Overview

Dataset statistics

Number of variables3
Number of observations117
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory26.1 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description복지소외계층발굴 및 민간자원연계 사업인 좋은이웃들 사업의 추진을 위해 활동하고 있는 시도, 시군구별 봉사자 현황에 대한 데이터입니다.
Author한국사회복지협의회
URLhttps://www.data.go.kr/data/15067607/fileData.do

Reproduction

Analysis started2023-12-12 13:05:40.875600
Analysis finished2023-12-12 13:05:41.509654
Duration0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
강원도
18 
경기도
17 
전라북도
14 
충청남도
13 
전라남도
11 
Other values (12)
44 

Length

Max length7
Median length5
Mean length3.9487179
Min length3

Unique

Unique4 ?
Unique (%)3.4%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 18
15.4%
경기도 17
14.5%
전라북도 14
12.0%
충청남도 13
11.1%
전라남도 11
9.4%
대구광역시 8
6.8%
경상남도 8
6.8%
충청북도 6
 
5.1%
서울특별시 5
 
4.3%
경상북도 5
 
4.3%
Other values (7) 12
10.3%

Length

2023-12-12T22:05:41.589692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원도 18
15.4%
경기도 17
14.5%
전라북도 14
12.0%
충청남도 13
11.1%
전라남도 11
9.4%
대구광역시 8
6.8%
경상남도 8
6.8%
충청북도 6
 
5.1%
경상북도 5
 
4.3%
서울특별시 5
 
4.3%
Other values (7) 12
10.3%

사업수행지역
Text

Distinct112
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T22:05:41.893612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.042735
Min length2

Characters and Unicode

Total characters356
Distinct characters99
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)92.3%

Sample

1st row구로구
2nd row금천구
3rd row도봉구
4th row서대문구
5th row영등포구
ValueCountFrequency (%)
북구 3
 
2.6%
남구 2
 
1.7%
서구 2
 
1.7%
중구 2
 
1.7%
전주시 1
 
0.9%
부안군 1
 
0.9%
김제시 1
 
0.9%
남원시 1
 
0.9%
무주군 1
 
0.9%
완주군 1
 
0.9%
Other values (102) 102
87.2%
2023-12-12T22:05:42.390296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 58
 
16.3%
군 40
 
11.2%
구 22
 
6.2%
주 16
 
4.5%
천 13
 
3.7%
산 11
 
3.1%
성 11
 
3.1%
양 9
 
2.5%
안 9
 
2.5%
광 7
 
2.0%
Other values (89) 160
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 356
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 58
 
16.3%
군 40
 
11.2%
구 22
 
6.2%
주 16
 
4.5%
천 13
 
3.7%
산 11
 
3.1%
성 11
 
3.1%
양 9
 
2.5%
안 9
 
2.5%
광 7
 
2.0%
Other values (89) 160
44.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 356
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 58
 
16.3%
군 40
 
11.2%
구 22
 
6.2%
주 16
 
4.5%
천 13
 
3.7%
산 11
 
3.1%
성 11
 
3.1%
양 9
 
2.5%
안 9
 
2.5%
광 7
 
2.0%
Other values (89) 160
44.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 356
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 58
 
16.3%
군 40
 
11.2%
구 22
 
6.2%
주 16
 
4.5%
천 13
 
3.7%
산 11
 
3.1%
성 11
 
3.1%
양 9
 
2.5%
안 9
 
2.5%
광 7
 
2.0%
Other values (89) 160
44.9%

봉사자수
Real number (ℝ)

Distinct108
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.53846
Minimum44
Maximum1614
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:05:42.598333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile145.4
Q1307
median464
Q3730
95-th percentile1375
Maximum1614
Range1570
Interquartile range (IQR)423

Descriptive statistics

Standard deviation380.41956
Coefficient of variation (CV)0.67029741
Kurtosis0.56063232
Mean567.53846
Median Absolute Deviation (MAD)189
Skewness1.1249973
Sum66402
Variance144719.04
MonotonicityNot monotonic
2023-12-12T22:05:42.751978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
351 3
 
2.6%
335 3
 
2.6%
491 2
 
1.7%
275 2
 
1.7%
659 2
 
1.7%
149 2
 
1.7%
610 2
 
1.7%
730 1
 
0.9%
425 1
 
0.9%
732 1
 
0.9%
Other values (98) 98
83.8%
ValueCountFrequency (%)
44 1
0.9%
51 1
0.9%
82 1
0.9%
115 1
0.9%
120 1
0.9%
135 1
0.9%
148 1
0.9%
149 2
1.7%
156 1
0.9%
159 1
0.9%
ValueCountFrequency (%)
1614 1
0.9%
1588 1
0.9%
1584 1
0.9%
1490 1
0.9%
1459 1
0.9%
1383 1
0.9%
1373 1
0.9%
1370 1
0.9%
1331 1
0.9%
1312 1
0.9%

Interactions

2023-12-12T22:05:41.272144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:05:42.858120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도봉사자수
시도1.0000.397
봉사자수0.3971.000
2023-12-12T22:05:42.940530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
봉사자수시도
봉사자수1.0000.157
시도0.1571.000

Missing values

2023-12-12T22:05:41.414132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:05:41.482519image/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

시도사업수행지역봉사자수
0서울특별시구로구291
1서울특별시금천구1312
2서울특별시도봉구135
3서울특별시서대문구1588
4서울특별시영등포구1614
5부산광역시남구538
6부산광역시북구1331
7부산광역시연제구115
8대구광역시남구199
9대구광역시달서구1459
시도사업수행지역봉사자수
107경상남도거제시401
108경상남도거창군1373
109경상남도김해시351
110경상남도사천시307
111경상남도의령군491
112경상남도진주시841
113경상남도창원시295
114경상남도함양군44
115제주특별자치도제주특별자치도1490
116세종특별자치시세종특별자치시355