Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory86.3 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
시군구명 is highly overall correlated with 행정구역코드 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 행정구역코드 and 6 other fieldsHigh correlation
행정구역코드 is highly overall correlated with 법정동코드 and 3 other fieldsHigh correlation
법정동코드 is highly overall correlated with 행정구역코드 and 3 other fieldsHigh correlation
수소이온농도값 is highly overall correlated with 탁도값 and 3 other fieldsHigh correlation
잔류염소값 is highly overall correlated with 정수장명 and 2 other fieldsHigh correlation
탁도값 is highly overall correlated with 수소이온농도값 and 3 other fieldsHigh correlation
행정구역코드 has unique valuesUnique

Reproduction

Analysis started2024-04-17 14:54:43.740195
Analysis finished2024-04-17 14:54:46.092007
Duration2.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정수장명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
고양정수장
28 
고암정수장
15 
고촌정수장
10 
금산정수장
10 
군포정수장
9 
Other values (11)
28 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row가야정수장
2nd row가야정수장
3rd row가조정수장
4th row가천정수장
5th row가천정수장

Common Values

ValueCountFrequency (%)
고양정수장 28
28.0%
고암정수장 15
15.0%
고촌정수장 10
 
10.0%
금산정수장 10
 
10.0%
군포정수장 9
 
9.0%
구천정수장 6
 
6.0%
경산정수장 5
 
5.0%
남동정수장 5
 
5.0%
가야정수장 2
 
2.0%
가천정수장 2
 
2.0%
Other values (6) 8
 
8.0%

Length

2024-04-17T23:54:46.140744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양정수장 28
28.0%
고암정수장 15
15.0%
고촌정수장 10
 
10.0%
금산정수장 10
 
10.0%
군포정수장 9
 
9.0%
구천정수장 6
 
6.0%
경산정수장 5
 
5.0%
남동정수장 5
 
5.0%
가야정수장 2
 
2.0%
가천정수장 2
 
2.0%
Other values (6) 8
 
8.0%

시도명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
48 
충청북도
15 
충청남도
12 
경상남도
11 
경상북도
7 
Other values (2)
7 

Length

Max length5
Median length4
Mean length3.57
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 48
48.0%
충청북도 15
 
15.0%
충청남도 12
 
12.0%
경상남도 11
 
11.0%
경상북도 7
 
7.0%
인천광역시 5
 
5.0%
전라남도 2
 
2.0%

Length

2024-04-17T23:54:46.241223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:54:46.335603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 48
48.0%
충청북도 15
 
15.0%
충청남도 12
 
12.0%
경상남도 11
 
11.0%
경상북도 7
 
7.0%
인천광역시 5
 
5.0%
전라남도 2
 
2.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
고양시
19 
제천시
15 
김포시
10 
금산군
10 
파주시
9 
Other values (11)
37 

Length

Max length5
Median length3
Mean length3.1
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row합천군
2nd row합천군
3rd row거창군
4th row성주군
5th row성주군

Common Values

ValueCountFrequency (%)
고양시 19
19.0%
제천시 15
15.0%
김포시 10
10.0%
금산군 10
10.0%
파주시 9
9.0%
군포시 9
9.0%
거제시 6
 
6.0%
경산시 5
 
5.0%
인천광역시 5
 
5.0%
거창군 3
 
3.0%
Other values (6) 9
9.0%

Length

2024-04-17T23:54:46.439952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 19
19.0%
제천시 15
15.0%
김포시 10
10.0%
금산군 10
10.0%
파주시 9
9.0%
군포시 9
9.0%
거제시 6
 
6.0%
경산시 5
 
5.0%
인천광역시 5
 
5.0%
거창군 3
 
3.0%
Other values (6) 9
9.0%

행정구역명
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T23:54:46.667884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.17
Min length2

Characters and Unicode

Total characters317
Distinct characters92
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

Unique98 ?
Unique (%)98.0%

Sample

1st row가야면
2nd row야로면
3rd row가조면
4th row금수면
5th row가천면
ValueCountFrequency (%)
금성면 2
 
2.0%
능포동 1
 
1.0%
남부면 1
 
1.0%
아주동 1
 
1.0%
동부면 1
 
1.0%
장승포동 1
 
1.0%
구례읍 1
 
1.0%
관인면 1
 
1.0%
신관동 1
 
1.0%
월송동 1
 
1.0%
Other values (89) 89
89.0%
2024-04-17T23:54:47.010433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 58
 
18.3%
면 33
 
10.4%
산 11
 
3.5%
읍 10
 
3.2%
금 9
 
2.8%
1 7
 
2.2%
정 6
 
1.9%
촌 6
 
1.9%
남 6
 
1.9%
수 5
 
1.6%
Other values (82) 166
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 300
94.6%
Decimal Number 17
 
5.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 58
 
19.3%
면 33
 
11.0%
산 11
 
3.7%
읍 10
 
3.3%
금 9
 
3.0%
정 6
 
2.0%
촌 6
 
2.0%
남 6
 
2.0%
수 5
 
1.7%
성 5
 
1.7%
Other values (78) 151
50.3%
Decimal Number
ValueCountFrequency (%)
1 7
41.2%
2 5
29.4%
3 4
23.5%
4 1
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 300
94.6%
Common 17
 
5.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 58
 
19.3%
면 33
 
11.0%
산 11
 
3.7%
읍 10
 
3.3%
금 9
 
3.0%
정 6
 
2.0%
촌 6
 
2.0%
남 6
 
2.0%
수 5
 
1.7%
성 5
 
1.7%
Other values (78) 151
50.3%
Common
ValueCountFrequency (%)
1 7
41.2%
2 5
29.4%
3 4
23.5%
4 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 300
94.6%
ASCII 17
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 58
 
19.3%
면 33
 
11.0%
산 11
 
3.7%
읍 10
 
3.3%
금 9
 
3.0%
정 6
 
2.0%
촌 6
 
2.0%
남 6
 
2.0%
수 5
 
1.7%
성 5
 
1.7%
Other values (78) 151
50.3%
ASCII
ValueCountFrequency (%)
1 7
41.2%
2 5
29.4%
3 4
23.5%
4 1
 
5.9%

행정구역코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2706989 × 109
Minimum2.820051 × 109
Maximum4.889034 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T23:54:47.128465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.820051 × 109
5-th percentile4.0627462 × 109
Q14.1410532 × 109
median4.1570555 × 109
Q34.4710342 × 109
95-th percentile4.8339026 × 109
Maximum4.889034 × 109
Range2.068983 × 109
Interquartile range (IQR)3.29981 × 108

Descriptive statistics

Standard deviation4.1989407 × 108
Coefficient of variation (CV)0.098319757
Kurtosis5.1555938
Mean4.2706989 × 109
Median Absolute Deviation (MAD)1.57976 × 108
Skewness-1.6973266
Sum4.2706989 × 1011
Variance1.7631103 × 1017
MonotonicityNot monotonic
2024-04-17T23:54:47.230807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4889033000 1
 
1.0%
4157025000 1
 
1.0%
4831053000 1
 
1.0%
4831033000 1
 
1.0%
4831054000 1
 
1.0%
4831032000 1
 
1.0%
4831051000 1
 
1.0%
4673025000 1
 
1.0%
4165040000 1
 
1.0%
4415057000 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2820051000 1
1.0%
2820053000 1
1.0%
2820054000 1
1.0%
2820055000 1
1.0%
2820055100 1
1.0%
4128151000 1
1.0%
4128152000 1
1.0%
4128153000 1
1.0%
4128154000 1
1.0%
4128155000 1
1.0%
ValueCountFrequency (%)
4889034000 1
1.0%
4889033000 1
1.0%
4888040000 1
1.0%
4888037000 1
1.0%
4888025000 1
1.0%
4831054000 1
1.0%
4831053000 1
1.0%
4831051000 1
1.0%
4831034000 1
1.0%
4831033000 1
1.0%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42706731
Minimum28200101
Maximum48890340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T23:54:47.337723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28200101
5-th percentile40627051
Q141410103
median41570345
Q344710342
95-th percentile48338836
Maximum48890340
Range20690239
Interquartile range (IQR)3300239.2

Descriptive statistics

Standard deviation4199045.1
Coefficient of variation (CV)0.098322793
Kurtosis5.1552246
Mean42706731
Median Absolute Deviation (MAD)1579759
Skewness-1.6972379
Sum4.2706731 × 109
Variance1.7631979 × 1013
MonotonicityNot monotonic
2024-04-17T23:54:47.440577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41410104 6
 
6.0%
28200102 3
 
3.0%
41281101 3
 
3.0%
41480101 3
 
3.0%
41281129 2
 
2.0%
41410105 2
 
2.0%
41480107 2
 
2.0%
41287104 2
 
2.0%
28200101 2
 
2.0%
44710320 1
 
1.0%
Other values (74) 74
74.0%
ValueCountFrequency (%)
28200101 2
2.0%
28200102 3
3.0%
41281101 3
3.0%
41281102 1
 
1.0%
41281104 1
 
1.0%
41281107 1
 
1.0%
41281109 1
 
1.0%
41281114 1
 
1.0%
41281117 1
 
1.0%
41281128 1
 
1.0%
ValueCountFrequency (%)
48890340 1
1.0%
48890330 1
1.0%
48880400 1
1.0%
48880370 1
1.0%
48880250 1
1.0%
48310340 1
1.0%
48310330 1
1.0%
48310320 1
1.0%
48310104 1
1.0%
48310102 1
1.0%

법정동명
Text

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T23:54:47.648346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.99
Min length2

Characters and Unicode

Total characters299
Distinct characters86
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

Unique73 ?
Unique (%)73.0%

Sample

1st row가야면
2nd row야로면
3rd row가조면
4th row금수면
5th row가천면
ValueCountFrequency (%)
산본동 6
 
6.0%
주교동 3
 
3.0%
금촌동 3
 
3.0%
간석동 3
 
3.0%
교하동 2
 
2.0%
금성면 2
 
2.0%
대화동 2
 
2.0%
구월동 2
 
2.0%
금정동 2
 
2.0%
화전동 2
 
2.0%
Other values (73) 73
73.0%
2024-04-17T23:54:47.959448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 60
20.1%
면 33
 
11.0%
산 12
 
4.0%
읍 10
 
3.3%
금 9
 
3.0%
촌 6
 
2.0%
본 6
 
2.0%
교 6
 
2.0%
화 6
 
2.0%
부 5
 
1.7%
Other values (76) 146
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 60
20.1%
면 33
 
11.0%
산 12
 
4.0%
읍 10
 
3.3%
금 9
 
3.0%
촌 6
 
2.0%
본 6
 
2.0%
교 6
 
2.0%
화 6
 
2.0%
부 5
 
1.7%
Other values (76) 146
48.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 60
20.1%
면 33
 
11.0%
산 12
 
4.0%
읍 10
 
3.3%
금 9
 
3.0%
촌 6
 
2.0%
본 6
 
2.0%
교 6
 
2.0%
화 6
 
2.0%
부 5
 
1.7%
Other values (76) 146
48.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 60
20.1%
면 33
 
11.0%
산 12
 
4.0%
읍 10
 
3.3%
금 9
 
3.0%
촌 6
 
2.0%
본 6
 
2.0%
교 6
 
2.0%
화 6
 
2.0%
부 5
 
1.7%
Other values (76) 146
48.8%

수소이온농도값
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.979
Minimum6.5
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T23:54:48.072091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile6.7
Q16.8
median6.9
Q37
95-th percentile7.6
Maximum7.6
Range1.1
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.29586203
Coefficient of variation (CV)0.042393184
Kurtosis0.42077128
Mean6.979
Median Absolute Deviation (MAD)0.1
Skewness1.2289412
Sum697.9
Variance0.087534343
MonotonicityNot monotonic
2024-04-17T23:54:48.153966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6.9 44
44.0%
6.7 22
22.0%
7.6 15
 
15.0%
7.0 11
 
11.0%
6.8 2
 
2.0%
7.4 2
 
2.0%
7.2 2
 
2.0%
6.6 1
 
1.0%
6.5 1
 
1.0%
ValueCountFrequency (%)
6.5 1
 
1.0%
6.6 1
 
1.0%
6.7 22
22.0%
6.8 2
 
2.0%
6.9 44
44.0%
7.0 11
 
11.0%
7.2 2
 
2.0%
7.4 2
 
2.0%
7.6 15
 
15.0%
ValueCountFrequency (%)
7.6 15
 
15.0%
7.4 2
 
2.0%
7.2 2
 
2.0%
7.0 11
 
11.0%
6.9 44
44.0%
6.8 2
 
2.0%
6.7 22
22.0%
6.6 1
 
1.0%
6.5 1
 
1.0%

잔류염소값
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7674
Minimum0.3
Maximum0.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T23:54:48.236108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.433
Q10.6
median0.82
Q30.94
95-th percentile0.94
Maximum0.94
Range0.64
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.17911466
Coefficient of variation (CV)0.23340456
Kurtosis0.27474045
Mean0.7674
Median Absolute Deviation (MAD)0.12
Skewness-1.0186111
Sum76.74
Variance0.032082061
MonotonicityNot monotonic
2024-04-17T23:54:48.329863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.94 28
28.0%
0.6 15
15.0%
0.87 11
 
11.0%
0.72 10
 
10.0%
0.88 9
 
9.0%
0.82 7
 
7.0%
0.78 6
 
6.0%
0.3 5
 
5.0%
0.44 2
 
2.0%
0.48 2
 
2.0%
Other values (3) 5
 
5.0%
ValueCountFrequency (%)
0.3 5
 
5.0%
0.44 2
 
2.0%
0.48 2
 
2.0%
0.53 2
 
2.0%
0.59 2
 
2.0%
0.6 15
15.0%
0.72 10
10.0%
0.73 1
 
1.0%
0.78 6
 
6.0%
0.82 7
7.0%
ValueCountFrequency (%)
0.94 28
28.0%
0.88 9
 
9.0%
0.87 11
 
11.0%
0.82 7
 
7.0%
0.78 6
 
6.0%
0.73 1
 
1.0%
0.72 10
 
10.0%
0.6 15
15.0%
0.59 2
 
2.0%
0.53 2
 
2.0%

탁도값
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0832
Minimum0.03
Maximum0.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T23:54:48.420391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.05
Q10.06
median0.06
Q30.06
95-th percentile0.35
Maximum0.47
Range0.44
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.076869035
Coefficient of variation (CV)0.92390667
Kurtosis11.605096
Mean0.0832
Median Absolute Deviation (MAD)0
Skewness3.4493437
Sum8.32
Variance0.0059088485
MonotonicityNot monotonic
2024-04-17T23:54:48.495492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.06 57
57.0%
0.05 22
 
22.0%
0.13 9
 
9.0%
0.35 5
 
5.0%
0.08 2
 
2.0%
0.09 2
 
2.0%
0.04 1
 
1.0%
0.03 1
 
1.0%
0.47 1
 
1.0%
ValueCountFrequency (%)
0.03 1
 
1.0%
0.04 1
 
1.0%
0.05 22
 
22.0%
0.06 57
57.0%
0.08 2
 
2.0%
0.09 2
 
2.0%
0.13 9
 
9.0%
0.35 5
 
5.0%
0.47 1
 
1.0%
ValueCountFrequency (%)
0.47 1
 
1.0%
0.35 5
 
5.0%
0.13 9
 
9.0%
0.09 2
 
2.0%
0.08 2
 
2.0%
0.06 57
57.0%
0.05 22
 
22.0%
0.04 1
 
1.0%
0.03 1
 
1.0%

Interactions

2024-04-17T23:54:45.611237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.134796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.647462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.941980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.271875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.667432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.405586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.701233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.005723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.332517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.726747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.462430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.754465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.067832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.396121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.788732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.521926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.821704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.133933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.467932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.853318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.582602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:44.887309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.202075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:54:45.550075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T23:54:48.560770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정수장명시도명시군구명행정구역명행정구역코드법정동코드법정동명수소이온농도값잔류염소값탁도값
정수장명1.0001.0001.0000.9901.0001.0000.9991.0001.0001.000
시도명1.0001.0001.0000.8971.0001.0000.9980.8380.9050.772
시군구명1.0001.0001.0000.9901.0001.0000.9991.0000.9991.000
행정구역명0.9900.8970.9901.0001.0001.0001.0000.9630.9621.000
행정구역코드1.0001.0001.0001.0001.0001.0001.0000.7340.8790.937
법정동코드1.0001.0001.0001.0001.0001.0001.0000.7250.8790.937
법정동명0.9990.9980.9991.0001.0001.0001.0000.9980.9981.000
수소이온농도값1.0000.8381.0000.9630.7340.7250.9981.0000.9420.788
잔류염소값1.0000.9050.9990.9620.8790.8790.9980.9421.0000.739
탁도값1.0000.7721.0001.0000.9370.9371.0000.7880.7391.000
2024-04-17T23:54:48.657954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정수장명시군구명시도명
정수장명1.0000.9600.950
시군구명0.9601.0000.950
시도명0.9500.9501.000
2024-04-17T23:54:48.732169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드법정동코드수소이온농도값잔류염소값탁도값정수장명시도명시군구명
행정구역코드1.0000.991-0.010-0.448-0.3620.9400.9890.940
법정동코드0.9911.000-0.010-0.448-0.3620.9400.9890.940
수소이온농도값-0.010-0.0101.000-0.358-0.5540.9560.6450.926
잔류염소값-0.448-0.448-0.3581.0000.2000.9560.7650.914
탁도값-0.362-0.362-0.5540.2001.0000.9400.6340.940
정수장명0.9400.9400.9560.9560.9401.0000.9500.960
시도명0.9890.9890.6450.7650.6340.9501.0000.950
시군구명0.9400.9400.9260.9140.9400.9600.9501.000

Missing values

2024-04-17T23:54:45.932695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T23:54:46.049024image/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

정수장명시도명시군구명행정구역명행정구역코드법정동코드법정동명수소이온농도값잔류염소값탁도값
0가야정수장경상남도합천군가야면488903300048890330가야면6.80.440.08
1가야정수장경상남도합천군야로면488903400048890340야로면6.80.440.08
2가조정수장경상남도거창군가조면488804000048880400가조면6.60.480.06
3가천정수장경상북도성주군금수면478403500047840350금수면7.40.820.09
4가천정수장경상북도성주군가천면478403400047840340가천면7.40.820.09
5개도정수장전라남도여수시화정면461303500046130350화정면7.00.870.04
6거창정수장경상남도거창군거창읍488802500048880250거창읍6.70.530.06
7거창정수장경상남도거창군남상면488803700048880370남상면6.70.530.06
8경산정수장경상북도경산시하양읍472902500047290250하양읍7.00.820.05
9경산정수장경상북도경산시북부동472905500047290112대평동7.00.820.05
정수장명시도명시군구명행정구역명행정구역코드법정동코드법정동명수소이온농도값잔류염소값탁도값
90금산정수장충청남도금산군제원면447103200044710320제원면6.70.870.06
91금산정수장충청남도금산군복수면447103800044710380복수면6.70.870.06
92금산정수장충청남도금산군남일면447103500044710350남일면6.70.870.06
93금산정수장충청남도금산군금성면447103100044710310금성면6.70.870.06
94금산정수장충청남도금산군금산읍447102500044710250금산읍6.70.870.06
95남동정수장인천광역시인천광역시구월1동282005100028200101구월동7.00.30.35
96남동정수장인천광역시인천광역시간석4동282005510028200102간석동7.00.30.35
97남동정수장인천광역시인천광역시간석3동282005500028200102간석동7.00.30.35
98남동정수장인천광역시인천광역시간석2동282005400028200101구월동7.00.30.35
99남동정수장인천광역시인천광역시간석1동282005300028200102간석동7.00.30.35