Overview

Dataset statistics

Number of variables7
Number of observations627
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.0 KiB
Average record size in memory57.2 B

Variable types

Text4
Categorical2
Numeric1

Dataset

Description한국농어촌공사에서 운영,관리하는 지하수관측소 정보(농어촌 지하수의 개발,이용 및 보전,관리를 위한 관측망 운영)
Author한국농어촌공사
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20191014000000001272

Alerts

대수층 has constant value ""Constant
관측소ID has unique valuesUnique
장비ID has unique valuesUnique
관측소명 has unique valuesUnique

Reproduction

Analysis started2023-12-11 03:43:51.034463
Analysis finished2023-12-11 03:43:51.818645
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소ID
Text

UNIQUE 

Distinct627
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2023-12-11T12:43:52.016331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters8778
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique627 ?
Unique (%)100.0%

Sample

1st rowCB-BUN-A2-0001
2nd rowCB-BUN-A2-0002
3rd rowCB-BUN-A2-0003
4th rowCB-BUN-A2-0004
5th rowCB-CHW-A2-0001
ValueCountFrequency (%)
cb-bun-a2-0001 1
 
0.2%
ic-ghi-a1-0001 1
 
0.2%
ic-ghi-a2-0001 1
 
0.2%
gw-ygu-a2-0001 1
 
0.2%
gw-ygu-a2-0002 1
 
0.2%
gw-yyg-a1-0001 1
 
0.2%
gw-yyg-a1-0002 1
 
0.2%
gw-yyg-a1-0003 1
 
0.2%
gw-yyg-a2-0001 1
 
0.2%
ic-ghi-a1-0003 1
 
0.2%
Other values (617) 617
98.4%
2023-12-11T12:43:52.478298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1881
21.4%
0 1874
21.3%
A 739
 
8.4%
G 673
 
7.7%
2 587
 
6.7%
N 465
 
5.3%
1 351
 
4.0%
J 296
 
3.4%
C 256
 
2.9%
B 224
 
2.6%
Other values (23) 1432
16.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3762
42.9%
Decimal Number 3135
35.7%
Dash Punctuation 1881
21.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 739
19.6%
G 673
17.9%
N 465
12.4%
J 296
7.9%
C 256
 
6.8%
B 224
 
6.0%
S 177
 
4.7%
H 160
 
4.3%
W 115
 
3.1%
Y 96
 
2.6%
Other values (12) 561
14.9%
Decimal Number
ValueCountFrequency (%)
0 1874
59.8%
2 587
 
18.7%
1 351
 
11.2%
3 105
 
3.3%
4 86
 
2.7%
5 58
 
1.9%
6 43
 
1.4%
7 18
 
0.6%
8 8
 
0.3%
9 5
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 1881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5016
57.1%
Latin 3762
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 739
19.6%
G 673
17.9%
N 465
12.4%
J 296
7.9%
C 256
 
6.8%
B 224
 
6.0%
S 177
 
4.7%
H 160
 
4.3%
W 115
 
3.1%
Y 96
 
2.6%
Other values (12) 561
14.9%
Common
ValueCountFrequency (%)
- 1881
37.5%
0 1874
37.4%
2 587
 
11.7%
1 351
 
7.0%
3 105
 
2.1%
4 86
 
1.7%
5 58
 
1.2%
6 43
 
0.9%
7 18
 
0.4%
8 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8778
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1881
21.4%
0 1874
21.3%
A 739
 
8.4%
G 673
 
7.7%
2 587
 
6.7%
N 465
 
5.3%
1 351
 
4.0%
J 296
 
3.4%
C 256
 
2.9%
B 224
 
2.6%
Other values (23) 1432
16.3%

장비ID
Text

UNIQUE 

Distinct627
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2023-12-11T12:43:52.897349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3762
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique627 ?
Unique (%)100.0%

Sample

1st rowW00014
2nd rowW00022
3rd rowR18205
4th rowR18206
5th rowS13024
ValueCountFrequency (%)
w00014 1
 
0.2%
r00001 1
 
0.2%
s17042 1
 
0.2%
r00231 1
 
0.2%
r00232 1
 
0.2%
r00203 1
 
0.2%
r00204 1
 
0.2%
s18161 1
 
0.2%
u14037 1
 
0.2%
r00003 1
 
0.2%
Other values (617) 617
98.4%
2023-12-11T12:43:53.517843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 841
22.4%
1 715
19.0%
S 338
9.0%
3 254
 
6.8%
5 245
 
6.5%
2 242
 
6.4%
R 228
 
6.1%
8 203
 
5.4%
4 194
 
5.2%
7 187
 
5.0%
Other values (4) 315
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3135
83.3%
Uppercase Letter 627
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 841
26.8%
1 715
22.8%
3 254
 
8.1%
5 245
 
7.8%
2 242
 
7.7%
8 203
 
6.5%
4 194
 
6.2%
7 187
 
6.0%
6 166
 
5.3%
9 88
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
S 338
53.9%
R 228
36.4%
U 37
 
5.9%
W 24
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3135
83.3%
Latin 627
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 841
26.8%
1 715
22.8%
3 254
 
8.1%
5 245
 
7.8%
2 242
 
7.7%
8 203
 
6.5%
4 194
 
6.2%
7 187
 
6.0%
6 166
 
5.3%
9 88
 
2.8%
Latin
ValueCountFrequency (%)
S 338
53.9%
R 228
36.4%
U 37
 
5.9%
W 24
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 841
22.4%
1 715
19.0%
S 338
9.0%
3 254
 
6.8%
5 245
 
6.5%
2 242
 
6.4%
R 228
 
6.1%
8 203
 
5.4%
4 194
 
5.2%
7 187
 
5.0%
Other values (4) 315
 
8.4%

관측소명
Text

UNIQUE 

Distinct627
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2023-12-11T12:43:53.945724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0047847
Min length3

Characters and Unicode

Total characters1884
Distinct characters155
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique627 ?
Unique (%)100.0%

Sample

1st row보은1
2nd row보은2
3rd row보은3
4th row보은4
5th row청원1
ValueCountFrequency (%)
보은1 1
 
0.2%
숭뇌1 1
 
0.2%
강화1 1
 
0.2%
양구1 1
 
0.2%
양구2 1
 
0.2%
강현1 1
 
0.2%
강현2 1
 
0.2%
조산1 1
 
0.2%
양양1 1
 
0.2%
숭뇌3 1
 
0.2%
Other values (617) 617
98.4%
2023-12-11T12:43:54.490631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 206
 
10.9%
2 168
 
8.9%
3 94
 
5.0%
4 73
 
3.9%
주 62
 
3.3%
천 62
 
3.3%
성 57
 
3.0%
산 52
 
2.8%
5 44
 
2.3%
안 41
 
2.2%
Other values (145) 1025
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1257
66.7%
Decimal Number 627
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 62
 
4.9%
천 62
 
4.9%
성 57
 
4.5%
산 52
 
4.1%
안 41
 
3.3%
양 32
 
2.5%
화 29
 
2.3%
평 27
 
2.1%
흥 24
 
1.9%
포 23
 
1.8%
Other values (136) 848
67.5%
Decimal Number
ValueCountFrequency (%)
1 206
32.9%
2 168
26.8%
3 94
15.0%
4 73
 
11.6%
5 44
 
7.0%
6 27
 
4.3%
7 10
 
1.6%
8 4
 
0.6%
9 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1257
66.7%
Common 627
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 62
 
4.9%
천 62
 
4.9%
성 57
 
4.5%
산 52
 
4.1%
안 41
 
3.3%
양 32
 
2.5%
화 29
 
2.3%
평 27
 
2.1%
흥 24
 
1.9%
포 23
 
1.8%
Other values (136) 848
67.5%
Common
ValueCountFrequency (%)
1 206
32.9%
2 168
26.8%
3 94
15.0%
4 73
 
11.6%
5 44
 
7.0%
6 27
 
4.3%
7 10
 
1.6%
8 4
 
0.6%
9 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1257
66.7%
ASCII 627
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 206
32.9%
2 168
26.8%
3 94
15.0%
4 73
 
11.6%
5 44
 
7.0%
6 27
 
4.3%
7 10
 
1.6%
8 4
 
0.6%
9 1
 
0.2%
Hangul
ValueCountFrequency (%)
주 62
 
4.9%
천 62
 
4.9%
성 57
 
4.5%
산 52
 
4.1%
안 41
 
3.3%
양 32
 
2.5%
화 29
 
2.3%
평 27
 
2.1%
흥 24
 
1.9%
포 23
 
1.8%
Other values (136) 848
67.5%

관측망구분
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
농촌지하수
446 
해수침투
181 

Length

Max length5
Median length5
Mean length4.7113238
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농촌지하수 446
71.1%
해수침투 181
28.9%

Length

2023-12-11T12:43:54.634448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:43:54.729119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농촌지하수 446
71.1%
해수침투 181
28.9%

대수층
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
암반
627 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
암반 627
100.0%

Length

2023-12-11T12:43:54.830691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:43:54.938620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
암반 627
100.0%

주소
Text

Distinct625
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2023-12-11T12:43:55.303289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length22.08453
Min length16

Characters and Unicode

Total characters13847
Distinct characters275
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique623 ?
Unique (%)99.4%

Sample

1st row충청북도 보은군 삼승면 탄금리 산23-3
2nd row충청북도 보은군 장안면 봉비리 825
3rd row충청북도 보은군 마로면 소여리 829-1
4th row충청북도 보은군 탄부면 사직리 산1-2
5th row충청북도 청주시 상당구 미원면 쌍이리 산39-16
ValueCountFrequency (%)
전라남도 138
 
4.4%
경상북도 93
 
3.0%
경상남도 90
 
2.9%
충청남도 74
 
2.4%
경기도 65
 
2.1%
강원도 63
 
2.0%
전라북도 52
 
1.7%
충청북도 35
 
1.1%
인천광역시 15
 
0.5%
강화군 13
 
0.4%
Other values (1692) 2498
79.7%
2023-12-11T12:43:56.111682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3025
21.8%
도 676
 
4.9%
리 605
 
4.4%
1 525
 
3.8%
면 465
 
3.4%
남 383
 
2.8%
군 379
 
2.7%
- 368
 
2.7%
시 287
 
2.1%
2 280
 
2.0%
Other values (265) 6854
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8054
58.2%
Space Separator 3025
 
21.8%
Decimal Number 2400
 
17.3%
Dash Punctuation 368
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 676
 
8.4%
리 605
 
7.5%
면 465
 
5.8%
남 383
 
4.8%
군 379
 
4.7%
시 287
 
3.6%
경 264
 
3.3%
북 210
 
2.6%
전 209
 
2.6%
상 208
 
2.6%
Other values (253) 4368
54.2%
Decimal Number
ValueCountFrequency (%)
1 525
21.9%
2 280
11.7%
4 235
9.8%
3 231
9.6%
6 203
 
8.5%
5 200
 
8.3%
8 194
 
8.1%
7 193
 
8.0%
0 172
 
7.2%
9 167
 
7.0%
Space Separator
ValueCountFrequency (%)
3025
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8054
58.2%
Common 5793
41.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 676
 
8.4%
리 605
 
7.5%
면 465
 
5.8%
남 383
 
4.8%
군 379
 
4.7%
시 287
 
3.6%
경 264
 
3.3%
북 210
 
2.6%
전 209
 
2.6%
상 208
 
2.6%
Other values (253) 4368
54.2%
Common
ValueCountFrequency (%)
3025
52.2%
1 525
 
9.1%
- 368
 
6.4%
2 280
 
4.8%
4 235
 
4.1%
3 231
 
4.0%
6 203
 
3.5%
5 200
 
3.5%
8 194
 
3.3%
7 193
 
3.3%
Other values (2) 339
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8054
58.2%
ASCII 5793
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3025
52.2%
1 525
 
9.1%
- 368
 
6.4%
2 280
 
4.8%
4 235
 
4.1%
3 231
 
4.0%
6 203
 
3.5%
5 200
 
3.5%
8 194
 
3.3%
7 193
 
3.3%
Other values (2) 339
 
5.9%
Hangul
ValueCountFrequency (%)
도 676
 
8.4%
리 605
 
7.5%
면 465
 
5.8%
남 383
 
4.8%
군 379
 
4.7%
시 287
 
3.6%
경 264
 
3.3%
북 210
 
2.6%
전 209
 
2.6%
상 208
 
2.6%
Other values (253) 4368
54.2%

주소코드
Real number (ℝ)

Distinct566
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5115528 × 109
Minimum2.871025 × 109
Maximum4.889043 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-12-11T12:43:56.277020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.871025 × 109
5-th percentile4.1360253 × 109
Q14.3720345 × 109
median4.615031 × 109
Q34.7170405 × 109
95-th percentile4.8840292 × 109
Maximum4.889043 × 109
Range2.018018 × 109
Interquartile range (IQR)3.45006 × 108

Descriptive statistics

Standard deviation3.5320169 × 108
Coefficient of variation (CV)0.078288276
Kurtosis9.3517223
Mean4.5115528 × 109
Median Absolute Deviation (MAD)1.7699999 × 108
Skewness-2.5618982
Sum2.8287436 × 1012
Variance1.2475143 × 1017
MonotonicityNot monotonic
2023-12-11T12:43:56.474497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4690031021 4
 
0.6%
2871039023 3
 
0.5%
4615031035 3
 
0.5%
4713025027 2
 
0.3%
4215025025 2
 
0.3%
4678037030 2
 
0.3%
4427032026 2
 
0.3%
4812531024 2
 
0.3%
4678025334 2
 
0.3%
4157035025 2
 
0.3%
Other values (556) 603
96.2%
ValueCountFrequency (%)
2871025027 1
 
0.2%
2871033024 2
0.3%
2871034022 1
 
0.2%
2871034027 2
0.3%
2871035022 1
 
0.2%
2871035023 1
 
0.2%
2871037021 1
 
0.2%
2871039023 3
0.5%
2871040026 1
 
0.2%
2872036022 2
0.3%
ValueCountFrequency (%)
4889043026 1
0.2%
4889037026 1
0.2%
4889036028 1
0.2%
4889035033 1
0.2%
4888040027 1
0.2%
4888037027 1
0.2%
4888032023 1
0.2%
4887035030 1
0.2%
4886038024 1
0.2%
4886037035 1
0.2%

Interactions

2023-12-11T12:43:51.452359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:43:56.596203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측망구분주소코드
관측망구분1.0000.251
주소코드0.2511.000
2023-12-11T12:43:56.679781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소코드관측망구분
주소코드1.0000.169
관측망구분0.1691.000

Missing values

2023-12-11T12:43:51.614691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:43:51.763119image/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.

Sample

관측소ID장비ID관측소명관측망구분대수층주소주소코드
0CB-BUN-A2-0001W00014보은1농촌지하수암반충청북도 보은군 삼승면 탄금리 산23-34372035023
1CB-BUN-A2-0002W00022보은2농촌지하수암반충청북도 보은군 장안면 봉비리 8254372032525
2CB-BUN-A2-0003R18205보은3농촌지하수암반충청북도 보은군 마로면 소여리 829-14372033026
3CB-BUN-A2-0004R18206보은4농촌지하수암반충청북도 보은군 탄부면 사직리 산1-24372034031
4CB-CHW-A2-0001S13024청원1농촌지하수암반충청북도 청주시 상당구 미원면 쌍이리 산39-164311132022
5CB-CHW-A2-0002S18136청주1농촌지하수암반충청북도 청주시 흥덕구 오송읍 서평리 583-354311325030
6CB-CHW-A2-0003S18139청주2농촌지하수암반충청북도 청주시 청원구 북이면 내둔리 6614311431043
7CB-CUJ-A2-0001U14043충주1농촌지하수암반충청북도 충주시 주덕읍 창전리 7114313025022
8CB-CUJ-A2-0002S17107충주2농촌지하수암반충청북도 충주시 신니면 견학리 산26-14313035034
9CB-CUJ-A2-0003S17106충주3농촌지하수암반충청북도 충주시 살미면 내사리 9014313031028
관측소ID장비ID관측소명관측망구분대수층주소주소코드
617JN-YGG-A1-0004S14028염산2해수침투암반전라남도 영광군 염산면 봉남리 12494687036023
618JN-YGG-A2-0001R00255영광1농촌지하수암반전라남도 영광군 영광읍 덕호리 8834687025029
619JN-YGG-A2-0002S13032영광2농촌지하수암반전라남도 영광군 군서면 만곡리 178-34687034030
620JN-YGG-A2-0003S16024영광3농촌지하수암반전라남도 영광군 군남면 동간리 7014687035022
621JN-YGG-A2-0004S16023영광4농촌지하수암반전라남도 영광군 법성면 진내리 9874687037021
622JN-YGG-A2-0005S16022영광5농촌지하수암반전라남도 영광군 염산면 오동리 16324687036028
623JN-YGG-A2-0006S18094영광6농촌지하수암반전라남도 영광군 묘량면 덕흥리 4624687032021
624JN-YGG-A2-0007S18095영광7농촌지하수암반전라남도 영광군 묘량면 운당리 886-184687032023
625US-BKU-A1-0001R00208북구1해수침투암반울산광역시 북구 정자동 2003120011300
626US-BKU-A1-0002R00209북구2해수침투암반울산광역시 북구 무룡동 573120011100