Overview

Dataset statistics

Number of variables5
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory48.4 B

Variable types

Text1
Numeric4

Dataset

Description신용보증기금 매출채권보험에 대한 데이터를 제공합니다. 업종(세부업종 포함)별 매출채권보험 취급건수 및 인수금액, 비중을 확인할 수 있습니다.
URLhttps://www.data.go.kr/data/15064317/fileData.do

Alerts

건수 is highly overall correlated with 구성비율 and 2 other fieldsHigh correlation
구성비율 is highly overall correlated with 건수 and 2 other fieldsHigh correlation
인수금액 is highly overall correlated with 건수 and 2 other fieldsHigh correlation
비중 is highly overall correlated with 건수 and 2 other fieldsHigh correlation
건수 has unique valuesUnique
인수금액 has unique valuesUnique
구성비율 has 1 (3.3%) zerosZeros
비중 has 1 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-12 23:22:15.597350
Analysis finished2023-12-12 23:22:17.276929
Duration1.68 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종구분
Text

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T08:22:17.387170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7.5
Mean length5.0666667
Min length2

Characters and Unicode

Total characters152
Distinct characters73
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)80.0%

Sample

1st row제조업
2nd row음식료
3rd row섬유,의복
4th row목재,종이,출판
5th row석유화학
ValueCountFrequency (%)
기타 4
 
13.3%
운송장비 2
 
6.7%
제조업 1
 
3.3%
컴퓨터시스템,정보처리 1
 
3.3%
과학기술 1
 
3.3%
건축기술엔지니어링 1
 
3.3%
광고대행,작성 1
 
3.3%
지식기반서비스업 1
 
3.3%
기타전문도매 1
 
3.3%
건축자재,철물도매 1
 
3.3%
Other values (16) 16
53.3%
2023-12-13T08:22:17.676598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 12
 
7.9%
, 8
 
5.3%
비 6
 
3.9%
매 6
 
3.9%
도 6
 
3.9%
업 5
 
3.3%
타 5
 
3.3%
재 3
 
2.0%
전 3
 
2.0%
스 3
 
2.0%
Other values (63) 95
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 144
94.7%
Other Punctuation 8
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 12
 
8.3%
비 6
 
4.2%
매 6
 
4.2%
도 6
 
4.2%
업 5
 
3.5%
타 5
 
3.5%
재 3
 
2.1%
전 3
 
2.1%
스 3
 
2.1%
제 3
 
2.1%
Other values (62) 92
63.9%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 144
94.7%
Common 8
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 12
 
8.3%
비 6
 
4.2%
매 6
 
4.2%
도 6
 
4.2%
업 5
 
3.5%
타 5
 
3.5%
재 3
 
2.1%
전 3
 
2.1%
스 3
 
2.1%
제 3
 
2.1%
Other values (62) 92
63.9%
Common
ValueCountFrequency (%)
, 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 144
94.7%
ASCII 8
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 12
 
8.3%
비 6
 
4.2%
매 6
 
4.2%
도 6
 
4.2%
업 5
 
3.5%
타 5
 
3.5%
재 3
 
2.1%
전 3
 
2.1%
스 3
 
2.1%
제 3
 
2.1%
Other values (62) 92
63.9%
ASCII
ValueCountFrequency (%)
, 8
100.0%

건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1646.7667
Minimum7
Maximum12366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T08:22:17.786665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile61.25
Q1297.75
median805
Q31893
95-th percentile6689.45
Maximum12366
Range12359
Interquartile range (IQR)1595.25

Descriptive statistics

Standard deviation2700.2403
Coefficient of variation (CV)1.6397225
Kurtosis10.710409
Mean1646.7667
Median Absolute Deviation (MAD)655
Skewness3.2507843
Sum49403
Variance7291297.9
MonotonicityNot monotonic
2023-12-13T08:22:17.896432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9767 1
 
3.3%
627 1
 
3.3%
7 1
 
3.3%
111 1
 
3.3%
32 1
 
3.3%
97 1
 
3.3%
153 1
 
3.3%
393 1
 
3.3%
1881 1
 
3.3%
2928 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
7 1
3.3%
32 1
3.3%
97 1
3.3%
111 1
3.3%
147 1
3.3%
153 1
3.3%
234 1
3.3%
266 1
3.3%
393 1
3.3%
553 1
3.3%
ValueCountFrequency (%)
12366 1
3.3%
9767 1
3.3%
2928 1
3.3%
2188 1
3.3%
2172 1
3.3%
2104 1
3.3%
2039 1
3.3%
1897 1
3.3%
1881 1
3.3%
1517 1
3.3%

구성비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.66
Minimum0
Maximum50.1
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T08:22:18.021939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.235
Q11.225
median3.25
Q37.675
95-th percentile27.08
Maximum50.1
Range50.1
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation10.938436
Coefficient of variation (CV)1.6424078
Kurtosis10.704671
Mean6.66
Median Absolute Deviation (MAD)2.65
Skewness3.248467
Sum199.8
Variance119.64938
MonotonicityNot monotonic
2023-12-13T08:22:18.134865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.6 2
 
6.7%
0.4 2
 
6.7%
2.5 2
 
6.7%
39.5 1
 
3.3%
0.0 1
 
3.3%
0.1 1
 
3.3%
1.6 1
 
3.3%
7.6 1
 
3.3%
11.9 1
 
3.3%
8.3 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.0 1
3.3%
0.1 1
3.3%
0.4 2
6.7%
0.6 2
6.7%
0.9 1
3.3%
1.1 1
3.3%
1.6 1
3.3%
2.2 1
3.3%
2.5 2
6.7%
2.6 1
3.3%
ValueCountFrequency (%)
50.1 1
3.3%
39.5 1
3.3%
11.9 1
3.3%
8.9 1
3.3%
8.8 1
3.3%
8.5 1
3.3%
8.3 1
3.3%
7.7 1
3.3%
7.6 1
3.3%
6.1 1
3.3%

인수금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.424666 × 1012
Minimum2.913 × 109
Maximum1.02728 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T08:22:18.243424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.913 × 109
5-th percentile1.796725 × 1010
Q11.8115325 × 1011
median7.342425 × 1011
Q31.091595 × 1012
95-th percentile7.3675745 × 1012
Maximum1.02728 × 1013
Range1.0269887 × 1013
Interquartile range (IQR)9.1044175 × 1011

Descriptive statistics

Standard deviation2.5045948 × 1012
Coefficient of variation (CV)1.7580225
Kurtosis8.2188001
Mean1.424666 × 1012
Median Absolute Deviation (MAD)4.95407 × 1011
Skewness2.9341141
Sum4.273998 × 1013
Variance6.2729953 × 1024
MonotonicityNot monotonic
2023-12-13T08:22:18.358419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10272800000000 1
 
3.3%
223976000000 1
 
3.3%
2913000000 1
 
3.3%
19818000000 1
 
3.3%
16453000000 1
 
3.3%
42730000000 1
 
3.3%
87878000000 1
 
3.3%
166879000000 1
 
3.3%
791102000000 1
 
3.3%
4655750000000 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
2913000000 1
3.3%
16453000000 1
3.3%
19818000000 1
3.3%
42730000000 1
3.3%
87878000000 1
3.3%
91582000000 1
3.3%
95867000000 1
3.3%
166879000000 1
3.3%
223976000000 1
3.3%
253695000000 1
3.3%
ValueCountFrequency (%)
10272800000000 1
3.3%
9586340000000 1
3.3%
4655750000000 1
3.3%
3186330000000 1
3.3%
1342500000000 1
3.3%
1312120000000 1
3.3%
1148850000000 1
3.3%
1121510000000 1
3.3%
1001850000000 1
3.3%
948697000000 1
3.3%

비중
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6633333
Minimum0
Maximum48.1
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T08:22:18.474695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.85
median3.45
Q35.075
95-th percentile34.505
Maximum48.1
Range48.1
Interquartile range (IQR)4.225

Descriptive statistics

Standard deviation11.730787
Coefficient of variation (CV)1.7604983
Kurtosis8.2202681
Mean6.6633333
Median Absolute Deviation (MAD)2.35
Skewness2.9344672
Sum199.9
Variance137.61137
MonotonicityNot monotonic
2023-12-13T08:22:18.580553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.4 3
 
10.0%
0.1 2
 
6.7%
4.0 1
 
3.3%
0.0 1
 
3.3%
0.2 1
 
3.3%
0.8 1
 
3.3%
3.7 1
 
3.3%
21.8 1
 
3.3%
5.2 1
 
3.3%
6.1 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.0 1
 
3.3%
0.1 2
6.7%
0.2 1
 
3.3%
0.4 3
10.0%
0.8 1
 
3.3%
1.0 1
 
3.3%
1.2 1
 
3.3%
1.8 1
 
3.3%
2.5 1
 
3.3%
3.0 1
 
3.3%
ValueCountFrequency (%)
48.1 1
3.3%
44.9 1
3.3%
21.8 1
3.3%
14.9 1
3.3%
6.3 1
3.3%
6.1 1
3.3%
5.4 1
3.3%
5.2 1
3.3%
4.7 1
3.3%
4.4 1
3.3%

Interactions

2023-12-13T08:22:16.842347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:15.996674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.259205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.531831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.911978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.067344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.325464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.600049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.979448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.129908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.389379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.664396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:17.060704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.192703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.469897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:16.749257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:22:18.653497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종구분건수구성비율인수금액비중
업종구분1.0000.9730.9731.0001.000
건수0.9731.0001.0000.9620.963
구성비율0.9731.0001.0000.9620.963
인수금액1.0000.9620.9621.0001.000
비중1.0000.9630.9631.0001.000
2023-12-13T08:22:18.740149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건수구성비율인수금액비중
건수1.0001.0000.9600.960
구성비율1.0001.0000.9620.961
인수금액0.9600.9621.0000.999
비중0.9600.9610.9991.000

Missing values

2023-12-13T08:22:17.152336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:22:17.243183image/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

업종구분건수구성비율인수금액비중
0제조업976739.51027280000000048.1
1음식료10254.18518770000004.0
2섬유,의복6992.86578530000003.1
3목재,종이,출판6422.65378940000002.5
4석유화학5532.26405830000003.0
5비금속소재11204.511488500000005.4
6철강21888.9318633000000014.9
7전기,전자10524.310018500000004.7
8기계11534.79071030000004.2
9운송장비7122.99486970000004.4
업종구분건수구성비율인수금액비중
20기계장비도매21048.513121200000006.1
21건축자재,철물도매20398.311215100000005.2
22기타전문도매292811.9465575000000021.8
23기타18817.67911020000003.7
24지식기반서비스업3931.61668790000000.8
25광고대행,작성1530.6878780000000.4
26건축기술엔지니어링970.4427300000000.2
27과학기술320.1164530000000.1
28기타1110.4198180000000.1
29건설업70.029130000000.0