Overview

Dataset statistics

Number of variables12
Number of observations151
Missing cells13
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 KiB
Average record size in memory98.9 B

Variable types

Numeric2
Text2
Categorical7
DateTime1

Dataset

Description부산광역시_먹는물공동시설(약수터)수질검사결과정보_20210630
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15083355

Alerts

미검사사유 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
검사여부 is highly overall correlated with 일반세균 중온 CFU_mL and 6 other fieldsHigh correlation
총대장균군 mL is highly overall correlated with 검사여부 and 1 other fieldsHigh correlation
암모니아성 질소 mg_L is highly overall correlated with 검사여부High correlation
검사결과 is highly overall correlated with 검사여부 and 1 other fieldsHigh correlation
분원성대장균군 mL is highly overall correlated with 검사여부High correlation
과망간산칼륨소비량 mg_L is highly overall correlated with 검사여부High correlation
번호 is highly overall correlated with 미검사사유High correlation
일반세균 중온 CFU_mL is highly overall correlated with 검사여부High correlation
검사여부 is highly imbalanced (82.4%)Imbalance
미검사사유 is highly imbalanced (82.4%)Imbalance
분원성대장균군 mL is highly imbalanced (80.0%)Imbalance
암모니아성 질소 mg_L is highly imbalanced (66.8%)Imbalance
검사일자 has 4 (2.6%) missing valuesMissing
일반세균 중온 CFU_mL has 5 (3.3%) missing valuesMissing
질산성 질소mg_L has 4 (2.6%) missing valuesMissing
번호 has unique valuesUnique
공동시설명 has unique valuesUnique
일반세균 중온 CFU_mL has 27 (17.9%) zerosZeros

Reproduction

Analysis started2024-03-13 13:14:54.325892
Analysis finished2024-03-13 13:14:55.955999
Duration1.63 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76
Minimum1
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-13T22:14:56.020205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.5
Q138.5
median76
Q3113.5
95-th percentile143.5
Maximum151
Range150
Interquartile range (IQR)75

Descriptive statistics

Standard deviation43.734045
Coefficient of variation (CV)0.57544796
Kurtosis-1.2
Mean76
Median Absolute Deviation (MAD)38
Skewness0
Sum11476
Variance1912.6667
MonotonicityStrictly increasing
2024-03-13T22:14:56.160269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
105 1
 
0.7%
98 1
 
0.7%
99 1
 
0.7%
100 1
 
0.7%
101 1
 
0.7%
102 1
 
0.7%
103 1
 
0.7%
104 1
 
0.7%
106 1
 
0.7%
Other values (141) 141
93.4%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
151 1
0.7%
150 1
0.7%
149 1
0.7%
148 1
0.7%
147 1
0.7%
146 1
0.7%
145 1
0.7%
144 1
0.7%
143 1
0.7%
142 1
0.7%

공동시설명
Text

UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-03-13T22:14:56.476963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length7.1721854
Min length5

Characters and Unicode

Total characters1083
Distinct characters163
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)100.0%

Sample

1st row금정구 수박골
2nd row금정구 삼밭골
3rd row금정구 호국사
4th row금정구 참샘골
5th row금정구 정암
ValueCountFrequency (%)
부산진구 24
 
7.9%
사상구 18
 
5.9%
사하구 15
 
4.9%
금정구 15
 
4.9%
북구 13
 
4.3%
남구 12
 
3.9%
영도구 10
 
3.3%
동래구 10
 
3.3%
해운대구 10
 
3.3%
서구 7
 
2.3%
Other values (151) 170
55.9%
2024-03-13T22:14:56.978352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153
 
14.1%
구 151
 
13.9%
산 45
 
4.2%
사 44
 
4.1%
정 36
 
3.3%
수 26
 
2.4%
부 26
 
2.4%
진 24
 
2.2%
동 23
 
2.1%
금 22
 
2.0%
Other values (153) 533
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 909
83.9%
Space Separator 153
 
14.1%
Open Punctuation 8
 
0.7%
Close Punctuation 8
 
0.7%
Decimal Number 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 151
 
16.6%
산 45
 
5.0%
사 44
 
4.8%
정 36
 
4.0%
수 26
 
2.9%
부 26
 
2.9%
진 24
 
2.6%
동 23
 
2.5%
금 22
 
2.4%
상 19
 
2.1%
Other values (147) 493
54.2%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
2 2
40.0%
4 1
20.0%
Space Separator
ValueCountFrequency (%)
153
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 909
83.9%
Common 174
 
16.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 151
 
16.6%
산 45
 
5.0%
사 44
 
4.8%
정 36
 
4.0%
수 26
 
2.9%
부 26
 
2.9%
진 24
 
2.6%
동 23
 
2.5%
금 22
 
2.4%
상 19
 
2.1%
Other values (147) 493
54.2%
Common
ValueCountFrequency (%)
153
87.9%
( 8
 
4.6%
) 8
 
4.6%
1 2
 
1.1%
2 2
 
1.1%
4 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 909
83.9%
ASCII 174
 
16.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153
87.9%
( 8
 
4.6%
) 8
 
4.6%
1 2
 
1.1%
2 2
 
1.1%
4 1
 
0.6%
Hangul
ValueCountFrequency (%)
구 151
 
16.6%
산 45
 
5.0%
사 44
 
4.8%
정 36
 
4.0%
수 26
 
2.9%
부 26
 
2.9%
진 24
 
2.6%
동 23
 
2.5%
금 22
 
2.4%
상 19
 
2.1%
Other values (147) 493
54.2%

검사여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
검사
147 
미검사
 
4

Length

Max length3
Median length2
Mean length2.0264901
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
검사 147
97.4%
미검사 4
 
2.6%

Length

2024-03-13T22:14:57.404830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:14:57.508975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
검사 147
97.4%
미검사 4
 
2.6%

미검사사유
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
<NA>
147 
수량부족
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 147
97.4%
수량부족 4
 
2.6%

Length

2024-03-13T22:14:57.633815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:14:57.749293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 147
97.4%
수량부족 4
 
2.6%

검사일자
Date

MISSING 

Distinct16
Distinct (%)10.9%
Missing4
Missing (%)2.6%
Memory size1.3 KiB
Minimum2021-04-07 00:00:00
Maximum2021-06-04 00:00:00
2024-03-13T22:14:57.855384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:14:57.989264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)

검사결과
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
적합
123 
부적합
24 
<NA>
 
4

Length

Max length4
Median length2
Mean length2.2119205
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
적합 123
81.5%
부적합 24
 
15.9%
<NA> 4
 
2.6%

Length

2024-03-13T22:14:58.116172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:14:58.247006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 123
81.5%
부적합 24
 
15.9%
na 4
 
2.6%

일반세균 중온 CFU_mL
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct46
Distinct (%)31.5%
Missing5
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean13.705479
Minimum0
Maximum88
Zeros27
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-13T22:14:58.376777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q319
95-th percentile55
Maximum88
Range88
Interquartile range (IQR)18

Descriptive statistics

Standard deviation18.643726
Coefficient of variation (CV)1.3603118
Kurtosis3.035238
Mean13.705479
Median Absolute Deviation (MAD)5
Skewness1.8329592
Sum2001
Variance347.58852
MonotonicityNot monotonic
2024-03-13T22:14:58.531226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 27
17.9%
1 17
 
11.3%
2 13
 
8.6%
4 10
 
6.6%
5 6
 
4.0%
11 5
 
3.3%
3 4
 
2.6%
14 3
 
2.0%
19 3
 
2.0%
27 3
 
2.0%
Other values (36) 55
36.4%
(Missing) 5
 
3.3%
ValueCountFrequency (%)
0 27
17.9%
1 17
11.3%
2 13
8.6%
3 4
 
2.6%
4 10
 
6.6%
5 6
 
4.0%
6 3
 
2.0%
7 2
 
1.3%
8 3
 
2.0%
9 2
 
1.3%
ValueCountFrequency (%)
88 1
0.7%
78 1
0.7%
75 1
0.7%
68 1
0.7%
65 1
0.7%
60 1
0.7%
59 1
0.7%
56 1
0.7%
52 1
0.7%
51 1
0.7%

총대장균군 mL
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
불검출
123 
검출
24 
<NA>
 
4

Length

Max length4
Median length3
Mean length2.8675497
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불검출 123
81.5%
검출 24
 
15.9%
<NA> 4
 
2.6%

Length

2024-03-13T22:14:58.705646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:14:58.893124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 123
81.5%
검출 24
 
15.9%
na 4
 
2.6%

분원성대장균군 mL
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
불검출
144 
<NA>
 
4
검출
 
3

Length

Max length4
Median length3
Mean length3.0066225
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불검출 144
95.4%
<NA> 4
 
2.6%
검출 3
 
2.0%

Length

2024-03-13T22:14:59.104148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:14:59.268609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 144
95.4%
na 4
 
2.6%
검출 3
 
2.0%

암모니아성 질소 mg_L
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
불검출
137 
불검출
 
10
<NA>
 
4

Length

Max length4
Median length3
Mean length3.0927152
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
불검출 137
90.7%
불검출 10
 
6.6%
<NA> 4
 
2.6%

Length

2024-03-13T22:14:59.428063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:14:59.575891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불검출 147
97.4%
na 4
 
2.6%

질산성 질소mg_L
Text

MISSING 

Distinct51
Distinct (%)34.7%
Missing4
Missing (%)2.6%
Memory size1.3 KiB
2024-03-13T22:14:59.817551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8095238
Min length1

Characters and Unicode

Total characters413
Distinct characters14
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

Unique20 ?
Unique (%)13.6%

Sample

1st row1.7
2nd row1.1
3rd row1.6
4th row2.9
5th row1.2
ValueCountFrequency (%)
1.5 10
 
6.8%
1.2 9
 
6.1%
2 7
 
4.8%
2.6 7
 
4.8%
0.9 7
 
4.8%
2.4 6
 
4.1%
1.1 5
 
3.4%
1.6 5
 
3.4%
1.3 5
 
3.4%
2.2 5
 
3.4%
Other values (41) 81
55.1%
2024-03-13T22:15:00.232856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 130
31.5%
1 64
15.5%
2 60
14.5%
4 24
 
5.8%
6 22
 
5.3%
5 21
 
5.1%
3 21
 
5.1%
9 18
 
4.4%
8 18
 
4.4%
0 16
 
3.9%
Other values (4) 19
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 274
66.3%
Other Punctuation 130
31.5%
Other Letter 9
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 64
23.4%
2 60
21.9%
4 24
 
8.8%
6 22
 
8.0%
5 21
 
7.7%
3 21
 
7.7%
9 18
 
6.6%
8 18
 
6.6%
0 16
 
5.8%
7 10
 
3.6%
Other Letter
ValueCountFrequency (%)
불 3
33.3%
검 3
33.3%
출 3
33.3%
Other Punctuation
ValueCountFrequency (%)
. 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 404
97.8%
Hangul 9
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 130
32.2%
1 64
15.8%
2 60
14.9%
4 24
 
5.9%
6 22
 
5.4%
5 21
 
5.2%
3 21
 
5.2%
9 18
 
4.5%
8 18
 
4.5%
0 16
 
4.0%
Hangul
ValueCountFrequency (%)
불 3
33.3%
검 3
33.3%
출 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 404
97.8%
Hangul 9
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 130
32.2%
1 64
15.8%
2 60
14.9%
4 24
 
5.9%
6 22
 
5.4%
5 21
 
5.2%
3 21
 
5.2%
9 18
 
4.5%
8 18
 
4.5%
0 16
 
4.0%
Hangul
ValueCountFrequency (%)
불 3
33.3%
검 3
33.3%
출 3
33.3%

과망간산칼륨소비량 mg_L
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0.4
81 
0.5
36 
0.3
18 
0.6
 
8
<NA>
 
4
Other values (2)
 
4

Length

Max length4
Median length3
Mean length3.0264901
Min length3

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row0.5
2nd row0.3
3rd row0.4
4th row0.4
5th row0.6

Common Values

ValueCountFrequency (%)
0.4 81
53.6%
0.5 36
23.8%
0.3 18
 
11.9%
0.6 8
 
5.3%
<NA> 4
 
2.6%
불검출 3
 
2.0%
0.7 1
 
0.7%

Length

2024-03-13T22:15:00.401054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:15:00.537128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.4 81
53.6%
0.5 36
23.8%
0.3 18
 
11.9%
0.6 8
 
5.3%
na 4
 
2.6%
불검출 3
 
2.0%
0.7 1
 
0.7%

Interactions

2024-03-13T22:14:55.219578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:14:55.025352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:14:55.319697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:14:55.121837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:15:00.644498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호검사여부검사일자검사결과일반세균 중온 CFU_mL총대장균군 mL분원성대장균군 mL암모니아성 질소 mg_L질산성 질소mg_L과망간산칼륨소비량 mg_L
번호1.0000.1640.9230.4610.0000.4610.2380.6290.6170.337
검사여부0.1641.000NaNNaNNaNNaNNaNNaNNaNNaN
검사일자0.923NaN1.0000.5670.0000.5670.4721.0000.5090.343
검사결과0.461NaN0.5671.0000.4950.9990.3810.0110.0000.127
일반세균 중온 CFU_mL0.000NaN0.0000.4951.0000.4950.2540.0980.0000.180
총대장균군 mL0.461NaN0.5670.9990.4951.0000.3810.0110.0000.127
분원성대장균군 mL0.238NaN0.4720.3810.2540.3811.0000.0000.0000.000
암모니아성 질소 mg_L0.629NaN1.0000.0110.0980.0110.0001.0000.3410.176
질산성 질소mg_L0.617NaN0.5090.0000.0000.0000.0000.3411.0000.334
과망간산칼륨소비량 mg_L0.337NaN0.3430.1270.1800.1270.0000.1760.3341.000
2024-03-13T22:15:00.795441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미검사사유검사여부총대장균군 mL암모니아성 질소 mg_L검사결과분원성대장균군 mL과망간산칼륨소비량 mg_L
미검사사유1.0001.000NaNNaNNaNNaNNaN
검사여부1.0001.0001.0001.0001.0001.0001.000
총대장균군 mLNaN1.0001.0000.0020.9750.2490.089
암모니아성 질소 mg_LNaN1.0000.0021.0000.0020.0000.124
검사결과NaN1.0000.9750.0021.0000.2490.089
분원성대장균군 mLNaN1.0000.2490.0000.2491.0000.000
과망간산칼륨소비량 mg_LNaN1.0000.0890.1240.0890.0001.000
2024-03-13T22:15:00.921225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호일반세균 중온 CFU_mL검사여부미검사사유검사결과총대장균군 mL분원성대장균군 mL암모니아성 질소 mg_L과망간산칼륨소비량 mg_L
번호1.0000.1950.1251.0000.3210.3210.1600.4870.215
일반세균 중온 CFU_mL0.1951.0001.0000.0000.4040.4040.1550.0700.080
검사여부0.1251.0001.0001.0001.0001.0001.0001.0001.000
미검사사유1.0000.0001.0001.0000.0000.0000.0000.0000.000
검사결과0.3210.4041.0000.0001.0000.9750.2490.0020.089
총대장균군 mL0.3210.4041.0000.0000.9751.0000.2490.0020.089
분원성대장균군 mL0.1600.1551.0000.0000.2490.2491.0000.0000.000
암모니아성 질소 mg_L0.4870.0701.0000.0000.0020.0020.0001.0000.124
과망간산칼륨소비량 mg_L0.2150.0801.0000.0000.0890.0890.0000.1241.000

Missing values

2024-03-13T22:14:55.430228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:14:55.620420image/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.
2024-03-13T22:14:55.798517image/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

번호공동시설명검사여부미검사사유검사일자검사결과일반세균 중온 CFU_mL총대장균군 mL분원성대장균군 mL암모니아성 질소 mg_L질산성 질소mg_L과망간산칼륨소비량 mg_L
01금정구 수박골검사<NA>2021-05-24적합0불검출불검출불검출1.70.5
12금정구 삼밭골검사<NA>2021-06-01적합4불검출불검출불검출1.10.3
23금정구 호국사검사<NA>2021-06-01적합9불검출불검출불검출1.60.4
34금정구 참샘골검사<NA>2021-05-31적합1불검출불검출불검출2.90.4
45금정구 정암검사<NA>2021-05-31적합1불검출불검출불검출1.20.6
56금정구 물망골검사<NA>2021-05-31적합1불검출불검출불검출1.30.4
67금정구 용머리검사<NA>2021-05-31적합2불검출불검출불검출1.70.6
78금정구 제일검사<NA>2021-05-24부적합39검출검출불검출2.80.5
89금정구 서곡검사<NA>2021-06-01적합0불검출불검출불검출10.4
910금정구 서천검사<NA>2021-05-24적합0불검출불검출불검출40.4
번호공동시설명검사여부미검사사유검사일자검사결과일반세균 중온 CFU_mL총대장균군 mL분원성대장균군 mL암모니아성 질소 mg_L질산성 질소mg_L과망간산칼륨소비량 mg_L
141142해운대구 체육공원검사<NA>2021-04-19적합4불검출불검출불검출4.30.4
142143해운대구 초록공원검사<NA>2021-04-19적합5불검출불검출불검출1.30.4
143144해운대구 장산검사<NA>2021-04-19적합0불검출불검출불검출1.10.4
144145해운대구 장천사검사<NA>2021-04-19적합9불검출불검출불검출0.90.4
145146해운대구 체육공원내검사<NA>2021-04-19적합41불검출불검출불검출0.80.6
146147해운대구 옥천사검사<NA>2021-04-19적합11불검출불검출불검출0.80.5
147148해운대구 고씨제당검사<NA>2021-04-19적합42불검출불검출불검출1.10.4
148149해운대구 장산제일검사<NA>2021-04-19적합0불검출불검출불검출0.80.6
149150해운대구 장산산림욕장검사<NA>2021-04-19적합13불검출불검출불검출10.5
150151해운대구 초록공원위검사<NA>2021-04-19적합25불검출불검출불검출1.50.5