Overview

Dataset statistics

Number of variables11
Number of observations6753
Missing cells14090
Missing cells (%)19.0%
Duplicate rows487
Duplicate rows (%)7.2%
Total size in memory626.6 KiB
Average record size in memory95.0 B

Variable types

Categorical5
Numeric6

Dataset

Description측정지점명,지점번호,지점구분,측정년,측정월,측정회차,수온,ph,do,남조류세포수,경보발령
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15606/S/1/datasetView.do

Alerts

Dataset has 487 (7.2%) duplicate rowsDuplicates
측정지점명 is highly overall correlated with 지점번호 and 1 other fieldsHigh correlation
지점구분 is highly overall correlated with 측정지점명 and 1 other fieldsHigh correlation
지점번호 is highly overall correlated with 측정지점명 and 1 other fieldsHigh correlation
수온 is highly overall correlated with do and 1 other fieldsHigh correlation
ph is highly overall correlated with doHigh correlation
do is highly overall correlated with 수온 and 1 other fieldsHigh correlation
남조류세포수 is highly overall correlated with 수온High correlation
경보발령 is highly imbalanced (90.4%)Imbalance
수온 has 3028 (44.8%) missing valuesMissing
ph has 5199 (77.0%) missing valuesMissing
do has 5199 (77.0%) missing valuesMissing
남조류세포수 has 664 (9.8%) missing valuesMissing
남조류세포수 is highly skewed (γ1 = 34.31794041)Skewed
남조류세포수 has 3138 (46.5%) zerosZeros

Reproduction

Analysis started2024-03-13 18:44:02.650036
Analysis finished2024-03-13 18:44:06.346887
Duration3.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정지점명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.9 KiB
성산대교
794 
마포대교
791 
한강대교
791 
한남대교
791 
성수대교
790 
Other values (4)
2796 

Length

Max length4
Median length4
Mean length3.8964904
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강동대교
2nd row광진교
3rd row잠실철교
4th row미사대교
5th row마포대교

Common Values

ValueCountFrequency (%)
성산대교 794
11.8%
마포대교 791
11.7%
한강대교 791
11.7%
한남대교 791
11.7%
성수대교 790
11.7%
강동대교 699
10.4%
광진교 699
10.4%
잠실철교 699
10.4%
미사대교 699
10.4%

Length

2024-03-14T03:44:06.397232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T03:44:06.491589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성산대교 794
11.8%
마포대교 791
11.7%
한강대교 791
11.7%
한남대교 791
11.7%
성수대교 790
11.7%
강동대교 699
10.4%
광진교 699
10.4%
잠실철교 699
10.4%
미사대교 699
10.4%

지점번호
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.9 KiB
1018G10
794 
1018G09
791 
1018G08
791 
1018G07
791 
1018G06
790 
Other values (4)
2796 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1018G02
2nd row1018G05
3rd row1018G04
4th row1018G01
5th row1018G09

Common Values

ValueCountFrequency (%)
1018G10 794
11.8%
1018G09 791
11.7%
1018G08 791
11.7%
1018G07 791
11.7%
1018G06 790
11.7%
1018G02 699
10.4%
1018G05 699
10.4%
1018G04 699
10.4%
1018G01 699
10.4%

Length

2024-03-14T03:44:06.614615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T03:44:06.704172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1018g10 794
11.8%
1018g09 791
11.7%
1018g08 791
11.7%
1018g07 791
11.7%
1018g06 790
11.7%
1018g02 699
10.4%
1018g05 699
10.4%
1018g04 699
10.4%
1018g01 699
10.4%

지점구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.9 KiB
2
3964 
1
2789 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 3964
58.7%
1 2789
41.3%

Length

2024-03-14T03:44:06.819371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T03:44:06.896687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3964
58.7%
1 2789
41.3%

측정년
Real number (ℝ)

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.5645
Minimum2011
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2024-03-14T03:44:06.963820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12014
median2018
Q32021
95-th percentile2023
Maximum2023
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.8501278
Coefficient of variation (CV)0.0019083047
Kurtosis-1.1518054
Mean2017.5645
Median Absolute Deviation (MAD)3
Skewness-0.13812484
Sum13624613
Variance14.823484
MonotonicityDecreasing
2024-03-14T03:44:07.052131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2019 1286
19.0%
2023 1152
17.1%
2015 531
7.9%
2012 468
 
6.9%
2011 459
 
6.8%
2013 450
 
6.7%
2016 422
 
6.2%
2018 376
 
5.6%
2014 368
 
5.4%
2017 360
 
5.3%
Other values (3) 881
13.0%
ValueCountFrequency (%)
2011 459
 
6.8%
2012 468
 
6.9%
2013 450
 
6.7%
2014 368
 
5.4%
2015 531
7.9%
2016 422
 
6.2%
2017 360
 
5.3%
2018 376
 
5.6%
2019 1286
19.0%
2020 340
 
5.0%
ValueCountFrequency (%)
2023 1152
17.1%
2022 250
 
3.7%
2021 291
 
4.3%
2020 340
 
5.0%
2019 1286
19.0%
2018 376
 
5.6%
2017 360
 
5.3%
2016 422
 
6.2%
2015 531
7.9%
2014 368
 
5.4%

측정월
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9487635
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2024-03-14T03:44:07.145689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median7
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7198986
Coefficient of variation (CV)0.39142195
Kurtosis-0.58531282
Mean6.9487635
Median Absolute Deviation (MAD)2
Skewness-0.28378311
Sum46925
Variance7.3978484
MonotonicityNot monotonic
2024-03-14T03:44:07.224670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 1131
16.7%
7 831
12.3%
5 717
10.6%
9 709
10.5%
6 705
10.4%
10 697
10.3%
4 594
8.8%
11 428
 
6.3%
3 262
 
3.9%
1 254
 
3.8%
Other values (2) 425
 
6.3%
ValueCountFrequency (%)
1 254
 
3.8%
2 233
 
3.5%
3 262
 
3.9%
4 594
8.8%
5 717
10.6%
6 705
10.4%
7 831
12.3%
8 1131
16.7%
9 709
10.5%
10 697
10.3%
ValueCountFrequency (%)
12 192
 
2.8%
11 428
 
6.3%
10 697
10.3%
9 709
10.5%
8 1131
16.7%
7 831
12.3%
6 705
10.4%
5 717
10.6%
4 594
8.8%
3 262
 
3.9%

측정회차
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.9 KiB
3회차
1573 
4회차
1521 
1회차
1507 
2회차
1487 
5회차
569 
Other values (4)
 
96

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1회차
2nd row1회차
3rd row1회차
4th row1회차
5th row2회차

Common Values

ValueCountFrequency (%)
3회차 1573
23.3%
4회차 1521
22.5%
1회차 1507
22.3%
2회차 1487
22.0%
5회차 569
 
8.4%
6회차 36
 
0.5%
7회차 36
 
0.5%
8회차 14
 
0.2%
9회차 10
 
0.1%

Length

2024-03-14T03:44:07.323977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T03:44:07.434830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3회차 1573
23.3%
4회차 1521
22.5%
1회차 1507
22.3%
2회차 1487
22.0%
5회차 569
 
8.4%
6회차 36
 
0.5%
7회차 36
 
0.5%
8회차 14
 
0.2%
9회차 10
 
0.1%

수온
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct300
Distinct (%)8.1%
Missing3028
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean18.40502
Minimum0.6
Maximum32.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2024-03-14T03:44:07.824227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile3.4
Q114.1
median20.6
Q323.9
95-th percentile26.8
Maximum32.2
Range31.6
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation7.046487
Coefficient of variation (CV)0.3828568
Kurtosis-0.25505066
Mean18.40502
Median Absolute Deviation (MAD)4.3
Skewness-0.81304097
Sum68558.7
Variance49.65298
MonotonicityNot monotonic
2024-03-14T03:44:07.935122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.6 52
 
0.8%
25.2 52
 
0.8%
24.9 49
 
0.7%
23.5 43
 
0.6%
22.4 41
 
0.6%
25.0 41
 
0.6%
25.1 39
 
0.6%
22.8 39
 
0.6%
24.5 35
 
0.5%
22.0 34
 
0.5%
Other values (290) 3300
48.9%
(Missing) 3028
44.8%
ValueCountFrequency (%)
0.6 3
 
< 0.1%
0.7 1
 
< 0.1%
0.8 1
 
< 0.1%
0.9 2
 
< 0.1%
1.0 3
 
< 0.1%
1.1 2
 
< 0.1%
1.2 6
0.1%
1.3 5
0.1%
1.4 7
0.1%
1.5 9
0.1%
ValueCountFrequency (%)
32.2 1
 
< 0.1%
31.4 1
 
< 0.1%
30.7 1
 
< 0.1%
30.6 1
 
< 0.1%
30.5 2
< 0.1%
30.3 1
 
< 0.1%
30.2 1
 
< 0.1%
30.1 1
 
< 0.1%
29.9 3
< 0.1%
29.7 1
 
< 0.1%

ph
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)1.9%
Missing5199
Missing (%)77.0%
Infinite0
Infinite (%)0.0%
Mean7.8933719
Minimum6.4
Maximum9.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2024-03-14T03:44:08.026891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile7.3
Q17.6
median7.9
Q38.1
95-th percentile8.6
Maximum9.5
Range3.1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.419227
Coefficient of variation (CV)0.053111269
Kurtosis0.5004877
Mean7.8933719
Median Absolute Deviation (MAD)0.3
Skewness0.39012718
Sum12266.3
Variance0.17575128
MonotonicityNot monotonic
2024-03-14T03:44:08.117751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7.9 166
 
2.5%
8.0 155
 
2.3%
7.8 147
 
2.2%
7.7 137
 
2.0%
7.5 135
 
2.0%
8.1 109
 
1.6%
7.6 109
 
1.6%
7.4 95
 
1.4%
8.3 92
 
1.4%
8.2 89
 
1.3%
Other values (20) 320
 
4.7%
(Missing) 5199
77.0%
ValueCountFrequency (%)
6.4 1
 
< 0.1%
6.5 1
 
< 0.1%
6.7 2
 
< 0.1%
6.8 4
 
0.1%
6.9 4
 
0.1%
7.0 6
 
0.1%
7.1 18
 
0.3%
7.2 20
 
0.3%
7.3 59
0.9%
7.4 95
1.4%
ValueCountFrequency (%)
9.5 1
 
< 0.1%
9.3 5
 
0.1%
9.2 2
 
< 0.1%
9.1 7
 
0.1%
9.0 12
 
0.2%
8.9 8
 
0.1%
8.8 12
 
0.2%
8.7 25
0.4%
8.6 30
0.4%
8.5 40
0.6%

do
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct76
Distinct (%)4.9%
Missing5199
Missing (%)77.0%
Infinite0
Infinite (%)0.0%
Mean9.4088803
Minimum6.1
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2024-03-14T03:44:08.246527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile7.4
Q18.4
median9.1
Q310.2
95-th percentile12.2
Maximum15.5
Range9.4
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.4018793
Coefficient of variation (CV)0.14899534
Kurtosis0.074773013
Mean9.4088803
Median Absolute Deviation (MAD)0.8
Skewness0.6793393
Sum14621.4
Variance1.9652656
MonotonicityNot monotonic
2024-03-14T03:44:08.352206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.1 71
 
1.1%
9.2 64
 
0.9%
8.9 56
 
0.8%
8.5 56
 
0.8%
9.0 55
 
0.8%
8.3 53
 
0.8%
8.6 52
 
0.8%
8.2 51
 
0.8%
8.8 50
 
0.7%
8.4 46
 
0.7%
Other values (66) 1000
 
14.8%
(Missing) 5199
77.0%
ValueCountFrequency (%)
6.1 1
 
< 0.1%
6.3 1
 
< 0.1%
6.4 2
 
< 0.1%
6.5 1
 
< 0.1%
6.6 2
 
< 0.1%
6.8 2
 
< 0.1%
6.9 4
 
0.1%
7.0 8
0.1%
7.1 14
0.2%
7.2 1
 
< 0.1%
ValueCountFrequency (%)
15.5 1
 
< 0.1%
13.8 1
 
< 0.1%
13.7 1
 
< 0.1%
13.6 1
 
< 0.1%
13.5 1
 
< 0.1%
13.4 1
 
< 0.1%
13.3 2
 
< 0.1%
13.2 1
 
< 0.1%
13.1 6
0.1%
12.9 3
< 0.1%

남조류세포수
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct804
Distinct (%)13.2%
Missing664
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean408.27164
Minimum0
Maximum150500
Zeros3138
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2024-03-14T03:44:08.454660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3176
95-th percentile1326
Maximum150500
Range150500
Interquartile range (IQR)176

Descriptive statistics

Standard deviation2904.8565
Coefficient of variation (CV)7.1150093
Kurtosis1557.8873
Mean408.27164
Median Absolute Deviation (MAD)0
Skewness34.31794
Sum2485966
Variance8438191.3
MonotonicityNot monotonic
2024-03-14T03:44:08.557716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3138
46.5%
30 52
 
0.8%
20 47
 
0.7%
13 33
 
0.5%
10 32
 
0.5%
50 31
 
0.5%
25 26
 
0.4%
38 26
 
0.4%
80 25
 
0.4%
15 25
 
0.4%
Other values (794) 2654
39.3%
(Missing) 664
 
9.8%
ValueCountFrequency (%)
0 3138
46.5%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 6
 
0.1%
6 17
 
0.3%
7 5
 
0.1%
8 6
 
0.1%
9 9
 
0.1%
10 32
 
0.5%
11 8
 
0.1%
ValueCountFrequency (%)
150500 1
< 0.1%
112828 1
< 0.1%
37911 1
< 0.1%
36382 1
< 0.1%
34450 1
< 0.1%
32791 1
< 0.1%
27076 1
< 0.1%
25779 1
< 0.1%
24347 1
< 0.1%
22720 1
< 0.1%

경보발령
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.9 KiB
정상
6623 
관심
 
101
경계
 
29

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 6623
98.1%
관심 101
 
1.5%
경계 29
 
0.4%

Length

2024-03-14T03:44:08.656478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T03:44:08.729255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 6623
98.1%
관심 101
 
1.5%
경계 29
 
0.4%

Interactions

2024-03-14T03:44:05.596461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:03.385617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:03.803384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.254622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.687969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.152315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.668434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:03.453756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:03.874861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.331750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.763600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.260294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.737562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:03.520401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:03.943465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.412957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.830258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.329830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.811382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:03.589495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.020827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.482553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.899899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.393072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.884451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:03.663722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.101366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.551428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.989208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.463962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.952564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:03.729982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.181715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:04.615672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.057509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T03:44:05.528993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T03:44:08.784526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정지점명지점번호지점구분측정년측정월측정회차수온phdo남조류세포수경보발령
측정지점명1.0001.0000.9640.0960.1300.0000.1530.2540.1880.0410.000
지점번호1.0001.0000.9640.0960.1300.0000.1530.2540.1880.0410.000
지점구분0.9640.9641.0000.2050.2540.0170.3020.2380.1900.0290.020
측정년0.0960.0960.2051.0000.4730.3050.4870.3680.3640.1290.405
측정월0.1300.1300.2540.4731.0000.1670.9120.6400.7750.0420.204
측정회차0.0000.0000.0170.3050.1671.0000.1920.2410.3110.1490.477
수온0.1530.1530.3020.4870.9120.1921.0000.4180.6810.1610.334
ph0.2540.2540.2380.3680.6400.2410.4181.0000.610NaN0.101
do0.1880.1880.1900.3640.7750.3110.6810.6101.000NaN0.040
남조류세포수0.0410.0410.0290.1290.0420.1490.161NaNNaN1.0000.317
경보발령0.0000.0000.0200.4050.2040.4770.3340.1010.0400.3171.000
2024-03-14T03:44:08.884852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정지점명지점구분지점번호측정회차경보발령
측정지점명1.0000.9941.0000.0000.000
지점구분0.9941.0000.9940.0170.032
지점번호1.0000.9941.0000.0000.000
측정회차0.0000.0170.0001.0000.239
경보발령0.0000.0320.0000.2391.000
2024-03-14T03:44:08.967781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정년측정월수온phdo남조류세포수측정지점명지점번호지점구분측정회차경보발령
측정년1.0000.0660.217-0.0430.0040.1250.0430.0430.1530.1440.267
측정월0.0661.0000.222-0.145-0.1190.3570.0590.0590.1950.0760.123
수온0.2170.2221.000-0.433-0.7220.5910.0700.0700.2310.0890.212
ph-0.043-0.145-0.4331.0000.579-0.1750.1180.1180.1820.1020.077
do0.004-0.119-0.7220.5791.000-0.3310.0870.0870.1490.1330.031
남조류세포수0.1250.3570.591-0.175-0.3311.0000.0230.0230.0350.0860.252
측정지점명0.0430.0590.0700.1180.0870.0231.0001.0000.9940.0000.000
지점번호0.0430.0590.0700.1180.0870.0231.0001.0000.9940.0000.000
지점구분0.1530.1950.2310.1820.1490.0350.9940.9941.0000.0170.032
측정회차0.1440.0760.0890.1020.1330.0860.0000.0000.0171.0000.239
경보발령0.2670.1230.2120.0770.0310.2520.0000.0000.0320.2391.000

Missing values

2024-03-14T03:44:06.059258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T03:44:06.190964image/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.
2024-03-14T03:44:06.294859image/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

측정지점명지점번호지점구분측정년측정월측정회차수온phdo남조류세포수경보발령
0강동대교1018G0212023121회차5.17.813.40정상
1광진교1018G0512023121회차5.17.813.50정상
2잠실철교1018G0412023121회차5.17.813.60정상
3미사대교1018G0112023121회차5.17.313.20정상
4마포대교1018G0922023112회차<NA><NA><NA>0정상
5성산대교1018G1022023112회차<NA><NA><NA>0정상
6미사대교1018G0112023111회차15.78.110.220정상
7강동대교1018G0212023111회차14.98.110.464정상
8광진교1018G0512023111회차14.88.110.30정상
9잠실철교1018G0412023111회차14.88.010.30정상
측정지점명지점번호지점구분측정년측정월측정회차수온phdo남조류세포수경보발령
6743광진교1018G051201114회차1.1<NA><NA>0정상
6744미사대교1018G011201115회차1.8<NA><NA>0정상
6745강동대교1018G021201115회차1.2<NA><NA>0정상
6746잠실철교1018G041201115회차1.7<NA><NA>0정상
6747광진교1018G051201115회차1.6<NA><NA>0정상
6748마포대교1018G092201114회차<NA><NA><NA><NA>정상
6749미사대교1018G011201111회차3.2<NA><NA>0정상
6750강동대교1018G021201111회차2.2<NA><NA>0정상
6751잠실철교1018G041201111회차2.6<NA><NA>0정상
6752광진교1018G051201111회차2.3<NA><NA>0정상

Duplicate rows

Most frequently occurring

측정지점명지점번호지점구분측정년측정월측정회차수온phdo남조류세포수경보발령# duplicates
102마포대교1018G092201973회차<NA><NA><NA>645정상15
103마포대교1018G092201974회차<NA><NA><NA>364정상15
104마포대교1018G092201981회차<NA><NA><NA>68정상15
105마포대교1018G092201982회차<NA><NA><NA>0정상15
106마포대교1018G092201983회차<NA><NA><NA>453정상15
107마포대교1018G092201984회차<NA><NA><NA>121정상15
208성산대교1018G102201973회차<NA><NA><NA>5419정상15
209성산대교1018G102201974회차<NA><NA><NA>1042정상15
210성산대교1018G102201981회차<NA><NA><NA>113정상15
211성산대교1018G102201982회차<NA><NA><NA>0정상15