Overview

Dataset statistics

Number of variables12
Number of observations2448
Missing cells1301
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory241.6 KiB
Average record size in memory101.1 B

Variable types

Numeric3
Categorical6
Text3

Dataset

Description연안침식 실태조사의 결과를 종합한 백서에 대한 연안침식백서_목록 정보로 파일명, 폴더순서등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15114198/fileData.do

Alerts

폴더그룹순번3(fd_group_dp_3) is highly overall correlated with 일련번호(sno) and 5 other fieldsHigh correlation
폴더그룹순번2(fd_group_dp_2) is highly overall correlated with 모니터링지점키(mnrg_spot_key) and 6 other fieldsHigh correlation
폴더그룹순번4(fd_group_dp_4) is highly overall correlated with 모니터링지점키(mnrg_spot_key) and 4 other fieldsHigh correlation
폴더그룹순번1(fd_group_dp_1) is highly overall correlated with 모니터링지점키(mnrg_spot_key) and 5 other fieldsHigh correlation
최상위목록번호(frt_upr_list_no) is highly overall correlated with 모니터링지점키(mnrg_spot_key) and 5 other fieldsHigh correlation
일련번호(sno) is highly overall correlated with 폴더그룹순번3(fd_group_dp_3)High correlation
모니터링지점키(mnrg_spot_key) is highly overall correlated with 폴더그룹순번1(fd_group_dp_1) and 4 other fieldsHigh correlation
상위목록번호(upr_list_no) is highly overall correlated with 폴더그룹순번1(fd_group_dp_1) and 5 other fieldsHigh correlation
목록단계번호(list_step_no) is highly overall correlated with 모니터링지점키(mnrg_spot_key) and 3 other fieldsHigh correlation
폴더그룹순번1(fd_group_dp_1) is highly imbalanced (92.9%)Imbalance
폴더그룹순번2(fd_group_dp_2) is highly imbalanced (93.3%)Imbalance
최상위목록번호(frt_upr_list_no) is highly imbalanced (92.9%)Imbalance
목록단계번호(list_step_no) is highly imbalanced (77.9%)Imbalance
폴더그룹순번5(fd_group_dp_5) has 197 (8.0%) missing valuesMissing
파일명(file_nm) has 131 (5.4%) missing valuesMissing
모니터링지점명(mnrg_spot_nm) has 197 (8.0%) missing valuesMissing
모니터링지점키(mnrg_spot_key) has 776 (31.7%) missing valuesMissing
일련번호(sno) has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:44:19.567252
Analysis finished2023-12-12 19:44:22.674873
Duration3.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호(sno)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2448
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1224.5
Minimum1
Maximum2448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2023-12-13T04:44:22.772914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile123.35
Q1612.75
median1224.5
Q31836.25
95-th percentile2325.65
Maximum2448
Range2447
Interquartile range (IQR)1223.5

Descriptive statistics

Standard deviation706.82105
Coefficient of variation (CV)0.57723238
Kurtosis-1.2
Mean1224.5
Median Absolute Deviation (MAD)612
Skewness0
Sum2997576
Variance499596
MonotonicityNot monotonic
2023-12-13T04:44:22.969023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1632 1
 
< 0.1%
1625 1
 
< 0.1%
1626 1
 
< 0.1%
1627 1
 
< 0.1%
1628 1
 
< 0.1%
1629 1
 
< 0.1%
1630 1
 
< 0.1%
1631 1
 
< 0.1%
1633 1
 
< 0.1%
Other values (2438) 2438
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2448 1
< 0.1%
2447 1
< 0.1%
2446 1
< 0.1%
2445 1
< 0.1%
2444 1
< 0.1%
2443 1
< 0.1%
2442 1
< 0.1%
2441 1
< 0.1%
2440 1
< 0.1%
2439 1
< 0.1%

폴더그룹순번1(fd_group_dp_1)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
03_실태조사 결과
2416 
02_실태조사 방법
 
19
01_실태조사 개요
 
13

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01_실태조사 개요
2nd row01_실태조사 개요
3rd row01_실태조사 개요
4th row01_실태조사 개요
5th row01_실태조사 개요

Common Values

ValueCountFrequency (%)
03_실태조사 결과 2416
98.7%
02_실태조사 방법 19
 
0.8%
01_실태조사 개요 13
 
0.5%

Length

2023-12-13T04:44:23.471439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:44:23.583620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
03_실태조사 2416
49.3%
결과 2416
49.3%
02_실태조사 19
 
0.4%
방법 19
 
0.4%
01_실태조사 13
 
0.3%
개요 13
 
0.3%

폴더그룹순번2(fd_group_dp_2)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
01_기본 모니터링
2381 
02_비디오 모니터링 결과
 
16
04_정밀 모니터링
 
7
04_조류관측 결과
 
7
03_비디오 모니터링
 
6
Other values (13)
 
31

Length

Max length15
Median length10
Mean length10.03268
Min length4

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row01_추진배경
3rd row02_사업목적
4th row03_연도별 변천이력
5th row03_연도별 변천이력

Common Values

ValueCountFrequency (%)
01_기본 모니터링 2381
97.3%
02_비디오 모니터링 결과 16
 
0.7%
04_정밀 모니터링 7
 
0.3%
04_조류관측 결과 7
 
0.3%
03_비디오 모니터링 6
 
0.2%
03_파랑관측 결과 5
 
0.2%
05_연안침식 등급평가 4
 
0.2%
04_침식등급 평가방법 4
 
0.2%
05_국외 모니터링 방법 4
 
0.2%
02_기본 모니터링 3
 
0.1%
Other values (8) 11
 
0.4%

Length

2023-12-13T04:44:23.727223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
모니터링 2418
49.2%
01_기본 2381
48.5%
결과 28
 
0.6%
02_비디오 16
 
0.3%
04_정밀 7
 
0.1%
04_조류관측 7
 
0.1%
03_비디오 6
 
0.1%
03_파랑관측 5
 
0.1%
05_연안침식 4
 
0.1%
등급평가 4
 
0.1%
Other values (18) 36
 
0.7%

폴더그룹순번3(fd_group_dp_3)
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
08_종합결과
567 
01_시설물 현황조사 결과
315 
07_침식이력 조사 결과
311 
02_항공사진 및 위성영상 분석
308 
05_파랑분석 결과
307 
Other values (39)
640 

Length

Max length22
Median length16
Mean length12.077206
Min length4

Unique

Unique34 ?
Unique (%)1.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row01_파랑관측 및 비디오모니터링 선정기준

Common Values

ValueCountFrequency (%)
08_종합결과 567
23.2%
01_시설물 현황조사 결과 315
12.9%
07_침식이력 조사 결과 311
12.7%
02_항공사진 및 위성영상 분석 308
12.6%
05_파랑분석 결과 307
12.5%
03_해변단면측량 결과 및 분석 260
10.6%
04_표층퇴적물조사 결과 235
9.6%
06_조류분석 결과 77
 
3.1%
<NA> 20
 
0.8%
02_지점별 결과 14
 
0.6%
Other values (34) 34
 
1.4%

Length

2023-12-13T04:44:23.896186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
결과 1521
25.0%
및 572
 
9.4%
분석 569
 
9.4%
08_종합결과 567
 
9.3%
01_시설물 315
 
5.2%
현황조사 315
 
5.2%
조사 312
 
5.1%
07_침식이력 311
 
5.1%
02_항공사진 308
 
5.1%
위성영상 308
 
5.1%
Other values (55) 975
16.1%

폴더그룹순번4(fd_group_dp_4)
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
03_경상남도
267 
07_전라남도_3
231 
09_충청남도
221 
01_강원도_1
191 
07_전라남도_1
184 
Other values (12)
1354 

Length

Max length10
Median length9
Mean length8.0796569
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
03_경상남도 267
10.9%
07_전라남도_3 231
9.4%
09_충청남도 221
9.0%
01_강원도_1 191
 
7.8%
07_전라남도_1 184
 
7.5%
07_전라남도_2 175
 
7.1%
02_경상북도_2 175
 
7.1%
11_인천광역시 173
 
7.1%
02_경상북도_1 170
 
6.9%
01_강원도_2 141
 
5.8%
Other values (7) 520
21.2%

Length

2023-12-13T04:44:24.064925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03_경상남도 267
10.9%
07_전라남도_3 231
9.4%
09_충청남도 221
9.0%
01_강원도_1 191
 
7.8%
07_전라남도_1 184
 
7.5%
07_전라남도_2 175
 
7.1%
02_경상북도_2 175
 
7.1%
11_인천광역시 173
 
7.1%
02_경상북도_1 170
 
6.9%
01_강원도_2 141
 
5.8%
Other values (7) 520
21.2%

폴더그룹순번5(fd_group_dp_5)
Text

MISSING 

Distinct343
Distinct (%)15.2%
Missing197
Missing (%)8.0%
Memory size19.3 KiB
2023-12-13T04:44:24.430932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length28
Mean length15.76677
Min length11

Characters and Unicode

Total characters35491
Distinct characters265
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)2.0%

Sample

1st row01_현내면 명파해변-대진해변
2nd row02_고성군 현내면 초도해변
3rd row03_고성군 거진읍 화진포해변
4th row04_고성군 거진읍 거진해변-송포해변
5th row05_거진읍 반암해변-죽왕면 기진해변
ValueCountFrequency (%)
해수욕장 1106
 
16.1%
죽왕면 40
 
0.6%
현남면 32
 
0.5%
서면 30
 
0.4%
근덕면 27
 
0.4%
명사십리 25
 
0.4%
백사장 25
 
0.4%
남면 25
 
0.4%
기성면 24
 
0.4%
토성면 24
 
0.4%
Other values (733) 5492
80.2%
2023-12-13T04:44:25.091489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4616
 
13.0%
_ 2304
 
6.5%
해 2026
 
5.7%
군 1302
 
3.7%
장 1296
 
3.7%
수 1260
 
3.6%
0 1188
 
3.3%
1 1176
 
3.3%
욕 1131
 
3.2%
면 852
 
2.4%
Other values (255) 18340
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23553
66.4%
Space Separator 4616
 
13.0%
Decimal Number 4612
 
13.0%
Connector Punctuation 2304
 
6.5%
Math Symbol 293
 
0.8%
Dash Punctuation 56
 
0.2%
Uppercase Letter 45
 
0.1%
Other Punctuation 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 2026
 
8.6%
군 1302
 
5.5%
장 1296
 
5.5%
수 1260
 
5.3%
욕 1131
 
4.8%
면 852
 
3.6%
시 844
 
3.6%
구 711
 
3.0%
변 657
 
2.8%
안 578
 
2.5%
Other values (238) 12896
54.8%
Decimal Number
ValueCountFrequency (%)
0 1188
25.8%
1 1176
25.5%
2 664
14.4%
3 308
 
6.7%
4 236
 
5.1%
5 226
 
4.9%
6 221
 
4.8%
7 206
 
4.5%
8 204
 
4.4%
9 183
 
4.0%
Other Punctuation
ValueCountFrequency (%)
· 8
66.7%
, 4
33.3%
Space Separator
ValueCountFrequency (%)
4616
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2304
100.0%
Math Symbol
ValueCountFrequency (%)
~ 293
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23553
66.4%
Common 11893
33.5%
Latin 45
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 2026
 
8.6%
군 1302
 
5.5%
장 1296
 
5.5%
수 1260
 
5.3%
욕 1131
 
4.8%
면 852
 
3.6%
시 844
 
3.6%
구 711
 
3.0%
변 657
 
2.8%
안 578
 
2.5%
Other values (238) 12896
54.8%
Common
ValueCountFrequency (%)
4616
38.8%
_ 2304
19.4%
0 1188
 
10.0%
1 1176
 
9.9%
2 664
 
5.6%
3 308
 
2.6%
~ 293
 
2.5%
4 236
 
2.0%
5 226
 
1.9%
6 221
 
1.9%
Other values (6) 661
 
5.6%
Latin
ValueCountFrequency (%)
X 45
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23553
66.4%
ASCII 11930
33.6%
None 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4616
38.7%
_ 2304
19.3%
0 1188
 
10.0%
1 1176
 
9.9%
2 664
 
5.6%
3 308
 
2.6%
~ 293
 
2.5%
4 236
 
2.0%
5 226
 
1.9%
6 221
 
1.9%
Other values (6) 698
 
5.9%
Hangul
ValueCountFrequency (%)
해 2026
 
8.6%
군 1302
 
5.5%
장 1296
 
5.5%
수 1260
 
5.3%
욕 1131
 
4.8%
면 852
 
3.6%
시 844
 
3.6%
구 711
 
3.0%
변 657
 
2.8%
안 578
 
2.5%
Other values (238) 12896
54.8%
None
ValueCountFrequency (%)
· 8
100.0%

파일명(file_nm)
Text

MISSING 

Distinct2317
Distinct (%)100.0%
Missing131
Missing (%)5.4%
Memory size19.3 KiB
2023-12-13T04:44:25.403322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters32438
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2317 ?
Unique (%)100.0%

Sample

1st rowCWP0000001.pdf
2nd rowCWP0000002.pdf
3rd rowCWP0000003.pdf
4th rowCWP0000004.pdf
5th rowCWP0000005.pdf
ValueCountFrequency (%)
cwp0000173.pdf 1
 
< 0.1%
cwp0001706.pdf 1
 
< 0.1%
cwp0001659.pdf 1
 
< 0.1%
cwp0001660.pdf 1
 
< 0.1%
cwp0001654.pdf 1
 
< 0.1%
cwp0001655.pdf 1
 
< 0.1%
cwp0001656.pdf 1
 
< 0.1%
cwp0001657.pdf 1
 
< 0.1%
cwp0001658.pdf 1
 
< 0.1%
cwp0001661.pdf 1
 
< 0.1%
Other values (2307) 2307
99.6%
2023-12-13T04:44:25.854736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8627
26.6%
C 2317
 
7.1%
. 2317
 
7.1%
f 2317
 
7.1%
d 2317
 
7.1%
W 2317
 
7.1%
p 2317
 
7.1%
P 2317
 
7.1%
1 1710
 
5.3%
2 1228
 
3.8%
Other values (7) 4654
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16219
50.0%
Uppercase Letter 6951
21.4%
Lowercase Letter 6951
21.4%
Other Punctuation 2317
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8627
53.2%
1 1710
 
10.5%
2 1228
 
7.6%
3 748
 
4.6%
4 698
 
4.3%
9 657
 
4.1%
8 654
 
4.0%
7 641
 
4.0%
5 631
 
3.9%
6 625
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
C 2317
33.3%
W 2317
33.3%
P 2317
33.3%
Lowercase Letter
ValueCountFrequency (%)
f 2317
33.3%
d 2317
33.3%
p 2317
33.3%
Other Punctuation
ValueCountFrequency (%)
. 2317
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18536
57.1%
Latin 13902
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8627
46.5%
. 2317
 
12.5%
1 1710
 
9.2%
2 1228
 
6.6%
3 748
 
4.0%
4 698
 
3.8%
9 657
 
3.5%
8 654
 
3.5%
7 641
 
3.5%
5 631
 
3.4%
Latin
ValueCountFrequency (%)
C 2317
16.7%
f 2317
16.7%
d 2317
16.7%
W 2317
16.7%
p 2317
16.7%
P 2317
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8627
26.6%
C 2317
 
7.1%
. 2317
 
7.1%
f 2317
 
7.1%
d 2317
 
7.1%
W 2317
 
7.1%
p 2317
 
7.1%
P 2317
 
7.1%
1 1710
 
5.3%
2 1228
 
3.8%
Other values (7) 4654
14.3%

모니터링지점명(mnrg_spot_nm)
Text

MISSING 

Distinct339
Distinct (%)15.1%
Missing197
Missing (%)8.0%
Memory size19.3 KiB
2023-12-13T04:44:26.233742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length8.7632163
Min length4

Characters and Unicode

Total characters19726
Distinct characters254
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)2.0%

Sample

1st row명파해변-대진해변
2nd row현내면 초도해변
3rd row거진읍 화진포해변
4th row거진읍 거진해변-송포해변
5th row반암해변-죽왕면 기진해변
ValueCountFrequency (%)
해수욕장 1106
 
24.0%
죽왕면 40
 
0.9%
현남면 32
 
0.7%
서면 30
 
0.7%
근덕면 27
 
0.6%
명사십리 25
 
0.5%
백사장 25
 
0.5%
남면 25
 
0.5%
토성면 24
 
0.5%
기성면 24
 
0.5%
Other values (436) 3250
70.5%
2023-12-13T04:44:26.819742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2374
 
12.0%
해 1885
 
9.6%
장 1266
 
6.4%
수 1174
 
6.0%
욕 1131
 
5.7%
면 836
 
4.2%
변 657
 
3.3%
구 573
 
2.9%
지 510
 
2.6%
리 413
 
2.1%
Other values (244) 8907
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16783
85.1%
Space Separator 2374
 
12.0%
Math Symbol 293
 
1.5%
Decimal Number 110
 
0.6%
Dash Punctuation 56
 
0.3%
Connector Punctuation 53
 
0.3%
Uppercase Letter 45
 
0.2%
Other Punctuation 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 1885
 
11.2%
장 1266
 
7.5%
수 1174
 
7.0%
욕 1131
 
6.7%
면 836
 
5.0%
변 657
 
3.9%
구 573
 
3.4%
지 510
 
3.0%
리 413
 
2.5%
포 291
 
1.7%
Other values (233) 8047
47.9%
Decimal Number
ValueCountFrequency (%)
1 52
47.3%
2 38
34.5%
3 15
 
13.6%
4 5
 
4.5%
Other Punctuation
ValueCountFrequency (%)
· 8
66.7%
, 4
33.3%
Space Separator
ValueCountFrequency (%)
2374
100.0%
Math Symbol
ValueCountFrequency (%)
~ 293
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 53
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16783
85.1%
Common 2898
 
14.7%
Latin 45
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 1885
 
11.2%
장 1266
 
7.5%
수 1174
 
7.0%
욕 1131
 
6.7%
면 836
 
5.0%
변 657
 
3.9%
구 573
 
3.4%
지 510
 
3.0%
리 413
 
2.5%
포 291
 
1.7%
Other values (233) 8047
47.9%
Common
ValueCountFrequency (%)
2374
81.9%
~ 293
 
10.1%
- 56
 
1.9%
_ 53
 
1.8%
1 52
 
1.8%
2 38
 
1.3%
3 15
 
0.5%
· 8
 
0.3%
4 5
 
0.2%
, 4
 
0.1%
Latin
ValueCountFrequency (%)
X 45
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16783
85.1%
ASCII 2935
 
14.9%
None 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2374
80.9%
~ 293
 
10.0%
- 56
 
1.9%
_ 53
 
1.8%
1 52
 
1.8%
X 45
 
1.5%
2 38
 
1.3%
3 15
 
0.5%
4 5
 
0.2%
, 4
 
0.1%
Hangul
ValueCountFrequency (%)
해 1885
 
11.2%
장 1266
 
7.5%
수 1174
 
7.0%
욕 1131
 
6.7%
면 836
 
5.0%
변 657
 
3.9%
구 573
 
3.4%
지 510
 
3.0%
리 413
 
2.5%
포 291
 
1.7%
Other values (233) 8047
47.9%
None
ValueCountFrequency (%)
· 8
100.0%

모니터링지점키(mnrg_spot_key)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct223
Distinct (%)13.3%
Missing776
Missing (%)31.7%
Infinite0
Infinite (%)0.0%
Mean43529205
Minimum26140001
Maximum50130009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2023-12-13T04:44:27.058063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26140001
5-th percentile28110002
Q142820011
median46820001
Q347770003
95-th percentile50110004
Maximum50130009
Range23990008
Interquartile range (IQR)4949992

Descriptive statistics

Standard deviation6973970.7
Coefficient of variation (CV)0.1602136
Kurtosis0.89637429
Mean43529205
Median Absolute Deviation (MAD)1490003
Skewness-1.5531616
Sum7.2780831 × 1010
Variance4.8636267 × 1013
MonotonicityNot monotonic
2023-12-13T04:44:27.258249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46780001 14
 
0.6%
46890001 9
 
0.4%
44825016 9
 
0.4%
44825010 9
 
0.4%
44825013 9
 
0.4%
44825002 9
 
0.4%
44825015 9
 
0.4%
44825001 9
 
0.4%
46130006 9
 
0.4%
46130009 9
 
0.4%
Other values (213) 1577
64.4%
(Missing) 776
31.7%
ValueCountFrequency (%)
26140001 8
0.3%
26350001 8
0.3%
26350002 7
0.3%
26380001 5
0.2%
26440001 5
0.2%
26440002 5
0.2%
26440003 5
0.2%
26500001 8
0.3%
26710001 8
0.3%
26710002 9
0.4%
ValueCountFrequency (%)
50130009 9
0.4%
50130008 5
0.2%
50130007 5
0.2%
50130006 8
0.3%
50130005 5
0.2%
50130004 9
0.4%
50130003 8
0.3%
50130002 8
0.3%
50130001 9
0.4%
50110006 5
0.2%

상위목록번호(upr_list_no)
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.248366
Minimum0
Maximum62
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2023-12-13T04:44:27.437785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41
Q150
median54
Q358
95-th percentile62
Maximum62
Range62
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.4277612
Coefficient of variation (CV)0.13949275
Kurtosis14.247202
Mean53.248366
Median Absolute Deviation (MAD)4
Skewness-2.8114154
Sum130352
Variance55.171636
MonotonicityNot monotonic
2023-12-13T04:44:27.605425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
52 259
10.6%
58 223
 
9.1%
60 213
 
8.7%
48 184
 
7.5%
56 176
 
7.2%
51 168
 
6.9%
57 167
 
6.8%
62 165
 
6.7%
50 163
 
6.7%
49 133
 
5.4%
Other values (28) 597
24.4%
ValueCountFrequency (%)
0 3
0.1%
1 5
0.2%
4 1
 
< 0.1%
6 3
0.1%
10 3
0.1%
14 5
0.2%
16 2
 
0.1%
19 5
0.2%
25 6
0.2%
33 7
0.3%
ValueCountFrequency (%)
62 165
6.7%
61 50
 
2.0%
60 213
8.7%
59 86
 
3.5%
58 223
9.1%
57 167
6.8%
56 176
7.2%
55 125
5.1%
54 90
3.7%
53 49
 
2.0%

최상위목록번호(frt_upr_list_no)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
33
2416 
14
 
19
1
 
13

Length

Max length2
Median length2
Mean length1.9946895
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
33 2416
98.7%
14 19
 
0.8%
1 13
 
0.5%

Length

2023-12-13T04:44:27.783217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:44:27.916606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
33 2416
98.7%
14 19
 
0.8%
1 13
 
0.5%

목록단계번호(list_step_no)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
5
2251 
4
 
121
3
 
56
2
 
17
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
5 2251
92.0%
4 121
 
4.9%
3 56
 
2.3%
2 17
 
0.7%
1 3
 
0.1%

Length

2023-12-13T04:44:28.078060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:44:28.237435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 2251
92.0%
4 121
 
4.9%
3 56
 
2.3%
2 17
 
0.7%
1 3
 
0.1%

Interactions

2023-12-13T04:44:21.692236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:44:20.855275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:44:21.236395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:44:21.819359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:44:21.001026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:44:21.362530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:44:21.942987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:44:21.123333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:44:21.537114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:44:28.354205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호(sno)폴더그룹순번1(fd_group_dp_1)폴더그룹순번2(fd_group_dp_2)폴더그룹순번3(fd_group_dp_3)폴더그룹순번4(fd_group_dp_4)모니터링지점키(mnrg_spot_key)상위목록번호(upr_list_no)최상위목록번호(frt_upr_list_no)목록단계번호(list_step_no)
일련번호(sno)1.0000.3670.3220.9410.4930.3580.6440.3670.446
폴더그룹순번1(fd_group_dp_1)0.3671.0001.0001.000NaNNaN0.9571.0000.534
폴더그룹순번2(fd_group_dp_2)0.3221.0001.0001.000NaNNaN0.9351.0000.828
폴더그룹순번3(fd_group_dp_3)0.9411.0001.0001.0000.0000.0000.9691.0000.851
폴더그룹순번4(fd_group_dp_4)0.493NaNNaN0.0001.0000.9850.971NaN0.247
모니터링지점키(mnrg_spot_key)0.358NaNNaN0.0000.9851.0000.860NaNNaN
상위목록번호(upr_list_no)0.6440.9570.9350.9690.9710.8601.0000.9570.959
최상위목록번호(frt_upr_list_no)0.3671.0001.0001.000NaNNaN0.9571.0000.534
목록단계번호(list_step_no)0.4460.5340.8280.8510.247NaN0.9590.5341.000
2023-12-13T04:44:28.560600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폴더그룹순번3(fd_group_dp_3)폴더그룹순번2(fd_group_dp_2)폴더그룹순번4(fd_group_dp_4)폴더그룹순번1(fd_group_dp_1)목록단계번호(list_step_no)최상위목록번호(frt_upr_list_no)
폴더그룹순번3(fd_group_dp_3)1.0000.9930.0000.9920.6450.992
폴더그룹순번2(fd_group_dp_2)0.9931.0001.0000.9970.6290.997
폴더그룹순번4(fd_group_dp_4)0.0001.0001.0001.0000.1941.000
폴더그룹순번1(fd_group_dp_1)0.9920.9971.0001.0000.4741.000
목록단계번호(list_step_no)0.6450.6290.1940.4741.0000.474
최상위목록번호(frt_upr_list_no)0.9920.9971.0001.0000.4741.000
2023-12-13T04:44:28.736600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호(sno)모니터링지점키(mnrg_spot_key)상위목록번호(upr_list_no)폴더그룹순번1(fd_group_dp_1)폴더그룹순번2(fd_group_dp_2)폴더그룹순번3(fd_group_dp_3)폴더그룹순번4(fd_group_dp_4)최상위목록번호(frt_upr_list_no)목록단계번호(list_step_no)
일련번호(sno)1.000-0.0670.1680.2360.1310.6920.2170.2360.201
모니터링지점키(mnrg_spot_key)-0.0671.000-0.4381.0001.0000.0000.9421.0001.000
상위목록번호(upr_list_no)0.168-0.4381.0000.9530.7340.7950.7840.9530.719
폴더그룹순번1(fd_group_dp_1)0.2361.0000.9531.0000.9970.9921.0001.0000.474
폴더그룹순번2(fd_group_dp_2)0.1311.0000.7340.9971.0000.9931.0000.9970.629
폴더그룹순번3(fd_group_dp_3)0.6920.0000.7950.9920.9931.0000.0000.9920.645
폴더그룹순번4(fd_group_dp_4)0.2170.9420.7841.0001.0000.0001.0001.0000.194
최상위목록번호(frt_upr_list_no)0.2361.0000.9531.0000.9970.9921.0001.0000.474
목록단계번호(list_step_no)0.2011.0000.7190.4740.6290.6450.1940.4741.000

Missing values

2023-12-13T04:44:22.127862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:44:22.363594image/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.
2023-12-13T04:44:22.562787image/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

일련번호(sno)폴더그룹순번1(fd_group_dp_1)폴더그룹순번2(fd_group_dp_2)폴더그룹순번3(fd_group_dp_3)폴더그룹순번4(fd_group_dp_4)폴더그룹순번5(fd_group_dp_5)파일명(file_nm)모니터링지점명(mnrg_spot_nm)모니터링지점키(mnrg_spot_key)상위목록번호(upr_list_no)최상위목록번호(frt_upr_list_no)목록단계번호(list_step_no)
0101_실태조사 개요<NA><NA><NA><NA>CWP0000001.pdf<NA><NA>011
1201_실태조사 개요01_추진배경<NA><NA><NA>CWP0000002.pdf<NA><NA>112
2301_실태조사 개요02_사업목적<NA><NA><NA>CWP0000003.pdf<NA><NA>112
3401_실태조사 개요03_연도별 변천이력<NA><NA><NA>CWP0000004.pdf<NA><NA>112
4501_실태조사 개요03_연도별 변천이력01_파랑관측 및 비디오모니터링 선정기준<NA><NA>CWP0000005.pdf<NA><NA>413
5601_실태조사 개요04_침식등급 평가방법<NA><NA><NA>CWP0000006.pdf<NA><NA>112
6701_실태조사 개요04_침식등급 평가방법01_’03∼’09년 평가방법<NA><NA>CWP0000007.pdf<NA><NA>613
7801_실태조사 개요04_침식등급 평가방법02_’10∼’12년 평가방법<NA><NA>CWP0000008.pdf<NA><NA>613
8901_실태조사 개요04_침식등급 평가방법03_’13∼’18년 평가방법<NA><NA>CWP0000009.pdf<NA><NA>613
91001_실태조사 개요05_국외 모니터링 방법<NA><NA><NA>CWP0000010.pdf<NA><NA>112
일련번호(sno)폴더그룹순번1(fd_group_dp_1)폴더그룹순번2(fd_group_dp_2)폴더그룹순번3(fd_group_dp_3)폴더그룹순번4(fd_group_dp_4)폴더그룹순번5(fd_group_dp_5)파일명(file_nm)모니터링지점명(mnrg_spot_nm)모니터링지점키(mnrg_spot_key)상위목록번호(upr_list_no)최상위목록번호(frt_upr_list_no)목록단계번호(list_step_no)
2438243903_실태조사 결과04_조류관측 결과02_서해안 조류특성<NA><NA>CWP0002439.pdf<NA><NA>37333
2439244003_실태조사 결과04_조류관측 결과03_남해안 조류특성<NA><NA>CWP0002440.pdf<NA><NA>37333
2440244103_실태조사 결과04_조류관측 결과04_동해안 조류특성<NA><NA>CWP0002441.pdf<NA><NA>37333
2441244203_실태조사 결과04_조류관측 결과05_최강 창조류 분포<NA><NA>CWP0002442.pdf<NA><NA>37333
2442244303_실태조사 결과04_조류관측 결과06_최강 낙조류 분포<NA><NA>CWP0002443.pdf<NA><NA>37333
2443244403_실태조사 결과05_연안침식 등급평가01_연안침식 등급평가 방법<NA><NA>CWP0002444.pdf<NA><NA>38333
2444244503_실태조사 결과05_연안침식 등급평가02_연안침식 등급평가 결과<NA><NA>CWP0002445.pdf<NA><NA>38333
2445244603_실태조사 결과05_연안침식 등급평가03_결과분석<NA><NA>CWP0002446.pdf<NA><NA>38333
2446244703_실태조사 결과06_침식원인 및 고찰<NA><NA><NA>CWP0002447.pdf<NA><NA>33332
2447244803_실태조사 결과07_연안침식 실태조사 평가<NA><NA><NA>CWP0002448.pdf<NA><NA>33332