Overview

Dataset statistics

Number of variables12
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory49.0 KiB
Average record size in memory100.3 B

Variable types

Text5
Numeric4
Categorical3

Dataset

Description해당 파일 데이터는 신용보증기금의 보증접수상담관계에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092871/fileData.do

Alerts

업무구분코드 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
Dataset has 1 (0.2%) duplicate rowsDuplicates
상담원장역할관계코드 is highly overall correlated with 관련금액High correlation
관련금액 is highly overall correlated with 상담원장역할관계코드High correlation
관련금액 has 446 (89.2%) zerosZeros

Reproduction

Analysis started2023-12-12 03:08:47.444852
Analysis finished2023-12-12 03:08:50.589544
Duration3.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct232
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:08:50.815823image/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

Unique47 ?
Unique (%)9.4%

Sample

1st row9dnlkd3D0b
2nd row9dnSZ3cc0s
3rd row9dnSMPA07I
4th row9dnSMPA07I
5th row9dnMQG9LIQ
ValueCountFrequency (%)
9dnskkckxu 5
 
1.0%
9dngtdwbuv 5
 
1.0%
9dnlayixof 5
 
1.0%
9dnsv24aib 5
 
1.0%
9dmpk1oano 5
 
1.0%
9dnsosf8r3 4
 
0.8%
9dnsyihcl7 4
 
0.8%
9dndnjhxhw 4
 
0.8%
9dnstpcwtg 4
 
0.8%
9dnsp5spt6 4
 
0.8%
Other values (222) 455
91.0%
2023-12-12T12:08:51.294501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 549
 
11.0%
9 528
 
10.6%
n 511
 
10.2%
S 224
 
4.5%
M 116
 
2.3%
X 86
 
1.7%
D 86
 
1.7%
L 81
 
1.6%
J 80
 
1.6%
O 77
 
1.5%
Other values (52) 2662
53.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2259
45.2%
Uppercase Letter 1779
35.6%
Decimal Number 962
19.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 549
24.3%
n 511
22.6%
m 73
 
3.2%
i 71
 
3.1%
v 70
 
3.1%
l 66
 
2.9%
p 63
 
2.8%
z 62
 
2.7%
t 59
 
2.6%
u 56
 
2.5%
Other values (16) 679
30.1%
Uppercase Letter
ValueCountFrequency (%)
S 224
 
12.6%
M 116
 
6.5%
X 86
 
4.8%
D 86
 
4.8%
L 81
 
4.6%
J 80
 
4.5%
O 77
 
4.3%
W 76
 
4.3%
N 75
 
4.2%
A 74
 
4.2%
Other values (16) 804
45.2%
Decimal Number
ValueCountFrequency (%)
9 528
54.9%
1 60
 
6.2%
2 59
 
6.1%
4 56
 
5.8%
3 54
 
5.6%
5 54
 
5.6%
7 46
 
4.8%
0 37
 
3.8%
6 34
 
3.5%
8 34
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4038
80.8%
Common 962
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 549
 
13.6%
n 511
 
12.7%
S 224
 
5.5%
M 116
 
2.9%
X 86
 
2.1%
D 86
 
2.1%
L 81
 
2.0%
J 80
 
2.0%
O 77
 
1.9%
W 76
 
1.9%
Other values (42) 2152
53.3%
Common
ValueCountFrequency (%)
9 528
54.9%
1 60
 
6.2%
2 59
 
6.1%
4 56
 
5.8%
3 54
 
5.6%
5 54
 
5.6%
7 46
 
4.8%
0 37
 
3.8%
6 34
 
3.5%
8 34
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 549
 
11.0%
9 528
 
10.6%
n 511
 
10.2%
S 224
 
4.5%
M 116
 
2.3%
X 86
 
1.7%
D 86
 
1.7%
L 81
 
1.6%
J 80
 
1.6%
O 77
 
1.5%
Other values (52) 2662
53.2%

상담원장역할관계코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.952
Minimum1
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:08:51.473022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile105
Maximum112
Range111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33.360569
Coefficient of variation (CV)2.5757079
Kurtosis3.9669769
Mean12.952
Median Absolute Deviation (MAD)0
Skewness2.4385326
Sum6476
Variance1112.9276
MonotonicityNot monotonic
2023-12-12T12:08:51.643290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 443
88.6%
105 47
 
9.4%
112 4
 
0.8%
110 3
 
0.6%
107 2
 
0.4%
106 1
 
0.2%
ValueCountFrequency (%)
1 443
88.6%
105 47
 
9.4%
106 1
 
0.2%
107 2
 
0.4%
110 3
 
0.6%
112 4
 
0.8%
ValueCountFrequency (%)
112 4
 
0.8%
110 3
 
0.6%
107 2
 
0.4%
106 1
 
0.2%
105 47
 
9.4%
1 443
88.6%

업무구분코드
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
G 500
100.0%

Length

2023-12-12T12:08:51.844917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:08:51.980842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

이력일련번호
Real number (ℝ)

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.316
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:08:52.113032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile9
Maximum21
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5952741
Coefficient of variation (CV)1.0842202
Kurtosis5.4611491
Mean3.316
Median Absolute Deviation (MAD)0
Skewness2.1456257
Sum1658
Variance12.925996
MonotonicityNot monotonic
2023-12-12T12:08:52.277208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 277
55.4%
3 75
 
15.0%
5 59
 
11.8%
7 38
 
7.6%
9 28
 
5.6%
11 8
 
1.6%
17 5
 
1.0%
19 3
 
0.6%
15 3
 
0.6%
13 2
 
0.4%
ValueCountFrequency (%)
1 277
55.4%
3 75
 
15.0%
5 59
 
11.8%
7 38
 
7.6%
9 28
 
5.6%
11 8
 
1.6%
13 2
 
0.4%
15 3
 
0.6%
17 5
 
1.0%
19 3
 
0.6%
ValueCountFrequency (%)
21 2
 
0.4%
19 3
 
0.6%
17 5
 
1.0%
15 3
 
0.6%
13 2
 
0.4%
11 8
 
1.6%
9 28
 
5.6%
7 38
7.6%
5 59
11.8%
3 75
15.0%

관련금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24211780
Minimum0
Maximum1.08 × 109
Zeros446
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:08:52.487206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.9 × 108
Maximum1.08 × 109
Range1.08 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation97461908
Coefficient of variation (CV)4.0253921
Kurtosis40.32135
Mean24211780
Median Absolute Deviation (MAD)0
Skewness5.6676079
Sum1.210589 × 1010
Variance9.4988235 × 1015
MonotonicityNot monotonic
2023-12-12T12:08:52.692517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 446
89.2%
142500000 5
 
1.0%
297000000 5
 
1.0%
190000000 4
 
0.8%
500000000 4
 
0.8%
95000000 3
 
0.6%
285000000 2
 
0.4%
16200000 2
 
0.4%
495000000 2
 
0.4%
300000000 1
 
0.2%
Other values (26) 26
 
5.2%
ValueCountFrequency (%)
0 446
89.2%
9600000 1
 
0.2%
14400000 1
 
0.2%
16200000 2
 
0.4%
18700000 1
 
0.2%
20000000 1
 
0.2%
23290000 1
 
0.2%
30000000 1
 
0.2%
32300000 1
 
0.2%
34000000 1
 
0.2%
ValueCountFrequency (%)
1080000000 1
 
0.2%
640000000 1
 
0.2%
600000000 1
 
0.2%
540000000 1
 
0.2%
500000000 4
0.8%
495000000 2
 
0.4%
300000000 1
 
0.2%
297000000 5
1.0%
285000000 2
 
0.4%
280500000 1
 
0.2%

유효개시일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
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 (%)
00:00.0 500
100.0%

Length

2023-12-12T12:08:52.876045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:08:52.998230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

유효종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
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 (%)
00:00.0 500
100.0%

Length

2023-12-12T12:08:53.150195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:08:53.276316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

최종수정수
Real number (ℝ)

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.51
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:08:53.398603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum21
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7365524
Coefficient of variation (CV)0.82850385
Kurtosis3.820533
Mean4.51
Median Absolute Deviation (MAD)2
Skewness1.7410836
Sum2255
Variance13.961824
MonotonicityNot monotonic
2023-12-12T12:08:53.551849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 109
21.8%
2 83
16.6%
4 59
11.8%
3 52
10.4%
5 46
9.2%
6 38
 
7.6%
8 28
 
5.6%
7 24
 
4.8%
9 24
 
4.8%
10 8
 
1.6%
Other values (10) 29
 
5.8%
ValueCountFrequency (%)
1 109
21.8%
2 83
16.6%
3 52
10.4%
4 59
11.8%
5 46
9.2%
6 38
 
7.6%
7 24
 
4.8%
8 28
 
5.6%
9 24
 
4.8%
10 8
 
1.6%
ValueCountFrequency (%)
21 2
 
0.4%
20 2
 
0.4%
19 1
 
0.2%
18 3
0.6%
17 3
0.6%
16 5
1.0%
14 3
0.6%
13 1
 
0.2%
12 2
 
0.4%
11 7
1.4%
Distinct332
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:08:53.987401image/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

Unique169 ?
Unique (%)33.8%

Sample

1st row33:03.2
2nd row32:38.3
3rd row31:52.2
4th row31:52.2
5th row31:51.3
ValueCountFrequency (%)
23:21.8 4
 
0.8%
24:46.3 3
 
0.6%
41:23.0 3
 
0.6%
45:55.1 3
 
0.6%
50:44.6 2
 
0.4%
56:36.9 2
 
0.4%
23:45.5 2
 
0.4%
46:31.3 2
 
0.4%
33:42.7 2
 
0.4%
45:42.6 2
 
0.4%
Other values (322) 475
95.0%
2023-12-12T12:08:54.534557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 396
11.3%
5 323
9.2%
2 322
9.2%
3 295
8.4%
0 284
8.1%
1 275
7.9%
6 173
 
4.9%
8 164
 
4.7%
Other values (2) 268
7.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 396
15.8%
5 323
12.9%
2 322
12.9%
3 295
11.8%
0 284
11.4%
1 275
11.0%
6 173
6.9%
8 164
6.6%
7 146
 
5.8%
9 122
 
4.9%
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%
4 396
11.3%
5 323
9.2%
2 322
9.2%
3 295
8.4%
0 284
8.1%
1 275
7.9%
6 173
 
4.9%
8 164
 
4.7%
Other values (2) 268
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 396
11.3%
5 323
9.2%
2 322
9.2%
3 295
8.4%
0 284
8.1%
1 275
7.9%
6 173
 
4.9%
8 164
 
4.7%
Other values (2) 268
7.7%
Distinct206
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:08:55.013809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.086
Min length4

Characters and Unicode

Total characters2043
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

Unique31 ?
Unique (%)6.2%

Sample

1st row4059
2nd row4216
3rd row5214
4th row5214
5th row5888
ValueCountFrequency (%)
4351 8
 
1.6%
5033 6
 
1.2%
9c743 5
 
1.0%
9c708 5
 
1.0%
5549 5
 
1.0%
4398 5
 
1.0%
3607 5
 
1.0%
5008 5
 
1.0%
4877 5
 
1.0%
5048 5
 
1.0%
Other values (196) 446
89.2%
2023-12-12T12:08:55.534108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 309
15.1%
5 277
13.6%
6 261
12.8%
0 210
10.3%
1 191
9.3%
3 181
8.9%
9 156
7.6%
2 153
7.5%
7 139
6.8%
8 123
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
97.9%
Uppercase Letter 43
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 309
15.4%
5 277
13.9%
6 261
13.1%
0 210
10.5%
1 191
9.6%
3 181
9.0%
9 156
7.8%
2 153
7.6%
7 139
7.0%
8 123
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
C 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
97.9%
Latin 43
 
2.1%

Most frequent character per script

Common
ValueCountFrequency (%)
4 309
15.4%
5 277
13.9%
6 261
13.1%
0 210
10.5%
1 191
9.6%
3 181
9.0%
9 156
7.8%
2 153
7.6%
7 139
7.0%
8 123
 
6.2%
Latin
ValueCountFrequency (%)
C 43
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 309
15.1%
5 277
13.6%
6 261
12.8%
0 210
10.3%
1 191
9.3%
3 181
8.9%
9 156
7.6%
2 153
7.5%
7 139
6.8%
8 123
 
6.0%
Distinct271
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:08:55.945970image/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

Unique110 ?
Unique (%)22.0%

Sample

1st row33:03.2
2nd row32:38.3
3rd row24:18.1
4th row24:18.1
5th row51:03.4
ValueCountFrequency (%)
53:06.3 5
 
1.0%
26:20.7 5
 
1.0%
56:03.4 5
 
1.0%
19:21.7 4
 
0.8%
26:43.4 4
 
0.8%
33:20.5 4
 
0.8%
29:24.9 4
 
0.8%
12:41.4 4
 
0.8%
09:27.1 4
 
0.8%
37:35.3 4
 
0.8%
Other values (261) 457
91.4%
2023-12-12T12:08:56.477246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 359
10.3%
2 348
9.9%
5 328
9.4%
3 307
8.8%
1 287
8.2%
0 281
8.0%
6 176
 
5.0%
9 142
 
4.1%
Other values (2) 272
7.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 359
14.4%
2 348
13.9%
5 328
13.1%
3 307
12.3%
1 287
11.5%
0 281
11.2%
6 176
7.0%
9 142
 
5.7%
7 141
 
5.6%
8 131
 
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%
4 359
10.3%
2 348
9.9%
5 328
9.4%
3 307
8.8%
1 287
8.2%
0 281
8.0%
6 176
 
5.0%
9 142
 
4.1%
Other values (2) 272
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 359
10.3%
2 348
9.9%
5 328
9.4%
3 307
8.8%
1 287
8.2%
0 281
8.0%
6 176
 
5.0%
9 142
 
4.1%
Other values (2) 272
7.8%
Distinct184
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:08:56.861235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.078
Min length4

Characters and Unicode

Total characters2039
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

Unique28 ?
Unique (%)5.6%

Sample

1st row4059
2nd row4216
3rd row5214
4th row5214
5th row4022
ValueCountFrequency (%)
4129 14
 
2.8%
3432 10
 
2.0%
4351 8
 
1.6%
4224 7
 
1.4%
4116 7
 
1.4%
3418 6
 
1.2%
3723 6
 
1.2%
5033 6
 
1.2%
4055 6
 
1.2%
4438 6
 
1.2%
Other values (174) 424
84.8%
2023-12-12T12:08:57.346245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 360
17.7%
3 311
15.3%
5 236
11.6%
2 187
9.2%
0 187
9.2%
6 175
8.6%
1 166
8.1%
9 136
 
6.7%
7 134
 
6.6%
8 108
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
98.1%
Uppercase Letter 39
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 360
18.0%
3 311
15.6%
5 236
11.8%
2 187
9.3%
0 187
9.3%
6 175
8.8%
1 166
8.3%
9 136
 
6.8%
7 134
 
6.7%
8 108
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
C 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
98.1%
Latin 39
 
1.9%

Most frequent character per script

Common
ValueCountFrequency (%)
4 360
18.0%
3 311
15.6%
5 236
11.8%
2 187
9.3%
0 187
9.3%
6 175
8.8%
1 166
8.3%
9 136
 
6.8%
7 134
 
6.7%
8 108
 
5.4%
Latin
ValueCountFrequency (%)
C 39
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 360
17.7%
3 311
15.3%
5 236
11.6%
2 187
9.2%
0 187
9.2%
6 175
8.6%
1 166
8.1%
9 136
 
6.7%
7 134
 
6.6%
8 108
 
5.3%

Interactions

2023-12-12T12:08:49.676933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:47.765859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:48.297195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:49.147233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:49.809571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:47.882833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:48.424514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:49.266277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:49.946686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:48.012058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:48.571937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:49.403723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:50.069607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:48.132057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:48.690679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:08:49.525514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:08:57.461167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상담원장역할관계코드이력일련번호관련금액최종수정수
상담원장역할관계코드1.0000.1650.9210.287
이력일련번호0.1651.0000.0000.964
관련금액0.9210.0001.0000.000
최종수정수0.2870.9640.0001.000
2023-12-12T12:08:57.573514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상담원장역할관계코드이력일련번호관련금액최종수정수
상담원장역할관계코드1.000-0.1740.962-0.289
이력일련번호-0.1741.000-0.1570.403
관련금액0.962-0.1571.000-0.263
최종수정수-0.2890.403-0.2631.000

Missing values

2023-12-12T12:08:50.262662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:08:50.502451image/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

상담ID상담원장역할관계코드업무구분코드이력일련번호관련금액유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnlkd3D0b1G1000:00.000:00.0133:03.2405933:03.24059
19dnSZ3cc0s1G1000:00.000:00.0132:38.3421632:38.34216
29dnSMPA07I1G5000:00.000:00.0431:52.2521424:18.15214
39dnSMPA07I1G1000:00.000:00.0531:52.2521424:18.15214
49dnMQG9LIQ1G1000:00.000:00.0531:51.3588851:03.44022
59dnMQG9LIQ1G5000:00.000:00.0431:51.3588851:03.44022
69dnLcSioyb1G7000:00.000:00.0631:37.1532427:52.65324
79dnLcSioyb1G1000:00.000:00.0731:37.1532427:52.65324
89dnAGrrMx71G3000:00.000:00.0231:29.5582023:20.52962
99dnAGrrMx71G1000:00.000:00.0331:29.5582023:20.52962
상담ID상담원장역할관계코드업무구분코드이력일련번호관련금액유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dnCor5DhV1G15000:00.000:00.01436:14.7484111:30.24841
4919dnSOJwWKr105G130000000000:00.000:00.0134:39.29C64134:39.29C641
4929dnSOHj1AO1G1000:00.000:00.0134:32.69C65934:32.69C659
4939dnM1OVb1j1G1000:00.000:00.0534:32.0616503:43.63940
4949dnM1OVb1j1G5000:00.000:00.0434:32.0616503:43.63940
4959dnSOHj1AO105G128050000000:00.000:00.0134:06.69C65934:06.69C659
4969dnCor5DhV1G13000:00.000:00.01233:47.3484111:30.24841
4979dnM2vnrQv1G7000:00.000:00.0632:08.8361644:52.43616
4989dnM2vnrQv1G1000:00.000:00.0732:08.8361644:52.43616
4999dnM2vnrQv1G5000:00.000:00.0431:42.0361644:52.43616

Duplicate rows

Most frequently occurring

상담ID상담원장역할관계코드업무구분코드이력일련번호관련금액유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호# duplicates
09dnSPPYNNg112G150000000000:00.000:00.0151:25.8435151:25.843512