Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory79.3 B

Variable types

Categorical2
Text1
Numeric6

Alerts

시도명 has constant value ""Constant
행정동코드 is highly overall correlated with 시군구명High correlation
인구수 is highly overall correlated with 급수인구 and 2 other fieldsHigh correlation
급수인구 is highly overall correlated with 인구수 and 2 other fieldsHigh correlation
급수율 is highly overall correlated with 인구수 and 1 other fieldsHigh correlation
사용량 is highly overall correlated with 인구수 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 행정동코드High correlation
행정동코드 has unique valuesUnique
인구수 has unique valuesUnique
급수인구 has 6 (6.0%) zerosZeros
급수율 has 6 (6.0%) zerosZeros
사용량 has 14 (14.0%) zerosZeros
1인1일 사용량 has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:27:17.595238
Analysis finished2023-12-10 12:27:28.818290
Duration11.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원도
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 100
100.0%

Length

2023-12-10T21:27:28.949367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:27:29.111053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 100
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강릉시
21 
원주시
13 
삼척시
12 
동해시
10 
정선군
8 
Other values (6)
36 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row삼척시
2nd row영월군
3rd row정선군
4th row삼척시
5th row속초시

Common Values

ValueCountFrequency (%)
강릉시 21
21.0%
원주시 13
13.0%
삼척시 12
12.0%
동해시 10
10.0%
정선군 8
 
8.0%
속초시 8
 
8.0%
영월군 6
 
6.0%
인제군 6
 
6.0%
양양군 6
 
6.0%
양구군 5
 
5.0%

Length

2023-12-10T21:27:29.259598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강릉시 21
21.0%
원주시 13
13.0%
삼척시 12
12.0%
동해시 10
10.0%
정선군 8
 
8.0%
속초시 8
 
8.0%
영월군 6
 
6.0%
인제군 6
 
6.0%
양양군 6
 
6.0%
양구군 5
 
5.0%

읍면동명
Text

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:27:29.681050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.96
Min length2

Characters and Unicode

Total characters296
Distinct characters97
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

Unique88 ?
Unique (%)88.0%

Sample

1st row근덕면
2nd row상동읍
3rd row고한읍
4th row교동
5th row조양동
ValueCountFrequency (%)
남면 4
 
4.0%
북면 2
 
2.0%
송정동 2
 
2.0%
교동 2
 
2.0%
중앙동 2
 
2.0%
현북면 1
 
1.0%
간성읍 1
 
1.0%
지정면 1
 
1.0%
신림면 1
 
1.0%
부곡동 1
 
1.0%
Other values (83) 83
83.0%
2023-12-10T21:27:30.526673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 49
 
16.6%
면 43
 
14.5%
읍 14
 
4.7%
남 10
 
3.4%
북 7
 
2.4%
성 6
 
2.0%
곡 6
 
2.0%
양 6
 
2.0%
정 6
 
2.0%
호 5
 
1.7%
Other values (87) 144
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 290
98.0%
Decimal Number 6
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 49
 
16.9%
면 43
 
14.8%
읍 14
 
4.8%
남 10
 
3.4%
북 7
 
2.4%
성 6
 
2.1%
곡 6
 
2.1%
양 6
 
2.1%
정 6
 
2.1%
호 5
 
1.7%
Other values (85) 138
47.6%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
1 3
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 290
98.0%
Common 6
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 49
 
16.9%
면 43
 
14.8%
읍 14
 
4.8%
남 10
 
3.4%
북 7
 
2.4%
성 6
 
2.1%
곡 6
 
2.1%
양 6
 
2.1%
정 6
 
2.1%
호 5
 
1.7%
Other values (85) 138
47.6%
Common
ValueCountFrequency (%)
2 3
50.0%
1 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 290
98.0%
ASCII 6
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 49
 
16.9%
면 43
 
14.8%
읍 14
 
4.8%
남 10
 
3.4%
북 7
 
2.4%
성 6
 
2.1%
곡 6
 
2.1%
양 6
 
2.1%
정 6
 
2.1%
호 5
 
1.7%
Other values (85) 138
47.6%
ASCII
ValueCountFrequency (%)
2 3
50.0%
1 3
50.0%

행정동코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2396225 × 109
Minimum4.213032 × 109
Maximum4.283035 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:27:30.764698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.213032 × 109
5-th percentile4.2130509 × 109
Q14.2150558 × 109
median4.2210585 × 109
Q34.2770325 × 109
95-th percentile4.2830253 × 109
Maximum4.283035 × 109
Range70003000
Interquartile range (IQR)61976750

Descriptive statistics

Standard deviation30231238
Coefficient of variation (CV)0.0071306438
Kurtosis-1.6522889
Mean4.2396225 × 109
Median Absolute Deviation (MAD)6030500
Skewness0.57329062
Sum4.2396225 × 1011
Variance9.1392775 × 1014
MonotonicityNot monotonic
2023-12-10T21:27:31.000810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223031000 1
 
1.0%
4282032000 1
 
1.0%
4213032000 1
 
1.0%
4213038000 1
 
1.0%
4213062000 1
 
1.0%
4213033000 1
 
1.0%
4213051500 1
 
1.0%
4213039000 1
 
1.0%
4217054000 1
 
1.0%
4217059000 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
4213032000 1
1.0%
4213033000 1
1.0%
4213037000 1
1.0%
4213038000 1
1.0%
4213039000 1
1.0%
4213051500 1
1.0%
4213052000 1
1.0%
4213056000 1
1.0%
4213057500 1
1.0%
4213060000 1
1.0%
ValueCountFrequency (%)
4283035000 1
1.0%
4283034000 1
1.0%
4283033000 1
1.0%
4283032000 1
1.0%
4283031000 1
1.0%
4283025000 1
1.0%
4282033000 1
1.0%
4282032000 1
1.0%
4282031000 1
1.0%
4282025300 1
1.0%

인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7531.39
Minimum693
Maximum31537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:27:31.308017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum693
5-th percentile1332.75
Q13353.75
median4950
Q39785.5
95-th percentile22746.9
Maximum31537
Range30844
Interquartile range (IQR)6431.75

Descriptive statistics

Standard deviation6749.8942
Coefficient of variation (CV)0.89623486
Kurtosis3.0867985
Mean7531.39
Median Absolute Deviation (MAD)2459.5
Skewness1.8205823
Sum753139
Variance45561072
MonotonicityNot monotonic
2023-12-10T21:27:31.566739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5771 1
 
1.0%
4278 1
 
1.0%
4151 1
 
1.0%
7047 1
 
1.0%
24873 1
 
1.0%
3218 1
 
1.0%
3084 1
 
1.0%
3833 1
 
1.0%
5848 1
 
1.0%
3670 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
693 1
1.0%
744 1
1.0%
758 1
1.0%
1101 1
1.0%
1157 1
1.0%
1342 1
1.0%
1389 1
1.0%
1610 1
1.0%
1699 1
1.0%
1816 1
1.0%
ValueCountFrequency (%)
31537 1
1.0%
29961 1
1.0%
28974 1
1.0%
25627 1
1.0%
24873 1
1.0%
22635 1
1.0%
21650 1
1.0%
21069 1
1.0%
19766 1
1.0%
17647 1
1.0%

급수인구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6812.64
Minimum0
Maximum31535
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:27:31.846220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12165.25
median4494
Q38733.25
95-th percentile22653.8
Maximum31535
Range31535
Interquartile range (IQR)6568

Descriptive statistics

Standard deviation6952.0799
Coefficient of variation (CV)1.0204678
Kurtosis3.10117
Mean6812.64
Median Absolute Deviation (MAD)2734
Skewness1.8171808
Sum681264
Variance48331415
MonotonicityNot monotonic
2023-12-10T21:27:32.103962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
6.0%
5335 1
 
1.0%
7069 1
 
1.0%
1081 1
 
1.0%
5582 1
 
1.0%
24873 1
 
1.0%
1000 1
 
1.0%
3084 1
 
1.0%
925 1
 
1.0%
5848 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0 6
6.0%
501 1
 
1.0%
577 1
 
1.0%
732 1
 
1.0%
914 1
 
1.0%
925 1
 
1.0%
1000 1
 
1.0%
1081 1
 
1.0%
1106 1
 
1.0%
1207 1
 
1.0%
ValueCountFrequency (%)
31535 1
1.0%
29771 1
1.0%
28921 1
1.0%
25530 1
1.0%
24873 1
1.0%
22537 1
1.0%
21085 1
1.0%
21069 1
1.0%
18891 1
1.0%
17325 1
1.0%

급수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.99
Minimum0
Maximum100
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:27:32.333271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q173
median93
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)27

Descriptive statistics

Standard deviation28.605465
Coefficient of variation (CV)0.35761301
Kurtosis1.9136034
Mean79.99
Median Absolute Deviation (MAD)7
Skewness-1.6959793
Sum7999
Variance818.27263
MonotonicityNot monotonic
2023-12-10T21:27:32.641641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
100 34
34.0%
0 6
 
6.0%
92 5
 
5.0%
98 5
 
5.0%
94 4
 
4.0%
79 3
 
3.0%
73 3
 
3.0%
82 2
 
2.0%
81 2
 
2.0%
63 2
 
2.0%
Other values (28) 34
34.0%
ValueCountFrequency (%)
0 6
6.0%
15 1
 
1.0%
24 1
 
1.0%
26 1
 
1.0%
28 1
 
1.0%
31 2
 
2.0%
40 1
 
1.0%
48 1
 
1.0%
52 1
 
1.0%
58 1
 
1.0%
ValueCountFrequency (%)
100 34
34.0%
99 2
 
2.0%
98 5
 
5.0%
97 1
 
1.0%
96 2
 
2.0%
95 2
 
2.0%
94 4
 
4.0%
92 5
 
5.0%
91 1
 
1.0%
90 1
 
1.0%

사용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2128.04
Minimum0
Maximum9145
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:27:32.940399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1481
median1866.5
Q32862
95-th percentile5987.15
Maximum9145
Range9145
Interquartile range (IQR)2381

Descriptive statistics

Standard deviation2018.9241
Coefficient of variation (CV)0.94872468
Kurtosis1.4717355
Mean2128.04
Median Absolute Deviation (MAD)1315
Skewness1.283885
Sum212804
Variance4076054.4
MonotonicityNot monotonic
2023-12-10T21:27:33.172561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
14.0%
365 2
 
2.0%
2449 2
 
2.0%
2251 1
 
1.0%
1502 1
 
1.0%
2039 1
 
1.0%
463 1
 
1.0%
1275 1
 
1.0%
7286 1
 
1.0%
385 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0 14
14.0%
167 1
 
1.0%
188 1
 
1.0%
196 1
 
1.0%
365 2
 
2.0%
372 1
 
1.0%
373 1
 
1.0%
382 1
 
1.0%
385 1
 
1.0%
463 1
 
1.0%
ValueCountFrequency (%)
9145 1
1.0%
7660 1
1.0%
7441 1
1.0%
7286 1
1.0%
7282 1
1.0%
5919 1
1.0%
5713 1
1.0%
5626 1
1.0%
5446 1
1.0%
5409 1
1.0%

1인1일 사용량
Real number (ℝ)

ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370.68
Minimum0
Maximum2541
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:27:33.474884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1239.75
median301.5
Q3440.5
95-th percentile914.75
Maximum2541
Range2541
Interquartile range (IQR)200.75

Descriptive statistics

Standard deviation336.47582
Coefficient of variation (CV)0.90772585
Kurtosis18.301108
Mean370.68
Median Absolute Deviation (MAD)102.5
Skewness3.4237844
Sum37068
Variance113215.98
MonotonicityNot monotonic
2023-12-10T21:27:33.717305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
14.0%
288 3
 
3.0%
239 2
 
2.0%
357 2
 
2.0%
242 2
 
2.0%
248 1
 
1.0%
267 1
 
1.0%
359 1
 
1.0%
228 1
 
1.0%
341 1
 
1.0%
Other values (72) 72
72.0%
ValueCountFrequency (%)
0 14
14.0%
189 1
 
1.0%
194 1
 
1.0%
204 1
 
1.0%
213 1
 
1.0%
218 1
 
1.0%
225 1
 
1.0%
228 1
 
1.0%
231 1
 
1.0%
233 1
 
1.0%
ValueCountFrequency (%)
2541 1
1.0%
1583 1
1.0%
1160 1
1.0%
1103 1
1.0%
948 1
1.0%
913 1
1.0%
761 1
1.0%
708 1
1.0%
640 1
1.0%
627 1
1.0%

Interactions

2023-12-10T21:27:26.244168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:20.857319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:22.207007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:23.375054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:24.233762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:25.119138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:26.558096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:21.110589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:22.529068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:23.516208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:24.421203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:25.305594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:27.439008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:21.275322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:22.722707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:23.648682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:24.574648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:25.535743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:27.732751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:21.471411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:22.892006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:23.793253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:24.698547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:25.682141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:27.939454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:21.765723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:23.107615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:23.927006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:24.851457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:25.851036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:28.164552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:21.983649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:23.233749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:24.080579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:24.976735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:26.039945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:27:33.871117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명읍면동명행정동코드인구수급수인구급수율사용량1인1일 사용량
시군구명1.0000.0001.0000.0000.2540.4470.3490.527
읍면동명0.0001.0000.0000.9860.9850.0000.9400.000
행정동코드1.0000.0001.0000.3340.3860.5780.2960.322
인구수0.0000.9860.3341.0000.9970.0000.8670.000
급수인구0.2540.9850.3860.9971.0000.0000.8590.000
급수율0.4470.0000.5780.0000.0001.0000.3000.000
사용량0.3490.9400.2960.8670.8590.3001.0000.337
1인1일 사용량0.5270.0000.3220.0000.0000.0000.3371.000
2023-12-10T21:27:34.111112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드인구수급수인구급수율사용량1인1일 사용량시군구명
행정동코드1.000-0.315-0.282-0.291-0.0480.3520.963
인구수-0.3151.0000.9530.6220.669-0.1210.000
급수인구-0.2820.9531.0000.7570.658-0.1260.105
급수율-0.2910.6220.7571.0000.429-0.0800.204
사용량-0.0480.6690.6580.4291.0000.4510.152
1인1일 사용량0.352-0.121-0.126-0.0800.4511.0000.286
시군구명0.9630.0000.1050.2040.1520.2861.000

Missing values

2023-12-10T21:27:28.357484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:27:28.674411image/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

시도명시군구명읍면동명행정동코드인구수급수인구급수율사용량1인1일 사용량
0강원도삼척시근덕면422303100057715335922251438
1강원도영월군상동읍4275025300115791479522566
2강원도정선군고한읍4277025300473546639854091160
3강원도삼척시교동422305300011577115211003220314
4강원도속초시조양동4221058000256272553010000
5강원도영월군북면42750330002304110648580426
6강원도원주시일산동4213056000880088001003097348
7강원도인제군인제읍428102500097537696793201398
8강원도정선군정선읍4277025000113209980882627263
9강원도삼척시가곡면42230350007440000
시도명시군구명읍면동명행정동코드인구수급수인구급수율사용량1인1일 사용량
90강원도강릉시옥계면42150350004024330282833247
91강원도강릉시옥천동4215054000390939091001860473
92강원도강릉시왕산면421503200016990000
93강원도강릉시주문진읍42150250001764717325987441424
94강원도강릉시중앙동4215052000569756971002464425
95강원도강릉시초당동4215058000531553151002494457
96강원도강릉시포남1동421505710011656116561003247273
97강원도강릉시포남2동421505720014878148441003876258
98강원도영월군영월읍42750250002165021085975626274
99강원도영월군수주면4275037000203257728188475