Overview

Dataset statistics

Number of variables8
Number of observations275
Missing cells243
Missing cells (%)11.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.9 KiB
Average record size in memory70.5 B

Variable types

Categorical1
Text2
Numeric5

Alerts

세입결산총계금액(원) is highly overall correlated with 일반회계액(원) and 3 other fieldsHigh correlation
일반회계액(원) is highly overall correlated with 세입결산총계금액(원) and 3 other fieldsHigh correlation
공기업특별회계액(원) is highly overall correlated with 세입결산총계금액(원) and 1 other fieldsHigh correlation
기타특별회계액(원) is highly overall correlated with 세입결산총계금액(원) and 2 other fieldsHigh correlation
기금액(원) is highly overall correlated with 세입결산총계금액(원) and 2 other fieldsHigh correlation
시군명 has 243 (88.4%) missing valuesMissing
세입결산총계금액(원) has unique valuesUnique
일반회계액(원) has unique valuesUnique
기타특별회계액(원) has unique valuesUnique
기금액(원) has unique valuesUnique
공기업특별회계액(원) has 118 (42.9%) zerosZeros

Reproduction

Analysis started2023-12-10 21:04:05.038004
Analysis finished2023-12-10 21:04:08.865905
Duration3.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2020
243 
2021
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2020 243
88.4%
2021 32
 
11.6%

Length

2023-12-11T06:04:08.925437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:04:09.021254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 243
88.4%
2021 32
 
11.6%

시군명
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing243
Missing (%)88.4%
Memory size2.3 KiB
2023-12-11T06:04:09.202406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.09375
Min length3

Characters and Unicode

Total characters99
Distinct characters41
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

Unique32 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row경기도
3rd row고양시
4th row과천시
5th row광명시
ValueCountFrequency (%)
경기도 1
 
3.1%
고양시 1
 
3.1%
화성시 1
 
3.1%
하남시 1
 
3.1%
포천시 1
 
3.1%
평택시 1
 
3.1%
파주시 1
 
3.1%
이천시 1
 
3.1%
의정부시 1
 
3.1%
의왕시 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T06:04:09.534045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

자치단체명
Text

Distinct243
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-11T06:04:09.922045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8872727
Min length4

Characters and Unicode

Total characters1344
Distinct characters133
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

Unique211 ?
Unique (%)76.7%

Sample

1st row경기가평군
2nd row경기본청
3rd row경기고양시
4th row경기과천시
5th row경기광명시
ValueCountFrequency (%)
경기가평군 2
 
0.7%
경기평택시 2
 
0.7%
경기안성시 2
 
0.7%
경기여주시 2
 
0.7%
경기용인시 2
 
0.7%
경기연천군 2
 
0.7%
경기양평군 2
 
0.7%
경기의왕시 2
 
0.7%
경기하남시 2
 
0.7%
경기이천시 2
 
0.7%
Other values (233) 255
92.7%
2023-12-11T06:04:10.432426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1344
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1344
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1344
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

세입결산총계금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3338366 × 1012
Minimum3.1237618 × 1011
Maximum5.4092826 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:04:10.875066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1237618 × 1011
5-th percentile5.2847058 × 1011
Q17.6035449 × 1011
median1.1049638 × 1012
Q31.7702301 × 1012
95-th percentile7.8169241 × 1012
Maximum5.4092826 × 1013
Range5.378045 × 1013
Interquartile range (IQR)1.0098756 × 1012

Descriptive statistics

Standard deviation5.3600029 × 1012
Coefficient of variation (CV)2.2966488
Kurtosis58.954352
Mean2.3338366 × 1012
Median Absolute Deviation (MAD)4.0509631 × 1011
Skewness7.1884476
Sum6.4180506 × 1014
Variance2.8729631 × 1025
MonotonicityNot monotonic
2023-12-11T06:04:11.029737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
975613183290 1
 
0.4%
581887342194 1
 
0.4%
13789568689826 1
 
0.4%
1038437299216 1
 
0.4%
697216546047 1
 
0.4%
801189259593 1
 
0.4%
636807198179 1
 
0.4%
823584067101 1
 
0.4%
951665670840 1
 
0.4%
529581599202 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
312376179711 1
0.4%
319449490079 1
0.4%
327862947324 1
0.4%
395907442187 1
0.4%
412973425509 1
0.4%
434759421290 1
0.4%
461291059162 1
0.4%
462124303884 1
0.4%
482362568430 1
0.4%
484957149612 1
0.4%
ValueCountFrequency (%)
54092826095504 1
0.4%
46896335582048 1
0.4%
41883849817856 1
0.4%
17033332927516 1
0.4%
15083740146035 1
0.4%
14692149769103 1
0.4%
13789568689826 1
0.4%
12694084368707 1
0.4%
11978036781973 1
0.4%
10112644918654 1
0.4%

일반회계액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8043523 × 1012
Minimum2.3467717 × 1011
Maximum3.6678665 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:04:11.179830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3467717 × 1011
5-th percentile4.5251992 × 1011
Q16.4376582 × 1011
median9.275699 × 1011
Q31.4507318 × 1012
95-th percentile5.8386954 × 1012
Maximum3.6678665 × 1013
Range3.6443988 × 1013
Interquartile range (IQR)8.0696598 × 1011

Descriptive statistics

Standard deviation3.9278108 × 1012
Coefficient of variation (CV)2.1768536
Kurtosis56.537954
Mean1.8043523 × 1012
Median Absolute Deviation (MAD)3.1906213 × 1011
Skewness7.0802246
Sum4.9619687 × 1014
Variance1.5427697 × 1025
MonotonicityNot monotonic
2023-12-11T06:04:11.369806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
746640362678 1
 
0.4%
527781585987 1
 
0.4%
10883377498020 1
 
0.4%
927569899070 1
 
0.4%
600251356796 1
 
0.4%
712671432628 1
 
0.4%
605054011154 1
 
0.4%
653435130469 1
 
0.4%
798897468470 1
 
0.4%
508371420960 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
234677174210 1
0.4%
261887243827 1
0.4%
291209624090 1
0.4%
310515436387 1
0.4%
347278983187 1
0.4%
379417714638 1
0.4%
389071528610 1
0.4%
409724095103 1
0.4%
415782352641 1
0.4%
430477979594 1
0.4%
ValueCountFrequency (%)
36678664720982 1
0.4%
35872828632696 1
0.4%
32880278236926 1
0.4%
12350082629930 1
0.4%
11161892700411 1
0.4%
10883377498020 1
0.4%
9658167075675 1
0.4%
9372756525281 1
0.4%
8900118156680 1
0.4%
8332660770356 1
0.4%

공기업특별회계액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1429074 × 1011
Minimum0
Maximum2.0352661 × 1012
Zeros118
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:04:11.570362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.2062635 × 1010
Q31.5583774 × 1011
95-th percentile3.7592012 × 1011
Maximum2.0352661 × 1012
Range2.0352661 × 1012
Interquartile range (IQR)1.5583774 × 1011

Descriptive statistics

Standard deviation2.0943003 × 1011
Coefficient of variation (CV)1.8324322
Kurtosis33.961022
Mean1.1429074 × 1011
Median Absolute Deviation (MAD)3.2062635 × 1010
Skewness4.7824292
Sum3.1429953 × 1013
Variance4.3860938 × 1022
MonotonicityNot monotonic
2023-12-11T06:04:11.746847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
42.9%
83577266291 1
 
0.4%
288363707003 1
 
0.4%
141750005010 1
 
0.4%
20216357335 1
 
0.4%
69946678773 1
 
0.4%
16533067227 1
 
0.4%
43750415626 1
 
0.4%
19108923737 1
 
0.4%
58883960433 1
 
0.4%
Other values (148) 148
53.8%
ValueCountFrequency (%)
0 118
42.9%
3468287272 1
 
0.4%
10641766990 1
 
0.4%
12309025057 1
 
0.4%
12795968604 1
 
0.4%
14599965708 1
 
0.4%
16478328200 1
 
0.4%
16533067227 1
 
0.4%
17141938626 1
 
0.4%
17434949856 1
 
0.4%
ValueCountFrequency (%)
2035266115486 1
0.4%
1515313761448 1
0.4%
1040786855176 1
0.4%
955499044328 1
0.4%
674201193751 1
0.4%
665457749460 1
0.4%
505614723335 1
0.4%
494537689897 1
0.4%
455067913241 1
0.4%
427858769927 1
0.4%

기타특별회계액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.856683 × 1011
Minimum3.0149302 × 108
Maximum1.1631604 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:04:11.971892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0149302 × 108
5-th percentile6.2940071 × 109
Q12.0534352 × 1010
median3.747316 × 1010
Q39.7044319 × 1010
95-th percentile6.0273067 × 1011
Maximum1.1631604 × 1013
Range1.1631303 × 1013
Interquartile range (IQR)7.6509967 × 1010

Descriptive statistics

Standard deviation8.0345202 × 1011
Coefficient of variation (CV)4.3273517
Kurtosis153.88695
Mean1.856683 × 1011
Median Absolute Deviation (MAD)2.1655079 × 1010
Skewness11.4366
Sum5.1058781 × 1013
Variance6.4553515 × 1023
MonotonicityNot monotonic
2023-12-11T06:04:12.153625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25938909481 1
 
0.4%
26199384421 1
 
0.4%
1262033659021 1
 
0.4%
57264139100 1
 
0.4%
13178628572 1
 
0.4%
40429992181 1
 
0.4%
13749926074 1
 
0.4%
44276303398 1
 
0.4%
30992634350 1
 
0.4%
5726661655 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
301493024 1
0.4%
2076690094 1
0.4%
2467489316 1
0.4%
2499346245 1
0.4%
3691994668 1
0.4%
3799987397 1
0.4%
4037232540 1
0.4%
4074919264 1
0.4%
4778318184 1
0.4%
4994037149 1
0.4%
ValueCountFrequency (%)
11631604036526 1
0.4%
3934345683354 1
0.4%
3478301581157 1
0.4%
2363764901251 1
0.4%
2313897843720 1
0.4%
1681702762627 1
0.4%
1262033659021 1
0.4%
962000238619 1
0.4%
893343788442 1
0.4%
836636234897 1
0.4%

기금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2952528 × 1011
Minimum3.720271 × 109
Maximum5.9914891 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:04:12.304844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.720271 × 109
5-th percentile8.8842938 × 109
Q13.4823065 × 1010
median7.1763798 × 1010
Q31.5662993 × 1011
95-th percentile9.8582888 × 1011
Maximum5.9914891 × 1012
Range5.9877688 × 1012
Interquartile range (IQR)1.2180687 × 1011

Descriptive statistics

Standard deviation6.3074623 × 1011
Coefficient of variation (CV)2.7480468
Kurtosis48.129467
Mean2.2952528 × 1011
Median Absolute Deviation (MAD)4.9529853 × 1010
Skewness6.4550387
Sum6.3119453 × 1013
Variance3.9784081 × 1023
MonotonicityNot monotonic
2023-12-11T06:04:12.449125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119456644840 1
 
0.4%
27906371786 1
 
0.4%
1644157532785 1
 
0.4%
53603261046 1
 
0.4%
83786560679 1
 
0.4%
48087834784 1
 
0.4%
18003260951 1
 
0.4%
77195577611 1
 
0.4%
121775568020 1
 
0.4%
15483516587 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
3720271038 1
0.4%
4764586588 1
0.4%
4859134428 1
0.4%
4973433324 1
0.4%
5146767501 1
0.4%
5330061874 1
0.4%
5336777027 1
0.4%
5609813214 1
0.4%
5904304466 1
0.4%
6031669160 1
0.4%
ValueCountFrequency (%)
5991489081353 1
0.4%
5215721235697 1
0.4%
4553127310796 1
0.4%
3154829805468 1
0.4%
1831904958055 1
0.4%
1769581829518 1
0.4%
1710885213216 1
0.4%
1644157532785 1
0.4%
1426525917856 1
0.4%
1328565598690 1
0.4%

Interactions

2023-12-11T06:04:08.057014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:05.434904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:06.017773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:06.662996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:07.359241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:08.171815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:05.539843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:06.138251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:06.763914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:07.482211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:08.270108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:05.655228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:06.282131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:06.896008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:07.653809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:08.404855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:05.780559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:06.427632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:07.052128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:07.800471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:08.512227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:05.913716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:06.562672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:07.205930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:04:07.932729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:04:12.564837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명세입결산총계금액(원)일반회계액(원)공기업특별회계액(원)기타특별회계액(원)기금액(원)
회계연도1.000NaN0.1080.0000.4570.1040.108
시군명NaN1.0001.0001.0001.0001.0001.000
세입결산총계금액(원)0.1081.0001.0000.9700.8340.9250.931
일반회계액(원)0.0001.0000.9701.0000.8070.8200.894
공기업특별회계액(원)0.4571.0000.8340.8071.0000.8470.960
기타특별회계액(원)0.1041.0000.9250.8200.8471.0000.960
기금액(원)0.1081.0000.9310.8940.9600.9601.000
2023-12-11T06:04:12.747176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세입결산총계금액(원)일반회계액(원)공기업특별회계액(원)기타특별회계액(원)기금액(원)회계연도
세입결산총계금액(원)1.0000.9750.6710.6440.7100.114
일반회계액(원)0.9751.0000.5880.6030.6180.000
공기업특별회계액(원)0.6710.5881.0000.4400.4750.340
기타특별회계액(원)0.6440.6030.4401.0000.5090.126
기금액(원)0.7100.6180.4750.5091.0000.080
회계연도0.1140.0000.3400.1260.0801.000

Missing values

2023-12-11T06:04:08.663668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:04:08.811774image/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

회계연도시군명자치단체명세입결산총계금액(원)일반회계액(원)공기업특별회계액(원)기타특별회계액(원)기금액(원)
02021가평군경기가평군9756131832907466403626788357726629125938909481119456644840
12021경기도경기본청468963355820483667866472098229183609635939343456833545991489081353
22021고양시경기고양시43072273812923111778765010303807370570435641563890455999681822
32021과천시경기과천시12149048468745477877008564550679132416416479896205632752881
42021광명시경기광명시158468027712110640757397209766104927232355583034099387657789
52021광주시경기광주시22083211828301528668581561280652669318268191858068130808073883
62021구리시경기구리시122836310565982963609303016041015597267713801088170603055569
72021군포시경기군포시126515726881392319618981814556775864742463260980153930059368
82021김포시경기김포시2344403945458184868314368018427598473991239416104220205400935
92021남양주시경기남양주시29552799672742379998467940232848853434224289015038118143630862
회계연도시군명자치단체명세입결산총계금액(원)일반회계액(원)공기업특별회계액(원)기타특별회계액(원)기금액(원)
2652020<NA>부산남구7172668545736885661391900211999085347500806849
2662020<NA>부산북구79105195723073477729337002592584961030348814250
2672020<NA>부산해운대구101057874000294771904220002689535380335964343999
2682020<NA>부산사하구8467349173138237262359700180352480194973433324
2692020<NA>부산금정구6830828431476588754801100194427764494764586588
2702020<NA>부산강서구60946075906053556777961703574433830038148641143
2712020<NA>부산연제구59659618676153809498746001954983905138951360250
2722020<NA>부산수영구52915397477047740977472001078358246040960617590
2732020<NA>부산사상구61806274501858625315060001220627035019603324068
2742020<NA>부산기장군880549611918744122111953011661197976619815520199