Overview

Dataset statistics

Number of variables12
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.4 KiB
Average record size in memory101.3 B

Variable types

Categorical3
Text5
Numeric3
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 시스템관리공통채번마스터에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093314/fileData.do

Alerts

최소채번값 has constant value ""Constant
채번증가단위 has constant value ""Constant
삭제여부 has constant value ""Constant
최대채번값 is highly overall correlated with 최종채번값 and 1 other fieldsHigh correlation
최종채번값 is highly overall correlated with 최대채번값 and 1 other fieldsHigh correlation
최종수정수 is highly overall correlated with 채번번호High correlation
채번번호 is highly overall correlated with 최대채번값 and 2 other fieldsHigh correlation
최종채번값 is highly skewed (γ1 = 22.33543522)Skewed

Reproduction

Analysis started2023-12-12 20:12:22.010327
Analysis finished2023-12-12 20:12:23.864838
Duration1.85 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

채번번호
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
G04
166 
B01
101 
B03
96 
U09
81 
G10
 
10
Other values (26)
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique16 ?
Unique (%)3.2%

Sample

1st rowU07
2nd rowU06
3rd rowU06
4th rowU27
5th rowU02

Common Values

ValueCountFrequency (%)
G04 166
33.2%
B01 101
20.2%
B03 96
19.2%
U09 81
16.2%
G10 10
 
2.0%
G08 5
 
1.0%
B20 5
 
1.0%
U06 4
 
0.8%
G22 4
 
0.8%
U27 2
 
0.4%
Other values (21) 26
 
5.2%

Length

2023-12-13T05:12:23.937635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g04 166
33.2%
b01 101
20.2%
b03 96
19.2%
u09 81
16.2%
g10 10
 
2.0%
g08 5
 
1.0%
b20 5
 
1.0%
u06 4
 
0.8%
g22 4
 
0.8%
g01 2
 
0.4%
Other values (21) 26
 
5.2%

채번관리항목값
Text

Distinct412
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T05:12:24.236890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length9.8
Min length1

Characters and Unicode

Total characters4900
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique330 ?
Unique (%)66.0%

Sample

1st row2021-10-19
2nd row2021-10-19KED
3rd row2021-10-19076
4th row2021-10-19KED
5th row
ValueCountFrequency (%)
gtpd2021 2
 
0.4%
gtph2021 2
 
0.4%
gthj2021 2
 
0.4%
gtqa2021 2
 
0.4%
gtme2021 2
 
0.4%
gtpj2021 2
 
0.4%
2021-10-19ked 2
 
0.4%
gqac2021 2
 
0.4%
gtle2021 2
 
0.4%
gtie2021 2
 
0.4%
Other values (401) 472
95.9%
2023-12-13T05:12:24.707743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 828
16.9%
0 767
15.7%
1 572
11.7%
G 364
 
7.4%
T 268
 
5.5%
5 210
 
4.3%
3 201
 
4.1%
4 194
 
4.0%
8 182
 
3.7%
6 177
 
3.6%
Other values (27) 1137
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3452
70.4%
Uppercase Letter 1424
29.1%
Dash Punctuation 16
 
0.3%
Space Separator 8
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 364
25.6%
T 268
18.8%
B 119
 
8.4%
F 98
 
6.9%
A 97
 
6.8%
H 67
 
4.7%
I 61
 
4.3%
Q 33
 
2.3%
P 33
 
2.3%
N 31
 
2.2%
Other values (15) 253
17.8%
Decimal Number
ValueCountFrequency (%)
2 828
24.0%
0 767
22.2%
1 572
16.6%
5 210
 
6.1%
3 201
 
5.8%
4 194
 
5.6%
8 182
 
5.3%
6 177
 
5.1%
7 175
 
5.1%
9 146
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3476
70.9%
Latin 1424
29.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 364
25.6%
T 268
18.8%
B 119
 
8.4%
F 98
 
6.9%
A 97
 
6.8%
H 67
 
4.7%
I 61
 
4.3%
Q 33
 
2.3%
P 33
 
2.3%
N 31
 
2.2%
Other values (15) 253
17.8%
Common
ValueCountFrequency (%)
2 828
23.8%
0 767
22.1%
1 572
16.5%
5 210
 
6.0%
3 201
 
5.8%
4 194
 
5.6%
8 182
 
5.2%
6 177
 
5.1%
7 175
 
5.0%
9 146
 
4.2%
Other values (2) 24
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 828
16.9%
0 767
15.7%
1 572
11.7%
G 364
 
7.4%
T 268
 
5.5%
5 210
 
4.3%
3 201
 
4.1%
4 194
 
4.0%
8 182
 
3.7%
6 177
 
3.6%
Other values (27) 1137
23.2%

최소채번값
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

2023-12-13T05:12:24.855766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:24.953573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

최대채번값
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40513631
Minimum999
Maximum1 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T05:12:25.059084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum999
5-th percentile999
Q1999
median99999
Q399999
95-th percentile99999
Maximum1 × 1010
Range9.999999 × 109
Interquartile range (IQR)99000

Descriptive statistics

Standard deviation6.3180648 × 108
Coefficient of variation (CV)15.594911
Kurtosis247.45605
Mean40513631
Median Absolute Deviation (MAD)0
Skewness15.762374
Sum2.0256816 × 1010
Variance3.9917942 × 1017
MonotonicityNot monotonic
2023-12-13T05:12:25.185595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
99999 291
58.2%
999 176
35.2%
9999999 12
 
2.4%
999999 7
 
1.4%
9999 7
 
1.4%
9999999999 2
 
0.4%
20000 2
 
0.4%
400000 1
 
0.2%
30000 1
 
0.2%
99999999 1
 
0.2%
ValueCountFrequency (%)
999 176
35.2%
9999 7
 
1.4%
20000 2
 
0.4%
30000 1
 
0.2%
99999 291
58.2%
400000 1
 
0.2%
999999 7
 
1.4%
9999999 12
 
2.4%
99999999 1
 
0.2%
9999999999 2
 
0.4%
ValueCountFrequency (%)
9999999999 2
 
0.4%
99999999 1
 
0.2%
9999999 12
 
2.4%
999999 7
 
1.4%
400000 1
 
0.2%
99999 291
58.2%
30000 1
 
0.2%
20000 2
 
0.4%
9999 7
 
1.4%
999 176
35.2%

채번증가단위
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

2023-12-13T05:12:25.295535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:25.387890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

최종채번값
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct322
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189956.45
Minimum1
Maximum90748979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T05:12:25.496382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median677.5
Q32384
95-th percentile5994.1
Maximum90748979
Range90748978
Interquartile range (IQR)2383

Descriptive statistics

Standard deviation4059586.6
Coefficient of variation (CV)21.371143
Kurtosis499.23344
Mean189956.45
Median Absolute Deviation (MAD)676.5
Skewness22.335435
Sum94978227
Variance1.6480243 × 1013
MonotonicityNot monotonic
2023-12-13T05:12:25.632895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 128
 
25.6%
2 17
 
3.4%
3 8
 
1.6%
5 6
 
1.2%
4 5
 
1.0%
6 4
 
0.8%
3535 2
 
0.4%
57 2
 
0.4%
18 2
 
0.4%
21 2
 
0.4%
Other values (312) 324
64.8%
ValueCountFrequency (%)
1 128
25.6%
2 17
 
3.4%
3 8
 
1.6%
4 5
 
1.0%
5 6
 
1.2%
6 4
 
0.8%
7 1
 
0.2%
8 2
 
0.4%
9 2
 
0.4%
10 1
 
0.2%
ValueCountFrequency (%)
90748979 1
0.2%
2410963 1
0.2%
671252 1
0.2%
140432 1
0.2%
94727 1
0.2%
74578 1
0.2%
46018 1
0.2%
42809 1
0.2%
32141 1
0.2%
15271 1
0.2%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-13T05:12:25.737841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.274
Minimum1
Maximum23022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T05:12:25.818002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum23022
Range23021
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1136.7623
Coefficient of variation (CV)12.454393
Kurtosis341.18789
Mean91.274
Median Absolute Deviation (MAD)0
Skewness17.658408
Sum45637
Variance1292228.4
MonotonicityNot monotonic
2023-12-13T05:12:25.941061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 485
97.0%
1508 1
 
0.2%
2 1
 
0.2%
14 1
 
0.2%
50 1
 
0.2%
172 1
 
0.2%
116 1
 
0.2%
1677 1
 
0.2%
16 1
 
0.2%
9078 1
 
0.2%
Other values (6) 6
 
1.2%
ValueCountFrequency (%)
1 485
97.0%
2 1
 
0.2%
13 1
 
0.2%
14 1
 
0.2%
16 1
 
0.2%
50 1
 
0.2%
90 1
 
0.2%
116 1
 
0.2%
172 1
 
0.2%
1508 1
 
0.2%
ValueCountFrequency (%)
23022 1
0.2%
9078 1
0.2%
4090 1
0.2%
3213 1
0.2%
2091 1
0.2%
1677 1
0.2%
1508 1
0.2%
172 1
0.2%
116 1
0.2%
90 1
0.2%

처리시각
Text

Distinct485
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T05:12:26.303571image/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

Unique470 ?
Unique (%)94.0%

Sample

1st row54:17.3
2nd row54:16.9
3rd row54:16.9
4th row54:16.8
5th row54:16.6
ValueCountFrequency (%)
40:30.5 2
 
0.4%
54:16.9 2
 
0.4%
15:52.7 2
 
0.4%
47:20.0 2
 
0.4%
47:37.2 2
 
0.4%
00:13.5 2
 
0.4%
26:28.1 2
 
0.4%
51:15.3 2
 
0.4%
29:34.6 2
 
0.4%
40:13.2 2
 
0.4%
Other values (475) 480
96.0%
2023-12-13T05:12:26.799542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 362
10.3%
5 326
9.3%
0 308
8.8%
3 307
8.8%
2 307
8.8%
1 300
8.6%
7 158
 
4.5%
8 158
 
4.5%
Other values (2) 274
7.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 362
14.5%
5 326
13.0%
0 308
12.3%
3 307
12.3%
2 307
12.3%
1 300
12.0%
7 158
6.3%
8 158
6.3%
9 142
 
5.7%
6 132
 
5.3%
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%
4 362
10.3%
5 326
9.3%
0 308
8.8%
3 307
8.8%
2 307
8.8%
1 300
8.6%
7 158
 
4.5%
8 158
 
4.5%
Other values (2) 274
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 362
10.3%
5 326
9.3%
0 308
8.8%
3 307
8.8%
2 307
8.8%
1 300
8.6%
7 158
 
4.5%
8 158
 
4.5%
Other values (2) 274
7.8%

처리직원번호
Text

Distinct361
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T05:12:27.215729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.242
Min length4

Characters and Unicode

Total characters2121
Distinct characters21
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

Unique298 ?
Unique (%)59.6%

Sample

1st row5074
2nd row5824
3rd row5824
4th row5824
5th row5824
ValueCountFrequency (%)
99023 18
 
3.6%
99016 13
 
2.6%
99001 9
 
1.8%
5267 6
 
1.2%
99006 6
 
1.2%
99002 5
 
1.0%
5801 5
 
1.0%
5235 5
 
1.0%
5462 4
 
0.8%
4775 4
 
0.8%
Other values (351) 425
85.0%
2023-12-13T05:12:27.758893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 324
15.3%
9 296
14.0%
0 224
10.6%
4 212
10.0%
6 206
9.7%
1 173
8.2%
3 166
7.8%
7 161
7.6%
2 154
7.3%
8 134
6.3%
Other values (11) 71
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2050
96.7%
Uppercase Letter 69
 
3.3%
Space Separator 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 324
15.8%
9 296
14.4%
0 224
10.9%
4 212
10.3%
6 206
10.0%
1 173
8.4%
3 166
8.1%
7 161
7.9%
2 154
7.5%
8 134
6.5%
Uppercase Letter
ValueCountFrequency (%)
C 51
73.9%
H 3
 
4.3%
B 3
 
4.3%
A 3
 
4.3%
T 2
 
2.9%
S 2
 
2.9%
U 2
 
2.9%
R 1
 
1.4%
E 1
 
1.4%
Y 1
 
1.4%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2052
96.7%
Latin 69
 
3.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 324
15.8%
9 296
14.4%
0 224
10.9%
4 212
10.3%
6 206
10.0%
1 173
8.4%
3 166
8.1%
7 161
7.8%
2 154
7.5%
8 134
6.5%
Latin
ValueCountFrequency (%)
C 51
73.9%
H 3
 
4.3%
B 3
 
4.3%
A 3
 
4.3%
T 2
 
2.9%
S 2
 
2.9%
U 2
 
2.9%
R 1
 
1.4%
E 1
 
1.4%
Y 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 324
15.3%
9 296
14.0%
0 224
10.6%
4 212
10.0%
6 206
9.7%
1 173
8.2%
3 166
7.8%
7 161
7.6%
2 154
7.3%
8 134
6.3%
Other values (11) 71
 
3.3%

최초처리시각
Text

Distinct466
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T05:12:28.499896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length7
Mean length8.102
Min length7

Characters and Unicode

Total characters4051
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

Unique459 ?
Unique (%)91.8%

Sample

1st row20:41.3
2nd row00:02.4
3rd row05:11.7
4th row04:22.3
5th row0001-01-01 00:00:00.000000
ValueCountFrequency (%)
0001-01-01 29
 
5.5%
00:00:00.000000 29
 
5.5%
51:15.3 2
 
0.4%
26:28.1 2
 
0.4%
29:34.6 2
 
0.4%
28:35.4 2
 
0.4%
40:30.5 2
 
0.4%
47:37.2 2
 
0.4%
16:23.6 1
 
0.2%
32:41.1 1
 
0.2%
Other values (457) 457
86.4%
2023-12-13T05:12:29.156278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 785
19.4%
: 529
13.1%
. 500
12.3%
1 382
9.4%
2 325
8.0%
5 316
7.8%
4 291
 
7.2%
3 282
 
7.0%
6 146
 
3.6%
8 146
 
3.6%
Other values (4) 349
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2935
72.5%
Other Punctuation 1029
 
25.4%
Dash Punctuation 58
 
1.4%
Space Separator 29
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 785
26.7%
1 382
13.0%
2 325
11.1%
5 316
10.8%
4 291
 
9.9%
3 282
 
9.6%
6 146
 
5.0%
8 146
 
5.0%
7 144
 
4.9%
9 118
 
4.0%
Other Punctuation
ValueCountFrequency (%)
: 529
51.4%
. 500
48.6%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Space Separator
ValueCountFrequency (%)
29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 785
19.4%
: 529
13.1%
. 500
12.3%
1 382
9.4%
2 325
8.0%
5 316
7.8%
4 291
 
7.2%
3 282
 
7.0%
6 146
 
3.6%
8 146
 
3.6%
Other values (4) 349
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 785
19.4%
: 529
13.1%
. 500
12.3%
1 382
9.4%
2 325
8.0%
5 316
7.8%
4 291
 
7.2%
3 282
 
7.0%
6 146
 
3.6%
8 146
 
3.6%
Other values (4) 349
8.6%

최초처리직원번호
Text

Distinct334
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T05:12:29.773807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.234
Min length4

Characters and Unicode

Total characters2117
Distinct characters23
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

Unique278 ?
Unique (%)55.6%

Sample

1st rowBATCH
2nd rowANYST
3rd rowACIOP
4th row5211
5th rowBATCH
ValueCountFrequency (%)
batch 30
 
6.0%
99016 27
 
5.4%
99001 15
 
3.0%
99023 11
 
2.2%
99006 7
 
1.4%
4775 5
 
1.0%
4155 5
 
1.0%
5801 5
 
1.0%
99002 5
 
1.0%
5235 4
 
0.8%
Other values (324) 386
77.2%
2023-12-13T05:12:30.495099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 327
15.4%
9 267
12.6%
4 234
11.1%
0 223
10.5%
1 171
8.1%
6 171
8.1%
3 155
7.3%
8 137
6.5%
2 135
6.4%
7 122
 
5.8%
Other values (13) 175
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1942
91.7%
Uppercase Letter 173
 
8.2%
Space Separator 2
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 34
19.7%
T 32
18.5%
C 32
18.5%
B 31
17.9%
H 30
17.3%
S 3
 
1.7%
I 2
 
1.2%
O 2
 
1.2%
P 2
 
1.2%
N 2
 
1.2%
Other values (2) 3
 
1.7%
Decimal Number
ValueCountFrequency (%)
5 327
16.8%
9 267
13.7%
4 234
12.0%
0 223
11.5%
1 171
8.8%
6 171
8.8%
3 155
8.0%
8 137
7.1%
2 135
7.0%
7 122
 
6.3%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1944
91.8%
Latin 173
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 34
19.7%
T 32
18.5%
C 32
18.5%
B 31
17.9%
H 30
17.3%
S 3
 
1.7%
I 2
 
1.2%
O 2
 
1.2%
P 2
 
1.2%
N 2
 
1.2%
Other values (2) 3
 
1.7%
Common
ValueCountFrequency (%)
5 327
16.8%
9 267
13.7%
4 234
12.0%
0 223
11.5%
1 171
8.8%
6 171
8.8%
3 155
8.0%
8 137
7.0%
2 135
6.9%
7 122
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2117
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 327
15.4%
9 267
12.6%
4 234
11.1%
0 223
10.5%
1 171
8.1%
6 171
8.1%
3 155
7.3%
8 137
6.5%
2 135
6.4%
7 122
 
5.8%
Other values (13) 175
8.3%

Interactions

2023-12-13T05:12:23.161008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:22.365432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:22.783568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:23.282487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:22.528505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:22.922303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:23.397133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:22.660750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:23.036992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:12:30.611247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
채번번호최대채번값최종채번값최종수정수
채번번호1.0001.0001.0001.000
최대채번값1.0001.0000.0000.692
최종채번값1.0000.0001.0000.893
최종수정수1.0000.6920.8931.000
2023-12-13T05:12:30.736782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최대채번값최종채번값최종수정수채번번호
최대채번값1.0000.8270.2350.970
최종채번값0.8271.0000.2640.970
최종수정수0.2350.2641.0000.972
채번번호0.9700.9700.9721.000

Missing values

2023-12-13T05:12:23.556119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:12:23.775323image/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

채번번호채번관리항목값최소채번값최대채번값채번증가단위최종채번값삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0U072021-10-19199999916322N154:17.3507420:41.3BATCH
1U062021-10-19KED1999999918775N154:16.9582400:02.4ANYST
2U062021-10-19076199999991371N154:16.9582405:11.7ACIOP
3U272021-10-19KED199999991512N154:16.8582404:22.35211
4U021400000194727N907854:16.658240001-01-01 00:00:00.000000BATCH
5U08199999999991671252N209154:14.652110001-01-01 00:00:00.000000BATCH
6U09FBTIL20211999991342N154:14.6521155:54.44406
7B05130000114750N2302254:05.954830001-01-01 00:00:00.000000BATCH
8U051999999999912410963N321354:03.852110001-01-01 00:00:00.000000BATCH
9B03GTIA202119999913300N154:00.69C72617:56.74799
채번번호채번관리항목값최소채번값최대채번값채번증가단위최종채번값삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490G04G046560384429199911N143:19.6477543:19.64775
491U09FBTHV20211999991573N143:11.5456207:04.84779
492G04C074658679809199911N143:02.8477443:02.84774
493G04C0746581298071999118N142:49.4589135:17.04258
494U09FBTPD202119999911172N142:07.0536101:08.35365
495B20TPA2021S19999149N141:22.8336856:06.43245
496G04G086551463123199911N140:42.1614340:42.16143
497G04G044639424404199913N140:39.1929800001-01-01 00:00:00.000000BATCH
498G10G069203864201199916N140:30.250870001-01-01 00:00:00.000000BATCH
499G04G086648127301199911N140:01.1588240:01.15882