Overview

Dataset statistics

Number of variables6
Number of observations1843
Missing cells920
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.7 KiB
Average record size in memory52.1 B

Variable types

Categorical1
Numeric4
Text1

Dataset

Description중장기개방계획에따른 경상남도 경남도립거창대학 데이터자료입니다.(대분류, 중분류, 소분류, 세분류, 세세분류, 직업명등의 데이터를 포함하고있습니다.)
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15066703

Alerts

소분류 has 62 (3.4%) missing valuesMissing
세분류 has 211 (11.4%) missing valuesMissing
세세분류 has 637 (34.6%) missing valuesMissing
소분류 has 124 (6.7%) zerosZeros
세분류 has 201 (10.9%) zerosZeros
세세분류 has 129 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-11 00:34:57.542605
Analysis finished2023-12-11 00:34:59.600155
Duration2.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
2
648 
8
341 
7
304 
1
122 
4
121 
Other values (5)
307 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 648
35.2%
8 341
18.5%
7 304
16.5%
1 122
 
6.6%
4 121
 
6.6%
3 97
 
5.3%
9 91
 
4.9%
5 59
 
3.2%
6 50
 
2.7%
A 10
 
0.5%

Length

2023-12-11T09:34:59.662538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:34:59.779582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 648
35.2%
8 341
18.5%
7 304
16.5%
1 122
 
6.6%
4 121
 
6.6%
3 97
 
5.3%
9 91
 
4.9%
5 59
 
3.2%
6 50
 
2.7%
a 10
 
0.5%

중분류
Real number (ℝ)

Distinct9
Distinct (%)0.5%
Missing10
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean4.3758865
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.3 KiB
2023-12-11T09:34:59.906812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4870861
Coefficient of variation (CV)0.56836165
Kurtosis-1.0516673
Mean4.3758865
Median Absolute Deviation (MAD)2
Skewness0.36291042
Sum8021
Variance6.1855973
MonotonicityNot monotonic
2023-12-11T09:35:00.025738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 310
16.8%
1 251
13.6%
2 247
13.4%
4 242
13.1%
5 216
11.7%
7 201
10.9%
8 160
8.7%
9 125
6.8%
6 81
 
4.4%
(Missing) 10
 
0.5%
ValueCountFrequency (%)
1 251
13.6%
2 247
13.4%
3 310
16.8%
4 242
13.1%
5 216
11.7%
6 81
 
4.4%
7 201
10.9%
8 160
8.7%
9 125
6.8%
ValueCountFrequency (%)
9 125
6.8%
8 160
8.7%
7 201
10.9%
6 81
 
4.4%
5 216
11.7%
4 242
13.1%
3 310
16.8%
2 247
13.4%
1 251
13.6%

소분류
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.6%
Missing62
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean2.7012914
Minimum0
Maximum9
Zeros124
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size16.3 KiB
2023-12-11T09:35:00.192879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2459811
Coefficient of variation (CV)0.83144716
Kurtosis1.5470798
Mean2.7012914
Median Absolute Deviation (MAD)1
Skewness1.4217413
Sum4811
Variance5.0444309
MonotonicityNot monotonic
2023-12-11T09:35:00.291492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 507
27.5%
2 452
24.5%
3 246
13.3%
4 163
 
8.8%
0 124
 
6.7%
9 106
 
5.8%
5 94
 
5.1%
6 48
 
2.6%
7 30
 
1.6%
8 11
 
0.6%
(Missing) 62
 
3.4%
ValueCountFrequency (%)
0 124
 
6.7%
1 507
27.5%
2 452
24.5%
3 246
13.3%
4 163
 
8.8%
5 94
 
5.1%
6 48
 
2.6%
7 30
 
1.6%
8 11
 
0.6%
9 106
 
5.8%
ValueCountFrequency (%)
9 106
 
5.8%
8 11
 
0.6%
7 30
 
1.6%
6 48
 
2.6%
5 94
 
5.1%
4 163
 
8.8%
3 246
13.3%
2 452
24.5%
1 507
27.5%
0 124
 
6.7%

세분류
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.6%
Missing211
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean2.8167892
Minimum0
Maximum9
Zeros201
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size16.3 KiB
2023-12-11T09:35:00.385838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5958298
Coefficient of variation (CV)0.92155628
Kurtosis0.76663453
Mean2.8167892
Median Absolute Deviation (MAD)1
Skewness1.2877764
Sum4597
Variance6.7383323
MonotonicityNot monotonic
2023-12-11T09:35:00.505526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 419
22.7%
2 355
19.3%
3 215
11.7%
0 201
10.9%
9 170
9.2%
4 142
 
7.7%
5 76
 
4.1%
6 36
 
2.0%
7 15
 
0.8%
8 3
 
0.2%
(Missing) 211
11.4%
ValueCountFrequency (%)
0 201
10.9%
1 419
22.7%
2 355
19.3%
3 215
11.7%
4 142
 
7.7%
5 76
 
4.1%
6 36
 
2.0%
7 15
 
0.8%
8 3
 
0.2%
9 170
9.2%
ValueCountFrequency (%)
9 170
9.2%
8 3
 
0.2%
7 15
 
0.8%
6 36
 
2.0%
5 76
 
4.1%
4 142
 
7.7%
3 215
11.7%
2 355
19.3%
1 419
22.7%
0 201
10.9%

세세분류
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.8%
Missing637
Missing (%)34.6%
Infinite0
Infinite (%)0.0%
Mean3.0704809
Minimum0
Maximum9
Zeros129
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size16.3 KiB
2023-12-11T09:35:00.615148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8120973
Coefficient of variation (CV)0.91584914
Kurtosis0.1261149
Mean3.0704809
Median Absolute Deviation (MAD)1
Skewness1.1569103
Sum3703
Variance7.9078914
MonotonicityNot monotonic
2023-12-11T09:35:00.725610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 297
16.1%
2 277
15.0%
9 169
 
9.2%
3 158
 
8.6%
0 129
 
7.0%
4 89
 
4.8%
5 44
 
2.4%
6 26
 
1.4%
7 11
 
0.6%
8 6
 
0.3%
(Missing) 637
34.6%
ValueCountFrequency (%)
0 129
7.0%
1 297
16.1%
2 277
15.0%
3 158
8.6%
4 89
 
4.8%
5 44
 
2.4%
6 26
 
1.4%
7 11
 
0.6%
8 6
 
0.3%
9 169
9.2%
ValueCountFrequency (%)
9 169
9.2%
8 6
 
0.3%
7 11
 
0.6%
6 26
 
1.4%
5 44
 
2.4%
4 89
 
4.8%
3 158
8.6%
2 277
15.0%
1 297
16.1%
0 129
7.0%

직업명
Text

Distinct1677
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
2023-12-11T09:35:00.962178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length9.9224091
Min length2

Characters and Unicode

Total characters18287
Distinct characters412
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1516 ?
Unique (%)82.3%

Sample

1st row번역가
2nd row번역가
3rd row통역가
4th row통역가
5th row기자 및 논설위원
ValueCountFrequency (%)
및 624
 
11.4%
조작원 189
 
3.5%
그 171
 
3.1%
외 171
 
3.1%
관련 102
 
1.9%
관리자 101
 
1.9%
연구원 98
 
1.8%
기술자 87
 
1.6%
종사원 84
 
1.5%
사무원 69
 
1.3%
Other values (1436) 3759
68.9%
2023-12-11T09:35:01.337589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3612
 
19.8%
원 1117
 
6.1%
기 643
 
3.5%
및 624
 
3.4%
사 580
 
3.2%
조 469
 
2.6%
관 441
 
2.4%
자 407
 
2.2%
작 282
 
1.5%
리 257
 
1.4%
Other values (402) 9855
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14668
80.2%
Space Separator 3612
 
19.8%
Uppercase Letter 4
 
< 0.1%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 1117
 
7.6%
기 643
 
4.4%
및 624
 
4.3%
사 580
 
4.0%
조 469
 
3.2%
관 441
 
3.0%
자 407
 
2.8%
작 282
 
1.9%
리 257
 
1.8%
전 251
 
1.7%
Other values (397) 9597
65.4%
Uppercase Letter
ValueCountFrequency (%)
P 2
50.0%
C 2
50.0%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
3612
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14668
80.2%
Common 3615
 
19.8%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 1117
 
7.6%
기 643
 
4.4%
및 624
 
4.3%
사 580
 
4.0%
조 469
 
3.2%
관 441
 
3.0%
자 407
 
2.8%
작 282
 
1.9%
리 257
 
1.8%
전 251
 
1.7%
Other values (397) 9597
65.4%
Common
ValueCountFrequency (%)
3612
99.9%
1 2
 
0.1%
9 1
 
< 0.1%
Latin
ValueCountFrequency (%)
P 2
50.0%
C 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14667
80.2%
ASCII 3619
 
19.8%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3612
99.8%
P 2
 
0.1%
C 2
 
0.1%
1 2
 
0.1%
9 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
원 1117
 
7.6%
기 643
 
4.4%
및 624
 
4.3%
사 580
 
4.0%
조 469
 
3.2%
관 441
 
3.0%
자 407
 
2.8%
작 282
 
1.9%
리 257
 
1.8%
전 251
 
1.7%
Other values (396) 9596
65.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

Interactions

2023-12-11T09:34:58.982442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:57.912474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.253648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.661800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:59.066981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:57.993432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.348341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.771432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:59.147331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.076521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.423424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.841011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:59.228890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.163271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.540192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:58.910187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:35:01.425423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류소분류세분류세세분류
대분류1.0000.5380.5800.4580.113
중분류0.5381.0000.4950.2340.000
소분류0.5800.4951.0000.2680.114
세분류0.4580.2340.2681.0000.114
세세분류0.1130.0000.1140.1141.000
2023-12-11T09:35:01.515114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중분류소분류세분류세세분류대분류
중분류1.0000.2590.0320.0040.280
소분류0.2591.0000.0570.0160.212
세분류0.0320.0571.000-0.0610.156
세세분류0.0040.016-0.0611.0000.035
대분류0.2800.2120.1560.0351.000

Missing values

2023-12-11T09:34:59.330948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:34:59.443866image/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.
2023-12-11T09:34:59.544251image/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

대분류중분류소분류세분류세세분류직업명
02812<NA>번역가
128120번역가
22813<NA>통역가
328130통역가
42814<NA>기자 및 논설위원
528141기자
628142논설위원
728143칼럼니스트
82815<NA>출판물 전문가
928151출판물 기획자
대분류중분류소분류세분류세세분류직업명
1833A<NA><NA><NA><NA>군인
1834A1<NA><NA><NA>군인
1835A11<NA><NA>장교
1836A111<NA>영관급 이상
1837A1110영관급 이상 장교
1838A112<NA>위관급
1839A1120위관급 장교
1840A12<NA><NA>장기 부사관 및 준위
1841A120<NA>장기 부사관 및 준위
1842A1200장기 부사관 및 준위