Overview

Dataset statistics

Number of variables5
Number of observations53
Missing cells53
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory43.5 B

Variable types

Categorical3
Text1
Unsupported1

Alerts

시설명 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 구분명 and 1 other fieldsHigh correlation
구분명 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
법인단체명 has 53 (100.0%) missing valuesMissing
법인단체명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-29 13:17:56.312016
Analysis finished2024-04-29 13:17:57.997103
Duration1.69 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size556.0 B
부천시
8 
성남시
7 
시흥시
6 
안산시
5 
안양시
4 
Other values (12)
23 

Length

Max length4
Median length3
Mean length3.0754717
Min length3

Unique

Unique5 ?
Unique (%)9.4%

Sample

1st row광명시
2nd row광명시
3rd row광명시
4th row광주시
5th row광주시

Common Values

ValueCountFrequency (%)
부천시 8
15.1%
성남시 7
13.2%
시흥시 6
11.3%
안산시 5
9.4%
안양시 4
7.5%
남양주시 4
7.5%
용인시 3
 
5.7%
광명시 3
 
5.7%
군포시 2
 
3.8%
양주시 2
 
3.8%
Other values (7) 9
17.0%

Length

2024-04-29T22:17:58.067507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부천시 8
15.1%
성남시 7
13.2%
시흥시 6
11.3%
안산시 5
9.4%
안양시 4
7.5%
남양주시 4
7.5%
용인시 3
 
5.7%
광명시 3
 
5.7%
광주시 2
 
3.8%
안성시 2
 
3.8%
Other values (7) 9
17.0%

구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
기본형
40 
확장형
13 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기본형
2nd row기본형
3rd row기본형
4th row확장형
5th row확장형

Common Values

ValueCountFrequency (%)
기본형 40
75.5%
확장형 13
 
24.5%

Length

2024-04-29T22:17:58.172139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T22:17:58.256231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기본형 40
75.5%
확장형 13
 
24.5%

시설명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size556.0 B
부천시무한돌봄센터
8 
성남시무한돌봄센터
7 
시흥시무한돌봄센터
6 
안산시무한돌봄센터
5 
안양시무한돌봄센터
4 
Other values (12)
23 

Length

Max length10
Median length9
Mean length9.0754717
Min length9

Unique

Unique5 ?
Unique (%)9.4%

Sample

1st row광명시무한돌봄센터
2nd row광명시무한돌봄센터
3rd row광명시무한돌봄센터
4th row광주시무한돌봄센터
5th row광주시무한돌봄센터

Common Values

ValueCountFrequency (%)
부천시무한돌봄센터 8
15.1%
성남시무한돌봄센터 7
13.2%
시흥시무한돌봄센터 6
11.3%
안산시무한돌봄센터 5
9.4%
안양시무한돌봄센터 4
7.5%
남양주시무한돌봄센터 4
7.5%
용인시무한돌봄센터 3
 
5.7%
광명시무한돌봄센터 3
 
5.7%
군포시무한돌봄센터 2
 
3.8%
양주시무한돌봄센터 2
 
3.8%
Other values (7) 9
17.0%

Length

2024-04-29T22:17:58.359893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부천시무한돌봄센터 8
15.1%
성남시무한돌봄센터 7
13.2%
시흥시무한돌봄센터 6
11.3%
안산시무한돌봄센터 5
9.4%
안양시무한돌봄센터 4
7.5%
남양주시무한돌봄센터 4
7.5%
용인시무한돌봄센터 3
 
5.7%
광명시무한돌봄센터 3
 
5.7%
광주시무한돌봄센터 2
 
3.8%
안성시무한돌봄센터 2
 
3.8%
Other values (7) 9
17.0%

네트워크팀명
Text

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size556.0 B
2024-04-29T22:17:58.558326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length10.90566
Min length7

Characters and Unicode

Total characters578
Distinct characters85
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)96.2%

Sample

1st row하안·소하네트워크팀
2nd row광명·학온네트워크팀
3rd row철산네트워크팀
4th row남부 네트워크팀
5th row북부 네트워크팀
ValueCountFrequency (%)
네트워크팀 11
 
16.4%
무한돌봄 3
 
4.5%
무한돌봄네트워크팀 2
 
3.0%
부흥네트워크팀 1
 
1.5%
안성시서부무한돌봄 1
 
1.5%
대야무한돌봄드림 1
 
1.5%
작은자리무한돌봄드림 1
 
1.5%
정왕무한돌봄드림 1
 
1.5%
목감무한돌봄드림 1
 
1.5%
초지무한돌봄네트워크팀 1
 
1.5%
Other values (44) 44
65.7%
2024-04-29T22:17:58.859812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
네 50
 
8.7%
트 50
 
8.7%
워 50
 
8.7%
크 50
 
8.7%
팀 50
 
8.7%
무 42
 
7.3%
한 42
 
7.3%
돌 39
 
6.7%
봄 39
 
6.7%
부 14
 
2.4%
Other values (75) 152
26.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 555
96.0%
Space Separator 14
 
2.4%
Decimal Number 7
 
1.2%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
네 50
 
9.0%
트 50
 
9.0%
워 50
 
9.0%
크 50
 
9.0%
팀 50
 
9.0%
무 42
 
7.6%
한 42
 
7.6%
돌 39
 
7.0%
봄 39
 
7.0%
부 14
 
2.5%
Other values (70) 129
23.2%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 3
42.9%
3 1
 
14.3%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 555
96.0%
Common 23
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
네 50
 
9.0%
트 50
 
9.0%
워 50
 
9.0%
크 50
 
9.0%
팀 50
 
9.0%
무 42
 
7.6%
한 42
 
7.6%
돌 39
 
7.0%
봄 39
 
7.0%
부 14
 
2.5%
Other values (70) 129
23.2%
Common
ValueCountFrequency (%)
14
60.9%
1 3
 
13.0%
2 3
 
13.0%
· 2
 
8.7%
3 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 555
96.0%
ASCII 21
 
3.6%
None 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
네 50
 
9.0%
트 50
 
9.0%
워 50
 
9.0%
크 50
 
9.0%
팀 50
 
9.0%
무 42
 
7.6%
한 42
 
7.6%
돌 39
 
7.0%
봄 39
 
7.0%
부 14
 
2.5%
Other values (70) 129
23.2%
ASCII
ValueCountFrequency (%)
14
66.7%
1 3
 
14.3%
2 3
 
14.3%
3 1
 
4.8%
None
ValueCountFrequency (%)
· 2
100.0%

법인단체명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing53
Missing (%)100.0%
Memory size609.0 B

Correlations

2024-04-29T22:17:58.951130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명구분명시설명네트워크팀명
시군명1.0001.0001.0000.000
구분명1.0001.0001.0000.000
시설명1.0001.0001.0000.000
네트워크팀명0.0000.0000.0001.000
2024-04-29T22:17:59.033387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분명시설명시군명
구분명1.0000.8400.840
시설명0.8401.0001.000
시군명0.8401.0001.000
2024-04-29T22:17:59.109738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명구분명시설명
시군명1.0000.8401.000
구분명0.8401.0000.840
시설명1.0000.8401.000

Missing values

2024-04-29T22:17:57.839985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T22:17:57.957707image/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

시군명구분명시설명네트워크팀명법인단체명
0광명시기본형광명시무한돌봄센터하안·소하네트워크팀<NA>
1광명시기본형광명시무한돌봄센터광명·학온네트워크팀<NA>
2광명시기본형광명시무한돌봄센터철산네트워크팀<NA>
3광주시확장형광주시무한돌봄센터남부 네트워크팀<NA>
4광주시확장형광주시무한돌봄센터북부 네트워크팀<NA>
5구리시기본형구리시무한돌봄센터무한돌봄네트워크팀<NA>
6군포시기본형군포시무한돌봄센터남부네트워크팀<NA>
7군포시기본형군포시무한돌봄센터북부네트워크팀<NA>
8남양주시확장형남양주시무한돌봄센터북부무한돌봄네트워크팀<NA>
9남양주시확장형남양주시무한돌봄센터동부무한돌봄네트워크팀<NA>
시군명구분명시설명네트워크팀명법인단체명
43안양시기본형안양시무한돌봄센터만안네트워크팀<NA>
44양주시확장형양주시무한돌봄센터무한돌봄 희망센터<NA>
45양주시확장형양주시무한돌봄센터무한돌봄 행복센터<NA>
46양평군확장형양평군무한돌봄센터양평군무한돌봄네트워크팀<NA>
47여주시기본형여주시무한돌봄센터여주시 무한돌봄 네트워크팀<NA>
48용인시기본형용인시무한돌봄센터기흥무한네트워크팀<NA>
49용인시기본형용인시무한돌봄센터수지무한네트워크팀<NA>
50용인시기본형용인시무한돌봄센터처인무한네트워크팀<NA>
51의왕시확장형의왕시무한돌봄센터무한돌봄네트워크팀<NA>
52포천시확장형포천시무한돌봄센터무한돌봄희망복지센터<NA>