Overview

Dataset statistics

Number of variables2
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory659.0 B
Average record size in memory21.3 B

Variable types

Text1
Numeric1

Dataset

Description중소벤처기업진흥공단의 중소기업 대상 정책자금의 집행의 지역별, 담보별 집중도비율을 공개하여, 중소기업의 포트폴리오별 정책자금 집중현황을 파악할 수 있도록 함.* 집중도비율 : 포트폴리오가 특정 업체 또는 담보에 대해 어느 정도 집중되어있는지에 대한 비율
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15017365/fileData.do

Alerts

지역 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:23:15.745709
Analysis finished2023-12-12 21:23:16.018137
Duration0.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-13T06:23:16.160651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length3
Min length2

Characters and Unicode

Total characters93
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row서울
2nd row서울동남부
3rd row서울북부
4th row인천
5th row인천서부
ValueCountFrequency (%)
서울 1
 
3.2%
전북서부 1
 
3.2%
경남서부 1
 
3.2%
경남동부 1
 
3.2%
경남 1
 
3.2%
울산 1
 
3.2%
부산동부 1
 
3.2%
부산 1
 
3.2%
전남동부 1
 
3.2%
전남 1
 
3.2%
Other values (21) 21
67.7%
2023-12-13T06:23:16.506220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 16
17.2%
북 10
10.8%
경 10
10.8%
남 8
8.6%
서 7
 
7.5%
동 7
 
7.5%
전 5
 
5.4%
기 4
 
4.3%
울 4
 
4.3%
산 3
 
3.2%
Other values (11) 19
20.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 16
17.2%
북 10
10.8%
경 10
10.8%
남 8
8.6%
서 7
 
7.5%
동 7
 
7.5%
전 5
 
5.4%
기 4
 
4.3%
울 4
 
4.3%
산 3
 
3.2%
Other values (11) 19
20.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 16
17.2%
북 10
10.8%
경 10
10.8%
남 8
8.6%
서 7
 
7.5%
동 7
 
7.5%
전 5
 
5.4%
기 4
 
4.3%
울 4
 
4.3%
산 3
 
3.2%
Other values (11) 19
20.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 16
17.2%
북 10
10.8%
경 10
10.8%
남 8
8.6%
서 7
 
7.5%
동 7
 
7.5%
전 5
 
5.4%
기 4
 
4.3%
울 4
 
4.3%
산 3
 
3.2%
Other values (11) 19
20.4%

백분율
Real number (ℝ)

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3206452
Minimum2.86
Maximum18.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T06:23:16.634717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile3.235
Q15.115
median6.02
Q38.855
95-th percentile12.9
Maximum18.33
Range15.47
Interquartile range (IQR)3.74

Descriptive statistics

Standard deviation3.5721515
Coefficient of variation (CV)0.48795583
Kurtosis1.5175599
Mean7.3206452
Median Absolute Deviation (MAD)1.73
Skewness1.2282096
Sum226.94
Variance12.760266
MonotonicityNot monotonic
2023-12-13T06:23:16.757290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6.02 2
 
6.5%
3.02 1
 
3.2%
12.69 1
 
3.2%
13.11 1
 
3.2%
11.03 1
 
3.2%
5.66 1
 
3.2%
4.97 1
 
3.2%
9.06 1
 
3.2%
5.37 1
 
3.2%
11.37 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
2.86 1
3.2%
3.02 1
3.2%
3.45 1
3.2%
3.48 1
3.2%
4.02 1
3.2%
4.97 1
3.2%
5.03 1
3.2%
5.06 1
3.2%
5.17 1
3.2%
5.18 1
3.2%
ValueCountFrequency (%)
18.33 1
3.2%
13.11 1
3.2%
12.69 1
3.2%
11.92 1
3.2%
11.53 1
3.2%
11.37 1
3.2%
11.03 1
3.2%
9.06 1
3.2%
8.65 1
3.2%
8.09 1
3.2%

Interactions

2023-12-13T06:23:15.809974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:23:16.881052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역백분율
지역1.0001.000
백분율1.0001.000

Missing values

2023-12-13T06:23:15.934093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:23:15.995882image/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

지역백분율
0서울3.02
1서울동남부5.62
2서울북부2.86
3인천5.18
4인천서부8.65
5경기3.48
6경기동부5.25
7경기서부3.45
8경기북부4.02
9강원11.92
지역백분율
21광주5.17
22전남11.53
23전남동부11.37
24부산5.37
25부산동부9.06
26울산4.97
27경남5.66
28경남동부6.02
29경남서부11.03
30제주13.11