Overview

Dataset statistics

Number of variables10
Number of observations22
Missing cells28
Missing cells (%)12.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory90.0 B

Variable types

Numeric4
Text5
Categorical1

Dataset

Description사회복지법인 행정 처분결과 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=U6EILGONRO1QDHQ12DA818864508&infSeq=1

Alerts

처분연도 is highly overall correlated with 처분결과내용High correlation
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 처분결과내용High correlation
처분결과내용 is highly overall correlated with 처분연도 and 3 other fieldsHigh correlation
시군명 has 4 (18.2%) missing valuesMissing
법인명 has 4 (18.2%) missing valuesMissing
소재지우편번호 has 4 (18.2%) missing valuesMissing
소재지지번주소 has 4 (18.2%) missing valuesMissing
소재지도로명주소 has 4 (18.2%) missing valuesMissing
WGS84위도 has 4 (18.2%) missing valuesMissing
WGS84경도 has 4 (18.2%) missing valuesMissing

Reproduction

Analysis started2023-12-10 21:46:15.087410
Analysis finished2023-12-10 21:46:17.114512
Duration2.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처분연도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.8636
Minimum2017
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T06:46:17.158082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32019
95-th percentile2021.95
Maximum2023
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5211354
Coefficient of variation (CV)0.00075346118
Kurtosis1.8725882
Mean2018.8636
Median Absolute Deviation (MAD)1
Skewness1.32354
Sum44415
Variance2.3138528
MonotonicityDecreasing
2023-12-11T06:46:17.236920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019 8
36.4%
2018 7
31.8%
2017 3
 
13.6%
2023 1
 
4.5%
2022 1
 
4.5%
2021 1
 
4.5%
2020 1
 
4.5%
ValueCountFrequency (%)
2017 3
 
13.6%
2018 7
31.8%
2019 8
36.4%
2020 1
 
4.5%
2021 1
 
4.5%
2022 1
 
4.5%
2023 1
 
4.5%
ValueCountFrequency (%)
2023 1
 
4.5%
2022 1
 
4.5%
2021 1
 
4.5%
2020 1
 
4.5%
2019 8
36.4%
2018 7
31.8%
2017 3
 
13.6%

시군명
Text

MISSING 

Distinct12
Distinct (%)66.7%
Missing4
Missing (%)18.2%
Memory size308.0 B
2023-12-11T06:46:17.371212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54
Distinct characters20
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

Unique8 ?
Unique (%)44.4%

Sample

1st row안양시
2nd row안양시
3rd row안양시
4th row양평군
5th row의왕시
ValueCountFrequency (%)
안양시 3
16.7%
화성시 3
16.7%
광주시 2
11.1%
성남시 2
11.1%
양평군 1
 
5.6%
의왕시 1
 
5.6%
평택시 1
 
5.6%
하남시 1
 
5.6%
김포시 1
 
5.6%
수원시 1
 
5.6%
Other values (2) 2
11.1%
2023-12-11T06:46:17.639735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 17
31.5%
성 6
 
11.1%
안 4
 
7.4%
양 4
 
7.4%
화 3
 
5.6%
남 3
 
5.6%
광 2
 
3.7%
주 2
 
3.7%
평 2
 
3.7%
김 1
 
1.9%
Other values (10) 10
18.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 17
31.5%
성 6
 
11.1%
안 4
 
7.4%
양 4
 
7.4%
화 3
 
5.6%
남 3
 
5.6%
광 2
 
3.7%
주 2
 
3.7%
평 2
 
3.7%
김 1
 
1.9%
Other values (10) 10
18.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 17
31.5%
성 6
 
11.1%
안 4
 
7.4%
양 4
 
7.4%
화 3
 
5.6%
남 3
 
5.6%
광 2
 
3.7%
주 2
 
3.7%
평 2
 
3.7%
김 1
 
1.9%
Other values (10) 10
18.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 17
31.5%
성 6
 
11.1%
안 4
 
7.4%
양 4
 
7.4%
화 3
 
5.6%
남 3
 
5.6%
광 2
 
3.7%
주 2
 
3.7%
평 2
 
3.7%
김 1
 
1.9%
Other values (10) 10
18.5%

법인명
Text

MISSING 

Distinct17
Distinct (%)94.4%
Missing4
Missing (%)18.2%
Memory size308.0 B
2023-12-11T06:46:17.800570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length6.5
Min length2

Characters and Unicode

Total characters117
Distinct characters55
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

Unique16 ?
Unique (%)88.9%

Sample

1st row백우현진복지재단
2nd row돕는사람들
3rd row안양시사회복지협의회
4th row양평군사회복지협의회
5th row의왕시사회복지협의회
ValueCountFrequency (%)
분당사회관 2
 
10.5%
돕는사람들 1
 
5.3%
백우현진복지재단 1
 
5.3%
성심동원 1
 
5.3%
한길 1
 
5.3%
지엠에스사회복지재단 1
 
5.3%
별 1
 
5.3%
바다의 1
 
5.3%
꿈나래터 1
 
5.3%
한국발달장애센터 1
 
5.3%
Other values (8) 8
42.1%
2023-12-11T06:46:18.093274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 14
 
12.0%
사 9
 
7.7%
지 9
 
7.7%
복 8
 
6.8%
의 7
 
6.0%
협 5
 
4.3%
시 4
 
3.4%
원 3
 
2.6%
성 3
 
2.6%
분 2
 
1.7%
Other values (45) 53
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 116
99.1%
Space Separator 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 14
 
12.1%
사 9
 
7.8%
지 9
 
7.8%
복 8
 
6.9%
의 7
 
6.0%
협 5
 
4.3%
시 4
 
3.4%
원 3
 
2.6%
성 3
 
2.6%
분 2
 
1.7%
Other values (44) 52
44.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 116
99.1%
Common 1
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 14
 
12.1%
사 9
 
7.8%
지 9
 
7.8%
복 8
 
6.9%
의 7
 
6.0%
협 5
 
4.3%
시 4
 
3.4%
원 3
 
2.6%
성 3
 
2.6%
분 2
 
1.7%
Other values (44) 52
44.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 116
99.1%
ASCII 1
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 14
 
12.1%
사 9
 
7.8%
지 9
 
7.8%
복 8
 
6.9%
의 7
 
6.0%
협 5
 
4.3%
시 4
 
3.4%
원 3
 
2.6%
성 3
 
2.6%
분 2
 
1.7%
Other values (44) 52
44.8%
ASCII
ValueCountFrequency (%)
1
100.0%

위반사항내용
Text

Distinct14
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-11T06:46:18.245614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length14
Mean length19.272727
Min length8

Characters and Unicode

Total characters424
Distinct characters61
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)36.4%

Sample

1st row사회복지사업법 제51조제6항 및 같은 법 시행령 제24조의2에 따른 처분 관련 정보의 공표사항 없음
2nd row사회복지사업법 제51조제6항 및 같은 법 시행령 제24조의2에 따른 처분 관련 정보의 공표사항 없음
3rd row사회복지사업법 제51조제6항 및 같은 법 시행령 제24조의2에 따른 처분 관련 정보의 공표사항 없음
4th row사회복지사업법 제51조제6항 및 같은 법 시행령 제24조의2에 따른 처분 관련 정보의 공표사항 없음
5th row재산 취득 미보고
ValueCountFrequency (%)
등 12
 
9.8%
부적정 8
 
6.6%
법인 7
 
5.7%
미보고 7
 
5.7%
재산 6
 
4.9%
따른 5
 
4.1%
없음 4
 
3.3%
운영 4
 
3.3%
제51조제6항 4
 
3.3%
임원 4
 
3.3%
Other values (26) 61
50.0%
2023-12-11T06:46:18.501537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
100
23.6%
사 17
 
4.0%
법 15
 
3.5%
정 14
 
3.3%
등 12
 
2.8%
제 12
 
2.8%
보 11
 
2.6%
적 10
 
2.4%
부 9
 
2.1%
2 8
 
1.9%
Other values (51) 216
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 300
70.8%
Space Separator 100
 
23.6%
Decimal Number 24
 
5.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 17
 
5.7%
법 15
 
5.0%
정 14
 
4.7%
등 12
 
4.0%
제 12
 
4.0%
보 11
 
3.7%
적 10
 
3.3%
부 9
 
3.0%
조 8
 
2.7%
항 8
 
2.7%
Other values (45) 184
61.3%
Decimal Number
ValueCountFrequency (%)
2 8
33.3%
5 4
16.7%
6 4
16.7%
1 4
16.7%
4 4
16.7%
Space Separator
ValueCountFrequency (%)
100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 300
70.8%
Common 124
29.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 17
 
5.7%
법 15
 
5.0%
정 14
 
4.7%
등 12
 
4.0%
제 12
 
4.0%
보 11
 
3.7%
적 10
 
3.3%
부 9
 
3.0%
조 8
 
2.7%
항 8
 
2.7%
Other values (45) 184
61.3%
Common
ValueCountFrequency (%)
100
80.6%
2 8
 
6.5%
5 4
 
3.2%
6 4
 
3.2%
1 4
 
3.2%
4 4
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 300
70.8%
ASCII 124
29.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
100
80.6%
2 8
 
6.5%
5 4
 
3.2%
6 4
 
3.2%
1 4
 
3.2%
4 4
 
3.2%
Hangul
ValueCountFrequency (%)
사 17
 
5.7%
법 15
 
5.0%
정 14
 
4.7%
등 12
 
4.0%
제 12
 
4.0%
보 11
 
3.7%
적 10
 
3.3%
부 9
 
3.0%
조 8
 
2.7%
항 8
 
2.7%
Other values (45) 184
61.3%

처분결과내용
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
시정명령
18 
<NA>
4 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row시정명령

Common Values

ValueCountFrequency (%)
시정명령 18
81.8%
<NA> 4
 
18.2%

Length

2023-12-11T06:46:18.600919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:46:18.680834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시정명령 18
81.8%
na 4
 
18.2%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)94.4%
Missing4
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean15108.667
Minimum10104
Maximum18598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T06:46:18.757817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10104
5-th percentile12193.3
Q113173.5
median14075.5
Q317713.5
95-th percentile18585.25
Maximum18598
Range8494
Interquartile range (IQR)4540

Descriptive statistics

Standard deviation2603.021
Coefficient of variation (CV)0.17228661
Kurtosis-1.1097914
Mean15108.667
Median Absolute Deviation (MAD)1750
Skewness0.00056454329
Sum271956
Variance6775718.5
MonotonicityNot monotonic
2023-12-11T06:46:18.840769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13616 2
 
9.1%
14059 1
 
4.5%
18583 1
 
4.5%
18102 1
 
4.5%
17505 1
 
4.5%
18577 1
 
4.5%
16200 1
 
4.5%
10104 1
 
4.5%
14001 1
 
4.5%
12722 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 4
18.2%
ValueCountFrequency (%)
10104 1
4.5%
12562 1
4.5%
12722 1
4.5%
12748 1
4.5%
13026 1
4.5%
13616 2
9.1%
14001 1
4.5%
14059 1
4.5%
14092 1
4.5%
16062 1
4.5%
ValueCountFrequency (%)
18598 1
4.5%
18583 1
4.5%
18577 1
4.5%
18102 1
4.5%
17783 1
4.5%
17505 1
4.5%
16200 1
4.5%
16062 1
4.5%
14092 1
4.5%
14059 1
4.5%

소재지지번주소
Text

MISSING 

Distinct17
Distinct (%)94.4%
Missing4
Missing (%)18.2%
Memory size308.0 B
2023-12-11T06:46:19.020424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length35
Mean length24.777778
Min length17

Characters and Unicode

Total characters446
Distinct characters99
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

Unique16 ?
Unique (%)88.9%

Sample

1st row경기도 안양시 만안구 안양동 782-6번지 현진빌딩 10층
2nd row경기도 안양시 동안구 관양동 954-6번지 인덕원성지스타위드 1106호
3rd row경기도 안양시 만안구 안양동 491-5번지 동신빌딩 202호
4th row경기도 양평군 양평읍 창대리 700-1
5th row경기도 의왕시 고천동 497-1번지
ValueCountFrequency (%)
경기도 18
 
18.9%
화성시 3
 
3.2%
안양시 3
 
3.2%
만안구 2
 
2.1%
성남시 2
 
2.1%
안양동 2
 
2.1%
광주시 2
 
2.1%
200번지 2
 
2.1%
분당구 2
 
2.1%
금곡동 2
 
2.1%
Other values (57) 57
60.0%
2023-12-11T06:46:19.309676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
17.3%
지 21
 
4.7%
기 18
 
4.0%
경 18
 
4.0%
도 18
 
4.0%
시 17
 
3.8%
번 17
 
3.8%
동 15
 
3.4%
- 14
 
3.1%
1 13
 
2.9%
Other values (89) 218
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 276
61.9%
Decimal Number 78
 
17.5%
Space Separator 77
 
17.3%
Dash Punctuation 14
 
3.1%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 21
 
7.6%
기 18
 
6.5%
경 18
 
6.5%
도 18
 
6.5%
시 17
 
6.2%
번 17
 
6.2%
동 15
 
5.4%
안 11
 
4.0%
양 8
 
2.9%
성 7
 
2.5%
Other values (76) 126
45.7%
Decimal Number
ValueCountFrequency (%)
1 13
16.7%
0 11
14.1%
2 10
12.8%
3 9
11.5%
4 8
10.3%
7 7
9.0%
5 7
9.0%
8 5
 
6.4%
6 4
 
5.1%
9 4
 
5.1%
Space Separator
ValueCountFrequency (%)
77
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 276
61.9%
Common 169
37.9%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 21
 
7.6%
기 18
 
6.5%
경 18
 
6.5%
도 18
 
6.5%
시 17
 
6.2%
번 17
 
6.2%
동 15
 
5.4%
안 11
 
4.0%
양 8
 
2.9%
성 7
 
2.5%
Other values (76) 126
45.7%
Common
ValueCountFrequency (%)
77
45.6%
- 14
 
8.3%
1 13
 
7.7%
0 11
 
6.5%
2 10
 
5.9%
3 9
 
5.3%
4 8
 
4.7%
7 7
 
4.1%
5 7
 
4.1%
8 5
 
3.0%
Other values (2) 8
 
4.7%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 276
61.9%
ASCII 170
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
45.3%
- 14
 
8.2%
1 13
 
7.6%
0 11
 
6.5%
2 10
 
5.9%
3 9
 
5.3%
4 8
 
4.7%
7 7
 
4.1%
5 7
 
4.1%
8 5
 
2.9%
Other values (3) 9
 
5.3%
Hangul
ValueCountFrequency (%)
지 21
 
7.6%
기 18
 
6.5%
경 18
 
6.5%
도 18
 
6.5%
시 17
 
6.2%
번 17
 
6.2%
동 15
 
5.4%
안 11
 
4.0%
양 8
 
2.9%
성 7
 
2.5%
Other values (76) 126
45.7%

소재지도로명주소
Text

MISSING 

Distinct17
Distinct (%)94.4%
Missing4
Missing (%)18.2%
Memory size308.0 B
2023-12-11T06:46:19.501338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length34
Mean length25.333333
Min length19

Characters and Unicode

Total characters456
Distinct characters98
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)88.9%

Sample

1st row경기도 안양시 만안구 병목안로6, 10층(안양동, 현진빌딩)
2nd row경기도 안양시 동안구 흥안대로427번길 38, 1106호(관양동)
3rd row경기도 안양시 만안구 안양로 153,202호(안양동, 동신빌딩)
4th row경기도 양평군 양평읍 창대리 700-1
5th row경기도 의왕시 오전로 27(고천동)
ValueCountFrequency (%)
경기도 18
 
19.6%
안양시 3
 
3.3%
화성시 3
 
3.3%
광주시 2
 
2.2%
성남시 2
 
2.2%
만안구 2
 
2.2%
33(금곡동 2
 
2.2%
정자일로 2
 
2.2%
분당구 2
 
2.2%
중봉1로 1
 
1.1%
Other values (55) 55
59.8%
2023-12-11T06:46:19.791754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
16.9%
기 19
 
4.2%
경 18
 
3.9%
도 18
 
3.9%
시 17
 
3.7%
안 16
 
3.5%
로 15
 
3.3%
1 15
 
3.3%
2 13
 
2.9%
5 11
 
2.4%
Other values (88) 237
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
59.2%
Decimal Number 78
 
17.1%
Space Separator 77
 
16.9%
Open Punctuation 9
 
2.0%
Close Punctuation 9
 
2.0%
Other Punctuation 7
 
1.5%
Dash Punctuation 6
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 19
 
7.0%
경 18
 
6.7%
도 18
 
6.7%
시 17
 
6.3%
안 16
 
5.9%
로 15
 
5.6%
동 11
 
4.1%
양 9
 
3.3%
길 7
 
2.6%
성 7
 
2.6%
Other values (73) 133
49.3%
Decimal Number
ValueCountFrequency (%)
1 15
19.2%
2 13
16.7%
5 11
14.1%
3 11
14.1%
0 9
11.5%
4 7
9.0%
9 3
 
3.8%
6 3
 
3.8%
8 3
 
3.8%
7 3
 
3.8%
Space Separator
ValueCountFrequency (%)
77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
59.2%
Common 186
40.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 19
 
7.0%
경 18
 
6.7%
도 18
 
6.7%
시 17
 
6.3%
안 16
 
5.9%
로 15
 
5.6%
동 11
 
4.1%
양 9
 
3.3%
길 7
 
2.6%
성 7
 
2.6%
Other values (73) 133
49.3%
Common
ValueCountFrequency (%)
77
41.4%
1 15
 
8.1%
2 13
 
7.0%
5 11
 
5.9%
3 11
 
5.9%
( 9
 
4.8%
) 9
 
4.8%
0 9
 
4.8%
, 7
 
3.8%
4 7
 
3.8%
Other values (5) 18
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
59.2%
ASCII 186
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
41.4%
1 15
 
8.1%
2 13
 
7.0%
5 11
 
5.9%
3 11
 
5.9%
( 9
 
4.8%
) 9
 
4.8%
0 9
 
4.8%
, 7
 
3.8%
4 7
 
3.8%
Other values (5) 18
 
9.7%
Hangul
ValueCountFrequency (%)
기 19
 
7.0%
경 18
 
6.7%
도 18
 
6.7%
시 17
 
6.3%
안 16
 
5.9%
로 15
 
5.6%
동 11
 
4.1%
양 9
 
3.3%
길 7
 
2.6%
성 7
 
2.6%
Other values (73) 133
49.3%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)94.4%
Missing4
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean37.31055
Minimum37.055551
Maximum37.622462
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T06:46:19.902395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.055551
5-th percentile37.069864
Q137.142038
median37.352992
Q337.398025
95-th percentile37.514711
Maximum37.622462
Range0.5669109
Interquartile range (IQR)0.25598702

Descriptive statistics

Standard deviation0.16459596
Coefficient of variation (CV)0.0044115127
Kurtosis-0.81425998
Mean37.31055
Median Absolute Deviation (MAD)0.095807881
Skewness-0.16878119
Sum671.5899
Variance0.027091831
MonotonicityNot monotonic
2023-12-11T06:46:20.003163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
37.3529918089 2
 
9.1%
37.3929712079 1
 
4.5%
37.072390097 1
 
4.5%
37.1767584043 1
 
4.5%
37.0842908001 1
 
4.5%
37.12274117 1
 
4.5%
37.3241726861 1
 
4.5%
37.6224620194 1
 
4.5%
37.3997094206 1
 
4.5%
37.3726047588 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 4
18.2%
ValueCountFrequency (%)
37.0555511222 1
4.5%
37.072390097 1
4.5%
37.0842908001 1
4.5%
37.12274117 1
4.5%
37.1304643221 1
4.5%
37.1767584043 1
4.5%
37.3241726861 1
4.5%
37.3486355343 1
4.5%
37.3529918089 2
9.1%
37.3726047588 1
4.5%
ValueCountFrequency (%)
37.6224620194 1
4.5%
37.4956956042 1
4.5%
37.4835511589 1
4.5%
37.4140482207 1
4.5%
37.3997094206 1
4.5%
37.3929712079 1
4.5%
37.3878721041 1
4.5%
37.3726047588 1
4.5%
37.3529918089 2
9.1%
37.3486355343 1
4.5%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)94.4%
Missing4
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean127.05664
Minimum126.69948
Maximum127.49945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T06:46:20.131623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.69948
5-th percentile126.82526
Q1126.92219
median126.99934
Q3127.19561
95-th percentile127.4114
Maximum127.49945
Range0.7999751
Interquartile range (IQR)0.27341931

Descriptive statistics

Standard deviation0.20288548
Coefficient of variation (CV)0.0015968113
Kurtosis0.14038556
Mean127.05664
Median Absolute Deviation (MAD)0.1062435
Skewness0.59842371
Sum2287.0195
Variance0.041162516
MonotonicityNot monotonic
2023-12-11T06:46:20.252146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
127.1055813728 2
 
9.1%
126.9723125739 1
 
4.5%
126.8474566663 1
 
4.5%
127.0175283043 1
 
4.5%
127.2645532197 1
 
4.5%
126.8704615724 1
 
4.5%
126.9751642943 1
 
4.5%
126.6994788321 1
 
4.5%
126.9195137838 1
 
4.5%
127.3958663469 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 4
18.2%
ValueCountFrequency (%)
126.6994788321 1
4.5%
126.8474566663 1
4.5%
126.8704615724 1
4.5%
126.9195137838 1
4.5%
126.9195390742 1
4.5%
126.9301559627 1
4.5%
126.9723125739 1
4.5%
126.9751642943 1
4.5%
126.9811474399 1
4.5%
127.0175283043 1
4.5%
ValueCountFrequency (%)
127.4994539365 1
4.5%
127.3958663469 1
4.5%
127.2645532197 1
4.5%
127.2340354548 1
4.5%
127.2256230217 1
4.5%
127.1055813728 2
9.1%
127.0560531716 1
4.5%
127.0175283043 1
4.5%
126.9811474399 1
4.5%
126.9751642943 1
4.5%

Interactions

2023-12-11T06:46:16.283495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:15.418916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:15.701882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:15.965884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:16.369653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:15.486051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:15.774282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:16.051132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:16.436226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:15.553459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:15.831367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:16.131096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:16.506371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:15.622107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:15.895689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:16.204120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:46:20.338531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분연도시군명법인명위반사항내용소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
처분연도1.0000.9531.0000.0000.0001.0001.0000.0000.258
시군명0.9531.0001.0000.7711.0001.0001.0000.9250.899
법인명1.0001.0001.0000.8471.0001.0001.0001.0001.000
위반사항내용0.0000.7710.8471.0000.9010.8470.8470.0000.853
소재지우편번호0.0001.0001.0000.9011.0001.0001.0000.5970.916
소재지지번주소1.0001.0001.0000.8471.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0000.8471.0001.0001.0001.0001.000
WGS84위도0.0000.9251.0000.0000.5971.0001.0001.0000.560
WGS84경도0.2580.8991.0000.8530.9161.0001.0000.5601.000
2023-12-11T06:46:20.458060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분연도소재지우편번호WGS84위도WGS84경도처분결과내용
처분연도1.000-0.1680.364-0.0111.000
소재지우편번호-0.1681.000-0.870-0.4321.000
WGS84위도0.364-0.8701.0000.1611.000
WGS84경도-0.011-0.4320.1611.0001.000
처분결과내용1.0001.0001.0001.0001.000

Missing values

2023-12-11T06:46:16.813204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:46:16.933291image/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.
2023-12-11T06:46:17.037745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

처분연도시군명법인명위반사항내용처분결과내용소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
02023<NA><NA>사회복지사업법 제51조제6항 및 같은 법 시행령 제24조의2에 따른 처분 관련 정보의 공표사항 없음<NA><NA><NA><NA><NA><NA>
12022<NA><NA>사회복지사업법 제51조제6항 및 같은 법 시행령 제24조의2에 따른 처분 관련 정보의 공표사항 없음<NA><NA><NA><NA><NA><NA>
22021<NA><NA>사회복지사업법 제51조제6항 및 같은 법 시행령 제24조의2에 따른 처분 관련 정보의 공표사항 없음<NA><NA><NA><NA><NA><NA>
32020<NA><NA>사회복지사업법 제51조제6항 및 같은 법 시행령 제24조의2에 따른 처분 관련 정보의 공표사항 없음<NA><NA><NA><NA><NA><NA>
42019안양시백우현진복지재단재산 취득 미보고시정명령14001경기도 안양시 만안구 안양동 782-6번지 현진빌딩 10층경기도 안양시 만안구 병목안로6, 10층(안양동, 현진빌딩)37.399709126.919514
52019안양시돕는사람들법인 임원 미보고 등시정명령14059경기도 안양시 동안구 관양동 954-6번지 인덕원성지스타위드 1106호경기도 안양시 동안구 흥안대로427번길 38, 1106호(관양동)37.392971126.972313
62019안양시안양시사회복지협의회정관에 따른 운영규정 부재시정명령14092경기도 안양시 만안구 안양동 491-5번지 동신빌딩 202호경기도 안양시 만안구 안양로 153,202호(안양동, 동신빌딩)37.387872126.930156
72019양평군양평군사회복지협의회임원 구성 부적정 등시정명령12562경기도 양평군 양평읍 창대리 700-1경기도 양평군 양평읍 창대리 700-137.483551127.499454
82019의왕시의왕시사회복지협의회법인 운영 부적정 등시정명령16062경기도 의왕시 고천동 497-1번지경기도 의왕시 오전로 27(고천동)37.348636126.981147
92019평택시평택시사회복지협의회재산 취득 미보고시정명령17783경기도 평택시 서정동 342번지경기도 평택시 서정역로 16(서정동)37.055551127.056053
처분연도시군명법인명위반사항내용처분결과내용소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
122018광주시향림원재산 취득 미보고 등시정명령12722경기도 광주시 곤지암읍 연곡리 81-2번지경기도 광주시 곤지암읍 광여로 555-5837.372605127.395866
132018광주시한국발달장애센터재산 취득 미보고 등시정명령12748경기도 광주시 탄벌동 674-1번지경기도 광주시 사기막길 95번길 5937.414048127.225623
142018김포시꿈나래터목적사업 미운영시정명령10104경기도 김포시 감정동 551-8번지 이삼메디컬센터 지층 B동 205호경기도 김포시 중봉1로 12, 지층 비205호(감정동, 이삼메디컬센터)37.622462126.699479
152018성남시분당사회관종사자 고용 부적정시정명령13616경기도 성남시 분당구 금곡동 200번지경기도 성남시 분당구 정자일로 33(금곡동)37.352992127.105581
162018성남시분당사회관법인 회계 운영 부적정 등시정명령13616경기도 성남시 분당구 금곡동 200번지경기도 성남시 분당구 정자일로 33(금곡동)37.352992127.105581
172018수원시바다의 별법인 재산 관리 부적정 등시정명령16200경기도 수원시 장안구 이목동 23-3번지경기도 수원시 장안구 장안로 458번길 142(이목동)37.324173126.975164
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212017화성시성혜원이사회 운영 부적정 등시정명령18583경기도 화성시 장안면 독정리 803-8번지경기도 화성시 장안면 포승장안로 1194-2437.07239126.847457