Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells61
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.6 KiB
Average record size in memory91.3 B

Variable types

Text6
Categorical1
Numeric2
DateTime2

Dataset

Description해당 파일 데이터는 신용보증기금의 공통일반부점연락처정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093126/fileData.do

Alerts

유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
전화번호 has 61 (12.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:33:20.458025
Analysis finished2023-12-12 19:33:21.327637
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부점코드
Text

Distinct141
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T04:33:21.560620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1500
Distinct characters25
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

Unique19 ?
Unique (%)3.8%

Sample

1st rowABE
2nd rowABE
3rd rowABE
4th rowABE
5th rowTAD
ValueCountFrequency (%)
tbr 19
 
3.8%
vao 16
 
3.2%
jac 15
 
3.0%
tle 13
 
2.6%
jhy 11
 
2.2%
jah 9
 
1.8%
qar 9
 
1.8%
tje 9
 
1.8%
qag 8
 
1.6%
abe 8
 
1.6%
Other values (131) 383
76.6%
2023-12-13T04:33:21.969041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 264
17.6%
T 158
 
10.5%
J 131
 
8.7%
V 85
 
5.7%
B 84
 
5.6%
N 83
 
5.5%
H 71
 
4.7%
Q 65
 
4.3%
E 64
 
4.3%
C 54
 
3.6%
Other values (15) 441
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 264
17.6%
T 158
 
10.5%
J 131
 
8.7%
V 85
 
5.7%
B 84
 
5.6%
N 83
 
5.5%
H 71
 
4.7%
Q 65
 
4.3%
E 64
 
4.3%
C 54
 
3.6%
Other values (15) 441
29.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 264
17.6%
T 158
 
10.5%
J 131
 
8.7%
V 85
 
5.7%
B 84
 
5.6%
N 83
 
5.5%
H 71
 
4.7%
Q 65
 
4.3%
E 64
 
4.3%
C 54
 
3.6%
Other values (15) 441
29.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 264
17.6%
T 158
 
10.5%
J 131
 
8.7%
V 85
 
5.7%
B 84
 
5.6%
N 83
 
5.5%
H 71
 
4.7%
Q 65
 
4.3%
E 64
 
4.3%
C 54
 
3.6%
Other values (15) 441
29.4%

전화구분코드
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
240 
3
209 
2
29 
4
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 240
48.0%
3 209
41.8%
2 29
 
5.8%
4 22
 
4.4%

Length

2023-12-13T04:33:22.097350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:33:22.189726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 240
48.0%
3 209
41.8%
2 29
 
5.8%
4 22
 
4.4%

이력일련번호
Real number (ℝ)

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.304
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T04:33:22.292656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0464506
Coefficient of variation (CV)0.88821639
Kurtosis3.7151068
Mean2.304
Median Absolute Deviation (MAD)0
Skewness1.869696
Sum1152
Variance4.1879599
MonotonicityNot monotonic
2023-12-13T04:33:22.450942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 291
58.2%
3 52
 
10.4%
2 51
 
10.2%
4 35
 
7.0%
5 25
 
5.0%
6 17
 
3.4%
7 12
 
2.4%
8 9
 
1.8%
9 4
 
0.8%
10 3
 
0.6%
ValueCountFrequency (%)
1 291
58.2%
2 51
 
10.2%
3 52
 
10.4%
4 35
 
7.0%
5 25
 
5.0%
6 17
 
3.4%
7 12
 
2.4%
8 9
 
1.8%
9 4
 
0.8%
10 3
 
0.6%
ValueCountFrequency (%)
14 1
 
0.2%
10 3
 
0.6%
9 4
 
0.8%
8 9
 
1.8%
7 12
 
2.4%
6 17
 
3.4%
5 25
5.0%
4 35
7.0%
3 52
10.4%
2 51
10.2%

전화번호
Text

MISSING 

Distinct325
Distinct (%)74.0%
Missing61
Missing (%)12.2%
Memory size4.0 KiB
2023-12-13T04:33:23.017035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length10.3918
Min length1

Characters and Unicode

Total characters4562
Distinct characters14
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

Unique259 ?
Unique (%)59.0%

Sample

1st row053 430 4491
2nd row053 430 4771
3rd row534304502
4th row534304471
5th row02 3114200
ValueCountFrequency (%)
02 84
 
11.0%
053 31
 
4.1%
1 24
 
3.2%
430 21
 
2.8%
710 19
 
2.5%
031 18
 
2.4%
051 12
 
1.6%
032 11
 
1.4%
0505071 11
 
1.4%
062 7
 
0.9%
Other values (357) 523
68.7%
2023-12-13T04:33:23.614920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 909
19.9%
5 478
10.5%
2 441
9.7%
441
9.7%
1 402
8.8%
3 380
8.3%
4 362
 
7.9%
6 289
 
6.3%
7 265
 
5.8%
9 246
 
5.4%
Other values (4) 349
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3975
87.1%
Space Separator 441
 
9.7%
Dash Punctuation 134
 
2.9%
Close Punctuation 9
 
0.2%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 909
22.9%
5 478
12.0%
2 441
11.1%
1 402
10.1%
3 380
9.6%
4 362
 
9.1%
6 289
 
7.3%
7 265
 
6.7%
9 246
 
6.2%
8 203
 
5.1%
Space Separator
ValueCountFrequency (%)
441
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4559
99.9%
Latin 3
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 909
19.9%
5 478
10.5%
2 441
9.7%
441
9.7%
1 402
8.8%
3 380
8.3%
4 362
 
7.9%
6 289
 
6.3%
7 265
 
5.8%
9 246
 
5.4%
Other values (3) 346
 
7.6%
Latin
ValueCountFrequency (%)
X 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 909
19.9%
5 478
10.5%
2 441
9.7%
441
9.7%
1 402
8.8%
3 380
8.3%
4 362
 
7.9%
6 289
 
6.3%
7 265
 
5.8%
9 246
 
5.4%
Other values (4) 349
 
7.7%

유효개시일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T04:33:23.809240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:23.930916image/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
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T04:33:24.071434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:24.205188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Real number (ℝ)

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.972
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T04:33:24.334074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0117929
Coefficient of variation (CV)0.67691551
Kurtosis3.5360886
Mean2.972
Median Absolute Deviation (MAD)1
Skewness1.6308415
Sum1486
Variance4.0473106
MonotonicityNot monotonic
2023-12-13T04:33:24.507613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 158
31.6%
1 112
22.4%
3 86
17.2%
4 54
 
10.8%
5 30
 
6.0%
6 27
 
5.4%
7 14
 
2.8%
8 9
 
1.8%
9 5
 
1.0%
10 3
 
0.6%
Other values (2) 2
 
0.4%
ValueCountFrequency (%)
1 112
22.4%
2 158
31.6%
3 86
17.2%
4 54
 
10.8%
5 30
 
6.0%
6 27
 
5.4%
7 14
 
2.8%
8 9
 
1.8%
9 5
 
1.0%
10 3
 
0.6%
ValueCountFrequency (%)
14 1
 
0.2%
13 1
 
0.2%
10 3
 
0.6%
9 5
 
1.0%
8 9
 
1.8%
7 14
 
2.8%
6 27
 
5.4%
5 30
 
6.0%
4 54
10.8%
3 86
17.2%

처리시각
Text

Distinct282
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T04:33:24.988931image/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

Unique105 ?
Unique (%)21.0%

Sample

1st row40:37.3
2nd row40:37.3
3rd row39:34.5
4th row39:34.5
5th row35:02.7
ValueCountFrequency (%)
20:39.0 4
 
0.8%
31:47.0 4
 
0.8%
11:11.9 4
 
0.8%
49:21.0 4
 
0.8%
27:05.0 4
 
0.8%
22:45.8 4
 
0.8%
35:32.0 4
 
0.8%
39:26.0 4
 
0.8%
36:54.0 4
 
0.8%
35:57.0 4
 
0.8%
Other values (272) 460
92.0%
2023-12-13T04:33:25.653650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 338
9.7%
3 326
9.3%
4 324
9.3%
1 323
9.2%
0 313
8.9%
5 311
8.9%
8 174
 
5.0%
9 163
 
4.7%
Other values (2) 228
6.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 338
13.5%
3 326
13.0%
4 324
13.0%
1 323
12.9%
0 313
12.5%
5 311
12.4%
8 174
7.0%
9 163
6.5%
7 123
 
4.9%
6 105
 
4.2%
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%
2 338
9.7%
3 326
9.3%
4 324
9.3%
1 323
9.2%
0 313
8.9%
5 311
8.9%
8 174
 
5.0%
9 163
 
4.7%
Other values (2) 228
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 338
9.7%
3 326
9.3%
4 324
9.3%
1 323
9.2%
0 313
8.9%
5 311
8.9%
8 174
 
5.0%
9 163
 
4.7%
Other values (2) 228
6.5%

처리직원번호
Text

Distinct114
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T04:33:26.093397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.032
Min length4

Characters and Unicode

Total characters2016
Distinct characters13
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

Unique37 ?
Unique (%)7.4%

Sample

1st row4454
2nd row4454
3rd row5228
4th row5228
5th row5391
ValueCountFrequency (%)
5314 128
25.6%
5099 53
 
10.6%
5803 40
 
8.0%
5434 14
 
2.8%
5372 11
 
2.2%
4308 8
 
1.6%
4679 7
 
1.4%
4853 7
 
1.4%
5364 6
 
1.2%
5424 6
 
1.2%
Other values (104) 220
44.0%
2023-12-13T04:33:26.679037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 430
21.3%
3 314
15.6%
4 310
15.4%
1 208
10.3%
9 191
9.5%
0 187
9.3%
8 104
 
5.2%
2 90
 
4.5%
6 84
 
4.2%
7 76
 
3.8%
Other values (3) 22
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1994
98.9%
Uppercase Letter 22
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 430
21.6%
3 314
15.7%
4 310
15.5%
1 208
10.4%
9 191
9.6%
0 187
9.4%
8 104
 
5.2%
2 90
 
4.5%
6 84
 
4.2%
7 76
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
E 8
36.4%
X 8
36.4%
A 6
27.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1994
98.9%
Latin 22
 
1.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 430
21.6%
3 314
15.7%
4 310
15.5%
1 208
10.4%
9 191
9.6%
0 187
9.4%
8 104
 
5.2%
2 90
 
4.5%
6 84
 
4.2%
7 76
 
3.8%
Latin
ValueCountFrequency (%)
E 8
36.4%
X 8
36.4%
A 6
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 430
21.3%
3 314
15.6%
4 310
15.4%
1 208
10.3%
9 191
9.5%
0 187
9.3%
8 104
 
5.2%
2 90
 
4.5%
6 84
 
4.2%
7 76
 
3.8%
Other values (3) 22
 
1.1%

최초처리시각
Text

Distinct105
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T04:33:27.104865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length7
Mean length11.408
Min length7

Characters and Unicode

Total characters5704
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

Unique15 ?
Unique (%)3.0%

Sample

1st row19:23.5
2nd row19:23.5
3rd row19:23.5
4th row19:23.5
5th row0001-01-01 00:00:00.000000
ValueCountFrequency (%)
0001-01-01 116
18.8%
00:00:00.000000 116
18.8%
36:54.0 19
 
3.1%
48:22.8 16
 
2.6%
24:58.3 15
 
2.4%
43:16.8 12
 
1.9%
05:06.6 11
 
1.8%
17:56.3 9
 
1.5%
35:32.0 9
 
1.5%
33:23.1 8
 
1.3%
Other values (96) 285
46.3%
2023-12-13T04:33:27.555687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2260
39.6%
: 616
 
10.8%
1 519
 
9.1%
. 500
 
8.8%
3 266
 
4.7%
4 248
 
4.3%
5 241
 
4.2%
- 232
 
4.1%
2 232
 
4.1%
6 164
 
2.9%
Other values (4) 426
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4240
74.3%
Other Punctuation 1116
 
19.6%
Dash Punctuation 232
 
4.1%
Space Separator 116
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2260
53.3%
1 519
 
12.2%
3 266
 
6.3%
4 248
 
5.8%
5 241
 
5.7%
2 232
 
5.5%
6 164
 
3.9%
8 140
 
3.3%
7 93
 
2.2%
9 77
 
1.8%
Other Punctuation
ValueCountFrequency (%)
: 616
55.2%
. 500
44.8%
Dash Punctuation
ValueCountFrequency (%)
- 232
100.0%
Space Separator
ValueCountFrequency (%)
116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5704
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2260
39.6%
: 616
 
10.8%
1 519
 
9.1%
. 500
 
8.8%
3 266
 
4.7%
4 248
 
4.3%
5 241
 
4.2%
- 232
 
4.1%
2 232
 
4.1%
6 164
 
2.9%
Other values (4) 426
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2260
39.6%
: 616
 
10.8%
1 519
 
9.1%
. 500
 
8.8%
3 266
 
4.7%
4 248
 
4.3%
5 241
 
4.2%
- 232
 
4.1%
2 232
 
4.1%
6 164
 
2.9%
Other values (4) 426
 
7.5%

최초처리직원번호
Text

Distinct58
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T04:33:27.767359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.066
Min length4

Characters and Unicode

Total characters2033
Distinct characters15
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

Unique19 ?
Unique (%)3.8%

Sample

1st row3513
2nd row3513
3rd row3513
4th row3513
5th row3682
ValueCountFrequency (%)
5099 69
13.8%
5314 58
11.6%
3682 51
 
10.2%
3513 39
 
7.8%
4169 36
 
7.2%
batch 27
 
5.4%
4800 26
 
5.2%
5803 24
 
4.8%
4837 15
 
3.0%
4948 12
 
2.4%
Other values (48) 143
28.6%
2023-12-13T04:33:28.060069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 293
14.4%
5 278
13.7%
4 255
12.5%
9 246
12.1%
0 214
10.5%
1 173
8.5%
8 170
8.4%
6 108
 
5.3%
2 100
 
4.9%
7 57
 
2.8%
Other values (5) 139
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1894
93.2%
Uppercase Letter 139
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 293
15.5%
5 278
14.7%
4 255
13.5%
9 246
13.0%
0 214
11.3%
1 173
9.1%
8 170
9.0%
6 108
 
5.7%
2 100
 
5.3%
7 57
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
A 31
22.3%
B 27
19.4%
T 27
19.4%
C 27
19.4%
H 27
19.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1894
93.2%
Latin 139
 
6.8%

Most frequent character per script

Common
ValueCountFrequency (%)
3 293
15.5%
5 278
14.7%
4 255
13.5%
9 246
13.0%
0 214
11.3%
1 173
9.1%
8 170
9.0%
6 108
 
5.7%
2 100
 
5.3%
7 57
 
3.0%
Latin
ValueCountFrequency (%)
A 31
22.3%
B 27
19.4%
T 27
19.4%
C 27
19.4%
H 27
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 293
14.4%
5 278
13.7%
4 255
12.5%
9 246
12.1%
0 214
10.5%
1 173
8.5%
8 170
8.4%
6 108
 
5.3%
2 100
 
4.9%
7 57
 
2.8%
Other values (5) 139
6.8%

Interactions

2023-12-13T04:33:20.903958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:20.717016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:20.995178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:20.819327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:33:28.147035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전화구분코드이력일련번호최종수정수최초처리직원번호
전화구분코드1.0000.0000.0000.428
이력일련번호0.0001.0000.9290.000
최종수정수0.0000.9291.0000.296
최초처리직원번호0.4280.0000.2961.000
2023-12-13T04:33:28.229941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호최종수정수전화구분코드
이력일련번호1.0000.2540.000
최종수정수0.2541.0000.000
전화구분코드0.0000.0001.000

Missing values

2023-12-13T04:33:21.118247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:33:21.264130image/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

부점코드전화구분코드이력일련번호전화번호유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0ABE11053 430 449100:00.000:00.0640:37.3445419:23.53513
1ABE31053 430 477100:00.000:00.0640:37.3445419:23.53513
2ABE1653430450200:00.000:00.0539:34.5522819:23.53513
3ABE3653430447100:00.000:00.0539:34.5522819:23.53513
4TAD1102 311420000:00.000:00.0335:02.753910001-01-01 00:00:00.0000003682
5TAD3102 311429800:00.000:00.0335:02.753910001-01-01 00:00:00.0000003682
6QAE11100:00.000:00.0252:33.6431129:57.85099
7QAE31100:00.000:00.0252:33.6431129:57.85099
8JOA11062 600 157500:00.000:00.0306:21.9416507:42.84308
9JOA13062-600-157500:00.000:00.0205:17.5416507:42.84308
부점코드전화구분코드이력일련번호전화번호유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490JAN3302--2182-01900:00.000:00.0213:16.1430155:45.14948
491JAN1202-2182-011400:00.000:00.0155:45.1494855:45.14948
492JAN3202-2182-019900:00.000:00.0155:45.1494855:45.14948
493VAA3102-2204-679100:00.000:00.0226:48.2500636:42.73513
494VAA1102-2204-670000:00.000:00.0226:48.2500636:42.73513
495JIE1231695271500:00.000:00.0115:57.5424515:57.54245
496JHA11031-230-155900:00.000:00.0124:38.4512424:38.45124
497VAM37505071821000:00.000:00.0617:47.9352257:46.33682
498VAM171588656500:00.000:00.0617:47.9352257:46.33682
499NCN1202-710-455400:00.000:00.0140:40.2301540:40.23015