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

Description사업종료로 차기 데이터 업데이트 불가
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15017364/fileData.do

Alerts

지역 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:17:21.818895
Analysis finished2023-12-12 12:17:22.120412
Duration0.3 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
2023-12-12T21:17:22.277230image/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%
2023-12-12T21:17:22.735844image/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)

Distinct24
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39322581
Minimum0.09
Maximum1.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:17:22.915188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.115
Q10.21
median0.27
Q30.54
95-th percentile0.885
Maximum1.35
Range1.26
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.28487877
Coefficient of variation (CV)0.72446611
Kurtosis2.9936512
Mean0.39322581
Median Absolute Deviation (MAD)0.1
Skewness1.6223092
Sum12.19
Variance0.081155914
MonotonicityNot monotonic
2023-12-12T21:17:23.067810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.22 3
 
9.7%
0.67 3
 
9.7%
0.17 2
 
6.5%
0.24 2
 
6.5%
0.23 2
 
6.5%
0.09 1
 
3.2%
0.35 1
 
3.2%
0.83 1
 
3.2%
0.39 1
 
3.2%
0.2 1
 
3.2%
Other values (14) 14
45.2%
ValueCountFrequency (%)
0.09 1
 
3.2%
0.1 1
 
3.2%
0.13 1
 
3.2%
0.17 2
6.5%
0.18 1
 
3.2%
0.19 1
 
3.2%
0.2 1
 
3.2%
0.22 3
9.7%
0.23 2
6.5%
0.24 2
6.5%
ValueCountFrequency (%)
1.35 1
 
3.2%
0.94 1
 
3.2%
0.83 1
 
3.2%
0.67 3
9.7%
0.63 1
 
3.2%
0.56 1
 
3.2%
0.52 1
 
3.2%
0.46 1
 
3.2%
0.43 1
 
3.2%
0.39 1
 
3.2%

Interactions

2023-12-12T21:17:21.889252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:17:23.171903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역백분율
지역1.0001.000
백분율1.0001.000

Missing values

2023-12-12T21:17:22.010658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:17:22.081482image/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
0서울0.09
1서울동남부0.19
2서울북부0.18
3인천0.17
4인천서부0.34
5경기0.1
6경기동부0.22
7경기서부0.13
8경기북부0.17
9강원0.67
지역백분율
21광주0.22
22전남0.63
23전남동부0.67
24부산0.2
25부산동부0.39
26울산0.24
27경남0.23
28경남동부0.22
29경남서부0.67
30제주0.83