Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1005.9 KiB
Average record size in memory103.0 B

Variable types

Categorical3
Text2
Numeric6

Dataset

Description구조별 기능별 세출예산 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=L02NOQO2F4A5RJHYXOK022315798&infSeq=1

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
회계연도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 회계연도High correlation
정책사업예산총계액(원) is highly overall correlated with 정책사업예산순계액(원)High correlation
행정운영경비총계액(원) is highly overall correlated with 재무활동순계액(원)High correlation
재무활동총계액(원) is highly overall correlated with 행정운영경비순계액(원)High correlation
정책사업예산순계액(원) is highly overall correlated with 정책사업예산총계액(원)High correlation
재무활동순계액(원) is highly overall correlated with 행정운영경비총계액(원)High correlation
행정운영경비순계액(원) is highly overall correlated with 재무활동총계액(원)High correlation
시군명 is highly imbalanced (76.7%)Imbalance
행정운영경비총계액(원) is highly skewed (γ1 = 29.09846087)Skewed
정책사업예산순계액(원) is highly skewed (γ1 = 23.87169123)Skewed
재무활동순계액(원) is highly skewed (γ1 = 38.39949181)Skewed
정책사업예산총계액(원) has 1144 (11.4%) zerosZeros
행정운영경비총계액(원) has 8015 (80.2%) zerosZeros
재무활동총계액(원) has 9467 (94.7%) zerosZeros
정책사업예산순계액(원) has 876 (8.8%) zerosZeros
재무활동순계액(원) has 8557 (85.6%) zerosZeros
행정운영경비순계액(원) has 9467 (94.7%) zerosZeros

Reproduction

Analysis started2023-12-10 21:54:32.574755
Analysis finished2023-12-10 21:54:38.020494
Duration5.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
8740 
2023
1260 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2023
5th row2022

Common Values

ValueCountFrequency (%)
2022 8740
87.4%
2023 1260
 
12.6%

Length

2023-12-11T06:54:38.086681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:54:38.166718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 8740
87.4%
2023 1260
 
12.6%

시군명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8740 
경기도
 
53
부천시
 
47
용인시
 
46
의왕시
 
45
Other values (28)
1069 

Length

Max length4
Median length4
Mean length3.8863
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row양주시
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 8740
87.4%
경기도 53
 
0.5%
부천시 47
 
0.5%
용인시 46
 
0.5%
의왕시 45
 
0.4%
파주시 43
 
0.4%
의정부시 43
 
0.4%
안산시 42
 
0.4%
평택시 42
 
0.4%
남양주시 42
 
0.4%
Other values (23) 857
 
8.6%

Length

2023-12-11T06:54:38.253283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8740
87.4%
경기도 53
 
0.5%
부천시 47
 
0.5%
용인시 46
 
0.5%
의왕시 45
 
0.4%
파주시 43
 
0.4%
의정부시 43
 
0.4%
안산시 42
 
0.4%
평택시 42
 
0.4%
남양주시 42
 
0.4%
Other values (23) 857
 
8.6%

자치단체명
Text

Distinct243
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T06:54:38.559911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8792
Min length4

Characters and Unicode

Total characters48792
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 row전북본청
2nd row전남구례군
3rd row경남김해시
4th row경기양주시
5th row강원원주시
ValueCountFrequency (%)
경기본청 109
 
1.1%
경기용인시 97
 
1.0%
경기부천시 96
 
1.0%
경기성남시 88
 
0.9%
경기안산시 88
 
0.9%
경기안성시 83
 
0.8%
경기고양시 82
 
0.8%
경기의왕시 82
 
0.8%
경기의정부시 82
 
0.8%
경기화성시 81
 
0.8%
Other values (233) 9112
91.1%
2023-12-11T06:54:38.986414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 4299
 
8.8%
시 4123
 
8.5%
군 3195
 
6.5%
남 2732
 
5.6%
기 2565
 
5.3%
구 2529
 
5.2%
북 2081
 
4.3%
전 1618
 
3.3%
천 1415
 
2.9%
산 1414
 
2.9%
Other values (123) 22821
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48792
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 4299
 
8.8%
시 4123
 
8.5%
군 3195
 
6.5%
남 2732
 
5.6%
기 2565
 
5.3%
구 2529
 
5.2%
북 2081
 
4.3%
전 1618
 
3.3%
천 1415
 
2.9%
산 1414
 
2.9%
Other values (123) 22821
46.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48792
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 4299
 
8.8%
시 4123
 
8.5%
군 3195
 
6.5%
남 2732
 
5.6%
기 2565
 
5.3%
구 2529
 
5.2%
북 2081
 
4.3%
전 1618
 
3.3%
천 1415
 
2.9%
산 1414
 
2.9%
Other values (123) 22821
46.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48792
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 4299
 
8.8%
시 4123
 
8.5%
군 3195
 
6.5%
남 2732
 
5.6%
기 2565
 
5.3%
구 2529
 
5.2%
북 2081
 
4.3%
전 1618
 
3.3%
천 1415
 
2.9%
산 1414
 
2.9%
Other values (123) 22821
46.8%

분야명
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
사회복지
1868 
환경
1319 
문화및관광
984 
일반공공행정
838 
산업ㆍ중소기업및에너지
822 
Other values (9)
4169 

Length

Max length11
Median length6
Mean length4.7852
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row산업ㆍ중소기업및에너지
2nd row교육
3rd row사회복지
4th row농림해양수산
5th row환경

Common Values

ValueCountFrequency (%)
사회복지 1868
18.7%
환경 1319
13.2%
문화및관광 984
9.8%
일반공공행정 838
8.4%
산업ㆍ중소기업및에너지 822
8.2%
국토및지역개발 780
7.8%
교통및물류 716
 
7.2%
농림해양수산 599
 
6.0%
기타 553
 
5.5%
보건 437
 
4.4%
Other values (4) 1084
10.8%

Length

2023-12-11T06:54:39.116154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사회복지 1868
18.7%
환경 1319
13.2%
문화및관광 984
9.8%
일반공공행정 838
8.4%
산업ㆍ중소기업및에너지 822
8.2%
국토및지역개발 780
7.8%
교통및물류 716
 
7.2%
농림해양수산 599
 
6.0%
기타 553
 
5.5%
보건 437
 
4.4%
Other values (4) 1084
10.8%

부문명
Text

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T06:54:39.366962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length5.1572
Min length2

Characters and Unicode

Total characters51572
Distinct characters116
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
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 row에너지및자원개발
2nd row평생ㆍ직업교육
3rd row기초생활보장
4th row농업ㆍ농촌
5th row자연
ValueCountFrequency (%)
기타 553
 
5.5%
상하수도ㆍ수질 454
 
4.5%
기초생활보장 430
 
4.3%
지역및도시 409
 
4.1%
대중교통ㆍ물류등기타 389
 
3.9%
폐기물 302
 
3.0%
예비비 294
 
2.9%
일반행정 280
 
2.8%
에너지및자원개발 268
 
2.7%
농업ㆍ농촌 267
 
2.7%
Other values (43) 6354
63.5%
2023-12-11T06:54:39.694834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ㆍ 3125
 
6.1%
기 2070
 
4.0%
지 1885
 
3.7%
업 1518
 
2.9%
및 1495
 
2.9%
도 1426
 
2.8%
보 1281
 
2.5%
수 1225
 
2.4%
원 1058
 
2.1%
반 1027
 
2.0%
Other values (106) 35462
68.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51572
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ㆍ 3125
 
6.1%
기 2070
 
4.0%
지 1885
 
3.7%
업 1518
 
2.9%
및 1495
 
2.9%
도 1426
 
2.8%
보 1281
 
2.5%
수 1225
 
2.4%
원 1058
 
2.1%
반 1027
 
2.0%
Other values (106) 35462
68.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51572
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
ㆍ 3125
 
6.1%
기 2070
 
4.0%
지 1885
 
3.7%
업 1518
 
2.9%
및 1495
 
2.9%
도 1426
 
2.8%
보 1281
 
2.5%
수 1225
 
2.4%
원 1058
 
2.1%
반 1027
 
2.0%
Other values (106) 35462
68.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48447
93.9%
Compat Jamo 3125
 
6.1%

Most frequent character per block

Compat Jamo
ValueCountFrequency (%)
ㆍ 3125
100.0%
Hangul
ValueCountFrequency (%)
기 2070
 
4.3%
지 1885
 
3.9%
업 1518
 
3.1%
및 1495
 
3.1%
도 1426
 
2.9%
보 1281
 
2.6%
수 1225
 
2.5%
원 1058
 
2.2%
반 1027
 
2.1%
일 1027
 
2.1%
Other values (105) 34435
71.1%

정책사업예산총계액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8785
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3658313 × 1010
Minimum0
Maximum6.3782536 × 1012
Zeros1144
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:54:39.859983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.5938725 × 108
median5.377244 × 109
Q32.0345966 × 1010
95-th percentile1.1853415 × 1011
Maximum6.3782536 × 1012
Range6.3782536 × 1012
Interquartile range (IQR)1.9586578 × 1010

Descriptive statistics

Standard deviation1.7314945 × 1011
Coefficient of variation (CV)5.1443294
Kurtosis532.75853
Mean3.3658313 × 1010
Median Absolute Deviation (MAD)5.349149 × 109
Skewness19.973257
Sum3.3658313 × 1014
Variance2.9980731 × 1022
MonotonicityNot monotonic
2023-12-11T06:54:40.029046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1144
 
11.4%
5000000 4
 
< 0.1%
1500000 4
 
< 0.1%
100000000 4
 
< 0.1%
50000000 4
 
< 0.1%
15000000 4
 
< 0.1%
1000000000 4
 
< 0.1%
301000000 4
 
< 0.1%
200000000 4
 
< 0.1%
10000000000 4
 
< 0.1%
Other values (8775) 8820
88.2%
ValueCountFrequency (%)
0 1144
11.4%
142000 1
 
< 0.1%
325000 1
 
< 0.1%
338000 1
 
< 0.1%
340000 1
 
< 0.1%
358000 1
 
< 0.1%
500000 1
 
< 0.1%
620000 1
 
< 0.1%
980000 1
 
< 0.1%
1000000 3
 
< 0.1%
ValueCountFrequency (%)
6378253632000 1
< 0.1%
6157030566000 1
< 0.1%
4306851623000 1
< 0.1%
4242699043000 1
< 0.1%
3746506601000 1
< 0.1%
3721279210000 1
< 0.1%
3514114374000 1
< 0.1%
3401749283000 1
< 0.1%
2994473249000 1
< 0.1%
2977867423000 1
< 0.1%

행정운영경비총계액(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1671
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6737201 × 109
Minimum0
Maximum1.6069856 × 1012
Zeros8015
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:54:40.180015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.00001 × 109
Maximum1.6069856 × 1012
Range1.6069856 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.4140806 × 1010
Coefficient of variation (CV)12.769028
Kurtosis1037.6223
Mean2.6737201 × 109
Median Absolute Deviation (MAD)0
Skewness29.098461
Sum2.6737201 × 1013
Variance1.1655946 × 1021
MonotonicityNot monotonic
2023-12-11T06:54:40.325707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8015
80.2%
1000000000 30
 
0.3%
500000000 19
 
0.2%
100000000 17
 
0.2%
200000000 17
 
0.2%
300000000 16
 
0.2%
10000000 10
 
0.1%
2000000000 10
 
0.1%
50000000 9
 
0.1%
5000000000 9
 
0.1%
Other values (1661) 1848
 
18.5%
ValueCountFrequency (%)
0 8015
80.2%
4000 1
 
< 0.1%
11000 1
 
< 0.1%
12000 1
 
< 0.1%
20000 1
 
< 0.1%
21000 1
 
< 0.1%
60000 1
 
< 0.1%
87000 1
 
< 0.1%
100000 2
 
< 0.1%
106000 1
 
< 0.1%
ValueCountFrequency (%)
1606985600000 1
< 0.1%
1266159132000 1
< 0.1%
1213360014000 1
< 0.1%
964314000000 1
< 0.1%
939728930000 1
< 0.1%
894145729000 1
< 0.1%
677041125000 1
< 0.1%
465000000000 1
< 0.1%
457047259000 1
< 0.1%
440942169000 1
< 0.1%

재무활동총계액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct533
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6550454 × 109
Minimum0
Maximum8.9103335 × 1011
Zeros9467
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:54:40.506248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile83229750
Maximum8.9103335 × 1011
Range8.9103335 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9293342 × 1010
Coefficient of variation (CV)8.0144947
Kurtosis299.53503
Mean3.6550454 × 109
Median Absolute Deviation (MAD)0
Skewness14.442918
Sum3.6550454 × 1013
Variance8.5809987 × 1020
MonotonicityNot monotonic
2023-12-11T06:54:40.642009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9467
94.7%
70326000 2
 
< 0.1%
111381349000 1
 
< 0.1%
70174000 1
 
< 0.1%
144337294000 1
 
< 0.1%
755185000 1
 
< 0.1%
3974695000 1
 
< 0.1%
71375000 1
 
< 0.1%
165659000 1
 
< 0.1%
104140000 1
 
< 0.1%
Other values (523) 523
 
5.2%
ValueCountFrequency (%)
0 9467
94.7%
8100000 1
 
< 0.1%
18011000 1
 
< 0.1%
20000000 1
 
< 0.1%
38302000 1
 
< 0.1%
39602000 1
 
< 0.1%
40600000 1
 
< 0.1%
41218000 1
 
< 0.1%
41539000 1
 
< 0.1%
42246000 1
 
< 0.1%
ValueCountFrequency (%)
891033353000 1
< 0.1%
878122749000 1
< 0.1%
779893740000 1
< 0.1%
599887570000 1
< 0.1%
549522450000 1
< 0.1%
538644653000 1
< 0.1%
450162224000 1
< 0.1%
433863117000 1
< 0.1%
386093193000 1
< 0.1%
359185675000 1
< 0.1%

정책사업예산순계액(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9016
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3884857 × 1010
Minimum0
Maximum4.2255814 × 1012
Zeros876
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:54:40.765292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.434165 × 108
median5.5326255 × 109
Q32.0136224 × 1010
95-th percentile9.7110573 × 1010
Maximum4.2255814 × 1012
Range4.2255814 × 1012
Interquartile range (IQR)1.9292808 × 1010

Descriptive statistics

Standard deviation8.8333271 × 1010
Coefficient of variation (CV)3.6982961
Kurtosis859.43657
Mean2.3884857 × 1010
Median Absolute Deviation (MAD)5.411695 × 109
Skewness23.871691
Sum2.3884857 × 1014
Variance7.8027667 × 1021
MonotonicityNot monotonic
2023-12-11T06:54:40.883832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 876
 
8.8%
38000000000 15
 
0.1%
5000000000 12
 
0.1%
100000000 5
 
0.1%
40700000000 5
 
0.1%
50000000 4
 
< 0.1%
200000000 4
 
< 0.1%
5000000 4
 
< 0.1%
10000000000 4
 
< 0.1%
10000000 4
 
< 0.1%
Other values (9006) 9067
90.7%
ValueCountFrequency (%)
0 876
8.8%
142000 1
 
< 0.1%
325000 1
 
< 0.1%
338000 1
 
< 0.1%
340000 1
 
< 0.1%
358000 1
 
< 0.1%
500000 1
 
< 0.1%
620000 1
 
< 0.1%
720000 1
 
< 0.1%
980000 1
 
< 0.1%
ValueCountFrequency (%)
4225581373000 1
< 0.1%
2993627099000 1
< 0.1%
2977047763000 1
< 0.1%
2114204465000 1
< 0.1%
1951288733000 1
< 0.1%
1774328000000 1
< 0.1%
1701130292000 1
< 0.1%
1396559455000 1
< 0.1%
1126076811000 1
< 0.1%
793284871000 1
< 0.1%

재무활동순계액(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1114
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0634495 × 109
Minimum0
Maximum9.64314 × 1011
Zeros8557
Zeros (%)85.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:54:41.005772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.4436288 × 109
Maximum9.64314 × 1011
Range9.64314 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5760016 × 1010
Coefficient of variation (CV)14.819712
Kurtosis1867.7682
Mean1.0634495 × 109
Median Absolute Deviation (MAD)0
Skewness38.399492
Sum1.0634495 × 1013
Variance2.483781 × 1020
MonotonicityNot monotonic
2023-12-11T06:54:41.136832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8557
85.6%
1000000000 27
 
0.3%
100000000 24
 
0.2%
200000000 20
 
0.2%
300000000 18
 
0.2%
500000000 17
 
0.2%
2000000000 16
 
0.2%
20000000 13
 
0.1%
10000000 12
 
0.1%
50000000 12
 
0.1%
Other values (1104) 1284
 
12.8%
ValueCountFrequency (%)
0 8557
85.6%
4000 1
 
< 0.1%
6000 1
 
< 0.1%
11000 1
 
< 0.1%
20000 1
 
< 0.1%
21000 1
 
< 0.1%
50000 1
 
< 0.1%
87000 1
 
< 0.1%
100000 2
 
< 0.1%
120000 1
 
< 0.1%
ValueCountFrequency (%)
964314000000 1
< 0.1%
628774125000 1
< 0.1%
465000000000 1
< 0.1%
418517089000 1
< 0.1%
391315404000 1
< 0.1%
372257293000 1
< 0.1%
323843910000 1
< 0.1%
238367000000 1
< 0.1%
183940101000 1
< 0.1%
180955968000 1
< 0.1%

행정운영경비순계액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct533
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6529837 × 109
Minimum0
Maximum8.9103335 × 1011
Zeros9467
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:54:41.498956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile83229750
Maximum8.9103335 × 1011
Range8.9103335 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9281649 × 1010
Coefficient of variation (CV)8.0158171
Kurtosis299.84336
Mean3.6529837 × 109
Median Absolute Deviation (MAD)0
Skewness14.448118
Sum3.6529837 × 1013
Variance8.5741498 × 1020
MonotonicityNot monotonic
2023-12-11T06:54:41.616444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9467
94.7%
70326000 2
 
< 0.1%
111381349000 1
 
< 0.1%
70174000 1
 
< 0.1%
144337294000 1
 
< 0.1%
755185000 1
 
< 0.1%
3974695000 1
 
< 0.1%
71375000 1
 
< 0.1%
165659000 1
 
< 0.1%
104140000 1
 
< 0.1%
Other values (523) 523
 
5.2%
ValueCountFrequency (%)
0 9467
94.7%
8100000 1
 
< 0.1%
18011000 1
 
< 0.1%
20000000 1
 
< 0.1%
38302000 1
 
< 0.1%
39602000 1
 
< 0.1%
40600000 1
 
< 0.1%
41218000 1
 
< 0.1%
41539000 1
 
< 0.1%
42246000 1
 
< 0.1%
ValueCountFrequency (%)
891033353000 1
< 0.1%
878122749000 1
< 0.1%
779893740000 1
< 0.1%
599887570000 1
< 0.1%
549522450000 1
< 0.1%
538644653000 1
< 0.1%
450162224000 1
< 0.1%
433863117000 1
< 0.1%
386093193000 1
< 0.1%
359185675000 1
< 0.1%

Interactions

2023-12-11T06:54:37.249813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:33.992668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.590368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:35.189757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.097726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.723185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:37.335816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.090576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.691756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:35.534847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.255449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.815085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:37.418930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.195261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.792125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:35.664578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.372537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.915590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:37.494145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.312495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.880738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:35.789216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.468918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.996308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:37.572352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.410301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.970421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:35.890633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.556564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:37.082173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:37.657591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:34.503649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:35.073047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:35.993922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:36.642771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:37.168789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:54:41.699938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명분야명부문명정책사업예산총계액(원)행정운영경비총계액(원)재무활동총계액(원)정책사업예산순계액(원)재무활동순계액(원)행정운영경비순계액(원)
회계연도1.000NaN0.0000.0440.0310.0000.0000.0000.0000.000
시군명NaN1.0000.0000.0000.1530.0000.0000.0000.0000.000
분야명0.0000.0001.0001.0000.0750.0960.3800.0570.0660.380
부문명0.0440.0001.0001.0000.1090.2400.4120.1060.1620.412
정책사업예산총계액(원)0.0310.1530.0750.1091.0000.4970.0000.8910.3800.000
행정운영경비총계액(원)0.0000.0000.0960.2400.4971.0000.0000.4090.8610.000
재무활동총계액(원)0.0000.0000.3800.4120.0000.0001.0000.0000.0001.000
정책사업예산순계액(원)0.0000.0000.0570.1060.8910.4090.0001.0000.4830.000
재무활동순계액(원)0.0000.0000.0660.1620.3800.8610.0000.4831.0000.000
행정운영경비순계액(원)0.0000.0000.3800.4120.0000.0001.0000.0000.0001.000
2023-12-11T06:54:41.832116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명분야명
회계연도1.0001.0000.000
시군명1.0001.0000.000
분야명0.0000.0001.000
2023-12-11T06:54:41.911135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정책사업예산총계액(원)행정운영경비총계액(원)재무활동총계액(원)정책사업예산순계액(원)재무활동순계액(원)행정운영경비순계액(원)회계연도시군명분야명
정책사업예산총계액(원)1.0000.165-0.3640.9060.146-0.3640.0230.0670.033
행정운영경비총계액(원)0.1651.000-0.0200.1690.815-0.0200.0000.0000.042
재무활동총계액(원)-0.364-0.0201.000-0.119-0.0031.0000.0000.0000.170
정책사업예산순계액(원)0.9060.169-0.1191.0000.155-0.1190.0000.0000.025
재무활동순계액(원)0.1460.815-0.0030.1551.000-0.0030.0000.0000.024
행정운영경비순계액(원)-0.364-0.0201.000-0.119-0.0031.0000.0000.0000.170
회계연도0.0230.0000.0000.0000.0000.0001.0001.0000.000
시군명0.0670.0000.0000.0000.0000.0001.0001.0000.000
분야명0.0330.0420.1700.0250.0240.1700.0000.0001.000

Missing values

2023-12-11T06:54:37.765700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:54:37.931836image/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

회계연도시군명자치단체명분야명부문명정책사업예산총계액(원)행정운영경비총계액(원)재무활동총계액(원)정책사업예산순계액(원)재무활동순계액(원)행정운영경비순계액(원)
73552022<NA>전북본청산업ㆍ중소기업및에너지에너지및자원개발8433471200072479700002114549300000
58432022<NA>전남구례군교육평생ㆍ직업교육8998690001440000000089986900014400000000
73762022<NA>경남김해시사회복지기초생활보장992535100006020000000099253510000200000000
9162023양주시경기양주시농림해양수산농업ㆍ농촌39982832000003998283200000
40132022<NA>강원원주시환경자연7702340000077023400000
28082022<NA>서울성동구일반공공행정일반행정42290598000855670400004157207500062439180000
103732022<NA>경기성남시문화및관광문화재657052800000654599500000
12792023이천시경기이천시예비비예비비563845800000563845800000
40392022<NA>강원원주시국토및지역개발지역및도시75510000001150000000007551000000115000000000
63882022<NA>경북고령군사회복지취약계층지원17565419000001755502300000
회계연도시군명자치단체명분야명부문명정책사업예산총계액(원)행정운영경비총계액(원)재무활동총계액(원)정책사업예산순계액(원)재무활동순계액(원)행정운영경비순계액(원)
90472022<NA>경기의왕시국토및지역개발지역및도시2358410007000000023584100070000000
90942022<NA>부산사상구교통및물류도로568322100000568322100000
8442023안양시경기안양시사회복지노인ㆍ청소년2222083730000022101568800000
29122022<NA>서울동작구사회복지노인ㆍ청소년1512769080000015127690800000
39302022<NA>강원본청산업ㆍ중소기업및에너지산업진흥ㆍ고도화5140000000041400000000
109192022<NA>부산부산진구사회복지노동449421800000449421800000
95732022<NA>인천본청공공질서및안전재난방재ㆍ민방위482150250005104571800005780823000510457180000
72272022<NA>충남보령시기타기타0750000005512226000005512226000
76552022<NA>충남서산시교통및물류대중교통ㆍ물류등기타3010972500020100000003003146800000
124412022<NA>경기오산시산업ㆍ중소기업및에너지산업기술지원1025670000010256700000

Duplicate rows

Most frequently occurring

회계연도시군명자치단체명분야명부문명정책사업예산총계액(원)행정운영경비총계액(원)재무활동총계액(원)정책사업예산순계액(원)재무활동순계액(원)행정운영경비순계액(원)# duplicates
02022<NA>경기시흥시일반공공행정재정ㆍ금융0000002
12022<NA>울산울주군사회복지사회복지일반0000002