Overview

Dataset statistics

Number of variables14
Number of observations332
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.7 KiB
Average record size in memory125.4 B

Variable types

DateTime1
Numeric13

Dataset

Description경상남도 양산시 읍면동별 수위 온도 풍속 습도 강수량 정보 등을 확인할 수 있습니다. 지역별 최고 강수량과 평균 강수량 등 보고서 출력을 할 수 있습니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3065864

Alerts

웅상출장소 is highly overall correlated with 물금읍 and 11 other fieldsHigh correlation
물금읍 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
동면 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
원동면 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
상북면 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
하북면 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
중앙동 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
삼성동 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
강서동 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
덕계동 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
이천 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
화제 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
웅상정수장 is highly overall correlated with 웅상출장소 and 11 other fieldsHigh correlation
일자 has unique valuesUnique
웅상출장소 has 265 (79.8%) zerosZeros
물금읍 has 273 (82.2%) zerosZeros
동면 has 268 (80.7%) zerosZeros
원동면 has 273 (82.2%) zerosZeros
상북면 has 261 (78.6%) zerosZeros
하북면 has 263 (79.2%) zerosZeros
중앙동 has 276 (83.1%) zerosZeros
삼성동 has 272 (81.9%) zerosZeros
강서동 has 273 (82.2%) zerosZeros
덕계동 has 273 (82.2%) zerosZeros
이천 has 253 (76.2%) zerosZeros
화제 has 270 (81.3%) zerosZeros
웅상정수장 has 260 (78.3%) zerosZeros

Reproduction

Analysis started2023-12-11 00:02:40.060595
Analysis finished2023-12-11 00:02:58.224160
Duration18.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

UNIQUE 

Distinct332
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Minimum2017-01-01 00:00:00
Maximum2017-11-28 00:00:00
2023-12-11T09:02:58.326555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:58.493027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

웅상출장소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1084337
Minimum0
Maximum123
Zeros265
Zeros (%)79.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:02:58.641352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13.675
Maximum123
Range123
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.6722979
Coefficient of variation (CV)4.113147
Kurtosis116.898
Mean2.1084337
Median Absolute Deviation (MAD)0
Skewness9.3080694
Sum700
Variance75.20875
MonotonicityNot monotonic
2023-12-11T09:02:58.777942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 265
79.8%
0.5 13
 
3.9%
4.0 4
 
1.2%
2.0 4
 
1.2%
5.0 3
 
0.9%
2.5 3
 
0.9%
1.0 3
 
0.9%
7.0 2
 
0.6%
1.5 2
 
0.6%
9.5 2
 
0.6%
Other values (26) 31
 
9.3%
ValueCountFrequency (%)
0.0 265
79.8%
0.5 13
 
3.9%
1.0 3
 
0.9%
1.5 2
 
0.6%
2.0 4
 
1.2%
2.5 3
 
0.9%
3.0 2
 
0.6%
3.5 1
 
0.3%
4.0 4
 
1.2%
5.0 3
 
0.9%
ValueCountFrequency (%)
123.0 1
0.3%
39.0 1
0.3%
38.0 1
0.3%
34.5 1
0.3%
32.0 1
0.3%
30.5 1
0.3%
22.5 1
0.3%
21.5 1
0.3%
19.0 2
0.6%
18.5 1
0.3%

물금읍
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.186747
Minimum0
Maximum112
Zeros273
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:02:58.913239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum112
Range112
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.1063574
Coefficient of variation (CV)4.1643398
Kurtosis79.985202
Mean2.186747
Median Absolute Deviation (MAD)0
Skewness7.935162
Sum726
Variance82.925745
MonotonicityNot monotonic
2023-12-11T09:02:59.096571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 273
82.2%
1 12
 
3.6%
2 7
 
2.1%
3 5
 
1.5%
11 4
 
1.2%
13 4
 
1.2%
25 3
 
0.9%
6 3
 
0.9%
8 3
 
0.9%
4 3
 
0.9%
Other values (14) 15
 
4.5%
ValueCountFrequency (%)
0 273
82.2%
1 12
 
3.6%
2 7
 
2.1%
3 5
 
1.5%
4 3
 
0.9%
6 3
 
0.9%
7 1
 
0.3%
8 3
 
0.9%
9 1
 
0.3%
10 1
 
0.3%
ValueCountFrequency (%)
112 1
 
0.3%
79 1
 
0.3%
36 1
 
0.3%
34 1
 
0.3%
33 1
 
0.3%
31 1
 
0.3%
25 3
0.9%
23 1
 
0.3%
21 1
 
0.3%
18 2
0.6%

동면
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1385542
Minimum0
Maximum118
Zeros268
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:02:59.501542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12.9
Maximum118
Range118
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.6390574
Coefficient of variation (CV)4.0396719
Kurtosis102.05098
Mean2.1385542
Median Absolute Deviation (MAD)0
Skewness8.6908933
Sum710
Variance74.633313
MonotonicityNot monotonic
2023-12-11T09:02:59.625366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 268
80.7%
1 16
 
4.8%
2 7
 
2.1%
16 4
 
1.2%
8 4
 
1.2%
3 3
 
0.9%
10 3
 
0.9%
12 2
 
0.6%
5 2
 
0.6%
4 2
 
0.6%
Other values (16) 21
 
6.3%
ValueCountFrequency (%)
0 268
80.7%
1 16
 
4.8%
2 7
 
2.1%
3 3
 
0.9%
4 2
 
0.6%
5 2
 
0.6%
6 2
 
0.6%
7 2
 
0.6%
8 4
 
1.2%
9 2
 
0.6%
ValueCountFrequency (%)
118 1
0.3%
49 1
0.3%
37 1
0.3%
36 1
0.3%
33 1
0.3%
30 1
0.3%
23 1
0.3%
22 2
0.6%
21 1
0.3%
17 1
0.3%

원동면
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8674699
Minimum0
Maximum110
Zeros273
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:02:59.764182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum110
Range110
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9754473
Coefficient of variation (CV)4.2707234
Kurtosis105.99838
Mean1.8674699
Median Absolute Deviation (MAD)0
Skewness8.9100798
Sum620
Variance63.60776
MonotonicityNot monotonic
2023-12-11T09:02:59.912055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 273
82.2%
2 11
 
3.3%
1 8
 
2.4%
5 5
 
1.5%
3 5
 
1.5%
4 4
 
1.2%
14 3
 
0.9%
7 3
 
0.9%
6 3
 
0.9%
12 2
 
0.6%
Other values (13) 15
 
4.5%
ValueCountFrequency (%)
0 273
82.2%
1 8
 
2.4%
2 11
 
3.3%
3 5
 
1.5%
4 4
 
1.2%
5 5
 
1.5%
6 3
 
0.9%
7 3
 
0.9%
8 1
 
0.3%
9 1
 
0.3%
ValueCountFrequency (%)
110 1
0.3%
39 1
0.3%
36 1
0.3%
35 1
0.3%
29 1
0.3%
27 2
0.6%
25 1
0.3%
24 1
0.3%
22 1
0.3%
18 1
0.3%

상북면
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9487952
Minimum0
Maximum103
Zeros261
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:03:00.075948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.95
Maximum103
Range103
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9455529
Coefficient of variation (CV)4.0771616
Kurtosis85.05635
Mean1.9487952
Median Absolute Deviation (MAD)0
Skewness7.9977387
Sum647
Variance63.131811
MonotonicityNot monotonic
2023-12-11T09:03:00.209565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.0 261
78.6%
0.5 13
 
3.9%
1.0 11
 
3.3%
8.5 5
 
1.5%
4.5 4
 
1.2%
1.5 4
 
1.2%
9.5 3
 
0.9%
2.5 3
 
0.9%
2.0 2
 
0.6%
6.0 2
 
0.6%
Other values (22) 24
 
7.2%
ValueCountFrequency (%)
0.0 261
78.6%
0.5 13
 
3.9%
1.0 11
 
3.3%
1.5 4
 
1.2%
2.0 2
 
0.6%
2.5 3
 
0.9%
3.5 1
 
0.3%
4.0 1
 
0.3%
4.5 4
 
1.2%
5.0 1
 
0.3%
ValueCountFrequency (%)
103.0 1
0.3%
42.5 1
0.3%
41.5 1
0.3%
37.5 1
0.3%
34.0 1
0.3%
33.0 1
0.3%
30.5 1
0.3%
22.0 1
0.3%
18.5 1
0.3%
16.0 2
0.6%

하북면
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1777108
Minimum0
Maximum90
Zeros263
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:03:00.327987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14
Maximum90
Range90
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.8744904
Coefficient of variation (CV)3.6159485
Kurtosis53.612863
Mean2.1777108
Median Absolute Deviation (MAD)0
Skewness6.3196457
Sum723
Variance62.007598
MonotonicityNot monotonic
2023-12-11T09:03:00.469899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 263
79.2%
1 17
 
5.1%
2 7
 
2.1%
3 7
 
2.1%
7 5
 
1.5%
14 4
 
1.2%
4 3
 
0.9%
8 3
 
0.9%
11 2
 
0.6%
10 2
 
0.6%
Other values (15) 19
 
5.7%
ValueCountFrequency (%)
0 263
79.2%
1 17
 
5.1%
2 7
 
2.1%
3 7
 
2.1%
4 3
 
0.9%
5 2
 
0.6%
6 2
 
0.6%
7 5
 
1.5%
8 3
 
0.9%
10 2
 
0.6%
ValueCountFrequency (%)
90 1
0.3%
45 1
0.3%
43 1
0.3%
34 1
0.3%
33 1
0.3%
31 2
0.6%
29 1
0.3%
28 1
0.3%
26 1
0.3%
23 2
0.6%

중앙동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9548193
Minimum0
Maximum104
Zeros276
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:03:00.624899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.45
Maximum104
Range104
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.082341
Coefficient of variation (CV)4.134572
Kurtosis84.196465
Mean1.9548193
Median Absolute Deviation (MAD)0
Skewness7.9624425
Sum649
Variance65.324237
MonotonicityNot monotonic
2023-12-11T09:03:00.787047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 276
83.1%
1 13
 
3.9%
3 6
 
1.8%
2 4
 
1.2%
6 3
 
0.9%
7 3
 
0.9%
9 3
 
0.9%
16 3
 
0.9%
11 3
 
0.9%
12 2
 
0.6%
Other values (16) 16
 
4.8%
ValueCountFrequency (%)
0 276
83.1%
1 13
 
3.9%
2 4
 
1.2%
3 6
 
1.8%
4 1
 
0.3%
5 1
 
0.3%
6 3
 
0.9%
7 3
 
0.9%
8 1
 
0.3%
9 3
 
0.9%
ValueCountFrequency (%)
104 1
0.3%
55 1
0.3%
36 1
0.3%
33 1
0.3%
31 1
0.3%
28 1
0.3%
27 1
0.3%
26 1
0.3%
20 1
0.3%
19 1
0.3%

삼성동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.186747
Minimum0
Maximum114
Zeros272
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:03:00.905864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12
Maximum114
Range114
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.007285
Coefficient of variation (CV)4.1190339
Kurtosis79.447676
Mean2.186747
Median Absolute Deviation (MAD)0
Skewness7.7701709
Sum726
Variance81.131183
MonotonicityNot monotonic
2023-12-11T09:03:01.055155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 272
81.9%
1 14
 
4.2%
7 6
 
1.8%
2 5
 
1.5%
4 5
 
1.5%
11 3
 
0.9%
15 3
 
0.9%
29 3
 
0.9%
3 3
 
0.9%
6 2
 
0.6%
Other values (14) 16
 
4.8%
ValueCountFrequency (%)
0 272
81.9%
1 14
 
4.2%
2 5
 
1.5%
3 3
 
0.9%
4 5
 
1.5%
5 1
 
0.3%
6 2
 
0.6%
7 6
 
1.8%
8 1
 
0.3%
9 1
 
0.3%
ValueCountFrequency (%)
114 1
 
0.3%
59 1
 
0.3%
46 1
 
0.3%
37 2
0.6%
35 1
 
0.3%
29 3
0.9%
22 1
 
0.3%
18 1
 
0.3%
16 1
 
0.3%
15 3
0.9%

강서동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9759036
Minimum0
Maximum94
Zeros273
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:03:01.179512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.45
Maximum94
Range94
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9187351
Coefficient of variation (CV)4.0076525
Kurtosis65.714252
Mean1.9759036
Median Absolute Deviation (MAD)0
Skewness7.1121205
Sum656
Variance62.706366
MonotonicityNot monotonic
2023-12-11T09:03:01.327088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 273
82.2%
1 12
 
3.6%
3 7
 
2.1%
2 6
 
1.8%
4 6
 
1.8%
7 4
 
1.2%
19 2
 
0.6%
10 2
 
0.6%
11 2
 
0.6%
17 2
 
0.6%
Other values (16) 16
 
4.8%
ValueCountFrequency (%)
0 273
82.2%
1 12
 
3.6%
2 6
 
1.8%
3 7
 
2.1%
4 6
 
1.8%
5 1
 
0.3%
6 1
 
0.3%
7 4
 
1.2%
9 1
 
0.3%
10 2
 
0.6%
ValueCountFrequency (%)
94 1
0.3%
59 1
0.3%
38 1
0.3%
34 1
0.3%
33 1
0.3%
28 1
0.3%
27 1
0.3%
26 1
0.3%
24 1
0.3%
21 1
0.3%

덕계동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1566265
Minimum0
Maximum113
Zeros273
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:03:01.464764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.9
Maximum113
Range113
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.337274
Coefficient of variation (CV)3.8658868
Kurtosis97.144168
Mean2.1566265
Median Absolute Deviation (MAD)0
Skewness8.3382748
Sum716
Variance69.510137
MonotonicityNot monotonic
2023-12-11T09:03:01.611985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 273
82.2%
1 9
 
2.7%
2 6
 
1.8%
4 5
 
1.5%
11 3
 
0.9%
9 3
 
0.9%
17 3
 
0.9%
7 3
 
0.9%
3 3
 
0.9%
5 2
 
0.6%
Other values (17) 22
 
6.6%
ValueCountFrequency (%)
0 273
82.2%
1 9
 
2.7%
2 6
 
1.8%
3 3
 
0.9%
4 5
 
1.5%
5 2
 
0.6%
6 2
 
0.6%
7 3
 
0.9%
8 2
 
0.6%
9 3
 
0.9%
ValueCountFrequency (%)
113 1
0.3%
38 1
0.3%
34 1
0.3%
33 1
0.3%
32 1
0.3%
31 1
0.3%
26 1
0.3%
23 1
0.3%
22 2
0.6%
19 1
0.3%

이천
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0060241
Minimum0
Maximum70
Zeros253
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:03:01.743570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.225
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.0244476
Coefficient of variation (CV)3.5016766
Kurtosis50.15835
Mean2.0060241
Median Absolute Deviation (MAD)0
Skewness6.3441851
Sum666
Variance49.342864
MonotonicityNot monotonic
2023-12-11T09:03:01.896464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 253
76.2%
0.5 11
 
3.3%
1.0 7
 
2.1%
2.0 5
 
1.5%
2.5 4
 
1.2%
3.0 4
 
1.2%
5.5 4
 
1.2%
7.5 4
 
1.2%
1.5 4
 
1.2%
3.5 3
 
0.9%
Other values (27) 33
 
9.9%
ValueCountFrequency (%)
0.0 253
76.2%
0.5 11
 
3.3%
1.0 7
 
2.1%
1.5 4
 
1.2%
2.0 5
 
1.5%
2.5 4
 
1.2%
3.0 4
 
1.2%
3.5 3
 
0.9%
4.0 1
 
0.3%
4.5 2
 
0.6%
ValueCountFrequency (%)
70.0 1
0.3%
66.0 1
0.3%
37.5 1
0.3%
29.0 1
0.3%
28.5 1
0.3%
28.0 1
0.3%
26.5 1
0.3%
22.0 1
0.3%
21.0 1
0.3%
18.0 1
0.3%

화제
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0677711
Minimum0
Maximum109.5
Zeros270
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:03:02.036114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.675
Maximum109.5
Range109.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.6095614
Coefficient of variation (CV)4.1636917
Kurtosis80.624865
Mean2.0677711
Median Absolute Deviation (MAD)0
Skewness7.8212183
Sum686.5
Variance74.124547
MonotonicityNot monotonic
2023-12-11T09:03:02.225785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 270
81.3%
1.0 8
 
2.4%
3.0 6
 
1.8%
0.5 6
 
1.8%
1.5 4
 
1.2%
2.5 3
 
0.9%
7.5 3
 
0.9%
5.5 2
 
0.6%
7.0 2
 
0.6%
18.5 1
 
0.3%
Other values (27) 27
 
8.1%
ValueCountFrequency (%)
0.0 270
81.3%
0.5 6
 
1.8%
1.0 8
 
2.4%
1.5 4
 
1.2%
2.0 1
 
0.3%
2.5 3
 
0.9%
3.0 6
 
1.8%
3.5 1
 
0.3%
4.0 1
 
0.3%
4.5 1
 
0.3%
ValueCountFrequency (%)
109.5 1
0.3%
54.5 1
0.3%
46.5 1
0.3%
36.5 1
0.3%
35.5 1
0.3%
30.5 1
0.3%
27.0 1
0.3%
24.5 1
0.3%
23.5 1
0.3%
22.5 1
0.3%

웅상정수장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1671687
Minimum0
Maximum104.5
Zeros260
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-11T09:03:02.350040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.4
Maximum104.5
Range104.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9911152
Coefficient of variation (CV)3.6873527
Kurtosis84.377146
Mean2.1671687
Median Absolute Deviation (MAD)0
Skewness7.7166683
Sum719.5
Variance63.857922
MonotonicityNot monotonic
2023-12-11T09:03:02.517360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 260
78.3%
0.5 16
 
4.8%
4.5 5
 
1.5%
1.0 4
 
1.2%
10.5 4
 
1.2%
6.0 4
 
1.2%
1.5 3
 
0.9%
2.0 3
 
0.9%
3.5 2
 
0.6%
2.5 2
 
0.6%
Other values (25) 29
 
8.7%
ValueCountFrequency (%)
0.0 260
78.3%
0.5 16
 
4.8%
1.0 4
 
1.2%
1.5 3
 
0.9%
2.0 3
 
0.9%
2.5 2
 
0.6%
3.5 2
 
0.6%
4.5 5
 
1.5%
5.0 2
 
0.6%
5.5 1
 
0.3%
ValueCountFrequency (%)
104.5 1
0.3%
38.5 1
0.3%
33.5 2
0.6%
32.0 1
0.3%
25.5 1
0.3%
25.0 1
0.3%
23.5 1
0.3%
23.0 1
0.3%
22.5 1
0.3%
21.5 1
0.3%

Interactions

2023-12-11T09:02:56.781609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:40.573545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.784541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.091294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.388415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:45.702052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.321501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.678885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.042378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.315712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.450923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:54.070857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.452667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:56.892677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:40.671936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.875447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.203649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.491900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:46.121549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.422562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.771254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.149266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.392790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.537264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:54.168216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.562691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:56.991510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:40.756682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.955281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.287253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.597671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:46.225732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.527313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.864793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.254371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.473779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.622546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:54.294289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.673382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.078753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:40.858245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.052717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.372361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.703200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:46.316565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.629045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.977722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.373985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.549773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.724729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:54.404940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.780986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.176074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:40.955273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.168002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.459290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.787181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:46.424164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.720410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:49.081904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.477168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.629014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.823319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:54.526938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.885245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.285555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.059496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.303835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.559342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.889053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:46.543582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.823636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:49.209135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.574038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.710171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.917414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:54.636618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.989975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.363129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.140811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.397298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.652018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.973116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:46.626764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.924418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:49.318100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.661216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.780769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:53.315517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:54.724875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:56.077099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.445963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.229161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.504793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.733711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:45.066480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:46.723979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.028080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:49.443028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.756258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.862484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:53.415863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:54.830812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:56.177736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.531940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.313058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.599263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.816927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:45.160617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:46.822144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.132953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:49.550114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.849845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.954615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:53.525147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:54.922615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:56.275674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.643039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.401381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.679284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:43.926208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:45.273505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:46.948263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.228067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:49.647102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:50.943712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.028645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:53.642986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.012813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:56.372328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.726399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.486641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.764643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.026311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:45.382744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.039124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.342488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:49.764960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.031292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.107038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:53.749485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.115516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:56.472144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.800497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.586362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.883003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.131055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:45.501136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.137997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.468295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:49.864264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.134319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.202793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:53.871318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.238011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:56.586395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:57.873173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:41.704556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:42.975169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:44.249521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:45.601662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:47.227602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:48.580900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:49.962579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:51.228567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:52.349531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:53.980881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:55.339927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:56.678599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:03:02.653975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
웅상출장소물금읍동면원동면상북면하북면중앙동삼성동강서동덕계동이천화제웅상정수장
웅상출장소1.0000.8490.8580.9580.8440.8510.8380.8410.8880.9770.7470.8200.977
물금읍0.8491.0000.9930.8180.9670.9520.9800.9160.9140.8550.8020.9610.844
동면0.8580.9931.0000.8200.9700.9540.9890.9350.9230.8510.7990.9660.871
원동면0.9580.8180.8201.0000.7950.8260.8360.8670.8510.9440.8360.8840.948
상북면0.8440.9670.9700.7951.0000.9630.9620.8710.8990.8230.8310.9570.854
하북면0.8510.9520.9540.8260.9631.0000.9520.8290.8370.8330.7820.9480.810
중앙동0.8380.9800.9890.8360.9620.9521.0000.9430.9270.8640.8130.9770.848
삼성동0.8410.9160.9350.8670.8710.8290.9431.0000.9730.8610.9310.9480.842
강서동0.8880.9140.9230.8510.8990.8370.9270.9731.0000.8940.9530.8650.877
덕계동0.9770.8550.8510.9440.8230.8330.8640.8610.8941.0000.8000.8300.973
이천0.7470.8020.7990.8360.8310.7820.8130.9310.9530.8001.0000.8170.753
화제0.8200.9610.9660.8840.9570.9480.9770.9480.8650.8300.8171.0000.811
웅상정수장0.9770.8440.8710.9480.8540.8100.8480.8420.8770.9730.7530.8111.000
2023-12-11T09:03:02.804780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
웅상출장소물금읍동면원동면상북면하북면중앙동삼성동강서동덕계동이천화제웅상정수장
웅상출장소1.0000.8260.8570.7860.8940.8280.8410.8710.8480.8870.7700.8180.888
물금읍0.8261.0000.8800.9000.8370.8170.9110.9130.8880.8400.8130.9290.828
동면0.8570.8801.0000.8910.8490.8270.9270.8930.8510.8450.8410.8940.855
원동면0.7860.9000.8911.0000.8470.7970.9010.8770.8680.8140.8760.9160.789
상북면0.8940.8370.8490.8471.0000.8360.8500.9080.8760.8890.8430.8570.863
하북면0.8280.8170.8270.7970.8361.0000.8250.8560.8330.8340.8270.8220.851
중앙동0.8410.9110.9270.9010.8500.8251.0000.9230.8940.8700.8450.9080.846
삼성동0.8710.9130.8930.8770.9080.8560.9231.0000.9460.8880.8240.9070.872
강서동0.8480.8880.8510.8680.8760.8330.8940.9461.0000.8600.8440.9130.858
덕계동0.8870.8400.8450.8140.8890.8340.8700.8880.8601.0000.8040.8200.919
이천0.7700.8130.8410.8760.8430.8270.8450.8240.8440.8041.0000.8440.801
화제0.8180.9290.8940.9160.8570.8220.9080.9070.9130.8200.8441.0000.831
웅상정수장0.8880.8280.8550.7890.8630.8510.8460.8720.8580.9190.8010.8311.000

Missing values

2023-12-11T09:02:57.982514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:02:58.144064image/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

일자웅상출장소물금읍동면원동면상북면하북면중앙동삼성동강서동덕계동이천화제웅상정수장
02017-01-010.00000.0000000.00.00.0
12017-01-020.00000.0000000.00.00.0
22017-01-030.00000.0000000.00.00.0
32017-01-040.00000.0000000.00.00.0
42017-01-057.00001.0200060.00.06.0
52017-01-060.00000.5000000.00.00.0
62017-01-070.00000.0000000.50.00.0
72017-01-084.03331.5411132.53.03.5
82017-01-090.00000.0000000.00.00.0
92017-01-100.00000.0000000.00.00.0
일자웅상출장소물금읍동면원동면상북면하북면중앙동삼성동강서동덕계동이천화제웅상정수장
3222017-11-190.00000.0000000.00.00.0
3232017-11-200.00000.0000000.00.00.0
3242017-11-210.00000.0000000.00.00.0
3252017-11-220.00000.0000000.00.00.0
3262017-11-230.00000.0000000.00.00.0
3272017-11-240.00000.0000000.00.00.0
3282017-11-250.00000.0000000.00.00.0
3292017-11-260.00000.0000000.00.00.0
3302017-11-270.00000.0000000.00.00.0
3312017-11-280.00000.0000000.00.00.0