Overview

Dataset statistics

Number of variables11
Number of observations2359
Missing cells67
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory223.6 KiB
Average record size in memory97.1 B

Variable types

Numeric9
Categorical1
Text1

Dataset

Description연도별, 기타용 지하수 정보제공(세부용도별 시설수, 이용량)를 아래와 같이 제공합니다. 제공정보 - 연도,시도,시군구,총계-개소수,총계-이용량,온천수-개소수,온천수-이용량,먹는샘물-개소수,먹는샘물-이용량,기타-개소수,기타-이용량 등 - 단위 톤
URLhttps://www.data.go.kr/data/15054544/fileData.do

Alerts

총계-개소수 is highly overall correlated with 총계-이용량 and 2 other fieldsHigh correlation
총계-이용량 is highly overall correlated with 총계-개소수 and 1 other fieldsHigh correlation
온천수-개소수 is highly overall correlated with 온천수-이용량High correlation
온천수-이용량 is highly overall correlated with 온천수-개소수High correlation
먹는샘물-개소수 is highly overall correlated with 먹는샘물-이용량High correlation
먹는샘물-이용량 is highly overall correlated with 먹는샘물-개소수High correlation
기타-개소수 is highly overall correlated with 총계-개소수 and 1 other fieldsHigh correlation
기타-이용량 is highly overall correlated with 총계-개소수 and 2 other fieldsHigh correlation
기타-이용량 has 65 (2.8%) missing valuesMissing
총계-이용량 has 143 (6.1%) zerosZeros
온천수-개소수 has 1825 (77.4%) zerosZeros
온천수-이용량 has 1865 (79.1%) zerosZeros
먹는샘물-개소수 has 1775 (75.2%) zerosZeros
먹는샘물-이용량 has 1829 (77.5%) zerosZeros
기타-개소수 has 244 (10.3%) zerosZeros
기타-이용량 has 401 (17.0%) zerosZeros

Reproduction

Analysis started2023-12-12 14:55:24.040706
Analysis finished2023-12-12 14:55:35.343694
Duration11.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.223
Minimum2007
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2023-12-12T23:55:35.394643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12010
median2014
Q32018
95-th percentile2022
Maximum2022
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6035681
Coefficient of variation (CV)0.0022855305
Kurtosis-1.1870422
Mean2014.223
Median Absolute Deviation (MAD)4
Skewness0.090524354
Sum4751552
Variance21.192839
MonotonicityIncreasing
2023-12-12T23:55:35.518489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2009 161
 
6.8%
2007 159
 
6.7%
2008 158
 
6.7%
2011 155
 
6.6%
2015 155
 
6.6%
2012 154
 
6.5%
2013 154
 
6.5%
2014 154
 
6.5%
2010 153
 
6.5%
2016 149
 
6.3%
Other values (6) 807
34.2%
ValueCountFrequency (%)
2007 159
6.7%
2008 158
6.7%
2009 161
6.8%
2010 153
6.5%
2011 155
6.6%
2012 154
6.5%
2013 154
6.5%
2014 154
6.5%
2015 155
6.6%
2016 149
6.3%
ValueCountFrequency (%)
2022 138
5.8%
2021 138
5.8%
2020 143
6.1%
2019 143
6.1%
2018 100
4.2%
2017 145
6.1%
2016 149
6.3%
2015 155
6.6%
2014 154
6.5%
2013 154
6.5%

시도
Categorical

Distinct17
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
경기도
357 
전라남도
286 
경상북도
264 
경상남도
216 
서울특별시
199 
Other values (12)
1037 

Length

Max length7
Median length4
Mean length4.0343366
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 357
15.1%
전라남도 286
12.1%
경상북도 264
11.2%
경상남도 216
9.2%
서울특별시 199
8.4%
강원도 196
8.3%
충청남도 182
7.7%
전라북도 166
7.0%
충청북도 140
 
5.9%
부산광역시 109
 
4.6%
Other values (7) 244
10.3%

Length

2023-12-12T23:55:35.667034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 357
15.1%
전라남도 286
12.1%
경상북도 264
11.2%
경상남도 216
9.2%
서울특별시 199
8.4%
강원도 196
8.3%
충청남도 182
7.7%
전라북도 166
7.0%
충청북도 140
 
5.9%
부산광역시 109
 
4.6%
Other values (7) 244
10.3%

시군구
Text

Distinct193
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-12-12T23:55:36.061763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0101738
Min length2

Characters and Unicode

Total characters7101
Distinct characters132
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

Unique8 ?
Unique (%)0.3%

Sample

1st row강릉시
2nd row고성군
3rd row삼척시
4th row속초시
5th row양양군
ValueCountFrequency (%)
중구 39
 
1.7%
동구 37
 
1.6%
서구 25
 
1.1%
강서구 18
 
0.8%
북구 17
 
0.7%
영덕군 16
 
0.7%
군위군 16
 
0.7%
김천시 16
 
0.7%
봉화군 16
 
0.7%
속초시 16
 
0.7%
Other values (183) 2143
90.8%
2023-12-12T23:55:36.594326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 1070
 
15.1%
시 871
 
12.3%
구 493
 
6.9%
주 269
 
3.8%
천 227
 
3.2%
양 213
 
3.0%
산 159
 
2.2%
영 154
 
2.2%
동 153
 
2.2%
성 152
 
2.1%
Other values (122) 3340
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7101
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 1070
 
15.1%
시 871
 
12.3%
구 493
 
6.9%
주 269
 
3.8%
천 227
 
3.2%
양 213
 
3.0%
산 159
 
2.2%
영 154
 
2.2%
동 153
 
2.2%
성 152
 
2.1%
Other values (122) 3340
47.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7101
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 1070
 
15.1%
시 871
 
12.3%
구 493
 
6.9%
주 269
 
3.8%
천 227
 
3.2%
양 213
 
3.0%
산 159
 
2.2%
영 154
 
2.2%
동 153
 
2.2%
성 152
 
2.1%
Other values (122) 3340
47.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7101
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 1070
 
15.1%
시 871
 
12.3%
구 493
 
6.9%
주 269
 
3.8%
천 227
 
3.2%
양 213
 
3.0%
산 159
 
2.2%
영 154
 
2.2%
동 153
 
2.2%
성 152
 
2.1%
Other values (122) 3340
47.0%

총계-개소수
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.301399
Minimum0
Maximum1155
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2023-12-12T23:55:36.779337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median6
Q314
95-th percentile84
Maximum1155
Range1155
Interquartile range (IQR)12

Descriptive statistics

Standard deviation97.74586
Coefficient of variation (CV)4.0222318
Kurtosis106.45495
Mean24.301399
Median Absolute Deviation (MAD)4
Skewness9.8238913
Sum57327
Variance9554.2531
MonotonicityNot monotonic
2023-12-12T23:55:36.947019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 411
17.4%
2 242
 
10.3%
3 186
 
7.9%
5 170
 
7.2%
6 149
 
6.3%
4 129
 
5.5%
9 91
 
3.9%
7 90
 
3.8%
8 87
 
3.7%
10 84
 
3.6%
Other values (130) 720
30.5%
ValueCountFrequency (%)
0 2
 
0.1%
1 411
17.4%
2 242
10.3%
3 186
7.9%
4 129
 
5.5%
5 170
7.2%
6 149
 
6.3%
7 90
 
3.8%
8 87
 
3.7%
9 91
 
3.9%
ValueCountFrequency (%)
1155 1
 
< 0.1%
1146 1
 
< 0.1%
1142 1
 
< 0.1%
1141 2
0.1%
1140 3
0.1%
1139 3
0.1%
1138 1
 
< 0.1%
1136 2
0.1%
1134 1
 
< 0.1%
373 1
 
< 0.1%

총계-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct858
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184058.07
Minimum0
Maximum6756033
Zeros143
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2023-12-12T23:55:37.106088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14117
median31081
Q3119673.5
95-th percentile913147.6
Maximum6756033
Range6756033
Interquartile range (IQR)115556.5

Descriptive statistics

Standard deviation526467.98
Coefficient of variation (CV)2.8603363
Kurtosis61.26701
Mean184058.07
Median Absolute Deviation (MAD)29949
Skewness6.6870271
Sum4.34193 × 108
Variance2.7716854 × 1011
MonotonicityNot monotonic
2023-12-12T23:55:37.281305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143
 
6.1%
365 26
 
1.1%
1460 18
 
0.8%
2446 16
 
0.7%
54750 16
 
0.7%
9454 15
 
0.6%
180 15
 
0.6%
30000 15
 
0.6%
27000 15
 
0.6%
63063 14
 
0.6%
Other values (848) 2066
87.6%
ValueCountFrequency (%)
0 143
6.1%
1 1
 
< 0.1%
2 2
 
0.1%
7 1
 
< 0.1%
14 3
 
0.1%
28 4
 
0.2%
30 1
 
< 0.1%
36 6
 
0.3%
37 1
 
< 0.1%
43 7
 
0.3%
ValueCountFrequency (%)
6756033 5
0.2%
3600413 2
 
0.1%
3327082 2
 
0.1%
3083210 2
 
0.1%
3073753 2
 
0.1%
3061930 1
 
< 0.1%
3061270 1
 
< 0.1%
3059330 1
 
< 0.1%
3057872 1
 
< 0.1%
3057826 1
 
< 0.1%

온천수-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.569309
Minimum0
Maximum70
Zeros1825
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2023-12-12T23:55:37.417649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.8112967
Coefficient of variation (CV)3.7030926
Kurtosis44.274256
Mean1.569309
Median Absolute Deviation (MAD)0
Skewness6.0502876
Sum3702
Variance33.77117
MonotonicityNot monotonic
2023-12-12T23:55:37.849789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1825
77.4%
1 145
 
6.1%
3 95
 
4.0%
2 85
 
3.6%
6 53
 
2.2%
4 36
 
1.5%
5 17
 
0.7%
26 16
 
0.7%
14 13
 
0.6%
34 12
 
0.5%
Other values (14) 62
 
2.6%
ValueCountFrequency (%)
0 1825
77.4%
1 145
 
6.1%
2 85
 
3.6%
3 95
 
4.0%
4 36
 
1.5%
5 17
 
0.7%
6 53
 
2.2%
7 2
 
0.1%
8 5
 
0.2%
10 11
 
0.5%
ValueCountFrequency (%)
70 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
45 3
 
0.1%
44 4
 
0.2%
43 5
 
0.2%
34 12
0.5%
33 3
 
0.1%
26 16
0.7%
23 11
0.5%

온천수-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)3.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40049.538
Minimum0
Maximum1220000
Zeros1865
Zeros (%)79.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2023-12-12T23:55:38.014269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile262800
Maximum1220000
Range1220000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation153852.8
Coefficient of variation (CV)3.8415624
Kurtosis28.530933
Mean40049.538
Median Absolute Deviation (MAD)0
Skewness5.1146239
Sum94436811
Variance2.3670684 × 1010
MonotonicityNot monotonic
2023-12-12T23:55:38.189410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1865
79.1%
36500 19
 
0.8%
54000 16
 
0.7%
754600 16
 
0.7%
262800 16
 
0.7%
57768 16
 
0.7%
365000 16
 
0.7%
7000 15
 
0.6%
20190 14
 
0.6%
30000 13
 
0.6%
Other values (79) 352
 
14.9%
ValueCountFrequency (%)
0 1865
79.1%
5 7
 
0.3%
6 2
 
0.1%
7 3
 
0.1%
8 3
 
0.1%
109 1
 
< 0.1%
256 5
 
0.2%
365 6
 
0.3%
466 10
 
0.4%
630 4
 
0.2%
ValueCountFrequency (%)
1220000 11
0.5%
990000 11
0.5%
974358 3
 
0.1%
936360 2
 
0.1%
776891 1
 
< 0.1%
754600 16
0.7%
718529 2
 
0.1%
711029 2
 
0.1%
622995 10
0.4%
618250 3
 
0.1%

먹는샘물-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4094955
Minimum0
Maximum87
Zeros1775
Zeros (%)75.2%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2023-12-12T23:55:38.343316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum87
Range87
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.5418477
Coefficient of variation (CV)4.6412688
Kurtosis132.17949
Mean1.4094955
Median Absolute Deviation (MAD)0
Skewness10.670958
Sum3325
Variance42.795771
MonotonicityNot monotonic
2023-12-12T23:55:38.474429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 1775
75.2%
1 231
 
9.8%
2 90
 
3.8%
3 62
 
2.6%
4 52
 
2.2%
8 20
 
0.8%
6 18
 
0.8%
11 15
 
0.6%
9 15
 
0.6%
12 10
 
0.4%
Other values (19) 71
 
3.0%
ValueCountFrequency (%)
0 1775
75.2%
1 231
 
9.8%
2 90
 
3.8%
3 62
 
2.6%
4 52
 
2.2%
5 9
 
0.4%
6 18
 
0.8%
7 1
 
< 0.1%
8 20
 
0.8%
9 15
 
0.6%
ValueCountFrequency (%)
87 6
0.3%
86 5
0.2%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 2
 
0.1%
20 4
0.2%
19 2
 
0.1%

먹는샘물-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct148
Distinct (%)6.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean17169.179
Minimum0
Maximum1053404
Zeros1829
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2023-12-12T23:55:38.688853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile82800
Maximum1053404
Range1053404
Interquartile range (IQR)0

Descriptive statistics

Standard deviation84292.438
Coefficient of variation (CV)4.9095205
Kurtosis90.209908
Mean17169.179
Median Absolute Deviation (MAD)0
Skewness8.8647209
Sum40484925
Variance7.1052151 × 109
MonotonicityNot monotonic
2023-12-12T23:55:38.877164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1829
77.5%
3650 17
 
0.7%
500 16
 
0.7%
83950 15
 
0.6%
4413 15
 
0.6%
76742 14
 
0.6%
201985 14
 
0.6%
720 13
 
0.6%
82800 12
 
0.5%
16790 12
 
0.5%
Other values (138) 401
 
17.0%
ValueCountFrequency (%)
0 1829
77.5%
36 6
 
0.3%
37 1
 
< 0.1%
183 1
 
< 0.1%
219 1
 
< 0.1%
253 2
 
0.1%
294 1
 
< 0.1%
365 9
 
0.4%
489 2
 
0.1%
500 16
 
0.7%
ValueCountFrequency (%)
1053404 1
 
< 0.1%
1038600 1
 
< 0.1%
1030055 1
 
< 0.1%
998335 2
0.1%
986932 1
 
< 0.1%
926153 1
 
< 0.1%
918076 1
 
< 0.1%
912600 3
0.1%
755550 3
0.1%
699636 2
0.1%

기타-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct126
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.322594
Minimum0
Maximum1152
Zeros244
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2023-12-12T23:55:39.049198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310
95-th percentile76.1
Maximum1152
Range1152
Interquartile range (IQR)9

Descriptive statistics

Standard deviation97.571003
Coefficient of variation (CV)4.5759443
Kurtosis108.33564
Mean21.322594
Median Absolute Deviation (MAD)3
Skewness9.9481994
Sum50300
Variance9520.1007
MonotonicityNot monotonic
2023-12-12T23:55:39.230419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 490
20.8%
2 264
11.2%
0 244
10.3%
4 173
 
7.3%
3 135
 
5.7%
6 134
 
5.7%
5 123
 
5.2%
7 75
 
3.2%
8 68
 
2.9%
9 56
 
2.4%
Other values (116) 597
25.3%
ValueCountFrequency (%)
0 244
10.3%
1 490
20.8%
2 264
11.2%
3 135
 
5.7%
4 173
 
7.3%
5 123
 
5.2%
6 134
 
5.7%
7 75
 
3.2%
8 68
 
2.9%
9 56
 
2.4%
ValueCountFrequency (%)
1152 1
 
< 0.1%
1146 1
 
< 0.1%
1142 1
 
< 0.1%
1141 2
0.1%
1140 3
0.1%
1139 3
0.1%
1138 1
 
< 0.1%
1136 2
0.1%
1134 1
 
< 0.1%
371 1
 
< 0.1%

기타-이용량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct714
Distinct (%)31.1%
Missing65
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean130458.26
Minimum0
Maximum6756033
Zeros401
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2023-12-12T23:55:39.398051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1480
median11815
Q357670
95-th percentile461435
Maximum6756033
Range6756033
Interquartile range (IQR)57190

Descriptive statistics

Standard deviation512237.04
Coefficient of variation (CV)3.9264437
Kurtosis73.620473
Mean130458.26
Median Absolute Deviation (MAD)11815
Skewness7.6351112
Sum2.9927126 × 108
Variance2.6238678 × 1011
MonotonicityNot monotonic
2023-12-12T23:55:39.613013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 401
 
17.0%
365 26
 
1.1%
480 17
 
0.7%
2446 16
 
0.7%
1470 16
 
0.7%
9454 15
 
0.6%
4117 15
 
0.6%
180 15
 
0.6%
27000 15
 
0.6%
18250 14
 
0.6%
Other values (704) 1744
73.9%
(Missing) 65
 
2.8%
ValueCountFrequency (%)
0 401
17.0%
1 4
 
0.2%
2 4
 
0.2%
3 2
 
0.1%
4 4
 
0.2%
7 1
 
< 0.1%
12 5
 
0.2%
14 4
 
0.2%
28 4
 
0.2%
30 1
 
< 0.1%
ValueCountFrequency (%)
6756033 5
0.2%
3586757 2
 
0.1%
3327082 2
 
0.1%
3083210 2
 
0.1%
3073753 2
 
0.1%
3061930 1
 
< 0.1%
3061270 1
 
< 0.1%
3059330 1
 
< 0.1%
3057872 1
 
< 0.1%
3054176 1
 
< 0.1%

Interactions

2023-12-12T23:55:33.967477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:24.929034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.021059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.963693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:28.195255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:29.463198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:30.945964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:31.921752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:32.888663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:34.070624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:25.045460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.122638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:27.091343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:28.406400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:29.600166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:31.038351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:32.018035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:33.021261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:34.174773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:25.142683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.232185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:27.195112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:28.546459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:29.742149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:31.132566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:32.154404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:33.145021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:34.307940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:25.278208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.330872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:27.298551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:28.690274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:29.869722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:31.258917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:32.278621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:33.269224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:34.419557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:25.426343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.441183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:27.440341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:28.812277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:29.997647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:31.419456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:32.394708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:33.392201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:34.548397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:25.564121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.548414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:27.543833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:28.948082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:30.109326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:31.537370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:32.501316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:33.522903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:34.647885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:25.691356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.639607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:27.659506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:29.065428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:30.249439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:31.642395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:32.593150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:33.636919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:34.754380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:25.804651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.753878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:27.765866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:29.184854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:30.353523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:31.726987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:32.680281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:33.746443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:34.850660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:25.912561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:26.855783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:27.988816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:29.312675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:30.471229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:31.817538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:32.776111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:33.864137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:55:39.753878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도총계-개소수총계-이용량온천수-개소수온천수-이용량먹는샘물-개소수먹는샘물-이용량기타-개소수기타-이용량
연도1.0000.0000.0000.0530.0000.0000.0000.0000.0000.061
시도0.0001.0000.3610.4010.5860.5020.2120.5890.3540.310
총계-개소수0.0000.3611.0000.7130.0000.2710.2160.0080.9990.728
총계-이용량0.0530.4010.7131.0000.6680.5350.1670.3460.7150.998
온천수-개소수0.0000.5860.0000.6681.0000.8510.2240.2240.0000.000
온천수-이용량0.0000.5020.2710.5350.8511.0000.4420.3110.2910.000
먹는샘물-개소수0.0000.2120.2160.1670.2240.4421.0000.3960.0000.103
먹는샘물-이용량0.0000.5890.0080.3460.2240.3110.3961.0000.0300.000
기타-개소수0.0000.3540.9990.7150.0000.2910.0000.0301.0000.731
기타-이용량0.0610.3100.7280.9980.0000.0000.1030.0000.7311.000
2023-12-12T23:55:39.913510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도총계-개소수총계-이용량온천수-개소수온천수-이용량먹는샘물-개소수먹는샘물-이용량기타-개소수기타-이용량시도
연도1.000-0.083-0.051-0.016-0.005-0.031-0.038-0.086-0.0990.000
총계-개소수-0.0831.0000.6950.1970.1690.3370.3030.7750.5630.194
총계-이용량-0.0510.6951.0000.2690.3090.2700.3140.4860.6260.193
온천수-개소수-0.0160.1970.2691.0000.9520.0450.062-0.197-0.2110.309
온천수-이용량-0.0050.1690.3090.9521.0000.0290.054-0.200-0.1800.221
먹는샘물-개소수-0.0310.3370.2700.0450.0291.0000.9340.064-0.0720.110
먹는샘물-이용량-0.0380.3030.3140.0620.0540.9341.0000.055-0.0770.293
기타-개소수-0.0860.7750.486-0.197-0.2000.0640.0551.0000.7890.190
기타-이용량-0.0990.5630.626-0.211-0.180-0.072-0.0770.7891.0000.144
시도0.0000.1940.1930.3090.2210.1100.2930.1900.1441.000

Missing values

2023-12-12T23:55:34.991608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:55:35.177094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T23:55:35.284995image/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

연도시도시군구총계-개소수총계-이용량온천수-개소수온천수-이용량먹는샘물-개소수먹는샘물-이용량기타-개소수기타-이용량
02007강원도강릉시310202720001300
12007강원도고성군128420000001284200
22007강원도삼척시620296000105202960
32007강원도속초시213284451155580020312887
42007강원도양양군315480115000002480
52007강원도영월군5683000000568300
62007강원도원주시513140000513140000
72007강원도인제군10817422035258406299
82007강원도철원군2360002360000000
92007강원도홍천군256210500020609350511700
연도시도시군구총계-개소수총계-이용량온천수-개소수온천수-이용량먹는샘물-개소수먹는샘물-이용량기타-개소수기타-이용량
23492022충청남도홍성군96102000096102
23502022충청북도괴산군24154751001515109993652
23512022충청북도단양군911326630003502
23522022충청북도보은군5227703201900022580
23532022충청북도영동군14393000014393
23542022충청북도옥천군294584530021446027443993
23552022충청북도음성군13123973000013123973
23562022충청북도진천군4322520000432252
23572022충청북도청주시24424235120440081729121546923
23582022충청북도충주시5230027321207279344132888581