Overview

Dataset statistics

Number of variables6
Number of observations1615
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.4 KiB
Average record size in memory49.1 B

Variable types

Text3
Numeric1
Categorical1
DateTime1

Dataset

Description통계청 나라통계시스템에서 사용하는 대표항목 분류 항목에 대한 데이터로써분류매핑아이디, 메타분류코드, 등록일시 등을 제공합니다.
Author통계청
URLhttps://www.data.go.kr/data/15123250/fileData.do

Alerts

등록자아이디 is highly imbalanced (95.0%)Imbalance
분류매핑아이디 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:38:55.888757
Analysis finished2023-12-12 14:38:56.371971
Duration0.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류매핑아이디
Text

UNIQUE 

Distinct1615
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2023-12-12T23:38:56.540678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters32300
Distinct characters12
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

Unique1615 ?
Unique (%)100.0%

Sample

1st row20121107MD0000000001
2nd row20121107MD0000000002
3rd row20121107MD0000000003
4th row20121107MD0000000004
5th row20121107MD0000000005
ValueCountFrequency (%)
20121107md0000000001 1
 
0.1%
20121116md0000000193 1
 
0.1%
20121116md0000000204 1
 
0.1%
20121116md0000000203 1
 
0.1%
20121116md0000000202 1
 
0.1%
20121116md0000000201 1
 
0.1%
20121116md0000000200 1
 
0.1%
20121116md0000000199 1
 
0.1%
20121116md0000000198 1
 
0.1%
20121116md0000000197 1
 
0.1%
Other values (1605) 1605
99.4%
2023-12-12T23:38:56.945250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14815
45.9%
1 6815
21.1%
2 4019
 
12.4%
M 1615
 
5.0%
D 1615
 
5.0%
9 605
 
1.9%
3 560
 
1.7%
6 557
 
1.7%
5 434
 
1.3%
7 428
 
1.3%
Other values (2) 837
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29070
90.0%
Uppercase Letter 3230
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14815
51.0%
1 6815
23.4%
2 4019
 
13.8%
9 605
 
2.1%
3 560
 
1.9%
6 557
 
1.9%
5 434
 
1.5%
7 428
 
1.5%
4 424
 
1.5%
8 413
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
M 1615
50.0%
D 1615
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29070
90.0%
Latin 3230
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14815
51.0%
1 6815
23.4%
2 4019
 
13.8%
9 605
 
2.1%
3 560
 
1.9%
6 557
 
1.9%
5 434
 
1.5%
7 428
 
1.5%
4 424
 
1.5%
8 413
 
1.4%
Latin
ValueCountFrequency (%)
M 1615
50.0%
D 1615
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14815
45.9%
1 6815
21.1%
2 4019
 
12.4%
M 1615
 
5.0%
D 1615
 
5.0%
9 605
 
1.9%
3 560
 
1.7%
6 557
 
1.7%
5 434
 
1.3%
7 428
 
1.3%
Other values (2) 837
 
2.6%

메타분류코드
Text

Distinct114
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2023-12-12T23:38:57.266175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters32300
Distinct characters13
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

Unique36 ?
Unique (%)2.2%

Sample

1st row20120925MD0000000071
2nd row20120925MD0000000117
3rd row20120925MD0000000034
4th row20120925MD0000000034
5th row20120925MD0000000034
ValueCountFrequency (%)
20120925md0000000034 123
 
7.6%
20120925md0000000043 58
 
3.6%
20120925md0000000039 54
 
3.3%
20120925md0000000155 52
 
3.2%
20120925md0000000118 50
 
3.1%
20120925md0000000150 50
 
3.1%
20120925md0000000153 48
 
3.0%
20120925md0000000113 46
 
2.8%
20120925md0000000109 43
 
2.7%
20120925md0000000146 41
 
2.5%
Other values (104) 1050
65.0%
2023-12-12T23:38:57.720095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15578
48.2%
2 5084
 
15.7%
1 2881
 
8.9%
5 2073
 
6.4%
9 1707
 
5.3%
M 1615
 
5.0%
D 1564
 
4.8%
4 567
 
1.8%
3 406
 
1.3%
7 296
 
0.9%
Other values (3) 529
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29070
90.0%
Uppercase Letter 3230
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15578
53.6%
2 5084
 
17.5%
1 2881
 
9.9%
5 2073
 
7.1%
9 1707
 
5.9%
4 567
 
2.0%
3 406
 
1.4%
7 296
 
1.0%
8 251
 
0.9%
6 227
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
M 1615
50.0%
D 1564
48.4%
C 51
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 29070
90.0%
Latin 3230
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15578
53.6%
2 5084
 
17.5%
1 2881
 
9.9%
5 2073
 
7.1%
9 1707
 
5.9%
4 567
 
2.0%
3 406
 
1.4%
7 296
 
1.0%
8 251
 
0.9%
6 227
 
0.8%
Latin
ValueCountFrequency (%)
M 1615
50.0%
D 1564
48.4%
C 51
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15578
48.2%
2 5084
 
15.7%
1 2881
 
8.9%
5 2073
 
6.4%
9 1707
 
5.3%
M 1615
 
5.0%
D 1564
 
4.8%
4 567
 
1.8%
3 406
 
1.3%
7 296
 
0.9%
Other values (3) 529
 
1.6%

생산아이디
Real number (ℝ)

Distinct15
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1200085
Minimum1200002
Maximum1200118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2023-12-12T23:38:57.866756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200002
5-th percentile1200076
Q11200080
median1200085
Q31200090
95-th percentile1200099
Maximum1200118
Range116
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.924145
Coefficient of variation (CV)9.1028096 × 10-6
Kurtosis22.715183
Mean1200085
Median Absolute Deviation (MAD)5
Skewness-3.5990353
Sum1.9381373 × 109
Variance119.33695
MonotonicityNot monotonic
2023-12-12T23:38:57.983722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1200085 277
17.2%
1200076 251
15.5%
1200090 249
15.4%
1200088 168
10.4%
1200091 158
9.8%
1200083 129
8.0%
1200093 125
7.7%
1200080 114
7.1%
1200099 84
 
5.2%
1200037 23
 
1.4%
Other values (5) 37
 
2.3%
ValueCountFrequency (%)
1200002 1
 
0.1%
1200003 7
 
0.4%
1200004 2
 
0.1%
1200037 23
 
1.4%
1200076 251
15.5%
1200077 18
 
1.1%
1200080 114
7.1%
1200083 129
8.0%
1200085 277
17.2%
1200088 168
10.4%
ValueCountFrequency (%)
1200118 9
 
0.6%
1200099 84
 
5.2%
1200093 125
7.7%
1200091 158
9.8%
1200090 249
15.4%
1200088 168
10.4%
1200085 277
17.2%
1200083 129
8.0%
1200080 114
7.1%
1200077 18
 
1.1%

생산항목아이디
Text

Distinct1483
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2023-12-12T23:38:58.252464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters32300
Distinct characters12
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

Unique1382 ?
Unique (%)85.6%

Sample

1st row12000830926DV0000026
2nd row12000830926DV0000040
3rd row12000830926DV0000051
4th row12000830926DV0000052
5th row12000830926DV0000053
ValueCountFrequency (%)
12000371015dv0000002 17
 
1.1%
12001181022dv0000130 5
 
0.3%
12000850920dv0000117 4
 
0.2%
12000850919dv0000050 3
 
0.2%
12000761005dv0000052 3
 
0.2%
12000830926dv0000109 3
 
0.2%
12000830926dv0000108 3
 
0.2%
12000830926dv0000105 3
 
0.2%
12000830926dv0000104 3
 
0.2%
12000850919dv0000053 3
 
0.2%
Other values (1473) 1568
97.1%
2023-12-12T23:38:58.592945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15376
47.6%
1 3718
 
11.5%
2 2976
 
9.2%
9 2318
 
7.2%
D 1615
 
5.0%
V 1615
 
5.0%
8 1215
 
3.8%
5 855
 
2.6%
6 706
 
2.2%
3 686
 
2.1%
Other values (2) 1220
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29070
90.0%
Uppercase Letter 3230
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15376
52.9%
1 3718
 
12.8%
2 2976
 
10.2%
9 2318
 
8.0%
8 1215
 
4.2%
5 855
 
2.9%
6 706
 
2.4%
3 686
 
2.4%
7 635
 
2.2%
4 585
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
D 1615
50.0%
V 1615
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29070
90.0%
Latin 3230
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15376
52.9%
1 3718
 
12.8%
2 2976
 
10.2%
9 2318
 
8.0%
8 1215
 
4.2%
5 855
 
2.9%
6 706
 
2.4%
3 686
 
2.4%
7 635
 
2.2%
4 585
 
2.0%
Latin
ValueCountFrequency (%)
D 1615
50.0%
V 1615
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15376
47.6%
1 3718
 
11.5%
2 2976
 
9.2%
9 2318
 
7.2%
D 1615
 
5.0%
V 1615
 
5.0%
8 1215
 
3.8%
5 855
 
2.6%
6 706
 
2.2%
3 686
 
2.1%
Other values (2) 1220
 
3.8%

등록자아이디
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
meta
1606 
cro000
 
9

Length

Max length6
Median length4
Mean length4.0111455
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
meta 1606
99.4%
cro000 9
 
0.6%

Length

2023-12-12T23:38:58.735585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:38:58.846741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
meta 1606
99.4%
cro000 9
 
0.6%

등록일시
Date

Distinct1495
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
Minimum2012-11-07 16:54:14
Maximum2013-12-20 11:19:07
2023-12-12T23:38:58.947438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:59.062242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T23:38:56.070729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:38:59.152731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생산아이디등록자아이디
생산아이디1.0000.033
등록자아이디0.0331.000
2023-12-12T23:38:59.228111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생산아이디등록자아이디
생산아이디1.0000.024
등록자아이디0.0241.000

Missing values

2023-12-12T23:38:56.209768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:38:56.322044image/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

분류매핑아이디메타분류코드생산아이디생산항목아이디등록자아이디등록일시
020121107MD000000000120120925MD0000000071120008312000830926DV0000026meta2012-11-07 16:54:14
120121107MD000000000220120925MD0000000117120008312000830926DV0000040meta2012-11-07 16:58:34
220121107MD000000000320120925MD0000000034120008312000830926DV0000051meta2012-11-07 17:02:15
320121107MD000000000420120925MD0000000034120008312000830926DV0000052meta2012-11-07 17:04:07
420121107MD000000000520120925MD0000000034120008312000830926DV0000053meta2012-11-07 17:05:06
520121107MD000000000620120925MD0000000034120008312000830926DV0000054meta2012-11-07 17:06:45
620121107MD000000000720120925MD0000000034120008312000830926DV0000055meta2012-11-07 17:07:21
720121107MD000000000820120925MD0000000019120008312000830926DV0000058meta2012-11-07 17:23:44
820121107MD000000000920120925MD0000000086120008312000830926DV0000090meta2012-11-07 17:58:22
920121107MD000000001020120925MD0000000020120008312000830926DV0000059meta2012-11-07 17:59:38
분류매핑아이디메타분류코드생산아이디생산항목아이디등록자아이디등록일시
160520121121MD000000016120120925MD0000000146120009112000910925DV0000003meta2012-11-21 14:02:54
160620121121MD000000016220120925MD0000000147120008512000851115DV0000059meta2012-11-21 14:15:56
160720121122MD000000000120120925MD0000000045120009012000901115DV0000059meta2012-11-22 09:34:27
160820121122MD000000000220120925MD0000000113120008512000850920DV0000133meta2012-11-22 13:32:15
160920121122MD000000000320120925MD0000000146120008512000851115DV0000031meta2012-11-22 13:36:00
161020121122MD000000000420120925MD0000000020120008512000850920DV0000145meta2012-11-22 13:37:36
161120121127MD000000000120121127MC0000000002120000312000030927DV0000039meta2012-11-27 10:51:55
161220121127MD000000000220120925MD0000000004120000412000041016DV0000163meta2012-11-27 11:05:03
161320121127MD000000000320120925MD0000000083120000412000041025DV0000077meta2012-11-27 13:35:40
161420131220MD000000000120121108MC0000000023120000212000021015DV0000022meta2013-12-20 11:19:07