Overview

Dataset statistics

Number of variables14
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.2 KiB
Average record size in memory121.3 B

Variable types

Text1
DateTime1
Categorical4
Numeric7
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 재정운영표정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092812/fileData.do

Alerts

명세서중분류 has constant value ""Constant
삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
제5항목값 is highly overall correlated with 제6항목값 and 2 other fieldsHigh correlation
제6항목값 is highly overall correlated with 제5항목값 and 2 other fieldsHigh correlation
제7항목값 is highly overall correlated with 제10항목값 and 1 other fieldsHigh correlation
제8항목값 is highly overall correlated with 제5항목값 and 2 other fieldsHigh correlation
제9항목값 is highly overall correlated with 제5항목값 and 2 other fieldsHigh correlation
제10항목값 is highly overall correlated with 제7항목값 and 1 other fieldsHigh correlation
제3항목값 is highly overall correlated with 제7항목값 and 1 other fieldsHigh correlation
제3항목값 is highly imbalanced (68.9%)Imbalance
재정운영표ID has unique valuesUnique
제2항목값 has 28 (5.6%) zerosZeros
제5항목값 has 491 (98.2%) zerosZeros
제6항목값 has 491 (98.2%) zerosZeros
제7항목값 has 102 (20.4%) zerosZeros
제8항목값 has 491 (98.2%) zerosZeros
제9항목값 has 491 (98.2%) zerosZeros
제10항목값 has 103 (20.6%) zerosZeros

Reproduction

Analysis started2023-12-12 09:32:35.115787
Analysis finished2023-12-12 09:32:42.377411
Duration7.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

재정운영표ID
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:32:42.616399image/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

Unique500 ?
Unique (%)100.0%

Sample

1st row9dnSMUqEer
2nd row9dnSMUqzd8
3rd row9dnSMUqy7R
4th row9dnSMUqy1x
5th row9dnSMUqyU9
ValueCountFrequency (%)
9dnsmuqeer 1
 
0.2%
9dnoghumra 1
 
0.2%
9dnoghukss 1
 
0.2%
9dnoghukas 1
 
0.2%
9dnoghukh2 1
 
0.2%
9dnoghukpu 1
 
0.2%
9dnoghukyc 1
 
0.2%
9dnoghulcc 1
 
0.2%
9dnoghullk 1
 
0.2%
9dnoghulsw 1
 
0.2%
Other values (490) 490
98.0%
2023-12-12T18:32:43.131023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 558
 
11.2%
9 516
 
10.3%
n 488
 
9.8%
o 303
 
6.1%
U 259
 
5.2%
q 200
 
4.0%
H 177
 
3.5%
y 174
 
3.5%
F 174
 
3.5%
M 164
 
3.3%
Other values (52) 1987
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2635
52.7%
Uppercase Letter 1584
31.7%
Decimal Number 781
 
15.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 558
21.2%
n 488
18.5%
o 303
11.5%
q 200
 
7.6%
y 174
 
6.6%
r 158
 
6.0%
g 158
 
6.0%
v 59
 
2.2%
u 57
 
2.2%
m 47
 
1.8%
Other values (16) 433
16.4%
Uppercase Letter
ValueCountFrequency (%)
U 259
16.4%
H 177
11.2%
F 174
11.0%
M 164
10.4%
S 163
10.3%
T 95
 
6.0%
Y 62
 
3.9%
P 43
 
2.7%
G 36
 
2.3%
L 35
 
2.2%
Other values (16) 376
23.7%
Decimal Number
ValueCountFrequency (%)
9 516
66.1%
0 56
 
7.2%
8 31
 
4.0%
4 31
 
4.0%
5 30
 
3.8%
3 28
 
3.6%
6 24
 
3.1%
2 24
 
3.1%
7 21
 
2.7%
1 20
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4219
84.4%
Common 781
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 558
 
13.2%
n 488
 
11.6%
o 303
 
7.2%
U 259
 
6.1%
q 200
 
4.7%
H 177
 
4.2%
y 174
 
4.1%
F 174
 
4.1%
M 164
 
3.9%
S 163
 
3.9%
Other values (42) 1559
37.0%
Common
ValueCountFrequency (%)
9 516
66.1%
0 56
 
7.2%
8 31
 
4.0%
4 31
 
4.0%
5 30
 
3.8%
3 28
 
3.6%
6 24
 
3.1%
2 24
 
3.1%
7 21
 
2.7%
1 20
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 558
 
11.2%
9 516
 
10.3%
n 488
 
9.8%
o 303
 
6.1%
U 259
 
5.2%
q 200
 
4.0%
H 177
 
3.5%
y 174
 
3.5%
F 174
 
3.5%
M 164
 
3.3%
Other values (52) 1987
39.7%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2021-07-01 00:00:00
Maximum2021-09-01 00:00:00
2023-12-12T18:32:43.286266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:43.399249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
신용보증기금
432 
산업기반신용보증기금
68 

Length

Max length10
Median length6
Mean length6.544
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신용보증기금 432
86.4%
산업기반신용보증기금 68
 
13.6%

Length

2023-12-12T18:32:43.563936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:43.710413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신용보증기금 432
86.4%
산업기반신용보증기금 68
 
13.6%

명세서중분류
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재무제표 500
100.0%

Length

2023-12-12T18:32:43.828343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:43.926601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재무제표 500
100.0%

제2항목값
Real number (ℝ)

ZEROS 

Distinct136
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1727415 × 109
Minimum0
Maximum7.91 × 1011
Zeros28
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:32:44.048263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141060175
median51030208
Q351190125
95-th percentile51310500
Maximum7.91 × 1011
Range7.91 × 1011
Interquartile range (IQR)10129950

Descriptive statistics

Standard deviation6.8412777 × 1010
Coefficient of variation (CV)11.083046
Kurtosis122.73528
Mean6.1727415 × 109
Median Absolute Deviation (MAD)240194
Skewness11.130739
Sum3.0863707 × 1012
Variance4.680308 × 1021
MonotonicityNot monotonic
2023-12-12T18:32:44.231787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
5.6%
51070000 8
 
1.6%
51310300 6
 
1.2%
51070900 6
 
1.2%
51020104 6
 
1.2%
41040302 6
 
1.2%
41040400 5
 
1.0%
41060000 5
 
1.0%
51311600 5
 
1.0%
41040103 5
 
1.0%
Other values (126) 420
84.0%
ValueCountFrequency (%)
0 28
5.6%
41010000 3
 
0.6%
41010200 3
 
0.6%
41010300 3
 
0.6%
41010301 3
 
0.6%
41010302 3
 
0.6%
41010500 3
 
0.6%
41040000 5
 
1.0%
41040100 5
 
1.0%
41040101 5
 
1.0%
ValueCountFrequency (%)
791000000000 3
0.6%
691000000000 1
 
0.2%
51330301 3
0.6%
51330300 3
0.6%
51330000 3
0.6%
51311600 5
1.0%
51311400 3
0.6%
51311000 2
 
0.4%
51310500 3
0.6%
51310300 6
1.2%

제3항목값
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
472 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 472
94.4%
1 28
 
5.6%

Length

2023-12-12T18:32:44.393244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:44.507439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 472
94.4%
1 28
 
5.6%

제5항목값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5860893 × 109
Minimum0
Maximum1.32 × 1012
Zeros491
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:32:44.626477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.32 × 1012
Range1.32 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.3446374 × 1010
Coefficient of variation (CV)14.938246
Kurtosis246.70024
Mean5.5860893 × 109
Median Absolute Deviation (MAD)0
Skewness15.727072
Sum2.7930447 × 1012
Variance6.9632974 × 1021
MonotonicityNot monotonic
2023-12-12T18:32:44.775164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 491
98.2%
1320000000000 2
 
0.4%
1798563866 2
 
0.4%
38785963215 2
 
0.4%
35141652094 2
 
0.4%
1592294214 1
 
0.2%
ValueCountFrequency (%)
0 491
98.2%
1592294214 1
 
0.2%
1798563866 2
 
0.4%
35141652094 2
 
0.4%
38785963215 2
 
0.4%
1320000000000 2
 
0.4%
ValueCountFrequency (%)
1320000000000 2
 
0.4%
38785963215 2
 
0.4%
35141652094 2
 
0.4%
1798563866 2
 
0.4%
1592294214 1
 
0.2%
0 491
98.2%

제6항목값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8273994 × 109
Minimum0
Maximum6.99 × 1011
Zeros491
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:32:44.921863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.99 × 1011
Range6.99 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.2212927 × 1010
Coefficient of variation (CV)9.1122437
Kurtosis90.283763
Mean6.8273994 × 109
Median Absolute Deviation (MAD)0
Skewness9.4379934
Sum3.4136997 × 1012
Variance3.8704483 × 1021
MonotonicityNot monotonic
2023-12-12T18:32:45.062456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 491
98.2%
699000000000 2
 
0.4%
15119330670 2
 
0.4%
527000000000 2
 
0.4%
459000000000 2
 
0.4%
13461033390 1
 
0.2%
ValueCountFrequency (%)
0 491
98.2%
13461033390 1
 
0.2%
15119330670 2
 
0.4%
459000000000 2
 
0.4%
527000000000 2
 
0.4%
699000000000 2
 
0.4%
ValueCountFrequency (%)
699000000000 2
 
0.4%
527000000000 2
 
0.4%
459000000000 2
 
0.4%
15119330670 2
 
0.4%
13461033390 1
 
0.2%
0 491
98.2%

제7항목값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct325
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3352238 × 1010
Minimum-1.15 × 1012
Maximum8.42 × 1011
Zeros102
Zeros (%)20.4%
Negative15
Negative (%)3.0%
Memory size4.5 KiB
2023-12-12T18:32:45.216537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.15 × 1012
5-th percentile0
Q11613654
median4.6760625 × 108
Q32.6100018 × 109
95-th percentile7.9573 × 1010
Maximum8.42 × 1011
Range1.992 × 1012
Interquartile range (IQR)2.6083882 × 109

Descriptive statistics

Standard deviation1.3570931 × 1011
Coefficient of variation (CV)10.163788
Kurtosis30.377805
Mean1.3352238 × 1010
Median Absolute Deviation (MAD)4.6760625 × 108
Skewness0.40786934
Sum6.6761188 × 1012
Variance1.8417017 × 1022
MonotonicityNot monotonic
2023-12-12T18:32:45.395645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102
 
20.4%
1613654 9
 
1.8%
1182861450 6
 
1.2%
177534242 4
 
0.8%
218518464 4
 
0.8%
232000000 4
 
0.8%
920727485 3
 
0.6%
818453866 3
 
0.6%
1417361450 3
 
0.6%
718440431 3
 
0.6%
Other values (315) 359
71.8%
ValueCountFrequency (%)
-1150000000000 1
0.2%
-990000000000 1
0.2%
-488000000000 2
0.4%
-424000000000 2
0.4%
-391000000000 1
0.2%
-342000000000 1
0.2%
-238000000000 1
0.2%
-24585306941 2
0.4%
-13320766804 2
0.4%
-11868739176 1
0.2%
ValueCountFrequency (%)
842000000000 1
0.2%
755000000000 1
0.2%
737000000000 2
0.4%
675000000000 2
0.4%
648000000000 1
0.2%
625000000000 2
0.4%
604000000000 1
0.2%
590000000000 2
0.4%
187000000000 1
0.2%
165000000000 1
0.2%

제8항목값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3989369 × 109
Minimum0
Maximum1.54 × 1012
Zeros491
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:32:45.522045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.54 × 1012
Range1.54 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.7319062 × 1010
Coefficient of variation (CV)15.20863
Kurtosis247.13704
Mean6.3989369 × 109
Median Absolute Deviation (MAD)0
Skewness15.747387
Sum3.1994685 × 1012
Variance9.4709999 × 1021
MonotonicityNot monotonic
2023-12-12T18:32:45.659336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 491
98.2%
1540000000000 2
 
0.4%
1710279741 2
 
0.4%
30474227199 2
 
0.4%
26801871509 2
 
0.4%
1495707572 1
 
0.2%
ValueCountFrequency (%)
0 491
98.2%
1495707572 1
 
0.2%
1710279741 2
 
0.4%
26801871509 2
 
0.4%
30474227199 2
 
0.4%
1540000000000 2
 
0.4%
ValueCountFrequency (%)
1540000000000 2
 
0.4%
30474227199 2
 
0.4%
26801871509 2
 
0.4%
1710279741 2
 
0.4%
1495707572 1
 
0.2%
0 491
98.2%

제9항목값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9809734 × 109
Minimum0
Maximum5.75 × 1011
Zeros491
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:32:45.794524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5.75 × 1011
Range5.75 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.4040658 × 1010
Coefficient of variation (CV)9.0354286
Kurtosis84.766534
Mean5.9809734 × 109
Median Absolute Deviation (MAD)0
Skewness9.2177353
Sum2.9904867 × 1012
Variance2.9203927 × 1021
MonotonicityNot monotonic
2023-12-12T18:32:45.922584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 491
98.2%
575000000000 2
 
0.4%
14010686730 2
 
0.4%
479000000000 2
 
0.4%
421000000000 2
 
0.4%
12465323840 1
 
0.2%
ValueCountFrequency (%)
0 491
98.2%
12465323840 1
 
0.2%
14010686730 2
 
0.4%
421000000000 2
 
0.4%
479000000000 2
 
0.4%
575000000000 2
 
0.4%
ValueCountFrequency (%)
575000000000 2
 
0.4%
479000000000 2
 
0.4%
421000000000 2
 
0.4%
14010686730 2
 
0.4%
12465323840 1
 
0.2%
0 491
98.2%

제10항목값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct313
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6091198 × 1010
Minimum-1.09 × 1012
Maximum9.64 × 1011
Zeros103
Zeros (%)20.6%
Negative13
Negative (%)2.6%
Memory size4.5 KiB
2023-12-12T18:32:46.098545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.09 × 1012
5-th percentile0
Q1756800
median3.9956507 × 108
Q32.4707822 × 109
95-th percentile7.9218905 × 1010
Maximum9.64 × 1011
Range2.054 × 1012
Interquartile range (IQR)2.4700254 × 109

Descriptive statistics

Standard deviation1.4373159 × 1011
Coefficient of variation (CV)8.9323116
Kurtosis29.886893
Mean1.6091198 × 1010
Median Absolute Deviation (MAD)3.9956507 × 108
Skewness1.8219381
Sum8.045599 × 1012
Variance2.0658771 × 1022
MonotonicityNot monotonic
2023-12-12T18:32:46.283839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
 
20.6%
1210492 9
 
1.8%
119000000000 5
 
1.0%
253000000 4
 
0.8%
67729454 4
 
0.8%
756800 3
 
0.6%
1425604550 3
 
0.6%
380168900 3
 
0.6%
17526923070 3
 
0.6%
517246583 3
 
0.6%
Other values (303) 360
72.0%
ValueCountFrequency (%)
-1090000000000 1
 
0.2%
-958000000000 1
 
0.2%
-448000000000 2
 
0.4%
-394000000000 2
 
0.4%
-357000000000 1
 
0.2%
-315000000000 1
 
0.2%
-26825747294 2
 
0.4%
-12300406989 2
 
0.4%
-10969616268 1
 
0.2%
0 103
20.6%
ValueCountFrequency (%)
964000000000 2
0.4%
940000000000 1
0.2%
817000000000 1
0.2%
731000000000 1
0.2%
711000000000 2
0.4%
697000000000 2
0.4%
643000000000 1
0.2%
626000000000 2
0.4%
181000000000 1
0.2%
161000000000 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%
2023-12-12T18:32:46.428075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
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

2023-12-12T18:32:46.566212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:46.683209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

Interactions

2023-12-12T18:32:40.934451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:35.662405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:36.591043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:37.541029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:38.473900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:39.376116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:40.253385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:41.041562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:35.790198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:36.731763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:37.693610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:38.592552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:39.526081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:40.359557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:41.128226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:35.922228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:36.870634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:37.819973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:38.718665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:39.640022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:40.456235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:41.231605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:36.052760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:37.021895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:37.937161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:38.846717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:39.770872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:40.569729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:41.346064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:36.193282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:37.157679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:38.057861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:38.985420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:39.909862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:40.654683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:41.490965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:36.350282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:37.300717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:38.194076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:39.135954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:40.043163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:40.758306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:41.606710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:36.473496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:37.424711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:38.327641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:39.267836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:40.157325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:40.851414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:32:46.754949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계년월국가결산회계구분제2항목값제3항목값제5항목값제6항목값제7항목값제8항목값제9항목값제10항목값
회계년월1.0000.1490.0000.0000.0340.0840.0260.0340.0840.166
국가결산회계구분0.1491.0000.0590.0810.0000.0000.0590.0000.0000.000
제2항목값0.0000.0591.0000.0000.2460.4950.5060.2460.4950.694
제3항목값0.0000.0810.0001.0000.0460.2980.4810.0460.2980.721
제5항목값0.0340.0000.2460.0461.0001.0000.5810.9231.0000.887
제6항목값0.0840.0000.4950.2981.0001.0000.7471.0001.0000.775
제7항목값0.0260.0590.5060.4810.5810.7471.0000.5810.7470.902
제8항목값0.0340.0000.2460.0460.9231.0000.5811.0001.0000.887
제9항목값0.0840.0000.4950.2981.0001.0000.7471.0001.0000.775
제10항목값0.1660.0000.6940.7210.8870.7750.9020.8870.7751.000
2023-12-12T18:32:46.902136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가결산회계구분제3항목값
국가결산회계구분1.0000.052
제3항목값0.0521.000
2023-12-12T18:32:47.036206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제2항목값제5항목값제6항목값제7항목값제8항목값제9항목값제10항목값국가결산회계구분제3항목값
제2항목값1.000-0.019-0.019-0.189-0.019-0.019-0.1640.0980.000
제5항목값-0.0191.0001.000-0.1261.0001.000-0.1240.0000.029
제6항목값-0.0191.0001.000-0.1261.0001.000-0.1240.0000.198
제7항목값-0.189-0.126-0.1261.000-0.126-0.1260.9390.0660.537
제8항목값-0.0191.0001.000-0.1261.0001.000-0.1240.0000.029
제9항목값-0.0191.0001.000-0.1261.0001.000-0.1240.0000.198
제10항목값-0.164-0.124-0.1240.939-0.124-0.1241.0000.0000.553
국가결산회계구분0.0980.0000.0000.0660.0000.0000.0001.0000.052
제3항목값0.0000.0290.1980.5370.0290.1980.5530.0521.000

Missing values

2023-12-12T18:32:41.757866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:32:42.297635image/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

재정운영표ID회계년월국가결산회계구분명세서중분류제2항목값제3항목값제5항목값제6항목값제7항목값제8항목값제9항목값제10항목값삭제여부최종수정수
09dnSMUqEer2021-09신용보증기금재무제표511200002002204933333001808639226N1
19dnSMUqzd82021-09신용보증기금재무제표51030203200333921500034745600N1
29dnSMUqy7R2021-09신용보증기금재무제표510302022009068000000103725000N1
39dnSMUqy1x2021-09신용보증기금재무제표5103020120019948829200234471070N1
49dnSMUqyU92021-09신용보증기금재무제표510302002001949833125002491597428N1
59dnSMUqyNb2021-09신용보증기금재무제표510301062001627773871001470382324N1
69dnSMUqyGP2021-09신용보증기금재무제표5103010520027524276600273142213N1
79dnSMUqyuD2021-09신용보증기금재무제표5103010420070362631500669212609N1
89dnSMUqybf2021-09신용보증기금재무제표510301012001145893619001063534489N1
99dnSMUqx152021-09신용보증기금재무제표510301002003752536571003476271635N1
재정운영표ID회계년월국가결산회계구분명세서중분류제2항목값제3항목값제5항목값제6항목값제7항목값제8항목값제9항목값제10항목값삭제여부최종수정수
4909dm0uadhL12021-08산업기반신용보증기금재무제표410400002003939772668005003822930N1
4919dm0uadhCH2021-08산업기반신용보증기금재무제표513116002000000N1
4929dm0uadht22021-08산업기반신용보증기금재무제표513110002000000N1
4939dm0uadhmr2021-08산업기반신용보증기금재무제표513100002000000N1
4949dm0uadhgu2021-08산업기반신용보증기금재무제표513002002000000N1
4959dm0uadg4a2021-08산업기반신용보증기금재무제표513001002002185184640067729454N1
4969dm0uadfno2021-08산업기반신용보증기금재무제표513000002002185184640067729454N1
4979dm0uadfhH2021-08산업기반신용보증기금재무제표512405002000000N1
4989dm0uadfbS2021-08산업기반신용보증기금재무제표512400002000000N1
4999dm0uade6i2021-08산업기반신용보증기금재무제표5107010020023200000000253000000N1