Overview

Dataset statistics

Number of variables3
Number of observations729
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.6 KiB
Average record size in memory26.2 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description성인지예산제도는 예산이 여성과 남성에게 미칠 영향을 미리 분석하여 이를 예산편성에 반영함으로써 여성과 남성이 동등하게 예산의 수혜를 받도록 하는 제도입니다.
URLhttps://www.data.go.kr/data/15043031/fileData.do

Reproduction

Analysis started2023-12-12 08:20:19.006282
Analysis finished2023-12-12 08:20:19.536693
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2021
243 
2022
243 
2023
243 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 243
33.3%
2022 243
33.3%
2023 243
33.3%

Length

2023-12-12T17:20:19.614464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:20:19.735369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 243
33.3%
2022 243
33.3%
2023 243
33.3%

자치단체명
Text

Distinct243
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2023-12-12T17:20:20.168523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.1152263
Min length3

Characters and Unicode

Total characters5916
Distinct characters139
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 row서울특별시
2nd row서울특별시 종로구
3rd row서울특별시 중구
4th row서울특별시 용산구
5th row서울특별시 성동구
ValueCountFrequency (%)
경기도 96
 
6.8%
서울특별시 78
 
5.5%
경상북도 72
 
5.1%
전라남도 69
 
4.9%
강원특별자치도 57
 
4.1%
경상남도 57
 
4.1%
부산광역시 51
 
3.6%
충청남도 48
 
3.4%
전라북도 45
 
3.2%
충청북도 36
 
2.6%
Other values (211) 798
56.7%
2023-12-12T17:20:20.795900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
678
 
11.5%
도 498
 
8.4%
시 474
 
8.0%
군 255
 
4.3%
구 249
 
4.2%
경 234
 
4.0%
남 210
 
3.5%
광 201
 
3.4%
북 171
 
2.9%
역 165
 
2.8%
Other values (129) 2781
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5238
88.5%
Space Separator 678
 
11.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 498
 
9.5%
시 474
 
9.0%
군 255
 
4.9%
구 249
 
4.8%
경 234
 
4.5%
남 210
 
4.0%
광 201
 
3.8%
북 171
 
3.3%
역 165
 
3.2%
특 141
 
2.7%
Other values (128) 2640
50.4%
Space Separator
ValueCountFrequency (%)
678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5238
88.5%
Common 678
 
11.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 498
 
9.5%
시 474
 
9.0%
군 255
 
4.9%
구 249
 
4.8%
경 234
 
4.5%
남 210
 
4.0%
광 201
 
3.8%
북 171
 
3.3%
역 165
 
3.2%
특 141
 
2.7%
Other values (128) 2640
50.4%
Common
ValueCountFrequency (%)
678
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5238
88.5%
ASCII 678
 
11.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
678
100.0%
Hangul
ValueCountFrequency (%)
도 498
 
9.5%
시 474
 
9.0%
군 255
 
4.9%
구 249
 
4.8%
경 234
 
4.5%
남 210
 
4.0%
광 201
 
3.8%
북 171
 
3.3%
역 165
 
3.2%
특 141
 
2.7%
Other values (128) 2640
50.4%

당초성인지사업수
Real number (ℝ)

Distinct178
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.702332
Minimum6
Maximum386
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T17:20:20.956520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q135
median59
Q394
95-th percentile171.8
Maximum386
Range380
Interquartile range (IQR)59

Descriptive statistics

Standard deviation52.018678
Coefficient of variation (CV)0.72548098
Kurtosis6.9839003
Mean71.702332
Median Absolute Deviation (MAD)27
Skewness2.1624021
Sum52271
Variance2705.9429
MonotonicityNot monotonic
2023-12-12T17:20:21.122299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 14
 
1.9%
50 14
 
1.9%
52 14
 
1.9%
62 13
 
1.8%
69 13
 
1.8%
33 12
 
1.6%
59 12
 
1.6%
46 12
 
1.6%
29 12
 
1.6%
39 11
 
1.5%
Other values (168) 602
82.6%
ValueCountFrequency (%)
6 1
 
0.1%
7 2
 
0.3%
9 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
14 4
0.5%
16 8
1.1%
17 7
1.0%
18 6
0.8%
19 7
1.0%
ValueCountFrequency (%)
386 1
0.1%
340 1
0.1%
332 2
0.3%
323 1
0.1%
318 1
0.1%
316 1
0.1%
309 1
0.1%
291 1
0.1%
259 1
0.1%
255 1
0.1%

Interactions

2023-12-12T17:20:19.183574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:20:21.223165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번당초성인지사업수
순번1.0000.000
당초성인지사업수0.0001.000
2023-12-12T17:20:21.306629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당초성인지사업수순번
당초성인지사업수1.0000.000
순번0.0001.000

Missing values

2023-12-12T17:20:19.384250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:20:19.488508image/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

순번자치단체명당초성인지사업수
02021서울특별시318
12021서울특별시 종로구62
22021서울특별시 중구24
32021서울특별시 용산구41
42021서울특별시 성동구74
52021서울특별시 광진구50
62021서울특별시 동대문구53
72021서울특별시 중랑구32
82021서울특별시 성북구96
92021서울특별시 강북구35
순번자치단체명당초성인지사업수
7192023경상남도 함안군44
7202023경상남도 창녕군61
7212023경상남도 고성군61
7222023경상남도 남해군45
7232023경상남도 하동군82
7242023경상남도 산청군52
7252023경상남도 함양군60
7262023경상남도 거창군69
7272023경상남도 합천군29
7282023제주특별자치도332