Overview

Dataset statistics

Number of variables12
Number of observations460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.7 KiB
Average record size in memory106.3 B

Variable types

Categorical2
Text2
Numeric8

Dataset

Description부산광역시 상수도사업본부 예산집행현황에 대한 데이터로 예산과목, 예산현액, 집행금액, 지출금액 등을 제공합니다.
Author부산광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15100643/fileData.do

Alerts

예산년도 has constant value ""Constant
반납금액 has constant value ""Constant
예산현액 is highly overall correlated with 집행금액 and 4 other fieldsHigh correlation
집행금액 is highly overall correlated with 예산현액 and 4 other fieldsHigh correlation
원인행위금액 is highly overall correlated with 예산현액 and 4 other fieldsHigh correlation
지출결의금액 is highly overall correlated with 예산현액 and 4 other fieldsHigh correlation
채무확정금액 is highly overall correlated with 예산현액 and 4 other fieldsHigh correlation
지출금액 is highly overall correlated with 예산현액 and 4 other fieldsHigh correlation
예산코드 has unique valuesUnique
예산과목명 has unique valuesUnique
집행금액 has 18 (3.9%) zerosZeros
지출결의금액 has 18 (3.9%) zerosZeros
채무확정금액 has 18 (3.9%) zerosZeros
지출금액 has 18 (3.9%) zerosZeros
과목경정금액 has 441 (95.9%) zerosZeros
전용금액 has 427 (92.8%) zerosZeros

Reproduction

Analysis started2023-12-11 22:58:08.361171
Analysis finished2023-12-11 22:58:15.878178
Duration7.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

예산년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023
460 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2023 460
100.0%

Length

2023-12-12T07:58:15.951940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:58:16.044689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 460
100.0%

예산코드
Text

UNIQUE 

Distinct460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-12T07:58:16.259897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.971739
Min length18

Characters and Unicode

Total characters9647
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique460 ?
Unique (%)100.0%

Sample

1st row200230231405405-01001
2nd row200230231405405-01003
3rd row200230231405405-01004
4th row200230231405405-01005
5th row200230233401401-01001
ValueCountFrequency (%)
200230231405405-01001 1
 
0.2%
700710751101101-03008 1
 
0.2%
700710751101101-03005 1
 
0.2%
700710751101101-02007 1
 
0.2%
700710751101101-02006 1
 
0.2%
700710751101101-02004 1
 
0.2%
700710751101101-02002 1
 
0.2%
700710751101101-02001 1
 
0.2%
700710751101101-01005 1
 
0.2%
700710751101101-01004 1
 
0.2%
Other values (450) 450
97.8%
2023-12-12T07:58:16.668734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3396
35.2%
1 1961
20.3%
2 1237
 
12.8%
7 1098
 
11.4%
4 509
 
5.3%
3 505
 
5.2%
- 460
 
4.8%
5 310
 
3.2%
6 112
 
1.2%
8 39
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9187
95.2%
Dash Punctuation 460
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3396
37.0%
1 1961
21.3%
2 1237
 
13.5%
7 1098
 
12.0%
4 509
 
5.5%
3 505
 
5.5%
5 310
 
3.4%
6 112
 
1.2%
8 39
 
0.4%
9 20
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9647
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3396
35.2%
1 1961
20.3%
2 1237
 
12.8%
7 1098
 
11.4%
4 509
 
5.3%
3 505
 
5.2%
- 460
 
4.8%
5 310
 
3.2%
6 112
 
1.2%
8 39
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9647
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3396
35.2%
1 1961
20.3%
2 1237
 
12.8%
7 1098
 
11.4%
4 509
 
5.3%
3 505
 
5.2%
- 460
 
4.8%
5 310
 
3.2%
6 112
 
1.2%
8 39
 
0.4%

예산과목명
Text

UNIQUE 

Distinct460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-12T07:58:16.997746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length79
Median length55
Mean length37.830435
Min length17

Characters and Unicode

Total characters17402
Distinct characters320
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique460 ?
Unique (%)100.0%

Sample

1st row토지 - 자산취득비 - 자산및물품취득비 - 부지취득
2nd row토지 - 자산취득비 - 자산및물품취득비 - 철마배수지 부지취득(21년이월)
3rd row토지 - 자산취득비 - 자산및물품취득비 - 금곡배수지 부지취득(21년이월)
4th row토지 - 자산취득비 - 자산및물품취득비 - 철마배수지 부지취득
5th row건물 - 시설비및부대비 - 시설비 - 건축물 보수보강
ValueCountFrequency (%)
1376
33.6%
일반운영비 116
 
2.8%
일반관리비 105
 
2.6%
시설비및부대비 102
 
2.5%
101
 
2.5%
시설비 88
 
2.1%
정수비 84
 
2.0%
인건비 62
 
1.5%
배·급수비 62
 
1.5%
구축물 53
 
1.3%
Other values (638) 1950
47.6%
2023-12-12T07:58:17.449731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3642
20.9%
1389
 
8.0%
- 1379
 
7.9%
850
 
4.9%
356
 
2.0%
332
 
1.9%
316
 
1.8%
304
 
1.7%
267
 
1.5%
264
 
1.5%
Other values (310) 8303
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11967
68.8%
Space Separator 3642
 
20.9%
Dash Punctuation 1379
 
7.9%
Open Punctuation 113
 
0.6%
Close Punctuation 113
 
0.6%
Other Punctuation 109
 
0.6%
Decimal Number 57
 
0.3%
Uppercase Letter 16
 
0.1%
Math Symbol 4
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1389
 
11.6%
850
 
7.1%
356
 
3.0%
332
 
2.8%
316
 
2.6%
304
 
2.5%
267
 
2.2%
264
 
2.2%
260
 
2.2%
235
 
2.0%
Other values (280) 7394
61.8%
Uppercase Letter
ValueCountFrequency (%)
S 3
18.8%
C 2
12.5%
T 2
12.5%
V 1
 
6.2%
A 1
 
6.2%
D 1
 
6.2%
P 1
 
6.2%
I 1
 
6.2%
U 1
 
6.2%
M 1
 
6.2%
Other values (2) 2
12.5%
Decimal Number
ValueCountFrequency (%)
2 21
36.8%
1 12
21.1%
3 8
 
14.0%
5 8
 
14.0%
6 4
 
7.0%
0 1
 
1.8%
4 1
 
1.8%
8 1
 
1.8%
7 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
· 107
98.2%
, 2
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
v 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
3642
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1379
100.0%
Open Punctuation
ValueCountFrequency (%)
( 113
100.0%
Close Punctuation
ValueCountFrequency (%)
) 113
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11967
68.8%
Common 5417
31.1%
Latin 18
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1389
 
11.6%
850
 
7.1%
356
 
3.0%
332
 
2.8%
316
 
2.6%
304
 
2.5%
267
 
2.2%
264
 
2.2%
260
 
2.2%
235
 
2.0%
Other values (280) 7394
61.8%
Common
ValueCountFrequency (%)
3642
67.2%
- 1379
 
25.5%
( 113
 
2.1%
) 113
 
2.1%
· 107
 
2.0%
2 21
 
0.4%
1 12
 
0.2%
3 8
 
0.1%
5 8
 
0.1%
6 4
 
0.1%
Other values (6) 10
 
0.2%
Latin
ValueCountFrequency (%)
S 3
16.7%
C 2
11.1%
T 2
11.1%
V 1
 
5.6%
A 1
 
5.6%
D 1
 
5.6%
P 1
 
5.6%
I 1
 
5.6%
U 1
 
5.6%
M 1
 
5.6%
Other values (4) 4
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11967
68.8%
ASCII 5328
30.6%
None 107
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3642
68.4%
- 1379
 
25.9%
( 113
 
2.1%
) 113
 
2.1%
2 21
 
0.4%
1 12
 
0.2%
3 8
 
0.2%
5 8
 
0.2%
6 4
 
0.1%
~ 4
 
0.1%
Other values (19) 24
 
0.5%
Hangul
ValueCountFrequency (%)
1389
 
11.6%
850
 
7.1%
356
 
3.0%
332
 
2.8%
316
 
2.6%
304
 
2.5%
267
 
2.2%
264
 
2.2%
260
 
2.2%
235
 
2.0%
Other values (280) 7394
61.8%
None
ValueCountFrequency (%)
· 107
100.0%

예산현액
Real number (ℝ)

HIGH CORRELATION 

Distinct409
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0393103 × 109
Minimum0
Maximum2.7 × 1010
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T07:58:17.624064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4785000
Q139234250
median1.7125 × 108
Q35.7933141 × 108
95-th percentile5.2090958 × 109
Maximum2.7 × 1010
Range2.7 × 1010
Interquartile range (IQR)5.4009716 × 108

Descriptive statistics

Standard deviation2.8820726 × 109
Coefficient of variation (CV)2.7730626
Kurtosis30.987032
Mean1.0393103 × 109
Median Absolute Deviation (MAD)1.5461599 × 108
Skewness5.1268748
Sum4.7808275 × 1011
Variance8.3063425 × 1018
MonotonicityNot monotonic
2023-12-12T07:58:17.793183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000 5
 
1.1%
20000000 5
 
1.1%
40000000 4
 
0.9%
30000000 4
 
0.9%
24000000 3
 
0.7%
50000000 3
 
0.7%
15000000 3
 
0.7%
220000000 3
 
0.7%
250000000 3
 
0.7%
100000000 3
 
0.7%
Other values (399) 424
92.2%
ValueCountFrequency (%)
0 1
 
0.2%
613000 1
 
0.2%
1000000 1
 
0.2%
1120000 1
 
0.2%
1340000 1
 
0.2%
1500000 1
 
0.2%
1620000 1
 
0.2%
1744000 1
 
0.2%
1900000 2
0.4%
2000000 3
0.7%
ValueCountFrequency (%)
27000000000 1
0.2%
20767946000 1
0.2%
19660285000 1
0.2%
19079179000 1
0.2%
17678037000 1
0.2%
15350000000 1
0.2%
13312800000 1
0.2%
13229663000 1
0.2%
13167000000 1
0.2%
11818636000 1
0.2%

집행금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct442
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5148785 × 108
Minimum0
Maximum2.7908035 × 1010
Zeros18
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T07:58:17.963717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile610251
Q119694245
median87421615
Q34.6230199 × 108
95-th percentile4.7298102 × 109
Maximum2.7908035 × 1010
Range2.7908035 × 1010
Interquartile range (IQR)4.4260775 × 108

Descriptive statistics

Standard deviation3.0955659 × 109
Coefficient of variation (CV)3.2533951
Kurtosis35.52083
Mean9.5148785 × 108
Median Absolute Deviation (MAD)84743295
Skewness5.6002529
Sum4.3768441 × 1011
Variance9.5825281 × 1018
MonotonicityNot monotonic
2023-12-12T07:58:18.104005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
3.9%
3900000 2
 
0.4%
23664000 1
 
0.2%
812589450 1
 
0.2%
4473189440 1
 
0.2%
9253260 1
 
0.2%
123841740 1
 
0.2%
24636680 1
 
0.2%
20250330 1
 
0.2%
556247910 1
 
0.2%
Other values (432) 432
93.9%
ValueCountFrequency (%)
0 18
3.9%
243000 1
 
0.2%
422900 1
 
0.2%
450000 1
 
0.2%
528000 1
 
0.2%
564100 1
 
0.2%
612680 1
 
0.2%
637230 1
 
0.2%
750000 1
 
0.2%
779430 1
 
0.2%
ValueCountFrequency (%)
27908035170 1
0.2%
25452177520 1
0.2%
22120587240 1
0.2%
21805184470 1
0.2%
18658288360 1
0.2%
17535375330 1
0.2%
16370403820 1
0.2%
14022200920 1
0.2%
12784709560 1
0.2%
11962308150 1
0.2%

원인행위금액
Real number (ℝ)

HIGH CORRELATION 

Distinct458
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1971672 × 109
Minimum0
Maximum3.8699446 × 1010
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T07:58:18.238614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2167026
Q124683952
median1.1402515 × 108
Q35.9240291 × 108
95-th percentile5.3117316 × 109
Maximum3.8699446 × 1010
Range3.8699446 × 1010
Interquartile range (IQR)5.6771896 × 108

Descriptive statistics

Standard deviation3.8505116 × 109
Coefficient of variation (CV)3.2163524
Kurtosis39.754863
Mean1.1971672 × 109
Median Absolute Deviation (MAD)1.08632 × 108
Skewness5.8091288
Sum5.5069691 × 1011
Variance1.4826439 × 1019
MonotonicityNot monotonic
2023-12-12T07:58:18.438453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90000000 2
 
0.4%
3900000 2
 
0.4%
23664000 1
 
0.2%
1797366040 1
 
0.2%
24636680 1
 
0.2%
20250330 1
 
0.2%
556247910 1
 
0.2%
22321320 1
 
0.2%
5215790 1
 
0.2%
164135070 1
 
0.2%
Other values (448) 448
97.4%
ValueCountFrequency (%)
0 1
0.2%
243000 1
0.2%
422900 1
0.2%
528000 1
0.2%
564100 1
0.2%
612680 1
0.2%
637230 1
0.2%
750000 1
0.2%
779430 1
0.2%
860000 1
0.2%
ValueCountFrequency (%)
38699446240 1
0.2%
30819814840 1
0.2%
27908035170 1
0.2%
25110040740 1
0.2%
21805184470 1
0.2%
21195751900 1
0.2%
19466019150 1
0.2%
19254195420 1
0.2%
14022200920 1
0.2%
13228956110 1
0.2%

지출결의금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct442
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5148785 × 108
Minimum0
Maximum2.7908035 × 1010
Zeros18
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T07:58:18.617992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile610251
Q119694245
median87421615
Q34.6230199 × 108
95-th percentile4.7298102 × 109
Maximum2.7908035 × 1010
Range2.7908035 × 1010
Interquartile range (IQR)4.4260775 × 108

Descriptive statistics

Standard deviation3.0955659 × 109
Coefficient of variation (CV)3.2533951
Kurtosis35.52083
Mean9.5148785 × 108
Median Absolute Deviation (MAD)84743295
Skewness5.6002529
Sum4.3768441 × 1011
Variance9.5825281 × 1018
MonotonicityNot monotonic
2023-12-12T07:58:18.786937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
3.9%
3900000 2
 
0.4%
23664000 1
 
0.2%
812589450 1
 
0.2%
4473189440 1
 
0.2%
9253260 1
 
0.2%
123841740 1
 
0.2%
24636680 1
 
0.2%
20250330 1
 
0.2%
556247910 1
 
0.2%
Other values (432) 432
93.9%
ValueCountFrequency (%)
0 18
3.9%
243000 1
 
0.2%
422900 1
 
0.2%
450000 1
 
0.2%
528000 1
 
0.2%
564100 1
 
0.2%
612680 1
 
0.2%
637230 1
 
0.2%
750000 1
 
0.2%
779430 1
 
0.2%
ValueCountFrequency (%)
27908035170 1
0.2%
25452177520 1
0.2%
22120587240 1
0.2%
21805184470 1
0.2%
18658288360 1
0.2%
17535375330 1
0.2%
16370403820 1
0.2%
14022200920 1
0.2%
12784709560 1
0.2%
11962308150 1
0.2%

채무확정금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct442
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5137191 × 108
Minimum0
Maximum2.7908035 × 1010
Zeros18
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T07:58:18.918747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile610251
Q119694245
median87421615
Q34.6230199 × 108
95-th percentile4.7298102 × 109
Maximum2.7908035 × 1010
Range2.7908035 × 1010
Interquartile range (IQR)4.4260775 × 108

Descriptive statistics

Standard deviation3.0954445 × 109
Coefficient of variation (CV)3.253664
Kurtosis35.526481
Mean9.5137191 × 108
Median Absolute Deviation (MAD)84743295
Skewness5.6007351
Sum4.3763108 × 1011
Variance9.5817767 × 1018
MonotonicityNot monotonic
2023-12-12T07:58:19.068030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
3.9%
3900000 2
 
0.4%
23664000 1
 
0.2%
812589450 1
 
0.2%
4473189440 1
 
0.2%
9253260 1
 
0.2%
123841740 1
 
0.2%
24636680 1
 
0.2%
20250330 1
 
0.2%
556247910 1
 
0.2%
Other values (432) 432
93.9%
ValueCountFrequency (%)
0 18
3.9%
243000 1
 
0.2%
422900 1
 
0.2%
450000 1
 
0.2%
528000 1
 
0.2%
564100 1
 
0.2%
612680 1
 
0.2%
637230 1
 
0.2%
750000 1
 
0.2%
779430 1
 
0.2%
ValueCountFrequency (%)
27908035170 1
0.2%
25452177520 1
0.2%
22120587240 1
0.2%
21805184470 1
0.2%
18658288360 1
0.2%
17535375330 1
0.2%
16370403820 1
0.2%
14022200920 1
0.2%
12784709560 1
0.2%
11952906360 1
0.2%

지출금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct442
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6907922 × 108
Minimum0
Maximum2.7908035 × 1010
Zeros18
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T07:58:19.525982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile610251
Q119869415
median88435075
Q34.833732 × 108
95-th percentile4.8733551 × 109
Maximum2.7908035 × 1010
Range2.7908035 × 1010
Interquartile range (IQR)4.6350378 × 108

Descriptive statistics

Standard deviation3.1025379 × 109
Coefficient of variation (CV)3.2015317
Kurtosis35.057696
Mean9.6907922 × 108
Median Absolute Deviation (MAD)85800625
Skewness5.5534625
Sum4.4577644 × 1011
Variance9.6257412 × 1018
MonotonicityNot monotonic
2023-12-12T07:58:19.712716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
3.9%
3900000 2
 
0.4%
23664000 1
 
0.2%
811904010 1
 
0.2%
4473189440 1
 
0.2%
9253260 1
 
0.2%
123841740 1
 
0.2%
24636680 1
 
0.2%
20250330 1
 
0.2%
556247910 1
 
0.2%
Other values (432) 432
93.9%
ValueCountFrequency (%)
0 18
3.9%
243000 1
 
0.2%
422900 1
 
0.2%
450000 1
 
0.2%
528000 1
 
0.2%
564100 1
 
0.2%
612680 1
 
0.2%
637230 1
 
0.2%
750000 1
 
0.2%
779430 1
 
0.2%
ValueCountFrequency (%)
27908035170 1
0.2%
25452177520 1
0.2%
22120587240 1
0.2%
21805184470 1
0.2%
18658288360 1
0.2%
17535375330 1
0.2%
16370403820 1
0.2%
14022200920 1
0.2%
12784709560 1
0.2%
11952906360 1
0.2%

반납금액
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
460 

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 (%)
0 460
100.0%

Length

2023-12-12T07:58:19.850124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:58:19.959288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 460
100.0%

과목경정금액
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411080.46
Minimum0
Maximum48352140
Zeros441
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T07:58:20.066389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum48352140
Range48352140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3733860.6
Coefficient of variation (CV)9.0830409
Kurtosis135.94238
Mean411080.46
Median Absolute Deviation (MAD)0
Skewness11.449386
Sum1.8909701 × 108
Variance1.3941715 × 1013
MonotonicityNot monotonic
2023-12-12T07:58:20.182453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 441
95.9%
3694920 1
 
0.2%
48352140 1
 
0.2%
6583200 1
 
0.2%
14915900 1
 
0.2%
2320000 1
 
0.2%
90000 1
 
0.2%
1816720 1
 
0.2%
19800 1
 
0.2%
4251920 1
 
0.2%
Other values (10) 10
 
2.2%
ValueCountFrequency (%)
0 441
95.9%
19800 1
 
0.2%
90000 1
 
0.2%
360000 1
 
0.2%
460410 1
 
0.2%
726000 1
 
0.2%
991010 1
 
0.2%
1816720 1
 
0.2%
2320000 1
 
0.2%
2728000 1
 
0.2%
ValueCountFrequency (%)
48352140 1
0.2%
46878000 1
0.2%
39107690 1
0.2%
14915900 1
0.2%
8110620 1
0.2%
6583200 1
0.2%
4620000 1
0.2%
4251920 1
0.2%
3694920 1
0.2%
3070680 1
0.2%

전용금액
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-281000
Maximum381000
Zeros427
Zeros (%)92.8%
Negative17
Negative (%)3.7%
Memory size4.2 KiB
2023-12-12T07:58:20.307842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-281000
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum381000
Range662000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation35855.338
Coefficient of variation (CV)nan
Kurtosis71.654322
Mean0
Median Absolute Deviation (MAD)0
Skewness3.1017708
Sum0
Variance1.2856053 × 109
MonotonicityNot monotonic
2023-12-12T07:58:20.523931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 427
92.8%
-20000 2
 
0.4%
-30000 2
 
0.4%
30000 2
 
0.4%
20000 2
 
0.4%
-55000 1
 
0.2%
2000 1
 
0.2%
-2950 1
 
0.2%
-2000 1
 
0.2%
55000 1
 
0.2%
Other values (20) 20
 
4.3%
ValueCountFrequency (%)
-281000 1
0.2%
-270000 1
0.2%
-190550 1
0.2%
-163947 1
0.2%
-100000 1
0.2%
-55000 1
0.2%
-50000 1
0.2%
-47900 1
0.2%
-30000 2
0.4%
-20000 2
0.4%
ValueCountFrequency (%)
381000 1
0.2%
354497 1
0.2%
270000 1
0.2%
55000 1
0.2%
50000 1
0.2%
47900 1
0.2%
30000 2
0.4%
20000 2
0.4%
15000 1
0.2%
2760 1
0.2%

Interactions

2023-12-12T07:58:14.592955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:08.869451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.684233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.433675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.170024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.842479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:12.595821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:13.682553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:14.705755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:08.965993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.800715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.516249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.257163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.929195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:12.695678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:13.811174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:14.817284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.066687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.890609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.602994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.334789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:12.021693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:12.783123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:13.923609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:14.925981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.178631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.985627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.705841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.428260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:12.111840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:13.161789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:14.028484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:15.048020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.261940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.090128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.800712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.505363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:12.210750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:13.271294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:14.153272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:15.170720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.357692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.182958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.885843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.581388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:12.297937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:13.364121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:14.275768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:15.288974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.447364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.266511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.976886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.667335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:12.402677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:13.471504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:14.394949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:15.411805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:09.552228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:10.350483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.072189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:11.752957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:12.490518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:13.585854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:58:14.498412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:58:20.646371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예산현액집행금액원인행위금액지출결의금액채무확정금액지출금액과목경정금액전용금액
예산현액1.0000.8950.9380.8950.8950.8950.8250.258
집행금액0.8951.0000.9691.0001.0001.0000.4450.536
원인행위금액0.9380.9691.0000.9690.9690.9690.7270.452
지출결의금액0.8951.0000.9691.0001.0001.0000.4450.536
채무확정금액0.8951.0000.9691.0001.0001.0000.4450.536
지출금액0.8951.0000.9691.0001.0001.0000.4450.536
과목경정금액0.8250.4450.7270.4450.4450.4451.0000.000
전용금액0.2580.5360.4520.5360.5360.5360.0001.000
2023-12-12T07:58:20.775612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예산현액집행금액원인행위금액지출결의금액채무확정금액지출금액과목경정금액전용금액
예산현액1.0000.9310.9720.9310.9310.9250.159-0.101
집행금액0.9311.0000.9511.0001.0000.9980.170-0.089
원인행위금액0.9720.9511.0000.9510.9510.9520.156-0.087
지출결의금액0.9311.0000.9511.0001.0000.9980.170-0.089
채무확정금액0.9311.0000.9511.0001.0000.9980.170-0.089
지출금액0.9250.9980.9520.9980.9981.0000.168-0.092
과목경정금액0.1590.1700.1560.1700.1700.1681.000-0.078
전용금액-0.101-0.089-0.087-0.089-0.089-0.092-0.0781.000

Missing values

2023-12-12T07:58:15.599294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:58:15.801950image/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

예산년도예산코드예산과목명예산현액집행금액원인행위금액지출결의금액채무확정금액지출금액반납금액과목경정금액전용금액
02023200230231405405-01001토지 - 자산취득비 - 자산및물품취득비 - 부지취득240000002366400023664000236640002366400023664000000
12023200230231405405-01003토지 - 자산취득비 - 자산및물품취득비 - 철마배수지 부지취득(21년이월)544521700544521700544521700544521700544521700544521700000
22023200230231405405-01004토지 - 자산취득비 - 자산및물품취득비 - 금곡배수지 부지취득(21년이월)746779200550121970550121970550121970550121970550121970000
32023200230231405405-01005토지 - 자산취득비 - 자산및물품취득비 - 철마배수지 부지취득1000000009456749094567490945674909456749094567490000
42023200230233401401-01001건물 - 시설비및부대비 - 시설비 - 건축물 보수보강141000000302978400302978400302978400302978400302978400000
52023200230233401401-01002건물 - 시설비및부대비 - 시설비 - 취·정수장 건축물 보수보강396000000642930160642930160642930160642930160642930160000
62023200230233401401-01004건물 - 시설비및부대비 - 시설비 - 청사 보수보강220000000289068370289068370289068370289068370289068370000
72023200230233401401-01005건물 - 시설비및부대비 - 시설비 - 직원사택 보수보강300000000274499400274499400274499400274499400274499400000
82023200230233401401-02001건물 - 시설비및부대비 - 감리비 - 직원사택 보수보강 감리500000039000003900000390000039000003900000000
92023200230234401401-01001구축물 - 시설비및부대비 - 시설비 - 취·정수 공정시설 보수보강300000000164035440260766540164035440164035440164035440000
예산년도예산코드예산과목명예산현액집행금액원인행위금액지출결의금액채무확정금액지출금액반납금액과목경정금액전용금액
4502023700710752214214-04005징수및수용가관리비 - 수선유지교체비 - 지원사업성 시설비 - 옥내급수관 직결급수 사업2970000000297000000048378142702970000000297000000048378142700149159000
4512023700710752214214-05002징수및수용가관리비 - 수선유지교체비 - 수선유지비 - 방수비 납부961496000574553040592371840574553040574553040569089440000
4522023700710752214214-05003징수및수용가관리비 - 수선유지교체비 - 수선유지비 - 블록 유수율 유지관리256575000012859371002035564200128593710012859371001285937100000
4532023700710752214214-05004징수및수용가관리비 - 수선유지교체비 - 수선유지비 - 앵글밸브 등 보호통내 시설물 정비22322500001625938120166233118016259381201625938120162593812000-190550
4542023700710752214214-05005징수및수용가관리비 - 수선유지교체비 - 수선유지비 - 누수수리 도급1331280000094673425409664027710946734254094673425409438456890065832000
4552023700710752214214-05011징수및수용가관리비 - 수선유지교체비 - 수선유지비 - 옥내 누수 및 수질점검37307470004561283750456169008045612837504561283750456128375000354497
4562023700710752214214-05014징수및수용가관리비 - 수선유지교체비 - 수선유지비 - 급수불편해소131670000009867462370100641127409867462370986746237098674623700483521400
4572023700710752304304-01001징수및수용가관리비 - 연금부담금등 - 연금부담금 - 공무직(무기계약)근로자 국민연금부담금203564000119304230119304230119304230119304230119304230000
4582023700710752304304-02001징수및수용가관리비 - 연금부담금등 - 국민건강보험부담금등 - 공무직(무기계약)근로자 보험부담금305321000191683730191683730191683730191683730191683730000
4592023700770782802802-14001전기손익수정손실 - 반환금기타 - 전기손익수정손실 - 전기손익수정손실429000000345919480345919480345919480345919480345919480000