Overview

Dataset statistics

Number of variables4
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory924.0 B
Average record size in memory38.5 B

Variable types

Text2
Numeric1
Categorical1

Dataset

Description경상남도_남해군 자원봉사단체에 대한 데이터로 단체명, 단체원수, 대표자 이름 등 항목에 대한 정보를 제공합니다.
Author경상남도 남해군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3063222

Alerts

직위 is highly imbalanced (58.6%)Imbalance
단체명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:16:27.466969
Analysis finished2023-12-10 23:16:27.871510
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단체명
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T08:16:28.041641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12.5
Mean length9.7916667
Min length5

Characters and Unicode

Total characters235
Distinct characters102
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row남해군자원봉사협의회임원단
2nd row남해기독신우회
3rd row남해청실회
4th row새남해로타리클럽
5th row천도교봉사회
ValueCountFrequency (%)
남해군자원봉사협의회임원단 1
 
3.3%
남해기독신우회 1
 
3.3%
그린리더 1
 
3.3%
남해군 1
 
3.3%
남해지부 1
 
3.3%
등불 1
 
3.3%
새마을부녀회 1
 
3.3%
보물섬힐링공연단 1
 
3.3%
사)대한민국경남남해군지부 1
 
3.3%
열매봉사단 1
 
3.3%
Other values (20) 20
66.7%
2023-12-11T08:16:28.416486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
남 17
 
7.2%
회 17
 
7.2%
해 16
 
6.8%
사 12
 
5.1%
군 8
 
3.4%
봉 8
 
3.4%
7
 
3.0%
협 5
 
2.1%
의 5
 
2.1%
지 5
 
2.1%
Other values (92) 135
57.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 221
94.0%
Space Separator 7
 
3.0%
Uppercase Letter 3
 
1.3%
Close Punctuation 2
 
0.9%
Open Punctuation 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 17
 
7.7%
회 17
 
7.7%
해 16
 
7.2%
사 12
 
5.4%
군 8
 
3.6%
봉 8
 
3.6%
협 5
 
2.3%
의 5
 
2.3%
지 5
 
2.3%
단 5
 
2.3%
Other values (86) 123
55.7%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
G 1
33.3%
S 1
33.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 221
94.0%
Common 11
 
4.7%
Latin 3
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 17
 
7.7%
회 17
 
7.7%
해 16
 
7.2%
사 12
 
5.4%
군 8
 
3.6%
봉 8
 
3.6%
협 5
 
2.3%
의 5
 
2.3%
지 5
 
2.3%
단 5
 
2.3%
Other values (86) 123
55.7%
Common
ValueCountFrequency (%)
7
63.6%
) 2
 
18.2%
( 2
 
18.2%
Latin
ValueCountFrequency (%)
I 1
33.3%
G 1
33.3%
S 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 221
94.0%
ASCII 14
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
남 17
 
7.7%
회 17
 
7.7%
해 16
 
7.2%
사 12
 
5.4%
군 8
 
3.6%
봉 8
 
3.6%
협 5
 
2.3%
의 5
 
2.3%
지 5
 
2.3%
단 5
 
2.3%
Other values (86) 123
55.7%
ASCII
ValueCountFrequency (%)
7
50.0%
) 2
 
14.3%
( 2
 
14.3%
I 1
 
7.1%
G 1
 
7.1%
S 1
 
7.1%

인원
Real number (ℝ)

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.083333
Minimum11
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:16:28.550654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12.75
Q122.5
median36.5
Q380.25
95-th percentile292.5
Maximum490
Range479
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation115.9906
Coefficient of variation (CV)1.3632587
Kurtosis6.0220578
Mean85.083333
Median Absolute Deviation (MAD)19.5
Skewness2.4339502
Sum2042
Variance13453.819
MonotonicityNot monotonic
2023-12-11T08:16:28.655401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20 2
 
8.3%
25 2
 
8.3%
37 1
 
4.2%
32 1
 
4.2%
222 1
 
4.2%
12 1
 
4.2%
60 1
 
4.2%
36 1
 
4.2%
65 1
 
4.2%
21 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
11 1
4.2%
12 1
4.2%
17 1
4.2%
20 2
8.3%
21 1
4.2%
23 1
4.2%
24 1
4.2%
25 2
8.3%
32 1
4.2%
36 1
4.2%
ValueCountFrequency (%)
490 1
4.2%
300 1
4.2%
250 1
4.2%
222 1
4.2%
82 1
4.2%
81 1
4.2%
80 1
4.2%
65 1
4.2%
60 1
4.2%
56 1
4.2%

직위
Categorical

IMBALANCE 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
회장
22 
지회장
 
2

Length

Max length3
Median length2
Mean length2.0833333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
회장 22
91.7%
지회장 2
 
8.3%

Length

2023-12-11T08:16:28.766221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:28.851062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
회장 22
91.7%
지회장 2
 
8.3%

성 명
Text

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T08:16:28.991638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72
Distinct characters41
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

Unique22 ?
Unique (%)91.7%

Sample

1st row양태종
2nd row박종철
3rd row고두수
4th row김종선
5th row이영미
ValueCountFrequency (%)
양태종 2
 
8.3%
최윤수 1
 
4.2%
박미선 1
 
4.2%
곽영순 1
 
4.2%
임양심 1
 
4.2%
한일균 1
 
4.2%
하숙희 1
 
4.2%
류영환 1
 
4.2%
이나경 1
 
4.2%
송홍주 1
 
4.2%
Other values (13) 13
54.2%
2023-12-11T08:16:29.270052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 6
 
8.3%
종 5
 
6.9%
양 4
 
5.6%
영 4
 
5.6%
김 4
 
5.6%
태 3
 
4.2%
순 3
 
4.2%
숙 3
 
4.2%
최 2
 
2.8%
미 2
 
2.8%
Other values (31) 36
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 6
 
8.3%
종 5
 
6.9%
양 4
 
5.6%
영 4
 
5.6%
김 4
 
5.6%
태 3
 
4.2%
순 3
 
4.2%
숙 3
 
4.2%
최 2
 
2.8%
미 2
 
2.8%
Other values (31) 36
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 6
 
8.3%
종 5
 
6.9%
양 4
 
5.6%
영 4
 
5.6%
김 4
 
5.6%
태 3
 
4.2%
순 3
 
4.2%
숙 3
 
4.2%
최 2
 
2.8%
미 2
 
2.8%
Other values (31) 36
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 6
 
8.3%
종 5
 
6.9%
양 4
 
5.6%
영 4
 
5.6%
김 4
 
5.6%
태 3
 
4.2%
순 3
 
4.2%
숙 3
 
4.2%
최 2
 
2.8%
미 2
 
2.8%
Other values (31) 36
50.0%

Interactions

2023-12-11T08:16:27.633299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:16:29.384082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단체명인원직위성 명
단체명1.0001.0001.0001.000
인원1.0001.0000.0000.927
직위1.0000.0001.0000.000
성 명1.0000.9270.0001.000
2023-12-11T08:16:29.481889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인원직위
인원1.0000.000
직위0.0001.000

Missing values

2023-12-11T08:16:27.755250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:16:27.838343image/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

단체명인원직위성 명
0남해군자원봉사협의회임원단11회장양태종
1남해기독신우회24회장박종철
2남해청실회53회장고두수
3새남해로타리클럽81회장김종선
4천도교봉사회17회장이영미
5한국자유총연맹남해군지회80지회장양태종
6손사랑서금요법봉사단25회장김두엽
7남해로타리클럽82회장이봉언
8재향군인여성회250회장이양옥
9바르게살기운동남해군협의회490회장최태정
단체명인원직위성 명
14SGI 행복드림봉사회56회장김순덕
15남해신협두손모아봉사단23회장송홍주
16남해군화전농악보존회21회장이나경
17새남해라이온스클럽65회장류영환
18남해군사랑의 열매봉사단36회장하숙희
19(사)대한민국경남남해군지부60회장한일균
20보물섬힐링공연단12회장임양심
21새마을부녀회222회장곽영순
22등불 남해지부32회장박미선
23남해군 그린리더 협의회20회장정준순