Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells49955
Missing cells (%)83.3%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory556.6 KiB
Average record size in memory57.0 B

Variable types

Numeric1
Boolean1
Categorical1
Text2
DateTime1

Dataset

Description시스템에서 사용자들이 사용하는 메뉴들 중에서 사용하지 않을경우, 이름 변경이 필요 할 경우 관리자가 마감, 수정, 삭제 등을 관리하고 이력을 기록하는 데이터
Author농업정책보험금융원
URLhttps://www.data.go.kr/data/15123699/fileData.do

Alerts

마감여부 has constant value ""Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
코드약명 is highly imbalanced (99.3%)Imbalance
순번 has 9991 (99.9%) missing valuesMissing
마감여부 has 9991 (99.9%) missing valuesMissing
모듈명 has 9991 (99.9%) missing valuesMissing
모듈ID has 9991 (99.9%) missing valuesMissing
년월일 has 9991 (99.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 21:43:09.077321
Analysis finished2023-12-12 21:43:09.756514
Duration0.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)100.0%
Missing9991
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean7.6666667
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:43:09.802283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.4
Q15
median8
Q310
95-th percentile12.2
Maximum13
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.391165
Coefficient of variation (CV)0.44232587
Kurtosis-1.1714826
Mean7.6666667
Median Absolute Deviation (MAD)3
Skewness0.12088409
Sum69
Variance11.5
MonotonicityNot monotonic
2023-12-13T06:43:09.941657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 1
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
11 1
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
3 1
 
< 0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
(Missing) 9991
99.9%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
13 1
< 0.1%
11 1
< 0.1%
10 1
< 0.1%
9 1
< 0.1%
8 1
< 0.1%
6 1
< 0.1%
5 1
< 0.1%
4 1
< 0.1%
3 1
< 0.1%

마감여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)11.1%
Missing9991
Missing (%)99.9%
Memory size97.7 KiB
True
 
9
(Missing)
9991 
ValueCountFrequency (%)
True 9
 
0.1%
(Missing) 9991
99.9%
2023-12-13T06:43:10.038316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

코드약명
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9991 
프로그램유형코드
 
8
세무서코드
 
1

Length

Max length8
Median length4
Mean length4.0033
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9991
99.9%
프로그램유형코드 8
 
0.1%
세무서코드 1
 
< 0.1%

Length

2023-12-13T06:43:10.153130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:43:10.248880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9991
99.9%
프로그램유형코드 8
 
0.1%
세무서코드 1
 
< 0.1%

모듈명
Text

MISSING 

Distinct8
Distinct (%)88.9%
Missing9991
Missing (%)99.9%
Memory size156.2 KiB
2023-12-13T06:43:10.375839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6
Min length4

Characters and Unicode

Total characters54
Distinct characters32
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

Unique7 ?
Unique (%)77.8%

Sample

1st row각종실적조회
2nd row회수실적및전망
3rd row부가기능
4th row회수실적및전망
5th row예산관리
ValueCountFrequency (%)
회수실적및전망 2
22.2%
각종실적조회 1
11.1%
부가기능 1
11.1%
예산관리 1
11.1%
폐기예정거래 1
11.1%
원장관리 1
11.1%
농특통계_폐기예정 1
11.1%
사업별거래실적 1
11.1%
2023-12-13T06:43:10.642692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
실 4
 
7.4%
적 4
 
7.4%
회 3
 
5.6%
기 3
 
5.6%
예 3
 
5.6%
래 2
 
3.7%
거 2
 
3.7%
정 2
 
3.7%
폐 2
 
3.7%
리 2
 
3.7%
Other values (22) 27
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53
98.1%
Connector Punctuation 1
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 4
 
7.5%
적 4
 
7.5%
회 3
 
5.7%
기 3
 
5.7%
예 3
 
5.7%
래 2
 
3.8%
거 2
 
3.8%
정 2
 
3.8%
폐 2
 
3.8%
리 2
 
3.8%
Other values (21) 26
49.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53
98.1%
Common 1
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 4
 
7.5%
적 4
 
7.5%
회 3
 
5.7%
기 3
 
5.7%
예 3
 
5.7%
래 2
 
3.8%
거 2
 
3.8%
정 2
 
3.8%
폐 2
 
3.8%
리 2
 
3.8%
Other values (21) 26
49.1%
Common
ValueCountFrequency (%)
_ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53
98.1%
ASCII 1
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
실 4
 
7.5%
적 4
 
7.5%
회 3
 
5.7%
기 3
 
5.7%
예 3
 
5.7%
래 2
 
3.8%
거 2
 
3.8%
정 2
 
3.8%
폐 2
 
3.8%
리 2
 
3.8%
Other values (21) 26
49.1%
ASCII
ValueCountFrequency (%)
_ 1
100.0%

모듈ID
Text

MISSING 

Distinct9
Distinct (%)100.0%
Missing9991
Missing (%)99.9%
Memory size156.2 KiB
2023-12-13T06:43:10.789815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters18
Distinct characters11
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

Unique9 ?
Unique (%)100.0%

Sample

1st rowFA
2nd rowHR
3rd rowFI
4th rowMA
5th rowFD
ValueCountFrequency (%)
fa 1
11.1%
hr 1
11.1%
fi 1
11.1%
ma 1
11.1%
fd 1
11.1%
ic 1
11.1%
di 1
11.1%
in 1
11.1%
tj 1
11.1%
2023-12-13T06:43:11.022603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 4
22.2%
F 3
16.7%
A 2
11.1%
D 2
11.1%
H 1
 
5.6%
R 1
 
5.6%
M 1
 
5.6%
C 1
 
5.6%
N 1
 
5.6%
T 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 4
22.2%
F 3
16.7%
A 2
11.1%
D 2
11.1%
H 1
 
5.6%
R 1
 
5.6%
M 1
 
5.6%
C 1
 
5.6%
N 1
 
5.6%
T 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 18
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 4
22.2%
F 3
16.7%
A 2
11.1%
D 2
11.1%
H 1
 
5.6%
R 1
 
5.6%
M 1
 
5.6%
C 1
 
5.6%
N 1
 
5.6%
T 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 4
22.2%
F 3
16.7%
A 2
11.1%
D 2
11.1%
H 1
 
5.6%
R 1
 
5.6%
M 1
 
5.6%
C 1
 
5.6%
N 1
 
5.6%
T 1
 
5.6%

년월일
Date

MISSING 

Distinct5
Distinct (%)55.6%
Missing9991
Missing (%)99.9%
Memory size156.2 KiB
Minimum2023-01-23 00:00:00
Maximum2023-05-12 00:00:00
2023-12-13T06:43:11.126201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:43:11.216965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

Interactions

2023-12-13T06:43:09.362225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:43:11.284263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번코드약명모듈명모듈ID년월일
순번1.0001.0001.0001.0001.000
코드약명1.0001.0001.0001.0001.000
모듈명1.0001.0001.0001.0001.000
모듈ID1.0001.0001.0001.0001.000
년월일1.0001.0001.0001.0001.000
2023-12-13T06:43:11.364727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번코드약명
순번1.0000.000
코드약명0.0001.000

Missing values

2023-12-13T06:43:09.476435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:43:09.571716image/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.
2023-12-13T06:43:09.680137image/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

순번마감여부코드약명모듈명모듈ID년월일
6585<NA><NA><NA><NA><NA><NA>
10523<NA><NA><NA><NA><NA><NA>
693<NA><NA><NA><NA><NA><NA>
10149<NA><NA><NA><NA><NA><NA>
2481<NA><NA><NA><NA><NA><NA>
7444<NA><NA><NA><NA><NA><NA>
5729<NA><NA><NA><NA><NA><NA>
10373<NA><NA><NA><NA><NA><NA>
5772<NA><NA><NA><NA><NA><NA>
8990<NA><NA><NA><NA><NA><NA>
순번마감여부코드약명모듈명모듈ID년월일
12920<NA><NA><NA><NA><NA><NA>
11286<NA><NA><NA><NA><NA><NA>
9475<NA><NA><NA><NA><NA><NA>
15967<NA><NA><NA><NA><NA><NA>
3878<NA><NA><NA><NA><NA><NA>
11487<NA><NA><NA><NA><NA><NA>
2318<NA><NA><NA><NA><NA><NA>
11578<NA><NA><NA><NA><NA><NA>
10624<NA><NA><NA><NA><NA><NA>
534<NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

순번마감여부코드약명모듈명모듈ID년월일# duplicates
0<NA><NA><NA><NA><NA><NA>9991