Overview

Dataset statistics

Number of variables6
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.5 KiB
Average record size in memory52.3 B

Variable types

Text1
Categorical1
DateTime1
Numeric3

Dataset

Description해당 파일은 신용보증기금의 조사 재무제표 정보에 대한 데이터로, 법인개인구분코드, 결산종료일자, 재무항목구분값 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15121986/fileData.do

Alerts

재무항목금액 has 110 (22.0%) zerosZeros

Reproduction

Analysis started2023-12-12 23:50:48.004540
Analysis finished2023-12-12 23:50:49.151107
Duration1.15 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ID
Text

Distinct461
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T08:50:49.450440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique424 ?
Unique (%)84.8%

Sample

1st row********o9
2nd row********tB
3rd row********wT
4th row********zV
5th row********f0
ValueCountFrequency (%)
sv 4
 
0.8%
zm 3
 
0.6%
ot 3
 
0.6%
cd 3
 
0.6%
rj 3
 
0.6%
zo 3
 
0.6%
qq 3
 
0.6%
bo 3
 
0.6%
wg 3
 
0.6%
t8 3
 
0.6%
Other values (393) 469
93.8%
2023-12-13T08:50:50.010125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 4000
80.0%
K 26
 
0.5%
r 23
 
0.5%
o 23
 
0.5%
l 22
 
0.4%
X 22
 
0.4%
8 22
 
0.4%
z 22
 
0.4%
5 21
 
0.4%
H 21
 
0.4%
Other values (53) 798
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 4000
80.0%
Uppercase Letter 428
 
8.6%
Lowercase Letter 407
 
8.1%
Decimal Number 165
 
3.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 26
 
6.1%
X 22
 
5.1%
H 21
 
4.9%
Q 20
 
4.7%
T 19
 
4.4%
V 19
 
4.4%
S 18
 
4.2%
A 18
 
4.2%
L 18
 
4.2%
I 17
 
4.0%
Other values (16) 230
53.7%
Lowercase Letter
ValueCountFrequency (%)
r 23
 
5.7%
o 23
 
5.7%
l 22
 
5.4%
z 22
 
5.4%
q 20
 
4.9%
t 19
 
4.7%
i 18
 
4.4%
c 18
 
4.4%
v 18
 
4.4%
w 18
 
4.4%
Other values (16) 206
50.6%
Decimal Number
ValueCountFrequency (%)
8 22
13.3%
5 21
12.7%
7 19
11.5%
1 17
10.3%
4 16
9.7%
9 15
9.1%
6 15
9.1%
0 15
9.1%
2 14
8.5%
3 11
6.7%
Other Punctuation
ValueCountFrequency (%)
* 4000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4165
83.3%
Latin 835
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 26
 
3.1%
r 23
 
2.8%
o 23
 
2.8%
l 22
 
2.6%
X 22
 
2.6%
z 22
 
2.6%
H 21
 
2.5%
q 20
 
2.4%
Q 20
 
2.4%
T 19
 
2.3%
Other values (42) 617
73.9%
Common
ValueCountFrequency (%)
* 4000
96.0%
8 22
 
0.5%
5 21
 
0.5%
7 19
 
0.5%
1 17
 
0.4%
4 16
 
0.4%
9 15
 
0.4%
6 15
 
0.4%
0 15
 
0.4%
2 14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 4000
80.0%
K 26
 
0.5%
r 23
 
0.5%
o 23
 
0.5%
l 22
 
0.4%
X 22
 
0.4%
8 22
 
0.4%
z 22
 
0.4%
5 21
 
0.4%
H 21
 
0.4%
Other values (53) 798
 
16.0%

법인개인구분코드
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
439 
2
61 

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 (%)
1 439
87.8%
2 61
 
12.2%

Length

2023-12-13T08:50:50.177781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:50:50.277148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 439
87.8%
2 61
 
12.2%

결산종료일자
Date

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2004-12-31 00:00:00
Maximum2018-12-31 00:00:00
2023-12-13T08:50:50.369239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:50:50.480577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)

재무분석평가구분코드
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.468
Minimum11
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T08:50:50.569311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median12
Q313
95-th percentile15
Maximum16
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.042665
Coefficient of variation (CV)0.083627286
Kurtosis1.8504936
Mean12.468
Median Absolute Deviation (MAD)1
Skewness1.1663638
Sum6234
Variance1.0871503
MonotonicityNot monotonic
2023-12-13T08:50:50.684121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
12 231
46.2%
13 155
31.0%
11 64
 
12.8%
15 25
 
5.0%
14 16
 
3.2%
16 9
 
1.8%
ValueCountFrequency (%)
11 64
 
12.8%
12 231
46.2%
13 155
31.0%
14 16
 
3.2%
15 25
 
5.0%
16 9
 
1.8%
ValueCountFrequency (%)
16 9
 
1.8%
15 25
 
5.0%
14 16
 
3.2%
13 155
31.0%
12 231
46.2%
11 64
 
12.8%

재무항목구분값
Real number (ℝ)

Distinct187
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4286.586
Minimum100
Maximum11400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T08:50:50.810466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q11600
median3750
Q36512.5
95-th percentile10105
Maximum11400
Range11300
Interquartile range (IQR)4912.5

Descriptive statistics

Standard deviation3075.7978
Coefficient of variation (CV)0.71754021
Kurtosis-0.79131865
Mean4286.586
Median Absolute Deviation (MAD)2350
Skewness0.49140636
Sum2143293
Variance9460532.1
MonotonicityNot monotonic
2023-12-13T08:50:51.265149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1600 13
 
2.6%
100 12
 
2.4%
1500 11
 
2.2%
200 10
 
2.0%
2900 9
 
1.8%
6900 9
 
1.8%
2600 8
 
1.6%
1900 8
 
1.6%
2000 8
 
1.6%
7000 8
 
1.6%
Other values (177) 404
80.8%
ValueCountFrequency (%)
100 12
2.4%
110 3
 
0.6%
120 1
 
0.2%
150 4
 
0.8%
200 10
2.0%
225 4
 
0.8%
250 4
 
0.8%
275 1
 
0.2%
285 1
 
0.2%
300 1
 
0.2%
ValueCountFrequency (%)
11400 4
0.8%
11300 4
0.8%
11100 1
 
0.2%
11000 1
 
0.2%
10950 1
 
0.2%
10800 1
 
0.2%
10600 1
 
0.2%
10500 1
 
0.2%
10400 5
1.0%
10300 3
0.6%

재무항목금액
Real number (ℝ)

ZEROS 

Distinct238
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87681404
Minimum-9613
Maximum5.4464056 × 109
Zeros110
Zeros (%)22.0%
Negative12
Negative (%)2.4%
Memory size4.5 KiB
2023-12-13T08:50:51.438294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9613
5-th percentile0
Q11
median41
Q31123
95-th percentile2.2259518 × 108
Maximum5.4464056 × 109
Range5.4464153 × 109
Interquartile range (IQR)1122

Descriptive statistics

Standard deviation4.6972087 × 108
Coefficient of variation (CV)5.3571322
Kurtosis59.921906
Mean87681404
Median Absolute Deviation (MAD)41
Skewness7.2505976
Sum4.3840702 × 1010
Variance2.206377 × 1017
MonotonicityNot monotonic
2023-12-13T08:50:51.569686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 110
 
22.0%
1 21
 
4.2%
2 10
 
2.0%
4 8
 
1.6%
3 8
 
1.6%
12 7
 
1.4%
6 6
 
1.2%
5 6
 
1.2%
36 6
 
1.2%
10 5
 
1.0%
Other values (228) 313
62.6%
ValueCountFrequency (%)
-9613 3
 
0.6%
-4860 1
 
0.2%
-4644 1
 
0.2%
-987 1
 
0.2%
-569 3
 
0.6%
-500 1
 
0.2%
-109 1
 
0.2%
-57 1
 
0.2%
0 110
22.0%
1 21
 
4.2%
ValueCountFrequency (%)
5446405642 1
0.2%
3620708704 1
0.2%
3579212674 1
0.2%
3574837674 1
0.2%
3532208946 1
0.2%
2370182812 1
0.2%
2257334800 1
0.2%
1916581356 1
0.2%
1867192968 1
0.2%
1732933510 1
0.2%

Interactions

2023-12-13T08:50:48.707888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:50:48.209754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:50:48.454266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:50:48.788201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:50:48.293557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:50:48.545458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:50:48.880871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:50:48.371541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:50:48.627163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:50:51.660294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법인개인구분코드결산종료일자재무분석평가구분코드재무항목구분값재무항목금액
법인개인구분코드1.0000.8810.2510.1110.000
결산종료일자0.8811.0000.7570.3500.460
재무분석평가구분코드0.2510.7571.0000.5630.322
재무항목구분값0.1110.3500.5631.0000.063
재무항목금액0.0000.4600.3220.0631.000
2023-12-13T08:50:51.764097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재무분석평가구분코드재무항목구분값재무항목금액법인개인구분코드
재무분석평가구분코드1.000-0.490-0.2730.180
재무항목구분값-0.4901.000-0.0830.084
재무항목금액-0.273-0.0831.0000.000
법인개인구분코드0.1800.0840.0001.000

Missing values

2023-12-13T08:50:48.999615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:50:49.105325image/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

ID법인개인구분코드결산종료일자재무분석평가구분코드재무항목구분값재무항목금액
0********o912016-12-311215004
1********tB12016-12-31121525206
2********wT12016-12-311216001
3********zV12016-12-3112170060
4********f012016-12-3112190015
5********2q12016-12-3112585040
6********5r12016-12-311258750
7********8l12016-12-311259001123
8********Ci12016-12-311244001
9********E112016-12-3112445024
ID법인개인구분코드결산종료일자재무분석평가구분코드재무항목구분값재무항목금액
490********w112016-12-3113190018
491********zO12016-12-3113200027
492********as12016-12-311346000
493********du12016-12-31134900210
494********gk12016-12-311359003
495********i712016-12-31136900779
496********lW12016-12-3113700048
497********oQ12016-12-31137100731
498********rC12016-12-31137400731
499********1O12016-12-31154000