Overview

Dataset statistics

Number of variables13
Number of observations5685
Missing cells6524
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory644.1 KiB
Average record size in memory116.0 B

Variable types

Text1
Categorical1
Numeric11

Dataset

Description수질측정망 운영결과 자료 - 지점별 생물화학적산소요구량, 부유물질, 총질소, 총인, 총유기탄소 등(BOD, SS, TN, TP, TOC 수치)에 대한 결과를 제공 수심(m), 수온(℃), DO(㎎/L), BOD(㎎/L), COD(㎎/L), SS(㎎/L), TN(㎎/L), TP(㎎/L), TOC(㎎/L), 수소이온농도(ph)
URLhttps://www.data.go.kr/data/3033743/fileData.do

Alerts

수온 is highly overall correlated with 용존산소농도High correlation
용존산소농도 is highly overall correlated with 수온High correlation
화학적산소요구량 is highly overall correlated with 부유물질 and 2 other fieldsHigh correlation
부유물질 is highly overall correlated with 화학적산소요구량 and 2 other fieldsHigh correlation
총인 is highly overall correlated with 화학적산소요구량 and 1 other fieldsHigh correlation
총유기탄소 is highly overall correlated with 화학적산소요구량 and 1 other fieldsHigh correlation
수심 has 5023 (88.4%) missing valuesMissing
수온 has 167 (2.9%) missing valuesMissing
용존산소농도 has 167 (2.9%) missing valuesMissing
생물화학적산소요구량 has 167 (2.9%) missing valuesMissing
화학적산소요구량 has 167 (2.9%) missing valuesMissing
부유물질 has 167 (2.9%) missing valuesMissing
총질소 has 167 (2.9%) missing valuesMissing
총인 has 167 (2.9%) missing valuesMissing
총유기탄소 has 165 (2.9%) missing valuesMissing
수소이온농도 has 167 (2.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:57:37.859963
Analysis finished2023-12-12 15:57:57.294417
Duration19.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct111
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size44.5 KiB
2023-12-13T00:57:57.580120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.2494283
Min length2

Characters and Unicode

Total characters18473
Distinct characters90
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송천1
2nd row송천1
3rd row송천1
4th row송천1
5th row송천1
ValueCountFrequency (%)
송천1 60
 
1.0%
춘천2 60
 
1.0%
소양강2 60
 
1.0%
소양강1 60
 
1.0%
내린천2 60
 
1.0%
인북천2 60
 
1.0%
인북천1 60
 
1.0%
간성북천 60
 
1.0%
영월1 60
 
1.0%
원주천1 60
 
1.0%
Other values (102) 5145
89.6%
2023-12-13T00:57:58.169793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3696
20.0%
1292
 
7.0%
2 1269
 
6.9%
1 1109
 
6.0%
632
 
3.4%
3 489
 
2.6%
420
 
2.3%
383
 
2.1%
369
 
2.0%
346
 
1.9%
Other values (80) 8468
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15126
81.9%
Decimal Number 3287
 
17.8%
Space Separator 60
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3696
24.4%
1292
 
8.5%
632
 
4.2%
420
 
2.8%
383
 
2.5%
369
 
2.4%
346
 
2.3%
323
 
2.1%
263
 
1.7%
263
 
1.7%
Other values (73) 7139
47.2%
Decimal Number
ValueCountFrequency (%)
2 1269
38.6%
1 1109
33.7%
3 489
 
14.9%
4 240
 
7.3%
5 120
 
3.7%
6 60
 
1.8%
Space Separator
ValueCountFrequency (%)
60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15126
81.9%
Common 3347
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3696
24.4%
1292
 
8.5%
632
 
4.2%
420
 
2.8%
383
 
2.5%
369
 
2.4%
346
 
2.3%
323
 
2.1%
263
 
1.7%
263
 
1.7%
Other values (73) 7139
47.2%
Common
ValueCountFrequency (%)
2 1269
37.9%
1 1109
33.1%
3 489
 
14.6%
4 240
 
7.2%
5 120
 
3.6%
60
 
1.8%
6 60
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15126
81.9%
ASCII 3347
 
18.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3696
24.4%
1292
 
8.5%
632
 
4.2%
420
 
2.8%
383
 
2.5%
369
 
2.4%
346
 
2.3%
323
 
2.1%
263
 
1.7%
263
 
1.7%
Other values (73) 7139
47.2%
ASCII
ValueCountFrequency (%)
2 1269
37.9%
1 1109
33.1%
3 489
 
14.6%
4 240
 
7.2%
5 120
 
3.6%
60
 
1.8%
6 60
 
1.8%


Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.5 KiB
2022
1331 
2021
1295 
2018
1020 
2019
1020 
2020
1019 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2022 1331
23.4%
2021 1295
22.8%
2018 1020
17.9%
2019 1020
17.9%
2020 1019
17.9%

Length

2023-12-13T00:57:58.320580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:57:58.440189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 1331
23.4%
2021 1295
22.8%
2018 1020
17.9%
2019 1020
17.9%
2020 1019
17.9%


Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5241865
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:57:58.586636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4396354
Coefficient of variation (CV)0.52721292
Kurtosis-1.2129414
Mean6.5241865
Median Absolute Deviation (MAD)3
Skewness-0.0031145686
Sum37090
Variance11.831092
MonotonicityNot monotonic
2023-12-13T00:57:58.729894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 476
8.4%
3 476
8.4%
4 476
8.4%
5 476
8.4%
6 476
8.4%
7 476
8.4%
8 476
8.4%
9 476
8.4%
10 476
8.4%
11 476
8.4%
Other values (2) 925
16.3%
ValueCountFrequency (%)
1 450
7.9%
2 476
8.4%
3 476
8.4%
4 476
8.4%
5 476
8.4%
6 476
8.4%
7 476
8.4%
8 476
8.4%
9 476
8.4%
10 476
8.4%
ValueCountFrequency (%)
12 475
8.4%
11 476
8.4%
10 476
8.4%
9 476
8.4%
8 476
8.4%
7 476
8.4%
6 476
8.4%
5 476
8.4%
4 476
8.4%
3 476
8.4%

수심
Real number (ℝ)

MISSING 

Distinct187
Distinct (%)28.2%
Missing5023
Missing (%)88.4%
Infinite0
Infinite (%)0.0%
Mean2.2789275
Minimum0.16
Maximum3.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:57:58.882307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile1.2905
Q12.1725
median2.41
Q32.6
95-th percentile3.029
Maximum3.9
Range3.74
Interquartile range (IQR)0.4275

Descriptive statistics

Standard deviation0.60202776
Coefficient of variation (CV)0.26417153
Kurtosis2.2177485
Mean2.2789275
Median Absolute Deviation (MAD)0.2
Skewness-1.1424946
Sum1508.65
Variance0.36243743
MonotonicityNot monotonic
2023-12-13T00:57:59.059685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.37 17
 
0.3%
2.46 16
 
0.3%
2.53 14
 
0.2%
2.42 13
 
0.2%
2.43 13
 
0.2%
2.3 12
 
0.2%
2.57 12
 
0.2%
2.41 11
 
0.2%
2.44 10
 
0.2%
2.39 10
 
0.2%
Other values (177) 534
 
9.4%
(Missing) 5023
88.4%
ValueCountFrequency (%)
0.16 2
< 0.1%
0.21 1
 
< 0.1%
0.22 2
< 0.1%
0.25 3
0.1%
0.27 1
 
< 0.1%
0.29 2
< 0.1%
0.3 1
 
< 0.1%
0.32 1
 
< 0.1%
0.35 1
 
< 0.1%
0.36 1
 
< 0.1%
ValueCountFrequency (%)
3.9 1
< 0.1%
3.8 1
< 0.1%
3.79 1
< 0.1%
3.64 1
< 0.1%
3.62 2
< 0.1%
3.61 2
< 0.1%
3.59 1
< 0.1%
3.57 1
< 0.1%
3.43 1
< 0.1%
3.37 2
< 0.1%

수온
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct330
Distinct (%)6.0%
Missing167
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean13.893295
Minimum-2.7
Maximum213.6
Zeros19
Zeros (%)0.3%
Negative15
Negative (%)0.3%
Memory size50.1 KiB
2023-12-13T00:57:59.245272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.7
5-th percentile1.5
Q17.6
median14.2
Q320.2
95-th percentile25.4
Maximum213.6
Range216.3
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation8.0907555
Coefficient of variation (CV)0.58234967
Kurtosis65.91907
Mean13.893295
Median Absolute Deviation (MAD)6.3
Skewness2.6720742
Sum76663.2
Variance65.460325
MonotonicityNot monotonic
2023-12-13T00:57:59.418749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.2 39
 
0.7%
13.8 37
 
0.7%
22.0 37
 
0.7%
15.0 35
 
0.6%
21.3 34
 
0.6%
21.0 33
 
0.6%
21.6 32
 
0.6%
20.2 32
 
0.6%
19.5 31
 
0.5%
21.5 31
 
0.5%
Other values (320) 5177
91.1%
(Missing) 167
 
2.9%
ValueCountFrequency (%)
-2.7 1
 
< 0.1%
-1.5 2
< 0.1%
-1.4 1
 
< 0.1%
-1.3 1
 
< 0.1%
-1.2 2
< 0.1%
-1.0 4
0.1%
-0.9 1
 
< 0.1%
-0.6 1
 
< 0.1%
-0.2 1
 
< 0.1%
-0.1 1
 
< 0.1%
ValueCountFrequency (%)
213.6 1
< 0.1%
33.9 1
< 0.1%
32.9 1
< 0.1%
32.8 1
< 0.1%
32.3 1
< 0.1%
32.2 1
< 0.1%
32.0 1
< 0.1%
31.9 1
< 0.1%
31.8 1
< 0.1%
31.7 1
< 0.1%

용존산소농도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct128
Distinct (%)2.3%
Missing167
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean11.19199
Minimum5.8
Maximum19.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:57:59.595174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.8
5-th percentile8.5
Q19.6
median11
Q312.6
95-th percentile14.5
Maximum19.5
Range13.7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9579523
Coefficient of variation (CV)0.17494229
Kurtosis0.060201409
Mean11.19199
Median Absolute Deviation (MAD)1.5
Skewness0.50911717
Sum61757.4
Variance3.8335773
MonotonicityNot monotonic
2023-12-13T00:57:59.794152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.1 119
 
2.1%
10.1 119
 
2.1%
9.4 114
 
2.0%
11.5 111
 
2.0%
10.9 111
 
2.0%
9.5 110
 
1.9%
9.6 110
 
1.9%
10.2 110
 
1.9%
8.9 106
 
1.9%
11.1 105
 
1.8%
Other values (118) 4403
77.4%
(Missing) 167
 
2.9%
ValueCountFrequency (%)
5.8 1
 
< 0.1%
5.9 1
 
< 0.1%
6.2 1
 
< 0.1%
6.3 1
 
< 0.1%
6.5 2
< 0.1%
6.6 1
 
< 0.1%
6.7 1
 
< 0.1%
6.8 2
< 0.1%
6.9 2
< 0.1%
7.0 4
0.1%
ValueCountFrequency (%)
19.5 1
< 0.1%
19.4 1
< 0.1%
19.3 2
< 0.1%
19.2 1
< 0.1%
19.1 1
< 0.1%
18.9 1
< 0.1%
18.6 2
< 0.1%
18.5 2
< 0.1%
18.3 2
< 0.1%
18.2 1
< 0.1%

생물화학적산소요구량
Real number (ℝ)

MISSING 

Distinct66
Distinct (%)1.2%
Missing167
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1.0180319
Minimum0.1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:57:59.991762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.6
median0.9
Q31.3
95-th percentile2.2
Maximum12
Range11.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.74286112
Coefficient of variation (CV)0.72970318
Kurtosis37.817725
Mean1.0180319
Median Absolute Deviation (MAD)0.4
Skewness4.208098
Sum5617.5
Variance0.55184264
MonotonicityNot monotonic
2023-12-13T00:58:00.232032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 481
 
8.5%
0.4 471
 
8.3%
0.5 470
 
8.3%
0.7 441
 
7.8%
0.8 426
 
7.5%
0.9 393
 
6.9%
1.0 346
 
6.1%
0.3 324
 
5.7%
1.1 309
 
5.4%
1.2 264
 
4.6%
Other values (56) 1593
28.0%
ValueCountFrequency (%)
0.1 32
 
0.6%
0.2 78
 
1.4%
0.3 324
5.7%
0.4 471
8.3%
0.5 470
8.3%
0.6 481
8.5%
0.7 441
7.8%
0.8 426
7.5%
0.9 393
6.9%
1.0 346
6.1%
ValueCountFrequency (%)
12.0 1
< 0.1%
10.9 1
< 0.1%
10.3 1
< 0.1%
9.5 1
< 0.1%
9.2 2
< 0.1%
8.4 1
< 0.1%
8.0 1
< 0.1%
7.8 1
< 0.1%
7.6 1
< 0.1%
7.4 1
< 0.1%

화학적산소요구량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct107
Distinct (%)1.9%
Missing167
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean2.6891084
Minimum0
Maximum18.2
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:58:00.427100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.185
Q11.8
median2.4
Q33.2
95-th percentile5
Maximum18.2
Range18.2
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.3515259
Coefficient of variation (CV)0.50259258
Kurtosis12.532097
Mean2.6891084
Median Absolute Deviation (MAD)0.7
Skewness2.4343107
Sum14838.5
Variance1.8266223
MonotonicityNot monotonic
2023-12-13T00:58:00.588260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 247
 
4.3%
2.0 232
 
4.1%
1.9 228
 
4.0%
2.3 226
 
4.0%
2.4 224
 
3.9%
1.8 222
 
3.9%
2.1 219
 
3.9%
2.5 216
 
3.8%
2.8 206
 
3.6%
2.6 200
 
3.5%
Other values (97) 3298
58.0%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.3 1
 
< 0.1%
0.4 4
 
0.1%
0.5 15
 
0.3%
0.6 13
 
0.2%
0.7 20
 
0.4%
0.8 24
 
0.4%
0.9 30
 
0.5%
1.0 68
1.2%
1.1 100
1.8%
ValueCountFrequency (%)
18.2 1
< 0.1%
15.4 1
< 0.1%
13.8 1
< 0.1%
13.5 1
< 0.1%
13.1 1
< 0.1%
12.7 1
< 0.1%
12.4 2
< 0.1%
11.8 1
< 0.1%
11.5 1
< 0.1%
11.1 2
< 0.1%

부유물질
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct318
Distinct (%)5.8%
Missing167
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean3.966709
Minimum0
Maximum253.4
Zeros16
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:58:00.771958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.8
median1.7
Q33.6
95-th percentile13.6
Maximum253.4
Range253.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation9.9177582
Coefficient of variation (CV)2.5002485
Kurtosis172.01844
Mean3.966709
Median Absolute Deviation (MAD)1.1
Skewness10.744349
Sum21888.3
Variance98.361928
MonotonicityNot monotonic
2023-12-13T00:58:00.931945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 304
 
5.3%
1.0 250
 
4.4%
0.4 245
 
4.3%
0.8 232
 
4.1%
0.6 202
 
3.6%
1.2 193
 
3.4%
0.9 161
 
2.8%
1.4 157
 
2.8%
0.5 154
 
2.7%
2.0 132
 
2.3%
Other values (308) 3488
61.4%
(Missing) 167
 
2.9%
ValueCountFrequency (%)
0.0 16
 
0.3%
0.1 58
 
1.0%
0.2 304
5.3%
0.3 120
 
2.1%
0.4 245
4.3%
0.5 154
2.7%
0.6 202
3.6%
0.7 124
2.2%
0.8 232
4.1%
0.9 161
2.8%
ValueCountFrequency (%)
253.4 1
< 0.1%
204.0 1
< 0.1%
178.7 1
< 0.1%
150.9 1
< 0.1%
145.6 1
< 0.1%
134.3 1
< 0.1%
130.9 1
< 0.1%
120.7 1
< 0.1%
120.4 1
< 0.1%
112.0 1
< 0.1%

총질소
Real number (ℝ)

MISSING 

Distinct3288
Distinct (%)59.6%
Missing167
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean2.7856988
Minimum0
Maximum14.389
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:58:01.084761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9387
Q11.687
median2.5475
Q33.509
95-th percentile5.5728
Maximum14.389
Range14.389
Interquartile range (IQR)1.822

Descriptive statistics

Standard deviation1.530897
Coefficient of variation (CV)0.54955581
Kurtosis4.3268014
Mean2.7856988
Median Absolute Deviation (MAD)0.902
Skewness1.5376969
Sum15371.486
Variance2.3436455
MonotonicityNot monotonic
2023-12-13T00:58:01.268101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.786 7
 
0.1%
3.045 6
 
0.1%
3.562 6
 
0.1%
3.3 6
 
0.1%
3.66 6
 
0.1%
1.831 6
 
0.1%
1.661 6
 
0.1%
1.325 5
 
0.1%
1.727 5
 
0.1%
1.802 5
 
0.1%
Other values (3278) 5460
96.0%
(Missing) 167
 
2.9%
ValueCountFrequency (%)
0.0 1
< 0.1%
0.259 1
< 0.1%
0.32 1
< 0.1%
0.327 1
< 0.1%
0.33 1
< 0.1%
0.341 1
< 0.1%
0.344 1
< 0.1%
0.348 1
< 0.1%
0.356 1
< 0.1%
0.359 1
< 0.1%
ValueCountFrequency (%)
14.389 1
< 0.1%
13.935 1
< 0.1%
11.857 1
< 0.1%
11.684 1
< 0.1%
11.466 1
< 0.1%
11.447 1
< 0.1%
11.403 1
< 0.1%
11.39 1
< 0.1%
10.25 1
< 0.1%
10.148 1
< 0.1%

총인
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct192
Distinct (%)3.5%
Missing167
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean0.025949619
Minimum0
Maximum0.639
Zeros15
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:58:01.445367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.005
Q10.01
median0.016
Q30.029
95-th percentile0.079
Maximum0.639
Range0.639
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.032488366
Coefficient of variation (CV)1.2519785
Kurtosis55.980164
Mean0.025949619
Median Absolute Deviation (MAD)0.008
Skewness5.5155313
Sum143.19
Variance0.0010554939
MonotonicityNot monotonic
2023-12-13T00:58:01.608405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.007 247
 
4.3%
0.009 235
 
4.1%
0.008 235
 
4.1%
0.013 232
 
4.1%
0.012 229
 
4.0%
0.01 228
 
4.0%
0.006 223
 
3.9%
0.011 222
 
3.9%
0.014 197
 
3.5%
0.015 180
 
3.2%
Other values (182) 3290
57.9%
ValueCountFrequency (%)
0.0 15
 
0.3%
0.001 12
 
0.2%
0.002 33
 
0.6%
0.003 65
 
1.1%
0.004 110
1.9%
0.005 161
2.8%
0.006 223
3.9%
0.007 247
4.3%
0.008 235
4.1%
0.009 235
4.1%
ValueCountFrequency (%)
0.639 1
< 0.1%
0.483 1
< 0.1%
0.473 1
< 0.1%
0.418 1
< 0.1%
0.362 1
< 0.1%
0.344 1
< 0.1%
0.324 1
< 0.1%
0.323 1
< 0.1%
0.315 1
< 0.1%
0.288 1
< 0.1%

총유기탄소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)1.5%
Missing165
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1.8555435
Minimum0
Maximum11.7
Zeros18
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:58:01.892295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7
Q11.2
median1.6
Q32.3
95-th percentile3.7
Maximum11.7
Range11.7
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.0157208
Coefficient of variation (CV)0.54739802
Kurtosis7.3290114
Mean1.8555435
Median Absolute Deviation (MAD)0.5
Skewness1.9516514
Sum10242.6
Variance1.0316888
MonotonicityNot monotonic
2023-12-13T00:58:02.069237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4 335
 
5.9%
1.2 331
 
5.8%
1.3 323
 
5.7%
1.5 320
 
5.6%
1.6 275
 
4.8%
1.8 257
 
4.5%
1.1 255
 
4.5%
1.7 254
 
4.5%
1.9 242
 
4.3%
1.0 220
 
3.9%
Other values (71) 2708
47.6%
ValueCountFrequency (%)
0.0 18
 
0.3%
0.1 7
 
0.1%
0.2 16
 
0.3%
0.3 30
 
0.5%
0.4 45
 
0.8%
0.5 62
 
1.1%
0.6 90
1.6%
0.7 100
1.8%
0.8 141
2.5%
0.9 199
3.5%
ValueCountFrequency (%)
11.7 1
 
< 0.1%
8.5 1
 
< 0.1%
8.4 1
 
< 0.1%
8.3 1
 
< 0.1%
8.2 1
 
< 0.1%
8.1 1
 
< 0.1%
8.0 2
< 0.1%
7.9 1
 
< 0.1%
7.7 1
 
< 0.1%
7.6 3
0.1%

수소이온농도
Real number (ℝ)

MISSING 

Distinct52
Distinct (%)0.9%
Missing167
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean8.2012323
Minimum5.3
Maximum12.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2023-12-13T00:58:02.252222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.3
5-th percentile7.3
Q17.8
median8.2
Q38.5
95-th percentile9.1
Maximum12.5
Range7.2
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.56299576
Coefficient of variation (CV)0.068647703
Kurtosis1.8256753
Mean8.2012323
Median Absolute Deviation (MAD)0.4
Skewness0.33839291
Sum45254.4
Variance0.31696422
MonotonicityNot monotonic
2023-12-13T00:58:02.408005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.2 443
 
7.8%
8.3 410
 
7.2%
8.1 399
 
7.0%
8.4 379
 
6.7%
7.9 368
 
6.5%
8.5 353
 
6.2%
8.0 348
 
6.1%
7.8 329
 
5.8%
8.6 293
 
5.2%
7.7 279
 
4.9%
Other values (42) 1917
33.7%
ValueCountFrequency (%)
5.3 1
 
< 0.1%
5.4 1
 
< 0.1%
5.6 2
 
< 0.1%
6.1 1
 
< 0.1%
6.4 1
 
< 0.1%
6.5 1
 
< 0.1%
6.6 3
 
0.1%
6.7 5
 
0.1%
6.8 10
0.2%
6.9 18
0.3%
ValueCountFrequency (%)
12.5 1
< 0.1%
11.9 1
< 0.1%
11.3 1
< 0.1%
11.2 1
< 0.1%
10.9 2
< 0.1%
10.6 1
< 0.1%
10.5 1
< 0.1%
10.4 1
< 0.1%
10.3 2
< 0.1%
10.2 2
< 0.1%

Interactions

2023-12-13T00:57:54.957559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:40.236643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:41.671909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:42.781180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:44.266847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:45.931551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:47.316056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:48.889806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:51.015540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:52.440958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:53.742222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:55.058887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:40.334568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:41.766119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:42.894025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:44.413820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:46.024390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:47.420602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:49.050224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:51.163463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:52.554309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:53.827751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:55.198637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:40.437116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:41.879441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:43.032608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:44.558423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:46.138228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:47.568653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:49.659589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:51.282811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:52.680144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:53.940308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:55.313376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:40.529281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:41.973282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:43.221140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:44.691683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:46.261407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:47.687638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:49.792980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:51.407113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:52.777690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:54.054330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:55.428875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:40.631576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:42.071088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:43.350958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:44.880741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:46.414731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:47.811867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:49.943131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:51.540322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:52.892667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:54.173614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:55.560099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:40.732224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:42.165914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:43.483394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:45.057641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:46.554041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:47.971528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:50.091885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:51.676961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:53.019267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:54.271142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:55.699444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:40.831350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:42.273634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:43.593704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:45.208746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:46.681281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:48.132702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:50.244178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:51.796116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:53.161275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:54.391901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:55.815712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:40.920416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:42.390230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:43.710200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:45.364085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:46.797609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:48.282687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:50.432432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:51.899989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:53.267765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:54.504814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:55.965138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:41.022061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:42.481396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:43.837774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:45.507250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:46.932489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:48.449627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:50.562862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:52.021370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:53.409407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:54.620026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:56.071916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:41.127362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:42.573516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:43.971463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:45.680831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:47.051607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:48.585540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:50.700462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:52.169571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:53.516358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:54.729644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:56.200427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:41.232087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:42.675770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:44.105292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:45.816394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:47.185375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:48.722701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:50.832345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:52.298817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:53.625058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:57:54.840394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:58:02.959815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수심수온용존산소농도생물화학적산소요구량화학적산소요구량부유물질총질소총인총유기탄소수소이온농도
1.0000.0000.7330.0240.1110.1520.1190.0000.1810.0680.1390.170
0.0001.0000.2510.7060.7380.1780.3400.0850.2370.1450.2850.200
수심0.7330.2511.0000.1810.1970.0940.3110.0000.2730.0180.3350.341
수온0.0240.7060.1811.0000.6170.0500.3020.0710.1520.2300.4310.089
용존산소농도0.1110.7380.1970.6171.0000.0000.2470.0770.2130.1530.2370.211
생물화학적산소요구량0.1520.1780.0940.0500.0001.0000.6980.0000.7090.3630.6050.214
화학적산소요구량0.1190.3400.3110.3020.2470.6981.0000.3420.6670.6430.7120.129
부유물질0.0000.0850.0000.0710.0770.0000.3421.0000.1580.8500.3360.000
총질소0.1810.2370.2730.1520.2130.7090.6670.1581.0000.3080.4280.254
총인0.0680.1450.0180.2300.1530.3630.6430.8500.3081.0000.5060.000
총유기탄소0.1390.2850.3350.4310.2370.6050.7120.3360.4280.5061.0000.089
수소이온농도0.1700.2000.3410.0890.2110.2140.1290.0000.2540.0000.0891.000
2023-12-13T00:58:03.168751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수심수온용존산소농도생물화학적산소요구량화학적산소요구량부유물질총질소총인총유기탄소수소이온농도
1.0000.0310.247-0.231-0.099-0.038-0.010-0.0570.029-0.0330.0890.000
수심0.0311.0000.164-0.164-0.0460.0550.150-0.1560.094-0.0300.0430.387
수온0.2470.1641.000-0.8130.1510.3400.442-0.1900.2900.4000.0940.018
용존산소농도-0.231-0.164-0.8131.000-0.054-0.254-0.3810.161-0.254-0.3200.0700.046
생물화학적산소요구량-0.099-0.0460.151-0.0541.0000.3900.3080.0570.2700.4010.0810.064
화학적산소요구량-0.0380.0550.340-0.2540.3901.0000.6090.0520.5320.6440.0710.049
부유물질-0.0100.1500.442-0.3810.3080.6091.0000.0510.5470.5140.0240.000
총질소-0.057-0.156-0.1900.1610.0570.0520.0511.0000.2590.034-0.0530.076
총인0.0290.0940.290-0.2540.2700.5320.5470.2591.0000.4150.0390.039
총유기탄소-0.033-0.0300.400-0.3200.4010.6440.5140.0340.4151.0000.0350.080
수소이온농도0.0890.0430.0940.0700.0810.0710.024-0.0530.0390.0351.0000.071
0.0000.3870.0180.0460.0640.0490.0000.0760.0390.0800.0711.000

Missing values

2023-12-13T00:57:56.376284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:57:56.631533image/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.
2023-12-13T00:57:57.166635image/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

측정소명수심수온용존산소농도생물화학적산소요구량화학적산소요구량부유물질총질소총인총유기탄소수소이온농도
0송천120181<NA>8.212.91.63.161.17.080.0992.58.9
1송천120182<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2송천120183<NA>3.013.11.47.024.66.3160.0943.37.9
3송천120184<NA>13.89.91.02.922.23.2860.0532.27.9
4송천120185<NA>8.811.71.63.14.72.9110.0422.28.3
5송천120186<NA>23.710.21.73.76.03.8190.0642.88.6
6송천120187<NA>19.88.71.05.45.72.8270.0662.18.3
7송천120188<NA>20.78.30.23.515.61.8630.0891.08.0
8송천120189<NA>19.58.50.83.36.34.9210.0540.88.7
9송천1201810<NA>8.510.80.92.023.43.970.1021.48.1
측정소명수심수온용존산소농도생물화학적산소요구량화학적산소요구량부유물질총질소총인총유기탄소수소이온농도
5675황지220223<NA>6.711.41.84.75.34.0950.0633.47.9
5676황지220224<NA>15.910.92.23.73.03.7010.0353.38.7
5677황지220225<NA>17.511.11.63.33.83.6780.0512.18.7
5678황지220226<NA>16.110.71.12.82.12.880.0711.88.3
5679황지220227<NA>21.08.83.03.614.02.490.1252.78.5
5680황지220228<NA>22.09.21.22.81.42.7090.071.78.5
5681황지220229<NA>18.38.92.64.07.92.3020.1112.47.9
5682황지2202210<NA>13.111.51.21.81.32.5020.0141.28.7
5683황지2202211<NA>11.211.50.92.31.22.7470.0281.48.4
5684황지2202212<NA>1.714.22.32.31.53.4860.0721.38.4