Overview

Dataset statistics

Number of variables6
Number of observations631
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.8 KiB
Average record size in memory53.2 B

Variable types

Numeric5
Text1

Dataset

Description한국노인인력개발원에서 운영하는 노인일자리 취업연계에서 제공하는 시스템 관리 정보로 취업연계 직종코드에 관한 정보를 제공하는 데이터입니다.
Author한국노인인력개발원
URLhttps://www.data.go.kr/data/15067124/fileData.do

Alerts

직종코드 is highly overall correlated with 대분류 and 3 other fieldsHigh correlation
대분류 is highly overall correlated with 직종코드 and 2 other fieldsHigh correlation
중분류 is highly overall correlated with 직종코드 and 3 other fieldsHigh correlation
소분류 is highly overall correlated with 직종코드 and 3 other fieldsHigh correlation
세부분류 is highly overall correlated with 직종코드 and 2 other fieldsHigh correlation
직종코드 has unique valuesUnique
대분류 has 92 (14.6%) zerosZeros
중분류 has 10 (1.6%) zerosZeros
소분류 has 45 (7.1%) zerosZeros
세부분류 has 181 (28.7%) zerosZeros

Reproduction

Analysis started2023-12-12 02:39:42.177803
Analysis finished2023-12-12 02:39:45.193319
Duration3.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

직종코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct631
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8445376 × 1010
Minimum0
Maximum9.9090509 × 1010
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-12T11:39:45.283449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5080008 × 108
Q11.1515602 × 1010
median5.5151205 × 1010
Q38.8181354 × 1010
95-th percentile8.8888309 × 1010
Maximum9.9090509 × 1010
Range9.9090509 × 1010
Interquartile range (IQR)7.6665753 × 1010

Descriptive statistics

Standard deviation3.3716311 × 1010
Coefficient of variation (CV)0.69596552
Kurtosis-1.4847736
Mean4.8445376 × 1010
Median Absolute Deviation (MAD)3.3130903 × 1010
Skewness-0.077525563
Sum3.0569032 × 1013
Variance1.1367896 × 1021
MonotonicityStrictly increasing
2023-12-12T11:39:45.498950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.2%
77070100000 1
 
0.2%
66262406241 1
 
0.2%
66262406242 1
 
0.2%
66262406243 1
 
0.2%
66262406244 1
 
0.2%
66262406249 1
 
0.2%
70000000000 1
 
0.2%
77000000000 1
 
0.2%
77070107011 1
 
0.2%
Other values (621) 621
98.4%
ValueCountFrequency (%)
0 1
0.2%
100000000 1
0.2%
101100000 1
0.2%
101100111 1
0.2%
101100112 1
0.2%
101200000 1
0.2%
101200121 1
0.2%
101200122 1
0.2%
101200123 1
0.2%
101200124 1
0.2%
ValueCountFrequency (%)
99090509050 1
0.2%
99090500000 1
0.2%
99090409042 1
0.2%
99090409041 1
0.2%
99090400000 1
0.2%
99090309039 1
0.2%
99090309031 1
0.2%
99090300000 1
0.2%
99090209029 1
0.2%
99090209022 1
0.2%

대분류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3835182
Minimum0
Maximum9
Zeros92
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-12T11:39:45.658756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q38
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.063142
Coefficient of variation (CV)0.69878619
Kurtosis-1.4847487
Mean4.3835182
Median Absolute Deviation (MAD)3
Skewness-0.081151671
Sum2766
Variance9.382839
MonotonicityIncreasing
2023-12-12T11:39:45.786181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 157
24.9%
0 92
14.6%
1 79
12.5%
5 69
10.9%
2 56
 
8.9%
6 49
 
7.8%
4 45
 
7.1%
3 32
 
5.1%
7 32
 
5.1%
9 20
 
3.2%
ValueCountFrequency (%)
0 92
14.6%
1 79
12.5%
2 56
 
8.9%
3 32
 
5.1%
4 45
 
7.1%
5 69
10.9%
6 49
 
7.8%
7 32
 
5.1%
8 157
24.9%
9 20
 
3.2%
ValueCountFrequency (%)
9 20
 
3.2%
8 157
24.9%
7 32
 
5.1%
6 49
 
7.8%
5 69
10.9%
4 45
 
7.1%
3 32
 
5.1%
2 56
 
8.9%
1 79
12.5%
0 92
14.6%

중분류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.66878
Minimum0
Maximum90
Zeros10
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-12T11:39:45.922522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q115
median51
Q381
95-th percentile88
Maximum90
Range90
Interquartile range (IQR)66

Descriptive statistics

Standard deviation31.338393
Coefficient of variation (CV)0.68621043
Kurtosis-1.4817258
Mean45.66878
Median Absolute Deviation (MAD)31
Skewness-0.027879974
Sum28817
Variance982.09488
MonotonicityNot monotonic
2023-12-12T11:39:46.103206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2 42
 
6.7%
41 37
 
5.9%
15 36
 
5.7%
1 31
 
4.9%
30 31
 
4.9%
70 31
 
4.9%
81 29
 
4.6%
61 28
 
4.4%
82 26
 
4.1%
21 22
 
3.5%
Other values (26) 318
50.4%
ValueCountFrequency (%)
0 10
 
1.6%
1 31
4.9%
2 42
6.7%
3 18
2.9%
11 4
 
0.6%
12 8
 
1.3%
13 21
3.3%
14 9
 
1.4%
15 36
5.7%
21 22
3.5%
ValueCountFrequency (%)
90 19
3.0%
89 3
 
0.5%
88 21
3.3%
87 16
2.5%
86 21
3.3%
85 13
2.1%
84 9
 
1.4%
83 18
2.9%
82 26
4.1%
81 29
4.6%

소분류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct137
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean433.12203
Minimum0
Maximum905
Zeros45
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-12T11:39:46.324725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1134
median416
Q3811
95-th percentile882.5
Maximum905
Range905
Interquartile range (IQR)677

Descriptive statistics

Standard deviation322.6111
Coefficient of variation (CV)0.74485036
Kurtosis-1.5125785
Mean433.12203
Median Absolute Deviation (MAD)288
Skewness0.054197707
Sum273300
Variance104077.92
MonotonicityNot monotonic
2023-12-12T11:39:46.541019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45
 
7.1%
13 9
 
1.4%
702 9
 
1.4%
701 9
 
1.4%
531 9
 
1.4%
416 9
 
1.4%
414 9
 
1.4%
306 9
 
1.4%
140 8
 
1.3%
811 8
 
1.3%
Other values (127) 507
80.3%
ValueCountFrequency (%)
0 45
7.1%
11 3
 
0.5%
12 5
 
0.8%
13 9
 
1.4%
14 4
 
0.6%
15 4
 
0.6%
16 5
 
0.8%
21 2
 
0.3%
22 3
 
0.5%
23 5
 
0.8%
ValueCountFrequency (%)
905 2
 
0.3%
904 3
0.5%
903 3
0.5%
902 4
0.6%
901 6
1.0%
890 2
 
0.3%
885 5
0.8%
884 3
0.5%
883 4
0.6%
882 5
0.8%

세부분류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct451
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3304.3487
Minimum0
Maximum9050
Zeros181
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-12T11:39:46.726249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2144
Q36216.5
95-th percentile8733.5
Maximum9050
Range9050
Interquartile range (IQR)6216.5

Descriptive statistics

Standard deviation3352.3157
Coefficient of variation (CV)1.0145163
Kurtosis-1.3752042
Mean3304.3487
Median Absolute Deviation (MAD)2144
Skewness0.47118211
Sum2085044
Variance11238021
MonotonicityNot monotonic
2023-12-12T11:39:46.916974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 181
28.7%
6156 1
 
0.2%
7016 1
 
0.2%
7015 1
 
0.2%
7014 1
 
0.2%
7013 1
 
0.2%
7012 1
 
0.2%
7011 1
 
0.2%
6249 1
 
0.2%
6244 1
 
0.2%
Other values (441) 441
69.9%
ValueCountFrequency (%)
0 181
28.7%
111 1
 
0.2%
112 1
 
0.2%
121 1
 
0.2%
122 1
 
0.2%
123 1
 
0.2%
124 1
 
0.2%
131 1
 
0.2%
132 1
 
0.2%
133 1
 
0.2%
ValueCountFrequency (%)
9050 1
0.2%
9042 1
0.2%
9041 1
0.2%
9039 1
0.2%
9031 1
0.2%
9029 1
0.2%
9022 1
0.2%
9021 1
0.2%
9015 1
0.2%
9014 1
0.2%

직종명
Text

Distinct603
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-12-12T11:39:47.328136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length24
Mean length10.389857
Min length2

Characters and Unicode

Total characters6556
Distinct characters320
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique575 ?
Unique (%)91.1%

Sample

1st row경영·사무·금융·보험직
2nd row관리직(임원·부서장)
3rd row의회의원·고위공무원 및 기업 고위임원
4th row의회의원·고위공무원 및 공공단체임원
5th row기업 고위임원
ValueCountFrequency (%)
및 149
 
9.2%
기타 65
 
4.0%
조작원 56
 
3.5%
전문가 32
 
2.0%
사무원 30
 
1.9%
기술자 29
 
1.8%
종사원 28
 
1.7%
관리자 27
 
1.7%
시험원 23
 
1.4%
연구원 21
 
1.3%
Other values (711) 1158
71.6%
2023-12-12T11:39:47.958353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
991
 
15.1%
원 380
 
5.8%
· 309
 
4.7%
기 248
 
3.8%
사 238
 
3.6%
조 153
 
2.3%
및 149
 
2.3%
자 135
 
2.1%
공 105
 
1.6%
전 90
 
1.4%
Other values (310) 3758
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5205
79.4%
Space Separator 996
 
15.2%
Other Punctuation 321
 
4.9%
Close Punctuation 17
 
0.3%
Open Punctuation 17
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 380
 
7.3%
기 248
 
4.8%
사 238
 
4.6%
조 153
 
2.9%
및 149
 
2.9%
자 135
 
2.6%
공 105
 
2.0%
전 90
 
1.7%
가 90
 
1.7%
비 83
 
1.6%
Other values (303) 3534
67.9%
Other Punctuation
ValueCountFrequency (%)
· 309
96.3%
, 11
 
3.4%
? 1
 
0.3%
Space Separator
ValueCountFrequency (%)
991
99.5%
　 5
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5205
79.4%
Common 1351
 
20.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 380
 
7.3%
기 248
 
4.8%
사 238
 
4.6%
조 153
 
2.9%
및 149
 
2.9%
자 135
 
2.6%
공 105
 
2.0%
전 90
 
1.7%
가 90
 
1.7%
비 83
 
1.6%
Other values (303) 3534
67.9%
Common
ValueCountFrequency (%)
991
73.4%
· 309
 
22.9%
) 17
 
1.3%
( 17
 
1.3%
, 11
 
0.8%
　 5
 
0.4%
? 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5205
79.4%
ASCII 1037
 
15.8%
None 314
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
991
95.6%
) 17
 
1.6%
( 17
 
1.6%
, 11
 
1.1%
? 1
 
0.1%
Hangul
ValueCountFrequency (%)
원 380
 
7.3%
기 248
 
4.8%
사 238
 
4.6%
조 153
 
2.9%
및 149
 
2.9%
자 135
 
2.6%
공 105
 
2.0%
전 90
 
1.7%
가 90
 
1.7%
비 83
 
1.6%
Other values (303) 3534
67.9%
None
ValueCountFrequency (%)
· 309
98.4%
　 5
 
1.6%

Interactions

2023-12-12T11:39:44.489398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:42.564138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.069857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.575655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.985038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:44.592799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:42.685665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.160971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.669456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:44.093950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:44.681357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:42.804306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.235857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.760083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:44.192243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:44.778002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:42.905081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.344348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.835874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:44.289973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:44.877966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:42.984512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.474746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:43.906819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:44.397070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:39:48.073525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직종코드대분류중분류소분류세부분류
직종코드1.0001.0000.9970.9940.977
대분류1.0001.0000.9970.9940.977
중분류0.9970.9971.0000.9990.988
소분류0.9940.9940.9991.0000.993
세부분류0.9770.9770.9880.9931.000
2023-12-12T11:39:48.172512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직종코드대분류중분류소분류세부분류
직종코드1.0000.9880.9720.8670.505
대분류0.9881.0000.9570.8510.489
중분류0.9720.9571.0000.8880.521
소분류0.8670.8510.8881.0000.617
세부분류0.5050.4890.5210.6171.000

Missing values

2023-12-12T11:39:45.011903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:39:45.141040image/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

직종코드대분류중분류소분류세부분류직종명
000000경영·사무·금융·보험직
11000000000100관리직(임원·부서장)
210110000001110의회의원·고위공무원 및 기업 고위임원
31011001110111111의회의원·고위공무원 및 공공단체임원
41011001120111112기업 고위임원
510120000001120행정·경영·금융·보험 관리자
61012001210112121정부행정 관리자
71012001220112122경영지원 관리자
81012001230112123마케팅·광고·홍보 관리자
91012001240112124금융·보험 관리자
직종코드대분류중분류소분류세부분류직종명
621990902090229909029022가축 사육 종사원
622990902090299909029029기타 사육 종사원
623990903000009909030임업 종사자
624990903090319909039031조림?산림경영인 및 벌목원
625990903090399909039039임산물 채취 및 기타 임업 종사원
626990904000009909040어업 종사자
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