Overview

Dataset statistics

Number of variables15
Number of observations3599
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory464.1 KiB
Average record size in memory132.0 B

Variable types

Categorical5
Text1
Numeric9

Dataset

Description기능별 재원별 세출예산 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=FRF1VBDTJUJCA204NT5922371545&infSeq=1

Alerts

회계연도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 지방채금액(원) and 4 other fieldsHigh correlation
특별교부금(원) is highly overall correlated with 시군명High correlation
민자금액(원) is highly overall correlated with 시군명High correlation
국고보조금(원) is highly overall correlated with 지특보조금(원) and 2 other fieldsHigh correlation
지특보조금(원) is highly overall correlated with 국고보조금(원) and 1 other fieldsHigh correlation
기금보조금(원) is highly overall correlated with 국고보조금(원) and 1 other fieldsHigh correlation
시도비(원) is highly overall correlated with 국고보조금(원) and 2 other fieldsHigh correlation
지방채금액(원) is highly overall correlated with 시군명High correlation
기타금액(원) is highly overall correlated with 시군명High correlation
시군명 is highly imbalanced (78.2%)Imbalance
특별교부금(원) is highly imbalanced (99.1%)Imbalance
민자금액(원) is highly imbalanced (99.6%)Imbalance
국고보조금(원) is highly skewed (γ1 = 24.52248614)Skewed
기금보조금(원) is highly skewed (γ1 = 21.59106126)Skewed
특별교부세금액(원) is highly skewed (γ1 = 23.09918176)Skewed
소방안전교부세금액(원) is highly skewed (γ1 = 21.5221957)Skewed
지방채금액(원) is highly skewed (γ1 = 52.46405567)Skewed
기타금액(원) is highly skewed (γ1 = 38.89143502)Skewed
국고보조금(원) has 562 (15.6%) zerosZeros
지특보조금(원) has 1956 (54.3%) zerosZeros
기금보조금(원) has 1736 (48.2%) zerosZeros
특별교부세금액(원) has 3560 (98.9%) zerosZeros
소방안전교부세금액(원) has 3525 (97.9%) zerosZeros
시도비(원) has 313 (8.7%) zerosZeros
시군구비(원) has 251 (7.0%) zerosZeros
지방채금액(원) has 3509 (97.5%) zerosZeros
기타금액(원) has 3579 (99.4%) zerosZeros

Reproduction

Analysis started2023-12-10 21:08:38.866555
Analysis finished2023-12-10 21:08:47.760677
Duration8.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
2022
3180 
2023
419 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2022 3180
88.4%
2023 419
 
11.6%

Length

2023-12-11T06:08:47.822144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:08:47.935189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 3180
88.4%
2023 419
 
11.6%

시군명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct33
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
<NA>
3180 
경기도
 
14
안성시
 
14
시흥시
 
14
성남시
 
13
Other values (28)
364 

Length

Max length4
Median length4
Mean length3.8944151
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3180
88.4%
경기도 14
 
0.4%
안성시 14
 
0.4%
시흥시 14
 
0.4%
성남시 13
 
0.4%
안양시 13
 
0.4%
고양시 13
 
0.4%
안산시 13
 
0.4%
과천시 13
 
0.4%
수원시 13
 
0.4%
Other values (23) 299
 
8.3%

Length

2023-12-11T06:08:48.059521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3180
88.4%
안성시 14
 
0.4%
시흥시 14
 
0.4%
경기도 14
 
0.4%
파주시 13
 
0.4%
오산시 13
 
0.4%
용인시 13
 
0.4%
의왕시 13
 
0.4%
의정부시 13
 
0.4%
이천시 13
 
0.4%
Other values (23) 299
 
8.3%

자치단체명
Text

Distinct243
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
2023-12-11T06:08:48.373312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8827452
Min length4

Characters and Unicode

Total characters17573
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

Unique0 ?
Unique (%)0.0%

Sample

1st row경기가평군
2nd row경기가평군
3rd row경기가평군
4th row경기가평군
5th row경기가평군
ValueCountFrequency (%)
경기안성시 28
 
0.8%
경기시흥시 28
 
0.8%
경기본청 28
 
0.8%
경기용인시 26
 
0.7%
경기가평군 26
 
0.7%
경기의왕시 26
 
0.7%
경기화성시 26
 
0.7%
경기연천군 26
 
0.7%
경기오산시 26
 
0.7%
경기양주시 26
 
0.7%
Other values (233) 3333
92.6%
2023-12-11T06:08:48.786059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 1441
 
8.2%
시 1375
 
7.8%
군 1159
 
6.6%
구 1089
 
6.2%
남 956
 
5.4%
기 851
 
4.8%
북 746
 
4.2%
전 592
 
3.4%
산 537
 
3.1%
천 507
 
2.9%
Other values (123) 8320
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17573
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 1441
 
8.2%
시 1375
 
7.8%
군 1159
 
6.6%
구 1089
 
6.2%
남 956
 
5.4%
기 851
 
4.8%
북 746
 
4.2%
전 592
 
3.4%
산 537
 
3.1%
천 507
 
2.9%
Other values (123) 8320
47.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17573
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 1441
 
8.2%
시 1375
 
7.8%
군 1159
 
6.6%
구 1089
 
6.2%
남 956
 
5.4%
기 851
 
4.8%
북 746
 
4.2%
전 592
 
3.4%
산 537
 
3.1%
천 507
 
2.9%
Other values (123) 8320
47.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17573
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 1441
 
8.2%
시 1375
 
7.8%
군 1159
 
6.6%
구 1089
 
6.2%
남 956
 
5.4%
기 851
 
4.8%
북 746
 
4.2%
전 592
 
3.4%
산 537
 
3.1%
천 507
 
2.9%
Other values (123) 8320
47.3%

분야명
Categorical

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
일반공공행정
275 
공공질서및안전
275 
문화및관광
275 
환경
275 
사회복지
275 
Other values (9)
2224 

Length

Max length11
Median length6
Mean length4.7618783
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반공공행정
2nd row공공질서및안전
3rd row교육
4th row문화및관광
5th row환경

Common Values

ValueCountFrequency (%)
일반공공행정 275
 
7.6%
공공질서및안전 275
 
7.6%
문화및관광 275
 
7.6%
환경 275
 
7.6%
사회복지 275
 
7.6%
기타 275
 
7.6%
산업ㆍ중소기업및에너지 275
 
7.6%
교통및물류 275
 
7.6%
국토및지역개발 275
 
7.6%
예비비 275
 
7.6%
Other values (4) 849
23.6%

Length

2023-12-11T06:08:48.903745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반공공행정 275
 
7.6%
공공질서및안전 275
 
7.6%
문화및관광 275
 
7.6%
환경 275
 
7.6%
사회복지 275
 
7.6%
기타 275
 
7.6%
산업ㆍ중소기업및에너지 275
 
7.6%
교통및물류 275
 
7.6%
국토및지역개발 275
 
7.6%
예비비 275
 
7.6%
Other values (4) 849
23.6%

국고보조금(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2976
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6312897 × 1010
Minimum0
Maximum1.0934396 × 1013
Zeros562
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T06:08:49.009169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170000000
median8.0989 × 108
Q35.7284255 × 109
95-th percentile1.3091155 × 1011
Maximum1.0934396 × 1013
Range1.0934396 × 1013
Interquartile range (IQR)5.6584255 × 109

Descriptive statistics

Standard deviation3.1227809 × 1011
Coefficient of variation (CV)8.5996467
Kurtosis734.23253
Mean3.6312897 × 1010
Median Absolute Deviation (MAD)8.0989 × 108
Skewness24.522486
Sum1.3069012 × 1014
Variance9.7517604 × 1022
MonotonicityNot monotonic
2023-12-11T06:08:49.135201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 562
 
15.6%
14000000 6
 
0.2%
2000000 5
 
0.1%
8000000 4
 
0.1%
11700000 4
 
0.1%
15000000 4
 
0.1%
10000000 4
 
0.1%
50000000 4
 
0.1%
13000000 3
 
0.1%
12900000 3
 
0.1%
Other values (2966) 3000
83.4%
ValueCountFrequency (%)
0 562
15.6%
1000000 1
 
< 0.1%
1650000 1
 
< 0.1%
2000000 5
 
0.1%
2800000 1
 
< 0.1%
2875000 1
 
< 0.1%
3000000 1
 
< 0.1%
3200000 1
 
< 0.1%
3800000 1
 
< 0.1%
3900000 1
 
< 0.1%
ValueCountFrequency (%)
10934395776000 1
< 0.1%
9768867271000 1
< 0.1%
6088110107000 1
< 0.1%
4121120335000 1
< 0.1%
3456144422000 1
< 0.1%
3199299778000 1
< 0.1%
2877367827000 1
< 0.1%
2671851778000 1
< 0.1%
2564282299000 1
< 0.1%
2498040960000 1
< 0.1%

지특보조금(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1479
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.856794 × 109
Minimum0
Maximum2.575415 × 1011
Zeros1956
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T06:08:49.243529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.22275 × 109
95-th percentile9.10833 × 109
Maximum2.575415 × 1011
Range2.575415 × 1011
Interquartile range (IQR)1.22275 × 109

Descriptive statistics

Standard deviation1.3370774 × 1010
Coefficient of variation (CV)4.6803423
Kurtosis121.71737
Mean2.856794 × 109
Median Absolute Deviation (MAD)0
Skewness9.9402808
Sum1.0281601 × 1013
Variance1.7877759 × 1020
MonotonicityNot monotonic
2023-12-11T06:08:49.358503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1956
54.3%
400000000 10
 
0.3%
800000000 8
 
0.2%
300000000 8
 
0.2%
350000000 7
 
0.2%
500000000 6
 
0.2%
1000000000 6
 
0.2%
150000000 6
 
0.2%
1700000000 6
 
0.2%
280000000 5
 
0.1%
Other values (1469) 1581
43.9%
ValueCountFrequency (%)
0 1956
54.3%
700000 1
 
< 0.1%
900000 1
 
< 0.1%
5000000 1
 
< 0.1%
5199000 1
 
< 0.1%
6117000 1
 
< 0.1%
6715000 1
 
< 0.1%
8804000 1
 
< 0.1%
10000000 2
 
0.1%
10995000 1
 
< 0.1%
ValueCountFrequency (%)
257541500000 1
< 0.1%
216796133000 1
< 0.1%
190326490000 1
< 0.1%
179596200000 1
< 0.1%
167020000000 1
< 0.1%
155102000000 1
< 0.1%
152451680000 1
< 0.1%
147150000000 1
< 0.1%
145201000000 1
< 0.1%
143789800000 1
< 0.1%

기금보조금(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1801
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.73212 × 109
Minimum0
Maximum9.4501896 × 1011
Zeros1736
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T06:08:49.490098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20000000
Q31.331995 × 109
95-th percentile1.2437839 × 1010
Maximum9.4501896 × 1011
Range9.4501896 × 1011
Interquartile range (IQR)1.331995 × 109

Descriptive statistics

Standard deviation2.6035903 × 1010
Coefficient of variation (CV)6.9761698
Kurtosis614.73614
Mean3.73212 × 109
Median Absolute Deviation (MAD)20000000
Skewness21.591061
Sum1.34319 × 1013
Variance6.7786825 × 1020
MonotonicityNot monotonic
2023-12-11T06:08:49.608262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1736
48.2%
200000000 7
 
0.2%
20000000 6
 
0.2%
100000000 6
 
0.2%
1700000000 5
 
0.1%
80000000 5
 
0.1%
250000000 5
 
0.1%
500000000 5
 
0.1%
7200000 4
 
0.1%
400000000 4
 
0.1%
Other values (1791) 1816
50.5%
ValueCountFrequency (%)
0 1736
48.2%
1000000 1
 
< 0.1%
1100000 1
 
< 0.1%
1400000 1
 
< 0.1%
1500000 2
 
0.1%
2050000 1
 
< 0.1%
2100000 1
 
< 0.1%
2420000 1
 
< 0.1%
3000000 1
 
< 0.1%
3400000 1
 
< 0.1%
ValueCountFrequency (%)
945018965000 1
< 0.1%
578472114000 1
< 0.1%
453213288000 1
< 0.1%
423262779000 1
< 0.1%
419717466000 1
< 0.1%
274106138000 1
< 0.1%
273355319000 1
< 0.1%
240582688000 1
< 0.1%
234735487000 1
< 0.1%
229745108000 1
< 0.1%

특별교부세금액(원)
Real number (ℝ)

SKEWED  ZEROS 

Distinct33
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8407724.4
Minimum0
Maximum4.562 × 109
Zeros3560
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T06:08:49.708999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.562 × 109
Range4.562 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3587407 × 108
Coefficient of variation (CV)16.160624
Kurtosis630.20367
Mean8407724.4
Median Absolute Deviation (MAD)0
Skewness23.099182
Sum3.02594 × 1010
Variance1.8461764 × 1016
MonotonicityNot monotonic
2023-12-11T06:08:49.810157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 3560
98.9%
300000000 5
 
0.1%
150000000 2
 
0.1%
700000000 2
 
0.1%
20000000 2
 
0.1%
8000000 1
 
< 0.1%
4562000000 1
 
< 0.1%
9000000 1
 
< 0.1%
13000000 1
 
< 0.1%
800000000 1
 
< 0.1%
Other values (23) 23
 
0.6%
ValueCountFrequency (%)
0 3560
98.9%
2000000 1
 
< 0.1%
6000000 1
 
< 0.1%
8000000 1
 
< 0.1%
9000000 1
 
< 0.1%
13000000 1
 
< 0.1%
20000000 2
 
0.1%
23400000 1
 
< 0.1%
52000000 1
 
< 0.1%
60000000 1
 
< 0.1%
ValueCountFrequency (%)
4562000000 1
< 0.1%
3975465000 1
< 0.1%
2300000000 1
< 0.1%
2186000000 1
< 0.1%
1870000000 1
< 0.1%
1800000000 1
< 0.1%
1782000000 1
< 0.1%
1776000000 1
< 0.1%
1200000000 1
< 0.1%
1050000000 1
< 0.1%

소방안전교부세금액(원)
Real number (ℝ)

SKEWED  ZEROS 

Distinct74
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7779657 × 108
Minimum0
Maximum1.1462734 × 1011
Zeros3525
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T06:08:49.933023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.1462734 × 1011
Range1.1462734 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.702451 × 109
Coefficient of variation (CV)13.327922
Kurtosis561.98982
Mean2.7779657 × 108
Median Absolute Deviation (MAD)0
Skewness21.522196
Sum9.9978985 × 1011
Variance1.3708143 × 1019
MonotonicityNot monotonic
2023-12-11T06:08:50.071169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3525
97.9%
250000000 2
 
0.1%
55000000000 1
 
< 0.1%
2434000000 1
 
< 0.1%
27479538000 1
 
< 0.1%
29300000000 1
 
< 0.1%
48000000 1
 
< 0.1%
3068600000 1
 
< 0.1%
321000000 1
 
< 0.1%
73382400000 1
 
< 0.1%
Other values (64) 64
 
1.8%
ValueCountFrequency (%)
0 3525
97.9%
1500000 1
 
< 0.1%
5000000 1
 
< 0.1%
10000000 1
 
< 0.1%
40000000 1
 
< 0.1%
48000000 1
 
< 0.1%
50000000 1
 
< 0.1%
80000000 1
 
< 0.1%
96000000 1
 
< 0.1%
105000000 1
 
< 0.1%
ValueCountFrequency (%)
114627335000 1
< 0.1%
111623970000 1
< 0.1%
73382400000 1
< 0.1%
55000000000 1
< 0.1%
50000000000 1
< 0.1%
46600000000 1
< 0.1%
45000000000 1
< 0.1%
29959000000 1
< 0.1%
29300000000 1
< 0.1%
28491700000 1
< 0.1%

시도비(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3262
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7611696 × 1010
Minimum0
Maximum8.5824772 × 1012
Zeros313
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T06:08:50.181686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.99403 × 108
median1.488053 × 109
Q37.166447 × 109
95-th percentile1.5812073 × 1011
Maximum8.5824772 × 1012
Range8.5824772 × 1012
Interquartile range (IQR)6.867044 × 109

Descriptive statistics

Standard deviation3.2211233 × 1011
Coefficient of variation (CV)6.7654034
Kurtosis349.08397
Mean4.7611696 × 1010
Median Absolute Deviation (MAD)1.452787 × 109
Skewness16.667562
Sum1.7135449 × 1014
Variance1.0375635 × 1023
MonotonicityNot monotonic
2023-12-11T06:08:50.292712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 313
 
8.7%
70000000 5
 
0.1%
150000000 4
 
0.1%
135000000 3
 
0.1%
50000000 3
 
0.1%
10000000 2
 
0.1%
95000000 2
 
0.1%
129400000 2
 
0.1%
300000000 2
 
0.1%
1288000 2
 
0.1%
Other values (3252) 3261
90.6%
ValueCountFrequency (%)
0 313
8.7%
1288000 2
 
0.1%
1770000 1
 
< 0.1%
1800000 1
 
< 0.1%
1890000 1
 
< 0.1%
2000000 1
 
< 0.1%
3190000 1
 
< 0.1%
3219000 1
 
< 0.1%
3300000 1
 
< 0.1%
4000000 1
 
< 0.1%
ValueCountFrequency (%)
8582477248000 1
< 0.1%
7520751539000 1
< 0.1%
7283191724000 1
< 0.1%
5983964806000 1
< 0.1%
4277021185000 1
< 0.1%
4098376080000 1
< 0.1%
3555619887000 1
< 0.1%
3452762299000 1
< 0.1%
3172868991000 1
< 0.1%
3163854051000 1
< 0.1%

특별교부금(원)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
0
3593 
100000000
 
3
850000000
 
1
500000000
 
1
150000000
 
1

Length

Max length9
Median length1
Mean length1.013337
Min length1

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3593
99.8%
100000000 3
 
0.1%
850000000 1
 
< 0.1%
500000000 1
 
< 0.1%
150000000 1
 
< 0.1%

Length

2023-12-11T06:08:50.603400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:08:50.690495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3593
99.8%
100000000 3
 
0.1%
850000000 1
 
< 0.1%
500000000 1
 
< 0.1%
150000000 1
 
< 0.1%

시군구비(원)
Real number (ℝ)

ZEROS 

Distinct3349
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1960396 × 1010
Minimum0
Maximum4.8080042 × 1011
Zeros251
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T06:08:50.786605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.388713 × 109
median2.0355547 × 1010
Q35.4343966 × 1010
95-th percentile1.5184238 × 1011
Maximum4.8080042 × 1011
Range4.8080042 × 1011
Interquartile range (IQR)4.7955253 × 1010

Descriptive statistics

Standard deviation5.9987635 × 1010
Coefficient of variation (CV)1.4296251
Kurtosis12.706575
Mean4.1960396 × 1010
Median Absolute Deviation (MAD)1.685872 × 1010
Skewness3.1280341
Sum1.5101547 × 1014
Variance3.5985164 × 1021
MonotonicityNot monotonic
2023-12-11T06:08:50.908154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 251
 
7.0%
34417449000 1
 
< 0.1%
6573188000 1
 
< 0.1%
37749001000 1
 
< 0.1%
40765759000 1
 
< 0.1%
60186892000 1
 
< 0.1%
13461885000 1
 
< 0.1%
30367979000 1
 
< 0.1%
7582594000 1
 
< 0.1%
15708203000 1
 
< 0.1%
Other values (3339) 3339
92.8%
ValueCountFrequency (%)
0 251
7.0%
9536000 1
 
< 0.1%
23500000 1
 
< 0.1%
36840000 1
 
< 0.1%
62919000 1
 
< 0.1%
73287000 1
 
< 0.1%
78672000 1
 
< 0.1%
87280000 1
 
< 0.1%
88467000 1
 
< 0.1%
91883000 1
 
< 0.1%
ValueCountFrequency (%)
480800424000 1
< 0.1%
471238935000 1
< 0.1%
439332038000 1
< 0.1%
439090507000 1
< 0.1%
425432792000 1
< 0.1%
424697427000 1
< 0.1%
422225026000 1
< 0.1%
418692775000 1
< 0.1%
413709215000 1
< 0.1%
407893588000 1
< 0.1%

지방채금액(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct73
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7870853 × 108
Minimum0
Maximum1.354675 × 1012
Zeros3509
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T06:08:51.036565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.354675 × 1012
Range1.354675 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3733873 × 1010
Coefficient of variation (CV)27.009949
Kurtosis2953.1909
Mean8.7870853 × 108
Median Absolute Deviation (MAD)0
Skewness52.464056
Sum3.162472 × 1012
Variance5.6329672 × 1020
MonotonicityNot monotonic
2023-12-11T06:08:51.162245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3509
97.5%
2000000000 8
 
0.2%
1000000000 3
 
0.1%
4000000000 3
 
0.1%
2500000000 3
 
0.1%
16000000000 2
 
0.1%
1150000000 2
 
0.1%
51000000000 2
 
0.1%
7000000000 2
 
0.1%
14000000000 2
 
0.1%
Other values (63) 63
 
1.8%
ValueCountFrequency (%)
0 3509
97.5%
450000000 1
 
< 0.1%
600000000 1
 
< 0.1%
1000000000 3
 
0.1%
1150000000 2
 
0.1%
1500000000 1
 
< 0.1%
2000000000 8
 
0.2%
2050000000 1
 
< 0.1%
2100000000 1
 
< 0.1%
2400000000 1
 
< 0.1%
ValueCountFrequency (%)
1354675000000 1
< 0.1%
280000000000 1
< 0.1%
249800000000 1
< 0.1%
135333000000 1
< 0.1%
69800000000 1
< 0.1%
62330000000 1
< 0.1%
51000000000 2
0.1%
48600000000 1
< 0.1%
46264000000 1
< 0.1%
44400000000 1
< 0.1%

민자금액(원)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
0
3598 
56092000
 
1

Length

Max length8
Median length1
Mean length1.001945
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3598
> 99.9%
56092000 1
 
< 0.1%

Length

2023-12-11T06:08:51.284271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:08:51.377069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3598
> 99.9%
56092000 1
 
< 0.1%

기타금액(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0411478 × 108
Minimum0
Maximum1.4281985 × 1011
Zeros3579
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T06:08:51.460166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.4281985 × 1011
Range1.4281985 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8952935 × 109
Coefficient of variation (CV)27.808669
Kurtosis1741.3298
Mean1.0411478 × 108
Median Absolute Deviation (MAD)0
Skewness38.891435
Sum3.747091 × 1011
Variance8.3827244 × 1018
MonotonicityNot monotonic
2023-12-11T06:08:51.575390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3579
99.4%
324600000 1
 
< 0.1%
13278311000 1
 
< 0.1%
600000000 1
 
< 0.1%
3284745000 1
 
< 0.1%
55216217000 1
 
< 0.1%
70000000 1
 
< 0.1%
47372876000 1
 
< 0.1%
358785000 1
 
< 0.1%
142819851000 1
 
< 0.1%
Other values (11) 11
 
0.3%
ValueCountFrequency (%)
0 3579
99.4%
29000000 1
 
< 0.1%
70000000 1
 
< 0.1%
81900000 1
 
< 0.1%
324600000 1
 
< 0.1%
358785000 1
 
< 0.1%
400000000 1
 
< 0.1%
600000000 1
 
< 0.1%
750000000 1
 
< 0.1%
1553053000 1
 
< 0.1%
ValueCountFrequency (%)
142819851000 1
< 0.1%
56681375000 1
< 0.1%
55216217000 1
< 0.1%
47372876000 1
< 0.1%
31243860000 1
< 0.1%
13278311000 1
< 0.1%
8504125000 1
< 0.1%
6320400000 1
< 0.1%
3420000000 1
< 0.1%
3284745000 1
< 0.1%

Interactions

2023-12-11T06:08:46.619143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:39.982940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:40.944162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.644948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.426260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.217220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.010418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.763450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:45.592256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.700242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:40.278528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.016105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.734181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.509656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.304953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.091181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.851384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:45.679102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.801371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:40.362992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.089850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.825994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.596065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.402183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.169755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.935875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:45.991163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.882957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:40.436230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.156849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.901077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.685169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.490855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.245505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:45.013852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.075172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.965411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:40.509985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.230585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.974040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.777817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.569365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.321265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:45.093044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.162459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:47.079775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:40.587295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.316543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.056238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.865229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.657825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.410569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:45.196565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.252389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:47.177981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:40.673721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.390939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.141512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.957854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.741494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.498754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:45.308823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.340446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:47.273420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:40.767642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.469347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.230803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.038100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.826217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.584632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:45.400929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.430252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:47.360398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:40.863553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:41.555249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:42.331130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.135121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:43.919093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:44.669047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:45.493464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:08:46.529088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:08:51.758416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명분야명국고보조금(원)지특보조금(원)기금보조금(원)특별교부세금액(원)소방안전교부세금액(원)시도비(원)특별교부금(원)시군구비(원)지방채금액(원)민자금액(원)기타금액(원)
회계연도1.000NaN0.0000.0290.0700.0170.0000.0060.0500.0000.2500.0000.0000.000
시군명NaN1.0000.0000.0000.2320.4650.0000.0000.366NaN0.403NaNNaNNaN
분야명0.0000.0001.0000.1850.1170.1090.0500.2070.0980.0000.4560.0000.0000.000
국고보조금(원)0.0290.0000.1851.0000.7000.6860.4070.0000.7580.0000.0000.7080.0000.330
지특보조금(원)0.0700.2320.1170.7001.0000.6200.4150.0000.4880.0000.0000.4080.0000.277
기금보조금(원)0.0170.4650.1090.6860.6201.0000.1320.0000.6860.0000.0000.7400.0000.287
특별교부세금액(원)0.0000.0000.0500.4070.4150.1321.0000.0000.2880.0000.1650.0000.0000.000
소방안전교부세금액(원)0.0060.0000.2070.0000.0000.0000.0001.0000.2250.0000.0000.0000.0000.000
시도비(원)0.0500.3660.0980.7580.4880.6860.2880.2251.0000.0000.0000.9290.0000.000
특별교부금(원)0.000NaN0.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
시군구비(원)0.2500.4030.4560.0000.0000.0000.1650.0000.0000.0001.0000.0000.0000.000
지방채금액(원)0.000NaN0.0000.7080.4080.7400.0000.0000.9290.0000.0001.0000.0000.000
민자금액(원)0.000NaN0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
기타금액(원)0.000NaN0.0000.3300.2770.2870.0000.0000.0000.0000.0000.0000.0001.000
2023-12-11T06:08:51.915912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분야명회계연도시군명특별교부금(원)민자금액(원)
분야명1.0000.0000.0000.0000.000
회계연도0.0001.0001.0000.0000.000
시군명0.0001.0001.0001.0001.000
특별교부금(원)0.0000.0001.0001.0000.000
민자금액(원)0.0000.0001.0000.0001.000
2023-12-11T06:08:52.025642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국고보조금(원)지특보조금(원)기금보조금(원)특별교부세금액(원)소방안전교부세금액(원)시도비(원)시군구비(원)지방채금액(원)기타금액(원)회계연도시군명분야명특별교부금(원)민자금액(원)
국고보조금(원)1.0000.5140.5990.0700.1280.7880.2880.1480.0880.0310.0000.0690.0000.000
지특보조금(원)0.5141.0000.4120.0980.1340.5970.2010.1610.0960.0540.1000.0470.0000.000
기금보조금(원)0.5990.4121.0000.0260.1170.5750.1670.0980.0670.0120.2560.0530.0000.000
특별교부세금액(원)0.0700.0980.0261.0000.0030.0690.0760.018-0.0080.0000.0000.0220.0000.000
소방안전교부세금액(원)0.1280.1340.1170.0031.0000.225-0.2240.1920.1720.0070.0000.0780.0000.000
시도비(원)0.7880.5970.5750.0690.2251.0000.1350.1660.1130.0380.1770.0430.0000.000
시군구비(원)0.2880.2010.1670.076-0.2240.1351.000-0.040-0.0990.1920.1490.2030.0000.000
지방채금액(원)0.1480.1610.0980.0180.1920.166-0.0401.0000.2760.0001.0000.0000.0000.000
기타금액(원)0.0880.0960.067-0.0080.1720.113-0.0990.2761.0000.0001.0000.0000.0000.000
회계연도0.0310.0540.0120.0000.0070.0380.1920.0000.0001.0001.0000.0000.0000.000
시군명0.0000.1000.2560.0000.0000.1770.1491.0001.0001.0001.0000.0001.0001.000
분야명0.0690.0470.0530.0220.0780.0430.2030.0000.0000.0000.0001.0000.0000.000
특별교부금(원)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.000
민자금액(원)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.000

Missing values

2023-12-11T06:08:47.517278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:08:47.688184image/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

회계연도시군명자치단체명분야명국고보조금(원)지특보조금(원)기금보조금(원)특별교부세금액(원)소방안전교부세금액(원)시도비(원)특별교부금(원)시군구비(원)지방채금액(원)민자금액(원)기타금액(원)
02023가평군경기가평군일반공공행정17049700000001091325000030587844000000
12023가평군경기가평군공공질서및안전78561000744000000000102288600004888146000000
22023가평군경기가평군교육0000012528000005701347000000
32023가평군경기가평군문화및관광5380000000895618000004552550000026289354000000
42023가평군경기가평군환경2985782000016087624000001756397000064200397000000
52023가평군경기가평군사회복지79819759000175763000017527940000021094783000046321269000000
62023가평군경기가평군기타33276000260800004527900000350616000090258864000000
72023가평군경기가평군농림해양수산493606100015480000005911670000009075759000030666458000000
82023가평군경기가평군산업ㆍ중소기업및에너지718830000000075499400006404829000000
92023가평군경기가평군교통및물류90000009040000000003361781000025756954000000
회계연도시군명자치단체명분야명국고보조금(원)지특보조금(원)기금보조금(원)특별교부세금액(원)소방안전교부세금액(원)시도비(원)특별교부금(원)시군구비(원)지방채금액(원)민자금액(원)기타금액(원)
35892022<NA>제주본청환경94400821000348160000002952281500004000000063789044300000000
35902022<NA>제주본청사회복지66198880000028281446000623871450000067692303900000000
35912022<NA>제주본청보건196388280001465200020813342000006385753600000000
35922022<NA>제주본청농림해양수산10704249300013572080000132959616000020000000043591942400000000
35932022<NA>제주본청산업ㆍ중소기업및에너지8202721000018179407000272226020000015534858800000000
35942022<NA>제주본청교통및물류8829936000111808616000470000000000199300000033074415100000000
35952022<NA>제주본청국토및지역개발8008434000134200000003070000000025000000040503793200000000
35962022<NA>제주본청과학기술00000101739000000000
35972022<NA>제주본청예비비000005186224100000000
35982022<NA>경남함양군국토및지역개발21452000006418000000500000000002158284000042584907000000