Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells10277
Missing cells (%)25.7%
Duplicate rows14
Duplicate rows (%)0.1%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

Text2
Numeric2

Dataset

Description전북특별자치도 내 14개 시군 관광지명, 주소, 위도, 경도 제공함으로써 관광지 위치 정보 데이터를 수집하고 분석함으로써 관광 추세를 파악하고 미래의 관광 수요를 예측할 수 있습니다. 이는 지역 마케팅 및 자원 할당에 도움을 줄 수 있음
Author전북특별자치도
URLhttps://www.data.go.kr/data/15124617/fileData.do

Alerts

Dataset has 14 (0.1%) duplicate rowsDuplicates
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
주소 has 9983 (99.8%) missing valuesMissing

Reproduction

Analysis started2024-03-14 11:15:22.125700
Analysis finished2024-03-14 11:15:24.890325
Duration2.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관광지명
Text

Distinct9151
Distinct (%)92.4%
Missing98
Missing (%)1.0%
Memory size156.2 KiB
2024-03-14T20:15:25.740643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length23
Mean length6.2739851
Min length2

Characters and Unicode

Total characters62125
Distinct characters775
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8515 ?
Unique (%)86.0%

Sample

1st row항동하버라인8단지
2nd row시청앞
3rd row고강동철탑(시계지점)
4th row농업기술센터
5th row자갈치역.비프광장
ValueCountFrequency (%)
건너 35
 
0.3%
방면 34
 
0.3%
홈플러스 12
 
0.1%
입구 10
 
0.1%
부대앞 10
 
0.1%
앞 9
 
0.1%
마을회관 9
 
0.1%
현대아파트 8
 
0.1%
이마트 6
 
0.1%
신촌 5
 
< 0.1%
Other values (9225) 9984
98.6%
2024-03-14T20:15:26.995805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 2344
 
3.8%
동 1261
 
2.0%
교 1225
 
2.0%
구 1082
 
1.7%
아 1070
 
1.7%
지 1068
 
1.7%
트 1007
 
1.6%
. 961
 
1.5%
마 959
 
1.5%
파 948
 
1.5%
Other values (765) 50200
80.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 56191
90.4%
Decimal Number 2072
 
3.3%
Other Punctuation 994
 
1.6%
Close Punctuation 885
 
1.4%
Open Punctuation 881
 
1.4%
Uppercase Letter 808
 
1.3%
Space Separator 220
 
0.4%
Lowercase Letter 43
 
0.1%
Dash Punctuation 28
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 2344
 
4.2%
동 1261
 
2.2%
교 1225
 
2.2%
구 1082
 
1.9%
아 1070
 
1.9%
지 1068
 
1.9%
트 1007
 
1.8%
마 959
 
1.7%
파 948
 
1.7%
원 881
 
1.6%
Other values (709) 44346
78.9%
Uppercase Letter
ValueCountFrequency (%)
C 175
21.7%
I 123
15.2%
T 80
9.9%
G 66
 
8.2%
K 65
 
8.0%
S 59
 
7.3%
A 46
 
5.7%
L 37
 
4.6%
J 24
 
3.0%
H 23
 
2.8%
Other values (14) 110
13.6%
Lowercase Letter
ValueCountFrequency (%)
e 28
65.1%
t 3
 
7.0%
i 3
 
7.0%
m 1
 
2.3%
a 1
 
2.3%
l 1
 
2.3%
y 1
 
2.3%
b 1
 
2.3%
g 1
 
2.3%
k 1
 
2.3%
Other values (2) 2
 
4.7%
Decimal Number
ValueCountFrequency (%)
1 743
35.9%
2 643
31.0%
3 264
 
12.7%
4 140
 
6.8%
5 67
 
3.2%
6 54
 
2.6%
9 50
 
2.4%
7 45
 
2.2%
0 44
 
2.1%
8 22
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 961
96.7%
/ 18
 
1.8%
, 14
 
1.4%
& 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 885
100.0%
Open Punctuation
ValueCountFrequency (%)
( 881
100.0%
Space Separator
ValueCountFrequency (%)
220
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 56193
90.5%
Common 5081
 
8.2%
Latin 851
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 2344
 
4.2%
동 1261
 
2.2%
교 1225
 
2.2%
구 1082
 
1.9%
아 1070
 
1.9%
지 1068
 
1.9%
트 1007
 
1.8%
마 959
 
1.7%
파 948
 
1.7%
원 881
 
1.6%
Other values (710) 44348
78.9%
Latin
ValueCountFrequency (%)
C 175
20.6%
I 123
14.5%
T 80
9.4%
G 66
 
7.8%
K 65
 
7.6%
S 59
 
6.9%
A 46
 
5.4%
L 37
 
4.3%
e 28
 
3.3%
J 24
 
2.8%
Other values (26) 148
17.4%
Common
ValueCountFrequency (%)
. 961
18.9%
) 885
17.4%
( 881
17.3%
1 743
14.6%
2 643
12.7%
3 264
 
5.2%
220
 
4.3%
4 140
 
2.8%
5 67
 
1.3%
6 54
 
1.1%
Other values (9) 223
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 56191
90.4%
ASCII 5932
 
9.5%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 2344
 
4.2%
동 1261
 
2.2%
교 1225
 
2.2%
구 1082
 
1.9%
아 1070
 
1.9%
지 1068
 
1.9%
트 1007
 
1.8%
마 959
 
1.7%
파 948
 
1.7%
원 881
 
1.6%
Other values (709) 44346
78.9%
ASCII
ValueCountFrequency (%)
. 961
16.2%
) 885
14.9%
( 881
14.9%
1 743
12.5%
2 643
10.8%
3 264
 
4.5%
220
 
3.7%
C 175
 
3.0%
4 140
 
2.4%
I 123
 
2.1%
Other values (45) 897
15.1%
None
ValueCountFrequency (%)
㈜ 2
100.0%

주소
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing9983
Missing (%)99.8%
Memory size156.2 KiB
2024-03-14T20:15:27.695565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length14.352941
Min length10

Characters and Unicode

Total characters244
Distinct characters86
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

Unique17 ?
Unique (%)100.0%

Sample

1st row전북남원시함파우길65-14
2nd row전죽익산시왕궁면궁성로666
3rd row전북남원시산내면입석길94-129
4th row전북군산시비응도동95
5th row전북장수군장수읍승마로74
ValueCountFrequency (%)
전북남원시함파우길65-14 1
 
5.9%
전북군산시옥도면신시도리 1
 
5.9%
전북정읍시이평면하송리산17 1
 
5.9%
전북남원시만복사길7 1
 
5.9%
전북김제시만경읍화포3길63-12 1
 
5.9%
전북부안군진서면청자로1075 1
 
5.9%
전북고창군상하면상하농원길11-23 1
 
5.9%
전북남원시이백면목가길193 1
 
5.9%
전북익산시여산면천호산길140 1
 
5.9%
전죽익산시왕궁면궁성로666 1
 
5.9%
Other values (7) 7
41.2%
2024-03-14T20:15:28.630200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
전 17
 
7.0%
북 16
 
6.6%
면 12
 
4.9%
시 12
 
4.9%
1 11
 
4.5%
산 9
 
3.7%
군 8
 
3.3%
길 8
 
3.3%
6 8
 
3.3%
4 6
 
2.5%
Other values (76) 137
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 188
77.0%
Decimal Number 50
 
20.5%
Dash Punctuation 6
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 17
 
9.0%
북 16
 
8.5%
면 12
 
6.4%
시 12
 
6.4%
산 9
 
4.8%
군 8
 
4.3%
길 8
 
4.3%
원 5
 
2.7%
로 5
 
2.7%
청 4
 
2.1%
Other values (65) 92
48.9%
Decimal Number
ValueCountFrequency (%)
1 11
22.0%
6 8
16.0%
4 6
12.0%
3 5
10.0%
7 5
10.0%
2 5
10.0%
9 4
 
8.0%
5 3
 
6.0%
0 2
 
4.0%
8 1
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 188
77.0%
Common 56
 
23.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 17
 
9.0%
북 16
 
8.5%
면 12
 
6.4%
시 12
 
6.4%
산 9
 
4.8%
군 8
 
4.3%
길 8
 
4.3%
원 5
 
2.7%
로 5
 
2.7%
청 4
 
2.1%
Other values (65) 92
48.9%
Common
ValueCountFrequency (%)
1 11
19.6%
6 8
14.3%
4 6
10.7%
- 6
10.7%
3 5
8.9%
7 5
8.9%
2 5
8.9%
9 4
 
7.1%
5 3
 
5.4%
0 2
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 188
77.0%
ASCII 56
 
23.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
전 17
 
9.0%
북 16
 
8.5%
면 12
 
6.4%
시 12
 
6.4%
산 9
 
4.8%
군 8
 
4.3%
길 8
 
4.3%
원 5
 
2.7%
로 5
 
2.7%
청 4
 
2.1%
Other values (65) 92
48.9%
ASCII
ValueCountFrequency (%)
1 11
19.6%
6 8
14.3%
4 6
10.7%
- 6
10.7%
3 5
8.9%
7 5
8.9%
2 5
8.9%
9 4
 
7.1%
5 3
 
5.4%
0 2
 
3.6%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct9577
Distinct (%)96.7%
Missing98
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean36.512427
Minimum34.31847
Maximum38.24922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T20:15:28.869002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.31847
5-th percentile35.083541
Q135.801398
median36.66331
Q337.369888
95-th percentile37.743648
Maximum38.24922
Range3.93075
Interquartile range (IQR)1.56849

Descriptive statistics

Standard deviation0.94438049
Coefficient of variation (CV)0.025864632
Kurtosis-1.3145865
Mean36.512427
Median Absolute Deviation (MAD)0.77423
Skewness-0.28537345
Sum361546.05
Variance0.89185451
MonotonicityNot monotonic
2024-03-14T20:15:29.132176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.43898 3
 
< 0.1%
35.18561 3
 
< 0.1%
35.17514 3
 
< 0.1%
37.3244 3
 
< 0.1%
35.1506 3
 
< 0.1%
36.47506 3
 
< 0.1%
37.26282 3
 
< 0.1%
35.23753 3
 
< 0.1%
37.7147 3
 
< 0.1%
35.08443 3
 
< 0.1%
Other values (9567) 9872
98.7%
(Missing) 98
 
1.0%
ValueCountFrequency (%)
34.31847 1
< 0.1%
34.60735 1
< 0.1%
34.63848 1
< 0.1%
34.70844 1
< 0.1%
34.71971 1
< 0.1%
34.72649 1
< 0.1%
34.73136 1
< 0.1%
34.73885 1
< 0.1%
34.73993 1
< 0.1%
34.74192 1
< 0.1%
ValueCountFrequency (%)
38.24922 1
< 0.1%
38.211 1
< 0.1%
38.16365 1
< 0.1%
38.15828 1
< 0.1%
38.08557 1
< 0.1%
38.05262 1
< 0.1%
38.05254 1
< 0.1%
38.02891 1
< 0.1%
38.02751 1
< 0.1%
38.02424 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct7626
Distinct (%)77.0%
Missing98
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean127.65735
Minimum126.3916
Maximum129.4985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T20:15:29.423596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.3916
5-th percentile126.72221
Q1126.97983
median127.3096
Q3128.52168
95-th percentile129.07727
Maximum129.4985
Range3.1069
Interquartile range (IQR)1.54185

Descriptive statistics

Standard deviation0.82521171
Coefficient of variation (CV)0.0064642711
Kurtosis-1.2111606
Mean127.65735
Median Absolute Deviation (MAD)0.4612
Skewness0.56429979
Sum1264063.1
Variance0.68097436
MonotonicityNot monotonic
2024-03-14T20:15:29.866405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.166 7
 
0.1%
127.115 6
 
0.1%
126.9327 6
 
0.1%
127.0937 5
 
0.1%
127.0671 5
 
0.1%
127.1065 5
 
0.1%
127.1406 5
 
0.1%
128.9908 5
 
0.1%
126.9151 5
 
0.1%
127.1098 5
 
0.1%
Other values (7616) 9848
98.5%
(Missing) 98
 
1.0%
ValueCountFrequency (%)
126.3916 1
< 0.1%
126.4174 1
< 0.1%
126.4187 1
< 0.1%
126.4188 2
< 0.1%
126.4214 1
< 0.1%
126.4229 1
< 0.1%
126.4317 1
< 0.1%
126.4326 1
< 0.1%
126.4336 1
< 0.1%
126.4346 1
< 0.1%
ValueCountFrequency (%)
129.4985 1
< 0.1%
129.4969 1
< 0.1%
129.4965 1
< 0.1%
129.4923 1
< 0.1%
129.4847 1
< 0.1%
129.4675 1
< 0.1%
129.4542 1
< 0.1%
129.4501 1
< 0.1%
129.4319 1
< 0.1%
129.4317 2
< 0.1%

Interactions

2024-03-14T20:15:23.532217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:15:22.975971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:15:23.814767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:15:23.255098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:15:30.127408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소위도경도
주소1.0001.0001.000
위도1.0001.0000.813
경도1.0000.8131.000
2024-03-14T20:15:30.359107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.000-0.637
경도-0.6371.000

Missing values

2024-03-14T20:15:24.165043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:15:24.443732image/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.
2024-03-14T20:15:24.732714image/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

관광지명주소위도경도
74676항동하버라인8단지<NA>37.47468126.8203
70357시청앞<NA>36.98992127.1119
71818고강동철탑(시계지점)<NA>37.53397126.8219
98801농업기술센터<NA>35.86694129.2243
19905자갈치역.비프광장<NA>35.09787129.0292
29179대봉네거리1<NA>35.85404128.6026
71031이마트<NA>37.3591127.1206
78945금륜사<NA>37.2382127.2892
14220대평1리(대평원)<NA>36.68497127.1575
30754갈산3길입구<NA>36.65517127.5704
관광지명주소위도경도
59634미도아파트<NA>37.52788126.7089
5775토망대.오동<NA>36.41672127.5305
52295다식종점<NA>36.16499128.371
80801양지마을3반<NA>37.63735127.133
9606백운농원입구<NA>35.2473129.0337
71512오정동행정복지센터.대명초등학교<NA>37.52708126.7971
25509육동교회<NA>35.7638128.9049
10555우산마을<NA>35.15744128.5529
93962신대마을회관<NA>37.02375127.4087
35245안금마을<NA>35.28647128.8854

Duplicate rows

Most frequently occurring

관광지명주소위도경도# duplicates
13<NA><NA><NA><NA>98
0관호초등학교입구<NA>36.02059128.38352
1남서울대학교<NA>36.91262127.13532
2문화아파트<NA>35.98873128.40772
3북대구세무서건너<NA>35.88908128.5832
4상지사거리입구<NA>35.95936128.49392
5석적농협<NA>36.07611128.39522
6소정리입구<NA>36.71191127.15392
7용수교회건너<NA>36.06433128.60812
8이양농공단지<NA>34.90259126.98832