Overview

Dataset statistics

Number of variables10
Number of observations138
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 KiB
Average record size in memory89.0 B

Variable types

Categorical1
Text1
Numeric8

Dataset

Description전국 지하수 이용 시설의 위치 및 개발‧이용정보를 아래와 같이 제공합니다. 제공정보 - 시도,시군수,총계-개소수,총계-이용량,온천수-개소수,온천수-이용량,먹는샘물-개소수,먹는샘물-이용량,기타-개소수,기타-이용량 등
URLhttps://www.data.go.kr/data/15054535/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
총계-개소수 has 2 (1.4%) zerosZeros
총계-이용량 has 11 (8.0%) zerosZeros
온천수-개소수 has 108 (78.3%) zerosZeros
온천수-이용량 has 110 (79.7%) zerosZeros
먹는샘물-개소수 has 105 (76.1%) zerosZeros
먹는샘물-이용량 has 110 (79.7%) zerosZeros
기타-개소수 has 21 (15.2%) zerosZeros
기타-이용량 has 33 (23.9%) zerosZeros

Reproduction

Analysis started2023-12-12 05:33:08.931593
Analysis finished2023-12-12 05:33:16.287824
Duration7.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시구
Categorical

Distinct17
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
경기도
21 
전라남도
17 
경상북도
15 
충청남도
13 
강원도
12 
Other values (12)
60 

Length

Max length7
Median length4
Mean length4.0217391
Min length3

Unique

Unique2 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 21
15.2%
전라남도 17
12.3%
경상북도 15
10.9%
충청남도 13
9.4%
강원도 12
8.7%
경상남도 12
8.7%
서울특별시 11
8.0%
충청북도 9
6.5%
전라북도 9
6.5%
부산광역시 5
 
3.6%
Other values (7) 14
10.1%

Length

2023-12-12T14:33:16.417716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 21
15.2%
전라남도 17
12.3%
경상북도 15
10.9%
충청남도 13
9.4%
강원도 12
8.7%
경상남도 12
8.7%
서울특별시 11
8.0%
전라북도 9
6.5%
충청북도 9
6.5%
부산광역시 5
 
3.6%
Other values (7) 14
10.1%

시군수
Text

Distinct133
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T14:33:16.802739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters414
Distinct characters109
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

Unique129 ?
Unique (%)93.5%

Sample

1st row고성군
2nd row동해시
3rd row삼척시
4th row속초시
5th row양양군
ValueCountFrequency (%)
동구 3
 
2.2%
서구 2
 
1.4%
중구 2
 
1.4%
강서구 2
 
1.4%
완도군 1
 
0.7%
함평군 1
 
0.7%
진도군 1
 
0.7%
장흥군 1
 
0.7%
장성군 1
 
0.7%
곡성군 1
 
0.7%
Other values (123) 123
89.1%
2023-12-12T14:33:17.383853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 64
 
15.5%
시 52
 
12.6%
구 27
 
6.5%
주 15
 
3.6%
양 13
 
3.1%
천 13
 
3.1%
동 10
 
2.4%
영 10
 
2.4%
진 9
 
2.2%
산 9
 
2.2%
Other values (99) 192
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 414
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 64
 
15.5%
시 52
 
12.6%
구 27
 
6.5%
주 15
 
3.6%
양 13
 
3.1%
천 13
 
3.1%
동 10
 
2.4%
영 10
 
2.4%
진 9
 
2.2%
산 9
 
2.2%
Other values (99) 192
46.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 414
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 64
 
15.5%
시 52
 
12.6%
구 27
 
6.5%
주 15
 
3.6%
양 13
 
3.1%
천 13
 
3.1%
동 10
 
2.4%
영 10
 
2.4%
진 9
 
2.2%
산 9
 
2.2%
Other values (99) 192
46.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 414
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 64
 
15.5%
시 52
 
12.6%
구 27
 
6.5%
주 15
 
3.6%
양 13
 
3.1%
천 13
 
3.1%
동 10
 
2.4%
영 10
 
2.4%
진 9
 
2.2%
산 9
 
2.2%
Other values (99) 192
46.4%

총계-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.826087
Minimum0
Maximum1136
Zeros2
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T14:33:17.561305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q312.75
95-th percentile53.65
Maximum1136
Range1136
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation101.1685
Coefficient of variation (CV)4.6352099
Kurtosis109.76061
Mean21.826087
Median Absolute Deviation (MAD)3
Skewness10.111678
Sum3012
Variance10235.064
MonotonicityNot monotonic
2023-12-12T14:33:17.744135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 23
16.7%
3 17
12.3%
2 15
10.9%
5 10
 
7.2%
6 10
 
7.2%
4 8
 
5.8%
8 6
 
4.3%
7 6
 
4.3%
13 4
 
2.9%
14 3
 
2.2%
Other values (27) 36
26.1%
ValueCountFrequency (%)
0 2
 
1.4%
1 23
16.7%
2 15
10.9%
3 17
12.3%
4 8
 
5.8%
5 10
7.2%
6 10
7.2%
7 6
 
4.3%
8 6
 
4.3%
9 3
 
2.2%
ValueCountFrequency (%)
1136 1
0.7%
325 1
0.7%
156 1
0.7%
91 1
0.7%
84 1
0.7%
78 1
0.7%
63 1
0.7%
52 1
0.7%
49 1
0.7%
43 1
0.7%

총계-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173038.48
Minimum0
Maximum3073753
Zeros11
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T14:33:17.927203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13563.25
median27500
Q3110822.5
95-th percentile977954.55
Maximum3073753
Range3073753
Interquartile range (IQR)107259.25

Descriptive statistics

Standard deviation444458.39
Coefficient of variation (CV)2.5685524
Kurtosis21.919939
Mean173038.48
Median Absolute Deviation (MAD)26797.5
Skewness4.4050896
Sum23879310
Variance1.9754326 × 1011
MonotonicityNot monotonic
2023-12-12T14:33:18.066450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
8.0%
30799 1
 
0.7%
46572 1
 
0.7%
45625 1
 
0.7%
3723 1
 
0.7%
14600 1
 
0.7%
14696 1
 
0.7%
120682 1
 
0.7%
41585 1
 
0.7%
57302 1
 
0.7%
Other values (118) 118
85.5%
ValueCountFrequency (%)
0 11
8.0%
36 1
 
0.7%
43 1
 
0.7%
100 1
 
0.7%
180 1
 
0.7%
255 1
 
0.7%
365 1
 
0.7%
480 1
 
0.7%
620 1
 
0.7%
675 1
 
0.7%
ValueCountFrequency (%)
3073753 1
0.7%
2683952 1
0.7%
1791792 1
0.7%
1699957 1
0.7%
1535715 1
0.7%
1003450 1
0.7%
998335 1
0.7%
974358 1
0.7%
639285 1
0.7%
618250 1
0.7%

온천수-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4565217
Minimum0
Maximum43
Zeros108
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T14:33:18.193120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.15
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.4560213
Coefficient of variation (CV)3.7459251
Kurtosis35.495934
Mean1.4565217
Median Absolute Deviation (MAD)0
Skewness5.7285523
Sum201
Variance29.768169
MonotonicityNot monotonic
2023-12-12T14:33:18.301450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 108
78.3%
3 8
 
5.8%
1 6
 
4.3%
2 5
 
3.6%
5 2
 
1.4%
4 2
 
1.4%
6 2
 
1.4%
43 1
 
0.7%
33 1
 
0.7%
8 1
 
0.7%
Other values (2) 2
 
1.4%
ValueCountFrequency (%)
0 108
78.3%
1 6
 
4.3%
2 5
 
3.6%
3 8
 
5.8%
4 2
 
1.4%
5 2
 
1.4%
6 2
 
1.4%
8 1
 
0.7%
21 1
 
0.7%
26 1
 
0.7%
ValueCountFrequency (%)
43 1
 
0.7%
33 1
 
0.7%
26 1
 
0.7%
21 1
 
0.7%
8 1
 
0.7%
6 2
 
1.4%
5 2
 
1.4%
4 2
 
1.4%
3 8
5.8%
2 5
3.6%

온천수-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40919.435
Minimum0
Maximum974358
Zeros110
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T14:33:18.426356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile313791.75
Maximum974358
Range974358
Interquartile range (IQR)0

Descriptive statistics

Standard deviation141222.73
Coefficient of variation (CV)3.4512385
Kurtosis20.922714
Mean40919.435
Median Absolute Deviation (MAD)0
Skewness4.3730622
Sum5646882
Variance1.9943859 × 1010
MonotonicityNot monotonic
2023-12-12T14:33:18.627916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 110
79.7%
1167 1
 
0.7%
207279 1
 
0.7%
204400 1
 
0.7%
20190 1
 
0.7%
630 1
 
0.7%
2940 1
 
0.7%
7000 1
 
0.7%
28000 1
 
0.7%
109500 1
 
0.7%
Other values (19) 19
 
13.8%
ValueCountFrequency (%)
0 110
79.7%
5 1
 
0.7%
630 1
 
0.7%
1167 1
 
0.7%
2940 1
 
0.7%
3829 1
 
0.7%
5471 1
 
0.7%
7000 1
 
0.7%
20190 1
 
0.7%
26280 1
 
0.7%
ValueCountFrequency (%)
974358 1
0.7%
754600 1
0.7%
618250 1
0.7%
547500 1
0.7%
415000 1
0.7%
394605 1
0.7%
365000 1
0.7%
304755 1
0.7%
262800 1
0.7%
207279 1
0.7%

먹는샘물-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2173913
Minimum0
Maximum19
Zeros105
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T14:33:18.783740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.15
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5012805
Coefficient of variation (CV)2.8760519
Kurtosis12.970099
Mean1.2173913
Median Absolute Deviation (MAD)0
Skewness3.6218981
Sum168
Variance12.258965
MonotonicityNot monotonic
2023-12-12T14:33:18.915374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 105
76.1%
1 13
 
9.4%
2 4
 
2.9%
3 4
 
2.9%
4 2
 
1.4%
14 1
 
0.7%
10 1
 
0.7%
12 1
 
0.7%
18 1
 
0.7%
19 1
 
0.7%
Other values (5) 5
 
3.6%
ValueCountFrequency (%)
0 105
76.1%
1 13
 
9.4%
2 4
 
2.9%
3 4
 
2.9%
4 2
 
1.4%
6 1
 
0.7%
8 1
 
0.7%
9 1
 
0.7%
10 1
 
0.7%
12 1
 
0.7%
ValueCountFrequency (%)
19 1
0.7%
18 1
0.7%
16 1
0.7%
15 1
0.7%
14 1
0.7%
12 1
0.7%
10 1
0.7%
9 1
0.7%
8 1
0.7%
6 1
0.7%

먹는샘물-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17989.239
Minimum0
Maximum998335
Zeros110
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T14:33:19.357743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile93857.5
Maximum998335
Range998335
Interquartile range (IQR)0

Descriptive statistics

Standard deviation91810.137
Coefficient of variation (CV)5.1036142
Kurtosis96.481923
Mean17989.239
Median Absolute Deviation (MAD)0
Skewness9.2371527
Sum2482515
Variance8.4291012 × 109
MonotonicityNot monotonic
2023-12-12T14:33:19.507217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 110
79.7%
181096 1
 
0.7%
4413 1
 
0.7%
172912 1
 
0.7%
14460 1
 
0.7%
151099 1
 
0.7%
3240 1
 
0.7%
54000 1
 
0.7%
998335 1
 
0.7%
28025 1
 
0.7%
Other values (19) 19
 
13.8%
ValueCountFrequency (%)
0 110
79.7%
36 1
 
0.7%
500 1
 
0.7%
720 1
 
0.7%
730 1
 
0.7%
1095 1
 
0.7%
1632 1
 
0.7%
3240 1
 
0.7%
3650 1
 
0.7%
4413 1
 
0.7%
ValueCountFrequency (%)
998335 1
0.7%
211200 1
0.7%
181096 1
0.7%
172912 1
0.7%
163885 1
0.7%
151099 1
0.7%
150000 1
0.7%
83950 1
0.7%
76742 1
0.7%
54750 1
0.7%

기타-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.152174
Minimum0
Maximum1136
Zeros21
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T14:33:19.670131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile37.5
Maximum1136
Range1136
Interquartile range (IQR)6

Descriptive statistics

Standard deviation101.29396
Coefficient of variation (CV)5.2889013
Kurtosis110.30216
Mean19.152174
Median Absolute Deviation (MAD)2
Skewness10.152724
Sum2643
Variance10260.466
MonotonicityNot monotonic
2023-12-12T14:33:19.809281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 25
18.1%
0 21
15.2%
2 19
13.8%
4 12
8.7%
3 10
 
7.2%
6 8
 
5.8%
5 6
 
4.3%
7 3
 
2.2%
8 3
 
2.2%
20 3
 
2.2%
Other values (20) 28
20.3%
ValueCountFrequency (%)
0 21
15.2%
1 25
18.1%
2 19
13.8%
3 10
 
7.2%
4 12
8.7%
5 6
 
4.3%
6 8
 
5.8%
7 3
 
2.2%
8 3
 
2.2%
9 2
 
1.4%
ValueCountFrequency (%)
1136 1
 
0.7%
325 1
 
0.7%
156 1
 
0.7%
90 1
 
0.7%
84 1
 
0.7%
78 1
 
0.7%
63 1
 
0.7%
33 1
 
0.7%
28 3
2.2%
27 2
1.4%

기타-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114129.8
Minimum0
Maximum3073753
Zeros33
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T14:33:19.959034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1120
median5375.5
Q341083.75
95-th percentile460341.35
Maximum3073753
Range3073753
Interquartile range (IQR)40963.75

Descriptive statistics

Standard deviation423472.31
Coefficient of variation (CV)3.7104446
Kurtosis29.850059
Mean114129.8
Median Absolute Deviation (MAD)5375.5
Skewness5.3266538
Sum15749913
Variance1.793288 × 1011
MonotonicityNot monotonic
2023-12-12T14:33:20.154215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
23.9%
3551 1
 
0.7%
1951 1
 
0.7%
36465 1
 
0.7%
46572 1
 
0.7%
45625 1
 
0.7%
3723 1
 
0.7%
14600 1
 
0.7%
14696 1
 
0.7%
11182 1
 
0.7%
Other values (96) 96
69.6%
ValueCountFrequency (%)
0 33
23.9%
43 1
 
0.7%
100 1
 
0.7%
180 1
 
0.7%
184 1
 
0.7%
255 1
 
0.7%
260 1
 
0.7%
276 1
 
0.7%
365 1
 
0.7%
480 1
 
0.7%
ValueCountFrequency (%)
3073753 1
0.7%
2683952 1
0.7%
1791792 1
0.7%
1699957 1
0.7%
1535715 1
0.7%
639285 1
0.7%
552982 1
0.7%
443993 1
0.7%
236799 1
0.7%
227915 1
0.7%

Interactions

2023-12-12T14:33:14.924367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:09.353236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.219624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:11.032679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.041077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.735637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.442065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:14.165384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:15.021895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:09.459860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.330831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:11.133940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.143498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.811984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.517283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:14.244322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:15.148320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:09.573324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.438440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:11.222909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.247891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.923775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.598655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:14.336609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:15.365858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:09.662892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.520356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:11.297802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.330353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.008594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.679663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:14.417551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:15.484775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:09.786602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.630159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:11.389465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.409128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.089623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.766878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:14.501568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:15.602491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:09.911526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.737025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:11.486339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.491258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.168676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.860062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:14.591102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:15.715970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.025433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.848846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:11.566594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.574237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.245747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.942149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:14.691970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:15.832558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.126158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:10.938647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:11.658261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:12.650744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:13.350120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:14.030026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:14.800784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:33:20.284559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시구총계-개소수총계-이용량온천수-개소수온천수-이용량먹는샘물-개소수먹는샘물-이용량기타-개소수기타-이용량
시구1.0000.0000.0000.0670.2540.3610.6600.0000.000
총계-개소수0.0001.0000.9950.0000.0000.0000.0001.0000.928
총계-이용량0.0000.9951.0000.5990.7260.3350.7610.9950.952
온천수-개소수0.0670.0000.5991.0000.9270.4440.7230.0000.000
온천수-이용량0.2540.0000.7260.9271.0000.5250.7220.0000.000
먹는샘물-개소수0.3610.0000.3350.4440.5251.0000.9210.0000.000
먹는샘물-이용량0.6600.0000.7610.7230.7220.9211.0000.0000.000
기타-개소수0.0001.0000.9950.0000.0000.0000.0001.0000.928
기타-이용량0.0000.9280.9520.0000.0000.0000.0000.9281.000
2023-12-12T14:33:20.458425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총계-개소수총계-이용량온천수-개소수온천수-이용량먹는샘물-개소수먹는샘물-이용량기타-개소수기타-이용량시구
총계-개소수1.0000.6360.2090.1700.2810.2250.7170.5390.000
총계-이용량0.6361.0000.2710.3500.2540.3240.3550.5400.000
온천수-개소수0.2090.2711.0000.9530.0430.027-0.272-0.2510.006
온천수-이용량0.1700.3500.9531.0000.0120.044-0.272-0.2140.096
먹는샘물-개소수0.2810.2540.0430.0121.0000.890-0.054-0.0810.141
먹는샘물-이용량0.2250.3240.0270.0440.8901.000-0.063-0.0900.410
기타-개소수0.7170.355-0.272-0.272-0.054-0.0631.0000.8010.000
기타-이용량0.5390.540-0.251-0.214-0.081-0.0900.8011.0000.000
시구0.0000.0000.0060.0960.1410.4100.0000.0001.000

Missing values

2023-12-12T14:33:15.996018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:33:16.193821image/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

시구시군수총계-개소수총계-이용량온천수-개소수온천수-이용량먹는샘물-개소수먹는샘물-이용량기타-개소수기타-이용량
0강원도고성군20002000
1강원도동해시1356000000135600
2강원도삼척시3214110000321411
3강원도속초시95918211670074751
4강원도양양군248000002480
5강원도영월군4216360000421636
6강원도인제군3262802262800010
7강원도철원군2300002300000000
8강원도태백시21800000021800
9강원도평창군14181096001418109600
시구시군수총계-개소수총계-이용량온천수-개소수온천수-이용량먹는샘물-개소수먹는샘물-이용량기타-개소수기타-이용량
128충청남도홍성군96102000096102
129충청북도괴산군24154751001515109993652
130충청북도단양군911326630003502
131충청북도보은군5227703201900022580
132충청북도영동군14393000014393
133충청북도옥천군294584530021446027443993
134충청북도음성군13123973000013123973
135충청북도진천군4322520000432252
136충청북도청주시24424235120440081729121546923
137충청북도충주시5230027321207279344132888581