Overview

Dataset statistics

Number of variables6
Number of observations1936
Missing cells1171
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.4 KiB
Average record size in memory52.1 B

Variable types

Numeric4
Text2

Dataset

Description중장기개방계획에따른 경상남도 경남도립거창대학 데이터자료입니다.ksic분류의 대분류, 중분류, 소분류, 세분류, 명칭, 분류명의 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15066702

Alerts

중분류 has 76 (3.9%) missing valuesMissing
소분류 has 304 (15.7%) missing valuesMissing
세분류 has 791 (40.9%) missing valuesMissing
중분류 has 62 (3.2%) zerosZeros
소분류 has 289 (14.9%) zerosZeros
세분류 has 180 (9.3%) zerosZeros

Reproduction

Analysis started2023-12-11 00:58:48.272899
Analysis finished2023-12-11 00:58:50.391140
Duration2.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Real number (ℝ)

Distinct76
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.308368
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2023-12-11T09:58:50.477226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q123
median42
Q365
95-th percentile91
Maximum99
Range98
Interquartile range (IQR)42

Descriptive statistics

Standard deviation26.515452
Coefficient of variation (CV)0.5984299
Kurtosis-0.91259249
Mean44.308368
Median Absolute Deviation (MAD)20
Skewness0.38186734
Sum85781
Variance703.0692
MonotonicityNot monotonic
2023-12-11T09:58:50.608352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 121
 
6.2%
47 108
 
5.6%
29 66
 
3.4%
10 63
 
3.3%
85 53
 
2.7%
23 47
 
2.4%
20 46
 
2.4%
13 46
 
2.4%
26 46
 
2.4%
42 45
 
2.3%
Other values (66) 1295
66.9%
ValueCountFrequency (%)
1 41
2.1%
2 11
 
0.6%
3 14
 
0.7%
5 7
 
0.4%
6 11
 
0.6%
7 16
 
0.8%
8 6
 
0.3%
10 63
3.3%
11 16
 
0.8%
12 5
 
0.3%
ValueCountFrequency (%)
99 5
 
0.3%
98 7
 
0.4%
97 4
 
0.2%
96 26
1.3%
95 23
1.2%
94 23
1.2%
91 39
2.0%
90 27
1.4%
87 17
0.9%
86 21
1.1%

중분류
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.5%
Missing76
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean3.1225806
Minimum0
Maximum9
Zeros62
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2023-12-11T09:58:50.736262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.635265
Coefficient of variation (CV)0.84393817
Kurtosis0.2139947
Mean3.1225806
Median Absolute Deviation (MAD)1
Skewness1.1966814
Sum5808
Variance6.9446216
MonotonicityNot monotonic
2023-12-11T09:58:50.832644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 560
28.9%
2 483
24.9%
9 198
 
10.2%
3 189
 
9.8%
4 154
 
8.0%
5 80
 
4.1%
0 62
 
3.2%
7 55
 
2.8%
6 50
 
2.6%
8 29
 
1.5%
(Missing) 76
 
3.9%
ValueCountFrequency (%)
0 62
 
3.2%
1 560
28.9%
2 483
24.9%
3 189
 
9.8%
4 154
 
8.0%
5 80
 
4.1%
6 50
 
2.6%
7 55
 
2.8%
8 29
 
1.5%
9 198
 
10.2%
ValueCountFrequency (%)
9 198
 
10.2%
8 29
 
1.5%
7 55
 
2.8%
6 50
 
2.6%
5 80
 
4.1%
4 154
 
8.0%
3 189
 
9.8%
2 483
24.9%
1 560
28.9%
0 62
 
3.2%

소분류
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.6%
Missing304
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean2.65625
Minimum0
Maximum9
Zeros289
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2023-12-11T09:58:50.931151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9077537
Coefficient of variation (CV)1.0946837
Kurtosis0.55212761
Mean2.65625
Median Absolute Deviation (MAD)1
Skewness1.3883008
Sum4335
Variance8.4550314
MonotonicityNot monotonic
2023-12-11T09:58:51.046835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 468
24.2%
2 389
20.1%
0 289
14.9%
9 238
12.3%
3 148
 
7.6%
4 47
 
2.4%
5 21
 
1.1%
6 18
 
0.9%
7 10
 
0.5%
8 4
 
0.2%
(Missing) 304
15.7%
ValueCountFrequency (%)
0 289
14.9%
1 468
24.2%
2 389
20.1%
3 148
 
7.6%
4 47
 
2.4%
5 21
 
1.1%
6 18
 
0.9%
7 10
 
0.5%
8 4
 
0.2%
9 238
12.3%
ValueCountFrequency (%)
9 238
12.3%
8 4
 
0.2%
7 10
 
0.5%
6 18
 
0.9%
5 21
 
1.1%
4 47
 
2.4%
3 148
 
7.6%
2 389
20.1%
1 468
24.2%
0 289
14.9%

세분류
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.9%
Missing791
Missing (%)40.9%
Infinite0
Infinite (%)0.0%
Mean2.8541485
Minimum0
Maximum9
Zeros180
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2023-12-11T09:58:51.147023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9531071
Coefficient of variation (CV)1.0346719
Kurtosis0.22701229
Mean2.8541485
Median Absolute Deviation (MAD)1
Skewness1.2632805
Sum3268
Variance8.7208416
MonotonicityNot monotonic
2023-12-11T09:58:51.267106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 307
 
15.9%
2 257
 
13.3%
9 183
 
9.5%
0 180
 
9.3%
3 127
 
6.6%
4 56
 
2.9%
5 20
 
1.0%
6 11
 
0.6%
7 3
 
0.2%
8 1
 
0.1%
(Missing) 791
40.9%
ValueCountFrequency (%)
0 180
9.3%
1 307
15.9%
2 257
13.3%
3 127
6.6%
4 56
 
2.9%
5 20
 
1.0%
6 11
 
0.6%
7 3
 
0.2%
8 1
 
0.1%
9 183
9.5%
ValueCountFrequency (%)
9 183
9.5%
8 1
 
0.1%
7 3
 
0.2%
6 11
 
0.6%
5 20
 
1.0%
4 56
 
2.9%
3 127
6.6%
2 257
13.3%
1 307
15.9%
0 180
9.3%

명칭
Text

Distinct1634
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-12-11T09:58:51.613725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length21
Mean length10.281508
Min length1

Characters and Unicode

Total characters19905
Distinct characters426
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

Unique1380 ?
Unique (%)71.3%

Sample

1st row비동력식 수공구 제조업
2nd row톱 및 호환성공구 제조업
3rd row금속파스너 및 나사제품 제조업
4th row금속 스프링 제조업
5th row금속선 가공제품 제조업
ValueCountFrequency (%)
및 619
 
10.7%
제조업 559
 
9.6%
기타 330
 
5.7%
서비스업 120
 
2.1%
도매업 97
 
1.7%
소매업 86
 
1.5%
그외 82
 
1.4%
운영업 71
 
1.2%
운송업 46
 
0.8%
기계 32
 
0.6%
Other values (1485) 3768
64.9%
2023-12-11T09:58:52.162197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3877
 
19.5%
업 1658
 
8.3%
제 813
 
4.1%
기 700
 
3.5%
조 700
 
3.5%
및 619
 
3.1%
타 343
 
1.7%
품 326
 
1.6%
용 307
 
1.5%
비 287
 
1.4%
Other values (416) 10275
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15989
80.3%
Space Separator 3877
 
19.5%
Other Punctuation 29
 
0.1%
Decimal Number 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 1658
 
10.4%
제 813
 
5.1%
기 700
 
4.4%
조 700
 
4.4%
및 619
 
3.9%
타 343
 
2.1%
품 326
 
2.0%
용 307
 
1.9%
비 287
 
1.8%
물 251
 
1.6%
Other values (412) 9985
62.4%
Other Punctuation
ValueCountFrequency (%)
· 20
69.0%
; 9
31.0%
Space Separator
ValueCountFrequency (%)
3877
100.0%
Decimal Number
ValueCountFrequency (%)
1 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15989
80.3%
Common 3916
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 1658
 
10.4%
제 813
 
5.1%
기 700
 
4.4%
조 700
 
4.4%
및 619
 
3.9%
타 343
 
2.1%
품 326
 
2.0%
용 307
 
1.9%
비 287
 
1.8%
물 251
 
1.6%
Other values (412) 9985
62.4%
Common
ValueCountFrequency (%)
3877
99.0%
· 20
 
0.5%
1 10
 
0.3%
; 9
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15989
80.3%
ASCII 3896
 
19.6%
None 20
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3877
99.5%
1 10
 
0.3%
; 9
 
0.2%
Hangul
ValueCountFrequency (%)
업 1658
 
10.4%
제 813
 
5.1%
기 700
 
4.4%
조 700
 
4.4%
및 619
 
3.9%
타 343
 
2.1%
품 326
 
2.0%
용 307
 
1.9%
비 287
 
1.8%
물 251
 
1.6%
Other values (412) 9985
62.4%
None
ValueCountFrequency (%)
· 20
100.0%

분류명
Text

Distinct184
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-12-11T09:58:52.443458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length6.178719
Min length2

Characters and Unicode

Total characters11962
Distinct characters259
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)7.2%

Sample

1st row제조업
2nd row제조업
3rd row제조업
4th row제조업
5th row제조업
ValueCountFrequency (%)
제조업 725
19.3%
및 685
18.2%
소매업 225
 
6.0%
도매 217
 
5.8%
서비스업 149
 
4.0%
운수업 79
 
2.1%
전문 74
 
2.0%
출판 70
 
1.9%
협회 66
 
1.8%
단체 66
 
1.8%
Other values (327) 1399
37.3%
2023-12-11T09:58:52.804583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2012
16.8%
업 1821
 
15.2%
제 766
 
6.4%
조 737
 
6.2%
및 685
 
5.7%
매 457
 
3.8%
소 246
 
2.1%
도 240
 
2.0%
비 179
 
1.5%
스 165
 
1.4%
Other values (249) 4654
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9792
81.9%
Space Separator 2012
 
16.8%
Other Punctuation 158
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 1821
18.6%
제 766
 
7.8%
조 737
 
7.5%
및 685
 
7.0%
매 457
 
4.7%
소 246
 
2.5%
도 240
 
2.5%
비 179
 
1.8%
스 165
 
1.7%
사 155
 
1.6%
Other values (247) 4341
44.3%
Space Separator
ValueCountFrequency (%)
2012
100.0%
Other Punctuation
ValueCountFrequency (%)
· 158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9792
81.9%
Common 2170
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 1821
18.6%
제 766
 
7.8%
조 737
 
7.5%
및 685
 
7.0%
매 457
 
4.7%
소 246
 
2.5%
도 240
 
2.5%
비 179
 
1.8%
스 165
 
1.7%
사 155
 
1.6%
Other values (247) 4341
44.3%
Common
ValueCountFrequency (%)
2012
92.7%
· 158
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9792
81.9%
ASCII 2012
 
16.8%
None 158
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2012
100.0%
Hangul
ValueCountFrequency (%)
업 1821
18.6%
제 766
 
7.8%
조 737
 
7.5%
및 685
 
7.0%
매 457
 
4.7%
소 246
 
2.5%
도 240
 
2.5%
비 179
 
1.8%
스 165
 
1.7%
사 155
 
1.6%
Other values (247) 4341
44.3%
None
ValueCountFrequency (%)
· 158
100.0%

Interactions

2023-12-11T09:58:49.792191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:48.833243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.190670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.504418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.872567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:48.927329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.279988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.577940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.947255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.017159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.356922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.648793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:50.030685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.102226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.431018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:49.716752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:58:52.892141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류소분류세분류
대분류1.0000.6080.3350.149
중분류0.6081.0000.3970.156
소분류0.3350.3971.0000.085
세분류0.1490.1560.0851.000
2023-12-11T09:58:53.023085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류소분류세분류
대분류1.0000.035-0.040-0.010
중분류0.0351.0000.0620.074
소분류-0.0400.0621.0000.073
세분류-0.0100.0740.0731.000

Missing values

2023-12-11T09:58:50.128197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:58:50.234536image/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.
2023-12-11T09:58:50.329060image/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

대분류중분류소분류세분류명칭분류명
025933비동력식 수공구 제조업제조업
125934톱 및 호환성공구 제조업제조업
225941금속파스너 및 나사제품 제조업제조업
325942금속 스프링 제조업제조업
425943금속선 가공제품 제조업제조업
52599<NA>그외 기타 금속가공제품 제조업제조업
625991금속캔 및 기타 포장용기 제조업제조업
725992금고 제조업제조업
825994금속위생용품 제조업제조업
925995금속표시판 제조업제조업
대분류중분류소분류세분류명칭분류명
19266921<NA>스포츠 및 레크레이션 용품 임대업부동산업 및 임대업
192769220음반 및 비디오물 임대업부동산업 및 임대업
192869291서적 임대업부동산업 및 임대업
192969299그외 기타 개인 및 가정용품 임대업부동산업 및 임대업
193069310건설 및 토목공사용 기계장비 임대업부동산업 및 임대업
19316939<NA>기타 산업용 기계 및 장비 임대업부동산업 및 임대업
19326940<NA>무형재산권 임대업부동산업 및 임대업
193364<NA><NA><NA>금융업금융 및 보험업
19346411<NA>중앙은행금융 및 보험업
193564110중앙은행금융 및 보험업