Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1002
Duplicate rows (%)10.0%
Total size in memory1.5 MiB
Average record size in memory161.0 B

Variable types

Numeric8
Categorical4
Text6

Dataset

Description부산광역시해운대구_재정정보공개시스템_세부사업별예산현액및지출액_20230113
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15050175

Alerts

수입대체경비(ETC_AMT) has constant value ""Constant
Dataset has 1002 (10.0%) duplicate rowsDuplicates
회계구분코드(FIS_FG_CD) is highly overall correlated with 회계구분(FIS_FG_NM)High correlation
예산현액(BGT_CURR_AMT) is highly overall correlated with 지출액(EXPD_AMT) and 2 other fieldsHigh correlation
지출액(EXPD_AMT) is highly overall correlated with 예산현액(BGT_CURR_AMT) and 1 other fieldsHigh correlation
편성액(COMPO_AMT) is highly overall correlated with 예산현액(BGT_CURR_AMT) and 2 other fieldsHigh correlation
시구군예산액(SIGUNGU_CURR_AMT) is highly overall correlated with 예산현액(BGT_CURR_AMT) and 1 other fieldsHigh correlation
회계구분(FIS_FG_NM) is highly overall correlated with 회계구분코드(FIS_FG_CD)High correlation
회계구분(FIS_FG_NM) is highly imbalanced (89.2%)Imbalance
지출액(EXPD_AMT) is highly skewed (γ1 = 20.7390702)Skewed
예산현액(BGT_CURR_AMT) has 193 (1.9%) zerosZeros
지출액(EXPD_AMT) has 1046 (10.5%) zerosZeros
편성액(COMPO_AMT) has 614 (6.1%) zerosZeros
이월액(FORWD_AMT) has 9221 (92.2%) zerosZeros
변경금액(CHNG_AMT) has 9739 (97.4%) zerosZeros
시구군예산액(SIGUNGU_CURR_AMT) has 2385 (23.8%) zerosZeros

Reproduction

Analysis started2023-12-10 16:45:36.808451
Analysis finished2023-12-10 16:45:50.268801
Duration13.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도(FIS_YEAR)
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.2841
Minimum2015
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:45:50.341311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12017
median2020
Q32021
95-th percentile2022
Maximum2023
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3755739
Coefficient of variation (CV)0.0011764436
Kurtosis-1.0998251
Mean2019.2841
Median Absolute Deviation (MAD)2
Skewness-0.34182342
Sum20192841
Variance5.6433515
MonotonicityNot monotonic
2023-12-11T01:45:50.525694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2022 1903
19.0%
2021 1621
16.2%
2020 1376
13.8%
2019 1054
10.5%
2017 976
9.8%
2018 973
9.7%
2016 883
8.8%
2015 846
8.5%
2023 368
 
3.7%
ValueCountFrequency (%)
2015 846
8.5%
2016 883
8.8%
2017 976
9.8%
2018 973
9.7%
2019 1054
10.5%
2020 1376
13.8%
2021 1621
16.2%
2022 1903
19.0%
2023 368
 
3.7%
ValueCountFrequency (%)
2023 368
 
3.7%
2022 1903
19.0%
2021 1621
16.2%
2020 1376
13.8%
2019 1054
10.5%
2018 973
9.7%
2017 976
9.8%
2016 883
8.8%
2015 846
8.5%

회계구분코드(FIS_FG_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.1981
Minimum100
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:45:50.700118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100
median100
Q3100
95-th percentile210
Maximum460
Range360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation49.223606
Coefficient of variation (CV)0.44668289
Kurtosis31.482415
Mean110.1981
Median Absolute Deviation (MAD)0
Skewness5.5346217
Sum1101981
Variance2422.9634
MonotonicityNot monotonic
2023-12-11T01:45:50.856278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100 9472
94.7%
215 217
 
2.2%
420 81
 
0.8%
210 48
 
0.5%
211 27
 
0.3%
410 22
 
0.2%
450 22
 
0.2%
225 21
 
0.2%
411 19
 
0.2%
440 18
 
0.2%
Other values (7) 53
 
0.5%
ValueCountFrequency (%)
100 9472
94.7%
210 48
 
0.5%
211 27
 
0.3%
212 12
 
0.1%
215 217
 
2.2%
220 7
 
0.1%
225 21
 
0.2%
410 22
 
0.2%
411 19
 
0.2%
412 4
 
< 0.1%
ValueCountFrequency (%)
460 13
 
0.1%
450 22
 
0.2%
440 18
 
0.2%
430 13
 
0.1%
420 81
0.8%
414 1
 
< 0.1%
413 3
 
< 0.1%
412 4
 
< 0.1%
411 19
 
0.2%
410 22
 
0.2%

회계구분(FIS_FG_NM)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반회계
9472 
주차장특별회계
 
217
사회복지기금
 
81
의료급여기금특별회계
 
48
지하수관리특별회계
 
27
Other values (14)
 
155

Length

Max length15
Median length4
Mean length4.1777
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반회계 9472
94.7%
주차장특별회계 217
 
2.2%
사회복지기금 81
 
0.8%
의료급여기금특별회계 48
 
0.5%
지하수관리특별회계 27
 
0.3%
식품진흥기금 22
 
0.2%
재난관리기금 22
 
0.2%
기반시설특별회계 21
 
0.2%
양성평등기금 16
 
0.2%
옥외광고발전기금 14
 
0.1%
Other values (9) 60
 
0.6%

Length

2023-12-11T01:45:51.046211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반회계 9472
94.7%
주차장특별회계 217
 
2.2%
사회복지기금 81
 
0.8%
의료급여기금특별회계 48
 
0.5%
지하수관리특별회계 27
 
0.3%
식품진흥기금 22
 
0.2%
재난관리기금 22
 
0.2%
기반시설특별회계 21
 
0.2%
양성평등기금 16
 
0.2%
옥외광고발전기금 14
 
0.1%
Other values (9) 60
 
0.6%

부서명(DEPT_NM)
Text

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:45:51.379958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.9341
Min length3

Characters and Unicode

Total characters49341
Distinct characters113
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

Unique0 ?
Unique (%)0.0%

Sample

1st row일자리경제과
2nd row건강증진과
3rd row좌3동
4th row보건정책과
5th row도시관리과
ValueCountFrequency (%)
가족복지과 852
 
8.5%
복지정책과 614
 
6.1%
건설과 564
 
5.6%
행복나눔과 546
 
5.5%
보건정책과 505
 
5.1%
일자리경제과 501
 
5.0%
늘푸른과 452
 
4.5%
노인장애인복지과 427
 
4.3%
안전총괄과 324
 
3.2%
관광문화과 316
 
3.2%
Other values (67) 4899
49.0%
2023-12-11T01:45:51.880085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 8358
 
16.9%
복 2579
 
5.2%
지 2371
 
4.8%
정 2042
 
4.1%
건 1561
 
3.2%
책 1186
 
2.4%
행 1141
 
2.3%
관 1095
 
2.2%
인 1050
 
2.1%
리 912
 
1.8%
Other values (103) 27046
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48474
98.2%
Decimal Number 867
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 8358
 
17.2%
복 2579
 
5.3%
지 2371
 
4.9%
정 2042
 
4.2%
건 1561
 
3.2%
책 1186
 
2.4%
행 1141
 
2.4%
관 1095
 
2.3%
인 1050
 
2.2%
리 912
 
1.9%
Other values (99) 26179
54.0%
Decimal Number
ValueCountFrequency (%)
1 329
37.9%
2 313
36.1%
3 142
16.4%
4 83
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48474
98.2%
Common 867
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 8358
 
17.2%
복 2579
 
5.3%
지 2371
 
4.9%
정 2042
 
4.2%
건 1561
 
3.2%
책 1186
 
2.4%
행 1141
 
2.4%
관 1095
 
2.3%
인 1050
 
2.2%
리 912
 
1.9%
Other values (99) 26179
54.0%
Common
ValueCountFrequency (%)
1 329
37.9%
2 313
36.1%
3 142
16.4%
4 83
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48474
98.2%
ASCII 867
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 8358
 
17.2%
복 2579
 
5.3%
지 2371
 
4.9%
정 2042
 
4.2%
건 1561
 
3.2%
책 1186
 
2.4%
행 1141
 
2.4%
관 1095
 
2.3%
인 1050
 
2.2%
리 912
 
1.9%
Other values (99) 26179
54.0%
ASCII
ValueCountFrequency (%)
1 329
37.9%
2 313
36.1%
3 142
16.4%
4 83
 
9.6%

정책사업명(PBIZ_NM)
Text

Distinct263
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:45:52.287767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length9.9191
Min length3

Characters and Unicode

Total characters99191
Distinct characters228
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st row지속적이고 안정적 일자리 제공
2nd row건강 증진
3rd row좌3동 사업경비
4th row건강 정책
5th row보행환경개선
ValueCountFrequency (%)
및 1431
 
5.8%
지원 1210
 
4.9%
조성 1015
 
4.1%
육성 733
 
3.0%
사업경비 728
 
3.0%
관리 602
 
2.4%
보육가족지원 520
 
2.1%
건강 520
 
2.1%
지방도 451
 
1.8%
건설 451
 
1.8%
Other values (346) 17002
68.9%
2023-12-11T01:45:52.932166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14664
 
14.8%
지 4413
 
4.4%
성 2502
 
2.5%
원 2379
 
2.4%
정 2212
 
2.2%
건 1930
 
1.9%
관 1810
 
1.8%
경 1780
 
1.8%
비 1748
 
1.8%
도 1606
 
1.6%
Other values (218) 64147
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81558
82.2%
Space Separator 14664
 
14.8%
Open Punctuation 903
 
0.9%
Close Punctuation 903
 
0.9%
Decimal Number 811
 
0.8%
Other Punctuation 288
 
0.3%
Dash Punctuation 32
 
< 0.1%
Uppercase Letter 32
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 4413
 
5.4%
성 2502
 
3.1%
원 2379
 
2.9%
정 2212
 
2.7%
건 1930
 
2.4%
관 1810
 
2.2%
경 1780
 
2.2%
비 1748
 
2.1%
도 1606
 
2.0%
동 1576
 
1.9%
Other values (208) 59602
73.1%
Decimal Number
ValueCountFrequency (%)
1 301
37.1%
2 285
35.1%
3 142
17.5%
4 83
 
10.2%
Space Separator
ValueCountFrequency (%)
14664
100.0%
Open Punctuation
ValueCountFrequency (%)
( 903
100.0%
Close Punctuation
ValueCountFrequency (%)
) 903
100.0%
Other Punctuation
ValueCountFrequency (%)
· 288
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81558
82.2%
Common 17601
 
17.7%
Latin 32
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 4413
 
5.4%
성 2502
 
3.1%
원 2379
 
2.9%
정 2212
 
2.7%
건 1930
 
2.4%
관 1810
 
2.2%
경 1780
 
2.2%
비 1748
 
2.1%
도 1606
 
2.0%
동 1576
 
1.9%
Other values (208) 59602
73.1%
Common
ValueCountFrequency (%)
14664
83.3%
( 903
 
5.1%
) 903
 
5.1%
1 301
 
1.7%
· 288
 
1.6%
2 285
 
1.6%
3 142
 
0.8%
4 83
 
0.5%
- 32
 
0.2%
Latin
ValueCountFrequency (%)
U 32
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81558
82.2%
ASCII 17345
 
17.5%
None 288
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14664
84.5%
( 903
 
5.2%
) 903
 
5.2%
1 301
 
1.7%
2 285
 
1.6%
3 142
 
0.8%
4 83
 
0.5%
- 32
 
0.2%
U 32
 
0.2%
Hangul
ValueCountFrequency (%)
지 4413
 
5.4%
성 2502
 
3.1%
원 2379
 
2.9%
정 2212
 
2.7%
건 1930
 
2.4%
관 1810
 
2.2%
경 1780
 
2.2%
비 1748
 
2.1%
도 1606
 
2.0%
동 1576
 
1.9%
Other values (208) 59602
73.1%
None
ValueCountFrequency (%)
· 288
100.0%

단위사업명(UBIZ_NM)
Text

Distinct335
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:45:53.352027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length8.9642
Min length2

Characters and Unicode

Total characters89642
Distinct characters293
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.2%

Sample

1st row일자리 찾기 지원
2nd row방문건강 관리
3rd row청사관리
4th row청사관리
5th row애향길보행환경개선
ValueCountFrequency (%)
지원 1756
 
7.8%
및 1057
 
4.7%
관리 961
 
4.3%
운영 760
 
3.4%
지방행정 497
 
2.2%
기초사무수행 497
 
2.2%
장애인 372
 
1.7%
구축 342
 
1.5%
추진 339
 
1.5%
도로정비 336
 
1.5%
Other values (478) 15525
69.2%
2023-12-11T01:45:54.025030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12481
 
13.9%
지 4124
 
4.6%
원 2685
 
3.0%
사 2341
 
2.6%
정 2099
 
2.3%
리 2057
 
2.3%
관 2012
 
2.2%
기 1877
 
2.1%
비 1388
 
1.5%
행 1363
 
1.5%
Other values (283) 57215
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76137
84.9%
Space Separator 12481
 
13.9%
Other Punctuation 490
 
0.5%
Uppercase Letter 352
 
0.4%
Close Punctuation 69
 
0.1%
Open Punctuation 69
 
0.1%
Decimal Number 44
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 4124
 
5.4%
원 2685
 
3.5%
사 2341
 
3.1%
정 2099
 
2.8%
리 2057
 
2.7%
관 2012
 
2.6%
기 1877
 
2.5%
비 1388
 
1.8%
행 1363
 
1.8%
전 1354
 
1.8%
Other values (268) 54837
72.0%
Decimal Number
ValueCountFrequency (%)
0 22
50.0%
3 11
25.0%
2 7
 
15.9%
9 2
 
4.5%
1 2
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
N 271
77.0%
H 27
 
7.7%
O 27
 
7.7%
W 27
 
7.7%
Other Punctuation
ValueCountFrequency (%)
\ 271
55.3%
· 217
44.3%
, 2
 
0.4%
Space Separator
ValueCountFrequency (%)
12481
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%
Open Punctuation
ValueCountFrequency (%)
( 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76137
84.9%
Common 13153
 
14.7%
Latin 352
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 4124
 
5.4%
원 2685
 
3.5%
사 2341
 
3.1%
정 2099
 
2.8%
리 2057
 
2.7%
관 2012
 
2.6%
기 1877
 
2.5%
비 1388
 
1.8%
행 1363
 
1.8%
전 1354
 
1.8%
Other values (268) 54837
72.0%
Common
ValueCountFrequency (%)
12481
94.9%
\ 271
 
2.1%
· 217
 
1.6%
) 69
 
0.5%
( 69
 
0.5%
0 22
 
0.2%
3 11
 
0.1%
2 7
 
0.1%
9 2
 
< 0.1%
1 2
 
< 0.1%
Latin
ValueCountFrequency (%)
N 271
77.0%
H 27
 
7.7%
O 27
 
7.7%
W 27
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76137
84.9%
ASCII 13288
 
14.8%
None 217
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12481
93.9%
N 271
 
2.0%
\ 271
 
2.0%
) 69
 
0.5%
( 69
 
0.5%
H 27
 
0.2%
O 27
 
0.2%
W 27
 
0.2%
0 22
 
0.2%
3 11
 
0.1%
Other values (4) 13
 
0.1%
Hangul
ValueCountFrequency (%)
지 4124
 
5.4%
원 2685
 
3.5%
사 2341
 
3.1%
정 2099
 
2.8%
리 2057
 
2.7%
관 2012
 
2.6%
기 1877
 
2.5%
비 1388
 
1.8%
행 1363
 
1.8%
전 1354
 
1.8%
Other values (268) 54837
72.0%
None
ValueCountFrequency (%)
· 217
100.0%

세부사업명(DBIZ_NM)
Text

Distinct2469
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:45:54.314606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length29
Mean length11.9357
Min length2

Characters and Unicode

Total characters119357
Distinct characters586
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique588 ?
Unique (%)5.9%

Sample

1st row사회적기업 활성화
2nd row방문보건사업 운영
3rd row청사 유지 보수
4th row주차부지 관리
5th row애향길 보행환경개선지구 사업
ValueCountFrequency (%)
지원 1711
 
6.5%
운영 1514
 
5.8%
및 611
 
2.3%
관리 582
 
2.2%
기본경비 317
 
1.2%
사업 292
 
1.1%
청사 217
 
0.8%
정비 209
 
0.8%
등 198
 
0.8%
일원 183
 
0.7%
Other values (3216) 20473
77.8%
2023-12-11T01:45:54.777503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16323
 
13.7%
지 4505
 
3.8%
원 3653
 
3.1%
사 3098
 
2.6%
운 2567
 
2.2%
영 2406
 
2.0%
비 2153
 
1.8%
업 1915
 
1.6%
관 1913
 
1.6%
리 1841
 
1.5%
Other values (576) 78983
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99275
83.2%
Space Separator 16323
 
13.7%
Close Punctuation 1007
 
0.8%
Open Punctuation 1007
 
0.8%
Decimal Number 904
 
0.8%
Other Punctuation 375
 
0.3%
Uppercase Letter 305
 
0.3%
Lowercase Letter 65
 
0.1%
Dash Punctuation 61
 
0.1%
Math Symbol 26
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 4505
 
4.5%
원 3653
 
3.7%
사 3098
 
3.1%
운 2567
 
2.6%
영 2406
 
2.4%
비 2153
 
2.2%
업 1915
 
1.9%
관 1913
 
1.9%
리 1841
 
1.9%
보 1538
 
1.5%
Other values (513) 73686
74.2%
Uppercase Letter
ValueCountFrequency (%)
C 70
23.0%
T 35
11.5%
V 31
10.2%
P 28
 
9.2%
E 27
 
8.9%
A 24
 
7.9%
D 19
 
6.2%
I 16
 
5.2%
U 10
 
3.3%
L 10
 
3.3%
Other values (10) 35
11.5%
Lowercase Letter
ValueCountFrequency (%)
o 18
27.7%
a 8
12.3%
r 8
12.3%
l 7
 
10.8%
e 4
 
6.2%
c 3
 
4.6%
d 3
 
4.6%
m 3
 
4.6%
g 3
 
4.6%
p 2
 
3.1%
Other values (5) 6
 
9.2%
Decimal Number
ValueCountFrequency (%)
1 246
27.2%
2 196
21.7%
3 144
15.9%
9 101
11.2%
7 50
 
5.5%
0 47
 
5.2%
4 45
 
5.0%
5 29
 
3.2%
8 27
 
3.0%
6 19
 
2.1%
Other Punctuation
ValueCountFrequency (%)
· 233
62.1%
, 106
28.3%
' 16
 
4.3%
. 9
 
2.4%
& 9
 
2.4%
! 1
 
0.3%
\ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 967
96.0%
』 39
 
3.9%
」 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 967
96.0%
『 39
 
3.9%
「 1
 
0.1%
Letter Number
ValueCountFrequency (%)
Ⅱ 5
55.6%
Ⅰ 4
44.4%
Space Separator
ValueCountFrequency (%)
16323
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%
Math Symbol
ValueCountFrequency (%)
~ 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99275
83.2%
Common 19703
 
16.5%
Latin 379
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 4505
 
4.5%
원 3653
 
3.7%
사 3098
 
3.1%
운 2567
 
2.6%
영 2406
 
2.4%
비 2153
 
2.2%
업 1915
 
1.9%
관 1913
 
1.9%
리 1841
 
1.9%
보 1538
 
1.5%
Other values (513) 73686
74.2%
Latin
ValueCountFrequency (%)
C 70
18.5%
T 35
 
9.2%
V 31
 
8.2%
P 28
 
7.4%
E 27
 
7.1%
A 24
 
6.3%
D 19
 
5.0%
o 18
 
4.7%
I 16
 
4.2%
U 10
 
2.6%
Other values (27) 101
26.6%
Common
ValueCountFrequency (%)
16323
82.8%
) 967
 
4.9%
( 967
 
4.9%
1 246
 
1.2%
· 233
 
1.2%
2 196
 
1.0%
3 144
 
0.7%
, 106
 
0.5%
9 101
 
0.5%
- 61
 
0.3%
Other values (16) 359
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99261
83.2%
ASCII 19760
 
16.6%
None 313
 
0.3%
Compat Jamo 14
 
< 0.1%
Number Forms 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16323
82.6%
) 967
 
4.9%
( 967
 
4.9%
1 246
 
1.2%
2 196
 
1.0%
3 144
 
0.7%
, 106
 
0.5%
9 101
 
0.5%
C 70
 
0.4%
- 61
 
0.3%
Other values (46) 579
 
2.9%
Hangul
ValueCountFrequency (%)
지 4505
 
4.5%
원 3653
 
3.7%
사 3098
 
3.1%
운 2567
 
2.6%
영 2406
 
2.4%
비 2153
 
2.2%
업 1915
 
1.9%
관 1913
 
1.9%
리 1841
 
1.9%
보 1538
 
1.5%
Other values (512) 73672
74.2%
None
ValueCountFrequency (%)
· 233
74.4%
『 39
 
12.5%
』 39
 
12.5%
「 1
 
0.3%
」 1
 
0.3%
Compat Jamo
ValueCountFrequency (%)
ㆍ 14
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 5
55.6%
Ⅰ 4
44.4%

사업구분(SUBSD_BGT_FG_NM)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
자체
5549 
보조
4451 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자체
2nd row보조
3rd row자체
4th row자체
5th row보조

Common Values

ValueCountFrequency (%)
자체 5549
55.5%
보조 4451
44.5%

Length

2023-12-11T01:45:54.957818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:45:55.048689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자체 5549
55.5%
보조 4451
44.5%

예산현액(BGT_CURR_AMT)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5845
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9526276 × 108
Minimum0
Maximum1.32714 × 1011
Zeros193
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:45:55.156588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile850000
Q110144500
median46893000
Q31.8615375 × 108
95-th percentile1.5 × 109
Maximum1.32714 × 1011
Range1.32714 × 1011
Interquartile range (IQR)1.7600925 × 108

Descriptive statistics

Standard deviation4.1114104 × 109
Coefficient of variation (CV)6.9068833
Kurtosis407.94307
Mean5.9526276 × 108
Median Absolute Deviation (MAD)43252000
Skewness17.861911
Sum5.9526276 × 1012
Variance1.6903696 × 1019
MonotonicityNot monotonic
2023-12-11T01:45:55.299807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 193
 
1.9%
20000000 105
 
1.1%
100000000 101
 
1.0%
50000000 91
 
0.9%
3000000 81
 
0.8%
30000000 77
 
0.8%
300000000 75
 
0.8%
5000000 74
 
0.7%
10000000 71
 
0.7%
1000000 68
 
0.7%
Other values (5835) 9064
90.6%
ValueCountFrequency (%)
0 193
1.9%
21000 2
 
< 0.1%
22000 1
 
< 0.1%
25000 1
 
< 0.1%
27000 1
 
< 0.1%
33000 1
 
< 0.1%
44000 1
 
< 0.1%
54000 1
 
< 0.1%
80000 2
 
< 0.1%
88000 3
 
< 0.1%
ValueCountFrequency (%)
132714000000 1
< 0.1%
117351000000 1
< 0.1%
111686000000 2
< 0.1%
93809954000 1
< 0.1%
82705196000 1
< 0.1%
78925263000 1
< 0.1%
78166000000 2
< 0.1%
76259581000 1
< 0.1%
61619971000 1
< 0.1%
59386412000 1
< 0.1%

지출액(EXPD_AMT)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6855
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5669755 × 108
Minimum0
Maximum1.32217 × 1011
Zeros1046
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:45:55.684508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14729145
median30299895
Q31.2377027 × 108
95-th percentile9.971008 × 108
Maximum1.32217 × 1011
Range1.32217 × 1011
Interquartile range (IQR)1.1904112 × 108

Descriptive statistics

Standard deviation3.8085824 × 109
Coefficient of variation (CV)8.3393975
Kurtosis529.77095
Mean4.5669755 × 108
Median Absolute Deviation (MAD)29775895
Skewness20.73907
Sum4.5669755 × 1012
Variance1.45053 × 1019
MonotonicityNot monotonic
2023-12-11T01:45:55.840459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1046
 
10.5%
20000000 57
 
0.6%
5000000 42
 
0.4%
10000000 40
 
0.4%
2000000 40
 
0.4%
1000000 37
 
0.4%
30000000 34
 
0.3%
100000000 33
 
0.3%
50000000 32
 
0.3%
4000000 31
 
0.3%
Other values (6845) 8608
86.1%
ValueCountFrequency (%)
0 1046
10.5%
8950 1
 
< 0.1%
20170 2
 
< 0.1%
21050 1
 
< 0.1%
24000 1
 
< 0.1%
24610 1
 
< 0.1%
27000 2
 
< 0.1%
31960 1
 
< 0.1%
38500 1
 
< 0.1%
43560 1
 
< 0.1%
ValueCountFrequency (%)
132217000000 1
< 0.1%
116583000000 1
< 0.1%
111506000000 2
< 0.1%
93807526070 1
< 0.1%
82610643610 1
< 0.1%
78252720480 1
< 0.1%
77701398200 2
< 0.1%
76032672420 1
< 0.1%
61121324800 1
< 0.1%
59280650120 1
< 0.1%

분야(FLD_NM)
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
사회복지
2728 
일반공공행정
1737 
보건
804 
문화및관광
780 
농림해양수산
719 
Other values (11)
3232 

Length

Max length11
Median length7
Mean length4.7097
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사회복지
2nd row보건
3rd row일반공공행정
4th row보건
5th row교통및물류

Common Values

ValueCountFrequency (%)
사회복지 2728
27.3%
일반공공행정 1737
17.4%
보건 804
 
8.0%
문화및관광 780
 
7.8%
농림해양수산 719
 
7.2%
국토및지역개발 644
 
6.4%
기타 608
 
6.1%
교통및물류 587
 
5.9%
공공질서및안전 379
 
3.8%
수송및교통 336
 
3.4%
Other values (6) 678
 
6.8%

Length

2023-12-11T01:45:55.995312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사회복지 2728
27.3%
일반공공행정 1737
17.4%
보건 804
 
8.0%
문화및관광 780
 
7.8%
농림해양수산 719
 
7.2%
국토및지역개발 644
 
6.4%
기타 608
 
6.1%
교통및물류 587
 
5.9%
공공질서및안전 379
 
3.8%
수송및교통 336
 
3.4%
Other values (6) 678
 
6.8%

사업시작일자(BIZ_START_YMD)
Text

Distinct132
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:45:56.186105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9824
Min length2

Characters and Unicode

Total characters99824
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)0.5%

Sample

1st row2022-01-01
2nd row2022-01-01
3rd row2021-01-01
4th row2022-01-01
5th row2019-01-01
ValueCountFrequency (%)
2022-01-01 1722
17.2%
2021-01-01 1524
15.2%
2020-01-01 1301
13.0%
2019-01-01 1112
11.1%
2017-01-01 951
9.5%
2018-01-01 949
9.5%
2015-01-01 882
8.8%
2016-01-01 845
8.5%
2023-01-01 349
 
3.5%
n 22
 
0.2%
Other values (122) 343
 
3.4%
2023-12-11T01:45:56.567457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 31126
31.2%
1 26174
26.2%
- 19956
20.0%
2 17026
17.1%
9 1209
 
1.2%
7 1001
 
1.0%
8 997
 
1.0%
5 924
 
0.9%
6 909
 
0.9%
3 408
 
0.4%
Other values (3) 94
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79824
80.0%
Dash Punctuation 19956
 
20.0%
Other Punctuation 22
 
< 0.1%
Uppercase Letter 22
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31126
39.0%
1 26174
32.8%
2 17026
21.3%
9 1209
 
1.5%
7 1001
 
1.3%
8 997
 
1.2%
5 924
 
1.2%
6 909
 
1.1%
3 408
 
0.5%
4 50
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 19956
100.0%
Other Punctuation
ValueCountFrequency (%)
\ 22
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99802
> 99.9%
Latin 22
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31126
31.2%
1 26174
26.2%
- 19956
20.0%
2 17026
17.1%
9 1209
 
1.2%
7 1001
 
1.0%
8 997
 
1.0%
5 924
 
0.9%
6 909
 
0.9%
3 408
 
0.4%
Other values (2) 72
 
0.1%
Latin
ValueCountFrequency (%)
N 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31126
31.2%
1 26174
26.2%
- 19956
20.0%
2 17026
17.1%
9 1209
 
1.2%
7 1001
 
1.0%
8 997
 
1.0%
5 924
 
0.9%
6 909
 
0.9%
3 408
 
0.4%
Other values (3) 94
 
0.1%

사업종료일자(BIZ_END_YMD)
Text

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:45:56.773873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9824
Min length2

Characters and Unicode

Total characters99824
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st row2026-12-31
2nd row2026-12-31
3rd row2025-12-31
4th row2022-12-31
5th row2020-12-31
ValueCountFrequency (%)
2026-12-31 1508
15.1%
2025-12-31 1259
12.6%
2021-12-31 1234
12.3%
2024-12-31 1152
11.5%
2022-12-31 1141
11.4%
2023-12-31 1064
10.6%
2020-12-31 1021
10.2%
2019-12-31 911
9.1%
2027-12-31 296
 
3.0%
2018-12-31 109
 
1.1%
Other values (49) 305
 
3.0%
2023-12-11T01:45:57.118545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 29815
29.9%
1 22342
22.4%
- 19956
20.0%
0 11121
 
11.1%
3 11043
 
11.1%
6 1568
 
1.6%
5 1294
 
1.3%
4 1166
 
1.2%
9 931
 
0.9%
7 407
 
0.4%
Other values (3) 181
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79824
80.0%
Dash Punctuation 19956
 
20.0%
Other Punctuation 22
 
< 0.1%
Uppercase Letter 22
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29815
37.4%
1 22342
28.0%
0 11121
 
13.9%
3 11043
 
13.8%
6 1568
 
2.0%
5 1294
 
1.6%
4 1166
 
1.5%
9 931
 
1.2%
7 407
 
0.5%
8 137
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 19956
100.0%
Other Punctuation
ValueCountFrequency (%)
\ 22
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99802
> 99.9%
Latin 22
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29815
29.9%
1 22342
22.4%
- 19956
20.0%
0 11121
 
11.1%
3 11043
 
11.1%
6 1568
 
1.6%
5 1294
 
1.3%
4 1166
 
1.2%
9 931
 
0.9%
7 407
 
0.4%
Other values (2) 159
 
0.2%
Latin
ValueCountFrequency (%)
N 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29815
29.9%
1 22342
22.4%
- 19956
20.0%
0 11121
 
11.1%
3 11043
 
11.1%
6 1568
 
1.6%
5 1294
 
1.3%
4 1166
 
1.2%
9 931
 
0.9%
7 407
 
0.4%
Other values (3) 181
 
0.2%

편성액(COMPO_AMT)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5545
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5418022 × 108
Minimum-11573000
Maximum1.33 × 1011
Zeros614
Zeros (%)6.1%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-11T01:45:57.320011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-11573000
5-th percentile0
Q17000000
median38827500
Q31.510975 × 108
95-th percentile1.2681588 × 109
Maximum1.33 × 1011
Range1.3301157 × 1011
Interquartile range (IQR)1.440975 × 108

Descriptive statistics

Standard deviation4.0331216 × 109
Coefficient of variation (CV)7.2776355
Kurtosis434.16239
Mean5.5418022 × 108
Median Absolute Deviation (MAD)36827500
Skewness18.419979
Sum5.5418022 × 1012
Variance1.626607 × 1019
MonotonicityNot monotonic
2023-12-11T01:45:57.476470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 614
 
6.1%
20000000 100
 
1.0%
100000000 99
 
1.0%
50000000 93
 
0.9%
3000000 79
 
0.8%
30000000 76
 
0.8%
300000000 73
 
0.7%
5000000 71
 
0.7%
1000000 69
 
0.7%
2000000 69
 
0.7%
Other values (5535) 8657
86.6%
ValueCountFrequency (%)
-11573000 1
 
< 0.1%
-1820000 1
 
< 0.1%
0 614
6.1%
21000 2
 
< 0.1%
22000 1
 
< 0.1%
25000 1
 
< 0.1%
27000 1
 
< 0.1%
33000 1
 
< 0.1%
44000 1
 
< 0.1%
54000 1
 
< 0.1%
ValueCountFrequency (%)
133000000000 1
< 0.1%
117000000000 1
< 0.1%
112000000000 2
< 0.1%
93809954000 1
< 0.1%
82705196000 1
< 0.1%
78925263000 1
< 0.1%
78166000000 2
< 0.1%
76259581000 1
< 0.1%
61619971000 1
< 0.1%
59386412000 1
< 0.1%

이월액(FORWD_AMT)
Real number (ℝ)

ZEROS 

Distinct524
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38521590
Minimum0
Maximum1.1251002 × 1010
Zeros9221
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:45:57.634622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile77455696
Maximum1.1251002 × 1010
Range1.1251002 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1521561 × 108
Coefficient of variation (CV)8.1828296
Kurtosis464.57805
Mean38521590
Median Absolute Deviation (MAD)0
Skewness18.156103
Sum3.852159 × 1011
Variance9.9360879 × 1016
MonotonicityNot monotonic
2023-12-11T01:45:57.813254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9221
92.2%
300000000 17
 
0.2%
500000000 14
 
0.1%
10000000 12
 
0.1%
200000000 11
 
0.1%
100000000 9
 
0.1%
400000000 8
 
0.1%
40000000 8
 
0.1%
20000000 8
 
0.1%
150000000 7
 
0.1%
Other values (514) 685
 
6.9%
ValueCountFrequency (%)
0 9221
92.2%
1000000 3
 
< 0.1%
1190000 1
 
< 0.1%
1890000 2
 
< 0.1%
2000000 1
 
< 0.1%
2024000 1
 
< 0.1%
2200000 1
 
< 0.1%
2257470 1
 
< 0.1%
2404160 1
 
< 0.1%
3000000 2
 
< 0.1%
ValueCountFrequency (%)
11251001630 2
< 0.1%
7641436550 1
< 0.1%
6465136220 1
< 0.1%
6385632800 1
< 0.1%
6155822980 2
< 0.1%
4874867530 1
< 0.1%
4705922990 2
< 0.1%
4485532230 1
< 0.1%
4387292790 1
< 0.1%
3998712700 1
< 0.1%

변경금액(CHNG_AMT)
Real number (ℝ)

ZEROS 

Distinct204
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2617253.3
Minimum-4.8725967 × 1010
Maximum4.8725967 × 1010
Zeros9739
Zeros (%)97.4%
Negative97
Negative (%)1.0%
Memory size166.0 KiB
2023-12-11T01:45:58.016784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.8725967 × 1010
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.8725967 × 1010
Range9.7451934 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1097625 × 109
Coefficient of variation (CV)424.018
Kurtosis1859.0793
Mean2617253.3
Median Absolute Deviation (MAD)0
Skewness8.3073924
Sum2.6172533 × 1010
Variance1.2315728 × 1018
MonotonicityNot monotonic
2023-12-11T01:45:58.180547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9739
97.4%
-2000000 6
 
0.1%
2000000 5
 
0.1%
1000000 5
 
0.1%
3000000 4
 
< 0.1%
48725967000 3
 
< 0.1%
-10000000000 3
 
< 0.1%
-10000000 3
 
< 0.1%
7300000 3
 
< 0.1%
-1000000 3
 
< 0.1%
Other values (194) 226
 
2.3%
ValueCountFrequency (%)
-48725967000 2
< 0.1%
-10000000000 3
< 0.1%
-3273768000 2
< 0.1%
-2825775000 1
 
< 0.1%
-1269659000 2
< 0.1%
-823162000 1
 
< 0.1%
-382456000 1
 
< 0.1%
-362625000 1
 
< 0.1%
-179742000 1
 
< 0.1%
-147131000 1
 
< 0.1%
ValueCountFrequency (%)
48725967000 3
< 0.1%
10000000000 1
 
< 0.1%
1944450000 2
< 0.1%
730000000 1
 
< 0.1%
450000000 1
 
< 0.1%
362625000 1
 
< 0.1%
353692000 2
< 0.1%
310000000 1
 
< 0.1%
301153000 1
 
< 0.1%
300000000 1
 
< 0.1%

수입대체경비(ETC_AMT)
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-11T01:45:58.331517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:45:58.429073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

시구군예산액(SIGUNGU_CURR_AMT)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4798
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6714104 × 108
Minimum0
Maximum6.1619971 × 1010
Zeros2385
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:45:58.553158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1400000
median16000000
Q377155925
95-th percentile6.7989215 × 108
Maximum6.1619971 × 1010
Range6.1619971 × 1010
Interquartile range (IQR)76755925

Descriptive statistics

Standard deviation2.1024616 × 109
Coefficient of variation (CV)7.8702307
Kurtosis452.04119
Mean2.6714104 × 108
Median Absolute Deviation (MAD)16000000
Skewness19.328739
Sum2.6714104 × 1012
Variance4.4203448 × 1018
MonotonicityNot monotonic
2023-12-11T01:45:58.709257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2385
 
23.8%
100000000 74
 
0.7%
50000000 74
 
0.7%
20000000 72
 
0.7%
30000000 63
 
0.6%
1000000 63
 
0.6%
3000000 61
 
0.6%
5000000 59
 
0.6%
10000000 52
 
0.5%
500000 47
 
0.5%
Other values (4788) 7050
70.5%
ValueCountFrequency (%)
0 2385
23.8%
19000 2
 
< 0.1%
20000 1
 
< 0.1%
21000 2
 
< 0.1%
22000 1
 
< 0.1%
25000 1
 
< 0.1%
27000 1
 
< 0.1%
33000 1
 
< 0.1%
40000 6
 
0.1%
44000 1
 
< 0.1%
ValueCountFrequency (%)
61619971000 1
 
< 0.1%
59382988000 1
 
< 0.1%
58725675000 3
< 0.1%
49112878000 1
 
< 0.1%
46042149000 1
 
< 0.1%
42361353000 1
 
< 0.1%
42303876000 1
 
< 0.1%
41546041000 1
 
< 0.1%
41108274000 1
 
< 0.1%
31754818000 1
 
< 0.1%

Interactions

2023-12-11T01:45:48.413910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:40.531990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:41.626801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:42.755926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:43.750019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:44.871605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:46.362513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:47.311828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:48.575450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:40.676360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:41.776941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:42.912419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:43.856999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:45.021287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:46.499986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:47.427595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:48.733783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:40.822807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:41.896920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:43.074271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:43.975246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:45.200434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:46.624466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:47.580627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:48.909027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:40.961932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:42.038892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:43.177259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:44.094898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:45.357777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:46.767871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:47.748208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:49.068526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:41.081116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:42.188538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:43.280514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:44.238659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:45.511786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:46.886460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:47.873316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:49.190829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:41.231442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:42.340950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:43.401348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:44.415161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:45.634499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:46.987532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:48.002677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:49.325262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:41.357536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:42.480093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:43.508435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:44.596077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:46.089628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:47.083290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:48.139063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:49.471168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:41.501964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:42.607951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:43.627589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:44.734101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:46.221385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:47.192190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:48.263323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:45:58.837456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도(FIS_YEAR)회계구분코드(FIS_FG_CD)회계구분(FIS_FG_NM)부서명(DEPT_NM)사업구분(SUBSD_BGT_FG_NM)예산현액(BGT_CURR_AMT)지출액(EXPD_AMT)분야(FLD_NM)사업종료일자(BIZ_END_YMD)편성액(COMPO_AMT)이월액(FORWD_AMT)변경금액(CHNG_AMT)시구군예산액(SIGUNGU_CURR_AMT)
회계연도(FIS_YEAR)1.0000.0560.0750.6400.0150.0340.0060.3860.9820.0120.1100.0410.035
회계구분코드(FIS_FG_CD)0.0561.0001.0000.7140.1990.1620.0000.5310.1420.1000.0880.1510.210
회계구분(FIS_FG_NM)0.0751.0001.0000.8060.1680.3930.0000.5380.3190.3150.0000.4740.594
부서명(DEPT_NM)0.6400.7140.8061.0000.6080.4030.0180.9660.5880.1490.1110.6020.447
사업구분(SUBSD_BGT_FG_NM)0.0150.1990.1680.6081.0000.0530.0590.6140.1260.0490.0760.0450.036
예산현액(BGT_CURR_AMT)0.0340.1620.3930.4030.0531.0000.9980.2650.6300.9990.1540.7350.807
지출액(EXPD_AMT)0.0060.0000.0000.0180.0590.9981.0000.0280.6270.9990.0000.2090.666
분야(FLD_NM)0.3860.5310.5380.9660.6140.2650.0281.0000.4220.2970.1370.1020.426
사업종료일자(BIZ_END_YMD)0.9820.1420.3190.5880.1260.6300.6270.4221.0000.6300.0000.1810.000
편성액(COMPO_AMT)0.0120.1000.3150.1490.0490.9990.9990.2970.6301.0000.0000.0000.735
이월액(FORWD_AMT)0.1100.0880.0000.1110.0760.1540.0000.1370.0000.0001.0000.0000.209
변경금액(CHNG_AMT)0.0410.1510.4740.6020.0450.7350.2090.1020.1810.0000.0001.0000.609
시구군예산액(SIGUNGU_CURR_AMT)0.0350.2100.5940.4470.0360.8070.6660.4260.0000.7350.2090.6091.000
2023-12-11T01:45:59.030976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분야(FLD_NM)회계구분(FIS_FG_NM)사업구분(SUBSD_BGT_FG_NM)
분야(FLD_NM)1.0000.2000.487
회계구분(FIS_FG_NM)0.2001.0000.149
사업구분(SUBSD_BGT_FG_NM)0.4870.1491.000
2023-12-11T01:45:59.165402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도(FIS_YEAR)회계구분코드(FIS_FG_CD)예산현액(BGT_CURR_AMT)지출액(EXPD_AMT)편성액(COMPO_AMT)이월액(FORWD_AMT)변경금액(CHNG_AMT)시구군예산액(SIGUNGU_CURR_AMT)회계구분(FIS_FG_NM)사업구분(SUBSD_BGT_FG_NM)분야(FLD_NM)
회계연도(FIS_YEAR)1.000-0.0320.016-0.137-0.0320.0760.0060.0040.0310.0100.137
회계구분코드(FIS_FG_CD)-0.0321.0000.087-0.0690.0910.005-0.0070.1400.9990.1320.274
예산현액(BGT_CURR_AMT)0.0160.0871.0000.7840.8630.2660.0460.5620.1580.0410.107
지출액(EXPD_AMT)-0.137-0.0690.7841.0000.7230.1650.0630.4600.0000.0460.011
편성액(COMPO_AMT)-0.0320.0910.8630.7231.000-0.109-0.0280.5070.1230.0370.121
이월액(FORWD_AMT)0.0760.0050.2660.165-0.1091.000-0.0020.1610.0000.0570.048
변경금액(CHNG_AMT)0.006-0.0070.0460.063-0.028-0.0021.0000.0580.2740.0550.045
시구군예산액(SIGUNGU_CURR_AMT)0.0040.1400.5620.4600.5070.1610.0581.0000.2800.0360.190
회계구분(FIS_FG_NM)0.0310.9990.1580.0000.1230.0000.2740.2801.0000.1490.200
사업구분(SUBSD_BGT_FG_NM)0.0100.1320.0410.0460.0370.0570.0550.0360.1491.0000.487
분야(FLD_NM)0.1370.2740.1070.0110.1210.0480.0450.1900.2000.4871.000

Missing values

2023-12-11T01:45:49.708324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:45:50.067228image/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

회계연도(FIS_YEAR)회계구분코드(FIS_FG_CD)회계구분(FIS_FG_NM)부서명(DEPT_NM)정책사업명(PBIZ_NM)단위사업명(UBIZ_NM)세부사업명(DBIZ_NM)사업구분(SUBSD_BGT_FG_NM)예산현액(BGT_CURR_AMT)지출액(EXPD_AMT)분야(FLD_NM)사업시작일자(BIZ_START_YMD)사업종료일자(BIZ_END_YMD)편성액(COMPO_AMT)이월액(FORWD_AMT)변경금액(CHNG_AMT)수입대체경비(ETC_AMT)시구군예산액(SIGUNGU_CURR_AMT)
129502022100일반회계일자리경제과지속적이고 안정적 일자리 제공일자리 찾기 지원사회적기업 활성화자체7000000067800000사회복지2022-01-012026-12-317000000000070000000
110132022100일반회계건강증진과건강 증진방문건강 관리방문보건사업 운영보조3000000030000000보건2022-01-012026-12-313000000000021000000
79652021100일반회계좌3동좌3동 사업경비청사관리청사 유지 보수자체2820000025955510일반공공행정2021-01-012025-12-312820000000028200000
98182022100일반회계보건정책과건강 정책청사관리주차부지 관리자체00보건2022-01-012022-12-3100000
70842020100일반회계도시관리과보행환경개선애향길보행환경개선애향길 보행환경개선지구 사업보조15592816101445381090교통및물류2019-01-012020-12-310155928161000500000000
125752022100일반회계늘푸른과도시숲 및 산림편의시설 확충산림자원 확충 및 관리사방댐의 준설 관리보조70000006440000농림해양수산2022-01-012026-12-317000000000630000
39352019215주차장특별회계교통행정과주차질서 확립주차장 건설 및 관리그린주차사업 운영보조240000008000000수송및교통2019-01-012023-12-31240000000007200000
65532015100일반회계일자리복지전략단창조도시 조성도시재생사업 추진희망마을 만들기보조200000000국토및지역개발2015-01-012015-12-31200000000005000000
61322015100일반회계주민복지과저소득층 생활안정 지원자활지원근로능력있는 수급자의 탈수급 지원사업 운영보조619068000590390000사회복지2015-01-012019-12-316190680000000
123492022100일반회계가족복지과아동 건전 육성요보호 아동 지원입양대상아동 보호비 지원보조60000000사회복지2022-07-012026-12-3160000000000
회계연도(FIS_YEAR)회계구분코드(FIS_FG_CD)회계구분(FIS_FG_NM)부서명(DEPT_NM)정책사업명(PBIZ_NM)단위사업명(UBIZ_NM)세부사업명(DBIZ_NM)사업구분(SUBSD_BGT_FG_NM)예산현액(BGT_CURR_AMT)지출액(EXPD_AMT)분야(FLD_NM)사업시작일자(BIZ_START_YMD)사업종료일자(BIZ_END_YMD)편성액(COMPO_AMT)이월액(FORWD_AMT)변경금액(CHNG_AMT)수입대체경비(ETC_AMT)시구군예산액(SIGUNGU_CURR_AMT)
120742023100일반회계건설과지방도 건설 확포장 및 건설사업 활성화\N소송패소 배상금 지급자체6133530000교통및물류2023-01-012027-12-3130173200031162100000613353000
99902022100일반회계홍보협력과선진 도시환경 조성쾌적한 도시환경 유지관리도시환경 정비보조7088000070534820일반공공행정2022-01-012026-12-317088000000070880000
48462019100일반회계가족복지과보육가족지원보육사업 운영어린이집 확충보조597825000572431230사회복지2019-01-012023-12-3147782500012000000000149456000
23302017100일반회계늘푸른과도시숲 및 산림편의시설 확충산림자원 확충 및 관리사방댐 준설 관리보조70000007000000농림해양수산2017-01-012021-12-317000000000630000
17412017100일반회계건설과하수도 관리하수도 정비하수관로 개보수보조17000000001665447430환경보호2017-01-012017-12-313000000001400000000000
28302018100일반회계관광시설관리사업소수영강시민공원 관리수변관리공중화장실 관리자체4563500043920290문화및관광2018-01-012022-12-314563500000045635000
11962016460신청사건립기금재무과재무활동(행정지원과)보전지출보전지출자체76780670000일반공공행정2016-01-012020-12-3176780670000007678067000
122942022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로정비좌동순환로(부흥초등학교 일원) 보도정비자체300000000274429820교통및물류2022-01-012022-12-31300000000000300000000
107972022100일반회계환경위생과수질환경 보전상수도·식수관리먹는 물 공동시설 관리자체67440006596180환경2022-01-012026-12-3167440000006744000
44252019100일반회계행정지원과지역정보화 지원구정정보화 추진사이버구정 강화자체20000001590000일반공공행정2019-01-012023-12-3120000000002000000

Duplicate rows

Most frequently occurring

회계연도(FIS_YEAR)회계구분코드(FIS_FG_CD)회계구분(FIS_FG_NM)부서명(DEPT_NM)정책사업명(PBIZ_NM)단위사업명(UBIZ_NM)세부사업명(DBIZ_NM)사업구분(SUBSD_BGT_FG_NM)예산현액(BGT_CURR_AMT)지출액(EXPD_AMT)분야(FLD_NM)사업시작일자(BIZ_START_YMD)사업종료일자(BIZ_END_YMD)편성액(COMPO_AMT)이월액(FORWD_AMT)변경금액(CHNG_AMT)수입대체경비(ETC_AMT)시구군예산액(SIGUNGU_CURR_AMT)# duplicates
6072022100일반회계가족복지과아동 건전 육성아동복지시설 지원지역아동센터 출석전자카드 운영비 지원보조00사회복지2022-01-012026-12-31000003
6162022100일반회계가족복지과아동 건전 육성요보호 아동 지원가정위탁지원운영(아동용품구입비)보조20000000사회복지2022-01-012026-12-31200000000003
6202022100일반회계가족복지과아동 건전 육성요보호 아동 지원입양대상아동 보호비 지원보조60000000사회복지2022-07-012026-12-31600000000003
6212022100일반회계가족복지과아동 건전 육성요보호 아동 지원입양비용 지원보조54000000사회복지2022-01-012026-12-31540000000003
6572022100일반회계건강증진과건강 증진장애인보건관리 전달체계 구축사업장애인 보건관리 전달체계 구축사업보조339900000보건2022-01-012026-12-313399000000084970003
6762022100일반회계건설과연안환경정비연안 유지관리우동 1437번지 일원 외 3개소 태풍피해 복구보조100000000100000000환경2021-01-012029-12-3110000000000003
6852022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로건설수영강변로~해운대로간 도로개설자체12000000051791100교통및물류2022-01-012023-12-311200000000001200000003
6932022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로정비반송로867번길 33-7 도로정비자체500000000교통및물류2022-01-012022-12-3150000000000500000003
6942022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로정비반송로877번길 7-1 일원 도로정비자체600000000교통및물류2022-01-012022-12-3160000000000600000003
6962022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로정비반송마루 진입로 보행환경 개선자체2500000000교통및물류2022-12-012023-12-3125000000000003