Overview

Dataset statistics

Number of variables14
Number of observations500
Missing cells500
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.2 KiB
Average record size in memory119.3 B

Variable types

Text5
Categorical6
Unsupported1
Boolean1
Numeric1

Dataset

Description해당 데이터는 신용보증기금의 세무관리 관련된 내용을 확인하실 수 있는 파일데이터로 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092663/fileData.do

Alerts

거래년도 has constant value ""Constant
매출대상구분코드 has constant value ""Constant
삭제여부 has constant value ""Constant
회계구분코드 is highly imbalanced (85.0%)Imbalance
회계매입매출구분코드 is highly imbalanced (84.7%)Imbalance
계산서구분코드 is highly imbalanced (52.5%)Imbalance
주민등록번호통합ID has 500 (100.0%) missing valuesMissing
세금계산서집계ID has unique valuesUnique
주민등록번호통합ID is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 22:57:46.692382
Analysis finished2023-12-11 22:57:47.334760
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

세금계산서집계ID
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:57:47.513624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st row9dnSY0xQVy
2nd row9dnSYOWtqV
3rd row9dnB0N6UZy
4th row9dnSYzZmti
5th row9dnLg9LICI
ValueCountFrequency (%)
9dnsy0xqvy 1
 
0.2%
9dlmpgjzvj 1
 
0.2%
9dnjac036o 1
 
0.2%
9dnk8wrfzl 1
 
0.2%
9dnk8adekh 1
 
0.2%
9dm3lv7gg4 1
 
0.2%
9dnk8y34ft 1
 
0.2%
9dnk9cyb9v 1
 
0.2%
9dnk9h7tds 1
 
0.2%
9dnk9kokkx 1
 
0.2%
Other values (490) 490
98.0%
2023-12-12T07:57:47.827357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 561
 
11.2%
9 559
 
11.2%
n 439
 
8.8%
M 128
 
2.6%
J 125
 
2.5%
l 106
 
2.1%
L 100
 
2.0%
m 97
 
1.9%
O 91
 
1.8%
K 83
 
1.7%
Other values (52) 2711
54.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2305
46.1%
Uppercase Letter 1686
33.7%
Decimal Number 1009
20.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 561
24.3%
n 439
19.0%
l 106
 
4.6%
m 97
 
4.2%
z 65
 
2.8%
k 60
 
2.6%
y 58
 
2.5%
w 58
 
2.5%
r 57
 
2.5%
h 57
 
2.5%
Other values (16) 747
32.4%
Uppercase Letter
ValueCountFrequency (%)
M 128
 
7.6%
J 125
 
7.4%
L 100
 
5.9%
O 91
 
5.4%
K 83
 
4.9%
N 79
 
4.7%
S 76
 
4.5%
E 63
 
3.7%
A 63
 
3.7%
Y 61
 
3.6%
Other values (16) 817
48.5%
Decimal Number
ValueCountFrequency (%)
9 559
55.4%
5 59
 
5.8%
8 56
 
5.6%
3 55
 
5.5%
2 54
 
5.4%
6 51
 
5.1%
7 49
 
4.9%
0 45
 
4.5%
1 42
 
4.2%
4 39
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3991
79.8%
Common 1009
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 561
 
14.1%
n 439
 
11.0%
M 128
 
3.2%
J 125
 
3.1%
l 106
 
2.7%
L 100
 
2.5%
m 97
 
2.4%
O 91
 
2.3%
K 83
 
2.1%
N 79
 
2.0%
Other values (42) 2182
54.7%
Common
ValueCountFrequency (%)
9 559
55.4%
5 59
 
5.8%
8 56
 
5.6%
3 55
 
5.5%
2 54
 
5.4%
6 51
 
5.1%
7 49
 
4.9%
0 45
 
4.5%
1 42
 
4.2%
4 39
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 561
 
11.2%
9 559
 
11.2%
n 439
 
8.8%
M 128
 
2.6%
J 125
 
2.5%
l 106
 
2.1%
L 100
 
2.0%
m 97
 
1.9%
O 91
 
1.8%
K 83
 
1.7%
Other values (52) 2711
54.2%

회계구분코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
G
477 
I
 
20
S
 
2
R
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
G 477
95.4%
I 20
 
4.0%
S 2
 
0.4%
R 1
 
0.2%

Length

2023-12-12T07:57:47.948066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:48.040542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 477
95.4%
i 20
 
4.0%
s 2
 
0.4%
r 1
 
0.2%

거래년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2021
500 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 500
100.0%

Length

2023-12-12T07:57:48.118831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:48.183820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 500
100.0%

분기구분코드
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4
357 
3
143 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 357
71.4%
3 143
28.6%

Length

2023-12-12T07:57:48.251040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:48.320116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 357
71.4%
3 143
28.6%

회계매입매출구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
489 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 489
97.8%
1 11
 
2.2%

Length

2023-12-12T07:57:48.391527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:48.461570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 489
97.8%
1 11
 
2.2%

계산서구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
449 
2
51 

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 (%)
1 449
89.8%
2 51
 
10.2%

Length

2023-12-12T07:57:48.538988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:48.610413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 449
89.8%
2 51
 
10.2%

매출대상구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-12T07:57:48.686053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:48.754899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

주민등록번호통합ID
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T07:57:48.808133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.488
Minimum1
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T07:57:48.879212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile14.05
Maximum152
Range151
Interquartile range (IQR)2

Descriptive statistics

Standard deviation11.95859
Coefficient of variation (CV)2.6645699
Kurtosis75.235206
Mean4.488
Median Absolute Deviation (MAD)0
Skewness7.792112
Sum2244
Variance143.00787
MonotonicityNot monotonic
2023-12-12T07:57:48.973348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 269
53.8%
2 64
 
12.8%
3 63
 
12.6%
4 18
 
3.6%
5 15
 
3.0%
11 7
 
1.4%
7 7
 
1.4%
14 6
 
1.2%
9 6
 
1.2%
8 6
 
1.2%
Other values (22) 39
 
7.8%
ValueCountFrequency (%)
1 269
53.8%
2 64
 
12.8%
3 63
 
12.6%
4 18
 
3.6%
5 15
 
3.0%
6 5
 
1.0%
7 7
 
1.4%
8 6
 
1.2%
9 6
 
1.2%
10 4
 
0.8%
ValueCountFrequency (%)
152 1
0.2%
120 1
0.2%
102 1
0.2%
65 1
0.2%
64 1
0.2%
56 1
0.2%
51 1
0.2%
48 1
0.2%
46 1
0.2%
32 2
0.4%

처리시각
Text

Distinct494
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:57:49.244683image/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

Unique489 ?
Unique (%)97.8%

Sample

1st row11:37.6
2nd row08:45.6
3rd row08:44.8
4th row05:04.4
5th row03:22.1
ValueCountFrequency (%)
33:35.1 3
 
0.6%
54:19.2 2
 
0.4%
48:49.5 2
 
0.4%
33:07.9 2
 
0.4%
44:49.5 2
 
0.4%
24:28.1 1
 
0.2%
13:44.4 1
 
0.2%
41:16.6 1
 
0.2%
37:15.2 1
 
0.2%
19:45.3 1
 
0.2%
Other values (484) 484
96.8%
2023-12-12T07:57:49.657161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 341
9.7%
1 329
9.4%
4 327
9.3%
3 326
9.3%
2 302
8.6%
0 296
8.5%
8 155
 
4.4%
6 149
 
4.3%
Other values (2) 275
7.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 341
13.6%
1 329
13.2%
4 327
13.1%
3 326
13.0%
2 302
12.1%
0 296
11.8%
8 155
6.2%
6 149
6.0%
9 148
5.9%
7 127
 
5.1%
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%
5 341
9.7%
1 329
9.4%
4 327
9.3%
3 326
9.3%
2 302
8.6%
0 296
8.5%
8 155
 
4.4%
6 149
 
4.3%
Other values (2) 275
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 341
9.7%
1 329
9.4%
4 327
9.3%
3 326
9.3%
2 302
8.6%
0 296
8.5%
8 155
 
4.4%
6 149
 
4.3%
Other values (2) 275
7.9%

처리직원번호
Text

Distinct273
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:57:49.941507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.348
Min length4

Characters and Unicode

Total characters2174
Distinct characters11
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

Unique157 ?
Unique (%)31.4%

Sample

1st row9A103
2nd row4594
3rd row4594
4th row5353
5th row92980
ValueCountFrequency (%)
4818 8
 
1.6%
6010 7
 
1.4%
9a050 7
 
1.4%
9a057 6
 
1.2%
9a013 6
 
1.2%
9a111 6
 
1.2%
5576 6
 
1.2%
5761 5
 
1.0%
6022 5
 
1.0%
5456 5
 
1.0%
Other values (263) 439
87.8%
2023-12-12T07:57:50.330569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 360
16.6%
9 278
12.8%
0 274
12.6%
4 182
8.4%
1 177
8.1%
6 159
7.3%
A 156
7.2%
2 153
7.0%
3 149
6.9%
7 145
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2018
92.8%
Uppercase Letter 156
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 360
17.8%
9 278
13.8%
0 274
13.6%
4 182
9.0%
1 177
8.8%
6 159
7.9%
2 153
7.6%
3 149
7.4%
7 145
7.2%
8 141
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
A 156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2018
92.8%
Latin 156
 
7.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 360
17.8%
9 278
13.8%
0 274
13.6%
4 182
9.0%
1 177
8.8%
6 159
7.9%
2 153
7.6%
3 149
7.4%
7 145
7.2%
8 141
 
7.0%
Latin
ValueCountFrequency (%)
A 156
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 360
16.6%
9 278
12.8%
0 274
12.6%
4 182
8.4%
1 177
8.1%
6 159
7.3%
A 156
7.2%
2 153
7.0%
3 149
6.9%
7 145
6.7%

최초처리시각
Text

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:57:50.731953image/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

Unique491 ?
Unique (%)98.2%

Sample

1st row11:37.6
2nd row08:45.6
3rd row25:41.3
4th row05:04.4
5th row35:21.3
ValueCountFrequency (%)
33:35.1 3
 
0.6%
43:06.2 2
 
0.4%
03:35.3 2
 
0.4%
54:19.2 2
 
0.4%
30:35.2 1
 
0.2%
09:08.4 1
 
0.2%
53:58.7 1
 
0.2%
35:50.5 1
 
0.2%
35:16.9 1
 
0.2%
33:54.5 1
 
0.2%
Other values (485) 485
97.0%
2023-12-12T07:57:51.169891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 341
9.7%
2 321
9.2%
3 317
9.1%
1 317
9.1%
4 317
9.1%
0 291
8.3%
8 164
 
4.7%
7 152
 
4.3%
Other values (2) 280
8.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 341
13.6%
2 321
12.8%
3 317
12.7%
1 317
12.7%
4 317
12.7%
0 291
11.6%
8 164
6.6%
7 152
6.1%
6 151
6.0%
9 129
 
5.2%
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%
5 341
9.7%
2 321
9.2%
3 317
9.1%
1 317
9.1%
4 317
9.1%
0 291
8.3%
8 164
 
4.7%
7 152
 
4.3%
Other values (2) 280
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 341
9.7%
2 321
9.2%
3 317
9.1%
1 317
9.1%
4 317
9.1%
0 291
8.3%
8 164
 
4.7%
7 152
 
4.3%
Other values (2) 280
8.0%

최초처리직원번호
Text

Distinct286
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:57:51.556314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.352
Min length4

Characters and Unicode

Total characters2176
Distinct characters11
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

Unique174 ?
Unique (%)34.8%

Sample

1st row9A103
2nd row4594
3rd row5890
4th row5353
5th row92980
ValueCountFrequency (%)
4818 8
 
1.6%
6010 7
 
1.4%
5576 7
 
1.4%
9a050 7
 
1.4%
9a111 6
 
1.2%
9a057 6
 
1.2%
9a013 6
 
1.2%
5681 5
 
1.0%
5456 5
 
1.0%
5203 5
 
1.0%
Other values (276) 438
87.6%
2023-12-12T07:57:52.244270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 351
16.1%
9 283
13.0%
0 269
12.4%
4 191
8.8%
1 187
8.6%
6 157
7.2%
8 155
7.1%
A 154
7.1%
2 153
7.0%
7 138
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2022
92.9%
Uppercase Letter 154
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 351
17.4%
9 283
14.0%
0 269
13.3%
4 191
9.4%
1 187
9.2%
6 157
7.8%
8 155
7.7%
2 153
7.6%
7 138
 
6.8%
3 138
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
A 154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2022
92.9%
Latin 154
 
7.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 351
17.4%
9 283
14.0%
0 269
13.3%
4 191
9.4%
1 187
9.2%
6 157
7.8%
8 155
7.7%
2 153
7.6%
7 138
 
6.8%
3 138
 
6.8%
Latin
ValueCountFrequency (%)
A 154
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 351
16.1%
9 283
13.0%
0 269
12.4%
4 191
8.8%
1 187
8.6%
6 157
7.2%
8 155
7.1%
A 154
7.1%
2 153
7.0%
7 138
 
6.3%

Interactions

2023-12-12T07:57:47.014951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:57:52.329711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계구분코드분기구분코드회계매입매출구분코드계산서구분코드최종수정수
회계구분코드1.0000.0180.0000.0730.000
분기구분코드0.0181.0000.0340.0560.320
회계매입매출구분코드0.0000.0341.0000.3660.000
계산서구분코드0.0730.0560.3661.0000.000
최종수정수0.0000.3200.0000.0001.000
2023-12-12T07:57:52.441959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계구분코드계산서구분코드분기구분코드회계매입매출구분코드
회계구분코드1.0000.0480.0120.000
계산서구분코드0.0481.0000.0360.238
분기구분코드0.0120.0361.0000.022
회계매입매출구분코드0.0000.2380.0221.000
2023-12-12T07:57:52.539809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수회계구분코드분기구분코드회계매입매출구분코드계산서구분코드
최종수정수1.0000.0000.2390.0000.000
회계구분코드0.0001.0000.0120.0000.048
분기구분코드0.2390.0121.0000.0220.036
회계매입매출구분코드0.0000.0000.0221.0000.238
계산서구분코드0.0000.0480.0360.2381.000

Missing values

2023-12-12T07:57:47.121914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:57:47.259800image/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

세금계산서집계ID회계구분코드거래년도분기구분코드회계매입매출구분코드계산서구분코드매출대상구분코드주민등록번호통합ID삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnSY0xQVyG20214211<NA>N111:37.69A10311:37.69A103
19dnSYOWtqVG20214211<NA>N108:45.6459408:45.64594
29dnB0N6UZyG20214211<NA>N2008:44.8459425:41.35890
39dnSYzZmtiG20214211<NA>N105:04.4535305:04.45353
49dnLg9LICIG20214211<NA>N1503:22.19298035:21.392980
59dnSX3LZZdG20214211<NA>N259:27.99A06157:09.29A061
69dnSX8fmhAG20214211<NA>N158:15.1544158:15.15441
79dnChbXHKGG20214211<NA>N254:01.4600135:52.76001
89dlnMgg1ASG20213211<NA>N1048:46.8359223:03.15684
99dnzad0XUpG20214211<NA>N343:14.59080529:28.591118
세금계산서집계ID회계구분코드거래년도분기구분코드회계매입매출구분코드계산서구분코드매출대상구분코드주민등록번호통합ID삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dnJFjNbv2G20214211<NA>N110:57.3527610:57.35276
4919dnJE5AW6PG20214211<NA>N107:27.5581407:27.55814
4929dnJEZeQOTG20214211<NA>N105:53.6605505:53.66055
4939dnJEuyoMXG20214211<NA>N259:49.8572758:20.25727
4949dnJElkXTZG20214211<NA>N256:04.0450056:04.04500
4959dnJEiHJBKG20214211<NA>N155:25.1591555:25.15915
4969dnJDT2TYpG20214211<NA>N252:33.2571249:20.75712
4979dnJD5AZvYI20214211<NA>N152:11.3542752:11.35427
4989dnJDQiqFDG20214211<NA>N148:25.3601048:25.36010
4999dnJDrZlOmG20214221<NA>N142:26.1567742:26.15677