Overview

Dataset statistics

Number of variables10
Number of observations35
Missing cells121
Missing cells (%)34.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory91.8 B

Variable types

Text2
Numeric5
Categorical3

Dataset

Description전라남도 보건환경연구원 홈페이지에 게시된 폐수 및 폐기물 관련 검사항목과 수수료에 관한 사항을 정리한 파일입니다.
Author전라남도
URLhttps://www.data.go.kr/data/15041963/fileData.do

Alerts

폐수 is highly overall correlated with 침출수 (29) and 5 other fieldsHigh correlation
침출수 (29) is highly overall correlated with 폐수 and 5 other fieldsHigh correlation
하수_분뇨 is highly overall correlated with 폐수 and 2 other fieldsHigh correlation
폐기물(12) is highly overall correlated with 폐수 and 2 other fieldsHigh correlation
폐기물(11) is highly overall correlated with 폐수 and 2 other fieldsHigh correlation
개인 하수 (2) is highly overall correlated with 폐수 and 2 other fieldsHigh correlation
폐기물(기타) is highly overall correlated with 폐수 and 1 other fieldsHigh correlation
개인 하수 (2) is highly imbalanced (76.5%)Imbalance
개인하수(BOD 제거율) is highly imbalanced (81.3%)Imbalance
폐기물(기타) is highly imbalanced (65.8%)Imbalance
폐수 has 7 (20.0%) missing valuesMissing
침출수 (29) has 8 (22.9%) missing valuesMissing
하수_분뇨 has 28 (80.0%) missing valuesMissing
폐기물(12) has 25 (71.4%) missing valuesMissing
폐기물(11) has 26 (74.3%) missing valuesMissing
폐기물(7) has 27 (77.1%) missing valuesMissing
검사항목 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:58:21.510741
Analysis finished2023-12-12 16:58:24.524372
Duration3.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

검사항목
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-13T01:58:24.667532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length7.1428571
Min length2

Characters and Unicode

Total characters250
Distinct characters118
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row수소이온농도(pH)
2nd rowBOD
3rd rowBOD 제거율
4th rowCOD
5th row색도(투과율법)
ValueCountFrequency (%)
bod 2
 
4.5%
수소이온농도(ph 1
 
2.3%
철(fe 1
 
2.3%
비소(as 1
 
2.3%
6가크롬(cr6 1
 
2.3%
수은(hg 1
 
2.3%
유기인 1
 
2.3%
pcb 1
 
2.3%
음이온계면활성제 1
 
2.3%
abs 1
 
2.3%
Other values (33) 33
75.0%
2023-12-13T01:58:24.994143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 24
 
9.6%
) 23
 
9.2%
C 9
 
3.6%
9
 
3.6%
수 5
 
2.0%
물 5
 
2.0%
소 5
 
2.0%
성 4
 
1.6%
질 4
 
1.6%
총 4
 
1.6%
Other values (108) 158
63.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
52.8%
Uppercase Letter 42
 
16.8%
Open Punctuation 24
 
9.6%
Close Punctuation 23
 
9.2%
Lowercase Letter 12
 
4.8%
Space Separator 9
 
3.6%
Decimal Number 2
 
0.8%
Dash Punctuation 2
 
0.8%
Other Punctuation 2
 
0.8%
Math Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 5
 
3.8%
물 5
 
3.8%
소 5
 
3.8%
성 4
 
3.0%
질 4
 
3.0%
총 4
 
3.0%
유 4
 
3.0%
류 3
 
2.3%
기 3
 
2.3%
이 3
 
2.3%
Other values (76) 92
69.7%
Uppercase Letter
ValueCountFrequency (%)
C 9
21.4%
P 4
9.5%
B 4
9.5%
S 3
 
7.1%
D 3
 
7.1%
O 3
 
7.1%
T 3
 
7.1%
H 3
 
7.1%
F 2
 
4.8%
N 2
 
4.8%
Other values (4) 6
14.3%
Lowercase Letter
ValueCountFrequency (%)
n 3
25.0%
r 2
16.7%
g 1
 
8.3%
e 1
 
8.3%
d 1
 
8.3%
s 1
 
8.3%
p 1
 
8.3%
b 1
 
8.3%
u 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
· 1
50.0%
, 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Decimal Number
ValueCountFrequency (%)
6 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
52.8%
Common 64
25.6%
Latin 54
21.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 5
 
3.8%
물 5
 
3.8%
소 5
 
3.8%
성 4
 
3.0%
질 4
 
3.0%
총 4
 
3.0%
유 4
 
3.0%
류 3
 
2.3%
기 3
 
2.3%
이 3
 
2.3%
Other values (76) 92
69.7%
Latin
ValueCountFrequency (%)
C 9
16.7%
P 4
 
7.4%
B 4
 
7.4%
S 3
 
5.6%
n 3
 
5.6%
D 3
 
5.6%
O 3
 
5.6%
T 3
 
5.6%
H 3
 
5.6%
F 2
 
3.7%
Other values (13) 17
31.5%
Common
ValueCountFrequency (%)
( 24
37.5%
) 23
35.9%
9
 
14.1%
6 2
 
3.1%
- 2
 
3.1%
· 1
 
1.6%
+ 1
 
1.6%
_ 1
 
1.6%
, 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
52.8%
ASCII 117
46.8%
None 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 24
20.5%
) 23
19.7%
C 9
 
7.7%
9
 
7.7%
P 4
 
3.4%
B 4
 
3.4%
S 3
 
2.6%
n 3
 
2.6%
D 3
 
2.6%
O 3
 
2.6%
Other values (21) 32
27.4%
Hangul
ValueCountFrequency (%)
수 5
 
3.8%
물 5
 
3.8%
소 5
 
3.8%
성 4
 
3.0%
질 4
 
3.0%
총 4
 
3.0%
유 4
 
3.0%
류 3
 
2.3%
기 3
 
2.3%
이 3
 
2.3%
Other values (76) 92
69.7%
None
ValueCountFrequency (%)
· 1
100.0%

폐수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)71.4%
Missing7
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean28210.714
Minimum800
Maximum455000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T01:58:25.108508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum800
5-th percentile2835
Q16150
median6900
Q313125
95-th percentile88485
Maximum455000
Range454200
Interquartile range (IQR)6975

Descriptive statistics

Standard deviation86626.116
Coefficient of variation (CV)3.0706814
Kurtosis23.973247
Mean28210.714
Median Absolute Deviation (MAD)2150
Skewness4.8111605
Sum789900
Variance7.504084 × 109
MonotonicityNot monotonic
2023-12-13T01:58:25.204228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6900 8
22.9%
13900 2
 
5.7%
7400 1
 
2.9%
455000 1
 
2.9%
6200 1
 
2.9%
13200 1
 
2.9%
125200 1
 
2.9%
20300 1
 
2.9%
10600 1
 
2.9%
800 1
 
2.9%
Other values (10) 10
28.6%
(Missing) 7
20.0%
ValueCountFrequency (%)
800 1
 
2.9%
2800 1
 
2.9%
2900 1
 
2.9%
3400 1
 
2.9%
3700 1
 
2.9%
5800 1
 
2.9%
6000 1
 
2.9%
6200 1
 
2.9%
6900 8
22.9%
7300 1
 
2.9%
ValueCountFrequency (%)
455000 1
2.9%
125200 1
2.9%
20300 1
2.9%
15400 1
2.9%
13900 2
5.7%
13200 1
2.9%
13100 1
2.9%
10600 1
2.9%
7800 1
2.9%
7400 1
2.9%

침출수 (29)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)63.0%
Missing8
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean12700
Minimum800
Maximum125200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T01:58:25.301638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum800
5-th percentile2830
Q16550
median6900
Q311850
95-th percentile20300
Maximum125200
Range124400
Interquartile range (IQR)5300

Descriptive statistics

Standard deviation23007.975
Coefficient of variation (CV)1.8116516
Kurtosis24.334052
Mean12700
Median Absolute Deviation (MAD)1100
Skewness4.8319882
Sum342900
Variance5.2936692 × 108
MonotonicityNot monotonic
2023-12-13T01:58:25.711555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6900 9
25.7%
20300 2
 
5.7%
13900 2
 
5.7%
5800 1
 
2.9%
6200 1
 
2.9%
125200 1
 
2.9%
10600 1
 
2.9%
7400 1
 
2.9%
800 1
 
2.9%
7800 1
 
2.9%
Other values (7) 7
20.0%
(Missing) 8
22.9%
ValueCountFrequency (%)
800 1
 
2.9%
2800 1
 
2.9%
2900 1
 
2.9%
3400 1
 
2.9%
3700 1
 
2.9%
5800 1
 
2.9%
6200 1
 
2.9%
6900 9
25.7%
7300 1
 
2.9%
7400 1
 
2.9%
ValueCountFrequency (%)
125200 1
 
2.9%
20300 2
 
5.7%
15400 1
 
2.9%
13900 2
 
5.7%
13100 1
 
2.9%
10600 1
 
2.9%
7800 1
 
2.9%
7400 1
 
2.9%
7300 1
 
2.9%
6900 9
25.7%

개인 하수 (2)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
33 
5800
 
1
2800
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)5.7%

Sample

1st row<NA>
2nd row5800
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 33
94.3%
5800 1
 
2.9%
2800 1
 
2.9%

Length

2023-12-13T01:58:25.833809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:58:25.930262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 33
94.3%
5800 1
 
2.9%
2800 1
 
2.9%

개인하수(BOD 제거율)
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
34 
11600
 
1

Length

Max length5
Median length4
Mean length4.0285714
Min length4

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row<NA>
2nd row<NA>
3rd row11600
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 34
97.1%
11600 1
 
2.9%

Length

2023-12-13T01:58:26.034009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:58:26.132818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 34
97.1%
11600 1
 
2.9%

하수_분뇨
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing28
Missing (%)80.0%
Infinite0
Infinite (%)0.0%
Mean69171.429
Minimum2800
Maximum455000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T01:58:26.216395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2800
5-th percentile2980
Q13550
median5800
Q36750
95-th percentile320690
Maximum455000
Range452200
Interquartile range (IQR)3200

Descriptive statistics

Standard deviation170142.42
Coefficient of variation (CV)2.459721
Kurtosis6.9978905
Mean69171.429
Median Absolute Deviation (MAD)2100
Skewness2.6452284
Sum484200
Variance2.8948442 × 1010
MonotonicityNot monotonic
2023-12-13T01:58:26.307714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5800 1
 
2.9%
7300 1
 
2.9%
2800 1
 
2.9%
3700 1
 
2.9%
3400 1
 
2.9%
6200 1
 
2.9%
455000 1
 
2.9%
(Missing) 28
80.0%
ValueCountFrequency (%)
2800 1
2.9%
3400 1
2.9%
3700 1
2.9%
5800 1
2.9%
6200 1
2.9%
7300 1
2.9%
455000 1
2.9%
ValueCountFrequency (%)
455000 1
2.9%
7300 1
2.9%
6200 1
2.9%
5800 1
2.9%
3700 1
2.9%
3400 1
2.9%
2800 1
2.9%

폐기물(12)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)80.0%
Missing25
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean16640
Minimum2200
Maximum29000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T01:58:26.437611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2200
5-th percentile6070
Q112625
median15100
Q320875
95-th percentile28640
Maximum29000
Range26800
Interquartile range (IQR)8250

Descriptive statistics

Standard deviation8100.3704
Coefficient of variation (CV)0.4868011
Kurtosis0.089143983
Mean16640
Median Absolute Deviation (MAD)3800
Skewness0.070679682
Sum166400
Variance65616000
MonotonicityNot monotonic
2023-12-13T01:58:26.558691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
15100 3
 
8.6%
22200 1
 
2.9%
16900 1
 
2.9%
11800 1
 
2.9%
29000 1
 
2.9%
28200 1
 
2.9%
2200 1
 
2.9%
10800 1
 
2.9%
(Missing) 25
71.4%
ValueCountFrequency (%)
2200 1
 
2.9%
10800 1
 
2.9%
11800 1
 
2.9%
15100 3
8.6%
16900 1
 
2.9%
22200 1
 
2.9%
28200 1
 
2.9%
29000 1
 
2.9%
ValueCountFrequency (%)
29000 1
 
2.9%
28200 1
 
2.9%
22200 1
 
2.9%
16900 1
 
2.9%
15100 3
8.6%
11800 1
 
2.9%
10800 1
 
2.9%
2200 1
 
2.9%

폐기물(11)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)77.8%
Missing26
Missing (%)74.3%
Infinite0
Infinite (%)0.0%
Mean18244.444
Minimum10800
Maximum29000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T01:58:26.679219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10800
5-th percentile11200
Q115100
median15100
Q322200
95-th percentile28680
Maximum29000
Range18200
Interquartile range (IQR)7100

Descriptive statistics

Standard deviation6697.5949
Coefficient of variation (CV)0.36710325
Kurtosis-0.77188718
Mean18244.444
Median Absolute Deviation (MAD)3300
Skewness0.81145032
Sum164200
Variance44857778
MonotonicityNot monotonic
2023-12-13T01:58:26.791512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
15100 3
 
8.6%
22200 1
 
2.9%
16900 1
 
2.9%
11800 1
 
2.9%
29000 1
 
2.9%
28200 1
 
2.9%
10800 1
 
2.9%
(Missing) 26
74.3%
ValueCountFrequency (%)
10800 1
 
2.9%
11800 1
 
2.9%
15100 3
8.6%
16900 1
 
2.9%
22200 1
 
2.9%
28200 1
 
2.9%
29000 1
 
2.9%
ValueCountFrequency (%)
29000 1
 
2.9%
28200 1
 
2.9%
22200 1
 
2.9%
16900 1
 
2.9%
15100 3
8.6%
11800 1
 
2.9%
10800 1
 
2.9%

폐기물(7)
Text

MISSING 

Distinct6
Distinct (%)75.0%
Missing27
Missing (%)77.1%
Memory size412.0 B
2023-12-13T01:58:26.908739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.75
Min length3

Characters and Unicode

Total characters38
Distinct characters15
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

Unique5 ?
Unique (%)62.5%

Sample

1st row22200
2nd row15100
3rd row15100
4th row15100
5th row16900
ValueCountFrequency (%)
15100 3
33.3%
22200 1
 
11.1%
16900 1
 
11.1%
11800 1
 
11.1%
고형물 1
 
11.1%
강열 1
 
11.1%
감량 1
 
11.1%
2023-12-13T01:58:27.211609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12
31.6%
1 9
23.7%
5 3
 
7.9%
2 3
 
7.9%
6 1
 
2.6%
9 1
 
2.6%
8 1
 
2.6%
고 1
 
2.6%
형 1
 
2.6%
물 1
 
2.6%
Other values (5) 5
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
78.9%
Other Letter 7
 
18.4%
Space Separator 1
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12
40.0%
1 9
30.0%
5 3
 
10.0%
2 3
 
10.0%
6 1
 
3.3%
9 1
 
3.3%
8 1
 
3.3%
Other Letter
ValueCountFrequency (%)
고 1
14.3%
형 1
14.3%
물 1
14.3%
강 1
14.3%
열 1
14.3%
감 1
14.3%
량 1
14.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31
81.6%
Hangul 7
 
18.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12
38.7%
1 9
29.0%
5 3
 
9.7%
2 3
 
9.7%
6 1
 
3.2%
9 1
 
3.2%
8 1
 
3.2%
1
 
3.2%
Hangul
ValueCountFrequency (%)
고 1
14.3%
형 1
14.3%
물 1
14.3%
강 1
14.3%
열 1
14.3%
감 1
14.3%
량 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
81.6%
Hangul 7
 
18.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12
38.7%
1 9
29.0%
5 3
 
9.7%
2 3
 
9.7%
6 1
 
3.2%
9 1
 
3.2%
8 1
 
3.2%
1
 
3.2%
Hangul
ValueCountFrequency (%)
고 1
14.3%
형 1
14.3%
물 1
14.3%
강 1
14.3%
열 1
14.3%
감 1
14.3%
량 1
14.3%

폐기물(기타)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
31 
4000
 
2
123000
 
1
2200
 
1

Length

Max length6
Median length4
Mean length4.0571429
Min length4

Unique

Unique2 ?
Unique (%)5.7%

Sample

1st row4000
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 31
88.6%
4000 2
 
5.7%
123000 1
 
2.9%
2200 1
 
2.9%

Length

2023-12-13T01:58:27.346335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:58:27.467517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
88.6%
4000 2
 
5.7%
123000 1
 
2.9%
2200 1
 
2.9%

Interactions

2023-12-13T01:58:23.684336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:21.967933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.384639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.867768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.266631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.771953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.034461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.495828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.949333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.355341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.867221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.124493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.593920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.036678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.440521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.929808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.204854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.680464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.118431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.507555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:24.010420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.293360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:22.783718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.193572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:23.586270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:58:27.557868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검사항목폐수침출수 (29)개인 하수 (2)하수_분뇨폐기물(12)폐기물(11)폐기물(7)폐기물(기타)
검사항목1.0001.0001.0000.0001.0001.0001.0001.0001.000
폐수1.0001.0001.000NaN0.000NaNNaN1.0000.000
침출수 (29)1.0001.0001.000NaNNaN1.0001.0001.000NaN
개인 하수 (2)0.000NaNNaN1.000NaNNaNNaNNaNNaN
하수_분뇨1.0000.000NaNNaN1.000NaNNaNNaNNaN
폐기물(12)1.000NaN1.000NaNNaN1.0001.0001.000NaN
폐기물(11)1.000NaN1.000NaNNaN1.0001.0001.000NaN
폐기물(7)1.0001.0001.000NaNNaN1.0001.0001.0000.000
폐기물(기타)1.0000.000NaNNaNNaNNaNNaN0.0001.000
2023-12-13T01:58:27.680040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개인 하수 (2)폐기물(기타)개인하수(BOD 제거율)
개인 하수 (2)1.000NaNNaN
폐기물(기타)NaN1.000NaN
개인하수(BOD 제거율)NaNNaN1.000
2023-12-13T01:58:27.774674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐수침출수 (29)하수_분뇨폐기물(12)폐기물(11)개인 하수 (2)개인하수(BOD 제거율)폐기물(기타)
폐수1.0001.0001.0000.8050.8051.0000.0001.000
침출수 (29)1.0001.0001.0000.8050.8051.0000.0001.000
하수_분뇨1.0001.0001.000NaNNaN1.0000.000NaN
폐기물(12)0.8050.805NaN1.0001.0000.0000.0000.000
폐기물(11)0.8050.805NaN1.0001.0000.0000.0000.000
개인 하수 (2)1.0001.0001.0000.0000.0001.0000.0000.000
개인하수(BOD 제거율)0.0000.0000.0000.0000.0000.0001.0000.000
폐기물(기타)1.0001.000NaN0.0000.0000.0000.0001.000

Missing values

2023-12-13T01:58:24.148092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:58:24.300735image/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.
2023-12-13T01:58:24.427048image/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

검사항목폐수침출수 (29)개인 하수 (2)개인하수(BOD 제거율)하수_분뇨폐기물(12)폐기물(11)폐기물(7)폐기물(기타)
0수소이온농도(pH)800800<NA><NA><NA><NA><NA><NA>4000
1BOD580058005800<NA>5800<NA><NA><NA><NA>
2BOD 제거율<NA><NA><NA>11600<NA><NA><NA><NA><NA>
3COD73007300<NA><NA>7300<NA><NA><NA><NA>
4색도(투과율법)29002900<NA><NA><NA><NA><NA><NA><NA>
5부유물질(SS)280028002800<NA>2800<NA><NA><NA><NA>
6총노말헥산(추출 물질(n_H)6000<NA><NA><NA><NA><NA><NA><NA><NA>
7노말헥산(추출물질중 광유류)1540015400<NA><NA><NA><NA><NA><NA><NA>
8총질소(T-N)37003700<NA><NA>3700<NA><NA><NA><NA>
9총인(T-P)34003400<NA><NA>3400<NA><NA><NA><NA>
검사항목폐수침출수 (29)개인 하수 (2)개인하수(BOD 제거율)하수_분뇨폐기물(12)폐기물(11)폐기물(7)폐기물(기타)
25음이온계면활성제 (ABS)13200<NA><NA><NA><NA><NA><NA><NA><NA>
26휘발성 저급탄화1390013900<NA><NA><NA>2820028200<NA><NA>
27수소류 (TCE,PCE)<NA><NA><NA><NA><NA><NA><NA><NA><NA>
28총대장균군 (폐·하수)62006200<NA><NA>6200<NA><NA><NA><NA>
29셀레늄<NA>6900<NA><NA><NA><NA><NA><NA><NA>
30디에틸헥실 프탈레이트<NA>20300<NA><NA><NA><NA><NA><NA><NA>
31수분<NA><NA><NA><NA><NA>2200<NA><NA><NA>
32기름성분<NA><NA><NA><NA><NA>1080010800<NA><NA>
33유기물함량<NA><NA><NA><NA><NA><NA><NA>고형물2200
34생태독성 (물벼룩)455000<NA><NA><NA>455000<NA><NA>강열 감량4000