Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells256
Missing cells (%)47.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory155.4 B

Variable types

Text9
Numeric3
Categorical3
DateTime2
Unsupported1

Dataset

Description샘플 데이터
Author경기도일자리재단
URLhttps://www.bigdata-region.kr/#/dataset/79a4dee7-0e1b-41d0-902c-5e2fb74f5d81

Alerts

회원유형명 has constant value ""Constant
시군구명 has constant value ""Constant
동명 has constant value ""Constant
출생년도 is highly overall correlated with 취업역량정보번호 and 4 other fieldsHigh correlation
직업역량기준점수 is highly overall correlated with 출생년도 and 1 other fieldsHigh correlation
우편번호 is highly overall correlated with 취업역량정보번호 and 4 other fieldsHigh correlation
취업역량정보번호 is highly overall correlated with 출생년도 and 1 other fieldsHigh correlation
직업역량점수 is highly overall correlated with 직업역량분야평균점수 and 2 other fieldsHigh correlation
직업역량분야평균점수 is highly overall correlated with 직업역량점수 and 2 other fieldsHigh correlation
출생년도 is highly imbalanced (73.5%)Imbalance
우편번호 is highly imbalanced (73.5%)Imbalance
직업역량직업명 has 30 (100.0%) missing valuesMissing
회원유형명 has 28 (93.3%) missing valuesMissing
성별코드 has 28 (93.3%) missing valuesMissing
직업명 has 28 (93.3%) missing valuesMissing
시도명 has 28 (93.3%) missing valuesMissing
시군구명 has 29 (96.7%) missing valuesMissing
동명 has 29 (96.7%) missing valuesMissing
가입경로명 has 28 (93.3%) missing valuesMissing
가입목표명 has 28 (93.3%) missing valuesMissing
취업역량정보키값 has unique valuesUnique
등록일시 has unique valuesUnique
직업역량직업명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 14:12:12.885637
Analysis finished2023-12-10 14:12:16.761041
Duration3.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

취업역량정보키값
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:12:17.026605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length33
Mean length33.5
Min length33

Characters and Unicode

Total characters1005
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row132_4.44_3.08_2.22_20140509001314
2nd row136_3.31_3.19_1.65_20140523061934
3rd row136_3.31_3.25_1.65_20140523061438
4th row137_3.87_3.56_1.93_20140523072627
5th row138_4.06_3.81_2.03_20140523202838
ValueCountFrequency (%)
132_4.44_3.08_2.22_20140509001314 1
 
3.3%
136_3.31_3.19_1.65_20140523061934 1
 
3.3%
181_2.81_3.29_1.40_20140526230048 1
 
3.3%
180_4.43_3.78_2.21_20140526170831 1
 
3.3%
166_3.04_3.14_1.52_20140526104816 1
 
3.3%
165_3.00_3.24_1.50_20140526104704 1
 
3.3%
164_3.50_3.49_1.75_20140526104605 1
 
3.3%
163_3.28_3.49_1.64_20140526103352 1
 
3.3%
162_3.61_3.71_1.80_20140526101309 1
 
3.3%
161_3.76_3.73_1.88_20140526101422 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:12:17.687896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 139
13.8%
1 137
13.6%
_ 122
12.1%
2 113
11.2%
4 92
9.2%
. 90
9.0%
3 85
8.5%
5 82
8.2%
6 54
 
5.4%
8 37
 
3.7%
Other values (2) 54
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 793
78.9%
Connector Punctuation 122
 
12.1%
Other Punctuation 90
 
9.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139
17.5%
1 137
17.3%
2 113
14.2%
4 92
11.6%
3 85
10.7%
5 82
10.3%
6 54
 
6.8%
8 37
 
4.7%
7 29
 
3.7%
9 25
 
3.2%
Connector Punctuation
ValueCountFrequency (%)
_ 122
100.0%
Other Punctuation
ValueCountFrequency (%)
. 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 139
13.8%
1 137
13.6%
_ 122
12.1%
2 113
11.2%
4 92
9.2%
. 90
9.0%
3 85
8.5%
5 82
8.2%
6 54
 
5.4%
8 37
 
3.7%
Other values (2) 54
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 139
13.8%
1 137
13.6%
_ 122
12.1%
2 113
11.2%
4 92
9.2%
. 90
9.0%
3 85
8.5%
5 82
8.2%
6 54
 
5.4%
8 37
 
3.7%
Other values (2) 54
 
5.4%

취업역량정보번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.33333
Minimum132
Maximum195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:12:17.901066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum132
5-th percentile136
Q1144.5
median153.5
Q3161.75
95-th percentile180.55
Maximum195
Range63
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation14.465973
Coefficient of variation (CV)0.093732005
Kurtosis0.98250554
Mean154.33333
Median Absolute Deviation (MAD)9
Skewness0.82578797
Sum4630
Variance209.26437
MonotonicityIncreasing
2023-12-10T23:12:18.094707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
136 2
 
6.7%
132 1
 
3.3%
195 1
 
3.3%
181 1
 
3.3%
180 1
 
3.3%
166 1
 
3.3%
165 1
 
3.3%
164 1
 
3.3%
163 1
 
3.3%
162 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
132 1
3.3%
136 2
6.7%
137 1
3.3%
138 1
3.3%
140 1
3.3%
141 1
3.3%
144 1
3.3%
146 1
3.3%
147 1
3.3%
148 1
3.3%
ValueCountFrequency (%)
195 1
3.3%
181 1
3.3%
180 1
3.3%
166 1
3.3%
165 1
3.3%
164 1
3.3%
163 1
3.3%
162 1
3.3%
161 1
3.3%
160 1
3.3%

직업역량점수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.74
Minimum56.2
Maximum89.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:12:18.665535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56.2
5-th percentile57.59
Q165.75
median71.6
Q381.15
95-th percentile88.71
Maximum89.2
Range33
Interquartile range (IQR)15.4

Descriptive statistics

Standard deviation10.163376
Coefficient of variation (CV)0.13972197
Kurtosis-1.0270252
Mean72.74
Median Absolute Deviation (MAD)8.2
Skewness0.10437822
Sum2182.2
Variance103.29421
MonotonicityNot monotonic
2023-12-10T23:12:18.867712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
70.4 2
 
6.7%
60.0 2
 
6.7%
66.2 2
 
6.7%
70.0 2
 
6.7%
88.8 1
 
3.3%
74.2 1
 
3.3%
87.6 1
 
3.3%
56.2 1
 
3.3%
88.6 1
 
3.3%
60.8 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
56.2 1
3.3%
56.6 1
3.3%
58.8 1
3.3%
60.0 2
6.7%
60.8 1
3.3%
64.6 1
3.3%
65.6 1
3.3%
66.2 2
6.7%
70.0 2
6.7%
70.4 2
6.7%
ValueCountFrequency (%)
89.2 1
3.3%
88.8 1
3.3%
88.6 1
3.3%
87.6 1
3.3%
85.6 1
3.3%
84.4 1
3.3%
82.4 1
3.3%
81.2 1
3.3%
81.0 1
3.3%
77.4 1
3.3%

직업역량분야평균점수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.333333
Minimum28
Maximum44.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:12:19.096732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile28.74
Q132.85
median35.7
Q340.55
95-th percentile44.31
Maximum44.6
Range16.6
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation5.0907173
Coefficient of variation (CV)0.14011148
Kurtosis-1.0229478
Mean36.333333
Median Absolute Deviation (MAD)4.1
Skewness0.10517108
Sum1090
Variance25.915402
MonotonicityNot monotonic
2023-12-10T23:12:19.332389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
35.2 2
 
6.7%
37.0 2
 
6.7%
30.0 2
 
6.7%
33.0 2
 
6.7%
35.0 2
 
6.7%
44.4 1
 
3.3%
44.6 1
 
3.3%
43.8 1
 
3.3%
28.0 1
 
3.3%
44.2 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
28.0 1
3.3%
28.2 1
3.3%
29.4 1
3.3%
30.0 2
6.7%
30.4 1
3.3%
32.2 1
3.3%
32.8 1
3.3%
33.0 2
6.7%
35.0 2
6.7%
35.2 2
6.7%
ValueCountFrequency (%)
44.6 1
3.3%
44.4 1
3.3%
44.2 1
3.3%
43.8 1
3.3%
42.8 1
3.3%
42.2 1
3.3%
41.2 1
3.3%
40.6 1
3.3%
40.4 1
3.3%
38.6 1
3.3%

직업역량기준점수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
200.0
19 
200.5
4 
200.6
3 
200.4
2 
200.7
2 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row200.0
2nd row200.6
3rd row200.6
4th row200.5
5th row200.0

Common Values

ValueCountFrequency (%)
200.0 19
63.3%
200.5 4
 
13.3%
200.6 3
 
10.0%
200.4 2
 
6.7%
200.7 2
 
6.7%

Length

2023-12-10T23:12:19.624976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:19.840514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
200.0 19
63.3%
200.5 4
 
13.3%
200.6 3
 
10.0%
200.4 2
 
6.7%
200.7 2
 
6.7%

등록일시
Date

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2014-05-09 00:13:00
Maximum2014-05-27 01:26:00
2023-12-10T23:12:20.048103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:20.253350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

직업역량직업명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

회원유형명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2023-12-10T23:12:20.461360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반 학습자
2nd row일반 학습자
ValueCountFrequency (%)
일반 2
50.0%
학습자 2
50.0%
2023-12-10T23:12:20.814479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 2
16.7%
반 2
16.7%
2
16.7%
학 2
16.7%
습 2
16.7%
자 2
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
83.3%
Space Separator 2
 
16.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 2
20.0%
반 2
20.0%
학 2
20.0%
습 2
20.0%
자 2
20.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
83.3%
Common 2
 
16.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 2
20.0%
반 2
20.0%
학 2
20.0%
습 2
20.0%
자 2
20.0%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
83.3%
ASCII 2
 
16.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 2
20.0%
반 2
20.0%
학 2
20.0%
습 2
20.0%
자 2
20.0%
ASCII
ValueCountFrequency (%)
2
100.0%

출생년도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
28 
1973
 
1
1968
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 28
93.3%
1973 1
 
3.3%
1968 1
 
3.3%

Length

2023-12-10T23:12:20.968236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:21.076334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
93.3%
1973 1
 
3.3%
1968 1
 
3.3%

성별코드
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2023-12-10T23:12:21.154312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowM
2nd rowF
ValueCountFrequency (%)
m 1
50.0%
f 1
50.0%
2023-12-10T23:12:21.441721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 1
50.0%
F 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1
50.0%
F 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1
50.0%
F 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 1
50.0%
F 1
50.0%

직업명
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2023-12-10T23:12:21.691935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Characters and Unicode

Total characters5
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row회사원
2nd row기타
ValueCountFrequency (%)
회사원 1
50.0%
기타 1
50.0%
2023-12-10T23:12:22.116056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 1
20.0%
사 1
20.0%
원 1
20.0%
기 1
20.0%
타 1
20.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 1
20.0%
사 1
20.0%
원 1
20.0%
기 1
20.0%
타 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 1
20.0%
사 1
20.0%
원 1
20.0%
기 1
20.0%
타 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 1
20.0%
사 1
20.0%
원 1
20.0%
기 1
20.0%
타 1
20.0%

우편번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
28 
17081
 
1
7302
 
1

Length

Max length5
Median length4
Mean length4.0333333
Min length4

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 28
93.3%
17081 1
 
3.3%
7302 1
 
3.3%

Length

2023-12-10T23:12:22.326845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:22.496453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
93.3%
17081 1
 
3.3%
7302 1
 
3.3%

시도명
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2023-12-10T23:12:22.699689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3.5
Mean length3.5
Min length2

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row경기
2nd row서울특별시
ValueCountFrequency (%)
경기 1
50.0%
서울특별시 1
50.0%
2023-12-10T23:12:23.471730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 1
14.3%
기 1
14.3%
서 1
14.3%
울 1
14.3%
특 1
14.3%
별 1
14.3%
시 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 1
14.3%
기 1
14.3%
서 1
14.3%
울 1
14.3%
특 1
14.3%
별 1
14.3%
시 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 1
14.3%
기 1
14.3%
서 1
14.3%
울 1
14.3%
특 1
14.3%
별 1
14.3%
시 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 1
14.3%
기 1
14.3%
서 1
14.3%
울 1
14.3%
특 1
14.3%
별 1
14.3%
시 1
14.3%

시군구명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T23:12:23.842873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row용인시 기흥구
ValueCountFrequency (%)
용인시 1
50.0%
기흥구 1
50.0%
2023-12-10T23:12:24.408123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 1
14.3%
인 1
14.3%
시 1
14.3%
1
14.3%
기 1
14.3%
흥 1
14.3%
구 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
85.7%
Space Separator 1
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 1
16.7%
인 1
16.7%
시 1
16.7%
기 1
16.7%
흥 1
16.7%
구 1
16.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
85.7%
Common 1
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 1
16.7%
인 1
16.7%
시 1
16.7%
기 1
16.7%
흥 1
16.7%
구 1
16.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
85.7%
ASCII 1
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 1
16.7%
인 1
16.7%
시 1
16.7%
기 1
16.7%
흥 1
16.7%
구 1
16.7%
ASCII
ValueCountFrequency (%)
1
100.0%

동명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T23:12:24.579062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row보라동
ValueCountFrequency (%)
보라동 1
100.0%
2023-12-10T23:12:24.963249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
보 1
33.3%
라 1
33.3%
동 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 1
33.3%
라 1
33.3%
동 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 1
33.3%
라 1
33.3%
동 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
보 1
33.3%
라 1
33.3%
동 1
33.3%

가입경로명
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2023-12-10T23:12:25.257961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9.5
Mean length9.5
Min length7

Characters and Unicode

Total characters19
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row인터넷(취업포털;검색)
2nd row신문기사/뉴스
ValueCountFrequency (%)
인터넷(취업포털;검색 1
50.0%
신문기사/뉴스 1
50.0%
2023-12-10T23:12:26.032209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 1
 
5.3%
색 1
 
5.3%
뉴 1
 
5.3%
/ 1
 
5.3%
사 1
 
5.3%
기 1
 
5.3%
문 1
 
5.3%
신 1
 
5.3%
) 1
 
5.3%
검 1
 
5.3%
Other values (9) 9
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15
78.9%
Other Punctuation 2
 
10.5%
Close Punctuation 1
 
5.3%
Open Punctuation 1
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 1
 
6.7%
색 1
 
6.7%
뉴 1
 
6.7%
사 1
 
6.7%
기 1
 
6.7%
문 1
 
6.7%
신 1
 
6.7%
검 1
 
6.7%
터 1
 
6.7%
털 1
 
6.7%
Other values (5) 5
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
; 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15
78.9%
Common 4
 
21.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 1
 
6.7%
색 1
 
6.7%
뉴 1
 
6.7%
사 1
 
6.7%
기 1
 
6.7%
문 1
 
6.7%
신 1
 
6.7%
검 1
 
6.7%
터 1
 
6.7%
털 1
 
6.7%
Other values (5) 5
33.3%
Common
ValueCountFrequency (%)
/ 1
25.0%
) 1
25.0%
; 1
25.0%
( 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15
78.9%
ASCII 4
 
21.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 1
 
6.7%
색 1
 
6.7%
뉴 1
 
6.7%
사 1
 
6.7%
기 1
 
6.7%
문 1
 
6.7%
신 1
 
6.7%
검 1
 
6.7%
터 1
 
6.7%
털 1
 
6.7%
Other values (5) 5
33.3%
ASCII
ValueCountFrequency (%)
/ 1
25.0%
) 1
25.0%
; 1
25.0%
( 1
25.0%

가입목표명
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing28
Missing (%)93.3%
Memory size372.0 B
2023-12-10T23:12:26.386357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5
Min length2

Characters and Unicode

Total characters10
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row자기계발을 위해
2nd row기타
ValueCountFrequency (%)
자기계발을 1
33.3%
위해 1
33.3%
기타 1
33.3%
2023-12-10T23:12:26.911257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 2
20.0%
자 1
10.0%
계 1
10.0%
발 1
10.0%
을 1
10.0%
1
10.0%
위 1
10.0%
해 1
10.0%
타 1
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9
90.0%
Space Separator 1
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 2
22.2%
자 1
11.1%
계 1
11.1%
발 1
11.1%
을 1
11.1%
위 1
11.1%
해 1
11.1%
타 1
11.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9
90.0%
Common 1
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 2
22.2%
자 1
11.1%
계 1
11.1%
발 1
11.1%
을 1
11.1%
위 1
11.1%
해 1
11.1%
타 1
11.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9
90.0%
ASCII 1
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 2
22.2%
자 1
11.1%
계 1
11.1%
발 1
11.1%
을 1
11.1%
위 1
11.1%
해 1
11.1%
타 1
11.1%
ASCII
ValueCountFrequency (%)
1
100.0%

데이터기준일자
Date

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2014-05-09 00:00:00
Maximum2014-05-27 00:00:00
2023-12-10T23:12:27.086231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:27.265304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

Interactions

2023-12-10T23:12:14.940298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:13.682778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:14.348815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:15.173099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:13.872723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:14.515993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:15.427449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:14.148293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:14.697978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:12:27.475874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취업역량정보키값취업역량정보번호직업역량점수직업역량분야평균점수직업역량기준점수등록일시출생년도성별코드직업명우편번호시도명가입경로명가입목표명데이터기준일자
취업역량정보키값1.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0001.000
취업역량정보번호1.0001.0000.6670.6670.3461.0000.0000.0000.0000.0000.0000.0000.0000.893
직업역량점수1.0000.6671.0001.0000.8701.0000.0000.0000.0000.0000.0000.0000.0000.622
직업역량분야평균점수1.0000.6671.0001.0000.8701.0000.0000.0000.0000.0000.0000.0000.0000.622
직업역량기준점수1.0000.3460.8700.8701.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
등록일시1.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0001.000
출생년도0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
성별코드0.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
직업명0.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
우편번호0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
시도명0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
가입경로명0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
가입목표명0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
데이터기준일자1.0000.8930.6220.6220.0001.0000.0000.0000.0000.0000.0000.0000.0001.000
2023-12-10T23:12:27.723305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출생년도직업역량기준점수우편번호
출생년도1.0001.0001.000
직업역량기준점수1.0001.0001.000
우편번호1.0001.0001.000
2023-12-10T23:12:27.891746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취업역량정보번호직업역량점수직업역량분야평균점수직업역량기준점수출생년도우편번호
취업역량정보번호1.000-0.099-0.1000.1291.0001.000
직업역량점수-0.0991.0001.0000.4771.0001.000
직업역량분야평균점수-0.1001.0001.0000.4771.0001.000
직업역량기준점수0.1290.4770.4771.0001.0001.000
출생년도1.0001.0001.0001.0001.0001.000
우편번호1.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T23:12:15.680274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:12:16.190647image/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.
2023-12-10T23:12:16.553265image/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

취업역량정보키값취업역량정보번호직업역량점수직업역량분야평균점수직업역량기준점수등록일시직업역량직업명회원유형명출생년도성별코드직업명우편번호시도명시군구명동명가입경로명가입목표명데이터기준일자
0132_4.44_3.08_2.22_2014050900131413288.844.4200.02014-05-09 00:13<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-09
1136_3.31_3.19_1.65_2014052306193413666.233.0200.62014-05-23 06:19<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-23
2136_3.31_3.25_1.65_2014052306143813666.233.0200.62014-05-23 06:14<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-23
3137_3.87_3.56_1.93_2014052307262713777.438.6200.52014-05-23 07:26<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-23
4138_4.06_3.81_2.03_2014052320283813881.240.6200.02014-05-23 20:28<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-23
5140_3.52_3.66_1.76_2014052407040414070.435.2200.02014-05-24 07:04<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-24
6141_3.52_3.59_1.76_2014052408353814170.435.2200.02014-05-24 08:35<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-24
7144_4.05_3.82_2.02_20140525083954_142555914481.040.4200.42014-05-25 08:39<NA>일반 학습자1973M회사원17081경기용인시 기흥구보라동인터넷(취업포털;검색)자기계발을 위해2014-05-25
8146_2.94_3.38_1.47_2014052515400114658.829.4200.02014-05-25 15:40<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-25
9147_2.83_3.10_1.41_2014052517170314756.628.2200.72014-05-25 17:17<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-25
취업역량정보키값취업역량정보번호직업역량점수직업역량분야평균점수직업역량기준점수등록일시직업역량직업명회원유형명출생년도성별코드직업명우편번호시도명시군구명동명가입경로명가입목표명데이터기준일자
20160_4.12_3.81_2.06_2014052610052416082.441.2200.02014-05-26 10:05<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
21161_3.76_3.73_1.88_2014052610142216175.237.6200.02014-05-26 10:14<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
22162_3.61_3.71_1.80_2014052610130916272.236.0200.52014-05-26 10:13<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
23163_3.28_3.49_1.64_2014052610335216365.632.8200.02014-05-26 10:33<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
24164_3.50_3.49_1.75_2014052610460516470.035.0200.02014-05-26 10:46<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
25165_3.00_3.24_1.50_2014052610470416560.030.0200.02014-05-26 10:47<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
26166_3.04_3.14_1.52_2014052610481616660.830.4200.02014-05-26 10:48<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
27180_4.43_3.78_2.21_2014052617083118088.644.2200.42014-05-26 17:08<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
28181_2.81_3.29_1.40_2014052623004818156.228.0200.72014-05-26 23:00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
29195_4.38_3.90_2.19_2014052701260919587.643.8200.02014-05-27 01:26<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-27