Overview

Dataset statistics

Number of variables7
Number of observations123
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory61.1 B

Variable types

Numeric4
Categorical2
Text1

Dataset

Description부산광역시사하구_재정정보공개시스템_통계목_20221021
Author부산광역시 사하구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15092043

Alerts

연번 is highly overall correlated with 통계명코드 and 4 other fieldsHigh correlation
통계명코드 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
통계목분류명 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
통계목편성코드 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
통계목분류코드 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
통계목편성목명 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
연번 has unique valuesUnique
통계명코드 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:41:09.130706
Analysis finished2023-12-10 16:41:12.805059
Duration3.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T01:41:12.924931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.1
Q131.5
median62
Q392.5
95-th percentile116.9
Maximum123
Range122
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.651087
Coefficient of variation (CV)0.57501753
Kurtosis-1.2
Mean62
Median Absolute Deviation (MAD)31
Skewness0
Sum7626
Variance1271
MonotonicityStrictly increasing
2023-12-11T01:41:13.156389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
79 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
Other values (113) 113
91.9%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
123 1
0.8%
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%

통계목분류코드
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
경상이전
49 
물건비
28 
자본지출
15 
내부거래
9 
보전재원
7 
Other values (3)
15 

Length

Max length6
Median length4
Mean length3.8780488
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인건비
2nd row인건비
3rd row인건비
4th row인건비
5th row물건비

Common Values

ValueCountFrequency (%)
경상이전 49
39.8%
물건비 28
22.8%
자본지출 15
 
12.2%
내부거래 9
 
7.3%
보전재원 7
 
5.7%
예비비및기타 6
 
4.9%
융자및출자 5
 
4.1%
인건비 4
 
3.3%

Length

2023-12-11T01:41:13.351126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:41:13.528419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상이전 49
39.8%
물건비 28
22.8%
자본지출 15
 
12.2%
내부거래 9
 
7.3%
보전재원 7
 
5.7%
예비비및기타 6
 
4.9%
융자및출자 5
 
4.1%
인건비 4
 
3.3%

통계명코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36590.398
Minimum10101
Maximum80205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T01:41:13.724212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile20112.8
Q120702.5
median30709
Q340451.5
95-th percentile70602.9
Maximum80205
Range70104
Interquartile range (IQR)19749

Descriptive statistics

Standard deviation17796.769
Coefficient of variation (CV)0.48637811
Kurtosis0.3362878
Mean36590.398
Median Absolute Deviation (MAD)9793
Skewness1.1026255
Sum4500619
Variance3.1672499 × 108
MonotonicityStrictly increasing
2023-12-11T01:41:13.891956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10101 1
 
0.8%
31104 1
 
0.8%
40402 1
 
0.8%
40401 1
 
0.8%
40303 1
 
0.8%
40302 1
 
0.8%
40301 1
 
0.8%
40202 1
 
0.8%
40201 1
 
0.8%
40104 1
 
0.8%
Other values (113) 113
91.9%
ValueCountFrequency (%)
10101 1
0.8%
10102 1
0.8%
10103 1
0.8%
10104 1
0.8%
20101 1
0.8%
20102 1
0.8%
20103 1
0.8%
20201 1
0.8%
20202 1
0.8%
20203 1
0.8%
ValueCountFrequency (%)
80205 1
0.8%
80204 1
0.8%
80203 1
0.8%
80202 1
0.8%
80201 1
0.8%
80101 1
0.8%
70603 1
0.8%
70602 1
0.8%
70601 1
0.8%
70502 1
0.8%

통계목명
Text

Distinct121
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T01:41:14.212815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.0162602
Min length2

Characters and Unicode

Total characters863
Distinct characters142
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique119 ?
Unique (%)96.7%

Sample

1st row보수
2nd row기타직보수
3rd row무기계약근로자보수
4th row기간제근로자등보수
5th row사무관리비
ValueCountFrequency (%)
국내여비 2
 
1.6%
기관운영업무추진비 2
 
1.6%
기타자본이전 1
 
0.8%
통화금융기관차입금이자상환 1
 
0.8%
공사,공단자본전출금 1
 
0.8%
공기업특별회계자본전출금 1
 
0.8%
예비군육성지원자본보조 1
 
0.8%
공기관등에대한대행사업비 1
 
0.8%
자치단체자본보조 1
 
0.8%
민간대행사업비 1
 
0.8%
Other values (111) 111
90.2%
2023-12-11T01:41:14.777369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
금 81
 
9.4%
비 46
 
5.3%
상 31
 
3.6%
보 30
 
3.5%
자 25
 
2.9%
기 24
 
2.8%
원 18
 
2.1%
공 17
 
2.0%
조 15
 
1.7%
전 15
 
1.7%
Other values (132) 561
65.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 853
98.8%
Other Punctuation 6
 
0.7%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
금 81
 
9.5%
비 46
 
5.4%
상 31
 
3.6%
보 30
 
3.5%
자 25
 
2.9%
기 24
 
2.8%
원 18
 
2.1%
공 17
 
2.0%
조 15
 
1.8%
전 15
 
1.8%
Other values (129) 551
64.6%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 853
98.8%
Common 10
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
금 81
 
9.5%
비 46
 
5.4%
상 31
 
3.6%
보 30
 
3.5%
자 25
 
2.9%
기 24
 
2.8%
원 18
 
2.1%
공 17
 
2.0%
조 15
 
1.8%
전 15
 
1.8%
Other values (129) 551
64.6%
Common
ValueCountFrequency (%)
, 6
60.0%
( 2
 
20.0%
) 2
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 853
98.8%
ASCII 10
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
금 81
 
9.5%
비 46
 
5.4%
상 31
 
3.6%
보 30
 
3.5%
자 25
 
2.9%
기 24
 
2.8%
원 18
 
2.1%
공 17
 
2.0%
조 15
 
1.8%
전 15
 
1.8%
Other values (129) 551
64.6%
ASCII
ValueCountFrequency (%)
, 6
60.0%
( 2
 
20.0%
) 2
 
20.0%

통계목분류명
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean361.78862
Minimum100
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T01:41:14.944386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q1200
median300
Q3400
95-th percentile700
Maximum800
Range700
Interquartile range (IQR)200

Descriptive statistics

Standard deviation178.57373
Coefficient of variation (CV)0.49358582
Kurtosis0.32931675
Mean361.78862
Median Absolute Deviation (MAD)100
Skewness1.1118009
Sum44500
Variance31888.578
MonotonicityIncreasing
2023-12-11T01:41:15.105496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
300 49
39.8%
200 28
22.8%
400 15
 
12.2%
700 9
 
7.3%
600 7
 
5.7%
800 6
 
4.9%
500 5
 
4.1%
100 4
 
3.3%
ValueCountFrequency (%)
100 4
 
3.3%
200 28
22.8%
300 49
39.8%
400 15
 
12.2%
500 5
 
4.1%
600 7
 
5.7%
700 9
 
7.3%
800 6
 
4.9%
ValueCountFrequency (%)
800 6
 
4.9%
700 9
 
7.3%
600 7
 
5.7%
500 5
 
4.1%
400 15
 
12.2%
300 49
39.8%
200 28
22.8%
100 4
 
3.3%

통계목편성목명
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
일반보상금
12 
민간이전
10 
의회비
9 
자치단체등이전
9 
차입금원금상환
 
6
Other values (33)
77 

Length

Max length10
Median length8
Mean length5.097561
Min length2

Unique

Unique12 ?
Unique (%)9.8%

Sample

1st row인건비
2nd row인건비
3rd row인건비
4th row인건비
5th row일반운영비

Common Values

ValueCountFrequency (%)
일반보상금 12
 
9.8%
민간이전 10
 
8.1%
의회비 9
 
7.3%
자치단체등이전 9
 
7.3%
차입금원금상환 6
 
4.9%
반환금기타 5
 
4.1%
여비 5
 
4.1%
인건비 4
 
3.3%
시설비및부대비 4
 
3.3%
업무추진비 4
 
3.3%
Other values (28) 55
44.7%

Length

2023-12-11T01:41:15.315964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반보상금 12
 
9.8%
민간이전 10
 
8.1%
의회비 9
 
7.3%
자치단체등이전 9
 
7.3%
차입금원금상환 6
 
4.9%
반환금기타 5
 
4.1%
여비 5
 
4.1%
업무추진비 4
 
3.3%
차입금이자상환 4
 
3.3%
융자금 4
 
3.3%
Other values (28) 55
44.7%

통계목편성코드
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365.86992
Minimum101
Maximum802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T01:41:15.471638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile201.1
Q1207
median307
Q3404.5
95-th percentile706
Maximum802
Range701
Interquartile range (IQR)197.5

Descriptive statistics

Standard deviation177.97158
Coefficient of variation (CV)0.48643404
Kurtosis0.33628103
Mean365.86992
Median Absolute Deviation (MAD)98
Skewness1.1026717
Sum45002
Variance31673.885
MonotonicityIncreasing
2023-12-11T01:41:15.637503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
301 12
 
9.8%
307 10
 
8.1%
205 9
 
7.3%
308 9
 
7.3%
601 6
 
4.9%
202 5
 
4.1%
802 5
 
4.1%
101 4
 
3.3%
501 4
 
3.3%
311 4
 
3.3%
Other values (28) 55
44.7%
ValueCountFrequency (%)
101 4
 
3.3%
201 3
 
2.4%
202 5
4.1%
203 4
 
3.3%
204 3
 
2.4%
205 9
7.3%
206 1
 
0.8%
207 3
 
2.4%
301 12
9.8%
302 2
 
1.6%
ValueCountFrequency (%)
802 5
4.1%
801 1
 
0.8%
706 3
2.4%
705 2
 
1.6%
704 1
 
0.8%
703 1
 
0.8%
702 1
 
0.8%
701 1
 
0.8%
602 1
 
0.8%
601 6
4.9%

Interactions

2023-12-11T01:41:11.691457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:09.594489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:10.164048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:11.055054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:11.844462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:09.721878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:10.602113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:11.210234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:12.056295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:09.873532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:10.743652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:11.354906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:12.311056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:10.022144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:10.892616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:41:11.530172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:41:15.776010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번통계목분류코드통계명코드통계목분류명통계목편성목명통계목편성코드
연번1.0000.8710.8690.8710.9820.871
통계목분류코드0.8711.0001.0001.0001.0001.000
통계명코드0.8691.0001.0001.0001.0001.000
통계목분류명0.8711.0001.0001.0001.0001.000
통계목편성목명0.9821.0001.0001.0001.0001.000
통계목편성코드0.8711.0001.0001.0001.0001.000
2023-12-11T01:41:15.893452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계목편성목명통계목분류코드
통계목편성목명1.0000.860
통계목분류코드0.8601.000
2023-12-11T01:41:16.022337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번통계명코드통계목분류명통계목편성코드통계목분류코드통계목편성목명
연번1.0001.0000.9600.9990.6610.752
통계명코드1.0001.0000.9600.9991.0000.860
통계목분류명0.9600.9601.0000.9621.0000.860
통계목편성코드0.9990.9990.9621.0001.0000.860
통계목분류코드0.6611.0001.0001.0001.0000.860
통계목편성목명0.7520.8600.8600.8600.8601.000

Missing values

2023-12-11T01:41:12.552839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:41:12.729689image/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

연번통계목분류코드통계명코드통계목명통계목분류명통계목편성목명통계목편성코드
01인건비10101보수100인건비101
12인건비10102기타직보수100인건비101
23인건비10103무기계약근로자보수100인건비101
34인건비10104기간제근로자등보수100인건비101
45물건비20101사무관리비200일반운영비201
56물건비20102공공운영비200일반운영비201
67물건비20103행사운영비200일반운영비201
78물건비20201국내여비200여비202
89물건비20202월액여비200여비202
910물건비20203국외업무여비200여비202
연번통계목분류코드통계명코드통계목명통계목분류명통계목편성목명통계목편성코드
113114내부거래70502예수금이자상환700예수금원리금상환705
114115내부거래70601감가상각비700기타내부거래706
115116내부거래70602당기순이익700기타내부거래706
116117내부거래70603적립금700기타내부거래706
117118예비비및기타80101예비비800예비비801
118119예비비및기타80201국고보조금반환금800반환금기타802
119120예비비및기타80202시,도비보조금반환금800반환금기타802
120121예비비및기타80203과오납금등800반환금기타802
121122예비비및기타80204잡손금800반환금기타802
122123예비비및기타80205당겨쓰기충당금800반환금기타802