Overview

Dataset statistics

Number of variables21
Number of observations500
Missing cells250
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.6 KiB
Average record size in memory171.3 B

Variable types

Text5
Numeric2
DateTime3
Categorical11

Dataset

Description해당 파일 데이터는 신용보증기금의 공통일반 직원정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093115/fileData.do

Alerts

부점발령일자 has constant value ""Constant
주재근무발령일자 has constant value ""Constant
주재근무부점코드 has constant value ""Constant
퇴사일자 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
생년월일 is highly imbalanced (83.7%)Imbalance
직위코드 is highly imbalanced (52.5%)Imbalance
주재복귀발령일자 is highly imbalanced (80.6%)Imbalance
로그인유형코드 is highly imbalanced (88.2%)Imbalance
이메일 has 250 (50.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 00:26:22.372072
Analysis finished2023-12-12 00:26:22.755696
Duration0.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

직원번호
Text

Distinct327
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:26:23.004875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.132
Min length4

Characters and Unicode

Total characters2066
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)40.4%

Sample

1st row2938
2nd row3640
3rd row4456
4th row5473
5th row4036
ValueCountFrequency (%)
93138 6
 
1.2%
3587 6
 
1.2%
3506 5
 
1.0%
3047 5
 
1.0%
2938 4
 
0.8%
5883 4
 
0.8%
6210 4
 
0.8%
6137 4
 
0.8%
2366 3
 
0.6%
5419 3
 
0.6%
Other values (317) 456
91.2%
2023-12-12T09:26:23.512802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 291
14.1%
3 269
13.0%
4 233
11.3%
9 219
10.6%
6 186
9.0%
1 186
9.0%
2 180
8.7%
0 175
8.5%
8 145
7.0%
7 119
5.8%
Other values (12) 63
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2003
97.0%
Uppercase Letter 63
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 29
46.0%
L 7
 
11.1%
E 5
 
7.9%
X 5
 
7.9%
D 4
 
6.3%
Q 3
 
4.8%
T 3
 
4.8%
J 2
 
3.2%
C 2
 
3.2%
M 1
 
1.6%
Other values (2) 2
 
3.2%
Decimal Number
ValueCountFrequency (%)
5 291
14.5%
3 269
13.4%
4 233
11.6%
9 219
10.9%
6 186
9.3%
1 186
9.3%
2 180
9.0%
0 175
8.7%
8 145
7.2%
7 119
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2003
97.0%
Latin 63
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 29
46.0%
L 7
 
11.1%
E 5
 
7.9%
X 5
 
7.9%
D 4
 
6.3%
Q 3
 
4.8%
T 3
 
4.8%
J 2
 
3.2%
C 2
 
3.2%
M 1
 
1.6%
Other values (2) 2
 
3.2%
Common
ValueCountFrequency (%)
5 291
14.5%
3 269
13.4%
4 233
11.6%
9 219
10.9%
6 186
9.3%
1 186
9.3%
2 180
9.0%
0 175
8.7%
8 145
7.2%
7 119
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 291
14.1%
3 269
13.0%
4 233
11.3%
9 219
10.6%
6 186
9.0%
1 186
9.0%
2 180
8.7%
0 175
8.5%
8 145
7.0%
7 119
5.8%
Other values (12) 63
 
3.0%

이력일련번호
Real number (ℝ)

Distinct115
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.036
Minimum1
Maximum319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:26:23.706366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q383
95-th percentile173.05
Maximum319
Range318
Interquartile range (IQR)82

Descriptive statistics

Standard deviation68.118448
Coefficient of variation (CV)1.6204788
Kurtosis2.7320502
Mean42.036
Median Absolute Deviation (MAD)0
Skewness1.7435489
Sum21018
Variance4640.1229
MonotonicityNot monotonic
2023-12-12T09:26:23.845676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 327
65.4%
122 4
 
0.8%
87 4
 
0.8%
99 4
 
0.8%
79 4
 
0.8%
84 4
 
0.8%
83 3
 
0.6%
73 3
 
0.6%
121 3
 
0.6%
128 3
 
0.6%
Other values (105) 141
28.2%
ValueCountFrequency (%)
1 327
65.4%
7 1
 
0.2%
9 1
 
0.2%
11 1
 
0.2%
15 1
 
0.2%
18 1
 
0.2%
25 2
 
0.4%
26 2
 
0.4%
27 2
 
0.4%
28 2
 
0.4%
ValueCountFrequency (%)
319 1
0.2%
318 1
0.2%
317 1
0.2%
312 1
0.2%
282 1
0.2%
281 1
0.2%
280 1
0.2%
279 1
0.2%
278 1
0.2%
273 1
0.2%

부점발령일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T09:26:23.973599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:24.078417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

생년월일
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
488 
0001-01-01 00:00:00.000000
 
12

Length

Max length26
Median length7
Mean length7.456
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 488
97.6%
0001-01-01 00:00:00.000000 12
 
2.4%

Length

2023-12-12T09:26:24.192189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:24.280372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 488
95.3%
0001-01-01 12
 
2.3%
00:00:00.000000 12
 
2.3%

성별구분코드
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
307 
2
177 
 
12
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 307
61.4%
2 177
35.4%
12
 
2.4%
4 4
 
0.8%

Length

2023-12-12T09:26:24.368336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:24.462793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 307
62.9%
2 177
36.3%
4 4
 
0.8%

직급코드
Categorical

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A4
131 
A5
124 
A3
90 
A2
42 
ZP
27 
Other values (8)
86 

Length

Max length2
Median length2
Mean length1.98
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZP
2nd rowA2
3rd rowA4
4th rowA4
5th rowA3

Common Values

ValueCountFrequency (%)
A4 131
26.2%
A5 124
24.8%
A3 90
18.0%
A2 42
 
8.4%
ZP 27
 
5.4%
DA 27
 
5.4%
DC 25
 
5.0%
10
 
2.0%
A1 9
 
1.8%
ZI 7
 
1.4%
Other values (3) 8
 
1.6%

Length

2023-12-12T09:26:24.560928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a4 131
26.7%
a5 124
25.3%
a3 90
18.4%
a2 42
 
8.6%
zp 27
 
5.5%
da 27
 
5.5%
dc 25
 
5.1%
a1 9
 
1.8%
zi 7
 
1.4%
a6 4
 
0.8%
Other values (2) 4
 
0.8%

직위코드
Categorical

IMBALANCE 

Distinct19
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
K01
317 
C02
72 
B05
 
30
H02
 
27
B16
 
9
Other values (14)
45 

Length

Max length3
Median length3
Mean length2.98
Min length1

Unique

Unique6 ?
Unique (%)1.2%

Sample

1st rowK01
2nd rowC27
3rd rowK01
4th rowK01
5th rowC02

Common Values

ValueCountFrequency (%)
K01 317
63.4%
C02 72
 
14.4%
B05 30
 
6.0%
H02 27
 
5.4%
B16 9
 
1.8%
I03 7
 
1.4%
C05 6
 
1.2%
N01 5
 
1.0%
5
 
1.0%
C24 5
 
1.0%
Other values (9) 17
 
3.4%

Length

2023-12-12T09:26:24.678612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
k01 317
64.0%
c02 72
 
14.5%
b05 30
 
6.1%
h02 27
 
5.5%
b16 9
 
1.8%
i03 7
 
1.4%
c05 6
 
1.2%
c24 5
 
1.0%
c36 5
 
1.0%
n01 5
 
1.0%
Other values (8) 12
 
2.4%

호칭명
Categorical

Distinct30
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
차장
71 
<NA>
66 
과장
53 
대리
51 
주임
41 
Other values (25)
218 

Length

Max length18
Median length2
Mean length2.93
Min length2

Unique

Unique5 ?
Unique (%)1.0%

Sample

1st row부장
2nd row수석부지점장
3rd row차장
4th row과장
5th row팀장

Common Values

ValueCountFrequency (%)
차장 71
14.2%
<NA> 66
13.2%
과장 53
10.6%
대리 51
10.2%
주임 41
8.2%
고객팀장 39
7.8%
팀장 36
7.2%
부장 33
6.6%
지점장 26
 
5.2%
선임차장 21
 
4.2%
Other values (20) 63
12.6%

Length

2023-12-12T09:26:24.841430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
차장 71
13.8%
na 66
12.8%
과장 53
10.3%
대리 52
10.1%
주임 41
7.9%
고객팀장 39
7.6%
팀장 36
7.0%
부장 33
6.4%
지점장 26
 
5.0%
선임차장 21
 
4.1%
Other values (28) 78
15.1%

주재근무발령일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
500 

Length

Max length26
Median length26
Mean length26
Min length26

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
3rd row0001-01-01 00:00:00.000000
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 500
100.0%

Length

2023-12-12T09:26:24.963916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:25.076786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 500
50.0%
00:00:00.000000 500
50.0%

주재복귀발령일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
485 
00:00.0
 
15

Length

Max length26
Median length26
Mean length25.43
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
3rd row0001-01-01 00:00:00.000000
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 485
97.0%
00:00.0 15
 
3.0%

Length

2023-12-12T09:26:25.180705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:25.290325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 485
49.2%
00:00:00.000000 485
49.2%
00:00.0 15
 
1.5%

주재근무부점코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
500
100.0%

Length

2023-12-12T09:26:25.469485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:25.576908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

퇴사일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
500 

Length

Max length26
Median length26
Mean length26
Min length26

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
3rd row0001-01-01 00:00:00.000000
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 500
100.0%

Length

2023-12-12T09:26:25.722794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:25.828654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 500
50.0%
00:00:00.000000 500
50.0%

이메일
Text

MISSING 

Distinct163
Distinct (%)65.2%
Missing250
Missing (%)50.0%
Memory size4.0 KiB
2023-12-12T09:26:26.061478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length48
Mean length47.616
Min length24

Characters and Unicode

Total characters11904
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)40.4%

Sample

1st rowAAHHPsuGTvBRWyRF3K1J7M6ts+o9QA7CvKK3XME6iDpUFQ==
2nd rowAAEMvKsK/We5ijXwYxGVhhU+SWcr9hZZ8cAo6vxrCr4DjA==
3rd rowAAEYXBQPSj3tphaadcW4CxhVKw6fJ+341FFD8wjmla0akw==
4th rowAAF/kX4Hv2OcIak78DKM5uMnRxfh46urOHY6ANz0bwDvGg==
5th rowAAHMlT7XtVHkX+uWvWMPwpngxwZlwlafXr/mrx1qOFEs1w==
ValueCountFrequency (%)
aafy+nbqwqbpnt6aqdzzmwmbgzs+c8gml/kzplrchin56w 6
 
2.4%
aahxeauqrkwkwvaqb4luzwz8olgqvn8/yr09s6ecnmb1fg 5
 
2.0%
aae4lqszztmm2h7eslvzplwkjjizrontcqxhwpbhu6gipg 5
 
2.0%
aahhpsugtvbrwyrf3k1j7m6ts+o9qa7cvkk3xme6idpufq 4
 
1.6%
aagcftqnfcsw3u3/0ebmx1phvbc8vlmyguwfzxqterloeq 3
 
1.2%
aahacp5dcyqmnszfvg9vpajoofwbbds2+n9hkwpaxvmhra 3
 
1.2%
aagai2xcstmhzlbwstfiuv8csnmgi1z8nl4l7j7cmojqkg 3
 
1.2%
aah4oozvs8obccu12qifg+wurkyuys/eij3hqyaxdndlcq 3
 
1.2%
aag6ikgj8byqoqtpsxkl+cbj5q9orrnzpnidprnpnqkcta 3
 
1.2%
aag21pxhysnldejavh/gvncijthy+zje6zcoqtx/7ydlaw 3
 
1.2%
Other values (153) 212
84.8%
2023-12-12T09:26:26.490013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 757
 
6.4%
= 492
 
4.1%
F 246
 
2.1%
Q 237
 
2.0%
g 234
 
2.0%
G 221
 
1.9%
H 216
 
1.8%
S 205
 
1.7%
E 203
 
1.7%
w 200
 
1.7%
Other values (55) 8893
74.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5228
43.9%
Lowercase Letter 4286
36.0%
Decimal Number 1596
 
13.4%
Math Symbol 640
 
5.4%
Other Punctuation 154
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 757
 
14.5%
F 246
 
4.7%
Q 237
 
4.5%
G 221
 
4.2%
H 216
 
4.1%
S 205
 
3.9%
E 203
 
3.9%
Z 194
 
3.7%
P 188
 
3.6%
Y 185
 
3.5%
Other values (16) 2576
49.3%
Lowercase Letter
ValueCountFrequency (%)
g 234
 
5.5%
w 200
 
4.7%
t 191
 
4.5%
c 181
 
4.2%
a 172
 
4.0%
o 171
 
4.0%
k 171
 
4.0%
i 170
 
4.0%
n 169
 
3.9%
l 169
 
3.9%
Other values (16) 2458
57.3%
Decimal Number
ValueCountFrequency (%)
6 190
11.9%
1 187
11.7%
8 164
10.3%
7 164
10.3%
5 152
9.5%
9 152
9.5%
3 151
9.5%
2 147
9.2%
0 146
9.1%
4 143
9.0%
Math Symbol
ValueCountFrequency (%)
= 492
76.9%
+ 148
 
23.1%
Other Punctuation
ValueCountFrequency (%)
/ 154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9514
79.9%
Common 2390
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 757
 
8.0%
F 246
 
2.6%
Q 237
 
2.5%
g 234
 
2.5%
G 221
 
2.3%
H 216
 
2.3%
S 205
 
2.2%
E 203
 
2.1%
w 200
 
2.1%
Z 194
 
2.0%
Other values (42) 6801
71.5%
Common
ValueCountFrequency (%)
= 492
20.6%
6 190
 
7.9%
1 187
 
7.8%
8 164
 
6.9%
7 164
 
6.9%
/ 154
 
6.4%
5 152
 
6.4%
9 152
 
6.4%
3 151
 
6.3%
+ 148
 
6.2%
Other values (3) 436
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 757
 
6.4%
= 492
 
4.1%
F 246
 
2.1%
Q 237
 
2.0%
g 234
 
2.0%
G 221
 
1.9%
H 216
 
1.8%
S 205
 
1.7%
E 203
 
1.7%
w 200
 
1.7%
Other values (55) 8893
74.7%

유효개시일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T09:26:26.641748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:26.729739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

유효종료일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T09:26:26.847566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:26.957347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Real number (ℝ)

Distinct197
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.912
Minimum1
Maximum412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:26:27.104790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q178
median109
Q3143
95-th percentile220.15
Maximum412
Range411
Interquartile range (IQR)65

Descriptive statistics

Standard deviation59.746628
Coefficient of variation (CV)0.52449811
Kurtosis2.3390667
Mean113.912
Median Absolute Deviation (MAD)32
Skewness1.0131514
Sum56956
Variance3569.6596
MonotonicityNot monotonic
2023-12-12T09:26:27.259798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 9
 
1.8%
102 8
 
1.6%
122 8
 
1.6%
121 7
 
1.4%
84 7
 
1.4%
71 7
 
1.4%
87 7
 
1.4%
165 7
 
1.4%
130 6
 
1.2%
80 6
 
1.2%
Other values (187) 428
85.6%
ValueCountFrequency (%)
1 5
1.0%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
12 1
 
0.2%
14 1
 
0.2%
15 1
 
0.2%
ValueCountFrequency (%)
412 1
0.2%
319 1
0.2%
318 1
0.2%
317 1
0.2%
316 1
0.2%
312 1
0.2%
311 1
0.2%
310 1
0.2%
282 1
0.2%
281 1
0.2%

처리시각
Text

Distinct496
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:26:27.623403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique493 ?
Unique (%)98.6%

Sample

1st row53:32.1
2nd row49:58.9
3rd row47:22.6
4th row46:15.1
5th row45:05.0
ValueCountFrequency (%)
31:35.1 3
 
0.6%
11:41.2 2
 
0.4%
10:18.9 2
 
0.4%
07:19.3 1
 
0.2%
09:46.0 1
 
0.2%
14:38.5 1
 
0.2%
22:57.1 1
 
0.2%
23:07.5 1
 
0.2%
25:06.5 1
 
0.2%
25:16.1 1
 
0.2%
Other values (486) 486
97.2%
2023-12-12T09:26:28.479712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 364
10.4%
3 331
9.5%
2 326
9.3%
4 301
8.6%
5 299
8.5%
0 271
7.7%
9 168
 
4.8%
7 154
 
4.4%
Other values (2) 286
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 364
14.6%
3 331
13.2%
2 326
13.0%
4 301
12.0%
5 299
12.0%
0 271
10.8%
9 168
6.7%
7 154
6.2%
6 145
 
5.8%
8 141
 
5.6%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 364
10.4%
3 331
9.5%
2 326
9.3%
4 301
8.6%
5 299
8.5%
0 271
7.7%
9 168
 
4.8%
7 154
 
4.4%
Other values (2) 286
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 364
10.4%
3 331
9.5%
2 326
9.3%
4 301
8.6%
5 299
8.5%
0 271
7.7%
9 168
 
4.8%
7 154
 
4.4%
Other values (2) 286
8.2%

처리직원번호
Text

Distinct324
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:26:28.863325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.128
Min length4

Characters and Unicode

Total characters2064
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique197 ?
Unique (%)39.4%

Sample

1st row2938
2nd row3640
3rd row4456
4th row5473
5th row4036
ValueCountFrequency (%)
3587 6
 
1.2%
93138 6
 
1.2%
6009 5
 
1.0%
3506 5
 
1.0%
2938 4
 
0.8%
6210 4
 
0.8%
5883 4
 
0.8%
6137 4
 
0.8%
3047 4
 
0.8%
6104 3
 
0.6%
Other values (314) 455
91.0%
2023-12-12T09:26:29.381436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 291
14.1%
3 264
12.8%
9 225
10.9%
4 225
10.9%
6 190
9.2%
1 185
9.0%
0 183
8.9%
2 177
8.6%
8 145
7.0%
7 119
5.8%
Other values (13) 60
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2004
97.1%
Uppercase Letter 60
 
2.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 31
51.7%
T 5
 
8.3%
D 4
 
6.7%
C 4
 
6.7%
Q 3
 
5.0%
H 3
 
5.0%
B 2
 
3.3%
L 2
 
3.3%
J 2
 
3.3%
E 1
 
1.7%
Other values (3) 3
 
5.0%
Decimal Number
ValueCountFrequency (%)
5 291
14.5%
3 264
13.2%
9 225
11.2%
4 225
11.2%
6 190
9.5%
1 185
9.2%
0 183
9.1%
2 177
8.8%
8 145
7.2%
7 119
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2004
97.1%
Latin 60
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 31
51.7%
T 5
 
8.3%
D 4
 
6.7%
C 4
 
6.7%
Q 3
 
5.0%
H 3
 
5.0%
B 2
 
3.3%
L 2
 
3.3%
J 2
 
3.3%
E 1
 
1.7%
Other values (3) 3
 
5.0%
Common
ValueCountFrequency (%)
5 291
14.5%
3 264
13.2%
9 225
11.2%
4 225
11.2%
6 190
9.5%
1 185
9.2%
0 183
9.1%
2 177
8.8%
8 145
7.2%
7 119
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 291
14.1%
3 264
12.8%
9 225
10.9%
4 225
10.9%
6 190
9.2%
1 185
9.0%
0 183
8.9%
2 177
8.6%
8 145
7.0%
7 119
5.8%
Other values (13) 60
 
2.9%

최초처리시각
Text

Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:26:29.594959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length7
Mean length15.094
Min length7

Characters and Unicode

Total characters7547
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)3.8%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
3rd row0001-01-01 00:00:00.000000
4th row30:35.0
5th row0001-01-01 00:00:00.000000
ValueCountFrequency (%)
0001-01-01 213
29.9%
00:00:00.000000 213
29.9%
27:53.0 27
 
3.8%
14:35.3 25
 
3.5%
13:27.0 24
 
3.4%
30:35.0 15
 
2.1%
48:40.5 14
 
2.0%
30:33.9 13
 
1.8%
17:04.2 13
 
1.8%
47:51.9 9
 
1.3%
Other values (52) 147
20.6%
2023-12-12T09:26:29.972600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3850
51.0%
1 806
 
10.7%
: 713
 
9.4%
. 500
 
6.6%
- 426
 
5.6%
3 252
 
3.3%
213
 
2.8%
4 181
 
2.4%
5 173
 
2.3%
2 151
 
2.0%
Other values (4) 282
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5695
75.5%
Other Punctuation 1213
 
16.1%
Dash Punctuation 426
 
5.6%
Space Separator 213
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3850
67.6%
1 806
 
14.2%
3 252
 
4.4%
4 181
 
3.2%
5 173
 
3.0%
2 151
 
2.7%
7 134
 
2.4%
9 63
 
1.1%
6 44
 
0.8%
8 41
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 713
58.8%
. 500
41.2%
Dash Punctuation
ValueCountFrequency (%)
- 426
100.0%
Space Separator
ValueCountFrequency (%)
213
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7547
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3850
51.0%
1 806
 
10.7%
: 713
 
9.4%
. 500
 
6.6%
- 426
 
5.6%
3 252
 
3.3%
213
 
2.8%
4 181
 
2.4%
5 173
 
2.3%
2 151
 
2.0%
Other values (4) 282
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3850
51.0%
1 806
 
10.7%
: 713
 
9.4%
. 500
 
6.6%
- 426
 
5.6%
3 252
 
3.3%
213
 
2.8%
4 181
 
2.4%
5 173
 
2.3%
2 151
 
2.0%
Other values (4) 282
 
3.7%

최초처리직원번호
Categorical

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
213 
5099
71 
5314
54 
5803
53 
3513
24 
Other values (9)
85 

Length

Max length5
Median length4
Mean length4.478
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row5099
5th row

Common Values

ValueCountFrequency (%)
213
42.6%
5099 71
 
14.2%
5314 54
 
10.8%
5803 53
 
10.6%
3513 24
 
4.8%
BATCH 24
 
4.8%
4800 19
 
3.8%
4169 16
 
3.2%
4451 9
 
1.8%
4062 5
 
1.0%
Other values (4) 12
 
2.4%

Length

2023-12-12T09:26:30.104787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5099 71
24.7%
5314 54
18.8%
5803 53
18.5%
3513 24
 
8.4%
batch 24
 
8.4%
4800 19
 
6.6%
4169 16
 
5.6%
4451 9
 
3.1%
4062 5
 
1.7%
6009 5
 
1.7%
Other values (3) 7
 
2.4%

로그인유형코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
488 
3
 
7
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 488
97.6%
3 7
 
1.4%
2 5
 
1.0%

Length

2023-12-12T09:26:30.240883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:30.363643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 488
97.6%
3 7
 
1.4%
2 5
 
1.0%

Sample

직원번호이력일련번호부점발령일자생년월일성별구분코드직급코드직위코드호칭명주재근무발령일자주재복귀발령일자주재근무부점코드퇴사일자이메일유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호로그인유형코드
02938100:00.000:00.01ZPK01부장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAHHPsuGTvBRWyRF3K1J7M6ts+o9QA7CvKK3XME6iDpUFQ==00:00.000:00.031953:32.129380001-01-01 00:00:00.0000001
13640100:00.000:00.01A2C27수석부지점장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAEMvKsK/We5ijXwYxGVhhU+SWcr9hZZ8cAo6vxrCr4DjA==00:00.000:00.014049:58.936400001-01-01 00:00:00.0000001
24456100:00.000:00.02A4K01차장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAEYXBQPSj3tphaadcW4CxhVKw6fJ+341FFD8wjmla0akw==00:00.000:00.012147:22.644560001-01-01 00:00:00.0000001
35473100:00.000:00.02A4K01과장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>00:00.000:00.07946:15.1547330:35.050991
44036100:00.000:00.01A3C02팀장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAF/kX4Hv2OcIak78DKM5uMnRxfh46urOHY6ANz0bwDvGg==00:00.000:00.010345:05.040360001-01-01 00:00:00.0000001
53012100:00.000:00.01A1B05지점장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAHMlT7XtVHkX+uWvWMPwpngxwZlwlafXr/mrx1qOFEs1w==00:00.000:00.016844:15.730120001-01-01 00:00:00.0000001
63779100:00.000:00.01A3C02고객팀장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAETUVFzvvJdF4r1yXDyL/gLIHXNWcUttQzVA4Enn9qoig==00:00.000:00.021242:25.737790001-01-01 00:00:00.0000001
74994100:00.000:00.02A4K01차장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAEbbzm6Q7jzxzgK13vMN7TW00:00.000:00.020932:30.1499412:13.548001
81864100:00.000:00.01ZPK01부장/정책보증0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAEy+3DYdY3ghTm+L5oO1kaTFzkZ0eNHn89jUPe6dk9zfQ==00:00.000:00.018328:25.418640001-01-01 00:00:00.0000001
990175100:00.000:00.01DAK01부장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>00:00.000:00.015926:27.2901750001-01-01 00:00:00.0000001
직원번호이력일련번호부점발령일자생년월일성별구분코드직급코드직위코드호칭명주재근무발령일자주재복귀발령일자주재근무부점코드퇴사일자이메일유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호로그인유형코드
4909483612500:00.000:00.01DAK01부부장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>00:00.000:00.012453:53.99483609:32.635131
4916003100:00.000:00.01A5K01<NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>00:00.000:00.02653:19.7600313:27.058031
492343212800:00.000:00.01A3C02고객팀장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAFh5MeTBxxXntLBOR5vK2M/T6ENDvtvlFSR+Oq7KNlKlQ==00:00.000:00.012751:45.134320001-01-01 00:00:00.0000001
49349939800:00.000:00.02A4K01차장0001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.000000<NA>00:00.000:00.09751:32.2499312:13.548001
494907065200:00.000:00.01DAK01부장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>00:00.000:00.05150:54.5907060001-01-01 00:00:00.0000001
4954029100:00.000:00.01A3C36팀장0001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000AAEVK3brsP0Cj6bRpkRyXwReZjkb+041wSu41azw1pGVZQ==00:00.000:00.016749:24.040290001-01-01 00:00:00.0000001
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