Overview

Dataset statistics

Number of variables22
Number of observations500
Missing cells564
Missing cells (%)5.1%
Duplicate rows18
Duplicate rows (%)3.6%
Total size in memory89.0 KiB
Average record size in memory182.3 B

Variable types

Numeric6
Categorical10
Text5
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 보증고객 추천기업 정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093261/fileData.do

Alerts

이메일전송일자 has constant value ""Constant
Dataset has 18 (3.6%) duplicate rowsDuplicates
입력일자 is highly imbalanced (81.6%)Imbalance
자금용도명 is highly imbalanced (76.1%)Imbalance
추천기업규모코드 is highly imbalanced (79.0%)Imbalance
유효기한 is highly imbalanced (68.9%)Imbalance
삭제여부 is highly imbalanced (90.6%)Imbalance
담당자전화번호 has 195 (39.0%) missing valuesMissing
담당자휴대폰번호 has 153 (30.6%) missing valuesMissing
담당자이메일 has 216 (43.2%) missing valuesMissing
추천금액 is highly skewed (γ1 = 22.27887793)Skewed
추천금액 has 47 (9.4%) zerosZeros
운전자금추천금액 has 381 (76.2%) zerosZeros
시설자금추천금액 has 281 (56.2%) zerosZeros

Reproduction

Analysis started2023-12-12 11:04:18.611316
Analysis finished2023-12-12 11:04:19.247558
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상생보증구분코드
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.542
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:04:19.321520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4836612
Coefficient of variation (CV)0.97705005
Kurtosis0.8981285
Mean2.542
Median Absolute Deviation (MAD)0
Skewness1.4323306
Sum1271
Variance6.1685731
MonotonicityNot monotonic
2023-12-12T20:04:19.508690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 337
67.4%
4 66
 
13.2%
5 35
 
7.0%
8 28
 
5.6%
9 19
 
3.8%
6 13
 
2.6%
11 2
 
0.4%
ValueCountFrequency (%)
1 337
67.4%
4 66
 
13.2%
5 35
 
7.0%
6 13
 
2.6%
8 28
 
5.6%
9 19
 
3.8%
11 2
 
0.4%
ValueCountFrequency (%)
11 2
 
0.4%
9 19
 
3.8%
8 28
 
5.6%
6 13
 
2.6%
5 35
 
7.0%
4 66
 
13.2%
1 337
67.4%

상생보증대기업구분코드
Categorical

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
339 
43
 
28
14
 
26
15
 
22
22
 
22
Other values (9)
63 

Length

Max length2
Median length1
Mean length1.322
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row15
2nd row15
3rd row
4th row44
5th row

Common Values

ValueCountFrequency (%)
339
67.8%
43 28
 
5.6%
14 26
 
5.2%
15 22
 
4.4%
22 22
 
4.4%
29 19
 
3.8%
42 11
 
2.2%
37 9
 
1.8%
44 6
 
1.2%
10 6
 
1.2%
Other values (4) 12
 
2.4%

Length

2023-12-12T20:04:19.741093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
43 28
17.4%
14 26
16.1%
15 22
13.7%
22 22
13.7%
29 19
11.8%
42 11
 
6.8%
37 9
 
5.6%
44 6
 
3.7%
10 6
 
3.7%
21 6
 
3.7%
Other values (3) 6
 
3.7%

부점코드
Text

Distinct98
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T20:04:20.150535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.648
Min length1

Characters and Unicode

Total characters1324
Distinct characters26
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

Unique16 ?
Unique (%)3.2%

Sample

1st rowTAH
2nd rowTPA
3rd rowTQA
4th rowQAC
5th rowTPD
ValueCountFrequency (%)
tba 22
 
5.3%
qac 20
 
4.9%
tpd 11
 
2.7%
tpq 11
 
2.7%
tpp 9
 
2.2%
tav 9
 
2.2%
toa 9
 
2.2%
tal 9
 
2.2%
tbg 9
 
2.2%
tog 8
 
1.9%
Other values (87) 295
71.6%
2023-12-12T20:04:20.829417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 392
29.6%
A 135
 
10.2%
88
 
6.6%
H 79
 
6.0%
P 69
 
5.2%
Q 64
 
4.8%
B 61
 
4.6%
I 54
 
4.1%
O 43
 
3.2%
L 38
 
2.9%
Other values (16) 301
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1236
93.4%
Space Separator 88
 
6.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 392
31.7%
A 135
 
10.9%
H 79
 
6.4%
P 69
 
5.6%
Q 64
 
5.2%
B 61
 
4.9%
I 54
 
4.4%
O 43
 
3.5%
L 38
 
3.1%
C 35
 
2.8%
Other values (15) 266
21.5%
Space Separator
ValueCountFrequency (%)
88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1236
93.4%
Common 88
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 392
31.7%
A 135
 
10.9%
H 79
 
6.4%
P 69
 
5.6%
Q 64
 
5.2%
B 61
 
4.9%
I 54
 
4.4%
O 43
 
3.5%
L 38
 
3.1%
C 35
 
2.8%
Other values (15) 266
21.5%
Common
ValueCountFrequency (%)
88
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 392
29.6%
A 135
 
10.2%
88
 
6.6%
H 79
 
6.0%
P 69
 
5.2%
Q 64
 
4.8%
B 61
 
4.6%
I 54
 
4.1%
O 43
 
3.2%
L 38
 
2.9%
Other values (16) 301
22.7%

팀코드
Categorical

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
185 
2
137 
125 
3
36 
8
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 185
37.0%
2 137
27.4%
125
25.0%
3 36
 
7.2%
8 10
 
2.0%
4 7
 
1.4%

Length

2023-12-12T20:04:21.067999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:21.245495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 185
49.3%
2 137
36.5%
3 36
 
9.6%
8 10
 
2.7%
4 7
 
1.9%

입력일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
486 
0001-01-01 00:00:00.000000
 
14

Length

Max length26
Median length7
Mean length7.532
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 (%)
00:00.0 486
97.2%
0001-01-01 00:00:00.000000 14
 
2.8%

Length

2023-12-12T20:04:21.446055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:21.646825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 486
94.6%
0001-01-01 14
 
2.7%
00:00:00.000000 14
 
2.7%

담당팀입력일자
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
391 
00:00.0
109 

Length

Max length26
Median length26
Mean length21.858
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row0001-01-01 00:00:00.000000
4th row00:00.0
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 391
78.2%
00:00.0 109
 
21.8%

Length

2023-12-12T20:04:21.848674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:22.031938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 391
43.9%
00:00:00.000000 391
43.9%
00:00.0 109
 
12.2%

추천금액
Real number (ℝ)

SKEWED  ZEROS 

Distinct146
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2164524.9
Minimum0
Maximum9.9 × 108
Zeros47
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:04:22.240090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1150
median300
Q3600
95-th percentile339635
Maximum9.9 × 108
Range9.9 × 108
Interquartile range (IQR)450

Descriptive statistics

Standard deviation44321290
Coefficient of variation (CV)20.476221
Kurtosis497.49103
Mean2164524.9
Median Absolute Deviation (MAD)200
Skewness22.278878
Sum1.0822625 × 109
Variance1.9643767 × 1015
MonotonicityNot monotonic
2023-12-12T20:04:22.510792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 54
 
10.8%
0 47
 
9.4%
500 37
 
7.4%
1000 26
 
5.2%
200 24
 
4.8%
160 23
 
4.6%
100 16
 
3.2%
600 16
 
3.2%
150 10
 
2.0%
130 8
 
1.6%
Other values (136) 239
47.8%
ValueCountFrequency (%)
0 47
9.4%
30 1
 
0.2%
48 1
 
0.2%
50 6
 
1.2%
64 1
 
0.2%
80 1
 
0.2%
83 2
 
0.4%
90 2
 
0.4%
94 1
 
0.2%
95 1
 
0.2%
ValueCountFrequency (%)
990000000 1
0.2%
45565185 1
0.2%
18223093 1
0.2%
4075267 1
0.2%
3511008 1
0.2%
3063000 1
0.2%
2032320 1
0.2%
2000000 1
0.2%
1399128 1
0.2%
1333483 1
0.2%

운전자금추천금액
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.18
Minimum0
Maximum3000
Zeros381
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:04:22.774028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1000
Maximum3000
Range3000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation315.52242
Coefficient of variation (CV)2.283416
Kurtosis17.694373
Mean138.18
Median Absolute Deviation (MAD)0
Skewness3.4206233
Sum69090
Variance99554.396
MonotonicityNot monotonic
2023-12-12T20:04:22.992349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 381
76.2%
300 32
 
6.4%
500 22
 
4.4%
1000 18
 
3.6%
600 10
 
2.0%
200 5
 
1.0%
400 4
 
0.8%
150 3
 
0.6%
700 3
 
0.6%
1100 2
 
0.4%
Other values (18) 20
 
4.0%
ValueCountFrequency (%)
0 381
76.2%
80 1
 
0.2%
100 1
 
0.2%
150 3
 
0.6%
160 1
 
0.2%
200 5
 
1.0%
300 32
 
6.4%
310 1
 
0.2%
330 1
 
0.2%
340 1
 
0.2%
ValueCountFrequency (%)
3000 1
 
0.2%
2000 1
 
0.2%
1500 1
 
0.2%
1300 1
 
0.2%
1200 2
 
0.4%
1100 2
 
0.4%
1000 18
3.6%
950 1
 
0.2%
900 1
 
0.2%
800 2
 
0.4%

시설자금추천금액
Real number (ℝ)

ZEROS 

Distinct109
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.51
Minimum0
Maximum3296
Zeros281
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:04:23.203147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3180
95-th percentile752.25
Maximum3296
Range3296
Interquartile range (IQR)180

Descriptive statistics

Standard deviation370.90531
Coefficient of variation (CV)2.0102179
Kurtosis21.651295
Mean184.51
Median Absolute Deviation (MAD)0
Skewness3.9348695
Sum92255
Variance137570.75
MonotonicityNot monotonic
2023-12-12T20:04:23.400831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 281
56.2%
160 22
 
4.4%
130 8
 
1.6%
200 8
 
1.6%
120 6
 
1.2%
180 6
 
1.2%
150 6
 
1.2%
600 5
 
1.0%
100 5
 
1.0%
500 5
 
1.0%
Other values (99) 148
29.6%
ValueCountFrequency (%)
0 281
56.2%
30 1
 
0.2%
48 1
 
0.2%
64 1
 
0.2%
80 1
 
0.2%
83 2
 
0.4%
90 1
 
0.2%
94 1
 
0.2%
95 1
 
0.2%
99 2
 
0.4%
ValueCountFrequency (%)
3296 1
 
0.2%
3000 1
 
0.2%
2500 1
 
0.2%
2000 1
 
0.2%
1600 1
 
0.2%
1500 3
0.6%
1452 1
 
0.2%
1430 1
 
0.2%
1400 2
0.4%
1279 3
0.6%

자금용도명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
465 
0
 
34
운전자금
 
1

Length

Max length4
Median length4
Mean length3.796
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 465
93.0%
0 34
 
6.8%
운전자금 1
 
0.2%

Length

2023-12-12T20:04:23.573793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:23.698034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 465
93.0%
0 34
 
6.8%
운전자금 1
 
0.2%

거래희망은행명
Categorical

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
신한은행
153 
<NA>
146 
우리은행
113 
기업은행
51 
미정
21 
Other values (2)
16 

Length

Max length4
Median length4
Mean length3.824
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농협
2nd row농협
3rd row우리은행
4th row<NA>
5th row우리은행

Common Values

ValueCountFrequency (%)
신한은행 153
30.6%
<NA> 146
29.2%
우리은행 113
22.6%
기업은행 51
 
10.2%
미정 21
 
4.2%
14
 
2.8%
농협 2
 
0.4%

Length

2023-12-12T20:04:23.865338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:24.032629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신한은행 153
31.5%
na 146
30.0%
우리은행 113
23.3%
기업은행 51
 
10.5%
미정 21
 
4.3%
농협 2
 
0.4%

담당자전화번호
Text

MISSING 

Distinct205
Distinct (%)67.2%
Missing195
Missing (%)39.0%
Memory size4.0 KiB
2023-12-12T20:04:24.396664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length22.944262
Min length1

Characters and Unicode

Total characters6998
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)56.4%

Sample

1st rowAAEoJDpBZ3eI835sYF3dXnnv
2nd rowAAHMINFNbatgmOtqMJjMfnjO
3rd rowAAEPEKNrHSgeFsPrdc19kLL3
4th rowAAH+to4WMYoiExFb5eS1WMDk
5th rowAAHRkrmEv8sZm7qRzwD2WbPe
ValueCountFrequency (%)
aafkz16jqze2rd/rtb4rldg6 24
 
8.2%
aafazk6sorazmzn+ylo8mk6j 10
 
3.4%
aafdk2mgrcgk0tkuj2uqjjhi 7
 
2.4%
aafvvkx+pwiltj6hwbkiukge 6
 
2.1%
aaflfzlor4nt+291zvmfat4k 6
 
2.1%
aaeumxz0bmi8tnb016blmkkt 4
 
1.4%
aaecsmvnuynwlomeeedams4v 4
 
1.4%
aafxi5e7rzt+uhf+69fo5gj1 4
 
1.4%
aaflehxmg77nv60oedpi+yyd 4
 
1.4%
aah+ayr+jcg3qgp61l1igfck 3
 
1.0%
Other values (194) 219
75.3%
2023-12-12T20:04:25.501064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 664
 
9.5%
G 181
 
2.6%
R 177
 
2.5%
E 175
 
2.5%
6 168
 
2.4%
F 168
 
2.4%
H 150
 
2.1%
k 145
 
2.1%
D 139
 
2.0%
t 137
 
2.0%
Other values (55) 4894
69.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3377
48.3%
Lowercase Letter 2390
34.2%
Decimal Number 1028
 
14.7%
Math Symbol 118
 
1.7%
Other Punctuation 71
 
1.0%
Space Separator 14
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 664
19.7%
G 181
 
5.4%
R 177
 
5.2%
E 175
 
5.2%
F 168
 
5.0%
H 150
 
4.4%
D 139
 
4.1%
L 111
 
3.3%
Z 107
 
3.2%
M 103
 
3.1%
Other values (16) 1402
41.5%
Lowercase Letter
ValueCountFrequency (%)
k 145
 
6.1%
t 137
 
5.7%
z 133
 
5.6%
b 128
 
5.4%
j 118
 
4.9%
l 108
 
4.5%
m 101
 
4.2%
p 96
 
4.0%
h 93
 
3.9%
n 88
 
3.7%
Other values (16) 1243
52.0%
Decimal Number
ValueCountFrequency (%)
6 168
16.3%
2 130
12.6%
4 107
10.4%
7 100
9.7%
9 94
9.1%
1 94
9.1%
0 94
9.1%
5 85
8.3%
8 83
8.1%
3 73
7.1%
Math Symbol
ValueCountFrequency (%)
+ 118
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 71
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5767
82.4%
Common 1231
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 664
 
11.5%
G 181
 
3.1%
R 177
 
3.1%
E 175
 
3.0%
F 168
 
2.9%
H 150
 
2.6%
k 145
 
2.5%
D 139
 
2.4%
t 137
 
2.4%
z 133
 
2.3%
Other values (42) 3698
64.1%
Common
ValueCountFrequency (%)
6 168
13.6%
2 130
10.6%
+ 118
9.6%
4 107
8.7%
7 100
8.1%
9 94
7.6%
1 94
7.6%
0 94
7.6%
5 85
6.9%
8 83
6.7%
Other values (3) 158
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 664
 
9.5%
G 181
 
2.6%
R 177
 
2.5%
E 175
 
2.5%
6 168
 
2.4%
F 168
 
2.4%
H 150
 
2.1%
k 145
 
2.1%
D 139
 
2.0%
t 137
 
2.0%
Other values (55) 4894
69.9%

담당자휴대폰번호
Text

MISSING 

Distinct234
Distinct (%)67.4%
Missing153
Missing (%)30.6%
Memory size4.0 KiB
2023-12-12T20:04:25.858049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.072046
Min length1

Characters and Unicode

Total characters8006
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique196 ?
Unique (%)56.5%

Sample

1st rowAAEoJDpBZ3eI835sYF3dXnnv
2nd rowAAEoJDpBZ3eI835sYF3dXnnv
3rd rowAAHMINFNbatgmOtqMJjMfnjO
4th rowAAGDJ1CHL4y+M95FEiyOX8X6
5th rowAAEPEKNrHSgeFsPrdc19kLL3
ValueCountFrequency (%)
aae24edqkz6wpl79uivmj0oi 20
 
6.0%
aafvvkx+pwiltj6hwbkiukge 14
 
4.2%
aagc442dhv63/lwak3iy41qu 10
 
3.0%
aagen6lptc8vdpji3hzllwfn 8
 
2.4%
aaetbspf8uzo30gzalpluish 4
 
1.2%
aafmql+ubamt1spkqthzusxh 4
 
1.2%
aahuxg0jjak+0guj7fnouycf 4
 
1.2%
aagqv5pgmuz55bh3ibbh8y60 4
 
1.2%
aafpkc9uxw5zldfxagc89g97 3
 
0.9%
aafd0belnxe5qy8u5biag4zi 3
 
0.9%
Other values (223) 259
77.8%
2023-12-12T20:04:26.416057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 754
 
9.4%
E 213
 
2.7%
F 207
 
2.6%
H 179
 
2.2%
G 178
 
2.2%
I 169
 
2.1%
k 164
 
2.0%
L 159
 
2.0%
4 155
 
1.9%
W 150
 
1.9%
Other values (55) 5678
70.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3930
49.1%
Lowercase Letter 2660
33.2%
Decimal Number 1198
 
15.0%
Math Symbol 128
 
1.6%
Other Punctuation 76
 
0.9%
Space Separator 14
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 754
19.2%
E 213
 
5.4%
F 207
 
5.3%
H 179
 
4.6%
G 178
 
4.5%
I 169
 
4.3%
L 159
 
4.0%
W 150
 
3.8%
U 145
 
3.7%
Q 123
 
3.1%
Other values (16) 1653
42.1%
Lowercase Letter
ValueCountFrequency (%)
k 164
 
6.2%
j 142
 
5.3%
e 131
 
4.9%
z 126
 
4.7%
i 119
 
4.5%
v 106
 
4.0%
l 103
 
3.9%
n 102
 
3.8%
w 102
 
3.8%
p 101
 
3.8%
Other values (16) 1464
55.0%
Decimal Number
ValueCountFrequency (%)
4 155
12.9%
6 144
12.0%
0 133
11.1%
3 123
10.3%
8 112
9.3%
9 112
9.3%
7 108
9.0%
1 106
8.8%
5 103
8.6%
2 102
8.5%
Math Symbol
ValueCountFrequency (%)
+ 128
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 76
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6590
82.3%
Common 1416
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 754
 
11.4%
E 213
 
3.2%
F 207
 
3.1%
H 179
 
2.7%
G 178
 
2.7%
I 169
 
2.6%
k 164
 
2.5%
L 159
 
2.4%
W 150
 
2.3%
U 145
 
2.2%
Other values (42) 4272
64.8%
Common
ValueCountFrequency (%)
4 155
10.9%
6 144
10.2%
0 133
9.4%
+ 128
9.0%
3 123
8.7%
8 112
7.9%
9 112
7.9%
7 108
7.6%
1 106
7.5%
5 103
7.3%
Other values (3) 192
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 754
 
9.4%
E 213
 
2.7%
F 207
 
2.6%
H 179
 
2.2%
G 178
 
2.2%
I 169
 
2.1%
k 164
 
2.0%
L 159
 
2.0%
4 155
 
1.9%
W 150
 
1.9%
Other values (55) 5678
70.9%

담당자이메일
Text

MISSING 

Distinct189
Distinct (%)66.5%
Missing216
Missing (%)43.2%
Memory size4.0 KiB
2023-12-12T20:04:26.854443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length48
Mean length45.007042
Min length1

Characters and Unicode

Total characters12782
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)51.1%

Sample

1st rowAAE7r6piq06kF8IFqaSqfymT1QP7j+6CaYKiLbeZQ2WO+Q==
2nd rowAAE7r6piq06kF8IFqaSqfymT1QP7j+6CaYKiLbeZQ2WO+Q==
3rd rowAAF0P9KksKluPWga4Qu0VgNMi9AG9dqUldKzH8REganMeQ==
4th rowAAE1p35rf6BoiSThLwDBXJ6FB4NvsEa/kQlD4xEkrG6r5Q==
5th rowAAHAMFMqQDVUQCQ4h2HtUgvjc+41wKdgaN+y118TNCZExA==
ValueCountFrequency (%)
aae7r6piq06kf8ifqasqfymt1qp7j+6caykilbezq2wo+q 16
 
5.9%
aaekuveigfm7u0z3ufin7xpzqdy8mu76x0hurkf34zgq/q 12
 
4.4%
aah7++ggpcaonnqnhxvibe0lnz9se9uk7sh1bgcqsv6fma 6
 
2.2%
aaejz8pidxmmz37ixguottgzpd4+dis8ixj4u/tj/crr2a 4
 
1.5%
aafbarkboggo9brknjn6s8f0u52ekvduocm57t6ou7otmq 4
 
1.5%
aaescirfz5mjeorwo0x/cli04rswun+7jdb7gnurggskxa 3
 
1.1%
aaegcqbhqzyp6xu9iiykfq5qtzac433rwidyf0rral/8wg 3
 
1.1%
aafsgl9hftxuo/sg2zdj3aot8i8ofgdwadt1br0eht6xiw 3
 
1.1%
aae08z3b81k210hzibit3gmvzlp7whb669m1gjqlp/fspg 3
 
1.1%
aahndl2odchh7pieflhlblipiwfryihz6wqjfk1vnpstuw 3
 
1.1%
Other values (178) 213
78.9%
2023-12-12T20:04:27.431954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 770
 
6.0%
= 524
 
4.1%
F 287
 
2.2%
Q 275
 
2.2%
E 268
 
2.1%
g 240
 
1.9%
Z 231
 
1.8%
7 226
 
1.8%
H 224
 
1.8%
M 218
 
1.7%
Other values (56) 9519
74.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5572
43.6%
Lowercase Letter 4511
35.3%
Decimal Number 1808
 
14.1%
Math Symbol 727
 
5.7%
Other Punctuation 150
 
1.2%
Space Separator 14
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 770
 
13.8%
F 287
 
5.2%
Q 275
 
4.9%
E 268
 
4.8%
Z 231
 
4.1%
H 224
 
4.0%
M 218
 
3.9%
G 217
 
3.9%
U 203
 
3.6%
T 196
 
3.5%
Other values (16) 2683
48.2%
Lowercase Letter
ValueCountFrequency (%)
g 240
 
5.3%
q 209
 
4.6%
i 207
 
4.6%
w 206
 
4.6%
a 205
 
4.5%
k 183
 
4.1%
b 182
 
4.0%
u 179
 
4.0%
e 179
 
4.0%
r 177
 
3.9%
Other values (16) 2544
56.4%
Decimal Number
ValueCountFrequency (%)
7 226
12.5%
6 214
11.8%
3 192
10.6%
0 190
10.5%
4 178
9.8%
8 177
9.8%
2 170
9.4%
5 167
9.2%
1 160
8.8%
9 134
7.4%
Math Symbol
ValueCountFrequency (%)
= 524
72.1%
+ 203
 
27.9%
Other Punctuation
ValueCountFrequency (%)
/ 150
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10083
78.9%
Common 2699
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 770
 
7.6%
F 287
 
2.8%
Q 275
 
2.7%
E 268
 
2.7%
g 240
 
2.4%
Z 231
 
2.3%
H 224
 
2.2%
M 218
 
2.2%
G 217
 
2.2%
q 209
 
2.1%
Other values (42) 7144
70.9%
Common
ValueCountFrequency (%)
= 524
19.4%
7 226
8.4%
6 214
7.9%
+ 203
 
7.5%
3 192
 
7.1%
0 190
 
7.0%
4 178
 
6.6%
8 177
 
6.6%
2 170
 
6.3%
5 167
 
6.2%
Other values (4) 458
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 770
 
6.0%
= 524
 
4.1%
F 287
 
2.2%
Q 275
 
2.2%
E 268
 
2.1%
g 240
 
1.9%
Z 231
 
1.8%
7 226
 
1.8%
H 224
 
1.8%
M 218
 
1.7%
Other values (56) 9519
74.5%

비고내용
Categorical

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
태양광(발전)
199 
<NA>
149 
태양광(설치)
86 
 
14
태양광(ESS)
 
14
Other values (19)
38 

Length

Max length16
Median length7
Mean length6.128
Min length1

Unique

Unique13 ?
Unique (%)2.6%

Sample

1st row<NA>
2nd row<NA>
3rd row태양광(발전)
4th row<NA>
5th row태양광(발전)

Common Values

ValueCountFrequency (%)
태양광(발전) 199
39.8%
<NA> 149
29.8%
태양광(설치) 86
17.2%
14
 
2.8%
태양광(ESS) 14
 
2.8%
태양광(제조/설치) 9
 
1.8%
바이오(제조) 4
 
0.8%
태양광(제조) 4
 
0.8%
지열(설치) 3
 
0.6%
태양광(발전,ESS) 3
 
0.6%
Other values (14) 15
 
3.0%

Length

2023-12-12T20:04:27.665687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태양광(발전 200
40.9%
na 149
30.5%
태양광(설치 86
17.6%
태양광(ess 14
 
2.9%
태양광(제조/설치 9
 
1.8%
바이오(제조 4
 
0.8%
태양광(제조 4
 
0.8%
태양광(발전,ess 3
 
0.6%
지열(설치 3
 
0.6%
폐기물(제조/설치 2
 
0.4%
Other values (15) 15
 
3.1%

이메일전송일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
500 

Length

Max length26
Median length26
Mean length26
Min length26

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
3rd row0001-01-01 00:00:00.000000
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 500
100.0%

Length

2023-12-12T20:04:27.869792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:28.023821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 500
50.0%
00:00:00.000000 500
50.0%

추천기업규모코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
472 
3
 
26
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
472
94.4%
3 26
 
5.2%
2 2
 
0.4%

Length

2023-12-12T20:04:28.177468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:28.313606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 26
92.9%
2 2
 
7.1%

유효기한
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
472 
00:00.0
 
28

Length

Max length26
Median length26
Mean length24.936
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
3rd row0001-01-01 00:00:00.000000
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 472
94.4%
00:00.0 28
 
5.6%

Length

2023-12-12T20:04:28.454887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:04:28.597257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 472
48.6%
00:00:00.000000 472
48.6%
00:00.0 28
 
2.9%

삭제여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
494 
True
 
6
ValueCountFrequency (%)
False 494
98.8%
True 6
 
1.2%
2023-12-12T20:04:28.724476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.28
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:04:28.838173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum19
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6440821
Coefficient of variation (CV)0.72108865
Kurtosis33.808442
Mean2.28
Median Absolute Deviation (MAD)1
Skewness4.4760158
Sum1140
Variance2.703006
MonotonicityNot monotonic
2023-12-12T20:04:29.028962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 218
43.6%
1 145
29.0%
3 77
 
15.4%
4 29
 
5.8%
5 14
 
2.8%
6 8
 
1.6%
7 3
 
0.6%
8 2
 
0.4%
19 1
 
0.2%
16 1
 
0.2%
Other values (2) 2
 
0.4%
ValueCountFrequency (%)
1 145
29.0%
2 218
43.6%
3 77
 
15.4%
4 29
 
5.8%
5 14
 
2.8%
6 8
 
1.6%
7 3
 
0.6%
8 2
 
0.4%
10 1
 
0.2%
12 1
 
0.2%
ValueCountFrequency (%)
19 1
 
0.2%
16 1
 
0.2%
12 1
 
0.2%
10 1
 
0.2%
8 2
 
0.4%
7 3
 
0.6%
6 8
 
1.6%
5 14
 
2.8%
4 29
 
5.8%
3 77
15.4%

처리시각
Text

Distinct236
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T20:04:29.530963image/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

Unique162 ?
Unique (%)32.4%

Sample

1st row36:28.2
2nd row43:37.6
3rd row09:05.3
4th row00:53.9
5th row55:44.0
ValueCountFrequency (%)
49:48.6 20
 
4.0%
02:51.2 18
 
3.6%
11:47.7 13
 
2.6%
32:18.7 13
 
2.6%
49:16.2 12
 
2.4%
32:27.0 11
 
2.2%
48:44.5 10
 
2.0%
08:42.3 9
 
1.8%
39:58.8 8
 
1.6%
58:12.5 8
 
1.6%
Other values (226) 378
75.6%
2023-12-12T20:04:30.299937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 351
10.0%
2 340
9.7%
1 312
8.9%
5 289
8.3%
3 284
8.1%
0 259
7.4%
8 200
 
5.7%
7 193
 
5.5%
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 (%)
4 351
14.0%
2 340
13.6%
1 312
12.5%
5 289
11.6%
3 284
11.4%
0 259
10.4%
8 200
8.0%
7 193
7.7%
9 153
6.1%
6 119
 
4.8%
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%
4 351
10.0%
2 340
9.7%
1 312
8.9%
5 289
8.3%
3 284
8.1%
0 259
7.4%
8 200
 
5.7%
7 193
 
5.5%
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%
4 351
10.0%
2 340
9.7%
1 312
8.9%
5 289
8.3%
3 284
8.1%
0 259
7.4%
8 200
 
5.7%
7 193
 
5.5%
Other values (2) 272
7.8%

처리직원번호
Real number (ℝ)

Distinct44
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5377.182
Minimum2458
Maximum5932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:04:30.557524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2458
5-th percentile4655.95
Q15472
median5472
Q35552
95-th percentile5637
Maximum5932
Range3474
Interquartile range (IQR)80

Descriptive statistics

Standard deviation413.92549
Coefficient of variation (CV)0.076978144
Kurtosis17.172952
Mean5377.182
Median Absolute Deviation (MAD)56
Skewness-3.9006245
Sum2688591
Variance171334.31
MonotonicityNot monotonic
2023-12-12T20:04:30.771361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
5472 244
48.8%
5552 113
22.6%
5152 44
 
8.8%
5553 23
 
4.6%
5637 19
 
3.8%
5416 7
 
1.4%
5823 3
 
0.6%
5797 3
 
0.6%
5185 3
 
0.6%
4588 2
 
0.4%
Other values (34) 39
 
7.8%
ValueCountFrequency (%)
2458 1
0.2%
2810 1
0.2%
3055 1
0.2%
3136 1
0.2%
3254 1
0.2%
3443 1
0.2%
3461 1
0.2%
3464 1
0.2%
3496 1
0.2%
3732 1
0.2%
ValueCountFrequency (%)
5932 1
 
0.2%
5894 1
 
0.2%
5823 3
 
0.6%
5797 3
 
0.6%
5637 19
 
3.8%
5616 1
 
0.2%
5608 1
 
0.2%
5553 23
 
4.6%
5552 113
22.6%
5472 244
48.8%

Sample

상생보증구분코드상생보증대기업구분코드부점코드팀코드입력일자담당팀입력일자추천금액운전자금추천금액시설자금추천금액자금용도명거래희망은행명담당자전화번호담당자휴대폰번호담당자이메일비고내용이메일전송일자추천기업규모코드유효기한삭제여부최종수정수처리시각처리직원번호
0515TAH00:00.000:00.0100000<NA>농협<NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N136:28.24805
1515TPA00:00.000:00.099000000000<NA>농협<NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N143:37.64805
21TQA00:00.00001-01-01 00:00:00.0000001000100<NA>우리은행AAEoJDpBZ3eI835sYF3dXnnvAAEoJDpBZ3eI835sYF3dXnnvAAE7r6piq06kF8IFqaSqfymT1QP7j+6CaYKiLbeZQ2WO+Q==태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N209:05.35637
3444QAC00:00.000:00.050000<NA><NA><NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N100:53.95637
41TPD200:00.00001-01-01 00:00:00.0000003050305<NA>우리은행<NA>AAEoJDpBZ3eI835sYF3dXnnvAAE7r6piq06kF8IFqaSqfymT1QP7j+6CaYKiLbeZQ2WO+Q==태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N255:44.05552
51TPD100:00.00001-01-01 00:00:00.0000003003000<NA>우리은행AAHMINFNbatgmOtqMJjMfnjOAAHMINFNbatgmOtqMJjMfnjOAAF0P9KksKluPWga4Qu0VgNMi9AG9dqUldKzH8REganMeQ==태양광(설치)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N154:00.55552
643700:00.00001-01-01 00:00:00.000000000<NA><NA><NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N129:00.85797
7437QAC00:00.00001-01-01 00:00:00.00000030000<NA><NA><NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N107:17.15797
8444TAV200:00.000:00.030000<NA><NA><NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N238:17.83732
9444THU00:00.000:00.0100000<NA><NA><NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N205:28.55637
상생보증구분코드상생보증대기업구분코드부점코드팀코드입력일자담당팀입력일자추천금액운전자금추천금액시설자금추천금액자금용도명거래희망은행명담당자전화번호담당자휴대폰번호담당자이메일비고내용이메일전송일자추천기업규모코드유효기한삭제여부최종수정수처리시각처리직원번호
490443TLE00:00.000:00.030000<NA><NA><NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N159:29.25553
491443TLB00:00.000:00.030000<NA><NA><NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N159:29.25553
4921TQJ100:00.00001-01-01 00:00:00.0000002002000<NA>기업은행AAHgzdSsimehGe0C2Vxjr+hUAAFy5HEnNjM/aswB/5epRDn+AAF9OIBCLHKYTeN4+EaaUMP3rJzJBKB43Kdj1IOR5y4a6w==수열(설치)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N348:10.85472
4931TPO100:00.00001-01-01 00:00:00.0000004950495<NA>신한은행AAE/zhbuRTt2S0xzGyv7HU7GAAGAPDojD4ortYAgVeb6qD7zAAHStJF5PdpO2EIUMtmf+lUd태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N348:10.85472
4941THS200:00.00001-01-01 00:00:00.0000004260426<NA>기업은행AAFlfzlOr4Nt+291ZVMFAt4KAAGen6LPtc8vDPji3HZlLWfNAAH7++ggPCAoNNqNHXvibe0lNZ9SE9Uk7SH1bGcQsv6FMA==태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N348:10.85472
4951TNB100:00.00001-01-01 00:00:00.0000008280828<NA>우리은행AAGvR5DpNYz5SMjAPptmuruEAAH1gL294WQbv4r2gboZZM7uAAHLkIoGSS1KOF23T138aPnq495T4VjBC+yvXOCHjwgOCA==태양광(ESS)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N348:10.85472
4961THV200:00.00001-01-01 00:00:00.0000005005000<NA>기업은행AAHK054ErnXyJDwqWclKREkhAAGd8F+dUFexp7hYcOpKKIH/AAGghDEQhJGoEX4lP4/BXHNeDCk8a0F1+U7SG7mYThf9HQ==태양광(설치)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N745:50.15472
4971TOD100:00.00001-01-01 00:00:00.0000004004000<NA>신한은행AAGnFJG9KpPK+vqQhUa8ne/1AAHsZkrGK7wfHK68K7bMlehoAAFqM0eat22PBpTxHfVbfshCsWyGrpT8PiRYGVtr00DITw==태양광(제조/설치)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N245:50.15472
4981TAW200:00.00001-01-01 00:00:00.000000120012000<NA>기업은행AAGipLckQr8ZHsLRzyZWZjYdAAEuDJFsvexAxZum+Bq2nNpsAAG8brd2hda0aQR3Oe2J0ZYduCAZD6mIMYzeDmaE8RlrvA==태양광(제조)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N245:50.15472
4991THJ100:00.00001-01-01 00:00:00.0000006006000<NA>기업은행AAGKm3wAJOOL7R0zINfbmmtnAAFoiA60rukyhNlnKMtBPMxGAAEnLEFwc0BtaPzyF//MAV4rrE7Z441+PiuMI2ycTikdXw==태양광(설치)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N445:50.15472

Duplicate rows

Most frequently occurring

상생보증구분코드상생보증대기업구분코드부점코드팀코드입력일자담당팀입력일자추천금액운전자금추천금액시설자금추천금액자금용도명거래희망은행명담당자전화번호담당자휴대폰번호담당자이메일비고내용이메일전송일자추천기업규모코드유효기한삭제여부최종수정수처리시각처리직원번호# duplicates
1551500:00.000:00.0000<NA><NA><NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N145:07.851525
1751500:00.000:00.0000<NA><NA><NA><NA><NA><NA>0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N327:00.551525
41TBA200:00.00001-01-01 00:00:00.0000001600160<NA>우리은행AAFkz16JQZE2RD/Rtb4RLDG6AAE24edQkz6WPL79UIVMj0OI<NA>태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N249:16.254724
111TNA100:00.00001-01-01 00:00:00.0000001800180<NA>신한은행AAFAZk6SorazMzN+Ylo8mK6jAAGC442DHv63/LWAK3iY41qU<NA>태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N258:12.554724
11QAC100:00.00001-01-01 00:00:00.0000001600160<NA>신한은행AAFAZk6SorazMzN+Ylo8mK6jAAGC442DHv63/LWAK3iY41qU<NA>태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N242:40.154723
121TPD200:00.00001-01-01 00:00:00.0000001250125<NA>우리은행AAFVVkX+pWILTj6HWBkIUkGeAAFVVkX+pWILTj6HWBkIUkGeAAE7r6piq06kF8IFqaSqfymT1QP7j+6CaYKiLbeZQ2WO+Q==태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N149:45.255523
0100:00.00001-01-01 00:00:00.0000005500550<NA>신한은행AAExpI667wdjzgp5MBP+E6rbAAFCxI/zl0DTWizlWAhz6iH4AAGLNp+DJ8bXU2CGywRfDHhP+p7rZzL4LezQsoVwsXcMOA==태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N155:14.054722
21TBA100:00.00001-01-01 00:00:00.0000001600160<NA>우리은행AAFkz16JQZE2RD/Rtb4RLDG6AAE24edQkz6WPL79UIVMj0OIAAEkuVeiGFM7U0Z3Ufin7xpzqDY8Mu76X0huRkF34ZgQ/Q==태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N112:43.955522
31TBA200:00.00001-01-01 00:00:00.0000001600160<NA>우리은행AAFkz16JQZE2RD/Rtb4RLDG6AAE24edQkz6WPL79UIVMj0OIAAEkuVeiGFM7U0Z3Ufin7xpzqDY8Mu76X0huRkF34ZgQ/Q==태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N202:51.254722
51TBA200:00.00001-01-01 00:00:00.0000001600160<NA>우리은행AAFkz16JQZE2RD/Rtb4RLDG6AAF4QRkvkaB8l3URvCCoWCEa<NA>태양광(발전)0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N256:14.854722