Overview

Dataset statistics

Number of variables10
Number of observations51
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory90.6 B

Variable types

Categorical2
Text2
Numeric6

Alerts

기준년도 has constant value ""Constant
기준월 has constant value ""Constant
TOC is highly overall correlated with BOD and 4 other fieldsHigh correlation
BOD is highly overall correlated with TOC and 4 other fieldsHigh correlation
SS is highly overall correlated with TOC and 4 other fieldsHigh correlation
T-N is highly overall correlated with TOC and 4 other fieldsHigh correlation
T-P is highly overall correlated with TOC and 4 other fieldsHigh correlation
색도 is highly overall correlated with TOC and 4 other fieldsHigh correlation
시료번호 has unique valuesUnique
조사지점 has unique valuesUnique

Reproduction

Analysis started2024-04-14 04:44:56.231333
Analysis finished2024-04-14 04:45:01.341741
Duration5.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024
51 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 51
100.0%

Length

2024-04-14T13:45:01.391443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T13:45:01.456984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024 51
100.0%

기준월
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
3
51 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 51
100.0%

Length

2024-04-14T13:45:01.526003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T13:45:01.590714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 51
100.0%

시료번호
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024-04-14T13:45:01.742910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length3.7843137
Min length3

Characters and Unicode

Total characters193
Distinct characters63
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

Unique51 ?
Unique (%)100.0%

Sample

1st row포천천1
2nd row포천천2
3rd row포천천3
4th row금현천
5th row송우천
ValueCountFrequency (%)
포천천1 1
 
2.0%
신천18 1
 
2.0%
능안천 1
 
2.0%
홍죽천 1
 
2.0%
연곡천 1
 
2.0%
우고천 1
 
2.0%
석우천 1
 
2.0%
효촌천 1
 
2.0%
입암천 1
 
2.0%
덕계천 1
 
2.0%
Other values (41) 41
80.4%
2024-04-14T13:45:02.009161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 45
23.3%
1 14
 
7.3%
신 12
 
6.2%
강 8
 
4.1%
탄 8
 
4.1%
한 8
 
4.1%
2 7
 
3.6%
3 5
 
2.6%
영 5
 
2.6%
평 5
 
2.6%
Other values (53) 76
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
78.2%
Decimal Number 37
 
19.2%
Dash Punctuation 2
 
1.0%
Connector Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%
Open Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 45
29.8%
신 12
 
7.9%
강 8
 
5.3%
탄 8
 
5.3%
한 8
 
5.3%
영 5
 
3.3%
평 5
 
3.3%
연 5
 
3.3%
암 4
 
2.6%
우 3
 
2.0%
Other values (40) 48
31.8%
Decimal Number
ValueCountFrequency (%)
1 14
37.8%
2 7
18.9%
3 5
 
13.5%
5 3
 
8.1%
8 2
 
5.4%
7 2
 
5.4%
4 2
 
5.4%
0 1
 
2.7%
9 1
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151
78.2%
Common 42
 
21.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 45
29.8%
신 12
 
7.9%
강 8
 
5.3%
탄 8
 
5.3%
한 8
 
5.3%
영 5
 
3.3%
평 5
 
3.3%
연 5
 
3.3%
암 4
 
2.6%
우 3
 
2.0%
Other values (40) 48
31.8%
Common
ValueCountFrequency (%)
1 14
33.3%
2 7
16.7%
3 5
 
11.9%
5 3
 
7.1%
8 2
 
4.8%
- 2
 
4.8%
7 2
 
4.8%
4 2
 
4.8%
_ 1
 
2.4%
0 1
 
2.4%
Other values (3) 3
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
78.2%
ASCII 42
 
21.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 45
29.8%
신 12
 
7.9%
강 8
 
5.3%
탄 8
 
5.3%
한 8
 
5.3%
영 5
 
3.3%
평 5
 
3.3%
연 5
 
3.3%
암 4
 
2.6%
우 3
 
2.0%
Other values (40) 48
31.8%
ASCII
ValueCountFrequency (%)
1 14
33.3%
2 7
16.7%
3 5
 
11.9%
5 3
 
7.1%
8 2
 
4.8%
- 2
 
4.8%
7 2
 
4.8%
4 2
 
4.8%
_ 1
 
2.4%
0 1
 
2.4%
Other values (3) 3
 
7.1%

조사지점
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024-04-14T13:45:02.189198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length24.098039
Min length14

Characters and Unicode

Total characters1229
Distinct characters144
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)100.0%

Sample

1st row포천시 소흘읍 무봉리 이동교리교 (포천천 상류,부인터사거리 뒤)
2nd row포천시 신읍동 한내교 아래(실제 가스충전소 뒤/중류)
3rd row포천시 영중면 거사3리 농본교(포천천 하류)
4th row포천시 가산면 마산리 금현천교 아래(세창아파트 뒤)
5th row포천시 송우리 641-92(송우교 아래)
ValueCountFrequency (%)
포천시 18
 
6.9%
양주시 17
 
6.6%
연천군 10
 
3.9%
동두천시 6
 
2.3%
전곡읍 5
 
1.9%
청산면 5
 
1.9%
영중면 5
 
1.9%
합류전 5
 
1.9%
광적면 4
 
1.5%
4
 
1.5%
Other values (136) 180
69.5%
2024-04-14T13:45:02.478561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
208
 
16.9%
천 64
 
5.2%
리 46
 
3.7%
1 45
 
3.7%
시 42
 
3.4%
- 36
 
2.9%
) 36
 
2.9%
( 36
 
2.9%
면 30
 
2.4%
교 27
 
2.2%
Other values (134) 659
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 750
61.0%
Space Separator 208
 
16.9%
Decimal Number 154
 
12.5%
Dash Punctuation 36
 
2.9%
Close Punctuation 36
 
2.9%
Open Punctuation 36
 
2.9%
Other Punctuation 7
 
0.6%
Connector Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 64
 
8.5%
리 46
 
6.1%
시 42
 
5.6%
면 30
 
4.0%
교 27
 
3.6%
동 26
 
3.5%
포 22
 
2.9%
류 20
 
2.7%
전 19
 
2.5%
양 18
 
2.4%
Other values (117) 436
58.1%
Decimal Number
ValueCountFrequency (%)
1 45
29.2%
2 17
 
11.0%
3 16
 
10.4%
4 15
 
9.7%
6 15
 
9.7%
0 12
 
7.8%
5 11
 
7.1%
9 9
 
5.8%
8 7
 
4.5%
7 7
 
4.5%
Other Punctuation
ValueCountFrequency (%)
/ 4
57.1%
, 3
42.9%
Space Separator
ValueCountFrequency (%)
208
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 750
61.0%
Common 479
39.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 64
 
8.5%
리 46
 
6.1%
시 42
 
5.6%
면 30
 
4.0%
교 27
 
3.6%
동 26
 
3.5%
포 22
 
2.9%
류 20
 
2.7%
전 19
 
2.5%
양 18
 
2.4%
Other values (117) 436
58.1%
Common
ValueCountFrequency (%)
208
43.4%
1 45
 
9.4%
- 36
 
7.5%
) 36
 
7.5%
( 36
 
7.5%
2 17
 
3.5%
3 16
 
3.3%
4 15
 
3.1%
6 15
 
3.1%
0 12
 
2.5%
Other values (7) 43
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 750
61.0%
ASCII 479
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
208
43.4%
1 45
 
9.4%
- 36
 
7.5%
) 36
 
7.5%
( 36
 
7.5%
2 17
 
3.5%
3 16
 
3.3%
4 15
 
3.1%
6 15
 
3.1%
0 12
 
2.5%
Other values (7) 43
 
9.0%
Hangul
ValueCountFrequency (%)
천 64
 
8.5%
리 46
 
6.1%
시 42
 
5.6%
면 30
 
4.0%
교 27
 
3.6%
동 26
 
3.5%
포 22
 
2.9%
류 20
 
2.7%
전 19
 
2.5%
양 18
 
2.4%
Other values (117) 436
58.1%

TOC
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0843137
Minimum0.8
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-04-14T13:45:02.772820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.1
Q12.1
median3.1
Q35.35
95-th percentile9
Maximum17
Range16.2
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation3.0416688
Coefficient of variation (CV)0.74471967
Kurtosis5.231125
Mean4.0843137
Median Absolute Deviation (MAD)1.5
Skewness1.8803554
Sum208.3
Variance9.251749
MonotonicityNot monotonic
2024-04-14T13:45:02.880161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2.1 3
 
5.9%
1.3 3
 
5.9%
2.4 2
 
3.9%
8.2 2
 
3.9%
3.8 2
 
3.9%
5.4 2
 
3.9%
5.3 2
 
3.9%
1.1 2
 
3.9%
2.5 2
 
3.9%
2.2 2
 
3.9%
Other values (27) 29
56.9%
ValueCountFrequency (%)
0.8 1
 
2.0%
0.9 1
 
2.0%
1.1 2
3.9%
1.3 3
5.9%
1.4 1
 
2.0%
1.6 1
 
2.0%
1.7 1
 
2.0%
1.8 1
 
2.0%
2.0 1
 
2.0%
2.1 3
5.9%
ValueCountFrequency (%)
17.0 1
2.0%
9.7 1
2.0%
9.6 1
2.0%
8.4 1
2.0%
8.2 2
3.9%
7.7 1
2.0%
7.2 1
2.0%
7.1 2
3.9%
5.5 1
2.0%
5.4 2
3.9%

BOD
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5352941
Minimum0.2
Maximum12.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-04-14T13:45:02.976737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.5
Q11.35
median2
Q33.05
95-th percentile5.35
Maximum12.1
Range11.9
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation2.0316322
Coefficient of variation (CV)0.80133985
Kurtosis8.9663657
Mean2.5352941
Median Absolute Deviation (MAD)0.8
Skewness2.4211382
Sum129.3
Variance4.1275294
MonotonicityNot monotonic
2024-04-14T13:45:03.072628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1.8 3
 
5.9%
1.2 3
 
5.9%
1.4 3
 
5.9%
0.7 3
 
5.9%
1.7 3
 
5.9%
2.3 2
 
3.9%
4.4 2
 
3.9%
3.0 2
 
3.9%
2.4 2
 
3.9%
0.5 2
 
3.9%
Other values (23) 26
51.0%
ValueCountFrequency (%)
0.2 1
 
2.0%
0.3 1
 
2.0%
0.5 2
3.9%
0.7 3
5.9%
0.8 1
 
2.0%
0.9 1
 
2.0%
1.2 3
5.9%
1.3 1
 
2.0%
1.4 3
5.9%
1.5 1
 
2.0%
ValueCountFrequency (%)
12.1 1
2.0%
7.1 1
2.0%
5.4 1
2.0%
5.3 1
2.0%
4.8 1
2.0%
4.5 2
3.9%
4.4 2
3.9%
4.2 1
2.0%
4.0 1
2.0%
3.2 1
2.0%

SS
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0607843
Minimum1.5
Maximum84.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-04-14T13:45:03.182602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.75
Q13.4
median6.2
Q38.6
95-th percentile14.8
Maximum84.8
Range83.3
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation14.049357
Coefficient of variation (CV)1.5505674
Kurtosis21.628475
Mean9.0607843
Median Absolute Deviation (MAD)2.6
Skewness4.5544623
Sum462.1
Variance197.38443
MonotonicityNot monotonic
2024-04-14T13:45:03.276358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2.4 3
 
5.9%
2.7 3
 
5.9%
6.2 2
 
3.9%
7.6 2
 
3.9%
1.6 2
 
3.9%
5.5 2
 
3.9%
4.4 2
 
3.9%
8.4 2
 
3.9%
6.3 1
 
2.0%
2.1 1
 
2.0%
Other values (31) 31
60.8%
ValueCountFrequency (%)
1.5 1
 
2.0%
1.6 2
3.9%
1.9 1
 
2.0%
2.0 1
 
2.0%
2.1 1
 
2.0%
2.4 3
5.9%
2.7 3
5.9%
3.2 1
 
2.0%
3.6 1
 
2.0%
3.7 1
 
2.0%
ValueCountFrequency (%)
84.8 1
2.0%
65.2 1
2.0%
15.6 1
2.0%
14.0 1
2.0%
13.4 1
2.0%
12.5 1
2.0%
12.0 1
2.0%
11.5 1
2.0%
10.6 1
2.0%
10.2 1
2.0%

T-N
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8251373
Minimum1.758
Maximum47.558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-04-14T13:45:03.388449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.758
5-th percentile2.664
Q14.6245
median6.526
Q39.4865
95-th percentile13.988
Maximum47.558
Range45.8
Interquartile range (IQR)4.862

Descriptive statistics

Standard deviation6.6314771
Coefficient of variation (CV)0.84745824
Kurtosis26.201724
Mean7.8251373
Median Absolute Deviation (MAD)2.392
Skewness4.5190755
Sum399.082
Variance43.976488
MonotonicityNot monotonic
2024-04-14T13:45:03.497540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.869 2
 
3.9%
1.758 1
 
2.0%
5.752 1
 
2.0%
9.831 1
 
2.0%
9.968 1
 
2.0%
3.577 1
 
2.0%
8.455 1
 
2.0%
8.63 1
 
2.0%
9.461 1
 
2.0%
1.887 1
 
2.0%
Other values (40) 40
78.4%
ValueCountFrequency (%)
1.758 1
2.0%
1.887 1
2.0%
2.437 1
2.0%
2.891 1
2.0%
2.912 1
2.0%
3.028 1
2.0%
3.067 1
2.0%
3.386 1
2.0%
3.512 1
2.0%
3.577 1
2.0%
ValueCountFrequency (%)
47.558 1
2.0%
19.257 1
2.0%
14.487 1
2.0%
13.489 1
2.0%
11.968 1
2.0%
11.354 1
2.0%
9.968 1
2.0%
9.963 1
2.0%
9.844 1
2.0%
9.831 1
2.0%

T-P
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.076294118
Minimum0.011
Maximum0.521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-04-14T13:45:03.598938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.011
5-th percentile0.016
Q10.0285
median0.059
Q30.0965
95-th percentile0.17
Maximum0.521
Range0.51
Interquartile range (IQR)0.068

Descriptive statistics

Standard deviation0.081973482
Coefficient of variation (CV)1.0744404
Kurtosis17.625322
Mean0.076294118
Median Absolute Deviation (MAD)0.033
Skewness3.6871375
Sum3.891
Variance0.0067196518
MonotonicityNot monotonic
2024-04-14T13:45:03.730141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.026 3
 
5.9%
0.017 3
 
5.9%
0.035 2
 
3.9%
0.031 2
 
3.9%
0.033 1
 
2.0%
0.021 1
 
2.0%
0.521 1
 
2.0%
0.134 1
 
2.0%
0.087 1
 
2.0%
0.125 1
 
2.0%
Other values (35) 35
68.6%
ValueCountFrequency (%)
0.011 1
 
2.0%
0.013 1
 
2.0%
0.015 1
 
2.0%
0.017 3
5.9%
0.021 1
 
2.0%
0.022 1
 
2.0%
0.023 1
 
2.0%
0.026 3
5.9%
0.028 1
 
2.0%
0.029 1
 
2.0%
ValueCountFrequency (%)
0.521 1
2.0%
0.29 1
2.0%
0.206 1
2.0%
0.134 1
2.0%
0.128 1
2.0%
0.125 1
2.0%
0.115 1
2.0%
0.114 1
2.0%
0.113 1
2.0%
0.109 1
2.0%

색도
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.588235
Minimum5
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-04-14T13:45:03.822718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q18.5
median11
Q317
95-th percentile44.5
Maximum70
Range65
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation14.122573
Coefficient of variation (CV)0.85136076
Kurtosis4.3877008
Mean16.588235
Median Absolute Deviation (MAD)4
Skewness2.1028339
Sum846
Variance199.44706
MonotonicityNot monotonic
2024-04-14T13:45:03.903980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
9 6
11.8%
7 5
 
9.8%
12 5
 
9.8%
6 5
 
9.8%
10 4
 
7.8%
15 3
 
5.9%
11 3
 
5.9%
13 3
 
5.9%
8 2
 
3.9%
35 2
 
3.9%
Other values (13) 13
25.5%
ValueCountFrequency (%)
5 1
 
2.0%
6 5
9.8%
7 5
9.8%
8 2
 
3.9%
9 6
11.8%
10 4
7.8%
11 3
5.9%
12 5
9.8%
13 3
5.9%
14 1
 
2.0%
ValueCountFrequency (%)
70 1
2.0%
58 1
2.0%
51 1
2.0%
38 1
2.0%
36 1
2.0%
35 2
3.9%
34 1
2.0%
29 1
2.0%
28 1
2.0%
22 1
2.0%

Interactions

2024-04-14T13:45:00.796019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:57.913522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:58.618596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:59.339193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:59.921474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.385001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.863824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:58.105372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:58.743909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:59.445815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.009811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.470433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.925848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:58.216432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:58.873454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:59.557600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.093679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.537796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.985297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:58.307024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:58.981751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:59.654321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.166098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.594552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:01.042852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:58.395006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:59.097871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:59.758157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.239313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.652337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:01.105859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:58.504131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:59.222273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:44:59.840323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.313983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:45:00.724380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-14T13:45:03.971047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시료번호조사지점TOCBODSST-NT-P색도
시료번호1.0001.0001.0001.0001.0001.0001.0001.000
조사지점1.0001.0001.0001.0001.0001.0001.0001.000
TOC1.0001.0001.0000.9660.4780.7540.7160.828
BOD1.0001.0000.9661.0000.4420.7070.6870.768
SS1.0001.0000.4780.4421.0000.2090.7640.799
T-N1.0001.0000.7540.7070.2091.0000.7200.608
T-P1.0001.0000.7160.6870.7640.7201.0000.801
색도1.0001.0000.8280.7680.7990.6080.8011.000
2024-04-14T13:45:04.053437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TOCBODSST-NT-P색도
TOC1.0000.9680.6330.6960.6950.887
BOD0.9681.0000.6400.6770.6690.837
SS0.6330.6401.0000.5600.5160.540
T-N0.6960.6770.5601.0000.6570.639
T-P0.6950.6690.5160.6571.0000.552
색도0.8870.8370.5400.6390.5521.000

Missing values

2024-04-14T13:45:01.196236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T13:45:01.298513image/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

기준년도기준월시료번호조사지점TOCBODSST-NT-P색도
020243포천천1포천시 소흘읍 무봉리 이동교리교 (포천천 상류,부인터사거리 뒤)2.11.36.31.7580.0338
120243포천천2포천시 신읍동 한내교 아래(실제 가스충전소 뒤/중류)4.22.86.27.0790.06912
220243포천천3포천시 영중면 거사3리 농본교(포천천 하류)3.12.16.95.9570.03515
320243금현천포천시 가산면 마산리 금현천교 아래(세창아파트 뒤)8.44.565.25.8280.2938
420243송우천포천시 송우리 641-92(송우교 아래)4.83.13.713.4890.0620
520243고모천포천시 가산면 방축리 수정교 아래(소흘처리장 옆)3.02.03.64.1180.07612
620243설운천포천시 선단동 89-10(선단교 아래)2.01.24.95.5190.0299
720243영평천1포천시 이동면 노곡리 낭유대교 아래(영평천 상류)0.80.21.54.4470.0265
820243영평천2포천시 영중면 성동리 와룡교 아래(수입천 합류 후)1.30.72.44.8020.0267
920243영평천3포천시 영중면 영송리 영평교 아래(포천천 합류 후)2.41.72.75.8690.03110
기준년도기준월시료번호조사지점TOCBODSST-NT-P색도
4120243대전천연천군 청산면 대전리 564-51.10.51.64.8260.0316
4220243한탄강2포천시 관인면 냉정리 용담교 아래(관인면 냉정리 산118-1)1.10.52.42.4370.0156
4320243한탄강1포천시 창수면 운산리 영로교 아래0.90.31.63.0280.0116
4420243한탄강연1연천군 전곡읍 신답리 1(영평천 합류전)1.30.72.72.8910.01710
4520243한탄강연2연천군 청산면 궁평리 561-1(영평천 합류후)1.40.83.82.9120.0139
4620243한탄강연3연천군 전곡읍 전곡리 413-8(고탄교)1.60.92.43.3860.0179
4720243한탄강(분리둑)연천군 전곡읍 전곡리 681-16(사랑교 / 신천 )7.24.413.49.9630.09536
4820243한탄강연천군 전곡읍 전곡리 681-16(사랑교 / 한탄강 본류)4.01.87.66.5260.04922
4920243한탄강연4연천군 전곡읍 전곡리 681-25.32.36.17.5890.06128
5020243자일천포천시 영북면 자일리 1039-13.52.46.87.8280.05913