Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows966
Duplicate rows (%)9.7%
Total size in memory1.5 MiB
Average record size in memory161.0 B

Variable types

Numeric8
Categorical4
Text6

Dataset

Description부산광역시 해운대구의 재정정보공개시스템에 대한 데이터로 세부사업별 예산현액 및 지출액의 회계구분, 예산현액, 편성액, 이월액, 변경금액 등의 정보를 제공합니다
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/15050175/fileData.do

Alerts

수입대체경비(ETC_AMT) has constant value ""Constant
Dataset has 966 (9.7%) 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 (89.5%)Imbalance
지출액(EXPD_AMT) is highly skewed (γ1 = 24.10560016)Skewed
편성액(COMPO_AMT) is highly skewed (γ1 = 20.78424395)Skewed
시구군예산액(SIGUNGU_CURR_AMT) is highly skewed (γ1 = 20.62452515)Skewed
예산현액(BGT_CURR_AMT) has 189 (1.9%) zerosZeros
지출액(EXPD_AMT) has 1016 (10.2%) zerosZeros
편성액(COMPO_AMT) has 596 (6.0%) zerosZeros
이월액(FORWD_AMT) has 9221 (92.2%) zerosZeros
변경금액(CHNG_AMT) has 9747 (97.5%) zerosZeros
시구군예산액(SIGUNGU_CURR_AMT) has 2416 (24.2%) zerosZeros

Reproduction

Analysis started2023-12-12 04:11:31.100201
Analysis finished2023-12-12 04:11:44.160354
Duration13.06 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%
Mean2019.2639
Minimum2015
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:11:44.237302image/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 deviation2.365421
Coefficient of variation (CV)0.0011714273
Kurtosis-1.0932378
Mean2019.2639
Median Absolute Deviation (MAD)2
Skewness-0.3377934
Sum20192639
Variance5.5952163
MonotonicityNot monotonic
2023-12-12T13:11:44.392957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2022 1880
18.8%
2021 1621
16.2%
2020 1381
13.8%
2019 1069
10.7%
2018 1018
10.2%
2017 965
9.7%
2016 868
8.7%
2015 858
8.6%
2023 340
 
3.4%
ValueCountFrequency (%)
2015 858
8.6%
2016 868
8.7%
2017 965
9.7%
2018 1018
10.2%
2019 1069
10.7%
2020 1381
13.8%
2021 1621
16.2%
2022 1880
18.8%
2023 340
 
3.4%
ValueCountFrequency (%)
2023 340
 
3.4%
2022 1880
18.8%
2021 1621
16.2%
2020 1381
13.8%
2019 1069
10.7%
2018 1018
10.2%
2017 965
9.7%
2016 868
8.7%
2015 858
8.6%

회계구분코드(FIS_FG_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.9247
Minimum100
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:11:44.587372image/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 deviation48.798209
Coefficient of variation (CV)0.44392396
Kurtosis32.34296
Mean109.9247
Median Absolute Deviation (MAD)0
Skewness5.6149931
Sum1099247
Variance2381.2652
MonotonicityNot monotonic
2023-12-12T13:11:44.755035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100 9492
94.9%
215 199
 
2.0%
420 75
 
0.8%
210 42
 
0.4%
411 27
 
0.3%
211 24
 
0.2%
225 23
 
0.2%
450 22
 
0.2%
212 20
 
0.2%
410 20
 
0.2%
Other values (7) 56
 
0.6%
ValueCountFrequency (%)
100 9492
94.9%
210 42
 
0.4%
211 24
 
0.2%
212 20
 
0.2%
215 199
 
2.0%
220 6
 
0.1%
225 23
 
0.2%
410 20
 
0.2%
411 27
 
0.3%
412 3
 
< 0.1%
ValueCountFrequency (%)
460 13
 
0.1%
450 22
 
0.2%
440 16
 
0.2%
430 15
 
0.1%
420 75
0.8%
414 1
 
< 0.1%
413 2
 
< 0.1%
412 3
 
< 0.1%
411 27
 
0.3%
410 20
 
0.2%

회계구분(FIS_FG_NM)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반회계
9492 
주차장특별회계
 
199
사회복지기금
 
75
의료급여기금특별회계
 
42
지하수관리특별회계
 
24
Other values (14)
 
168

Length

Max length15
Median length4
Mean length4.1765
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반회계 9492
94.9%
주차장특별회계 199
 
2.0%
사회복지기금 75
 
0.8%
의료급여기금특별회계 42
 
0.4%
지하수관리특별회계 24
 
0.2%
기반시설특별회계 23
 
0.2%
재난관리기금 22
 
0.2%
식품진흥기금 20
 
0.2%
옥외광고발전기금 20
 
0.2%
발전소주변지역지원사업특별회계 20
 
0.2%
Other values (9) 63
 
0.6%

Length

2023-12-12T13:11:44.920525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반회계 9492
94.9%
주차장특별회계 199
 
2.0%
사회복지기금 75
 
0.8%
의료급여기금특별회계 42
 
0.4%
지하수관리특별회계 24
 
0.2%
기반시설특별회계 23
 
0.2%
재난관리기금 22
 
0.2%
옥외광고발전기금 20
 
0.2%
발전소주변지역지원사업특별회계 20
 
0.2%
식품진흥기금 20
 
0.2%
Other values (9) 63
 
0.6%
Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:11:45.293372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.9251
Min length3

Characters and Unicode

Total characters49251
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 row기획조정실
2nd row의회사무국
3rd row도시디자인과
4th row일자리경제과
5th row건축과
ValueCountFrequency (%)
가족복지과 830
 
8.3%
복지정책과 633
 
6.3%
건설과 587
 
5.9%
행복나눔과 538
 
5.4%
보건정책과 517
 
5.2%
일자리경제과 506
 
5.1%
늘푸른과 456
 
4.6%
노인장애인복지과 429
 
4.3%
관광문화과 342
 
3.4%
안전총괄과 311
 
3.1%
Other values (67) 4851
48.5%
2023-12-12T13:11:45.782846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8339
 
16.9%
2560
 
5.2%
2356
 
4.8%
2029
 
4.1%
1598
 
3.2%
1206
 
2.4%
1117
 
2.3%
1107
 
2.2%
1057
 
2.1%
907
 
1.8%
Other values (103) 26975
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48352
98.2%
Decimal Number 899
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8339
 
17.2%
2560
 
5.3%
2356
 
4.9%
2029
 
4.2%
1598
 
3.3%
1206
 
2.5%
1117
 
2.3%
1107
 
2.3%
1057
 
2.2%
907
 
1.9%
Other values (99) 26076
53.9%
Decimal Number
ValueCountFrequency (%)
1 339
37.7%
2 332
36.9%
3 144
16.0%
4 84
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48352
98.2%
Common 899
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8339
 
17.2%
2560
 
5.3%
2356
 
4.9%
2029
 
4.2%
1598
 
3.3%
1206
 
2.5%
1117
 
2.3%
1107
 
2.3%
1057
 
2.2%
907
 
1.9%
Other values (99) 26076
53.9%
Common
ValueCountFrequency (%)
1 339
37.7%
2 332
36.9%
3 144
16.0%
4 84
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48352
98.2%
ASCII 899
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8339
 
17.2%
2560
 
5.3%
2356
 
4.9%
2029
 
4.2%
1598
 
3.3%
1206
 
2.5%
1117
 
2.3%
1107
 
2.3%
1057
 
2.2%
907
 
1.9%
Other values (99) 26076
53.9%
ASCII
ValueCountFrequency (%)
1 339
37.7%
2 332
36.9%
3 144
16.0%
4 84
 
9.3%
Distinct261
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:11:46.124946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length9.9315
Min length3

Characters and Unicode

Total characters99315
Distinct characters225
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

Unique12 ?
Unique (%)0.1%

Sample

1st row재무활동(기획조정실)
2nd row지방의회 운영 지원
3rd row광고물관리
4th row해양환경 보전
5th row건축관리
ValueCountFrequency (%)
1438
 
5.8%
지원 1232
 
5.0%
조성 1040
 
4.2%
사업경비 740
 
3.0%
육성 698
 
2.8%
관리 614
 
2.5%
건강 528
 
2.1%
보육가족지원 509
 
2.1%
지방도 470
 
1.9%
건설 470
 
1.9%
Other values (344) 17021
68.7%
2023-12-12T13:11:46.602082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14760
 
14.9%
4476
 
4.5%
2487
 
2.5%
2404
 
2.4%
2162
 
2.2%
1939
 
2.0%
1842
 
1.9%
1789
 
1.8%
1720
 
1.7%
1637
 
1.6%
Other values (215) 64099
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81617
82.2%
Space Separator 14760
 
14.9%
Open Punctuation 877
 
0.9%
Close Punctuation 877
 
0.9%
Decimal Number 832
 
0.8%
Other Punctuation 290
 
0.3%
Dash Punctuation 31
 
< 0.1%
Uppercase Letter 31
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4476
 
5.5%
2487
 
3.0%
2404
 
2.9%
2162
 
2.6%
1939
 
2.4%
1842
 
2.3%
1789
 
2.2%
1720
 
2.1%
1637
 
2.0%
1578
 
1.9%
Other values (205) 59583
73.0%
Decimal Number
ValueCountFrequency (%)
1 305
36.7%
2 299
35.9%
3 144
17.3%
4 84
 
10.1%
Space Separator
ValueCountFrequency (%)
14760
100.0%
Open Punctuation
ValueCountFrequency (%)
( 877
100.0%
Close Punctuation
ValueCountFrequency (%)
) 877
100.0%
Other Punctuation
ValueCountFrequency (%)
· 290
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81617
82.2%
Common 17667
 
17.8%
Latin 31
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4476
 
5.5%
2487
 
3.0%
2404
 
2.9%
2162
 
2.6%
1939
 
2.4%
1842
 
2.3%
1789
 
2.2%
1720
 
2.1%
1637
 
2.0%
1578
 
1.9%
Other values (205) 59583
73.0%
Common
ValueCountFrequency (%)
14760
83.5%
( 877
 
5.0%
) 877
 
5.0%
1 305
 
1.7%
2 299
 
1.7%
· 290
 
1.6%
3 144
 
0.8%
4 84
 
0.5%
- 31
 
0.2%
Latin
ValueCountFrequency (%)
U 31
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81617
82.2%
ASCII 17408
 
17.5%
None 290
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14760
84.8%
( 877
 
5.0%
) 877
 
5.0%
1 305
 
1.8%
2 299
 
1.7%
3 144
 
0.8%
4 84
 
0.5%
- 31
 
0.2%
U 31
 
0.2%
Hangul
ValueCountFrequency (%)
4476
 
5.5%
2487
 
3.0%
2404
 
2.9%
2162
 
2.6%
1939
 
2.4%
1842
 
2.3%
1789
 
2.2%
1720
 
2.1%
1637
 
2.0%
1578
 
1.9%
Other values (205) 59583
73.0%
None
ValueCountFrequency (%)
· 290
100.0%
Distinct334
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:11:46.890469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length9.0132
Min length2

Characters and Unicode

Total characters90132
Distinct characters294
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

Unique15 ?
Unique (%)0.1%

Sample

1st row\N
2nd row국내외교류
3rd row옥외광고물 관리
4th row어업기반시설 조성
5th row건축행정 추진
ValueCountFrequency (%)
지원 1753
 
7.8%
1099
 
4.9%
관리 991
 
4.4%
운영 764
 
3.4%
지방행정 508
 
2.2%
기초사무수행 508
 
2.2%
장애인 373
 
1.6%
구축 349
 
1.5%
도로정비 341
 
1.5%
추진 324
 
1.4%
Other values (479) 15607
69.0%
2023-12-12T13:11:47.379241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12655
 
14.0%
4173
 
4.6%
2669
 
3.0%
2342
 
2.6%
2108
 
2.3%
2086
 
2.3%
2067
 
2.3%
1872
 
2.1%
1370
 
1.5%
1362
 
1.5%
Other values (284) 57428
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76526
84.9%
Space Separator 12655
 
14.0%
Other Punctuation 454
 
0.5%
Uppercase Letter 333
 
0.4%
Close Punctuation 62
 
0.1%
Open Punctuation 62
 
0.1%
Decimal Number 40
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4173
 
5.5%
2669
 
3.5%
2342
 
3.1%
2108
 
2.8%
2086
 
2.7%
2067
 
2.7%
1872
 
2.4%
1370
 
1.8%
1362
 
1.8%
1337
 
1.7%
Other values (269) 55140
72.1%
Decimal Number
ValueCountFrequency (%)
0 22
55.0%
3 11
27.5%
2 5
 
12.5%
1 1
 
2.5%
9 1
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
N 246
73.9%
H 29
 
8.7%
W 29
 
8.7%
O 29
 
8.7%
Other Punctuation
ValueCountFrequency (%)
\ 246
54.2%
· 206
45.4%
, 2
 
0.4%
Space Separator
ValueCountFrequency (%)
12655
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%
Open Punctuation
ValueCountFrequency (%)
( 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76526
84.9%
Common 13273
 
14.7%
Latin 333
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4173
 
5.5%
2669
 
3.5%
2342
 
3.1%
2108
 
2.8%
2086
 
2.7%
2067
 
2.7%
1872
 
2.4%
1370
 
1.8%
1362
 
1.8%
1337
 
1.7%
Other values (269) 55140
72.1%
Common
ValueCountFrequency (%)
12655
95.3%
\ 246
 
1.9%
· 206
 
1.6%
) 62
 
0.5%
( 62
 
0.5%
0 22
 
0.2%
3 11
 
0.1%
2 5
 
< 0.1%
, 2
 
< 0.1%
1 1
 
< 0.1%
Latin
ValueCountFrequency (%)
N 246
73.9%
H 29
 
8.7%
W 29
 
8.7%
O 29
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76526
84.9%
ASCII 13400
 
14.9%
None 206
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12655
94.4%
N 246
 
1.8%
\ 246
 
1.8%
) 62
 
0.5%
( 62
 
0.5%
H 29
 
0.2%
W 29
 
0.2%
O 29
 
0.2%
0 22
 
0.2%
3 11
 
0.1%
Other values (4) 9
 
0.1%
Hangul
ValueCountFrequency (%)
4173
 
5.5%
2669
 
3.5%
2342
 
3.1%
2108
 
2.8%
2086
 
2.7%
2067
 
2.7%
1872
 
2.4%
1370
 
1.8%
1362
 
1.8%
1337
 
1.7%
Other values (269) 55140
72.1%
None
ValueCountFrequency (%)
· 206
100.0%
Distinct2489
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:11:47.669677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length29
Mean length11.9136
Min length2

Characters and Unicode

Total characters119136
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

Unique612 ?
Unique (%)6.1%

Sample

1st row보전지출(재정안정화계정)
2nd row국내의정활동
3rd row불법광고물 정비
4th row송정항 어항개발 계획수립 용역
5th row무허가건축물 행정대집행
ValueCountFrequency (%)
지원 1706
 
6.5%
운영 1548
 
5.9%
관리 624
 
2.4%
591
 
2.2%
기본경비 309
 
1.2%
사업 286
 
1.1%
청사 203
 
0.8%
196
 
0.7%
정비 195
 
0.7%
일원 185
 
0.7%
Other values (3234) 20500
77.8%
2023-12-12T13:11:48.124705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16357
 
13.7%
4457
 
3.7%
3618
 
3.0%
3066
 
2.6%
2567
 
2.2%
2397
 
2.0%
2118
 
1.8%
1964
 
1.6%
1913
 
1.6%
1894
 
1.6%
Other values (576) 78785
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99114
83.2%
Space Separator 16357
 
13.7%
Open Punctuation 987
 
0.8%
Close Punctuation 987
 
0.8%
Decimal Number 853
 
0.7%
Other Punctuation 384
 
0.3%
Uppercase Letter 290
 
0.2%
Lowercase Letter 66
 
0.1%
Dash Punctuation 59
 
< 0.1%
Math Symbol 26
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4457
 
4.5%
3618
 
3.7%
3066
 
3.1%
2567
 
2.6%
2397
 
2.4%
2118
 
2.1%
1964
 
2.0%
1913
 
1.9%
1894
 
1.9%
1505
 
1.5%
Other values (514) 73615
74.3%
Uppercase Letter
ValueCountFrequency (%)
C 59
20.3%
P 31
10.7%
E 30
10.3%
T 30
10.3%
V 26
9.0%
A 25
8.6%
I 18
 
6.2%
D 18
 
6.2%
L 11
 
3.8%
U 11
 
3.8%
Other values (9) 31
10.7%
Lowercase Letter
ValueCountFrequency (%)
o 19
28.8%
l 7
 
10.6%
p 6
 
9.1%
r 5
 
7.6%
e 5
 
7.6%
a 5
 
7.6%
b 3
 
4.5%
t 3
 
4.5%
u 3
 
4.5%
g 2
 
3.0%
Other values (5) 8
12.1%
Decimal Number
ValueCountFrequency (%)
1 227
26.6%
2 187
21.9%
3 132
15.5%
9 93
10.9%
4 58
 
6.8%
0 45
 
5.3%
7 36
 
4.2%
5 34
 
4.0%
8 26
 
3.0%
6 15
 
1.8%
Other Punctuation
ValueCountFrequency (%)
· 243
63.3%
, 109
28.4%
' 12
 
3.1%
. 10
 
2.6%
& 6
 
1.6%
! 3
 
0.8%
\ 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 948
96.0%
37
 
3.7%
2
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 948
96.0%
37
 
3.7%
2
 
0.2%
Letter Number
ValueCountFrequency (%)
7
53.8%
6
46.2%
Space Separator
ValueCountFrequency (%)
16357
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%
Math Symbol
ValueCountFrequency (%)
~ 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99114
83.2%
Common 19653
 
16.5%
Latin 369
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4457
 
4.5%
3618
 
3.7%
3066
 
3.1%
2567
 
2.6%
2397
 
2.4%
2118
 
2.1%
1964
 
2.0%
1913
 
1.9%
1894
 
1.9%
1505
 
1.5%
Other values (514) 73615
74.3%
Latin
ValueCountFrequency (%)
C 59
16.0%
P 31
 
8.4%
E 30
 
8.1%
T 30
 
8.1%
V 26
 
7.0%
A 25
 
6.8%
o 19
 
5.1%
I 18
 
4.9%
D 18
 
4.9%
L 11
 
3.0%
Other values (26) 102
27.6%
Common
ValueCountFrequency (%)
16357
83.2%
( 948
 
4.8%
) 948
 
4.8%
· 243
 
1.2%
1 227
 
1.2%
2 187
 
1.0%
3 132
 
0.7%
, 109
 
0.6%
9 93
 
0.5%
- 59
 
0.3%
Other values (16) 350
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99095
83.2%
ASCII 19688
 
16.5%
None 321
 
0.3%
Compat Jamo 19
 
< 0.1%
Number Forms 13
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16357
83.1%
( 948
 
4.8%
) 948
 
4.8%
1 227
 
1.2%
2 187
 
0.9%
3 132
 
0.7%
, 109
 
0.6%
9 93
 
0.5%
C 59
 
0.3%
- 59
 
0.3%
Other values (45) 569
 
2.9%
Hangul
ValueCountFrequency (%)
4457
 
4.5%
3618
 
3.7%
3066
 
3.1%
2567
 
2.6%
2397
 
2.4%
2118
 
2.1%
1964
 
2.0%
1913
 
1.9%
1894
 
1.9%
1505
 
1.5%
Other values (513) 73596
74.3%
None
ValueCountFrequency (%)
· 243
75.7%
37
 
11.5%
37
 
11.5%
2
 
0.6%
2
 
0.6%
Compat Jamo
ValueCountFrequency (%)
19
100.0%
Number Forms
ValueCountFrequency (%)
7
53.8%
6
46.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
자체
5535 
보조
4465 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자체 5535
55.4%
보조 4465
44.6%

Length

2023-12-12T13:11:48.264329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:11:48.365560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자체 5535
55.4%
보조 4465
44.6%

예산현액(BGT_CURR_AMT)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5842
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3839787 × 108
Minimum0
Maximum1.49552 × 1011
Zeros189
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:11:48.503043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile865200
Q110000000
median46249000
Q31.89056 × 108
95-th percentile1.4947492 × 109
Maximum1.49552 × 1011
Range1.49552 × 1011
Interquartile range (IQR)1.79056 × 108

Descriptive statistics

Standard deviation3.6081238 × 109
Coefficient of variation (CV)6.7015937
Kurtosis558.23299
Mean5.3839787 × 108
Median Absolute Deviation (MAD)42892500
Skewness19.995713
Sum5.3839787 × 1012
Variance1.3018557 × 1019
MonotonicityNot monotonic
2023-12-12T13:11:49.019884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 189
 
1.9%
20000000 104
 
1.0%
100000000 97
 
1.0%
3000000 87
 
0.9%
50000000 86
 
0.9%
300000000 79
 
0.8%
5000000 78
 
0.8%
30000000 76
 
0.8%
1000000 71
 
0.7%
10000000 70
 
0.7%
Other values (5832) 9063
90.6%
ValueCountFrequency (%)
0 189
1.9%
1000 1
 
< 0.1%
21000 1
 
< 0.1%
25000 1
 
< 0.1%
27000 1
 
< 0.1%
33000 1
 
< 0.1%
40000 1
 
< 0.1%
44000 1
 
< 0.1%
54000 1
 
< 0.1%
80000 3
 
< 0.1%
ValueCountFrequency (%)
149552000000 1
 
< 0.1%
111686000000 1
 
< 0.1%
93809954000 1
 
< 0.1%
82705196000 1
 
< 0.1%
78166000000 2
< 0.1%
61619971000 1
 
< 0.1%
59800000000 1
 
< 0.1%
59386412000 1
 
< 0.1%
58725675000 3
< 0.1%
49119478000 1
 
< 0.1%

지출액(EXPD_AMT)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6893
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0815738 × 108
Minimum0
Maximum1.49245 × 1011
Zeros1016
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:11:49.243743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14714960
median30253015
Q31.2560326 × 108
95-th percentile9.9619361 × 108
Maximum1.49245 × 1011
Range1.49245 × 1011
Interquartile range (IQR)1.208883 × 108

Descriptive statistics

Standard deviation3.2774745 × 109
Coefficient of variation (CV)8.0299285
Kurtosis780.6677
Mean4.0815738 × 108
Median Absolute Deviation (MAD)29676815
Skewness24.1056
Sum4.0815738 × 1012
Variance1.0741839 × 1019
MonotonicityNot monotonic
2023-12-12T13:11:49.421277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1016
 
10.2%
20000000 59
 
0.6%
5000000 47
 
0.5%
1000000 43
 
0.4%
2000000 36
 
0.4%
10000000 36
 
0.4%
30000000 35
 
0.4%
3000000 35
 
0.4%
4000000 34
 
0.3%
50000000 32
 
0.3%
Other values (6883) 8627
86.3%
ValueCountFrequency (%)
0 1016
10.2%
150 1
 
< 0.1%
8950 1
 
< 0.1%
20170 1
 
< 0.1%
24610 1
 
< 0.1%
27000 2
 
< 0.1%
31960 1
 
< 0.1%
38500 1
 
< 0.1%
40000 1
 
< 0.1%
44000 1
 
< 0.1%
ValueCountFrequency (%)
149245000000 1
< 0.1%
111506000000 1
< 0.1%
93807526070 1
< 0.1%
82610643610 1
< 0.1%
77701398200 2
< 0.1%
61121324800 1
< 0.1%
59734262000 1
< 0.1%
59280650120 1
< 0.1%
48684773900 1
< 0.1%
46537715140 1
< 0.1%

분야(FLD_NM)
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
사회복지
2701 
일반공공행정
1742 
보건
808 
문화및관광
800 
농림해양수산
743 
Other values (11)
3206 

Length

Max length11
Median length7
Mean length4.7214
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반공공행정
2nd row일반공공행정
3rd row국토및지역개발
4th row농림해양수산
5th row국토및지역개발

Common Values

ValueCountFrequency (%)
사회복지 2701
27.0%
일반공공행정 1742
17.4%
보건 808
 
8.1%
문화및관광 800
 
8.0%
농림해양수산 743
 
7.4%
국토및지역개발 666
 
6.7%
기타 585
 
5.9%
교통및물류 559
 
5.6%
공공질서및안전 364
 
3.6%
수송및교통 342
 
3.4%
Other values (6) 690
 
6.9%

Length

2023-12-12T13:11:49.583897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사회복지 2701
27.0%
일반공공행정 1742
17.4%
보건 808
 
8.1%
문화및관광 800
 
8.0%
농림해양수산 743
 
7.4%
국토및지역개발 666
 
6.7%
기타 585
 
5.9%
교통및물류 559
 
5.6%
공공질서및안전 364
 
3.6%
수송및교통 342
 
3.4%
Other values (6) 690
 
6.9%
Distinct125
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:11:49.823986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.984
Min length2

Characters and Unicode

Total characters99840
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

Unique39 ?
Unique (%)0.4%

Sample

1st row2023-01-01
2nd row2020-01-01
3rd row2016-01-01
4th row2021-01-01
5th row2022-01-01
ValueCountFrequency (%)
2022-01-01 1699
17.0%
2021-01-01 1536
15.4%
2020-01-01 1334
13.3%
2019-01-01 1112
11.1%
2018-01-01 991
9.9%
2017-01-01 945
9.4%
2015-01-01 880
8.8%
2016-01-01 823
8.2%
2023-01-01 321
 
3.2%
n 20
 
0.2%
Other values (115) 339
 
3.4%
2023-12-12T13:11:50.267862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 31168
31.2%
1 26221
26.3%
- 19960
20.0%
2 16966
17.0%
9 1210
 
1.2%
8 1041
 
1.0%
7 995
 
1.0%
5 921
 
0.9%
6 893
 
0.9%
3 380
 
0.4%
Other values (3) 85
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79840
80.0%
Dash Punctuation 19960
 
20.0%
Other Punctuation 20
 
< 0.1%
Uppercase Letter 20
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31168
39.0%
1 26221
32.8%
2 16966
21.2%
9 1210
 
1.5%
8 1041
 
1.3%
7 995
 
1.2%
5 921
 
1.2%
6 893
 
1.1%
3 380
 
0.5%
4 45
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 19960
100.0%
Other Punctuation
ValueCountFrequency (%)
\ 20
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99820
> 99.9%
Latin 20
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31168
31.2%
1 26221
26.3%
- 19960
20.0%
2 16966
17.0%
9 1210
 
1.2%
8 1041
 
1.0%
7 995
 
1.0%
5 921
 
0.9%
6 893
 
0.9%
3 380
 
0.4%
Other values (2) 65
 
0.1%
Latin
ValueCountFrequency (%)
N 20
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31168
31.2%
1 26221
26.3%
- 19960
20.0%
2 16966
17.0%
9 1210
 
1.2%
8 1041
 
1.0%
7 995
 
1.0%
5 921
 
0.9%
6 893
 
0.9%
3 380
 
0.4%
Other values (3) 85
 
0.1%
Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:11:50.445298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.984
Min length2

Characters and Unicode

Total characters99840
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

Unique18 ?
Unique (%)0.2%

Sample

1st row2027-12-31
2nd row2024-12-31
3rd row2020-12-31
4th row2025-12-31
5th row2026-12-31
ValueCountFrequency (%)
2026-12-31 1490
14.9%
2025-12-31 1283
12.8%
2021-12-31 1209
12.1%
2022-12-31 1174
11.7%
2024-12-31 1165
11.7%
2023-12-31 1064
10.6%
2020-12-31 1017
10.2%
2019-12-31 922
9.2%
2027-12-31 269
 
2.7%
2018-12-31 116
 
1.2%
Other values (49) 291
 
2.9%
2023-12-12T13:11:50.785947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 29839
29.9%
1 22336
22.4%
- 19960
20.0%
0 11116
 
11.1%
3 11041
 
11.1%
6 1545
 
1.5%
5 1324
 
1.3%
4 1177
 
1.2%
9 945
 
0.9%
7 379
 
0.4%
Other values (3) 178
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79840
80.0%
Dash Punctuation 19960
 
20.0%
Other Punctuation 20
 
< 0.1%
Uppercase Letter 20
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29839
37.4%
1 22336
28.0%
0 11116
 
13.9%
3 11041
 
13.8%
6 1545
 
1.9%
5 1324
 
1.7%
4 1177
 
1.5%
9 945
 
1.2%
7 379
 
0.5%
8 138
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 19960
100.0%
Other Punctuation
ValueCountFrequency (%)
\ 20
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99820
> 99.9%
Latin 20
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29839
29.9%
1 22336
22.4%
- 19960
20.0%
0 11116
 
11.1%
3 11041
 
11.1%
6 1545
 
1.5%
5 1324
 
1.3%
4 1177
 
1.2%
9 945
 
0.9%
7 379
 
0.4%
Other values (2) 158
 
0.2%
Latin
ValueCountFrequency (%)
N 20
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29839
29.9%
1 22336
22.4%
- 19960
20.0%
0 11116
 
11.1%
3 11041
 
11.1%
6 1545
 
1.5%
5 1324
 
1.3%
4 1177
 
1.2%
9 945
 
0.9%
7 379
 
0.4%
Other values (3) 178
 
0.2%

편성액(COMPO_AMT)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5561
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9673005 × 108
Minimum-11573000
Maximum1.5 × 1011
Zeros596
Zeros (%)6.0%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T13:11:51.000695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-11573000
5-th percentile0
Q16900000
median38226000
Q31.511625 × 108
95-th percentile1.2819775 × 109
Maximum1.5 × 1011
Range1.5001157 × 1011
Interquartile range (IQR)1.442625 × 108

Descriptive statistics

Standard deviation3.5230469 × 109
Coefficient of variation (CV)7.092478
Kurtosis605.53971
Mean4.9673005 × 108
Median Absolute Deviation (MAD)36226000
Skewness20.784244
Sum4.9673005 × 1012
Variance1.241186 × 1019
MonotonicityNot monotonic
2023-12-12T13:11:51.213599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 596
 
6.0%
20000000 99
 
1.0%
100000000 93
 
0.9%
3000000 86
 
0.9%
50000000 85
 
0.9%
30000000 76
 
0.8%
5000000 75
 
0.8%
1000000 73
 
0.7%
300000000 71
 
0.7%
10000000 66
 
0.7%
Other values (5551) 8680
86.8%
ValueCountFrequency (%)
-11573000 1
 
< 0.1%
-1820000 1
 
< 0.1%
0 596
6.0%
1000 1
 
< 0.1%
21000 1
 
< 0.1%
25000 1
 
< 0.1%
27000 1
 
< 0.1%
33000 1
 
< 0.1%
40000 1
 
< 0.1%
44000 1
 
< 0.1%
ValueCountFrequency (%)
150000000000 1
< 0.1%
112000000000 1
< 0.1%
93809954000 1
< 0.1%
82705196000 1
< 0.1%
78166000000 2
< 0.1%
61619971000 1
< 0.1%
59800000000 1
< 0.1%
59386412000 1
< 0.1%
49119478000 1
< 0.1%
48725967000 2
< 0.1%

이월액(FORWD_AMT)
Real number (ℝ)

ZEROS 

Distinct524
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38848051
Minimum0
Maximum1.1251002 × 1010
Zeros9221
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:11:51.398782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile80054729
Maximum1.1251002 × 1010
Range1.1251002 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1668344 × 108
Coefficient of variation (CV)8.1518488
Kurtosis456.60114
Mean38848051
Median Absolute Deviation (MAD)0
Skewness17.993993
Sum3.8848051 × 1011
Variance1.002884 × 1017
MonotonicityNot monotonic
2023-12-12T13:11:51.582477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9221
92.2%
300000000 19
 
0.2%
500000000 15
 
0.1%
100000000 12
 
0.1%
200000000 11
 
0.1%
10000000 10
 
0.1%
20000000 8
 
0.1%
400000000 7
 
0.1%
22000000 7
 
0.1%
1000000000 7
 
0.1%
Other values (514) 683
 
6.8%
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%
2257470 1
 
< 0.1%
3000000 3
 
< 0.1%
3495600 1
 
< 0.1%
4050000 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 1
< 0.1%
4485532230 2
< 0.1%
4387292790 1
< 0.1%
3998712700 2
< 0.1%

변경금액(CHNG_AMT)
Real number (ℝ)

ZEROS 

Distinct200
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2895967
Minimum-4.8725967 × 1010
Maximum4.8725967 × 1010
Zeros9747
Zeros (%)97.5%
Negative95
Negative (%)0.9%
Memory size166.0 KiB
2023-12-12T13:11:51.796413image/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 deviation1.0999361 × 109
Coefficient of variation (CV)379.81652
Kurtosis1925.1285
Mean2895967
Median Absolute Deviation (MAD)0
Skewness8.534443
Sum2.895967 × 1010
Variance1.2098594 × 1018
MonotonicityNot monotonic
2023-12-12T13:11:51.966080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9747
97.5%
-2000000 7
 
0.1%
2000000 6
 
0.1%
1000000 5
 
0.1%
3000000 4
 
< 0.1%
200000000 3
 
< 0.1%
-1000000 3
 
< 0.1%
48725967000 3
 
< 0.1%
22000000 2
 
< 0.1%
54447000 2
 
< 0.1%
Other values (190) 218
 
2.2%
ValueCountFrequency (%)
-48725967000 2
< 0.1%
-10000000000 2
< 0.1%
-2825775000 2
< 0.1%
-1269659000 2
< 0.1%
-823162000 1
< 0.1%
-382456000 1
< 0.1%
-138971000 2
< 0.1%
-120591000 1
< 0.1%
-113700000 1
< 0.1%
-83129000 1
< 0.1%
ValueCountFrequency (%)
48725967000 3
< 0.1%
1944450000 1
 
< 0.1%
730000000 1
 
< 0.1%
450000000 2
< 0.1%
443881000 1
 
< 0.1%
366000000 1
 
< 0.1%
353692000 2
< 0.1%
310000000 2
< 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

2023-12-12T13:11:52.113491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:11:52.246043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

시구군예산액(SIGUNGU_CURR_AMT)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct4765
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5436917 × 108
Minimum0
Maximum6.1619971 × 1010
Zeros2416
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:11:52.420191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1288000
median15422000
Q374575500
95-th percentile6.575292 × 108
Maximum6.1619971 × 1010
Range6.1619971 × 1010
Interquartile range (IQR)74287500

Descriptive statistics

Standard deviation2.0062488 × 109
Coefficient of variation (CV)7.887154
Kurtosis516.34422
Mean2.5436917 × 108
Median Absolute Deviation (MAD)15422000
Skewness20.624525
Sum2.5436917 × 1012
Variance4.0250342 × 1018
MonotonicityNot monotonic
2023-12-12T13:11:52.599994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2416
 
24.2%
20000000 82
 
0.8%
50000000 74
 
0.7%
100000000 72
 
0.7%
30000000 62
 
0.6%
1000000 61
 
0.6%
3000000 58
 
0.6%
5000000 58
 
0.6%
10000000 54
 
0.5%
500000 49
 
0.5%
Other values (4755) 7014
70.1%
ValueCountFrequency (%)
0 2416
24.2%
1000 1
 
< 0.1%
18000 1
 
< 0.1%
19000 2
 
< 0.1%
20000 1
 
< 0.1%
21000 1
 
< 0.1%
25000 1
 
< 0.1%
27000 1
 
< 0.1%
33000 1
 
< 0.1%
40000 5
 
0.1%
ValueCountFrequency (%)
61619971000 1
 
< 0.1%
59382988000 1
 
< 0.1%
58725675000 3
< 0.1%
49112878000 1
 
< 0.1%
47073849000 1
 
< 0.1%
42361353000 1
 
< 0.1%
41546041000 1
 
< 0.1%
34905728000 1
 
< 0.1%
31754818000 1
 
< 0.1%
24765000000 1
 
< 0.1%

Interactions

2023-12-12T13:11:42.302368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:35.246371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:36.370058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.171194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.964930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:39.097495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:39.983134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:41.259030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:42.440316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:35.384264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:36.473296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.281401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:38.082181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:39.201775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:40.178204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:41.396603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:42.606387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:35.547652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:36.567034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.386718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:38.212833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:39.305821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:40.321346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:41.522752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:42.754313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:35.687015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:36.667739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.482767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:38.314826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:39.405570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:40.490696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:41.660582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:42.875744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:35.855968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:36.768017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.590088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:38.410850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:39.520078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:40.664930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:41.781930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:43.020443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:35.999779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:36.874467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.685986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:38.508374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:39.630296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:40.840190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:41.911269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:43.161430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:36.128819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:36.980710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.775963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:38.603549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:39.743454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:40.984753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:42.055370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:43.332458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:36.253187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.073970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:37.866466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:38.691390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:39.854867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:41.111288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:42.175370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:11:52.756925image/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)1.0000.0450.0800.6370.0290.0240.0220.3910.9820.0140.1220.0510.034
회계구분코드(FIS_FG_CD)0.0451.0001.0000.7220.1990.1260.0000.5250.1880.0930.0980.3070.204
회계구분(FIS_FG_NM)0.0801.0001.0000.8320.1660.3500.0000.5460.3500.3060.0570.5040.604
부서명(DEPT_NM)0.6370.7220.8321.0000.6140.3620.0000.9660.5930.1520.1320.5790.448
사업구분(SUBSD_BGT_FG_NM)0.0290.1990.1660.6141.0000.0300.0440.6130.1230.0270.0590.0830.034
예산현액(BGT_CURR_AMT)0.0240.1260.3500.3620.0301.0000.9960.2630.5730.9990.1590.5400.884
지출액(EXPD_AMT)0.0220.0000.0000.0000.0440.9961.0000.0340.5710.9970.0000.0000.810
분야(FLD_NM)0.3910.5250.5460.9660.6130.2630.0341.0000.4080.2600.1350.1180.447
사업종료일자(BIZ_END_YMD)0.9820.1880.3500.5930.1230.5730.5710.4081.0000.5730.2630.2340.000
편성액(COMPO_AMT)0.0140.0930.3060.1520.0270.9990.9970.2600.5731.0000.0000.0000.851
이월액(FORWD_AMT)0.1220.0980.0570.1320.0590.1590.0000.1350.2630.0001.0000.0000.226
변경금액(CHNG_AMT)0.0510.3070.5040.5790.0830.5400.0000.1180.2340.0000.0001.0000.617
시구군예산액(SIGUNGU_CURR_AMT)0.0340.2040.6040.4480.0340.8840.8100.4470.0000.8510.2260.6171.000
2023-12-12T13:11:52.953181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업구분(SUBSD_BGT_FG_NM)분야(FLD_NM)회계구분(FIS_FG_NM)
사업구분(SUBSD_BGT_FG_NM)1.0000.4870.147
분야(FLD_NM)0.4871.0000.204
회계구분(FIS_FG_NM)0.1470.2041.000
2023-12-12T13:11:53.089803image/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)1.000-0.0300.017-0.125-0.0310.0720.0060.0050.0340.0260.139
회계구분코드(FIS_FG_CD)-0.0301.0000.086-0.0750.096-0.011-0.0030.1390.9990.1320.270
예산현액(BGT_CURR_AMT)0.0170.0861.0000.7900.8620.2710.0550.5550.1450.0300.111
지출액(EXPD_AMT)-0.125-0.0750.7901.0000.7330.1660.0660.4580.0000.0440.014
편성액(COMPO_AMT)-0.0310.0960.8620.7331.000-0.103-0.0220.5060.1240.0270.110
이월액(FORWD_AMT)0.072-0.0110.2710.166-0.1031.0000.0030.1560.0240.0450.048
변경금액(CHNG_AMT)0.006-0.0030.0550.066-0.0220.0031.0000.0650.3080.0560.047
시구군예산액(SIGUNGU_CURR_AMT)0.0050.1390.5550.4580.5060.1560.0651.0000.2870.0330.202
회계구분(FIS_FG_NM)0.0340.9990.1450.0000.1240.0240.3080.2871.0000.1470.204
사업구분(SUBSD_BGT_FG_NM)0.0260.1320.0300.0440.0270.0450.0560.0330.1471.0000.487
분야(FLD_NM)0.1390.2700.1110.0140.1100.0480.0470.2020.2040.4871.000

Missing values

2023-12-12T13:11:43.560194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:11:43.960094image/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)
121242023412통합재정안정화기금기획조정실재무활동(기획조정실)\N보전지출(재정안정화계정)자체247650000000일반공공행정2023-01-012027-12-312476500000000024765000000
72072020100일반회계의회사무국지방의회 운영 지원국내외교류국내의정활동자체156110002892900일반공공행정2020-01-012024-12-311561100000015611000
6642016411옥외광고정비기금도시디자인과광고물관리옥외광고물 관리불법광고물 정비자체245294000230025010국토및지역개발2016-01-012020-12-31245294000000245294000
102682021100일반회계일자리경제과해양환경 보전어업기반시설 조성송정항 어항개발 계획수립 용역자체3604390036043620농림해양수산2021-01-012025-12-3122862000131819000036043900
130652022100일반회계건축과건축관리건축행정 추진무허가건축물 행정대집행자체150000000국토및지역개발2022-01-012026-12-311500000000015000000
41882019100일반회계반송1동반송1동 사업경비지방행정 기초사무수행민방위 비상급수시설 관리자체34400001625850일반공공행정2019-01-012023-12-3134400000003440000
37692019100일반회계미래도시과상하수도·수질관리하수도 수질관리하수구 관리보조212319000157951950환경보호2019-01-012023-12-311625200004979900000182319000
101972021100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로건설반여1동 우회도로 개설자체434873000373669100교통및물류1996-01-012021-12-3104348730000034873000
68272021100일반회계교통정책과교통 안전 확보교통관련시설물 정비자전거 관련 시설물 유지보수자체96200005136030교통및물류2021-01-012025-12-3196200000009620000
33342018100일반회계보건정책과건강 정책구강건강 관리아동주치의사업 지원보조3800000038000000보건2018-01-012022-12-313800000000019000000
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)
82782021100일반회계노인장애인복지과노인복지 지원노인사회참여 및 여가생활 지원경로당 환경개선보조2000000019944000사회복지2021-01-012021-12-312000000000010000000
20052017100일반회계복지정책과아동 건전 육성요보호 아동 지원입양축하금 지원보조70000002000000사회복지2017-01-012021-12-3170000000000
102712021100일반회계건강증진과건강 증진구민 정신건강증진정신질환자 치료비 지원 사업보조5597600017661750보건2021-01-012025-12-315597600000013994000
95382021100일반회계노인장애인복지과노인일자리창출노인일자리 지원노인사회활동지원사업(자체)자체850000850000사회복지2021-01-012025-12-31850000000850000
31912018100일반회계행복나눔과장애인 자립기반 조성장애인 생활안정장애수당(차상위)보조617630000614460000사회복지2018-01-012022-12-316176300000000
128042022100일반회계보건정책과건강 정책구민건강증진 기타사업 추진헌혈장려 사업자체80000008000000보건2022-01-012026-12-3180000000008000000
98012022100일반회계노인장애인복지과노인복지 지원노인복지시설 지원노인복지관 직원급여보전액 지원보조1326000012042000사회복지2022-01-012026-12-31132600000000
18572017100일반회계보건정책과건강 정책구민건강증진 기타사업 추진기초정신건강증진센터 운영보조228860000228860000보건2017-01-012021-12-3122886000000051115000
116332022100일반회계노인장애인복지과장애인 자립기반 조성장애인 편의 제공장애인 인식개선자체10000001000000사회복지2022-01-012026-12-3110000000001000000
51722021100일반회계안전총괄과민방위 운영민방위교육 훈련국민참여 민방위의 날 훈련보조19000001892000공공질서및안전2021-01-012025-12-311900000000665000

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
5722022100일반회계가족복지과아동 건전 육성아동복지시설 지원지역아동센터 공기청정기 지원보조00사회복지2022-01-012026-12-31000003
5752022100일반회계가족복지과아동 건전 육성아동복지시설 지원학대피해아동쉼터 건전육성지원보조22900000사회복지2022-01-012026-12-31229000000003
5862022100일반회계가족복지과아동 건전 육성요보호 아동 지원입양대상아동 보호비 지원보조60000000사회복지2022-07-012026-12-31600000000003
5872022100일반회계가족복지과아동 건전 육성요보호 아동 지원입양비용 지원보조54000000사회복지2022-01-012026-12-31540000000003
6312022100일반회계건강증진과건강 증진치매관리체계 구축치매안심센터 운영 지원(자체)자체800000보건2022-01-012026-12-3180000000800003
6502022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로정비달맞이길 추리문학관 일원 도로(계단) 정비보조3000000000교통및물류2022-12-262023-12-3130000000000003
6552022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로정비반송마루 진입로 보행환경 개선자체2500000000교통및물류2022-12-012023-12-3125000000000003
6622022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로정비삼어로 일원 방음시설 설치보조18000000000교통및물류2020-01-012026-12-31180000000000003
6672022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로정비우동 해운대로 594번길 일원 보도설치자체00교통및물류2020-01-012026-12-31000003
6702022100일반회계건설과지방도 건설 확포장 및 건설사업 활성화도로정비운봉길 51-10 일원 도로정비자체300000000교통및물류2022-01-012022-12-3130000000000300000003