Overview

Dataset statistics

Number of variables19
Number of observations3105
Missing cells4402
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory488.3 KiB
Average record size in memory161.0 B

Variable types

Numeric6
Text7
Categorical5
DateTime1

Dataset

Description지하수 보조측정망 제원에 대한 내용입니다.- 관정번호, 관측소명, 관측소코드, 허가신고번호, 주소, 경도, 위도, 표고, 관리기관명, 설치일자, 관정구분, 암반충적구분, 케이싱 높이, 설치심도, 굴착구경, 관측주기내용, 관측항목명, 지하수상세용도코드, 음용여부* 지하수 관련 사이트는 www.gims.go.kr 을 참고하여주시기 바랍니다.
Author한국수자원공사
URLhttps://www.data.go.kr/data/15104450/fileData.do

Alerts

암반충적구분 is highly overall correlated with 관리기관명 and 1 other fieldsHigh correlation
관정구분 is highly overall correlated with 관리기관명 and 1 other fieldsHigh correlation
관정번호 is highly overall correlated with 관리기관명 and 1 other fieldsHigh correlation
케이싱높이 is highly overall correlated with 관리기관명High correlation
지하수상세용도코드 is highly overall correlated with 관리기관명 and 1 other fieldsHigh correlation
관리기관명 is highly overall correlated with 관정번호 and 6 other fieldsHigh correlation
관측주기내용 is highly overall correlated with 관정번호 and 1 other fieldsHigh correlation
음용여부 is highly overall correlated with 지하수상세용도코드 and 1 other fieldsHigh correlation
관리기관명 is highly imbalanced (94.8%)Imbalance
관측주기내용 is highly imbalanced (56.8%)Imbalance
허가신고번호 has 995 (32.0%) missing valuesMissing
경도 has 75 (2.4%) missing valuesMissing
위도 has 75 (2.4%) missing valuesMissing
표고 has 1535 (49.4%) missing valuesMissing
설치일자 has 128 (4.1%) missing valuesMissing
케이싱높이 has 591 (19.0%) missing valuesMissing
설치심도 has 577 (18.6%) missing valuesMissing
굴착구경 has 331 (10.7%) missing valuesMissing
지하수상세용도코드 has 83 (2.7%) missing valuesMissing
관정번호 has unique valuesUnique
표고 has 128 (4.1%) zerosZeros
케이싱높이 has 1713 (55.2%) zerosZeros

Reproduction

Analysis started2023-12-12 12:01:53.750159
Analysis finished2023-12-12 12:02:00.384975
Duration6.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관정번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean752411.85
Minimum718298
Maximum777828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2023-12-12T21:02:00.472138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum718298
5-th percentile735563.2
Q1751853
median752630
Q3753406
95-th percentile770130.8
Maximum777828
Range59530
Interquartile range (IQR)1553

Descriptive statistics

Standard deviation8398.8707
Coefficient of variation (CV)0.011162598
Kurtosis4.7603585
Mean752411.85
Median Absolute Deviation (MAD)777
Skewness-0.87303691
Sum2.3362388 × 109
Variance70541030
MonotonicityNot monotonic
2023-12-12T21:02:00.619447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
753955 1
 
< 0.1%
751474 1
 
< 0.1%
752811 1
 
< 0.1%
753174 1
 
< 0.1%
752413 1
 
< 0.1%
752021 1
 
< 0.1%
753716 1
 
< 0.1%
753717 1
 
< 0.1%
753658 1
 
< 0.1%
753028 1
 
< 0.1%
Other values (3095) 3095
99.7%
ValueCountFrequency (%)
718298 1
< 0.1%
718355 1
< 0.1%
718368 1
< 0.1%
718397 1
< 0.1%
718405 1
< 0.1%
718429 1
< 0.1%
718430 1
< 0.1%
718433 1
< 0.1%
718435 1
< 0.1%
718446 1
< 0.1%
ValueCountFrequency (%)
777828 1
< 0.1%
771729 1
< 0.1%
771072 1
< 0.1%
771071 1
< 0.1%
771070 1
< 0.1%
771069 1
< 0.1%
771068 1
< 0.1%
771067 1
< 0.1%
771066 1
< 0.1%
771065 1
< 0.1%

관측소명
Text

Distinct2938
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
2023-12-12T21:02:01.053326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length6.5275362
Min length2

Characters and Unicode

Total characters20268
Distinct characters516
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2775 ?
Unique (%)89.4%

Sample

1st row장수10
2nd row응암-0018
3rd row산월-0008
4th row순창33
5th row완주15
ValueCountFrequency (%)
보조지하수관측정 44
 
1.2%
관측소 35
 
0.9%
관측정 29
 
0.8%
북구 22
 
0.6%
0001 22
 
0.6%
마을상수도 22
 
0.6%
보조지하수 14
 
0.4%
진안 10
 
0.3%
한림 10
 
0.3%
생림 9
 
0.2%
Other values (3165) 3606
94.3%
2023-12-12T21:02:01.660263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1984
 
9.8%
1 895
 
4.4%
- 877
 
4.3%
718
 
3.5%
2 573
 
2.8%
J 430
 
2.1%
기 373
 
1.8%
3 354
 
1.7%
호 351
 
1.7%
동 335
 
1.7%
Other values (506) 13378
66.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12258
60.5%
Decimal Number 5065
25.0%
Uppercase Letter 1071
 
5.3%
Dash Punctuation 877
 
4.3%
Space Separator 718
 
3.5%
Close Punctuation 105
 
0.5%
Open Punctuation 104
 
0.5%
Lowercase Letter 41
 
0.2%
Connector Punctuation 20
 
0.1%
Other Punctuation 6
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 373
 
3.0%
호 351
 
2.9%
동 335
 
2.7%
원 333
 
2.7%
산 295
 
2.4%
수 279
 
2.3%
공 223
 
1.8%
리 216
 
1.8%
정 199
 
1.6%
구 197
 
1.6%
Other values (447) 9457
77.1%
Uppercase Letter
ValueCountFrequency (%)
J 430
40.1%
D 124
 
11.6%
M 82
 
7.7%
R 81
 
7.6%
W 78
 
7.3%
Q 45
 
4.2%
P 43
 
4.0%
G 38
 
3.5%
C 28
 
2.6%
H 28
 
2.6%
Other values (10) 94
 
8.8%
Lowercase Letter
ValueCountFrequency (%)
d 4
 
9.8%
c 4
 
9.8%
b 4
 
9.8%
e 3
 
7.3%
a 3
 
7.3%
l 2
 
4.9%
k 2
 
4.9%
g 2
 
4.9%
f 2
 
4.9%
j 2
 
4.9%
Other values (10) 13
31.7%
Decimal Number
ValueCountFrequency (%)
0 1984
39.2%
1 895
17.7%
2 573
 
11.3%
3 354
 
7.0%
4 277
 
5.5%
5 234
 
4.6%
6 223
 
4.4%
7 193
 
3.8%
8 175
 
3.5%
9 157
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 5
83.3%
, 1
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 877
100.0%
Space Separator
ValueCountFrequency (%)
718
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Open Punctuation
ValueCountFrequency (%)
( 104
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 20
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12260
60.5%
Common 6895
34.0%
Latin 1113
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 373
 
3.0%
호 351
 
2.9%
동 335
 
2.7%
원 333
 
2.7%
산 295
 
2.4%
수 279
 
2.3%
공 223
 
1.8%
리 216
 
1.8%
정 199
 
1.6%
구 197
 
1.6%
Other values (448) 9459
77.2%
Latin
ValueCountFrequency (%)
J 430
38.6%
D 124
 
11.1%
M 82
 
7.4%
R 81
 
7.3%
W 78
 
7.0%
Q 45
 
4.0%
P 43
 
3.9%
G 38
 
3.4%
C 28
 
2.5%
H 28
 
2.5%
Other values (31) 136
 
12.2%
Common
ValueCountFrequency (%)
0 1984
28.8%
1 895
13.0%
- 877
12.7%
718
 
10.4%
2 573
 
8.3%
3 354
 
5.1%
4 277
 
4.0%
5 234
 
3.4%
6 223
 
3.2%
7 193
 
2.8%
Other values (7) 567
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12258
60.5%
ASCII 8007
39.5%
None 2
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1984
24.8%
1 895
11.2%
- 877
11.0%
718
 
9.0%
2 573
 
7.2%
J 430
 
5.4%
3 354
 
4.4%
4 277
 
3.5%
5 234
 
2.9%
6 223
 
2.8%
Other values (47) 1442
18.0%
Hangul
ValueCountFrequency (%)
기 373
 
3.0%
호 351
 
2.9%
동 335
 
2.7%
원 333
 
2.7%
산 295
 
2.4%
수 279
 
2.3%
공 223
 
1.8%
리 216
 
1.8%
정 199
 
1.6%
구 197
 
1.6%
Other values (447) 9457
77.1%
None
ValueCountFrequency (%)
㈜ 2
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

관측소코드
Text

Distinct3104
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
2023-12-12T21:02:01.964998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length13.712399
Min length5

Characters and Unicode

Total characters42577
Distinct characters37
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3103 ?
Unique (%)99.9%

Sample

1st rowJB-JSU-G1-0010
2nd rowSJ-SJN-G1-0018
3rd rowJB-GUV-G1-0008
4th rowJB-SNC-G1-0033
5th rowJB-WAN-G1-0015
ValueCountFrequency (%)
de-sog-g1-0016 2
 
0.1%
su-gdg-g1-0006 1
 
< 0.1%
cb-cjj-g1-0009 1
 
< 0.1%
gn-sac-g1-0030 1
 
< 0.1%
cb-ydn-g1-0013 1
 
< 0.1%
cb-cjj-g1-0013 1
 
< 0.1%
cb-ums-g1-0016 1
 
< 0.1%
su-sbk-g1-0002 1
 
< 0.1%
gn-yas-g1-0009 1
 
< 0.1%
cn-can-g1-0053 1
 
< 0.1%
Other values (3094) 3094
99.6%
2023-12-12T21:02:02.424883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 8650
20.3%
0 7802
18.3%
G 5159
12.1%
1 4395
10.3%
N 2031
 
4.8%
J 1661
 
3.9%
C 1532
 
3.6%
S 1458
 
3.4%
B 1038
 
2.4%
2 982
 
2.3%
Other values (27) 7869
18.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17487
41.1%
Decimal Number 16427
38.6%
Dash Punctuation 8650
20.3%
Lowercase Letter 10
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Other Letter 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 5159
29.5%
N 2031
 
11.6%
J 1661
 
9.5%
C 1532
 
8.8%
S 1458
 
8.3%
B 1038
 
5.9%
U 756
 
4.3%
D 585
 
3.3%
W 547
 
3.1%
A 437
 
2.5%
Other values (12) 2283
13.1%
Decimal Number
ValueCountFrequency (%)
0 7802
47.5%
1 4395
26.8%
2 982
 
6.0%
3 643
 
3.9%
4 554
 
3.4%
5 452
 
2.8%
9 427
 
2.6%
6 401
 
2.4%
7 388
 
2.4%
8 383
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 8650
100.0%
Lowercase Letter
ValueCountFrequency (%)
w 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Letter
ValueCountFrequency (%)
구 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25079
58.9%
Latin 17497
41.1%
Hangul 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 5159
29.5%
N 2031
 
11.6%
J 1661
 
9.5%
C 1532
 
8.8%
S 1458
 
8.3%
B 1038
 
5.9%
U 756
 
4.3%
D 585
 
3.3%
W 547
 
3.1%
A 437
 
2.5%
Other values (13) 2293
13.1%
Common
ValueCountFrequency (%)
- 8650
34.5%
0 7802
31.1%
1 4395
17.5%
2 982
 
3.9%
3 643
 
2.6%
4 554
 
2.2%
5 452
 
1.8%
9 427
 
1.7%
6 401
 
1.6%
7 388
 
1.5%
Other values (3) 385
 
1.5%
Hangul
ValueCountFrequency (%)
구 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42576
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 8650
20.3%
0 7802
18.3%
G 5159
12.1%
1 4395
10.3%
N 2031
 
4.8%
J 1661
 
3.9%
C 1532
 
3.6%
S 1458
 
3.4%
B 1038
 
2.4%
2 982
 
2.3%
Other values (26) 7868
18.5%
Hangul
ValueCountFrequency (%)
구 1
100.0%

허가신고번호
Text

MISSING 

Distinct1831
Distinct (%)86.8%
Missing995
Missing (%)32.0%
Memory size24.4 KiB
2023-12-12T21:02:02.743985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length9.6
Min length1

Characters and Unicode

Total characters20256
Distinct characters47
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1663 ?
Unique (%)78.8%

Sample

1st row1200900007
2nd row2201101721
3rd row2201200103
4th row1199800005
5th row2201800001
ValueCountFrequency (%)
35
 
1.7%
등록번호없음 18
 
0.9%
4201300008 11
 
0.5%
4201200020 9
 
0.4%
2199800001 7
 
0.3%
4201000009 7
 
0.3%
1200800001 5
 
0.2%
2200900004 5
 
0.2%
4201700109 5
 
0.2%
2201100004 4
 
0.2%
Other values (1822) 2005
95.0%
2023-12-12T21:02:03.329472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8370
41.3%
2 3572
17.6%
1 2917
 
14.4%
9 1359
 
6.7%
4 704
 
3.5%
3 654
 
3.2%
8 540
 
2.7%
7 540
 
2.7%
6 532
 
2.6%
5 511
 
2.5%
Other values (37) 557
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19699
97.3%
Other Letter 284
 
1.4%
Uppercase Letter 157
 
0.8%
Dash Punctuation 106
 
0.5%
Lowercase Letter 8
 
< 0.1%
Space Separator 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 52
18.3%
호 48
16.9%
양 30
10.6%
괴 22
7.7%
기 22
7.7%
등 19
 
6.7%
록 19
 
6.7%
음 18
 
6.3%
없 18
 
6.3%
번 18
 
6.3%
Other values (11) 18
 
6.3%
Decimal Number
ValueCountFrequency (%)
0 8370
42.5%
2 3572
18.1%
1 2917
 
14.8%
9 1359
 
6.9%
4 704
 
3.6%
3 654
 
3.3%
8 540
 
2.7%
7 540
 
2.7%
6 532
 
2.7%
5 511
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
e 2
25.0%
p 1
12.5%
b 1
12.5%
g 1
12.5%
u 1
12.5%
a 1
12.5%
k 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
W 111
70.7%
D 29
 
18.5%
Y 14
 
8.9%
S 1
 
0.6%
F 1
 
0.6%
A 1
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 106
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19807
97.8%
Hangul 284
 
1.4%
Latin 165
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 52
18.3%
호 48
16.9%
양 30
10.6%
괴 22
7.7%
기 22
7.7%
등 19
 
6.7%
록 19
 
6.7%
음 18
 
6.3%
없 18
 
6.3%
번 18
 
6.3%
Other values (11) 18
 
6.3%
Common
ValueCountFrequency (%)
0 8370
42.3%
2 3572
18.0%
1 2917
 
14.7%
9 1359
 
6.9%
4 704
 
3.6%
3 654
 
3.3%
8 540
 
2.7%
7 540
 
2.7%
6 532
 
2.7%
5 511
 
2.6%
Other values (3) 108
 
0.5%
Latin
ValueCountFrequency (%)
W 111
67.3%
D 29
 
17.6%
Y 14
 
8.5%
e 2
 
1.2%
p 1
 
0.6%
S 1
 
0.6%
b 1
 
0.6%
F 1
 
0.6%
g 1
 
0.6%
u 1
 
0.6%
Other values (3) 3
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19972
98.6%
Hangul 284
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8370
41.9%
2 3572
17.9%
1 2917
 
14.6%
9 1359
 
6.8%
4 704
 
3.5%
3 654
 
3.3%
8 540
 
2.7%
7 540
 
2.7%
6 532
 
2.7%
5 511
 
2.6%
Other values (16) 273
 
1.4%
Hangul
ValueCountFrequency (%)
산 52
18.3%
호 48
16.9%
양 30
10.6%
괴 22
7.7%
기 22
7.7%
등 19
 
6.7%
록 19
 
6.7%
음 18
 
6.3%
없 18
 
6.3%
번 18
 
6.3%
Other values (11) 18
 
6.3%

주소
Text

Distinct2954
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
2023-12-12T21:02:03.728616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length20.940741
Min length7

Characters and Unicode

Total characters65021
Distinct characters328
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2839 ?
Unique (%)91.4%

Sample

1st row전라북도 장수군 장수읍 대성리 709
2nd row세종특별자치시 세종시 연동면 응암리 917
3rd row전라북도 군산시 대야면 산월리 982
4th row전라북도 순창군 구림면 월정리 산158-1
5th row전라북도 완주군 동상면 사봉리 431-3
ValueCountFrequency (%)
경상남도 693
 
4.8%
충청북도 414
 
2.8%
제주특별자치도 376
 
2.6%
서울특별시 265
 
1.8%
전라북도 262
 
1.8%
충청남도 254
 
1.7%
부산광역시 236
 
1.6%
제주시 211
 
1.5%
경기도 206
 
1.4%
창원시 196
 
1.3%
Other values (5011) 11422
78.6%
2023-12-12T21:02:04.334780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12778
 
19.7%
시 2573
 
4.0%
도 2397
 
3.7%
1 2300
 
3.5%
리 1785
 
2.7%
동 1762
 
2.7%
- 1708
 
2.6%
2 1477
 
2.3%
남 1399
 
2.2%
구 1312
 
2.0%
Other values (318) 35530
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39042
60.0%
Space Separator 12778
 
19.7%
Decimal Number 11489
 
17.7%
Dash Punctuation 1708
 
2.6%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2573
 
6.6%
도 2397
 
6.1%
리 1785
 
4.6%
동 1762
 
4.5%
남 1399
 
3.6%
구 1312
 
3.4%
면 1261
 
3.2%
주 1029
 
2.6%
산 993
 
2.5%
경 947
 
2.4%
Other values (304) 23584
60.4%
Decimal Number
ValueCountFrequency (%)
1 2300
20.0%
2 1477
12.9%
3 1225
10.7%
4 1184
10.3%
5 1074
9.3%
6 967
8.4%
7 886
 
7.7%
8 860
 
7.5%
9 776
 
6.8%
0 740
 
6.4%
Space Separator
ValueCountFrequency (%)
12778
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1708
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39042
60.0%
Common 25979
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2573
 
6.6%
도 2397
 
6.1%
리 1785
 
4.6%
동 1762
 
4.5%
남 1399
 
3.6%
구 1312
 
3.4%
면 1261
 
3.2%
주 1029
 
2.6%
산 993
 
2.5%
경 947
 
2.4%
Other values (304) 23584
60.4%
Common
ValueCountFrequency (%)
12778
49.2%
1 2300
 
8.9%
- 1708
 
6.6%
2 1477
 
5.7%
3 1225
 
4.7%
4 1184
 
4.6%
5 1074
 
4.1%
6 967
 
3.7%
7 886
 
3.4%
8 860
 
3.3%
Other values (4) 1520
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39042
60.0%
ASCII 25979
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12778
49.2%
1 2300
 
8.9%
- 1708
 
6.6%
2 1477
 
5.7%
3 1225
 
4.7%
4 1184
 
4.6%
5 1074
 
4.1%
6 967
 
3.7%
7 886
 
3.4%
8 860
 
3.3%
Other values (4) 1520
 
5.9%
Hangul
ValueCountFrequency (%)
시 2573
 
6.6%
도 2397
 
6.1%
리 1785
 
4.6%
동 1762
 
4.5%
남 1399
 
3.6%
구 1312
 
3.4%
면 1261
 
3.2%
주 1029
 
2.6%
산 993
 
2.5%
경 947
 
2.4%
Other values (304) 23584
60.4%

경도
Text

MISSING 

Distinct2881
Distinct (%)95.1%
Missing75
Missing (%)2.4%
Memory size24.4 KiB
2023-12-12T21:02:04.816899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length10.311881
Min length1

Characters and Unicode

Total characters31245
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2742 ?
Unique (%)90.5%

Sample

1st row36 42 14.8
2nd row35 57 6.85
3rd row35 26 7.01
4th row35 53 5.15
5th row36 3 9.8
ValueCountFrequency (%)
35 1172
 
13.0%
36 694
 
7.7%
37 655
 
7.3%
33 371
 
4.1%
34 179
 
2.0%
15 107
 
1.2%
30 91
 
1.0%
31 87
 
1.0%
14 86
 
1.0%
16 85
 
0.9%
Other values (2159) 5455
60.7%
2023-12-12T21:02:05.530787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5952
19.0%
3 5352
17.1%
5 3269
10.5%
. 2646
8.5%
1 2574
8.2%
2 2365
 
7.6%
4 2153
 
6.9%
6 1860
 
6.0%
7 1851
 
5.9%
8 1211
 
3.9%
Other values (3) 2012
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22646
72.5%
Space Separator 5952
 
19.0%
Other Punctuation 2646
 
8.5%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 5352
23.6%
5 3269
14.4%
1 2574
11.4%
2 2365
10.4%
4 2153
9.5%
6 1860
 
8.2%
7 1851
 
8.2%
8 1211
 
5.3%
9 1106
 
4.9%
0 905
 
4.0%
Space Separator
ValueCountFrequency (%)
5952
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2646
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31245
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5952
19.0%
3 5352
17.1%
5 3269
10.5%
. 2646
8.5%
1 2574
8.2%
2 2365
 
7.6%
4 2153
 
6.9%
6 1860
 
6.0%
7 1851
 
5.9%
8 1211
 
3.9%
Other values (3) 2012
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5952
19.0%
3 5352
17.1%
5 3269
10.5%
. 2646
8.5%
1 2574
8.2%
2 2365
 
7.6%
4 2153
 
6.9%
6 1860
 
6.0%
7 1851
 
5.9%
8 1211
 
3.9%
Other values (3) 2012
 
6.4%

위도
Text

MISSING 

Distinct2883
Distinct (%)95.1%
Missing75
Missing (%)2.4%
Memory size24.4 KiB
2023-12-12T21:02:06.056788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length11.147525
Min length1

Characters and Unicode

Total characters33777
Distinct characters13
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

Unique2746 ?
Unique (%)90.6%

Sample

1st row127 12 51.2
2nd row126 48 59.39
3rd row127 2 52.1
4th row127 18 44.92
5th row127 6 32.4
ValueCountFrequency (%)
127 1001
 
11.1%
128 790
 
8.8%
126 772
 
8.6%
129 266
 
3.0%
35 156
 
1.7%
36 84
 
0.9%
1 80
 
0.9%
40 77
 
0.9%
37 77
 
0.9%
41 74
 
0.8%
Other values (2189) 5603
62.4%
2023-12-12T21:02:06.751035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5951
17.6%
1 5020
14.9%
2 4981
14.7%
. 2663
7.9%
3 2297
 
6.8%
5 2270
 
6.7%
7 2154
 
6.4%
4 2117
 
6.3%
6 2050
 
6.1%
8 1922
 
5.7%
Other values (3) 2352
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25162
74.5%
Space Separator 5951
 
17.6%
Other Punctuation 2664
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5020
20.0%
2 4981
19.8%
3 2297
9.1%
5 2270
9.0%
7 2154
8.6%
4 2117
8.4%
6 2050
8.1%
8 1922
 
7.6%
9 1429
 
5.7%
0 922
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 2663
> 99.9%
, 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33777
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5951
17.6%
1 5020
14.9%
2 4981
14.7%
. 2663
7.9%
3 2297
 
6.8%
5 2270
 
6.7%
7 2154
 
6.4%
4 2117
 
6.3%
6 2050
 
6.1%
8 1922
 
5.7%
Other values (3) 2352
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33777
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5951
17.6%
1 5020
14.9%
2 4981
14.7%
. 2663
7.9%
3 2297
 
6.8%
5 2270
 
6.7%
7 2154
 
6.4%
4 2117
 
6.3%
6 2050
 
6.1%
8 1922
 
5.7%
Other values (3) 2352
 
7.0%

표고
Real number (ℝ)

MISSING  ZEROS 

Distinct902
Distinct (%)57.5%
Missing1535
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean68.403756
Minimum0
Maximum628
Zeros128
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2023-12-12T21:02:07.287144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.471
median40
Q385.0375
95-th percentile247.3334
Maximum628
Range628
Interquartile range (IQR)68.5665

Descriptive statistics

Standard deviation86.813457
Coefficient of variation (CV)1.2691329
Kurtosis11.026988
Mean68.403756
Median Absolute Deviation (MAD)28.47
Skewness2.9135915
Sum107393.9
Variance7536.5763
MonotonicityNot monotonic
2023-12-12T21:02:07.464230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 128
 
4.1%
87.31 36
 
1.2%
8.0 33
 
1.1%
10.0 16
 
0.5%
28.0 12
 
0.4%
50.0 12
 
0.4%
23.0 12
 
0.4%
80.0 12
 
0.4%
11.0 11
 
0.4%
150.0 10
 
0.3%
Other values (892) 1288
41.5%
(Missing) 1535
49.4%
ValueCountFrequency (%)
0.0 128
4.1%
1.0 2
 
0.1%
2.0 5
 
0.2%
3.0 3
 
0.1%
3.8 1
 
< 0.1%
4.0 8
 
0.3%
4.18 1
 
< 0.1%
4.86 1
 
< 0.1%
5.0 4
 
0.1%
5.53 1
 
< 0.1%
ValueCountFrequency (%)
628.0 1
< 0.1%
627.0 1
< 0.1%
626.86 1
< 0.1%
615.2 1
< 0.1%
595.12 1
< 0.1%
582.0 1
< 0.1%
565.0 1
< 0.1%
563.0 1
< 0.1%
560.0 1
< 0.1%
545.7 1
< 0.1%

관리기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
<NA>
3036 
거창군청
 
22
동작구청
 
8
서초구청
 
6
남해군청
 
5
Other values (17)
 
28

Length

Max length9
Median length4
Mean length4.0070853
Min length3

Unique

Unique12 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3036
97.8%
거창군청 22
 
0.7%
동작구청 8
 
0.3%
서초구청 6
 
0.2%
남해군청 5
 
0.2%
송파구청 4
 
0.1%
울산광역시 울주군 4
 
0.1%
창원시청 4
 
0.1%
동구청 2
 
0.1%
부산진구청 2
 
0.1%
Other values (12) 12
 
0.4%

Length

2023-12-12T21:02:07.677717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3036
97.6%
거창군청 22
 
0.7%
동작구청 8
 
0.3%
서초구청 6
 
0.2%
남해군청 5
 
0.2%
울주군 4
 
0.1%
창원시청 4
 
0.1%
울산광역시 4
 
0.1%
송파구청 4
 
0.1%
동구청 2
 
0.1%
Other values (14) 15
 
0.5%

설치일자
Date

MISSING 

Distinct1071
Distinct (%)36.0%
Missing128
Missing (%)4.1%
Memory size24.4 KiB
Minimum1957-07-22 00:00:00
Maximum2022-06-08 00:00:00
2023-12-12T21:02:07.836766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:07.979270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관정구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
2
2210 
1
493 
<NA>
394 
3
 
8

Length

Max length4
Median length1
Mean length1.3806763
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 2210
71.2%
1 493
 
15.9%
<NA> 394
 
12.7%
3 8
 
0.3%

Length

2023-12-12T21:02:08.143937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:02:08.285297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2210
71.2%
1 493
 
15.9%
na 394
 
12.7%
3 8
 
0.3%

암반충적구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
2
2210 
1
493 
<NA>
394 
3
 
8

Length

Max length4
Median length1
Mean length1.3806763
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 2210
71.2%
1 493
 
15.9%
<NA> 394
 
12.7%
3 8
 
0.3%

Length

2023-12-12T21:02:08.430751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:02:08.534306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2210
71.2%
1 493
 
15.9%
na 394
 
12.7%
3 8
 
0.3%

케이싱높이
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct114
Distinct (%)4.5%
Missing591
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean9.4568377
Minimum-5
Maximum300
Zeros1713
Zeros (%)55.2%
Negative48
Negative (%)1.5%
Memory size27.4 KiB
2023-12-12T21:02:08.654411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile0
Q10
median0
Q31
95-th percentile61
Maximum300
Range305
Interquartile range (IQR)1

Descriptive statistics

Standard deviation26.838061
Coefficient of variation (CV)2.837953
Kurtosis38.397758
Mean9.4568377
Median Absolute Deviation (MAD)0
Skewness5.1000197
Sum23774.49
Variance720.28153
MonotonicityNot monotonic
2023-12-12T21:02:08.817072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1713
55.2%
30.0 120
 
3.9%
1.0 101
 
3.3%
12.0 45
 
1.4%
80.0 45
 
1.4%
3.0 31
 
1.0%
40.0 28
 
0.9%
0.3 25
 
0.8%
50.0 24
 
0.8%
-1.0 24
 
0.8%
Other values (104) 358
 
11.5%
(Missing) 591
 
19.0%
ValueCountFrequency (%)
-5.0 1
 
< 0.1%
-3.0 2
 
0.1%
-2.0 21
 
0.7%
-1.0 24
 
0.8%
0.0 1713
55.2%
0.05 4
 
0.1%
0.06 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 11
 
0.4%
ValueCountFrequency (%)
300.0 6
0.2%
168.0 1
 
< 0.1%
165.0 1
 
< 0.1%
164.0 1
 
< 0.1%
159.0 1
 
< 0.1%
150.0 10
0.3%
130.0 6
0.2%
120.0 4
 
0.1%
110.0 1
 
< 0.1%
107.0 1
 
< 0.1%

설치심도
Real number (ℝ)

MISSING 

Distinct233
Distinct (%)9.2%
Missing577
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean113.78972
Minimum0
Maximum610
Zeros11
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2023-12-12T21:02:08.985055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.35
Q180
median100
Q3138
95-th percentile200
Maximum610
Range610
Interquartile range (IQR)58

Descriptive statistics

Standard deviation56.12833
Coefficient of variation (CV)0.49326362
Kurtosis13.046199
Mean113.78972
Median Absolute Deviation (MAD)20
Skewness2.5225822
Sum287660.42
Variance3150.3895
MonotonicityNot monotonic
2023-12-12T21:02:09.153224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 697
22.4%
80.0 338
10.9%
150.0 253
 
8.1%
120.0 145
 
4.7%
200.0 81
 
2.6%
90.0 70
 
2.3%
70.0 66
 
2.1%
110.0 45
 
1.4%
130.0 44
 
1.4%
180.0 38
 
1.2%
Other values (223) 751
24.2%
(Missing) 577
18.6%
ValueCountFrequency (%)
0.0 11
0.4%
1.0 1
 
< 0.1%
4.0 1
 
< 0.1%
8.0 2
 
0.1%
10.0 2
 
0.1%
10.3 1
 
< 0.1%
11.0 4
 
0.1%
11.8 2
 
0.1%
11.9 2
 
0.1%
12.0 8
0.3%
ValueCountFrequency (%)
610.0 1
 
< 0.1%
550.0 3
0.1%
515.0 1
 
< 0.1%
500.0 1
 
< 0.1%
480.0 1
 
< 0.1%
470.0 2
0.1%
450.0 1
 
< 0.1%
412.0 1
 
< 0.1%
408.0 1
 
< 0.1%
400.0 2
0.1%

굴착구경
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)1.0%
Missing331
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean197.10418
Minimum12
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2023-12-12T21:02:09.286849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile150
Q1150
median200
Q3200
95-th percentile250
Maximum1500
Range1488
Interquartile range (IQR)50

Descriptive statistics

Standard deviation53.125985
Coefficient of variation (CV)0.26953251
Kurtosis131.19161
Mean197.10418
Median Absolute Deviation (MAD)0
Skewness5.5200265
Sum546767
Variance2822.3703
MonotonicityNot monotonic
2023-12-12T21:02:09.422052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
200 1441
46.4%
150 666
21.4%
250 413
 
13.3%
300 86
 
2.8%
100 56
 
1.8%
350 42
 
1.4%
50 11
 
0.4%
254 7
 
0.2%
80 6
 
0.2%
165 5
 
0.2%
Other values (19) 41
 
1.3%
(Missing) 331
 
10.7%
ValueCountFrequency (%)
12 2
 
0.1%
25 3
 
0.1%
30 2
 
0.1%
32 5
 
0.2%
40 4
 
0.1%
50 11
 
0.4%
65 3
 
0.1%
75 5
 
0.2%
80 6
 
0.2%
100 56
1.8%
ValueCountFrequency (%)
1500 1
 
< 0.1%
520 1
 
< 0.1%
350 42
 
1.4%
300 86
 
2.8%
280 1
 
< 0.1%
254 7
 
0.2%
250 413
 
13.3%
240 1
 
< 0.1%
210 1
 
< 0.1%
200 1441
46.4%

관측주기내용
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
자동
2328 
수동
362 
1시간 간격
 
223
반자동
 
117
1
 
30
Other values (3)
 
45

Length

Max length6
Median length2
Mean length2.3123994
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동 2328
75.0%
수동 362
 
11.7%
1시간 간격 223
 
7.2%
반자동 117
 
3.8%
1 30
 
1.0%
2 26
 
0.8%
<NA> 12
 
0.4%
3 7
 
0.2%

Length

2023-12-12T21:02:09.584440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:02:09.703755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동 2328
70.0%
수동 362
 
10.9%
1시간 223
 
6.7%
간격 223
 
6.7%
반자동 117
 
3.5%
1 30
 
0.9%
2 26
 
0.8%
na 12
 
0.4%
3 7
 
0.2%

관측항목명
Text

Distinct126
Distinct (%)4.1%
Missing12
Missing (%)0.4%
Memory size24.4 KiB
2023-12-12T21:02:09.889052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length30
Mean length15.217588
Min length2

Characters and Unicode

Total characters47068
Distinct characters51
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)2.2%

Sample

1st row수위, 수온, 전기전도도
2nd row수위, 수온, 전기전도도
3rd row수위, 수온, 전기전도도
4th row수위, 수온, 전기전도도
5th row수위, 수온, 전기전도도
ValueCountFrequency (%)
전기전도도 1566
22.1%
수위 1484
20.9%
수온 1319
18.6%
수위,수온,전기전도도 692
9.8%
수위,수온,전기전도도(3항목 284
 
4.0%
수질검사 276
 
3.9%
온도 223
 
3.1%
지하수위 154
 
2.2%
항목 133
 
1.9%
19개 130
 
1.8%
Other values (115) 835
11.8%
2023-12-12T21:02:10.326038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 7142
15.2%
수 6748
14.3%
도 6491
13.8%
전 6117
13.0%
4003
8.5%
온 3305
7.0%
위 3081
6.5%
기 2882
6.1%
항 698
 
1.5%
목 698
 
1.5%
Other values (41) 5903
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33639
71.5%
Other Punctuation 7200
 
15.3%
Space Separator 4003
 
8.5%
Decimal Number 1032
 
2.2%
Close Punctuation 565
 
1.2%
Open Punctuation 565
 
1.2%
Uppercase Letter 64
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 6748
20.1%
도 6491
19.3%
전 6117
18.2%
온 3305
9.8%
위 3081
9.2%
기 2882
8.6%
항 698
 
2.1%
목 698
 
2.1%
사 476
 
1.4%
검 476
 
1.4%
Other values (21) 2667
 
7.9%
Decimal Number
ValueCountFrequency (%)
3 333
32.3%
1 239
23.2%
9 223
21.6%
5 133
 
12.9%
4 44
 
4.3%
2 19
 
1.8%
6 12
 
1.2%
7 10
 
1.0%
8 10
 
1.0%
0 9
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
C 23
35.9%
E 23
35.9%
P 9
 
14.1%
H 9
 
14.1%
Other Punctuation
ValueCountFrequency (%)
, 7142
99.2%
. 57
 
0.8%
\ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
4003
100.0%
Close Punctuation
ValueCountFrequency (%)
) 565
100.0%
Open Punctuation
ValueCountFrequency (%)
( 565
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33639
71.5%
Common 13365
 
28.4%
Latin 64
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 6748
20.1%
도 6491
19.3%
전 6117
18.2%
온 3305
9.8%
위 3081
9.2%
기 2882
8.6%
항 698
 
2.1%
목 698
 
2.1%
사 476
 
1.4%
검 476
 
1.4%
Other values (21) 2667
 
7.9%
Common
ValueCountFrequency (%)
, 7142
53.4%
4003
30.0%
) 565
 
4.2%
( 565
 
4.2%
3 333
 
2.5%
1 239
 
1.8%
9 223
 
1.7%
5 133
 
1.0%
. 57
 
0.4%
4 44
 
0.3%
Other values (6) 61
 
0.5%
Latin
ValueCountFrequency (%)
C 23
35.9%
E 23
35.9%
P 9
 
14.1%
H 9
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33639
71.5%
ASCII 13429
 
28.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 7142
53.2%
4003
29.8%
) 565
 
4.2%
( 565
 
4.2%
3 333
 
2.5%
1 239
 
1.8%
9 223
 
1.7%
5 133
 
1.0%
. 57
 
0.4%
4 44
 
0.3%
Other values (10) 125
 
0.9%
Hangul
ValueCountFrequency (%)
수 6748
20.1%
도 6491
19.3%
전 6117
18.2%
온 3305
9.8%
위 3081
9.2%
기 2882
8.6%
항 698
 
2.1%
목 698
 
2.1%
사 476
 
1.4%
검 476
 
1.4%
Other values (21) 2667
 
7.9%

지하수상세용도코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)0.8%
Missing83
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean31.256453
Minimum10
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.4 KiB
2023-12-12T21:02:10.471052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q114
median32
Q351
95-th percentile51
Maximum51
Range41
Interquartile range (IQR)37

Descriptive statistics

Standard deviation16.009227
Coefficient of variation (CV)0.5121895
Kurtosis-1.6800949
Mean31.256453
Median Absolute Deviation (MAD)18
Skewness0.035026752
Sum94457
Variance256.29535
MonotonicityNot monotonic
2023-12-12T21:02:10.613575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
51 876
28.2%
14 539
17.4%
40 300
 
9.7%
12 299
 
9.6%
32 231
 
7.4%
31 171
 
5.5%
17 160
 
5.2%
44 101
 
3.3%
19 78
 
2.5%
11 54
 
1.7%
Other values (15) 213
 
6.9%
(Missing) 83
 
2.7%
ValueCountFrequency (%)
10 40
 
1.3%
11 54
 
1.7%
12 299
9.6%
13 50
 
1.6%
14 539
17.4%
16 50
 
1.6%
17 160
 
5.2%
18 2
 
0.1%
19 78
 
2.5%
23 1
 
< 0.1%
ValueCountFrequency (%)
51 876
28.2%
47 3
 
0.1%
46 1
 
< 0.1%
45 34
 
1.1%
44 101
 
3.3%
43 7
 
0.2%
42 1
 
< 0.1%
40 300
 
9.7%
36 1
 
< 0.1%
34 1
 
< 0.1%

음용여부
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
2
1595 
0
855 
1
599 
<NA>
 
56

Length

Max length4
Median length1
Mean length1.0541063
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 1595
51.4%
0 855
27.5%
1 599
 
19.3%
<NA> 56
 
1.8%

Length

2023-12-12T21:02:10.758739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:02:10.890040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1595
51.4%
0 855
27.5%
1 599
 
19.3%
na 56
 
1.8%

Interactions

2023-12-12T21:01:58.836530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:55.432394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:55.968528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:56.744663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:57.385578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:58.111334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:58.990640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:55.518977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:56.056464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:56.843116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:57.489319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:58.250667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:59.135336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:55.605986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:56.140937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:56.935608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:57.592202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:58.382263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:59.249484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:55.690781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:56.220498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:57.064335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:57.699281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:58.494032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:59.367121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:55.778275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:56.311256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:57.169814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:57.835973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:58.599707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:59.482488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:55.880399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:56.418406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:57.281365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:58.004634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:58.712789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:02:10.970907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관정번호표고관리기관명관정구분암반충적구분케이싱높이설치심도굴착구경관측주기내용지하수상세용도코드음용여부
관정번호1.0000.0000.8450.4590.4590.1890.1680.0840.7860.7350.334
표고0.0001.0000.0000.0590.0590.1230.7470.1080.1430.2590.202
관리기관명0.8450.0001.0000.9130.9130.9520.6060.5890.9570.9620.868
관정구분0.4590.0590.9131.0001.0000.1470.2590.0780.2290.2670.260
암반충적구분0.4590.0590.9131.0001.0000.1470.2590.0780.2290.2670.260
케이싱높이0.1890.1230.9520.1470.1471.0000.1260.1130.2700.1640.256
설치심도0.1680.7470.6060.2590.2590.1261.0000.2040.0820.2640.241
굴착구경0.0840.1080.5890.0780.0780.1130.2041.0000.1770.3190.148
관측주기내용0.7860.1430.9570.2290.2290.2700.0820.1771.0000.5950.444
지하수상세용도코드0.7350.2590.9620.2670.2670.1640.2640.3190.5951.0000.681
음용여부0.3340.2020.8680.2600.2600.2560.2410.1480.4440.6811.000
2023-12-12T21:02:11.107519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
암반충적구분관리기관명관측주기내용음용여부관정구분
암반충적구분1.0000.7510.0970.0841.000
관리기관명0.7511.0000.7410.5330.751
관측주기내용0.0970.7411.0000.3340.097
음용여부0.0840.5330.3341.0000.084
관정구분1.0000.7510.0970.0841.000
2023-12-12T21:02:11.229968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관정번호표고케이싱높이설치심도굴착구경지하수상세용도코드관리기관명관정구분암반충적구분관측주기내용음용여부
관정번호1.0000.1300.027-0.0720.0840.1190.5880.2130.2130.6160.268
표고0.1301.0000.1120.1770.0990.1190.0000.0350.0350.0750.122
케이싱높이0.0270.1121.000-0.136-0.0380.0750.6300.0980.0980.1630.178
설치심도-0.0720.177-0.1361.0000.089-0.1760.2970.1970.1970.0430.147
굴착구경0.0840.099-0.0380.0891.000-0.0540.2780.0580.0580.1200.111
지하수상세용도코드0.1190.1190.075-0.176-0.0541.0000.7450.1650.1650.3530.534
관리기관명0.5880.0000.6300.2970.2780.7451.0000.7510.7510.7410.533
관정구분0.2130.0350.0980.1970.0580.1650.7511.0001.0000.0970.084
암반충적구분0.2130.0350.0980.1970.0580.1650.7511.0001.0000.0970.084
관측주기내용0.6160.0750.1630.0430.1200.3530.7410.0970.0971.0000.334
음용여부0.2680.1220.1780.1470.1110.5340.5330.0840.0840.3341.000

Missing values

2023-12-12T21:01:59.663397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:01:59.923102image/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.
2023-12-12T21:02:00.179781image/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

관정번호관측소명관측소코드허가신고번호주소경도위도표고관리기관명설치일자관정구분암반충적구분케이싱높이설치심도굴착구경관측주기내용관측항목명지하수상세용도코드음용여부
0753955장수10JB-JSU-G1-0010<NA>전라북도 장수군 장수읍 대성리 709<NA><NA><NA><NA><NA>22<NA><NA><NA>자동수위, 수온, 전기전도도<NA><NA>
1770450응암-0018SJ-SJN-G1-0018<NA>세종특별자치시 세종시 연동면 응암리 91736 42 14.8127 12 51.280.0<NA>2020-12-22110.5<NA>200자동수위, 수온, 전기전도도510
2770649산월-0008JB-GUV-G1-00081200900007전라북도 군산시 대야면 산월리 98235 57 6.85126 48 59.397.0<NA>2021-04-14220.1370.0250자동수위, 수온, 전기전도도310
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