Overview

Dataset statistics

Number of variables7
Number of observations32
Missing cells32
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory64.1 B

Variable types

Text2
Categorical2
Numeric1
Boolean1
Unsupported1

Dataset

Description전라남도 여수시 공영자전거 운영 키오스크정보(키오스크ID, 키오스크상태, 거치대 개수, 아이피 정보, 포트 번호, 삭제 여부 등)을 제공합니다.
Author전라남도 여수시
URLhttps://www.data.go.kr/data/15049724/fileData.do

Alerts

DELFLAG has constant value ""Constant
KIOSK_STATE is highly imbalanced (79.9%)Imbalance
PORT is highly imbalanced (53.4%)Imbalance
KIOSK_VERSION has 32 (100.0%) missing valuesMissing
KIOSK_ID has unique valuesUnique
IP has unique valuesUnique
KIOSK_VERSION is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 21:32:11.232272
Analysis finished2023-12-12 21:32:11.782709
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

KIOSK_ID
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T06:32:12.038896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters448
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st rowE3208700000019
2nd rowE3208500000012
3rd rowE3208500000013
4th rowE3208500000014
5th rowE320890000001F
ValueCountFrequency (%)
e3208700000019 1
 
3.1%
e3208500000012 1
 
3.1%
e320820000000f 1
 
3.1%
e320820000000e 1
 
3.1%
e320820000000d 1
 
3.1%
e320820000000c 1
 
3.1%
e320820000000b 1
 
3.1%
e320820000000a 1
 
3.1%
e3208200000009 1
 
3.1%
e3208200000008 1
 
3.1%
Other values (22) 22
68.8%
2023-12-13T06:32:12.386994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 241
53.8%
2 51
 
11.4%
8 36
 
8.0%
E 34
 
7.6%
3 34
 
7.6%
1 18
 
4.0%
9 7
 
1.6%
5 6
 
1.3%
6 6
 
1.3%
7 3
 
0.7%
Other values (6) 12
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 404
90.2%
Uppercase Letter 44
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 241
59.7%
2 51
 
12.6%
8 36
 
8.9%
3 34
 
8.4%
1 18
 
4.5%
9 7
 
1.7%
5 6
 
1.5%
6 6
 
1.5%
7 3
 
0.7%
4 2
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
E 34
77.3%
F 2
 
4.5%
A 2
 
4.5%
B 2
 
4.5%
D 2
 
4.5%
C 2
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 404
90.2%
Latin 44
 
9.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 241
59.7%
2 51
 
12.6%
8 36
 
8.9%
3 34
 
8.4%
1 18
 
4.5%
9 7
 
1.7%
5 6
 
1.5%
6 6
 
1.5%
7 3
 
0.7%
4 2
 
0.5%
Latin
ValueCountFrequency (%)
E 34
77.3%
F 2
 
4.5%
A 2
 
4.5%
B 2
 
4.5%
D 2
 
4.5%
C 2
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 241
53.8%
2 51
 
11.4%
8 36
 
8.0%
E 34
 
7.6%
3 34
 
7.6%
1 18
 
4.0%
9 7
 
1.6%
5 6
 
1.3%
6 6
 
1.3%
7 3
 
0.7%
Other values (6) 12
 
2.7%

KIOSK_STATE
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
0
31 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
96.9%
2 1
 
3.1%

Length

2023-12-13T06:32:12.544379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:32:12.655672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
96.9%
2 1
 
3.1%

LOCKER_CNT
Real number (ℝ)

Distinct9
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.875
Minimum10
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T06:32:12.761357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q112
median15
Q320
95-th percentile22.25
Maximum30
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.8576511
Coefficient of variation (CV)0.30599377
Kurtosis0.70587027
Mean15.875
Median Absolute Deviation (MAD)4.5
Skewness0.83762696
Sum508
Variance23.596774
MonotonicityNot monotonic
2023-12-13T06:32:12.911020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
20 9
28.1%
15 8
25.0%
10 5
15.6%
12 5
15.6%
25 1
 
3.1%
30 1
 
3.1%
13 1
 
3.1%
11 1
 
3.1%
19 1
 
3.1%
ValueCountFrequency (%)
10 5
15.6%
11 1
 
3.1%
12 5
15.6%
13 1
 
3.1%
15 8
25.0%
19 1
 
3.1%
20 9
28.1%
25 1
 
3.1%
30 1
 
3.1%
ValueCountFrequency (%)
30 1
 
3.1%
25 1
 
3.1%
20 9
28.1%
19 1
 
3.1%
15 8
25.0%
13 1
 
3.1%
12 5
15.6%
11 1
 
3.1%
10 5
15.6%

IP
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T06:32:13.146074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.625
Min length16

Characters and Unicode

Total characters564
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row192.168.25.2:49162
2nd row192.168.18.2:49156
3rd row192.168.19.2:49163
4th row192.168.20.2:49176
5th row10.10.11.253:56118
ValueCountFrequency (%)
192.168.25.2:49162 1
 
3.1%
192.168.18.2:49156 1
 
3.1%
192.168.15.2:49159 1
 
3.1%
192.168.14.2:49163 1
 
3.1%
192.168.13.2:54708 1
 
3.1%
192.168.12.2:49156 1
 
3.1%
192.168.11.2:49159 1
 
3.1%
10.10.11.253:49159 1
 
3.1%
192.168.9.2:1032 1
 
3.1%
192.168.8.2:49163 1
 
3.1%
Other values (22) 22
68.8%
2023-12-13T06:32:13.873542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 119
21.1%
. 96
17.0%
2 78
13.8%
9 58
10.3%
6 47
 
8.3%
8 33
 
5.9%
: 32
 
5.7%
4 28
 
5.0%
0 22
 
3.9%
5 21
 
3.7%
Other values (2) 30
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 436
77.3%
Other Punctuation 128
 
22.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 119
27.3%
2 78
17.9%
9 58
13.3%
6 47
 
10.8%
8 33
 
7.6%
4 28
 
6.4%
0 22
 
5.0%
5 21
 
4.8%
3 20
 
4.6%
7 10
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 96
75.0%
: 32
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 119
21.1%
. 96
17.0%
2 78
13.8%
9 58
10.3%
6 47
 
8.3%
8 33
 
5.9%
: 32
 
5.7%
4 28
 
5.0%
0 22
 
3.9%
5 21
 
3.7%
Other values (2) 30
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 119
21.1%
. 96
17.0%
2 78
13.8%
9 58
10.3%
6 47
 
8.3%
8 33
 
5.9%
: 32
 
5.7%
4 28
 
5.0%
0 22
 
3.9%
5 21
 
3.7%
Other values (2) 30
 
5.3%

PORT
Categorical

IMBALANCE 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
1522
27 
0
4 
5467
 
1

Length

Max length4
Median length4
Mean length3.625
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row1522
2nd row1522
3rd row1522
4th row1522
5th row1522

Common Values

ValueCountFrequency (%)
1522 27
84.4%
0 4
 
12.5%
5467 1
 
3.1%

Length

2023-12-13T06:32:14.052917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:32:14.196667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1522 27
84.4%
0 4
 
12.5%
5467 1
 
3.1%

DELFLAG
Boolean

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size164.0 B
False
32 
ValueCountFrequency (%)
False 32
100.0%
2023-12-13T06:32:14.283991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

KIOSK_VERSION
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

Interactions

2023-12-13T06:32:11.437461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:32:14.357766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
KIOSK_IDKIOSK_STATELOCKER_CNTIPPORT
KIOSK_ID1.0001.0001.0001.0001.000
KIOSK_STATE1.0001.0000.0001.0000.000
LOCKER_CNT1.0000.0001.0001.0000.000
IP1.0001.0001.0001.0001.000
PORT1.0000.0000.0001.0001.000
2023-12-13T06:32:14.469682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PORTKIOSK_STATE
PORT1.0000.000
KIOSK_STATE0.0001.000
2023-12-13T06:32:14.570077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOCKER_CNTKIOSK_STATEPORT
LOCKER_CNT1.0000.0000.000
KIOSK_STATE0.0001.0000.000
PORT0.0000.0001.000

Missing values

2023-12-13T06:32:11.602494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:32:11.729882image/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

KIOSK_IDKIOSK_STATELOCKER_CNTIPPORTDELFLAGKIOSK_VERSION
0E3208700000019025192.168.25.2:491621522N<NA>
1E3208500000012030192.168.18.2:491561522N<NA>
2E3208500000013020192.168.19.2:491631522N<NA>
3E3208500000014020192.168.20.2:491761522N<NA>
4E320890000001F01010.10.11.253:561181522N<NA>
5E320890000002001010.10.11.253:522731522N<NA>
6E3208600000015020192.168.21.2:491591522N<NA>
7E3208600000016015192.168.22.2:491661522N<NA>
8E3208600000017015192.168.23.2:491631522N<NA>
9E3208600000018015192.168.24.2:491701522N<NA>
KIOSK_IDKIOSK_STATELOCKER_CNTIPPORTDELFLAGKIOSK_VERSION
22E3208200000007012192.168.7.2:491631522N<NA>
23E3208200000008015192.168.8.2:491631522N<NA>
24E3208200000009015192.168.9.2:10321522N<NA>
25E320820000000A01010.10.11.253:491591522N<NA>
26E320820000000B019192.168.11.2:491591522N<NA>
27E320820000000C015192.168.12.2:491561522N<NA>
28E320820000000D020192.168.13.2:547081522N<NA>
29E320820000000E020192.168.14.2:491631522N<NA>
30E320820000000F020192.168.15.2:491591522N<NA>
31E3208200000010020192.168.16.2:10381522N<NA>