Overview

Dataset statistics

Number of variables13
Number of observations500
Missing cells962
Missing cells (%)14.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.8 KiB
Average record size in memory110.3 B

Variable types

Text3
Categorical5
Numeric3
Unsupported1
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 고객기본정보고객관계에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093057/fileData.do

Alerts

광역지역구분코드 has constant value ""Constant
등록직원번호 is highly overall correlated with 처리직원번호High correlation
해제직원번호 is highly overall correlated with 처리직원번호 and 4 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 등록직원번호 and 1 other fieldsHigh correlation
고객관계구분코드 is highly overall correlated with 해제직원번호 and 1 other fieldsHigh correlation
주력기업여부 is highly overall correlated with 고객관계구분코드High correlation
해제부점코드 is highly overall correlated with 해제직원번호High correlation
삭제여부 is highly overall correlated with 해제직원번호High correlation
최종수정수 is highly overall correlated with 해제직원번호High correlation
고객관계구분코드 is highly imbalanced (58.2%)Imbalance
해제부점코드 is highly imbalanced (84.2%)Imbalance
삭제여부 is highly imbalanced (97.9%)Imbalance
최종수정수 is highly imbalanced (64.5%)Imbalance
해제직원번호 has 460 (92.0%) missing valuesMissing
해제사유내용 has 500 (100.0%) missing valuesMissing
해제사유내용 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 15:08:21.606097
Analysis finished2023-12-12 15:08:23.302788
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

제1고객ID
Text

Distinct424
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:08:23.496367image/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

Unique387 ?
Unique (%)77.4%

Sample

1st rowaaaaaakL8V
2nd rowaaaaaakL8V
3rd rowaaaaaakL8V
4th rowaaaaaakL8V
5th rowaaaaaakL8V
ValueCountFrequency (%)
aaaaadfgro 10
 
2.0%
aaaaad9xj1 7
 
1.4%
aaaaacc6ka 6
 
1.2%
aaaaaakl8v 5
 
1.0%
9dnokfcu6e 5
 
1.0%
9dbtioambb 5
 
1.0%
aaaaabeohf 5
 
1.0%
aaaaaaqgs8 4
 
0.8%
9dnm14ag9l 4
 
0.8%
9cupxf2gud 3
 
0.6%
Other values (414) 446
89.2%
2023-12-13T00:08:23.922136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 526
 
10.5%
9 467
 
9.3%
d 437
 
8.7%
n 354
 
7.1%
S 345
 
6.9%
c 104
 
2.1%
K 78
 
1.6%
O 76
 
1.5%
X 76
 
1.5%
J 67
 
1.3%
Other values (52) 2470
49.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2431
48.6%
Uppercase Letter 1699
34.0%
Decimal Number 870
 
17.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 526
21.6%
d 437
18.0%
n 354
14.6%
c 104
 
4.3%
b 64
 
2.6%
m 63
 
2.6%
g 55
 
2.3%
p 53
 
2.2%
w 52
 
2.1%
f 52
 
2.1%
Other values (16) 671
27.6%
Uppercase Letter
ValueCountFrequency (%)
S 345
20.3%
K 78
 
4.6%
O 76
 
4.5%
X 76
 
4.5%
J 67
 
3.9%
F 64
 
3.8%
L 61
 
3.6%
G 58
 
3.4%
W 58
 
3.4%
V 58
 
3.4%
Other values (16) 758
44.6%
Decimal Number
ValueCountFrequency (%)
9 467
53.7%
4 53
 
6.1%
1 50
 
5.7%
3 49
 
5.6%
8 48
 
5.5%
5 45
 
5.2%
6 44
 
5.1%
2 43
 
4.9%
0 41
 
4.7%
7 30
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4130
82.6%
Common 870
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 526
 
12.7%
d 437
 
10.6%
n 354
 
8.6%
S 345
 
8.4%
c 104
 
2.5%
K 78
 
1.9%
O 76
 
1.8%
X 76
 
1.8%
J 67
 
1.6%
F 64
 
1.5%
Other values (42) 2003
48.5%
Common
ValueCountFrequency (%)
9 467
53.7%
4 53
 
6.1%
1 50
 
5.7%
3 49
 
5.6%
8 48
 
5.5%
5 45
 
5.2%
6 44
 
5.1%
2 43
 
4.9%
0 41
 
4.7%
7 30
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 526
 
10.5%
9 467
 
9.3%
d 437
 
8.7%
n 354
 
7.1%
S 345
 
6.9%
c 104
 
2.1%
K 78
 
1.6%
O 76
 
1.5%
X 76
 
1.5%
J 67
 
1.3%
Other values (52) 2470
49.4%

고객관계구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
105
390 
104
104 
101
 
5
102
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row104
2nd row104
3rd row104
4th row104
5th row104

Common Values

ValueCountFrequency (%)
105 390
78.0%
104 104
 
20.8%
101 5
 
1.0%
102 1
 
0.2%

Length

2023-12-13T00:08:24.097716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:08:24.195260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
105 390
78.0%
104 104
 
20.8%
101 5
 
1.0%
102 1
 
0.2%

제2고객ID
Text

Distinct457
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:08:24.473689image/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

Unique417 ?
Unique (%)83.4%

Sample

1st row9cz6oRrhhs
2nd row9cqjwyy8Wb
3rd row9bTgth4pEo
4th rowaaaaaawIqp
5th rowaaaaabU81Y
ValueCountFrequency (%)
9dnskw50wp 4
 
0.8%
9by9eyckyo 3
 
0.6%
9dnsjmiugp 2
 
0.4%
9cz6orrhhs 2
 
0.4%
9dnsua0ahk 2
 
0.4%
9dnsw6cbv8 2
 
0.4%
9bksm0mocd 2
 
0.4%
9dnsivstwq 2
 
0.4%
aaaaacy7wj 2
 
0.4%
9dnspsf69q 2
 
0.4%
Other values (447) 477
95.4%
2023-12-13T00:08:24.889400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 514
 
10.3%
d 458
 
9.2%
n 439
 
8.8%
S 415
 
8.3%
a 185
 
3.7%
c 91
 
1.8%
M 80
 
1.6%
K 79
 
1.6%
W 79
 
1.6%
J 76
 
1.5%
Other values (52) 2584
51.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2207
44.1%
Uppercase Letter 1873
37.5%
Decimal Number 920
18.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 458
20.8%
n 439
19.9%
a 185
 
8.4%
c 91
 
4.1%
b 67
 
3.0%
m 60
 
2.7%
o 59
 
2.7%
p 55
 
2.5%
f 52
 
2.4%
l 51
 
2.3%
Other values (16) 690
31.3%
Uppercase Letter
ValueCountFrequency (%)
S 415
22.2%
M 80
 
4.3%
K 79
 
4.2%
W 79
 
4.2%
J 76
 
4.1%
Y 72
 
3.8%
O 71
 
3.8%
Q 65
 
3.5%
L 65
 
3.5%
I 60
 
3.2%
Other values (16) 811
43.3%
Decimal Number
ValueCountFrequency (%)
9 514
55.9%
0 54
 
5.9%
6 53
 
5.8%
8 51
 
5.5%
5 49
 
5.3%
4 48
 
5.2%
7 43
 
4.7%
3 38
 
4.1%
1 37
 
4.0%
2 33
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4080
81.6%
Common 920
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 458
 
11.2%
n 439
 
10.8%
S 415
 
10.2%
a 185
 
4.5%
c 91
 
2.2%
M 80
 
2.0%
K 79
 
1.9%
W 79
 
1.9%
J 76
 
1.9%
Y 72
 
1.8%
Other values (42) 2106
51.6%
Common
ValueCountFrequency (%)
9 514
55.9%
0 54
 
5.9%
6 53
 
5.8%
8 51
 
5.5%
5 49
 
5.3%
4 48
 
5.2%
7 43
 
4.7%
3 38
 
4.1%
1 37
 
4.0%
2 33
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 514
 
10.3%
d 458
 
9.2%
n 439
 
8.8%
S 415
 
8.3%
a 185
 
3.7%
c 91
 
1.8%
M 80
 
1.6%
K 79
 
1.6%
W 79
 
1.6%
J 76
 
1.5%
Other values (52) 2584
51.7%

주력기업여부
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
384 
N
85 
Y
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
384
76.8%
N 85
 
17.0%
Y 31
 
6.2%

Length

2023-12-13T00:08:25.070738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:08:25.214679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 85
73.3%
y 31
 
26.7%

광역지역구분코드
Categorical

CONSTANT 

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

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 (%)
500
100.0%

Length

2023-12-13T00:08:25.328236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:08:25.428736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

등록직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)27.5%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean54531.175
Minimum2316
Maximum99023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:08:25.553978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2316
5-th percentile3467.65
Q15037.25
median88889
Q399002
95-th percentile99023
Maximum99023
Range96707
Interquartile range (IQR)93964.75

Descriptive statistics

Standard deviation45911.27
Coefficient of variation (CV)0.84192702
Kurtosis-1.96535
Mean54531.175
Median Absolute Deviation (MAD)10134
Skewness-0.15650001
Sum27156525
Variance2.1078447 × 109
MonotonicityNot monotonic
2023-12-13T00:08:25.750168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99001 67
 
13.4%
88889 61
 
12.2%
99006 32
 
6.4%
99023 30
 
6.0%
99002 26
 
5.2%
99016 24
 
4.8%
99007 23
 
4.6%
5937 10
 
2.0%
4020 9
 
1.8%
5931 7
 
1.4%
Other values (127) 209
41.8%
ValueCountFrequency (%)
2316 1
0.2%
2566 1
0.2%
2758 2
0.4%
2962 1
0.2%
3071 1
0.2%
3151 2
0.4%
3178 1
0.2%
3316 1
0.2%
3330 2
0.4%
3355 2
0.4%
ValueCountFrequency (%)
99023 30
6.0%
99016 24
 
4.8%
99015 2
 
0.4%
99014 1
 
0.2%
99007 23
 
4.6%
99006 32
6.4%
99002 26
 
5.2%
99001 67
13.4%
88889 61
12.2%
88888 4
 
0.8%

해제부점코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct30
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
460 
THA
 
5
TME
 
3
TAP
 
2
TMB
 
2
Other values (25)
 
28

Length

Max length3
Median length1
Mean length1.16
Min length1

Unique

Unique22 ?
Unique (%)4.4%

Sample

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

Common Values

ValueCountFrequency (%)
460
92.0%
THA 5
 
1.0%
TME 3
 
0.6%
TAP 2
 
0.4%
TMB 2
 
0.4%
NBN 2
 
0.4%
WAA 2
 
0.4%
TMA 2
 
0.4%
TQA 1
 
0.2%
TIE 1
 
0.2%
Other values (20) 20
 
4.0%

Length

2023-12-13T00:08:25.892652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tha 5
 
12.5%
tme 3
 
7.5%
tap 2
 
5.0%
tmb 2
 
5.0%
nbn 2
 
5.0%
waa 2
 
5.0%
tma 2
 
5.0%
tqd 1
 
2.5%
tjc 1
 
2.5%
tbk 1
 
2.5%
Other values (19) 19
47.5%

해제직원번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)87.5%
Missing460
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean14117.975
Minimum2500
Maximum99016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:08:26.038475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2500
5-th percentile2653
Q14018
median5114.5
Q35851.25
95-th percentile99001.05
Maximum99016
Range96516
Interquartile range (IQR)1833.25

Descriptive statistics

Standard deviation28677.126
Coefficient of variation (CV)2.0312493
Kurtosis5.953325
Mean14117.975
Median Absolute Deviation (MAD)958.5
Skewness2.7640795
Sum564719
Variance8.2237758 × 108
MonotonicityNot monotonic
2023-12-13T00:08:26.194190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2941 2
 
0.4%
5799 2
 
0.4%
99001 2
 
0.4%
4041 2
 
0.4%
2653 2
 
0.4%
5769 1
 
0.2%
5596 1
 
0.2%
5459 1
 
0.2%
3608 1
 
0.2%
4931 1
 
0.2%
Other values (25) 25
 
5.0%
(Missing) 460
92.0%
ValueCountFrequency (%)
2500 1
0.2%
2653 2
0.4%
2851 1
0.2%
2941 2
0.4%
3504 1
0.2%
3608 1
0.2%
3773 1
0.2%
3949 1
0.2%
4041 2
0.4%
4056 1
0.2%
ValueCountFrequency (%)
99016 1
0.2%
99002 1
0.2%
99001 2
0.4%
6200 1
0.2%
6127 1
0.2%
6107 1
0.2%
6039 1
0.2%
6016 1
0.2%
6008 1
0.2%
5799 2
0.4%

해제사유내용
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

삭제여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
499 
True
 
1
ValueCountFrequency (%)
False 499
99.8%
True 1
 
0.2%
2023-12-13T00:08:26.308510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
411 
2
73 
3
 
13
4
 
2
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 411
82.2%
2 73
 
14.6%
3 13
 
2.6%
4 2
 
0.4%
7 1
 
0.2%

Length

2023-12-13T00:08:26.400398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:08:26.515945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 411
82.2%
2 73
 
14.6%
3 13
 
2.6%
4 2
 
0.4%
7 1
 
0.2%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51645.304
Minimum2500
Maximum99023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:08:26.667468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2500
5-th percentile3590
Q15065.75
median88889
Q399002
95-th percentile99023
Maximum99023
Range96523
Interquartile range (IQR)93936.25

Descriptive statistics

Standard deviation46164.551
Coefficient of variation (CV)0.89387704
Kurtosis-1.9928171
Mean51645.304
Median Absolute Deviation (MAD)10134
Skewness-0.021303823
Sum25822652
Variance2.1311658 × 109
MonotonicityNot monotonic
2023-12-13T00:08:26.889785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99001 63
 
12.6%
88889 49
 
9.8%
99006 32
 
6.4%
99023 30
 
6.0%
99002 27
 
5.4%
99016 24
 
4.8%
99007 23
 
4.6%
4020 15
 
3.0%
5937 11
 
2.2%
5330 8
 
1.6%
Other values (124) 218
43.6%
ValueCountFrequency (%)
2500 1
 
0.2%
2653 2
 
0.4%
2851 1
 
0.2%
2941 2
 
0.4%
2962 1
 
0.2%
3071 1
 
0.2%
3178 1
 
0.2%
3423 5
1.0%
3472 1
 
0.2%
3504 3
0.6%
ValueCountFrequency (%)
99023 30
6.0%
99016 24
 
4.8%
99015 2
 
0.4%
99014 1
 
0.2%
99007 23
 
4.6%
99006 32
6.4%
99002 27
5.4%
99001 63
12.6%
88889 49
9.8%
88888 3
 
0.6%

최초처리직원번호
Text

Distinct134
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:08:27.133192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.546
Min length3

Characters and Unicode

Total characters2273
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)14.2%

Sample

1st row4020
2nd row4020
3rd row4020
4th row4020
5th row4020
ValueCountFrequency (%)
99001 67
 
13.4%
88889 61
 
12.2%
99006 32
 
6.4%
99023 30
 
6.0%
99002 26
 
5.2%
99016 24
 
4.8%
99007 23
 
4.6%
5937 10
 
2.0%
4020 9
 
1.8%
5931 7
 
1.4%
Other values (124) 211
42.2%
2023-12-13T00:08:27.563301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 565
24.9%
0 455
20.0%
8 300
13.2%
1 159
 
7.0%
3 159
 
7.0%
6 149
 
6.6%
5 141
 
6.2%
2 136
 
6.0%
4 103
 
4.5%
7 86
 
3.8%
Other values (5) 20
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2253
99.1%
Uppercase Letter 20
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 565
25.1%
0 455
20.2%
8 300
13.3%
1 159
 
7.1%
3 159
 
7.1%
6 149
 
6.6%
5 141
 
6.3%
2 136
 
6.0%
4 103
 
4.6%
7 86
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
B 4
20.0%
A 4
20.0%
T 4
20.0%
C 4
20.0%
H 4
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2253
99.1%
Latin 20
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
9 565
25.1%
0 455
20.2%
8 300
13.3%
1 159
 
7.1%
3 159
 
7.1%
6 149
 
6.6%
5 141
 
6.3%
2 136
 
6.0%
4 103
 
4.6%
7 86
 
3.8%
Latin
ValueCountFrequency (%)
B 4
20.0%
A 4
20.0%
T 4
20.0%
C 4
20.0%
H 4
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 565
24.9%
0 455
20.0%
8 300
13.2%
1 159
 
7.0%
3 159
 
7.0%
6 149
 
6.6%
5 141
 
6.2%
2 136
 
6.0%
4 103
 
4.5%
7 86
 
3.8%
Other values (5) 20
 
0.9%

Interactions

2023-12-13T00:08:22.505801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:22.070950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:22.283495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:22.606487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:22.143899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:22.353940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:22.760853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:22.209451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:22.425538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:08:27.675874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객관계구분코드주력기업여부등록직원번호해제부점코드해제직원번호삭제여부최종수정수처리직원번호
고객관계구분코드1.0000.6370.4130.000NaN0.6320.2700.386
주력기업여부0.6371.0000.7500.0720.0000.0000.4270.729
등록직원번호0.4130.7501.0000.3920.3670.0000.2860.997
해제부점코드0.0000.0720.3921.0001.0000.0000.7890.000
해제직원번호NaN0.0000.3671.0001.000NaN0.3420.975
삭제여부0.6320.0000.0000.000NaN1.0000.0500.000
최종수정수0.2700.4270.2860.7890.3420.0501.0000.367
처리직원번호0.3860.7290.9970.0000.9750.0000.3671.000
2023-12-13T00:08:27.807655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수해제부점코드고객관계구분코드주력기업여부삭제여부
최종수정수1.0000.4620.2230.3570.061
해제부점코드0.4621.0000.0000.0300.000
고객관계구분코드0.2230.0001.0000.6580.439
주력기업여부0.3570.0300.6581.0000.000
삭제여부0.0610.0000.4390.0001.000
2023-12-13T00:08:27.909764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록직원번호해제직원번호처리직원번호고객관계구분코드주력기업여부해제부점코드삭제여부최종수정수
등록직원번호1.0000.1130.9420.4030.4090.1900.0000.226
해제직원번호0.1131.0001.0001.0000.0000.5381.0000.540
처리직원번호0.9421.0001.0000.3740.3840.0000.0000.299
고객관계구분코드0.4031.0000.3741.0000.6580.0000.4390.223
주력기업여부0.4090.0000.3840.6581.0000.0300.0000.357
해제부점코드0.1900.5380.0000.0000.0301.0000.0000.462
삭제여부0.0001.0000.0000.4390.0000.0001.0000.061
최종수정수0.2260.5400.2990.2230.3570.4620.0611.000

Missing values

2023-12-13T00:08:22.894144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:08:23.085927image/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.
2023-12-13T00:08:23.239547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

제1고객ID고객관계구분코드제2고객ID주력기업여부광역지역구분코드등록직원번호해제부점코드해제직원번호해제사유내용삭제여부최종수정수처리직원번호최초처리직원번호
0aaaaaakL8V1049cz6oRrhhsN4020<NA><NA>N140204020
1aaaaaakL8V1049cqjwyy8WbN4020<NA><NA>N140204020
2aaaaaakL8V1049bTgth4pEoN4020<NA><NA>N140204020
3aaaaaakL8V104aaaaaawIqpN4020<NA><NA>N140204020
4aaaaaakL8V104aaaaabU81YY4020<NA><NA>N140204020
59dnS0E4R471059dnS0GEeHK99001<NA><NA>N19900199001
69dnS0EOLVo1059dnS0EPfUe5814<NA><NA>N158145814
79dnS0BkBjs1059dnS0EhsX699001<NA><NA>N19900199001
8aaaaadfgrO1049cz6oRrhhsN4020<NA><NA>N340204020
9aaaaadfgrO1049cqjwyy8WbN4020<NA><NA>N240204020
제1고객ID고객관계구분코드제2고객ID주력기업여부광역지역구분코드등록직원번호해제부점코드해제직원번호해제사유내용삭제여부최종수정수처리직원번호최초처리직원번호
490aaaaaeaNDy105aaaaaczMbON4629TOJ3608<NA>N23608BATCH
4919dnSGrAUOt1059dnSGrBEPr4232<NA><NA>N142324232
4929dnSGl8K3z1059dnSGqYb1C99016<NA><NA>N19901699016
4939dnSGhV7Ml105aaaaabDj3299002<NA><NA>N19900299002
494aaaaadUuLt105aaaaabDj32N<NA>WAM99002<NA>N299002BATCH
4959clv2HUVCJ1059dnSGgw64V99001<NA><NA>N19900199001
4969dnSGdehyI1059dnSGeNupV99016<NA><NA>N19901699016
4979dnSF4f0Dj1059dnSF5lHSR99016<NA><NA>N19901699016
4989dnSFBCNfo1059dnSF0RsZ299001<NA><NA>N19900199001
4999dnSFZHLzY1059dnSFZH9Ym88889<NA><NA>N18888988889