Overview

Dataset statistics

Number of variables8
Number of observations82
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory68.6 B

Variable types

Numeric3
Categorical3
Boolean1
Text1

Dataset

Description해당 파일 데이터는 신용보증기금의 공통일반PCOFF월마감일정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093133/fileData.do

Alerts

마감일자 has constant value ""Constant
삭제여부 has constant value ""Constant
기준년월 is highly overall correlated with 처리직원번호 and 1 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 기준년월 and 1 other fieldsHigh correlation
최초처리시각 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 기준년월 and 2 other fieldsHigh correlation
최초처리시각 is highly imbalanced (55.0%)Imbalance
기준년월 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:11:42.498382
Analysis finished2023-12-12 13:11:44.043879
Duration1.55 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201699.06
Minimum201401
Maximum202010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T22:11:44.124466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201401
5-th percentile201405.05
Q1201509.25
median201705.5
Q3201901.75
95-th percentile202005.95
Maximum202010
Range609
Interquartile range (IQR)392.5

Descriptive statistics

Standard deviation197.84059
Coefficient of variation (CV)0.00098087014
Kurtosis-1.2269171
Mean201699.06
Median Absolute Deviation (MAD)196.5
Skewness0.022947398
Sum16539323
Variance39140.897
MonotonicityNot monotonic
2023-12-12T22:11:44.307178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202010 1
 
1.2%
201506 1
 
1.2%
201510 1
 
1.2%
201511 1
 
1.2%
201509 1
 
1.2%
201601 1
 
1.2%
201602 1
 
1.2%
201605 1
 
1.2%
201604 1
 
1.2%
201606 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
201401 1
1.2%
201402 1
1.2%
201403 1
1.2%
201404 1
1.2%
201405 1
1.2%
201406 1
1.2%
201407 1
1.2%
201408 1
1.2%
201409 1
1.2%
201410 1
1.2%
ValueCountFrequency (%)
202010 1
1.2%
202009 1
1.2%
202008 1
1.2%
202007 1
1.2%
202006 1
1.2%
202005 1
1.2%
202004 1
1.2%
202003 1
1.2%
202002 1
1.2%
202001 1
1.2%

마감일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
00:00.0
82 

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 82
100.0%

Length

2023-12-12T22:11:44.448656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:11:44.529809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 82
100.0%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size214.0 B
False
82 
ValueCountFrequency (%)
False 82
100.0%
2023-12-12T22:11:44.613418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct46
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.841463
Minimum1
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T22:11:44.725840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.05
Q15
median14
Q338
95-th percentile73.7
Maximum135
Range134
Interquartile range (IQR)33

Descriptive statistics

Standard deviation28.438571
Coefficient of variation (CV)1.1005016
Kurtosis3.5725738
Mean25.841463
Median Absolute Deviation (MAD)12
Skewness1.7751094
Sum2119
Variance808.75233
MonotonicityNot monotonic
2023-12-12T22:11:44.859407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2 8
 
9.8%
1 5
 
6.1%
5 5
 
6.1%
4 5
 
6.1%
11 3
 
3.7%
12 3
 
3.7%
38 3
 
3.7%
9 3
 
3.7%
34 2
 
2.4%
53 2
 
2.4%
Other values (36) 43
52.4%
ValueCountFrequency (%)
1 5
6.1%
2 8
9.8%
3 1
 
1.2%
4 5
6.1%
5 5
6.1%
6 1
 
1.2%
7 1
 
1.2%
8 2
 
2.4%
9 3
 
3.7%
10 2
 
2.4%
ValueCountFrequency (%)
135 1
1.2%
127 1
1.2%
103 1
1.2%
94 1
1.2%
74 1
1.2%
68 1
1.2%
62 1
1.2%
59 1
1.2%
58 1
1.2%
56 1
1.2%

처리시각
Text

Distinct52
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-12T22:11:45.067271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters574
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

Unique37 ?
Unique (%)45.1%

Sample

1st row38:01.0
2nd row31:13.0
3rd row22:04.1
4th row33:33.7
5th row15:12.1
ValueCountFrequency (%)
53:40.9 5
 
6.1%
48:08.5 5
 
6.1%
10:15.5 5
 
6.1%
44:53.7 5
 
6.1%
22:42.0 3
 
3.7%
55:29.0 3
 
3.7%
36:24.2 3
 
3.7%
49:17.1 2
 
2.4%
50:57.9 2
 
2.4%
20:04.9 2
 
2.4%
Other values (42) 47
57.3%
2023-12-12T22:11:45.372213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 82
14.3%
. 82
14.3%
5 60
10.5%
0 57
9.9%
2 56
9.8%
4 55
9.6%
1 47
8.2%
3 42
7.3%
8 26
 
4.5%
7 25
 
4.4%
Other values (2) 42
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 410
71.4%
Other Punctuation 164
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 60
14.6%
0 57
13.9%
2 56
13.7%
4 55
13.4%
1 47
11.5%
3 42
10.2%
8 26
6.3%
7 25
6.1%
9 23
 
5.6%
6 19
 
4.6%
Other Punctuation
ValueCountFrequency (%)
: 82
50.0%
. 82
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 574
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 82
14.3%
. 82
14.3%
5 60
10.5%
0 57
9.9%
2 56
9.8%
4 55
9.6%
1 47
8.2%
3 42
7.3%
8 26
 
4.5%
7 25
 
4.4%
Other values (2) 42
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 82
14.3%
. 82
14.3%
5 60
10.5%
0 57
9.9%
2 56
9.8%
4 55
9.6%
1 47
8.2%
3 42
7.3%
8 26
 
4.5%
7 25
 
4.4%
Other values (2) 42
7.3%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5145.061
Minimum4800
Maximum5439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T22:11:45.466998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4800
5-th percentile4803.85
Q14877
median5219
Q35351
95-th percentile5439
Maximum5439
Range639
Interquartile range (IQR)474

Descriptive statistics

Standard deviation222.69795
Coefficient of variation (CV)0.043283832
Kurtosis-1.5087981
Mean5145.061
Median Absolute Deviation (MAD)132
Skewness-0.30511213
Sum421895
Variance49594.379
MonotonicityNot monotonic
2023-12-12T22:11:45.555351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4877 24
29.3%
5273 15
18.3%
5351 13
15.9%
5439 10
12.2%
5165 9
 
11.0%
5219 6
 
7.3%
4800 5
 
6.1%
ValueCountFrequency (%)
4800 5
 
6.1%
4877 24
29.3%
5165 9
 
11.0%
5219 6
 
7.3%
5273 15
18.3%
5351 13
15.9%
5439 10
12.2%
ValueCountFrequency (%)
5439 10
12.2%
5351 13
15.9%
5273 15
18.3%
5219 6
 
7.3%
5165 9
 
11.0%
4877 24
29.3%
4800 5
 
6.1%

최초처리시각
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct23
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
0001-01-01 00:00:00.000000
60 
31:13.0
 
1
21:41.2
 
1
33:33.7
 
1
15:12.1
 
1
Other values (18)
18 

Length

Max length26
Median length26
Mean length20.902439
Min length7

Unique

Unique22 ?
Unique (%)26.8%

Sample

1st row38:01.0
2nd row31:13.0
3rd row21:41.2
4th row33:33.7
5th row15:12.1

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 60
73.2%
31:13.0 1
 
1.2%
21:41.2 1
 
1.2%
33:33.7 1
 
1.2%
15:12.1 1
 
1.2%
28:51.9 1
 
1.2%
12:54.9 1
 
1.2%
27:33.0 1
 
1.2%
26:00.6 1
 
1.2%
34:27.9 1
 
1.2%
Other values (13) 13
 
15.9%

Length

2023-12-12T22:11:45.681085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0001-01-01 60
42.3%
00:00:00.000000 60
42.3%
26:25.1 1
 
0.7%
43:23.5 1
 
0.7%
22:11.0 1
 
0.7%
35:52.8 1
 
0.7%
20:04.9 1
 
0.7%
24:39.7 1
 
0.7%
42:28.7 1
 
0.7%
27:53.0 1
 
0.7%
Other values (14) 14
 
9.9%

최초처리직원번호
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size788.0 B
BATCH
60 
5351
11 
5439
10 
4866
 
1

Length

Max length5
Median length5
Mean length4.7317073
Min length4

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row5439
2nd row5439
3rd row5439
4th row5439
5th row5439

Common Values

ValueCountFrequency (%)
BATCH 60
73.2%
5351 11
 
13.4%
5439 10
 
12.2%
4866 1
 
1.2%

Length

2023-12-12T22:11:45.808313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:11:45.900713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
batch 60
73.2%
5351 11
 
13.4%
5439 10
 
12.2%
4866 1
 
1.2%

Interactions

2023-12-12T22:11:43.432384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:42.775488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:43.133923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:43.538796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:42.897068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:43.235589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:43.646643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:43.018475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:43.344225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:11:45.968102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
기준년월1.0000.6741.0000.9750.3310.977
최종수정수0.6741.0000.0000.5660.0000.255
처리시각1.0000.0001.0001.0000.8800.864
처리직원번호0.9750.5661.0001.0000.6540.899
최초처리시각0.3310.0000.8800.6541.0001.000
최초처리직원번호0.9770.2550.8640.8991.0001.000
2023-12-12T22:11:46.056420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초처리시각최초처리직원번호
최초처리시각1.0000.870
최초처리직원번호0.8701.000
2023-12-12T22:11:46.132948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월최종수정수처리직원번호최초처리시각최초처리직원번호
기준년월1.000-0.3890.7800.1240.776
최종수정수-0.3891.000-0.4680.0000.156
처리직원번호0.780-0.4681.0000.2240.764
최초처리시각0.1240.0000.2241.0000.870
최초처리직원번호0.7760.1560.7640.8701.000

Missing values

2023-12-12T22:11:43.792540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:11:43.983283image/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

기준년월마감일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
020201000:00.0N138:01.0543938:01.05439
120200900:00.0N131:13.0543931:13.05439
220200800:00.0N222:04.1543921:41.25439
320200700:00.0N133:33.7543933:33.75439
420200600:00.0N115:12.1543915:12.15439
520200500:00.0N128:51.9543928:51.95439
620200400:00.0N252:09.7543912:54.95439
720200300:00.0N236:06.1543927:33.05439
820200200:00.0N227:57.3543926:00.65439
920200100:00.0N235:08.8543934:27.95439
기준년월마감일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
7220140900:00.0N5851:39.648770001-01-01 00:00:00.000000BATCH
7320141100:00.0N5442:06.548770001-01-01 00:00:00.000000BATCH
7420140400:00.0N2636:58.648770001-01-01 00:00:00.000000BATCH
7520140600:00.0N3849:17.148770001-01-01 00:00:00.000000BATCH
7620140800:00.0N5049:17.148770001-01-01 00:00:00.000000BATCH
7720140700:00.0N4213:20.348770001-01-01 00:00:00.000000BATCH
7820140500:00.0N2430:02.148770001-01-01 00:00:00.000000BATCH
7920140300:00.0N1647:15.148770001-01-01 00:00:00.000000BATCH
8020140100:00.0N1142:13.248770001-01-01 00:00:00.000000BATCH
8120140200:00.0N942:13.248770001-01-01 00:00:00.000000BATCH