Overview

Dataset statistics

Number of variables6
Number of observations22
Missing cells4
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory56.0 B

Variable types

Categorical2
Text2
Numeric2

Dataset

Description양산시 지능형홈시스템내 경로당복지시스템 운영을 위한 디바이스 의 타입 및 제조사 모델명, 설치연도, 수량, 단가 등을 확인할 수 있습니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15092010

Alerts

설치연도 is highly overall correlated with 수량 and 1 other fieldsHigh correlation
수량 is highly overall correlated with 설치연도 and 1 other fieldsHigh correlation
차수 is highly overall correlated with 설치연도 and 1 other fieldsHigh correlation
제조사 및 모델명 has 1 (4.5%) missing valuesMissing
설치연도 has 1 (4.5%) missing valuesMissing
수량 has 1 (4.5%) missing valuesMissing
단가 has 1 (4.5%) missing valuesMissing

Reproduction

Analysis started2023-12-11 00:56:19.453273
Analysis finished2023-12-11 00:56:20.330090
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

차수
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2차
3 
5차
3 
6차
3 
7차
3 
1차
2 
Other values (5)
8 

Length

Max length4
Median length2
Mean length2.0909091
Min length2

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row1차
2nd row1차
3rd row2차
4th row2차
5th row2차

Common Values

ValueCountFrequency (%)
2차 3
13.6%
5차 3
13.6%
6차 3
13.6%
7차 3
13.6%
1차 2
9.1%
3차 2
9.1%
4차 2
9.1%
9차 2
9.1%
8차 1
 
4.5%
<NA> 1
 
4.5%

Length

2023-12-11T09:56:20.407120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:56:20.545898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2차 3
13.6%
5차 3
13.6%
6차 3
13.6%
7차 3
13.6%
1차 2
9.1%
3차 2
9.1%
4차 2
9.1%
9차 2
9.1%
8차 1
 
4.5%
na 1
 
4.5%

타입
Categorical

Distinct4
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size308.0 B
경로당 지원 PC
9 
전자테그 리더기
9 
데이터수집장치
3 
<NA>
1 

Length

Max length9
Median length8
Mean length8.0909091
Min length4

Unique

Unique1 ?
Unique (%)4.5%

Sample

1st row경로당 지원 PC
2nd row전자테그 리더기
3rd row경로당 지원 PC
4th row경로당 지원 PC
5th row전자테그 리더기

Common Values

ValueCountFrequency (%)
경로당 지원 PC 9
40.9%
전자테그 리더기 9
40.9%
데이터수집장치 3
 
13.6%
<NA> 1
 
4.5%

Length

2023-12-11T09:56:20.682969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:56:20.792461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경로당 9
18.4%
지원 9
18.4%
pc 9
18.4%
전자테그 9
18.4%
리더기 9
18.4%
데이터수집장치 3
 
6.1%
na 1
 
2.0%

제조사 및 모델명
Text

MISSING 

Distinct11
Distinct (%)52.4%
Missing1
Missing (%)4.5%
Memory size308.0 B
2023-12-11T09:56:20.917499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length9.6666667
Min length6

Characters and Unicode

Total characters203
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)33.3%

Sample

1st rowATEC A8TBFENP
2nd rowUDR-01
3rd rowATEC A8TBFENP
4th rowMSI AE1920
5th rowUDR-01
ValueCountFrequency (%)
udr-01 9
28.1%
atec 3
 
9.4%
msi 3
 
9.4%
iptime 3
 
9.4%
a8tbfenp 2
 
6.2%
ae1920 2
 
6.2%
a1004 2
 
6.2%
hp 2
 
6.2%
ae2210 1
 
3.1%
jone22l-415tx-001 1
 
3.1%
Other values (4) 4
12.5%
2023-12-11T09:56:21.173316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22
 
10.8%
1 18
 
8.9%
- 14
 
6.9%
R 12
 
5.9%
A 12
 
5.9%
E 12
 
5.9%
12
 
5.9%
2 11
 
5.4%
U 9
 
4.4%
D 9
 
4.4%
Other values (21) 72
35.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 114
56.2%
Decimal Number 63
31.0%
Dash Punctuation 14
 
6.9%
Space Separator 12
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 12
10.5%
A 12
10.5%
E 12
10.5%
U 9
 
7.9%
D 9
 
7.9%
T 9
 
7.9%
I 9
 
7.9%
P 7
 
6.1%
B 6
 
5.3%
M 6
 
5.3%
Other values (12) 23
20.2%
Decimal Number
ValueCountFrequency (%)
0 22
34.9%
1 18
28.6%
2 11
17.5%
4 5
 
7.9%
9 3
 
4.8%
8 2
 
3.2%
5 2
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 114
56.2%
Common 89
43.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 12
10.5%
A 12
10.5%
E 12
10.5%
U 9
 
7.9%
D 9
 
7.9%
T 9
 
7.9%
I 9
 
7.9%
P 7
 
6.1%
B 6
 
5.3%
M 6
 
5.3%
Other values (12) 23
20.2%
Common
ValueCountFrequency (%)
0 22
24.7%
1 18
20.2%
- 14
15.7%
12
13.5%
2 11
12.4%
4 5
 
5.6%
9 3
 
3.4%
8 2
 
2.2%
5 2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22
 
10.8%
1 18
 
8.9%
- 14
 
6.9%
R 12
 
5.9%
A 12
 
5.9%
E 12
 
5.9%
12
 
5.9%
2 11
 
5.4%
U 9
 
4.4%
D 9
 
4.4%
Other values (21) 72
35.5%

설치연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)38.1%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2016.1429
Minimum2011
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T09:56:21.283483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2017
Q32019
95-th percentile2020
Maximum2020
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1029939
Coefficient of variation (CV)0.0015390744
Kurtosis-1.2577206
Mean2016.1429
Median Absolute Deviation (MAD)2
Skewness-0.40247185
Sum42339
Variance9.6285714
MonotonicityNot monotonic
2023-12-11T09:56:21.369465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2019 3
13.6%
2020 3
13.6%
2015 3
13.6%
2017 3
13.6%
2018 3
13.6%
2011 2
9.1%
2012 2
9.1%
2013 2
9.1%
(Missing) 1
 
4.5%
ValueCountFrequency (%)
2011 2
9.1%
2012 2
9.1%
2013 2
9.1%
2015 3
13.6%
2017 3
13.6%
2018 3
13.6%
2019 3
13.6%
2020 3
13.6%
ValueCountFrequency (%)
2020 3
13.6%
2019 3
13.6%
2018 3
13.6%
2017 3
13.6%
2015 3
13.6%
2013 2
9.1%
2012 2
9.1%
2011 2
9.1%

수량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)52.4%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean30.333333
Minimum5
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T09:56:21.477253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q18
median14
Q351
95-th percentile70
Maximum101
Range96
Interquartile range (IQR)43

Descriptive statistics

Standard deviation27.46695
Coefficient of variation (CV)0.90550384
Kurtosis0.41637892
Mean30.333333
Median Absolute Deviation (MAD)8
Skewness1.0795808
Sum637
Variance754.43333
MonotonicityNot monotonic
2023-12-11T09:56:21.577477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
14 3
13.6%
8 3
13.6%
12 3
13.6%
36 2
9.1%
70 2
9.1%
52 2
9.1%
6 2
9.1%
50 1
 
4.5%
51 1
 
4.5%
101 1
 
4.5%
ValueCountFrequency (%)
5 1
 
4.5%
6 2
9.1%
8 3
13.6%
12 3
13.6%
14 3
13.6%
36 2
9.1%
50 1
 
4.5%
51 1
 
4.5%
52 2
9.1%
70 2
9.1%
ValueCountFrequency (%)
101 1
 
4.5%
70 2
9.1%
52 2
9.1%
51 1
 
4.5%
50 1
 
4.5%
36 2
9.1%
14 3
13.6%
12 3
13.6%
8 3
13.6%
6 2
9.1%

단가
Text

MISSING 

Distinct19
Distinct (%)90.5%
Missing1
Missing (%)4.5%
Memory size308.0 B
2023-12-11T09:56:21.730723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.952381
Min length8

Characters and Unicode

Total characters209
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)81.0%

Sample

1st row 1,180,000
2nd row 470,000
3rd row 1,195,000
4th row 1,293,600
5th row 1,089,000
ValueCountFrequency (%)
1,195,000 2
 
9.5%
1,089,000 2
 
9.5%
1,045,000 1
 
4.8%
88,000 1
 
4.8%
603,408 1
 
4.8%
506,000 1
 
4.8%
495,000 1
 
4.8%
902,000 1
 
4.8%
69,300 1
 
4.8%
528,000 1
 
4.8%
Other values (9) 9
42.9%
2023-12-11T09:56:22.058361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 79
37.8%
42
20.1%
, 32
15.3%
1 15
 
7.2%
9 8
 
3.8%
5 7
 
3.3%
8 7
 
3.3%
3 5
 
2.4%
6 5
 
2.4%
2 4
 
1.9%
Other values (2) 5
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 135
64.6%
Space Separator 42
 
20.1%
Other Punctuation 32
 
15.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79
58.5%
1 15
 
11.1%
9 8
 
5.9%
5 7
 
5.2%
8 7
 
5.2%
3 5
 
3.7%
6 5
 
3.7%
2 4
 
3.0%
4 4
 
3.0%
7 1
 
0.7%
Space Separator
ValueCountFrequency (%)
42
100.0%
Other Punctuation
ValueCountFrequency (%)
, 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 209
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 79
37.8%
42
20.1%
, 32
15.3%
1 15
 
7.2%
9 8
 
3.8%
5 7
 
3.3%
8 7
 
3.3%
3 5
 
2.4%
6 5
 
2.4%
2 4
 
1.9%
Other values (2) 5
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79
37.8%
42
20.1%
, 32
15.3%
1 15
 
7.2%
9 8
 
3.8%
5 7
 
3.3%
8 7
 
3.3%
3 5
 
2.4%
6 5
 
2.4%
2 4
 
1.9%
Other values (2) 5
 
2.4%

Interactions

2023-12-11T09:56:19.859621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:56:19.712075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:56:19.933409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:56:19.785973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:56:22.149486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차수타입제조사 및 모델명설치연도수량단가
차수1.0000.0000.0001.0000.9110.834
타입0.0001.0001.0000.0000.0001.000
제조사 및 모델명0.0001.0001.0000.0000.0000.927
설치연도1.0000.0000.0001.0000.8791.000
수량0.9110.0000.0000.8791.0000.000
단가0.8341.0000.9271.0000.0001.000
2023-12-11T09:56:22.268770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차수타입
차수1.0000.000
타입0.0001.000
2023-12-11T09:56:22.369115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치연도수량차수타입
설치연도1.000-0.6780.8950.000
수량-0.6781.0000.7000.000
차수0.8950.7001.0000.000
타입0.0000.0000.0001.000

Missing values

2023-12-11T09:56:20.037402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:56:20.151276image/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.
2023-12-11T09:56:20.254839image/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

차수타입제조사 및 모델명설치연도수량단가
01차경로당 지원 PCATEC A8TBFENP2019361,180,000
11차전자테그 리더기UDR-01201936470,000
22차경로당 지원 PCATEC A8TBFENP2020501,195,000
32차경로당 지원 PCMSI AE19202011511,293,600
42차전자테그 리더기UDR-0120111011,089,000
53차경로당 지원 PCMSI AE19202012701,320,000
63차전자테그 리더기UDR-012012701,089,000
74차경로당 지원 PCMSI AE22102013521,300,000
84차전자테그 리더기UDR-012013521,000,000
95차데이터수집장치IPTIME A1004201514100,000
차수타입제조사 및 모델명설치연도수량단가
126차데이터수집장치IPTIME A104R2017888,000
136차경로당 지원 PCHP 22-B020KR201781,045,000
146차전자테그 리더기UDR-0120178528,000
157차데이터수집장치IPTIME A100420181269,300
167차경로당 지원 PCHP 24-G201KR201812902,000
177차전자테그 리더기UDR-01201812495,000
188차전자테그 리더기UDR-0120195506,000
199차경로당 지원 PCATEC A9SB-5VBGB202061,195,000
209차전자테그 리더기UDR-0120206603,408
21<NA><NA><NA><NA><NA><NA>