Overview

Dataset statistics

Number of variables8
Number of observations1371
Missing cells167
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.8 KiB
Average record size in memory67.1 B

Variable types

Categorical1
Text4
Numeric3

Alerts

WGS84위도 is highly overall correlated with WGS84경도High correlation
WGS84경도 is highly overall correlated with WGS84위도High correlation
문화재한자명 has 87 (6.3%) missing valuesMissing
이미지경로 has 80 (5.8%) missing valuesMissing
WGS84위도 has 459 (33.5%) zerosZeros
WGS84경도 has 459 (33.5%) zerosZeros

Reproduction

Analysis started2023-12-10 20:59:58.948805
Analysis finished2023-12-10 21:00:01.523928
Duration2.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

종목명칭
Categorical

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
시도유형문화재
383 
시도기념물
224 
문화재자료
206 
보물
196 
국가등록문화재
95 
Other values (9)
267 

Length

Max length7
Median length5
Mean length5.2873815
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국가등록문화재
2nd row국가등록문화재
3rd row국가등록문화재
4th row국가등록문화재
5th row국가등록문화재

Common Values

ValueCountFrequency (%)
시도유형문화재 383
27.9%
시도기념물 224
16.3%
문화재자료 206
15.0%
보물 196
14.3%
국가등록문화재 95
 
6.9%
시도무형문화재 82
 
6.0%
사적 71
 
5.2%
천연기념물 29
 
2.1%
국가민속문화재 23
 
1.7%
시도등록문화재 18
 
1.3%
Other values (4) 44
 
3.2%

Length

2023-12-11T06:00:01.594630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
시도유형문화재 383
27.9%
시도기념물 224
16.3%
문화재자료 206
15.0%
보물 196
14.3%
국가등록문화재 95
 
6.9%
시도무형문화재 82
 
6.0%
사적 71
 
5.2%
천연기념물 29
 
2.1%
국가민속문화재 23
 
1.7%
시도등록문화재 18
 
1.3%
Other values (4) 44
 
3.2%

문화재명
Text

Distinct1362
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
2023-12-11T06:00:01.841928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length28
Mean length9.3267688
Min length2

Characters and Unicode

Total characters12787
Distinct characters483
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1353 ?
Unique (%)98.7%

Sample

1st row연천역 급수탑
2nd row파주 구 장단면사무소
3rd row파주 경의선 구 장단역 터
4th row경의선 장단역 증기기관차
5th row파주 경의선 장단역 죽음의 다리
ValueCountFrequency (%)
남양주 50
 
1.8%
및 44
 
1.6%
파주 38
 
1.4%
고양 33
 
1.2%
양주 29
 
1.1%
여주 28
 
1.0%
화성 27
 
1.0%
초상 26
 
1.0%
25
 
0.9%
안성 25
 
0.9%
Other values (1725) 2381
88.0%
2023-12-11T06:00:02.268710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1336
 
10.4%
사 454
 
3.6%
주 258
 
2.0%
성 246
 
1.9%
상 197
 
1.5%
지 194
 
1.5%
도 188
 
1.5%
양 186
 
1.5%
묘 184
 
1.4%
장 165
 
1.3%
Other values (473) 9379
73.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10966
85.8%
Space Separator 1336
 
10.4%
Decimal Number 237
 
1.9%
Open Punctuation 57
 
0.4%
Close Punctuation 57
 
0.4%
Dash Punctuation 55
 
0.4%
Other Punctuation 41
 
0.3%
Uppercase Letter 17
 
0.1%
Math Symbol 13
 
0.1%
Final Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 454
 
4.1%
주 258
 
2.4%
성 246
 
2.2%
상 197
 
1.8%
지 194
 
1.8%
도 188
 
1.7%
양 186
 
1.7%
묘 184
 
1.7%
장 165
 
1.5%
석 160
 
1.5%
Other values (434) 8734
79.6%
Uppercase Letter
ValueCountFrequency (%)
D 2
11.8%
K 2
11.8%
A 2
11.8%
R 2
11.8%
W 1
 
5.9%
P 1
 
5.9%
M 1
 
5.9%
C 1
 
5.9%
H 1
 
5.9%
T 1
 
5.9%
Other values (3) 3
17.6%
Decimal Number
ValueCountFrequency (%)
1 68
28.7%
2 50
21.1%
3 26
 
11.0%
0 25
 
10.5%
9 21
 
8.9%
5 14
 
5.9%
6 11
 
4.6%
4 11
 
4.6%
7 6
 
2.5%
8 5
 
2.1%
Other Punctuation
ValueCountFrequency (%)
· 16
39.0%
, 15
36.6%
? 6
 
14.6%
. 3
 
7.3%
: 1
 
2.4%
Math Symbol
ValueCountFrequency (%)
∼ 5
38.5%
~ 5
38.5%
> 1
 
7.7%
< 1
 
7.7%
～ 1
 
7.7%
Space Separator
ValueCountFrequency (%)
1336
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 4
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10966
85.8%
Common 1804
 
14.1%
Latin 17
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 454
 
4.1%
주 258
 
2.4%
성 246
 
2.2%
상 197
 
1.8%
지 194
 
1.8%
도 188
 
1.7%
양 186
 
1.7%
묘 184
 
1.7%
장 165
 
1.5%
석 160
 
1.5%
Other values (434) 8734
79.6%
Common
ValueCountFrequency (%)
1336
74.1%
1 68
 
3.8%
( 57
 
3.2%
) 57
 
3.2%
- 55
 
3.0%
2 50
 
2.8%
3 26
 
1.4%
0 25
 
1.4%
9 21
 
1.2%
· 16
 
0.9%
Other values (16) 93
 
5.2%
Latin
ValueCountFrequency (%)
D 2
11.8%
K 2
11.8%
A 2
11.8%
R 2
11.8%
W 1
 
5.9%
P 1
 
5.9%
M 1
 
5.9%
C 1
 
5.9%
H 1
 
5.9%
T 1
 
5.9%
Other values (3) 3
17.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10966
85.8%
ASCII 1791
 
14.0%
None 17
 
0.1%
Punctuation 8
 
0.1%
Math Operators 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1336
74.6%
1 68
 
3.8%
( 57
 
3.2%
) 57
 
3.2%
- 55
 
3.1%
2 50
 
2.8%
3 26
 
1.5%
0 25
 
1.4%
9 21
 
1.2%
, 15
 
0.8%
Other values (24) 81
 
4.5%
Hangul
ValueCountFrequency (%)
사 454
 
4.1%
주 258
 
2.4%
성 246
 
2.2%
상 197
 
1.8%
지 194
 
1.8%
도 188
 
1.7%
양 186
 
1.7%
묘 184
 
1.7%
장 165
 
1.5%
석 160
 
1.5%
Other values (434) 8734
79.6%
None
ValueCountFrequency (%)
· 16
94.1%
～ 1
 
5.9%
Math Operators
ValueCountFrequency (%)
∼ 5
100.0%
Punctuation
ValueCountFrequency (%)
’ 4
50.0%
‘ 4
50.0%

문화재한자명
Text

MISSING 

Distinct1275
Distinct (%)99.3%
Missing87
Missing (%)6.3%
Memory size10.8 KiB
2023-12-11T06:00:02.564935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length28
Mean length9.0514019
Min length2

Characters and Unicode

Total characters11622
Distinct characters1482
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1267 ?
Unique (%)98.7%

Sample

1st row漣川驛 給水塔
2nd row坡州 舊 長湍面事務所
3rd row坡州 京義線 舊 長湍驛 터
4th row京義線 長湍驛 蒸氣機關車
5th row坡州 京義線 長湍驛 죽음의 다리
ValueCountFrequency (%)
및 36
 
1.5%
南楊州 33
 
1.4%
坡州 32
 
1.3%
高陽 25
 
1.0%
華城 25
 
1.0%
楊州 25
 
1.0%
25
 
1.0%
肖像 24
 
1.0%
驪州 23
 
0.9%
安城 21
 
0.9%
Other values (1643) 2169
89.0%
2023-12-11T06:00:03.030545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1158
 
10.0%
寺 273
 
2.3%
州 171
 
1.5%
墓 164
 
1.4%
城 156
 
1.3%
石 132
 
1.1%
像 121
 
1.0%
里 118
 
1.0%
山 108
 
0.9%
圖 103
 
0.9%
Other values (1472) 9118
78.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10086
86.8%
Space Separator 1158
 
10.0%
Decimal Number 133
 
1.1%
Other Punctuation 60
 
0.5%
Close Punctuation 54
 
0.5%
Dash Punctuation 54
 
0.5%
Open Punctuation 50
 
0.4%
Uppercase Letter 12
 
0.1%
Math Symbol 11
 
0.1%
Initial Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
寺 273
 
2.7%
州 171
 
1.7%
墓 164
 
1.6%
城 156
 
1.5%
石 132
 
1.3%
像 121
 
1.2%
里 118
 
1.2%
山 108
 
1.1%
圖 103
 
1.0%
大 97
 
1.0%
Other values (1437) 8643
85.7%
Decimal Number
ValueCountFrequency (%)
1 39
29.3%
2 33
24.8%
9 17
12.8%
0 15
 
11.3%
3 13
 
9.8%
5 7
 
5.3%
8 3
 
2.3%
6 3
 
2.3%
7 2
 
1.5%
4 1
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
A 2
16.7%
R 2
16.7%
D 2
16.7%
G 1
8.3%
V 1
8.3%
W 1
8.3%
P 1
8.3%
K 1
8.3%
M 1
8.3%
Other Punctuation
ValueCountFrequency (%)
? 30
50.0%
· 14
23.3%
, 13
21.7%
. 2
 
3.3%
: 1
 
1.7%
Math Symbol
ValueCountFrequency (%)
~ 5
45.5%
∼ 3
27.3%
< 1
 
9.1%
> 1
 
9.1%
～ 1
 
9.1%
Space Separator
ValueCountFrequency (%)
1158
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 9474
81.5%
Common 1524
 
13.1%
Hangul 612
 
5.3%
Latin 12
 
0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
寺 273
 
2.9%
州 171
 
1.8%
墓 164
 
1.7%
城 156
 
1.6%
石 132
 
1.4%
像 121
 
1.3%
里 118
 
1.2%
山 108
 
1.1%
圖 103
 
1.1%
大 97
 
1.0%
Other values (1221) 8031
84.8%
Hangul
ValueCountFrequency (%)
및 61
 
10.0%
리 18
 
2.9%
나 18
 
2.9%
무 18
 
2.9%
의 16
 
2.6%
돌 13
 
2.1%
고 13
 
2.1%
이 12
 
2.0%
기 10
 
1.6%
가 9
 
1.5%
Other values (206) 424
69.3%
Common
ValueCountFrequency (%)
1158
76.0%
) 54
 
3.5%
- 54
 
3.5%
( 50
 
3.3%
1 39
 
2.6%
2 33
 
2.2%
? 30
 
2.0%
9 17
 
1.1%
0 15
 
1.0%
· 14
 
0.9%
Other values (16) 60
 
3.9%
Latin
ValueCountFrequency (%)
A 2
16.7%
R 2
16.7%
D 2
16.7%
G 1
8.3%
V 1
8.3%
W 1
8.3%
P 1
8.3%
K 1
8.3%
M 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 9216
79.3%
ASCII 1514
 
13.0%
Hangul 612
 
5.3%
CJK Compat Ideographs 258
 
2.2%
None 15
 
0.1%
Punctuation 4
 
< 0.1%
Math Operators 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1158
76.5%
) 54
 
3.6%
- 54
 
3.6%
( 50
 
3.3%
1 39
 
2.6%
2 33
 
2.2%
? 30
 
2.0%
9 17
 
1.1%
0 15
 
1.0%
3 13
 
0.9%
Other values (20) 51
 
3.4%
CJK
ValueCountFrequency (%)
寺 273
 
3.0%
州 171
 
1.9%
墓 164
 
1.8%
城 156
 
1.7%
石 132
 
1.4%
像 121
 
1.3%
里 118
 
1.3%
山 108
 
1.2%
圖 103
 
1.1%
大 97
 
1.1%
Other values (1169) 7773
84.3%
Hangul
ValueCountFrequency (%)
및 61
 
10.0%
리 18
 
2.9%
나 18
 
2.9%
무 18
 
2.9%
의 16
 
2.6%
돌 13
 
2.1%
고 13
 
2.1%
이 12
 
2.0%
기 10
 
1.6%
가 9
 
1.5%
Other values (206) 424
69.3%
CJK Compat Ideographs
ValueCountFrequency (%)
李 59
22.9%
龍 39
15.1%
立 21
 
8.1%
驪 17
 
6.6%
金 14
 
5.4%
漣 9
 
3.5%
蓮 9
 
3.5%
柳 8
 
3.1%
宅 8
 
3.1%
利 6
 
2.3%
Other values (42) 68
26.4%
None
ValueCountFrequency (%)
· 14
93.3%
～ 1
 
6.7%
Math Operators
ValueCountFrequency (%)
∼ 3
100.0%
Punctuation
ValueCountFrequency (%)
‘ 2
50.0%
’ 2
50.0%

문화재지정번호
Real number (ℝ)

Distinct698
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3429534.1
Minimum10000
Maximum22230000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-11T06:00:03.216800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile100000
Q1740000
median1770000
Q33595000
95-th percentile16310150
Maximum22230000
Range22220000
Interquartile range (IQR)2855000

Descriptive statistics

Standard deviation4708196
Coefficient of variation (CV)1.3728384
Kurtosis4.7786545
Mean3429534.1
Median Absolute Deviation (MAD)1250000
Skewness2.3273462
Sum4.7018913 × 109
Variance2.216711 × 1013
MonotonicityNot monotonic
2023-12-11T06:00:03.447810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 8
 
0.6%
40000 8
 
0.6%
90000 7
 
0.5%
10000 7
 
0.5%
30000 7
 
0.5%
50000 7
 
0.5%
60000 7
 
0.5%
70000 7
 
0.5%
80000 7
 
0.5%
120000 7
 
0.5%
Other values (688) 1299
94.7%
ValueCountFrequency (%)
10000 7
0.5%
20000 8
0.6%
30000 7
0.5%
40000 8
0.6%
50000 7
0.5%
60000 7
0.5%
70000 7
0.5%
80000 7
0.5%
90000 7
0.5%
100000 6
0.4%
ValueCountFrequency (%)
22230000 1
0.1%
22130000 1
0.1%
22070000 1
0.1%
21950000 1
0.1%
21850000 1
0.1%
21750000 1
0.1%
21740000 1
0.1%
21730000 1
0.1%
21710000 1
0.1%
21550000 1
0.1%

문화재지정번호명
Text

Distinct698
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
2023-12-11T06:00:03.873729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.8446389
Min length1

Characters and Unicode

Total characters3900
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique395 ?
Unique (%)28.8%

Sample

1st row45
2nd row76
3rd row77
4th row78
5th row79
ValueCountFrequency (%)
2 8
 
0.6%
4 8
 
0.6%
8 7
 
0.5%
14 7
 
0.5%
12 7
 
0.5%
9 7
 
0.5%
13 7
 
0.5%
5 7
 
0.5%
7 7
 
0.5%
6 7
 
0.5%
Other values (688) 1299
94.7%
2023-12-11T06:00:04.408230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 812
20.8%
2 550
14.1%
3 434
11.1%
4 330
8.5%
5 325
8.3%
7 290
 
7.4%
9 283
 
7.3%
6 278
 
7.1%
8 275
 
7.1%
0 253
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3830
98.2%
Dash Punctuation 70
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 812
21.2%
2 550
14.4%
3 434
11.3%
4 330
8.6%
5 325
8.5%
7 290
 
7.6%
9 283
 
7.4%
6 278
 
7.3%
8 275
 
7.2%
0 253
 
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 812
20.8%
2 550
14.1%
3 434
11.1%
4 330
8.5%
5 325
8.3%
7 290
 
7.4%
9 283
 
7.3%
6 278
 
7.1%
8 275
 
7.1%
0 253
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 812
20.8%
2 550
14.1%
3 434
11.1%
4 330
8.5%
5 325
8.3%
7 290
 
7.4%
9 283
 
7.3%
6 278
 
7.1%
8 275
 
7.1%
0 253
 
6.5%

이미지경로
Text

MISSING 

Distinct1291
Distinct (%)100.0%
Missing80
Missing (%)5.8%
Memory size10.8 KiB
2023-12-11T06:00:04.698891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length80
Median length79
Mean length66.607281
Min length58

Characters and Unicode

Total characters85990
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1291 ?
Unique (%)100.0%

Sample

1st rowhttp://www.cha.go.kr/unisearch/images/register/1668053.jpg
2nd rowhttp://www.cha.go.kr/unisearch/images/register/1668062.jpg
3rd rowhttp://www.cha.go.kr/unisearch/images/register/1668065.jpg
4th rowhttp://www.cha.go.kr/unisearch/images/register/1668066.jpg
5th rowhttp://www.cha.go.kr/unisearch/images/register/1668073.jpg
ValueCountFrequency (%)
http://www.cha.go.kr/unisearch/images/register/2017103013364300.jpg 1
 
0.1%
http://www.cha.go.kr/unisearch/images/intangible_cult_prop/1643633.jpg 1
 
0.1%
http://www.cha.go.kr/unisearch/images/intangible_cult_prop/2018091411014300.jpg 1
 
0.1%
http://www.cha.go.kr/unisearch/images/intangible_cult_prop/2022092109071900.jpg 1
 
0.1%
http://www.cha.go.kr/unisearch/images/intangible_cult_prop/2018012413072500.jpg 1
 
0.1%
http://www.cha.go.kr/unisearch/images/intangible_cult_prop/2018091410540200.jpg 1
 
0.1%
http://www.cha.go.kr/unisearch/images/intangible_cult_prop/2018090714183200.jpg 1
 
0.1%
http://www.cha.go.kr/unisearch/images/intangible_cult_prop/2018081009505200.jpg 1
 
0.1%
http://www.cha.go.kr/unisearch/images/intangible_cult_prop/2018091017320900.jpg 1
 
0.1%
http://www.cha.go.kr/unisearch/images/intangible_cult_prop/1643671.jpg 1
 
0.1%
Other values (1281) 1281
99.2%
2023-12-11T06:00:05.134478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 7746
 
9.0%
a 5255
 
6.1%
. 5164
 
6.0%
t 4617
 
5.4%
g 4349
 
5.1%
e 4237
 
4.9%
r 4224
 
4.9%
h 3944
 
4.6%
w 3873
 
4.5%
i 3659
 
4.3%
Other values (30) 38922
45.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57601
67.0%
Other Punctuation 14201
 
16.5%
Decimal Number 12680
 
14.7%
Connector Punctuation 1280
 
1.5%
Uppercase Letter 228
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5255
 
9.1%
t 4617
 
8.0%
g 4349
 
7.6%
e 4237
 
7.4%
r 4224
 
7.3%
h 3944
 
6.8%
w 3873
 
6.7%
i 3659
 
6.4%
p 3417
 
5.9%
c 3231
 
5.6%
Other values (12) 16795
29.2%
Decimal Number
ValueCountFrequency (%)
0 2536
20.0%
1 2532
20.0%
6 1696
13.4%
2 1468
11.6%
3 931
 
7.3%
4 784
 
6.2%
5 784
 
6.2%
7 682
 
5.4%
9 653
 
5.1%
8 614
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
P 76
33.3%
G 76
33.3%
J 62
27.2%
N 14
 
6.1%
Other Punctuation
ValueCountFrequency (%)
/ 7746
54.5%
. 5164
36.4%
: 1291
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57829
67.3%
Common 28161
32.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5255
 
9.1%
t 4617
 
8.0%
g 4349
 
7.5%
e 4237
 
7.3%
r 4224
 
7.3%
h 3944
 
6.8%
w 3873
 
6.7%
i 3659
 
6.3%
p 3417
 
5.9%
c 3231
 
5.6%
Other values (16) 17023
29.4%
Common
ValueCountFrequency (%)
/ 7746
27.5%
. 5164
18.3%
0 2536
 
9.0%
1 2532
 
9.0%
6 1696
 
6.0%
2 1468
 
5.2%
: 1291
 
4.6%
_ 1280
 
4.5%
3 931
 
3.3%
4 784
 
2.8%
Other values (4) 2733
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 7746
 
9.0%
a 5255
 
6.1%
. 5164
 
6.0%
t 4617
 
5.4%
g 4349
 
5.1%
e 4237
 
4.9%
r 4224
 
4.9%
h 3944
 
4.6%
w 3873
 
4.5%
i 3659
 
4.3%
Other values (30) 38922
45.3%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct734
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.918596
Minimum0
Maximum38.1811
Zeros459
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-11T06:00:05.335904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.26833
Q337.552449
95-th percentile37.874359
Maximum38.1811
Range38.1811
Interquartile range (IQR)37.552449

Descriptive statistics

Standard deviation17.686027
Coefficient of variation (CV)0.70975214
Kurtosis-1.5109118
Mean24.918596
Median Absolute Deviation (MAD)0.42042352
Skewness-0.700348
Sum34163.395
Variance312.79554
MonotonicityNot monotonic
2023-12-11T06:00:05.619089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 459
33.5%
37.3930295720453 25
 
1.8%
37.2683295776315 24
 
1.8%
37.1994932 14
 
1.0%
37.2677891 11
 
0.8%
37.4332925 11
 
0.8%
37.2122405 10
 
0.7%
37.3226816 10
 
0.7%
37.3142615 9
 
0.7%
37.0264259 8
 
0.6%
Other values (724) 790
57.6%
ValueCountFrequency (%)
0.0 459
33.5%
34.7230435 1
 
0.1%
36.9115936347341 1
 
0.1%
36.9120119369696 1
 
0.1%
36.913516632003 1
 
0.1%
36.9135428172787 1
 
0.1%
36.9291223889287 1
 
0.1%
36.9365881637217 1
 
0.1%
36.9398889335819 1
 
0.1%
36.9399403809912 1
 
0.1%
ValueCountFrequency (%)
38.1811 1
0.1%
38.1472076326323 1
0.1%
38.1307489425742 1
0.1%
38.1290001798844 1
0.1%
38.1283608897582 1
0.1%
38.11627881 1
0.1%
38.1020798779401 1
0.1%
38.0950707475056 1
0.1%
38.094 1
0.1%
38.093446998829 1
0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct734
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.540318
Minimum0
Maximum127.75394
Zeros459
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-11T06:00:05.792025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median126.92429
Q3127.14732
95-th percentile127.52843
Maximum127.75394
Range127.75394
Interquartile range (IQR)127.14732

Descriptive statistics

Standard deviation59.997648
Coefficient of variation (CV)0.70969271
Kurtosis-1.5109133
Mean84.540318
Median Absolute Deviation (MAD)0.36632118
Skewness-0.70087885
Sum115904.78
Variance3599.7177
MonotonicityNot monotonic
2023-12-11T06:00:06.023904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 459
33.5%
127.052183461948 25
 
1.8%
127.108755608431 24
 
1.8%
126.8311887 14
 
1.0%
127.1085108 11
 
0.8%
126.8783766 11
 
0.8%
127.0050501 10
 
0.7%
127.1260201 10
 
0.7%
126.9534116 9
 
0.7%
127.3976762 8
 
0.6%
Other values (724) 790
57.6%
ValueCountFrequency (%)
0.0 459
33.5%
126.2962311 1
 
0.1%
126.2969526 1
 
0.1%
126.3214742 1
 
0.1%
126.3215248 2
 
0.1%
126.342125 1
 
0.1%
126.3534785 1
 
0.1%
126.3742366 1
 
0.1%
126.3890348 1
 
0.1%
126.390863 1
 
0.1%
ValueCountFrequency (%)
127.753935715441 1
0.1%
127.7371884 1
0.1%
127.68842 1
0.1%
127.687464433933 1
0.1%
127.687354905746 1
0.1%
127.686981329034 1
0.1%
127.684184957909 1
0.1%
127.682546431735 1
0.1%
127.671151736346 1
0.1%
127.664932694879 1
0.1%

Interactions

2023-12-11T06:00:00.825761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:00.195204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:00.475772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:00.942710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:00.284617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:00.574925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:01.084864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:00.382651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:00.699003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:00:06.141508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종목명칭문화재지정번호WGS84위도WGS84경도
종목명칭1.0000.7280.5740.574
문화재지정번호0.7281.0000.5440.544
WGS84위도0.5740.5441.0001.000
WGS84경도0.5740.5441.0001.000
2023-12-11T06:00:06.254163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
문화재지정번호WGS84위도WGS84경도종목명칭
문화재지정번호1.000-0.217-0.1820.400
WGS84위도-0.2171.0000.6050.450
WGS84경도-0.1820.6051.0000.450
종목명칭0.4000.4500.4501.000

Missing values

2023-12-11T06:00:01.245166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:00:01.377017image/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.
2023-12-11T06:00:01.475384image/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

종목명칭문화재명문화재한자명문화재지정번호문화재지정번호명이미지경로WGS84위도WGS84경도
0국가등록문화재연천역 급수탑漣川驛 給水塔45000045http://www.cha.go.kr/unisearch/images/register/1668053.jpg38.10208127.074224
1국가등록문화재파주 구 장단면사무소坡州 舊 長湍面事務所76000076http://www.cha.go.kr/unisearch/images/register/1668062.jpg37.898804126.690366
2국가등록문화재파주 경의선 구 장단역 터坡州 京義線 舊 長湍驛 터77000077http://www.cha.go.kr/unisearch/images/register/1668065.jpg37.904783126.687797
3국가등록문화재경의선 장단역 증기기관차京義線 長湍驛 蒸氣機關車78000078http://www.cha.go.kr/unisearch/images/register/1668066.jpg37.890428126.736775
4국가등록문화재파주 경의선 장단역 죽음의 다리坡州 京義線 長湍驛 죽음의 다리79000079http://www.cha.go.kr/unisearch/images/register/1668073.jpg37.903679126.694418
5국가등록문화재구 포천성당舊 抱川聖堂2710000271http://www.cha.go.kr/unisearch/images/register/1668076.jpg37.898832127.197715
6국가등록문화재고양 구 일산역사高陽 舊 一山驛舍2940000294http://www.cha.go.kr/unisearch/images/register/1668080.jpg37.682966126.768535
7국가등록문화재남양주 구 팔당역南楊州 舊 八堂驛2950000295http://www.cha.go.kr/unisearch/images/register/1668086.jpg37.543458127.250893
8국가등록문화재양평 구 구둔역楊平 舊 九屯驛2960000296http://www.cha.go.kr/unisearch/images/register/1668093.jpg37.437353127.68842
9국가등록문화재미몽迷夢3420000342http://www.cha.go.kr/unisearch/images/register/1667113.jpg0.00.0
종목명칭문화재명문화재한자명문화재지정번호문화재지정번호명이미지경로WGS84위도WGS84경도
1361천연기념물한탄강 대교천 현무암 협곡漢灘江 大橋川 玄武岩 峽谷4360000436http://www.cha.go.kr/unisearch/images/natural_monument/1629831.jpg38.1811127.2872
1362천연기념물여주 효종대왕릉(영릉) 회양목驪州 孝宗大王陵(寧陵) 淮陽木4590000459http://www.cha.go.kr/unisearch/images/natural_monument/1629843.jpg37.311066127.610245
1363천연기념물포천 직두리 부부송抱川 稷頭里 夫婦松4600000460http://www.cha.go.kr/unisearch/images/natural_monument/1629850.jpg37.85567127.245202
1364천연기념물화성 전곡리 물푸레나무華城 前谷里 물푸레나무4700000470http://www.cha.go.kr/unisearch/images/natural_monument/1629865.jpg37.193977126.69811
1365천연기념물화성 융릉 개비자나무華城 隆陵 개비자나무5040000504http://www.cha.go.kr/unisearch/images/natural_monument/1629872.jpg37.208046126.989534
1366천연기념물포천 한탄강 현무암 협곡과 비둘기낭폭포抱川 漢灘江 玄武岩 峽谷과 비둘기낭瀑布5370000537http://www.cha.go.kr/unisearch/images/natural_monument/1629877.jpg38.079657127.217841
1367천연기념물포천 아우라지 베개용암抱川 아우라지 베개鎔巖5420000542http://www.cha.go.kr/unisearch/images/natural_monument/1629881.jpg38.042127.118
1368천연기념물포천 초과리 오리나무抱川 初果里 五里木5550000555http://www.cha.go.kr/unisearch/images/natural_monument/2019090610145900.jpg38.147208127.245595
1369천연기념물연천 임진강 두루미류 도래지(569)漣川 臨津江 두루미類 渡來地(569)5690000569http://www.cha.go.kr/unisearch/images/natural_monument/2022082914231700.jpg0.00.0
1370천연기념물화성 뿔공룡(코리아케라톱스 화성엔시스) 골격 화석華城 뿔恐龍(코리아케라톱스 화성엔시스) 骨格 化石5710000571http://www.cha.go.kr/unisearch/images/natural_monument/2023041316304200.jpg0.00.0