Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells19480
Missing cells (%)21.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory830.1 KiB
Average record size in memory85.0 B

Variable types

Text3
Numeric4
Categorical1
Unsupported1

Dataset

Description국립암센터에서 19년도 9월까지 국립암센터홈페이지를 통해 개방하는 협업정보
Author국립암센터
URLhttps://www.data.go.kr/data/15049625/fileData.do

Alerts

EXP_COMPANY has 3085 (30.9%) missing valuesMissing
EXP_TYPE1 has 3164 (31.6%) missing valuesMissing
EXP_DUTY has 3227 (32.3%) missing valuesMissing
EXP_BRANCH has 10000 (100.0%) missing valuesMissing
EXP_BRANCH is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 08:46:31.784807
Analysis finished2023-12-12 08:46:35.487392
Duration3.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

EXP_RSM_ID
Text

Distinct8041
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:46:35.875567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.2996
Min length1

Characters and Unicode

Total characters52996
Distinct characters11
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

Unique6593 ?
Unique (%)65.9%

Sample

1st row5,304
2nd row3,250
3rd row12,105
4th row6,102
5th row11,680
ValueCountFrequency (%)
6,817 7
 
0.1%
11,370 6
 
0.1%
6,496 6
 
0.1%
4,565 6
 
0.1%
12,107 6
 
0.1%
18,758 5
 
< 0.1%
17,319 5
 
< 0.1%
4,513 5
 
< 0.1%
12,359 5
 
< 0.1%
10,151 5
 
< 0.1%
Other values (8031) 9944
99.4%
2023-12-12T17:46:36.536898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 9392
17.7%
1 8509
16.1%
2 4233
8.0%
4 4168
7.9%
6 4038
7.6%
3 3996
7.5%
5 3987
7.5%
7 3856
7.3%
8 3800
7.2%
9 3525
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43604
82.3%
Other Punctuation 9392
 
17.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8509
19.5%
2 4233
9.7%
4 4168
9.6%
6 4038
9.3%
3 3996
9.2%
5 3987
9.1%
7 3856
8.8%
8 3800
8.7%
9 3525
8.1%
0 3492
8.0%
Other Punctuation
ValueCountFrequency (%)
, 9392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 9392
17.7%
1 8509
16.1%
2 4233
8.0%
4 4168
7.9%
6 4038
7.6%
3 3996
7.5%
5 3987
7.5%
7 3856
7.3%
8 3800
7.2%
9 3525
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 9392
17.7%
1 8509
16.1%
2 4233
8.0%
4 4168
7.9%
6 4038
7.6%
3 3996
7.5%
5 3987
7.5%
7 3856
7.3%
8 3800
7.2%
9 3525
 
6.7%

EXP_NO
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4956
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:46:36.710954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.96388461
Coefficient of variation (CV)0.64448022
Kurtosis10.321935
Mean1.4956
Median Absolute Deviation (MAD)0
Skewness2.7438813
Sum14956
Variance0.92907355
MonotonicityNot monotonic
2023-12-12T17:46:36.869816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 7064
70.6%
2 1708
 
17.1%
3 764
 
7.6%
4 281
 
2.8%
5 107
 
1.1%
6 36
 
0.4%
7 21
 
0.2%
8 11
 
0.1%
9 6
 
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
1 7064
70.6%
2 1708
 
17.1%
3 764
 
7.6%
4 281
 
2.8%
5 107
 
1.1%
6 36
 
0.4%
7 21
 
0.2%
8 11
 
0.1%
9 6
 
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 6
 
0.1%
8 11
 
0.1%
7 21
 
0.2%
6 36
 
0.4%
5 107
 
1.1%
4 281
 
2.8%
3 764
 
7.6%
2 1708
 
17.1%
1 7064
70.6%

EXP_COMPANY
Text

MISSING 

Distinct4729
Distinct (%)68.4%
Missing3085
Missing (%)30.9%
Memory size156.2 KiB
2023-12-12T17:46:37.192218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length40
Mean length7.7728127
Min length1

Characters and Unicode

Total characters53749
Distinct characters738
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4047 ?
Unique (%)58.5%

Sample

1st rowAccess college of Adelaide
2nd row강북삼성병원
3rd row한국전산개발
4th row충청북도장애인종합복지관
5th row순천향대학 천안병원
ValueCountFrequency (%)
국립암센터 168
 
1.9%
서울대학교병원 105
 
1.2%
병원 102
 
1.2%
서울아산병원 95
 
1.1%
삼성서울병원 94
 
1.1%
강북삼성병원 46
 
0.5%
일산병원 43
 
0.5%
가톨릭대학교 39
 
0.4%
강남성모병원 38
 
0.4%
신촌세브란스병원 36
 
0.4%
Other values (5064) 8012
91.3%
2023-12-12T17:46:37.695058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 3542
 
6.6%
병 2716
 
5.1%
1968
 
3.7%
대 1925
 
3.6%
학 1680
 
3.1%
교 1264
 
2.4%
주 1081
 
2.0%
) 990
 
1.8%
( 961
 
1.8%
국 869
 
1.6%
Other values (728) 36753
68.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46632
86.8%
Space Separator 1968
 
3.7%
Lowercase Letter 1473
 
2.7%
Uppercase Letter 1291
 
2.4%
Close Punctuation 990
 
1.8%
Open Punctuation 961
 
1.8%
Decimal Number 242
 
0.5%
Other Punctuation 134
 
0.2%
Dash Punctuation 36
 
0.1%
Other Symbol 11
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 3542
 
7.6%
병 2716
 
5.8%
대 1925
 
4.1%
학 1680
 
3.6%
교 1264
 
2.7%
주 1081
 
2.3%
국 869
 
1.9%
서 823
 
1.8%
성 789
 
1.7%
의 752
 
1.6%
Other values (651) 31191
66.9%
Lowercase Letter
ValueCountFrequency (%)
e 179
12.2%
a 135
9.2%
o 129
8.8%
i 128
8.7%
n 120
8.1%
t 119
8.1%
l 104
 
7.1%
r 98
 
6.7%
s 98
 
6.7%
c 68
 
4.6%
Other values (16) 295
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 150
 
11.6%
C 145
 
11.2%
G 97
 
7.5%
K 93
 
7.2%
L 86
 
6.7%
T 82
 
6.4%
I 77
 
6.0%
B 65
 
5.0%
A 62
 
4.8%
D 55
 
4.3%
Other values (16) 379
29.4%
Decimal Number
ValueCountFrequency (%)
1 64
26.4%
2 51
21.1%
3 31
12.8%
6 29
12.0%
5 23
 
9.5%
0 13
 
5.4%
7 12
 
5.0%
8 9
 
3.7%
9 7
 
2.9%
4 3
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 44
32.8%
. 38
28.4%
& 34
25.4%
/ 13
 
9.7%
' 4
 
3.0%
: 1
 
0.7%
Math Symbol
ValueCountFrequency (%)
+ 6
60.0%
> 2
 
20.0%
< 2
 
20.0%
Space Separator
ValueCountFrequency (%)
1968
100.0%
Close Punctuation
ValueCountFrequency (%)
) 990
100.0%
Open Punctuation
ValueCountFrequency (%)
( 961
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46618
86.7%
Common 4342
 
8.1%
Latin 2764
 
5.1%
Han 25
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 3542
 
7.6%
병 2716
 
5.8%
대 1925
 
4.1%
학 1680
 
3.6%
교 1264
 
2.7%
주 1081
 
2.3%
국 869
 
1.9%
서 823
 
1.8%
성 789
 
1.7%
의 752
 
1.6%
Other values (634) 31177
66.9%
Latin
ValueCountFrequency (%)
e 179
 
6.5%
S 150
 
5.4%
C 145
 
5.2%
a 135
 
4.9%
o 129
 
4.7%
i 128
 
4.6%
n 120
 
4.3%
t 119
 
4.3%
l 104
 
3.8%
r 98
 
3.5%
Other values (42) 1457
52.7%
Common
ValueCountFrequency (%)
1968
45.3%
) 990
22.8%
( 961
22.1%
1 64
 
1.5%
2 51
 
1.2%
, 44
 
1.0%
. 38
 
0.9%
- 36
 
0.8%
& 34
 
0.8%
3 31
 
0.7%
Other values (14) 125
 
2.9%
Han
ValueCountFrequency (%)
現 3
 
12.0%
舊 2
 
8.0%
株 2
 
8.0%
興 2
 
8.0%
區 2
 
8.0%
東 2
 
8.0%
振 1
 
4.0%
和 1
 
4.0%
同 1
 
4.0%
營 1
 
4.0%
Other values (8) 8
32.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46592
86.7%
ASCII 7106
 
13.2%
CJK 25
 
< 0.1%
Compat Jamo 15
 
< 0.1%
None 11
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 3542
 
7.6%
병 2716
 
5.8%
대 1925
 
4.1%
학 1680
 
3.6%
교 1264
 
2.7%
주 1081
 
2.3%
국 869
 
1.9%
서 823
 
1.8%
성 789
 
1.7%
의 752
 
1.6%
Other values (627) 31151
66.9%
ASCII
ValueCountFrequency (%)
1968
27.7%
) 990
13.9%
( 961
13.5%
e 179
 
2.5%
S 150
 
2.1%
C 145
 
2.0%
a 135
 
1.9%
o 129
 
1.8%
i 128
 
1.8%
n 120
 
1.7%
Other values (66) 2201
31.0%
None
ValueCountFrequency (%)
㈜ 11
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅇ 7
46.7%
ㄹ 2
 
13.3%
ㄴ 2
 
13.3%
ㅁ 2
 
13.3%
ㅎ 1
 
6.7%
ㄻ 1
 
6.7%
CJK
ValueCountFrequency (%)
現 3
 
12.0%
舊 2
 
8.0%
株 2
 
8.0%
興 2
 
8.0%
區 2
 
8.0%
東 2
 
8.0%
振 1
 
4.0%
和 1
 
4.0%
同 1
 
4.0%
營 1
 
4.0%
Other values (8) 8
32.0%

EXP_TYPE1
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)0.2%
Missing3164
Missing (%)31.6%
Infinite0
Infinite (%)0.0%
Mean6.7653599
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:46:37.849216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q313
95-th percentile13
Maximum15
Range14
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.0294762
Coefficient of variation (CV)0.74341592
Kurtosis-1.7032378
Mean6.7653599
Median Absolute Deviation (MAD)4
Skewness0.24033619
Sum46248
Variance25.295631
MonotonicityNot monotonic
2023-12-12T17:46:37.981933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
13 1571
15.7%
2 1355
13.6%
1 1011
 
10.1%
3 681
 
6.8%
5 460
 
4.6%
12 457
 
4.6%
14 328
 
3.3%
10 166
 
1.7%
6 153
 
1.5%
4 149
 
1.5%
Other values (5) 505
 
5.1%
(Missing) 3164
31.6%
ValueCountFrequency (%)
1 1011
10.1%
2 1355
13.6%
3 681
6.8%
4 149
 
1.5%
5 460
 
4.6%
6 153
 
1.5%
7 113
 
1.1%
8 128
 
1.3%
9 114
 
1.1%
10 166
 
1.7%
ValueCountFrequency (%)
15 5
 
0.1%
14 328
 
3.3%
13 1571
15.7%
12 457
 
4.6%
11 145
 
1.5%
10 166
 
1.7%
9 114
 
1.1%
8 128
 
1.3%
7 113
 
1.1%
6 153
 
1.5%

EXP_TYPE2
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정규직
3895 
<NA>
3183 
임시직
2922 

Length

Max length4
Median length3
Mean length3.3183
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정규직 3895
39.0%
<NA> 3183
31.8%
임시직 2922
29.2%

Length

2023-12-12T17:46:38.146796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:46:38.259465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정규직 3895
39.0%
na 3183
31.8%
임시직 2922
29.2%

EXP_DUTY
Text

MISSING 

Distinct1559
Distinct (%)23.0%
Missing3227
Missing (%)32.3%
Memory size156.2 KiB
2023-12-12T17:46:38.567667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length37
Mean length3.7571239
Min length1

Characters and Unicode

Total characters25447
Distinct characters388
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

Unique1194 ?
Unique (%)17.6%

Sample

1st row사원
2nd row계약직
3rd row사원
4th row사원
5th row간호사
ValueCountFrequency (%)
사원 1311
 
17.7%
간호사 742
 
10.0%
대리 305
 
4.1%
방사선사 257
 
3.5%
주임 186
 
2.5%
계약직 177
 
2.4%
인턴 176
 
2.4%
연구원 127
 
1.7%
과장 111
 
1.5%
임상병리사 108
 
1.5%
Other values (1462) 3888
52.6%
2023-12-12T17:46:39.078469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 4310
 
16.9%
원 2145
 
8.4%
간 1206
 
4.7%
호 1065
 
4.2%
712
 
2.8%
직 637
 
2.5%
리 608
 
2.4%
임 574
 
2.3%
장 546
 
2.1%
대 435
 
1.7%
Other values (378) 13209
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23399
92.0%
Space Separator 712
 
2.8%
Lowercase Letter 548
 
2.2%
Open Punctuation 174
 
0.7%
Close Punctuation 174
 
0.7%
Uppercase Letter 170
 
0.7%
Decimal Number 166
 
0.7%
Other Punctuation 78
 
0.3%
Dash Punctuation 22
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 4310
 
18.4%
원 2145
 
9.2%
간 1206
 
5.2%
호 1065
 
4.6%
직 637
 
2.7%
리 608
 
2.6%
임 574
 
2.5%
장 546
 
2.3%
대 435
 
1.9%
무 407
 
1.7%
Other values (311) 11466
49.0%
Lowercase Letter
ValueCountFrequency (%)
e 65
11.9%
t 61
11.1%
a 59
10.8%
r 56
10.2%
s 55
10.0%
n 52
9.5%
i 42
7.7%
c 28
 
5.1%
o 20
 
3.6%
f 19
 
3.5%
Other values (13) 91
16.6%
Uppercase Letter
ValueCountFrequency (%)
A 31
18.2%
C 15
8.8%
R 14
 
8.2%
S 14
 
8.2%
P 12
 
7.1%
D 11
 
6.5%
I 11
 
6.5%
M 10
 
5.9%
F 9
 
5.3%
N 7
 
4.1%
Other values (13) 36
21.2%
Decimal Number
ValueCountFrequency (%)
5 39
23.5%
6 25
15.1%
7 25
15.1%
8 19
11.4%
3 15
 
9.0%
1 15
 
9.0%
2 10
 
6.0%
0 8
 
4.8%
4 7
 
4.2%
9 3
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 35
44.9%
/ 26
33.3%
. 11
 
14.1%
& 4
 
5.1%
' 2
 
2.6%
Space Separator
ValueCountFrequency (%)
712
100.0%
Open Punctuation
ValueCountFrequency (%)
( 174
100.0%
Close Punctuation
ValueCountFrequency (%)
) 174
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23395
91.9%
Common 1329
 
5.2%
Latin 719
 
2.8%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 4310
 
18.4%
원 2145
 
9.2%
간 1206
 
5.2%
호 1065
 
4.6%
직 637
 
2.7%
리 608
 
2.6%
임 574
 
2.5%
장 546
 
2.3%
대 435
 
1.9%
무 407
 
1.7%
Other values (308) 11462
49.0%
Latin
ValueCountFrequency (%)
e 65
 
9.0%
t 61
 
8.5%
a 59
 
8.2%
r 56
 
7.8%
s 55
 
7.6%
n 52
 
7.2%
i 42
 
5.8%
A 31
 
4.3%
c 28
 
3.9%
o 20
 
2.8%
Other values (37) 250
34.8%
Common
ValueCountFrequency (%)
712
53.6%
( 174
 
13.1%
) 174
 
13.1%
5 39
 
2.9%
, 35
 
2.6%
/ 26
 
2.0%
6 25
 
1.9%
7 25
 
1.9%
- 22
 
1.7%
8 19
 
1.4%
Other values (10) 78
 
5.9%
Han
ValueCountFrequency (%)
長 2
50.0%
課 1
25.0%
次 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23350
91.8%
ASCII 2047
 
8.0%
Compat Jamo 45
 
0.2%
CJK 4
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 4310
 
18.5%
원 2145
 
9.2%
간 1206
 
5.2%
호 1065
 
4.6%
직 637
 
2.7%
리 608
 
2.6%
임 574
 
2.5%
장 546
 
2.3%
대 435
 
1.9%
무 407
 
1.7%
Other values (300) 11417
48.9%
ASCII
ValueCountFrequency (%)
712
34.8%
( 174
 
8.5%
) 174
 
8.5%
e 65
 
3.2%
t 61
 
3.0%
a 59
 
2.9%
r 56
 
2.7%
s 55
 
2.7%
n 52
 
2.5%
i 42
 
2.1%
Other values (56) 597
29.2%
Compat Jamo
ValueCountFrequency (%)
ㅇ 16
35.6%
ㄴ 12
26.7%
ㄻ 11
24.4%
ㅁ 2
 
4.4%
ㅍ 1
 
2.2%
ㅊ 1
 
2.2%
ㅋ 1
 
2.2%
ㄹ 1
 
2.2%
CJK
ValueCountFrequency (%)
長 2
50.0%
課 1
25.0%
次 1
25.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

EXP_WORK_SDATE
Real number (ℝ)

Distinct2329
Distinct (%)23.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13584673
Minimum197712
Maximum20100412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:46:39.251085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum197712
5-th percentile200701
Q1200801
median20020902
Q320060101
95-th percentile20070801
Maximum20100412
Range19902700
Interquartile range (IQR)19859300

Descriptive statistics

Standard deviation9293096.3
Coefficient of variation (CV)0.68408684
Kurtosis-1.4436455
Mean13584673
Median Absolute Deviation (MAD)40500.5
Skewness-0.74604982
Sum1.3581956 × 1011
Variance8.6361639 × 1013
MonotonicityNot monotonic
2023-12-12T17:46:39.670192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200701 1419
 
14.2%
200801 880
 
8.8%
200901 618
 
6.2%
20070101 106
 
1.1%
201001 86
 
0.9%
20060301 74
 
0.7%
20050301 54
 
0.5%
20050701 50
 
0.5%
20060201 50
 
0.5%
20060302 49
 
0.5%
Other values (2319) 6612
66.1%
ValueCountFrequency (%)
197712 1
< 0.1%
197802 1
< 0.1%
197911 1
< 0.1%
198205 1
< 0.1%
198212 1
< 0.1%
198407 1
< 0.1%
198501 1
< 0.1%
198504 1
< 0.1%
198703 1
< 0.1%
198807 1
< 0.1%
ValueCountFrequency (%)
20100412 1
< 0.1%
20100406 1
< 0.1%
20100222 1
< 0.1%
20100216 1
< 0.1%
20100104 1
< 0.1%
20091221 1
< 0.1%
20091220 1
< 0.1%
20091207 1
< 0.1%
20091130 1
< 0.1%
20091102 1
< 0.1%

EXP_WORK_EDATE
Real number (ℝ)

Distinct1984
Distinct (%)19.8%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean17185755
Minimum197904
Maximum20100822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:46:39.868053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum197904
5-th percentile200701
Q120030806
median20070101
Q320070920
95-th percentile20090101
Maximum20100822
Range19902918
Interquartile range (IQR)40114

Descriptive statistics

Standard deviation6987408.1
Coefficient of variation (CV)0.40658139
Kurtosis2.0802
Mean17185755
Median Absolute Deviation (MAD)10121.5
Skewness-2.0198197
Sum1.7182318 × 1011
Variance4.8823873 × 1013
MonotonicityNot monotonic
2023-12-12T17:46:40.029755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20070101 917
 
9.2%
20080101 591
 
5.9%
200701 503
 
5.0%
20090101 339
 
3.4%
200801 331
 
3.3%
200901 290
 
2.9%
20070511 205
 
2.1%
20071201 125
 
1.2%
20070228 109
 
1.1%
20070510 96
 
1.0%
Other values (1974) 6492
64.9%
ValueCountFrequency (%)
197904 1
< 0.1%
198512 1
< 0.1%
198801 1
< 0.1%
199006 1
< 0.1%
199301 1
< 0.1%
199402 1
< 0.1%
199703 1
< 0.1%
199709 1
< 0.1%
199902 1
< 0.1%
199905 1
< 0.1%
ValueCountFrequency (%)
20100822 1
< 0.1%
20100731 1
< 0.1%
20100730 1
< 0.1%
20100721 1
< 0.1%
20100712 1
< 0.1%
20100709 1
< 0.1%
20100701 1
< 0.1%
20100631 1
< 0.1%
20100630 2
< 0.1%
20100623 1
< 0.1%

EXP_BRANCH
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Interactions

2023-12-12T17:46:34.495118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:33.007404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:33.453410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:33.911170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:34.606373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:33.110255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:33.543656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:34.043395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:34.748659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:33.208661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:33.664967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:34.206114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:34.887015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:33.340923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:33.795125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:46:34.352313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:46:40.140012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
EXP_NOEXP_TYPE1EXP_TYPE2EXP_WORK_SDATEEXP_WORK_EDATE
EXP_NO1.0000.1850.0380.5770.328
EXP_TYPE10.1851.0000.5090.0220.041
EXP_TYPE20.0380.5091.0000.0000.000
EXP_WORK_SDATE0.5770.0220.0001.0000.696
EXP_WORK_EDATE0.3280.0410.0000.6961.000
2023-12-12T17:46:40.273680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
EXP_NOEXP_TYPE1EXP_WORK_SDATEEXP_WORK_EDATEEXP_TYPE2
EXP_NO1.0000.0690.3950.0580.029
EXP_TYPE10.0691.000-0.173-0.1550.397
EXP_WORK_SDATE0.395-0.1731.0000.4080.000
EXP_WORK_EDATE0.058-0.1550.4081.0000.000
EXP_TYPE20.0290.3970.0000.0001.000

Missing values

2023-12-12T17:46:35.048901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:46:35.214598image/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.
2023-12-12T17:46:35.395417image/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

EXP_RSM_IDEXP_NOEXP_COMPANYEXP_TYPE1EXP_TYPE2EXP_DUTYEXP_WORK_SDATEEXP_WORK_EDATEEXP_BRANCH
55065,3041Access college of Adelaide12정규직사원2006051520070102<NA>
96203,2501강북삼성병원1임시직계약직2006030120070317<NA>
1420912,1051<NA><NA><NA><NA>20080120080101<NA>
3356,1022한국전산개발13임시직사원1999100520000331<NA>
1103011,6801충청북도장애인종합복지관11정규직사원2002080120050115<NA>
95686,3261순천향대학 천안병원1정규직간호사1991081519940731<NA>
26297,2125도봉 종로엠학원13정규직영어강사/중.고등부2007011120070504<NA>
1311611,5711고대구로병원1임시직계약직직원2006022720080226<NA>
21388,6101<NA><NA><NA><NA>200701200701<NA>
145463,5811<NA><NA><NA><NA>200701200701<NA>
EXP_RSM_IDEXP_NOEXP_COMPANYEXP_TYPE1EXP_TYPE2EXP_DUTYEXP_WORK_SDATEEXP_WORK_EDATEEXP_BRANCH
42106,1854서울대학교병원1임시직간호사2006041020070411<NA>
765512,6571삼보컴퓨터지산대리점13정규직사원2003070120040630<NA>
39274,5641대구현대병원3정규직간호사20021120041001<NA>
99603,9112구로구보건소5임시직<NA>2006070820060303<NA>
662910,7236한화(주)드림파마13정규직사원2007070420080114<NA>
3971,8352서울대학교 병원 강남센터1임시직촉탁보건직2003092320070504<NA>
1578917,2931<NA><NA><NA><NA>200901200901<NA>
6485,9601현대증권주식회사10정규직사원2005010320070504<NA>
86357,9281<NA><NA><NA><NA>20070120070101<NA>
1789816,9941<NA><NA><NA><NA>20090120090101<NA>