Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory927.7 KiB
Average record size in memory95.0 B

Variable types

Numeric7
Text3

Dataset

Description한국산업인력공단 국가직무능력표준(NCS) 경력개발경로에 대한 데이터로 대분류,중분류,소분류,직무코드,직무명,역량 등의 데이터가 있습니다.
Author한국산업인력공단
URLhttps://www.data.go.kr/data/15088716/fileData.do

Alerts

직무역량수준(능력단위수준 이면서 세분류의 자식) is highly overall correlated with 수준(직급수준)High correlation
수준(직급수준) is highly overall correlated with 직무역량수준(능력단위수준 이면서 세분류의 자식)High correlation

Reproduction

Analysis started2023-12-12 22:15:19.764459
Analysis finished2023-12-12 22:15:27.830608
Duration8.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류코드
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.2056
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:15:27.885288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median15
Q319
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.7427194
Coefficient of variation (CV)0.37767135
Kurtosis-0.37128845
Mean15.2056
Median Absolute Deviation (MAD)4
Skewness-0.5340278
Sum152056
Variance32.978827
MonotonicityNot monotonic
2023-12-13T07:15:28.025818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 1241
12.4%
14 1132
11.3%
19 1048
10.5%
20 895
 
8.9%
24 601
 
6.0%
8 592
 
5.9%
17 557
 
5.6%
23 525
 
5.2%
12 502
 
5.0%
16 492
 
4.9%
Other values (14) 2415
24.1%
ValueCountFrequency (%)
1 35
 
0.4%
2 202
 
2.0%
3 325
3.2%
4 90
 
0.9%
5 153
 
1.5%
6 93
 
0.9%
7 169
 
1.7%
8 592
5.9%
9 313
3.1%
10 169
 
1.7%
ValueCountFrequency (%)
24 601
6.0%
23 525
5.2%
22 257
 
2.6%
21 211
 
2.1%
20 895
8.9%
19 1048
10.5%
18 245
 
2.5%
17 557
5.6%
16 492
 
4.9%
15 1241
12.4%

중분류코드
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9552
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:15:28.136478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2875568
Coefficient of variation (CV)0.77407852
Kurtosis1.1914775
Mean2.9552
Median Absolute Deviation (MAD)1
Skewness1.3936592
Sum29552
Variance5.2329163
MonotonicityNot monotonic
2023-12-13T07:15:28.247508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 3351
33.5%
2 2176
21.8%
3 1860
18.6%
4 653
 
6.5%
5 527
 
5.3%
8 514
 
5.1%
6 352
 
3.5%
7 247
 
2.5%
9 159
 
1.6%
10 133
 
1.3%
ValueCountFrequency (%)
1 3351
33.5%
2 2176
21.8%
3 1860
18.6%
4 653
 
6.5%
5 527
 
5.3%
6 352
 
3.5%
7 247
 
2.5%
8 514
 
5.1%
9 159
 
1.6%
10 133
 
1.3%
ValueCountFrequency (%)
11 28
 
0.3%
10 133
 
1.3%
9 159
 
1.6%
8 514
 
5.1%
7 247
 
2.5%
6 352
 
3.5%
5 527
 
5.3%
4 653
 
6.5%
3 1860
18.6%
2 2176
21.8%

소분류코드
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8316
Minimum0
Maximum19
Zeros36
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:15:28.347010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4804549
Coefficient of variation (CV)0.87599058
Kurtosis8.4344814
Mean2.8316
Median Absolute Deviation (MAD)1
Skewness2.5354047
Sum28316
Variance6.1526567
MonotonicityNot monotonic
2023-12-13T07:15:28.445674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 3262
32.6%
2 2751
27.5%
3 1756
17.6%
4 626
 
6.3%
5 426
 
4.3%
6 350
 
3.5%
7 246
 
2.5%
8 155
 
1.6%
10 112
 
1.1%
9 71
 
0.7%
Other values (10) 245
 
2.5%
ValueCountFrequency (%)
0 36
 
0.4%
1 3262
32.6%
2 2751
27.5%
3 1756
17.6%
4 626
 
6.3%
5 426
 
4.3%
6 350
 
3.5%
7 246
 
2.5%
8 155
 
1.6%
9 71
 
0.7%
ValueCountFrequency (%)
19 8
 
0.1%
18 16
 
0.2%
17 12
 
0.1%
16 3
 
< 0.1%
15 16
 
0.2%
14 28
 
0.3%
13 30
 
0.3%
12 59
0.6%
11 37
 
0.4%
10 112
1.1%

직무코드
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.339
Minimum0
Maximum19
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:15:28.555387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q34
95-th percentile9
Maximum19
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7077319
Coefficient of variation (CV)0.81094098
Kurtosis5.3037408
Mean3.339
Median Absolute Deviation (MAD)1
Skewness2.0193928
Sum33390
Variance7.3318122
MonotonicityNot monotonic
2023-12-13T07:15:28.644264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 2599
26.0%
2 2298
23.0%
3 1726
17.3%
4 1118
11.2%
5 704
 
7.0%
6 431
 
4.3%
7 327
 
3.3%
8 218
 
2.2%
9 153
 
1.5%
10 119
 
1.2%
Other values (10) 307
 
3.1%
ValueCountFrequency (%)
0 16
 
0.2%
1 2599
26.0%
2 2298
23.0%
3 1726
17.3%
4 1118
11.2%
5 704
 
7.0%
6 431
 
4.3%
7 327
 
3.3%
8 218
 
2.2%
9 153
 
1.5%
ValueCountFrequency (%)
19 8
 
0.1%
18 12
 
0.1%
17 11
 
0.1%
16 25
 
0.2%
15 24
 
0.2%
14 32
 
0.3%
13 28
 
0.3%
12 51
0.5%
11 100
1.0%
10 119
1.2%
Distinct1072
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:15:29.081691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length6.6709
Min length2

Characters and Unicode

Total characters66709
Distinct characters434
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row측정
2nd row의장품질관리
3rd row사회복지조직운영
4th rowPaaS엔지니어링
5th row증권상장업무
ValueCountFrequency (%)
이용 36
 
0.4%
제선 30
 
0.3%
네일미용 30
 
0.3%
지능형교통체계(its 29
 
0.3%
29
 
0.3%
건강운동관리 28
 
0.3%
목재가공 27
 
0.3%
선재가공 27
 
0.3%
이러닝콘텐츠개발 27
 
0.3%
펄프·종이제조 27
 
0.3%
Other values (1072) 9875
97.1%
2023-12-13T07:15:29.384261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1987
 
3.0%
1702
 
2.6%
1594
 
2.4%
1467
 
2.2%
1385
 
2.1%
1344
 
2.0%
1276
 
1.9%
1237
 
1.9%
1220
 
1.8%
1157
 
1.7%
Other values (424) 52340
78.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63623
95.4%
Uppercase Letter 984
 
1.5%
Other Punctuation 929
 
1.4%
Lowercase Letter 560
 
0.8%
Close Punctuation 192
 
0.3%
Open Punctuation 192
 
0.3%
Space Separator 165
 
0.2%
Decimal Number 37
 
0.1%
Other Number 27
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1987
 
3.1%
1702
 
2.7%
1594
 
2.5%
1467
 
2.3%
1385
 
2.2%
1344
 
2.1%
1276
 
2.0%
1237
 
1.9%
1220
 
1.9%
1157
 
1.8%
Other values (380) 49254
77.4%
Uppercase Letter
ValueCountFrequency (%)
I 177
18.0%
T 165
16.8%
S 108
11.0%
W 75
7.6%
D 75
7.6%
C 63
 
6.4%
M 47
 
4.8%
R 43
 
4.4%
A 40
 
4.1%
Q 30
 
3.0%
Other values (9) 161
16.4%
Lowercase Letter
ValueCountFrequency (%)
o 83
14.8%
a 76
13.6%
s 58
10.4%
r 55
9.8%
f 50
8.9%
c 47
8.4%
t 44
7.9%
i 25
 
4.5%
m 22
 
3.9%
y 22
 
3.9%
Other values (6) 78
13.9%
Other Punctuation
ValueCountFrequency (%)
· 878
94.5%
, 28
 
3.0%
/ 23
 
2.5%
Decimal Number
ValueCountFrequency (%)
3 25
67.6%
2 12
32.4%
Close Punctuation
ValueCountFrequency (%)
) 192
100.0%
Open Punctuation
ValueCountFrequency (%)
( 192
100.0%
Space Separator
ValueCountFrequency (%)
165
100.0%
Other Number
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63623
95.4%
Latin 1544
 
2.3%
Common 1542
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1987
 
3.1%
1702
 
2.7%
1594
 
2.5%
1467
 
2.3%
1385
 
2.2%
1344
 
2.1%
1276
 
2.0%
1237
 
1.9%
1220
 
1.9%
1157
 
1.8%
Other values (380) 49254
77.4%
Latin
ValueCountFrequency (%)
I 177
 
11.5%
T 165
 
10.7%
S 108
 
7.0%
o 83
 
5.4%
a 76
 
4.9%
W 75
 
4.9%
D 75
 
4.9%
C 63
 
4.1%
s 58
 
3.8%
r 55
 
3.6%
Other values (25) 609
39.4%
Common
ValueCountFrequency (%)
· 878
56.9%
) 192
 
12.5%
( 192
 
12.5%
165
 
10.7%
, 28
 
1.8%
27
 
1.8%
3 25
 
1.6%
/ 23
 
1.5%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63623
95.4%
ASCII 2181
 
3.3%
None 905
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1987
 
3.1%
1702
 
2.7%
1594
 
2.5%
1467
 
2.3%
1385
 
2.2%
1344
 
2.1%
1276
 
2.0%
1237
 
1.9%
1220
 
1.9%
1157
 
1.8%
Other values (380) 49254
77.4%
None
ValueCountFrequency (%)
· 878
97.0%
27
 
3.0%
ASCII
ValueCountFrequency (%)
) 192
 
8.8%
( 192
 
8.8%
I 177
 
8.1%
165
 
7.6%
T 165
 
7.6%
S 108
 
5.0%
o 83
 
3.8%
a 76
 
3.5%
W 75
 
3.4%
D 75
 
3.4%
Other values (32) 873
40.0%

직무역량코드
Real number (ℝ)

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6239
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:15:29.504829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q312
95-th percentile25
Maximum73
Range72
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.4449974
Coefficient of variation (CV)0.87750261
Kurtosis9.5891321
Mean9.6239
Median Absolute Deviation (MAD)4
Skewness2.4872855
Sum96239
Variance71.317981
MonotonicityNot monotonic
2023-12-13T07:15:29.627939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 714
 
7.1%
1 712
 
7.1%
5 693
 
6.9%
7 690
 
6.9%
6 690
 
6.9%
3 687
 
6.9%
2 680
 
6.8%
8 675
 
6.8%
9 609
 
6.1%
10 605
 
6.0%
Other values (63) 3245
32.5%
ValueCountFrequency (%)
1 712
7.1%
2 680
6.8%
3 687
6.9%
4 714
7.1%
5 693
6.9%
6 690
6.9%
7 690
6.9%
8 675
6.8%
9 609
6.1%
10 605
6.0%
ValueCountFrequency (%)
73 1
 
< 0.1%
72 1
 
< 0.1%
71 1
 
< 0.1%
70 2
< 0.1%
69 2
< 0.1%
68 2
< 0.1%
67 2
< 0.1%
66 3
< 0.1%
65 1
 
< 0.1%
64 2
< 0.1%
Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1443
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:15:29.730462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3128023
Coefficient of variation (CV)0.31677299
Kurtosis-0.42957288
Mean4.1443
Median Absolute Deviation (MAD)1
Skewness0.2485945
Sum41443
Variance1.7234499
MonotonicityNot monotonic
2023-12-13T07:15:29.821240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 2713
27.1%
3 2438
24.4%
4 2405
24.1%
2 1047
 
10.5%
6 974
 
9.7%
7 364
 
3.6%
8 57
 
0.6%
1 2
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 1047
 
10.5%
3 2438
24.4%
4 2405
24.1%
5 2713
27.1%
6 974
 
9.7%
7 364
 
3.6%
8 57
 
0.6%
ValueCountFrequency (%)
8 57
 
0.6%
7 364
 
3.6%
6 974
 
9.7%
5 2713
27.1%
4 2405
24.1%
3 2438
24.4%
2 1047
 
10.5%
1 2
 
< 0.1%
Distinct9797
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:15:30.073222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length49
Mean length10.0408
Min length2

Characters and Unicode

Total characters100408
Distinct characters752
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9677 ?
Unique (%)96.8%

Sample

1st row정밀측정
2nd row품질경영
3rd row사회복지조직 사무관리
4th rowPaaS 프론트엔드 설계
5th row공모예정가격 산출
ValueCountFrequency (%)
관리 533
 
2.3%
설계 335
 
1.4%
수립 279
 
1.2%
분석 269
 
1.2%
개발 186
 
0.8%
작성 164
 
0.7%
기획 152
 
0.7%
운영 144
 
0.6%
작업 142
 
0.6%
계획 126
 
0.5%
Other values (8138) 21003
90.0%
2023-12-13T07:15:30.476640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13337
 
13.3%
2717
 
2.7%
2356
 
2.3%
1962
 
2.0%
1853
 
1.8%
1841
 
1.8%
1543
 
1.5%
1516
 
1.5%
1408
 
1.4%
1343
 
1.3%
Other values (742) 70532
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83010
82.7%
Space Separator 13337
 
13.3%
Uppercase Letter 1422
 
1.4%
Lowercase Letter 1363
 
1.4%
Other Punctuation 602
 
0.6%
Open Punctuation 265
 
0.3%
Close Punctuation 264
 
0.3%
Decimal Number 91
 
0.1%
Connector Punctuation 22
 
< 0.1%
Other Number 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2717
 
3.3%
2356
 
2.8%
1962
 
2.4%
1853
 
2.2%
1841
 
2.2%
1543
 
1.9%
1516
 
1.8%
1408
 
1.7%
1343
 
1.6%
1314
 
1.6%
Other values (671) 65157
78.5%
Lowercase Letter
ValueCountFrequency (%)
o 153
11.2%
i 144
10.6%
e 137
10.1%
a 124
9.1%
n 117
 
8.6%
t 103
 
7.6%
r 94
 
6.9%
s 66
 
4.8%
c 61
 
4.5%
l 57
 
4.2%
Other values (16) 307
22.5%
Uppercase Letter
ValueCountFrequency (%)
T 156
 
11.0%
I 143
 
10.1%
S 141
 
9.9%
C 127
 
8.9%
P 117
 
8.2%
D 100
 
7.0%
M 74
 
5.2%
W 61
 
4.3%
U 55
 
3.9%
O 55
 
3.9%
Other values (15) 393
27.6%
Other Punctuation
ValueCountFrequency (%)
· 532
88.4%
/ 42
 
7.0%
, 11
 
1.8%
: 6
 
1.0%
& 5
 
0.8%
; 4
 
0.7%
. 2
 
0.3%
Decimal Number
ValueCountFrequency (%)
3 40
44.0%
2 34
37.4%
1 6
 
6.6%
5 4
 
4.4%
0 3
 
3.3%
4 2
 
2.2%
6 2
 
2.2%
Space Separator
ValueCountFrequency (%)
13337
100.0%
Open Punctuation
ValueCountFrequency (%)
( 265
100.0%
Close Punctuation
ValueCountFrequency (%)
) 264
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 22
100.0%
Other Number
ValueCountFrequency (%)
19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82992
82.7%
Common 14613
 
14.6%
Latin 2785
 
2.8%
Han 18
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2717
 
3.3%
2356
 
2.8%
1962
 
2.4%
1853
 
2.2%
1841
 
2.2%
1543
 
1.9%
1516
 
1.8%
1408
 
1.7%
1343
 
1.6%
1314
 
1.6%
Other values (664) 65139
78.5%
Latin
ValueCountFrequency (%)
T 156
 
5.6%
o 153
 
5.5%
i 144
 
5.2%
I 143
 
5.1%
S 141
 
5.1%
e 137
 
4.9%
C 127
 
4.6%
a 124
 
4.5%
P 117
 
4.2%
n 117
 
4.2%
Other values (41) 1426
51.2%
Common
ValueCountFrequency (%)
13337
91.3%
· 532
 
3.6%
( 265
 
1.8%
) 264
 
1.8%
/ 42
 
0.3%
3 40
 
0.3%
2 34
 
0.2%
_ 22
 
0.2%
19
 
0.1%
- 13
 
0.1%
Other values (10) 45
 
0.3%
Han
ValueCountFrequency (%)
4
22.2%
4
22.2%
3
16.7%
2
11.1%
2
11.1%
2
11.1%
1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82923
82.6%
ASCII 16847
 
16.8%
None 551
 
0.5%
Compat Jamo 69
 
0.1%
CJK 18
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13337
79.2%
( 265
 
1.6%
) 264
 
1.6%
T 156
 
0.9%
o 153
 
0.9%
i 144
 
0.9%
I 143
 
0.8%
S 141
 
0.8%
e 137
 
0.8%
C 127
 
0.8%
Other values (59) 1980
 
11.8%
Hangul
ValueCountFrequency (%)
2717
 
3.3%
2356
 
2.8%
1962
 
2.4%
1853
 
2.2%
1841
 
2.2%
1543
 
1.9%
1516
 
1.8%
1408
 
1.7%
1343
 
1.6%
1314
 
1.6%
Other values (663) 65070
78.5%
None
ValueCountFrequency (%)
· 532
96.6%
19
 
3.4%
Compat Jamo
ValueCountFrequency (%)
69
100.0%
CJK
ValueCountFrequency (%)
4
22.2%
4
22.2%
3
16.7%
2
11.1%
2
11.1%
2
11.1%
1
 
5.6%

수준(직급수준)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1466
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:15:30.600131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3184393
Coefficient of variation (CV)0.31795672
Kurtosis-0.42921164
Mean4.1466
Median Absolute Deviation (MAD)1
Skewness0.24932182
Sum41466
Variance1.7382823
MonotonicityNot monotonic
2023-12-13T07:15:30.711183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 2717
27.2%
3 2430
24.3%
4 2387
23.9%
2 1057
 
10.6%
6 976
 
9.8%
7 370
 
3.7%
8 60
 
0.6%
1 3
 
< 0.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 1057
 
10.6%
3 2430
24.3%
4 2387
23.9%
5 2717
27.2%
6 976
 
9.8%
7 370
 
3.7%
8 60
 
0.6%
ValueCountFrequency (%)
8 60
 
0.6%
7 370
 
3.7%
6 976
 
9.8%
5 2717
27.2%
4 2387
23.9%
3 2430
24.3%
2 1057
 
10.6%
1 3
 
< 0.1%
Distinct3196
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:15:31.006482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length32
Mean length8.688
Min length2

Characters and Unicode

Total characters86880
Distinct characters488
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1301 ?
Unique (%)13.0%

Sample

1st row고급기능자
2nd row부서장
3rd row복지경영 선임관리자
4th row선임 PaaS 엔지니어
5th row상장(IPO) 실무책임자
ValueCountFrequency (%)
실무자 902
 
4.8%
관리자 621
 
3.3%
담당자 328
 
1.7%
초급 321
 
1.7%
대리 282
 
1.5%
과장 262
 
1.4%
중급 260
 
1.4%
책임자 214
 
1.1%
고급 209
 
1.1%
초급기술자 205
 
1.1%
Other values (2211) 15371
81.0%
2023-12-13T07:15:31.410792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9078
 
10.4%
6175
 
7.1%
2749
 
3.2%
2701
 
3.1%
2293
 
2.6%
2027
 
2.3%
1945
 
2.2%
1837
 
2.1%
1699
 
2.0%
1663
 
1.9%
Other values (478) 54713
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73693
84.8%
Space Separator 9078
 
10.4%
Lowercase Letter 1214
 
1.4%
Uppercase Letter 928
 
1.1%
Other Punctuation 812
 
0.9%
Close Punctuation 430
 
0.5%
Open Punctuation 430
 
0.5%
Decimal Number 256
 
0.3%
Connector Punctuation 25
 
< 0.1%
Math Symbol 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6175
 
8.4%
2749
 
3.7%
2701
 
3.7%
2293
 
3.1%
2027
 
2.8%
1945
 
2.6%
1837
 
2.5%
1699
 
2.3%
1663
 
2.3%
1524
 
2.1%
Other values (421) 49080
66.6%
Uppercase Letter
ValueCountFrequency (%)
S 164
17.7%
I 102
11.0%
T 101
10.9%
W 93
10.0%
M 63
 
6.8%
D 62
 
6.7%
B 61
 
6.6%
P 49
 
5.3%
L 43
 
4.6%
J 31
 
3.3%
Other values (12) 159
17.1%
Lowercase Letter
ValueCountFrequency (%)
r 231
19.0%
e 172
14.2%
a 145
11.9%
n 135
11.1%
o 135
11.1%
i 117
9.6%
s 79
 
6.5%
t 54
 
4.4%
b 36
 
3.0%
u 31
 
2.6%
Other values (9) 79
 
6.5%
Decimal Number
ValueCountFrequency (%)
3 85
33.2%
2 63
24.6%
1 42
16.4%
4 38
14.8%
5 13
 
5.1%
6 8
 
3.1%
7 7
 
2.7%
Other Punctuation
ValueCountFrequency (%)
/ 404
49.8%
· 258
31.8%
, 140
 
17.2%
. 10
 
1.2%
Space Separator
ValueCountFrequency (%)
9078
100.0%
Close Punctuation
ValueCountFrequency (%)
) 430
100.0%
Open Punctuation
ValueCountFrequency (%)
( 430
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 25
100.0%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73693
84.8%
Common 11045
 
12.7%
Latin 2142
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6175
 
8.4%
2749
 
3.7%
2701
 
3.7%
2293
 
3.1%
2027
 
2.8%
1945
 
2.6%
1837
 
2.5%
1699
 
2.3%
1663
 
2.3%
1524
 
2.1%
Other values (421) 49080
66.6%
Latin
ValueCountFrequency (%)
r 231
 
10.8%
e 172
 
8.0%
S 164
 
7.7%
a 145
 
6.8%
n 135
 
6.3%
o 135
 
6.3%
i 117
 
5.5%
I 102
 
4.8%
T 101
 
4.7%
W 93
 
4.3%
Other values (31) 747
34.9%
Common
ValueCountFrequency (%)
9078
82.2%
) 430
 
3.9%
( 430
 
3.9%
/ 404
 
3.7%
· 258
 
2.3%
, 140
 
1.3%
3 85
 
0.8%
2 63
 
0.6%
1 42
 
0.4%
4 38
 
0.3%
Other values (6) 77
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73686
84.8%
ASCII 12929
 
14.9%
None 258
 
0.3%
Compat Jamo 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9078
70.2%
) 430
 
3.3%
( 430
 
3.3%
/ 404
 
3.1%
r 231
 
1.8%
e 172
 
1.3%
S 164
 
1.3%
a 145
 
1.1%
, 140
 
1.1%
n 135
 
1.0%
Other values (46) 1600
 
12.4%
Hangul
ValueCountFrequency (%)
6175
 
8.4%
2749
 
3.7%
2701
 
3.7%
2293
 
3.1%
2027
 
2.8%
1945
 
2.6%
1837
 
2.5%
1699
 
2.3%
1663
 
2.3%
1524
 
2.1%
Other values (420) 49073
66.6%
None
ValueCountFrequency (%)
· 258
100.0%
Compat Jamo
ValueCountFrequency (%)
7
100.0%

Interactions

2023-12-13T07:15:26.829298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:22.019286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:22.860191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.476390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:24.156081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:25.093484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:25.996998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:26.922491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:22.100298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:22.937280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.590656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:24.249982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:25.204610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:26.101288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:27.018364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:22.177781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.016314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.680326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:24.365074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:25.316952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:26.205331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:27.132419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:22.284802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.100422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.773422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:24.502619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:25.472252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:26.348743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:27.241868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:22.378381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.213133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.861567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:24.650657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:25.620490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:26.458974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:27.349640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:22.687294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.309174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.953171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:24.800472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:25.746269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:26.591028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:27.452316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:22.778312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:23.395001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:24.059610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:24.955167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:25.885339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:15:26.721764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:15:31.504529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류코드중분류코드소분류코드직무코드직무역량코드직무역량수준(능력단위수준 이면서 세분류의 자식)수준(직급수준)
대분류코드1.0000.6850.5470.3800.3610.2310.233
중분류코드0.6851.0000.4190.1990.1760.1790.178
소분류코드0.5470.4191.0000.2010.1320.0740.075
직무코드0.3800.1990.2011.0000.1260.0650.065
직무역량코드0.3610.1760.1320.1261.0000.1260.138
직무역량수준(능력단위수준 이면서 세분류의 자식)0.2310.1790.0740.0650.1261.0000.999
수준(직급수준)0.2330.1780.0750.0650.1380.9991.000
2023-12-13T07:15:31.609670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류코드중분류코드소분류코드직무코드직무역량코드직무역량수준(능력단위수준 이면서 세분류의 자식)수준(직급수준)
대분류코드1.000-0.0880.1200.0010.007-0.083-0.083
중분류코드-0.0881.0000.014-0.058-0.106-0.043-0.041
소분류코드0.1200.0141.000-0.089-0.0630.0150.016
직무코드0.001-0.058-0.0891.000-0.074-0.056-0.054
직무역량코드0.007-0.106-0.063-0.0741.000-0.147-0.150
직무역량수준(능력단위수준 이면서 세분류의 자식)-0.083-0.0430.015-0.056-0.1471.0000.993
수준(직급수준)-0.083-0.0410.016-0.054-0.1500.9931.000

Missing values

2023-12-13T07:15:27.622661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:15:27.765304image/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

대분류코드중분류코드소분류코드직무코드직무명직무역량코드직무역량수준(능력단위수준 이면서 세분류의 자식)직무역량명수준(직급수준)직급명
515915215측정53정밀측정3고급기능자
602715842의장품질관리16품질경영6부서장
11437112사회복지조직운영67사회복지조직 사무관리7복지경영 선임관리자
9884201216PaaS엔지니어링46PaaS 프론트엔드 설계6선임 PaaS 엔지니어
5713164증권상장업무125공모예정가격 산출5상장(IPO) 실무책임자
797418112방적185방적 원사 품질검사5과장
834919124원자력발전전기설비정비83차단기 정비3원자력발전 전기설비 정비사 (정류)
1230424133농업환경개선116농업환경 보전관리6농업환경개선 관리총괄부장
363414131유지관리97보수·보강 성능 평가7유지관리 특급 기술자
420414332건축설비시공82특수설비공사2시공 초급자
대분류코드중분류코드소분류코드직무코드직무명직무역량코드직무역량수준(능력단위수준 이면서 세분류의 자식)직무역량명수준(직급수준)직급명
1117122123특수인쇄35특수인쇄용 원고 제작5특수인쇄 전문가
94521931133D프린팅 소재개발733D프린팅 소재 개발 안전관리33D프린팅 소재 개발 안전관리자
8812191102철도신호제어시공16신호제어시공계획 수립6철도신호제어 시공 프로젝트 매니저(FM)
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