Overview

Dataset statistics

Number of variables10
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory89.0 B

Variable types

Categorical3
Text2
Numeric5

Dataset

Description샘플 데이터
Author지디에스컨설팅그룹
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=d2e82100-2dff-11ea-9713-eb3e5186fb38

Alerts

정수장명 is highly overall correlated with 행정구역코드 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 행정구역코드 and 6 other fieldsHigh correlation
시군구명 is highly overall correlated with 행정구역코드 and 6 other fieldsHigh correlation
행정구역코드 is highly overall correlated with 법정동 코드 and 4 other fieldsHigh correlation
법정동 코드 is highly overall correlated with 행정구역코드 and 4 other fieldsHigh correlation
수소이온농도 is highly overall correlated with 정수장명 and 2 other fieldsHigh correlation
잔류염소 is highly overall correlated with 정수장명 and 2 other fieldsHigh correlation
탁도 is highly overall correlated with 행정구역코드 and 4 other fieldsHigh correlation
행정구역명 has unique valuesUnique
행정구역코드 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:42:39.450479
Analysis finished2023-12-10 11:42:44.146810
Duration4.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정수장명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
가흥정수장
15 
가창정수장
8 
가은정수장
2 
가천정수장
2 
갈평정수장
2 
Other values (3)
4 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique2 ?
Unique (%)6.1%

Sample

1st row가은정수장
2nd row가은정수장
3rd row가조정수장
4th row가창정수장
5th row가창정수장

Common Values

ValueCountFrequency (%)
가흥정수장 15
45.5%
가창정수장 8
24.2%
가은정수장 2
 
6.1%
가천정수장 2
 
6.1%
갈평정수장 2
 
6.1%
감천정수장 2
 
6.1%
가조정수장 1
 
3.0%
갈말정수장 1
 
3.0%

Length

2023-12-10T20:42:44.665347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:44.852971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가흥정수장 15
45.5%
가창정수장 8
24.2%
가은정수장 2
 
6.1%
가천정수장 2
 
6.1%
갈평정수장 2
 
6.1%
감천정수장 2
 
6.1%
가조정수장 1
 
3.0%
갈말정수장 1
 
3.0%

시도명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
경상북도
23 
대구광역시
8 
경상남도
 
1
강원도
 
1

Length

Max length5
Median length4
Mean length4.2121212
Min length3

Unique

Unique2 ?
Unique (%)6.1%

Sample

1st row경상북도
2nd row경상북도
3rd row경상남도
4th row대구광역시
5th row대구광역시

Common Values

ValueCountFrequency (%)
경상북도 23
69.7%
대구광역시 8
 
24.2%
경상남도 1
 
3.0%
강원도 1
 
3.0%

Length

2023-12-10T20:42:45.058438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:45.246700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 23
69.7%
대구광역시 8
 
24.2%
경상남도 1
 
3.0%
강원도 1
 
3.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size396.0 B
영주시
15 
대구광역시
7 
문경시
2 
성주군
2 
포항시
2 
Other values (4)
5 

Length

Max length5
Median length3
Mean length3.4242424
Min length3

Unique

Unique3 ?
Unique (%)9.1%

Sample

1st row문경시
2nd row문경시
3rd row거창군
4th row달성군
5th row대구광역시

Common Values

ValueCountFrequency (%)
영주시 15
45.5%
대구광역시 7
21.2%
문경시 2
 
6.1%
성주군 2
 
6.1%
포항시 2
 
6.1%
예천군 2
 
6.1%
거창군 1
 
3.0%
달성군 1
 
3.0%
철원군 1
 
3.0%

Length

2023-12-10T20:42:45.454461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:45.673023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영주시 15
45.5%
대구광역시 7
21.2%
문경시 2
 
6.1%
성주군 2
 
6.1%
포항시 2
 
6.1%
예천군 2
 
6.1%
거창군 1
 
3.0%
달성군 1
 
3.0%
철원군 1
 
3.0%

행정구역명
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-10T20:42:46.003637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2727273
Min length2

Characters and Unicode

Total characters108
Distinct characters42
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row가은읍
2nd row농암면
3rd row가조면
4th row가창면
5th row상동
ValueCountFrequency (%)
가은읍 1
 
3.0%
안정면 1
 
3.0%
감천면 1
 
3.0%
오천읍 1
 
3.0%
동해면 1
 
3.0%
갈말읍 1
 
3.0%
휴천3동 1
 
3.0%
휴천2동 1
 
3.0%
영주2동 1
 
3.0%
가흥2동 1
 
3.0%
Other values (23) 23
69.7%
2023-12-10T20:42:46.554657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 17
15.7%
면 14
 
13.0%
가 6
 
5.6%
천 6
 
5.6%
산 5
 
4.6%
2 5
 
4.6%
1 5
 
4.6%
읍 3
 
2.8%
수 3
 
2.8%
휴 3
 
2.8%
Other values (32) 41
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97
89.8%
Decimal Number 11
 
10.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17
17.5%
면 14
 
14.4%
가 6
 
6.2%
천 6
 
6.2%
산 5
 
5.2%
읍 3
 
3.1%
수 3
 
3.1%
휴 3
 
3.1%
지 2
 
2.1%
망 2
 
2.1%
Other values (29) 36
37.1%
Decimal Number
ValueCountFrequency (%)
2 5
45.5%
1 5
45.5%
3 1
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97
89.8%
Common 11
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17
17.5%
면 14
 
14.4%
가 6
 
6.2%
천 6
 
6.2%
산 5
 
5.2%
읍 3
 
3.1%
수 3
 
3.1%
휴 3
 
3.1%
지 2
 
2.1%
망 2
 
2.1%
Other values (29) 36
37.1%
Common
ValueCountFrequency (%)
2 5
45.5%
1 5
45.5%
3 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97
89.8%
ASCII 11
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 17
17.5%
면 14
 
14.4%
가 6
 
6.2%
천 6
 
6.2%
산 5
 
5.2%
읍 3
 
3.1%
수 3
 
3.1%
휴 3
 
3.1%
지 2
 
2.1%
망 2
 
2.1%
Other values (29) 36
37.1%
ASCII
ValueCountFrequency (%)
2 5
45.5%
1 5
45.5%
3 1
 
9.1%

행정구역코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2382343 × 109
Minimum2.726062 × 109
Maximum4.88804 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-10T20:42:46.785815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.726062 × 109
5-th percentile2.7260636 × 109
Q14.2780256 × 109
median4.721038 × 109
Q34.721062 × 109
95-th percentile4.7900344 × 109
Maximum4.88804 × 109
Range2.161978 × 109
Interquartile range (IQR)4.430364 × 108

Descriptive statistics

Standard deviation8.6983446 × 108
Coefficient of variation (CV)0.2052351
Kurtosis-0.48400363
Mean4.2382343 × 109
Median Absolute Deviation (MAD)6999000
Skewness-1.2279165
Sum1.3986173 × 1011
Variance7.5661199 × 1017
MonotonicityNot monotonic
2023-12-10T20:42:47.002319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4728025300 1
 
3.0%
4721056000 1
 
3.0%
4721034000 1
 
3.0%
4721052500 1
 
3.0%
4721062000 1
 
3.0%
4721055000 1
 
3.0%
4721059000 1
 
3.0%
4721063000 1
 
3.0%
4721060000 1
 
3.0%
4728037000 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
2726062000 1
3.0%
2726063000 1
3.0%
2726064000 1
3.0%
2726065100 1
3.0%
2726065200 1
3.0%
2726066100 1
3.0%
2726066200 1
3.0%
2771031000 1
3.0%
4278025600 1
3.0%
4711125600 1
3.0%
ValueCountFrequency (%)
4888040000 1
3.0%
4790035000 1
3.0%
4790034000 1
3.0%
4784035000 1
3.0%
4784034000 1
3.0%
4728037000 1
3.0%
4728025300 1
3.0%
4721063000 1
3.0%
4721062000 1
3.0%
4721061000 1
3.0%

법정동 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42382101
Minimum27260109
Maximum48880400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-10T20:42:47.201357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27260109
5-th percentile27260111
Q142780256
median47210104
Q347210360
95-th percentile47900344
Maximum48880400
Range21620291
Interquartile range (IQR)4430104

Descriptive statistics

Standard deviation8698473.6
Coefficient of variation (CV)0.20523932
Kurtosis-0.48399834
Mean42382101
Median Absolute Deviation (MAD)70266
Skewness-1.2279175
Sum1.3986093 × 109
Variance7.5663443 × 1013
MonotonicityNot monotonic
2023-12-10T20:42:47.387177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
47210104 3
 
9.1%
27260113 2
 
6.1%
27260112 2
 
6.1%
47210101 2
 
6.1%
47210105 2
 
6.1%
47280253 1
 
3.0%
47210350 1
 
3.0%
47900350 1
 
3.0%
47900340 1
 
3.0%
47111256 1
 
3.0%
Other values (17) 17
51.5%
ValueCountFrequency (%)
27260109 1
3.0%
27260110 1
3.0%
27260111 1
3.0%
27260112 2
6.1%
27260113 2
6.1%
27710310 1
3.0%
42780256 1
3.0%
47111256 1
3.0%
47111320 1
3.0%
47210101 2
6.1%
ValueCountFrequency (%)
48880400 1
3.0%
47900350 1
3.0%
47900340 1
3.0%
47840350 1
3.0%
47840340 1
3.0%
47280370 1
3.0%
47280253 1
3.0%
47210380 1
3.0%
47210360 1
3.0%
47210350 1
3.0%

법정동명
Text

Distinct27
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-10T20:42:47.649380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9393939
Min length2

Characters and Unicode

Total characters97
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)66.7%

Sample

1st row가은읍
2nd row농암면
3rd row가조면
4th row가창면
5th row상동
ValueCountFrequency (%)
휴천동 3
 
9.1%
영주동 2
 
6.1%
가흥동 2
 
6.1%
범물동 2
 
6.1%
지산동 2
 
6.1%
상망동 1
 
3.0%
가은읍 1
 
3.0%
안정면 1
 
3.0%
감천면 1
 
3.0%
오천읍 1
 
3.0%
Other values (17) 17
51.5%
2023-12-10T20:42:48.120090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 17
17.5%
면 14
 
14.4%
가 6
 
6.2%
천 6
 
6.2%
산 5
 
5.2%
읍 3
 
3.1%
수 3
 
3.1%
휴 3
 
3.1%
흥 2
 
2.1%
주 2
 
2.1%
Other values (29) 36
37.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17
17.5%
면 14
 
14.4%
가 6
 
6.2%
천 6
 
6.2%
산 5
 
5.2%
읍 3
 
3.1%
수 3
 
3.1%
휴 3
 
3.1%
흥 2
 
2.1%
주 2
 
2.1%
Other values (29) 36
37.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17
17.5%
면 14
 
14.4%
가 6
 
6.2%
천 6
 
6.2%
산 5
 
5.2%
읍 3
 
3.1%
수 3
 
3.1%
휴 3
 
3.1%
흥 2
 
2.1%
주 2
 
2.1%
Other values (29) 36
37.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 17
17.5%
면 14
 
14.4%
가 6
 
6.2%
천 6
 
6.2%
산 5
 
5.2%
읍 3
 
3.1%
수 3
 
3.1%
휴 3
 
3.1%
흥 2
 
2.1%
주 2
 
2.1%
Other values (29) 36
37.1%

수소이온농도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0393939
Minimum6.7
Maximum7.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-10T20:42:48.298043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.7
5-th percentile6.82
Q17
median7
Q37.1
95-th percentile7.3
Maximum7.4
Range0.7
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.14348213
Coefficient of variation (CV)0.020382739
Kurtosis1.8728451
Mean7.0393939
Median Absolute Deviation (MAD)0
Skewness0.19286783
Sum232.3
Variance0.020587121
MonotonicityNot monotonic
2023-12-10T20:42:48.465027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7.0 17
51.5%
7.1 8
24.2%
7.3 3
 
9.1%
6.9 2
 
6.1%
6.7 2
 
6.1%
7.4 1
 
3.0%
ValueCountFrequency (%)
6.7 2
 
6.1%
6.9 2
 
6.1%
7.0 17
51.5%
7.1 8
24.2%
7.3 3
 
9.1%
7.4 1
 
3.0%
ValueCountFrequency (%)
7.4 1
 
3.0%
7.3 3
 
9.1%
7.1 8
24.2%
7.0 17
51.5%
6.9 2
 
6.1%
6.7 2
 
6.1%

잔류염소
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60030303
Minimum0.45
Maximum0.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-10T20:42:48.646671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile0.486
Q10.51
median0.57
Q30.57
95-th percentile0.838
Maximum0.85
Range0.4
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.11539294
Coefficient of variation (CV)0.19222448
Kurtosis0.1955044
Mean0.60030303
Median Absolute Deviation (MAD)0.06
Skewness1.139709
Sum19.81
Variance0.01331553
MonotonicityNot monotonic
2023-12-10T20:42:48.848166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.57 15
45.5%
0.51 8
24.2%
0.45 2
 
6.1%
0.83 2
 
6.1%
0.85 2
 
6.1%
0.76 2
 
6.1%
0.72 1
 
3.0%
0.68 1
 
3.0%
ValueCountFrequency (%)
0.45 2
 
6.1%
0.51 8
24.2%
0.57 15
45.5%
0.68 1
 
3.0%
0.72 1
 
3.0%
0.76 2
 
6.1%
0.83 2
 
6.1%
0.85 2
 
6.1%
ValueCountFrequency (%)
0.85 2
 
6.1%
0.83 2
 
6.1%
0.76 2
 
6.1%
0.72 1
 
3.0%
0.68 1
 
3.0%
0.57 15
45.5%
0.51 8
24.2%
0.45 2
 
6.1%

탁도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070909091
Minimum0.02
Maximum0.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-10T20:42:49.048444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.04
Q10.05
median0.08
Q30.08
95-th percentile0.104
Maximum0.14
Range0.12
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.024541245
Coefficient of variation (CV)0.34609449
Kurtosis2.4961444
Mean0.070909091
Median Absolute Deviation (MAD)0
Skewness0.91470342
Sum2.34
Variance0.00060227273
MonotonicityNot monotonic
2023-12-10T20:42:49.259704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.08 18
54.5%
0.05 8
24.2%
0.04 2
 
6.1%
0.06 2
 
6.1%
0.14 2
 
6.1%
0.02 1
 
3.0%
ValueCountFrequency (%)
0.02 1
 
3.0%
0.04 2
 
6.1%
0.05 8
24.2%
0.06 2
 
6.1%
0.08 18
54.5%
0.14 2
 
6.1%
ValueCountFrequency (%)
0.14 2
 
6.1%
0.08 18
54.5%
0.06 2
 
6.1%
0.05 8
24.2%
0.04 2
 
6.1%
0.02 1
 
3.0%

Interactions

2023-12-10T20:42:42.193814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:39.840939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:40.286778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:40.753253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:41.368248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:42.330670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:39.914410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:40.364309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:40.855250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:41.460268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:42.590906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:39.997190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:40.434613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:41.013171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:41.628281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:42.893307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:40.095641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:40.536489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:41.136644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:41.867737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:43.173999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:40.195728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:40.658592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:41.252411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:42.050809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:42:49.420766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정수장명시도명시군구명행정구역명행정구역코드법정동 코드법정동명수소이온농도잔류염소탁도
정수장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시도명1.0001.0001.0001.0001.0001.0001.0000.9501.0000.900
시군구명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
행정구역명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
행정구역코드1.0001.0001.0001.0001.0001.0001.0000.9631.0001.000
법정동 코드1.0001.0001.0001.0001.0001.0001.0000.9631.0001.000
법정동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
수소이온농도1.0000.9501.0001.0000.9630.9631.0001.0000.9620.967
잔류염소1.0001.0001.0001.0001.0001.0001.0000.9621.0000.936
탁도1.0000.9001.0001.0001.0001.0001.0000.9670.9361.000
2023-12-10T20:42:49.631019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정수장명시도명시군구명
정수장명1.0000.9280.980
시도명0.9281.0000.910
시군구명0.9800.9101.000
2023-12-10T20:42:49.839574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드법정동 코드수소이온농도잔류염소탁도정수장명시도명시군구명
행정구역코드1.0000.861-0.2180.4640.5860.9130.9830.894
법정동 코드0.8611.000-0.2190.4650.5870.9130.9830.894
수소이온농도-0.218-0.2191.000-0.129-0.2280.9620.8220.943
잔류염소0.4640.465-0.1291.0000.1740.9810.9470.961
탁도0.5860.587-0.2280.1741.0000.9620.7540.943
정수장명0.9130.9130.9620.9810.9621.0000.9280.980
시도명0.9830.9830.8220.9470.7540.9281.0000.910
시군구명0.8940.8940.9430.9610.9430.9800.9101.000

Missing values

2023-12-10T20:42:43.520781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:42:44.041835image/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

정수장명시도명시군구명행정구역명행정구역코드법정동 코드법정동명수소이온농도잔류염소탁도
0가은정수장경상북도문경시가은읍472802530047280253가은읍6.90.450.08
1가은정수장경상북도문경시농암면472803700047280370농암면6.90.450.08
2가조정수장경상남도거창군가조면488804000048880400가조면7.40.720.08
3가창정수장대구광역시달성군가창면277103100027710310가창면7.10.510.05
4가창정수장대구광역시대구광역시상동272606200027260109상동7.10.510.05
5가창정수장대구광역시대구광역시파동272606300027260110파동7.10.510.05
6가창정수장대구광역시대구광역시두산동272606400027260111두산동7.10.510.05
7가창정수장대구광역시대구광역시범물1동272606610027260113범물동7.10.510.05
8가창정수장대구광역시대구광역시지산1동272606510027260112지산동7.10.510.05
9가창정수장대구광역시대구광역시범물2동272606620027260113범물동7.10.510.05
정수장명시도명시군구명행정구역명행정구역코드법정동 코드법정동명수소이온농도잔류염소탁도
23가흥정수장경상북도영주시휴천1동472105900047210104휴천동7.00.570.08
24가흥정수장경상북도영주시가흥2동472106300047210105가흥동7.00.570.08
25가흥정수장경상북도영주시영주2동472105600047210101영주동7.00.570.08
26가흥정수장경상북도영주시휴천2동472106000047210104휴천동7.00.570.08
27가흥정수장경상북도영주시휴천3동472106100047210104휴천동7.00.570.08
28갈말정수장강원도철원군갈말읍427802560042780256갈말읍7.30.680.02
29갈평정수장경상북도포항시동해면471113200047111320동해면6.70.850.06
30갈평정수장경상북도포항시오천읍471112560047111256오천읍6.70.850.06
31감천정수장경상북도예천군감천면479003400047900340감천면7.30.760.14
32감천정수장경상북도예천군보문면479003500047900350보문면7.30.760.14