2%
ValueCountFrequency (%)
100 2
3.8%
113 1
1.9%
150 1
1.9%
300 1
1.9%
457 1
1.9%
550 1
1.9%
568 1
1.9%
890 1
1.9%
1572 1
1.9%
2185 1
1.9%
ValueCountFrequency (%)
33061 1
1.9%
10000 1
1.9%
5258 1
1.9%
3500 1
1.9%
3000 1
1.9%
2185 1
1.9%
1572 1
1.9%
890 1
1.9%
568 1
1.9%
550 1
1.9%

내국인
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)92.2%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean111963.96
Minimum950
Maximum638970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size596.0 B
2024-03-15T03:59:56.914087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum950
5-th percentile1761.5
Q16000
median42700
Q3130234.5
95-th percentile560729.5
Maximum638970
Range638020
Interquartile range (IQR)124234.5

Descriptive statistics

Standard deviation169800.17
Coefficient of variation (CV)1.5165609
Kurtosis3.4745956
Mean111963.96
Median Absolute Deviation (MAD)37900
Skewness2.0779318
Sum5710162
Variance2.8832097 × 1010
MonotonicityNot monotonic
2024-03-15T03:59:57.162711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
5000 4
 
7.7%
6000 2
 
3.8%
170000 1
 
1.9%
24900 1
 
1.9%
93083 1
 
1.9%
58852 1
 
1.9%
30000 1
 
1.9%
214180 1
 
1.9%
250747 1
 
1.9%
2800 1
 
1.9%
Other values (37) 37
71.2%
ValueCountFrequency (%)
950 1
 
1.9%
1500 1
 
1.9%
1523 1
 
1.9%
2000 1
 
1.9%
2800 1
 
1.9%
3000 1
 
1.9%
4000 1
 
1.9%
4800 1
 
1.9%
5000 4
7.7%
6000 2
3.8%
ValueCountFrequency (%)
638970 1
1.9%
616440 1
1.9%
598000 1
1.9%
523459 1
1.9%
420000 1
1.9%
400000 1
1.9%
250747 1
1.9%
214180 1
1.9%
174857 1
1.9%
170000 1
1.9%

Interactions

2024-03-15T03:59:49.659364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:59:48.165298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:59:48.912951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:59:49.896295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:59:48.404582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:59:49.164143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:59:50.047033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:59:48.667367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:59:49.410202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:59:57.333097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호축제명기간외국인내국인
번호1.0001.0000.9120.0000.531
축제명1.0001.0001.0001.0001.000
기간0.9121.0001.0001.0000.805
외국인0.0001.0001.0001.0000.706
내국인0.5311.0000.8050.7061.000
2024-03-15T03:59:57.499478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호외국인내국인
번호1.000-0.200-0.344
외국인-0.2001.0000.648
내국인-0.3440.6481.000

Missing values

2024-03-15T03:59:50.561431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:59:50.774162image/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.
2024-03-15T03:59:51.012151image/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

번호축제명기간외국인내국인
01전주세계소리축제2015-10-07~2015-10-11<NA>170000
12한지문화축제2015-05-02~2015-05-0545764859
23전주비빔밥축제2015-10-22~2015-10-2589085268
34전주국제영화제2015-04-30~2015-05-09<NA>420000
45전주대사습놀이2015-05-30~2015-06-01<NA>135378
56군산꽁당보리축제2015-05-02~2015-05-05<NA>160000
67군산시간여행축제2015-10-09~2015-10-11<NA>152272
78군산세계철새축제2015-11-06~2015-11-08<NA>37800
89익산보석대축제2015-10-28~2015-11-08<NA>43000
910익산서동&천만송이국화축제2015-10-30~2015-11-08<NA>616440
번호축제명기간외국인내국인
4243순창장류축제2015-10-29~2015-11-01568174857
4344추령장승축제2021-10-24<NA>1523
4445옥천벚꽃축제2015-04-02~2015-04-05<NA>5000
4546동학농민혁명무장기포2021-04-25<NA>1500
4647기념제와무장읍성축제<NA><NA><NA>
4748고창해풍고추축제2015-08-29~2015-08-30<NA>20000
4849모양성제2015-10-20~2015-10-26<NA>125091
4950고창갯벌체험축제2015-05-23~2015-05-25<NA>105000
5051부안마실축제2015-05-01~2015-05-03550122245
5152곰소젓갈축제2015-10-09~2015-10-1115059850