Overview

Dataset statistics

Number of variables7
Number of observations102
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory62.3 B

Variable types

Categorical4
Text1
Numeric2

Dataset

Description부산광역시 통합자금관리시스템 예상수입액수기등록 데이터로 기준일자,비고,예상수입액,일련번호,자치단체코드,회계년도,회계코드 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15121626/fileData.do

Alerts

자치단체코드 has constant value ""Constant
회계년도 has constant value ""Constant
일련번호 is highly overall correlated with 기준일자High correlation
기준일자 is highly overall correlated with 일련번호High correlation
회계코드 is highly imbalanced (77.0%)Imbalance
일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:54:56.349353
Analysis finished2023-12-12 02:54:57.607371
Duration1.26 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일자
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size948.0 B
2013-04-30
9 
2013-05-20
 
7
2013-05-14
 
6
2013-05-15
 
5
2013-05-24
 
5
Other values (30)
70 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique10 ?
Unique (%)9.8%

Sample

1st row2013-04-18
2nd row2013-04-05
3rd row2013-05-14
4th row2013-05-14
5th row2013-05-14

Common Values

ValueCountFrequency (%)
2013-04-30 9
 
8.8%
2013-05-20 7
 
6.9%
2013-05-14 6
 
5.9%
2013-05-15 5
 
4.9%
2013-05-24 5
 
4.9%
2013-04-01 5
 
4.9%
2013-05-09 4
 
3.9%
2013-04-25 4
 
3.9%
2013-06-11 4
 
3.9%
2013-05-28 4
 
3.9%
Other values (25) 49
48.0%

Length

2023-12-12T11:54:58.110643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-04-30 9
 
8.8%
2013-05-20 7
 
6.9%
2013-05-14 6
 
5.9%
2013-05-15 5
 
4.9%
2013-05-24 5
 
4.9%
2013-04-01 5
 
4.9%
2013-05-09 4
 
3.9%
2013-04-25 4
 
3.9%
2013-06-11 4
 
3.9%
2013-05-28 4
 
3.9%
Other values (25) 49
48.0%

비고
Text

Distinct91
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size948.0 B
2023-12-12T11:54:58.464837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length8.5980392
Min length3

Characters and Unicode

Total characters877
Distinct characters215
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)81.4%

Sample

1st row지역자율형사회서비스
2nd row취득세 감면분 보전
3rd row생계급여
4th row주거급여
5th row장애인연금
ValueCountFrequency (%)
지방교부세 5
 
3.4%
긴급복지 2
 
1.4%
자살예방 2
 
1.4%
장애아동가족지원 2
 
1.4%
지원 2
 
1.4%
사회서비스 2
 
1.4%
지역자율형 2
 
1.4%
영유아보육료 2
 
1.4%
자동차세(주행분 2
 
1.4%
운영 2
 
1.4%
Other values (117) 122
84.1%
2023-12-12T11:54:58.987554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45
 
5.1%
지 44
 
5.0%
사 28
 
3.2%
업 23
 
2.6%
자 19
 
2.2%
원 17
 
1.9%
세 16
 
1.8%
방 16
 
1.8%
비 14
 
1.6%
교 13
 
1.5%
Other values (205) 642
73.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 800
91.2%
Space Separator 45
 
5.1%
Uppercase Letter 11
 
1.3%
Close Punctuation 6
 
0.7%
Open Punctuation 6
 
0.7%
Decimal Number 4
 
0.5%
Dash Punctuation 3
 
0.3%
Other Punctuation 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 44
 
5.5%
사 28
 
3.5%
업 23
 
2.9%
자 19
 
2.4%
원 17
 
2.1%
세 16
 
2.0%
방 16
 
2.0%
비 14
 
1.8%
교 13
 
1.6%
장 13
 
1.6%
Other values (188) 597
74.6%
Uppercase Letter
ValueCountFrequency (%)
T 3
27.3%
C 2
18.2%
S 2
18.2%
M 1
 
9.1%
I 1
 
9.1%
A 1
 
9.1%
V 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
2 1
25.0%
3 1
25.0%
5 1
25.0%
1 1
25.0%
Space Separator
ValueCountFrequency (%)
45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 800
91.2%
Common 66
 
7.5%
Latin 11
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 44
 
5.5%
사 28
 
3.5%
업 23
 
2.9%
자 19
 
2.4%
원 17
 
2.1%
세 16
 
2.0%
방 16
 
2.0%
비 14
 
1.8%
교 13
 
1.6%
장 13
 
1.6%
Other values (188) 597
74.6%
Common
ValueCountFrequency (%)
45
68.2%
) 6
 
9.1%
( 6
 
9.1%
- 3
 
4.5%
, 1
 
1.5%
2 1
 
1.5%
3 1
 
1.5%
~ 1
 
1.5%
5 1
 
1.5%
1 1
 
1.5%
Latin
ValueCountFrequency (%)
T 3
27.3%
C 2
18.2%
S 2
18.2%
M 1
 
9.1%
I 1
 
9.1%
A 1
 
9.1%
V 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 800
91.2%
ASCII 77
 
8.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45
58.4%
) 6
 
7.8%
( 6
 
7.8%
T 3
 
3.9%
- 3
 
3.9%
C 2
 
2.6%
S 2
 
2.6%
, 1
 
1.3%
M 1
 
1.3%
I 1
 
1.3%
Other values (7) 7
 
9.1%
Hangul
ValueCountFrequency (%)
지 44
 
5.5%
사 28
 
3.5%
업 23
 
2.9%
자 19
 
2.4%
원 17
 
2.1%
세 16
 
2.0%
방 16
 
2.0%
비 14
 
1.8%
교 13
 
1.6%
장 13
 
1.6%
Other values (188) 597
74.6%

예상수입액
Real number (ℝ)

Distinct97
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.317598 × 109
Minimum1.25 × 108
Maximum8.8376 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T11:54:59.172024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.25 × 108
5-th percentile2.0025 × 108
Q16.305 × 108
median1.8685 × 109
Q31.072425 × 1010
95-th percentile3.99866 × 1010
Maximum8.8376 × 1010
Range8.8251 × 1010
Interquartile range (IQR)1.009375 × 1010

Descriptive statistics

Standard deviation1.6456472 × 1010
Coefficient of variation (CV)1.7661711
Kurtosis9.7986259
Mean9.317598 × 109
Median Absolute Deviation (MAD)1.553 × 109
Skewness2.8865982
Sum9.50395 × 1011
Variance2.7081548 × 1020
MonotonicityNot monotonic
2023-12-12T11:54:59.380921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22975000000 2
 
2.0%
15878000000 2
 
2.0%
1399000000 2
 
2.0%
438000000 2
 
2.0%
272000000 2
 
2.0%
3390000000 1
 
1.0%
3578000000 1
 
1.0%
1819000000 1
 
1.0%
12146000000 1
 
1.0%
154000000 1
 
1.0%
Other values (87) 87
85.3%
ValueCountFrequency (%)
125000000 1
1.0%
139000000 1
1.0%
154000000 1
1.0%
186000000 1
1.0%
193000000 1
1.0%
200000000 1
1.0%
205000000 1
1.0%
250000000 1
1.0%
259000000 1
1.0%
272000000 2
2.0%
ValueCountFrequency (%)
88376000000 1
1.0%
88367000000 1
1.0%
54661000000 1
1.0%
53020000000 1
1.0%
41355000000 1
1.0%
40300000000 1
1.0%
34032000000 1
1.0%
32715000000 1
1.0%
30400000000 1
1.0%
30000000000 1
1.0%

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.5
Minimum1
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T11:54:59.584321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.05
Q126.25
median51.5
Q376.75
95-th percentile96.95
Maximum102
Range101
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation29.588849
Coefficient of variation (CV)0.57454076
Kurtosis-1.2
Mean51.5
Median Absolute Deviation (MAD)25.5
Skewness0
Sum5253
Variance875.5
MonotonicityNot monotonic
2023-12-12T11:54:59.819848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 1
 
1.0%
14 1
 
1.0%
37 1
 
1.0%
36 1
 
1.0%
35 1
 
1.0%
34 1
 
1.0%
33 1
 
1.0%
32 1
 
1.0%
31 1
 
1.0%
30 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size948.0 B
6260000
102 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6260000
2nd row6260000
3rd row6260000
4th row6260000
5th row6260000

Common Values

ValueCountFrequency (%)
6260000 102
100.0%

Length

2023-12-12T11:54:59.971600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:55:00.085231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6260000 102
100.0%

회계년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size948.0 B
2013
102 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 102
100.0%

Length

2023-12-12T11:55:00.232199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:55:00.368218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 102
100.0%

회계코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size948.0 B
100
94 
235
 
4
260
 
2
220
 
1
230
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 94
92.2%
235 4
 
3.9%
260 2
 
2.0%
220 1
 
1.0%
230 1
 
1.0%

Length

2023-12-12T11:55:00.475877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:55:00.616387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 94
92.2%
235 4
 
3.9%
260 2
 
2.0%
220 1
 
1.0%
230 1
 
1.0%

Interactions

2023-12-12T11:54:57.130087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:54:56.837061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:54:57.244977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:54:56.973063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:55:00.716281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자비고예상수입액일련번호회계코드
기준일자1.0000.0000.0000.9800.000
비고0.0001.0000.0000.7360.910
예상수입액0.0000.0001.0000.0870.532
일련번호0.9800.7360.0871.0000.421
회계코드0.0000.9100.5320.4211.000
2023-12-12T11:55:00.835652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계코드기준일자
회계코드1.0000.000
기준일자0.0001.000
2023-12-12T11:55:00.957576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예상수입액일련번호기준일자회계코드
예상수입액1.000-0.3590.0000.357
일련번호-0.3591.0000.7190.180
기준일자0.0000.7191.0000.000
회계코드0.3570.1800.0001.000

Missing values

2023-12-12T11:54:57.379822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:54:57.535092image/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

기준일자비고예상수입액일련번호자치단체코드회계년도회계코드
02013-04-18지역자율형사회서비스2099000000962600002013100
12013-04-05취득세 감면분 보전40300000000762600002013100
22013-05-14생계급여327150000004462600002013100
32013-05-14주거급여75750000004562600002013100
42013-05-14장애인연금24450000004662600002013100
52013-05-14전통시장26290000004762600002013100
62013-05-14지역자율형 사회서비스13990000004862600002013100
72013-05-14재해위험지역 정비10000000004962600002013100
82013-05-23연안정비사업37800000006562600002013100
92013-05-21어린이안전영상정보인프라구축15500000006662600002013100
기준일자비고예상수입액일련번호자치단체코드회계년도회계코드
922013-05-28친환경농자재(유기질비료, 토양개량제)6820000008062600002013100
932013-05-28관광특구 활성화 지원사업8000000008162600002013100
942013-06-03부산교통공단 채무상환 지원18570000009062600002013235
952013-06-03부산-김해 경전철 국고보조금8670000009162600002013100
962013-06-03아이돌봄지원사업1390000009262600002013100
972013-06-04천연가스자동차 보급9000000009362600002013100
982013-06-11장안-임랑국지도건설13000000009962600002013100
992013-06-11지역자율형 사회서비스 투자사업139900000010062600002013100
1002013-06-11장애아동가족지원43800000010162600002013100
1012013-06-11희망키움통장38200000010262600002013100