Overview

Dataset statistics

Number of variables3
Number of observations90
Missing cells2
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory26.5 B

Variable types

Categorical1
Numeric1
Text1

Dataset

Description대전 지역특화 게임 콘텐츠 발굴 및 게임기업의 역량 강화를 위한 사업으로 이와 관련한 대전 지역 게임 기업 데이터 현황입니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15080697/fileData.do

Alerts

번호 has 1 (1.1%) missing valuesMissing
기업명 has 1 (1.1%) missing valuesMissing

Reproduction

Analysis started2024-04-17 18:42:07.327716
Analysis finished2024-04-17 18:42:07.684079
Duration0.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분(지역)
Categorical

Distinct6
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size852.0 B
유성구
63 
서구
15 
대덕구
 
6
중구
 
4
동구
 
1

Length

Max length4
Median length3
Mean length2.7888889
Min length2

Unique

Unique2 ?
Unique (%)2.2%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 63
70.0%
서구 15
 
16.7%
대덕구 6
 
6.7%
중구 4
 
4.4%
동구 1
 
1.1%
<NA> 1
 
1.1%

Length

2024-04-18T03:42:07.749751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:42:07.847365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 63
70.0%
서구 15
 
16.7%
대덕구 6
 
6.7%
중구 4
 
4.4%
동구 1
 
1.1%
na 1
 
1.1%

번호
Real number (ℝ)

MISSING 

Distinct63
Distinct (%)70.8%
Missing1
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean24.359551
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-04-18T03:42:07.943518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q16
median19
Q341
95-th percentile58.6
Maximum63
Range62
Interquartile range (IQR)35

Descriptive statistics

Standard deviation19.625614
Coefficient of variation (CV)0.80566403
Kurtosis-1.1494916
Mean24.359551
Median Absolute Deviation (MAD)15
Skewness0.48561103
Sum2168
Variance385.16471
MonotonicityNot monotonic
2024-04-18T03:42:08.048195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
5.6%
3 4
 
4.4%
4 4
 
4.4%
2 4
 
4.4%
5 3
 
3.3%
6 3
 
3.3%
11 2
 
2.2%
15 2
 
2.2%
13 2
 
2.2%
12 2
 
2.2%
Other values (53) 58
64.4%
ValueCountFrequency (%)
1 5
5.6%
2 4
4.4%
3 4
4.4%
4 4
4.4%
5 3
3.3%
6 3
3.3%
7 2
 
2.2%
8 2
 
2.2%
9 2
 
2.2%
10 2
 
2.2%
ValueCountFrequency (%)
63 1
1.1%
62 1
1.1%
61 1
1.1%
60 1
1.1%
59 1
1.1%
58 1
1.1%
57 1
1.1%
56 1
1.1%
55 1
1.1%
54 1
1.1%

기업명
Text

MISSING 

Distinct89
Distinct (%)100.0%
Missing1
Missing (%)1.1%
Memory size852.0 B
2024-04-18T03:42:08.266090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length5.9775281
Min length2

Characters and Unicode

Total characters532
Distinct characters155
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)100.0%

Sample

1st rowSUD㈜
2nd row루켄
3rd row㈜봄소프트
4th row㈜비햅틱스
5th row빅픽쳐스
ValueCountFrequency (%)
sud㈜ 1
 
1.1%
버터플라이임팩트㈜ 1
 
1.1%
소프트식스 1
 
1.1%
플레이캐슬 1
 
1.1%
㈜플레이지오닉스 1
 
1.1%
㈜누라임소프트 1
 
1.1%
sts 1
 
1.1%
팀애플몽키 1
 
1.1%
글림시스템즈 1
 
1.1%
주)플레이서 1
 
1.1%
Other values (81) 81
89.0%
2024-04-18T03:42:08.591112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 62
 
11.7%
이 37
 
7.0%
스 25
 
4.7%
트 13
 
2.4%
디 12
 
2.3%
아 12
 
2.3%
레 11
 
2.1%
에 11
 
2.1%
프 11
 
2.1%
임 10
 
1.9%
Other values (145) 328
61.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 442
83.1%
Other Symbol 62
 
11.7%
Uppercase Letter 11
 
2.1%
Open Punctuation 6
 
1.1%
Close Punctuation 6
 
1.1%
Lowercase Letter 3
 
0.6%
Space Separator 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 37
 
8.4%
스 25
 
5.7%
트 13
 
2.9%
디 12
 
2.7%
아 12
 
2.7%
레 11
 
2.5%
에 11
 
2.5%
프 11
 
2.5%
임 10
 
2.3%
즈 10
 
2.3%
Other values (131) 290
65.6%
Uppercase Letter
ValueCountFrequency (%)
S 3
27.3%
U 2
18.2%
T 2
18.2%
H 1
 
9.1%
D 1
 
9.1%
G 1
 
9.1%
C 1
 
9.1%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
a 1
33.3%
m 1
33.3%
Other Symbol
ValueCountFrequency (%)
㈜ 62
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 504
94.7%
Common 14
 
2.6%
Latin 14
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 62
 
12.3%
이 37
 
7.3%
스 25
 
5.0%
트 13
 
2.6%
디 12
 
2.4%
아 12
 
2.4%
레 11
 
2.2%
에 11
 
2.2%
프 11
 
2.2%
임 10
 
2.0%
Other values (132) 300
59.5%
Latin
ValueCountFrequency (%)
S 3
21.4%
U 2
14.3%
T 2
14.3%
e 1
 
7.1%
a 1
 
7.1%
m 1
 
7.1%
H 1
 
7.1%
D 1
 
7.1%
G 1
 
7.1%
C 1
 
7.1%
Common
ValueCountFrequency (%)
( 6
42.9%
) 6
42.9%
2
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 442
83.1%
None 62
 
11.7%
ASCII 28
 
5.3%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 62
100.0%
Hangul
ValueCountFrequency (%)
이 37
 
8.4%
스 25
 
5.7%
트 13
 
2.9%
디 12
 
2.7%
아 12
 
2.7%
레 11
 
2.5%
에 11
 
2.5%
프 11
 
2.5%
임 10
 
2.3%
즈 10
 
2.3%
Other values (131) 290
65.6%
ASCII
ValueCountFrequency (%)
( 6
21.4%
) 6
21.4%
S 3
10.7%
U 2
 
7.1%
T 2
 
7.1%
2
 
7.1%
e 1
 
3.6%
a 1
 
3.6%
m 1
 
3.6%
H 1
 
3.6%
Other values (3) 3
10.7%

Interactions

2024-04-18T03:42:07.439912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:42:08.664315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분(지역)번호기업명
구분(지역)1.0000.5551.000
번호0.5551.0001.000
기업명1.0001.0001.000
2024-04-18T03:42:08.727922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호구분(지역)
번호1.0000.253
구분(지역)0.2531.000

Missing values

2024-04-18T03:42:07.524487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:42:07.579536image/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.
2024-04-18T03:42:07.642427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분(지역)번호기업명
0유성구1SUD㈜
1유성구2루켄
2유성구3㈜봄소프트
3유성구4㈜비햅틱스
4유성구5빅픽쳐스
5유성구6아이오에스
6유성구7㈜세컨그라운드
7유성구8(주)아이에이치테크
8유성구9에스엔소프트
9유성구10어크로스페이스㈜
구분(지역)번호기업명
80서구12하바나코퍼레이션
81서구13㈜비앤디
82서구14㈜프라임메디랩
83서구15㈜라이브젠
84중구1(주)비주얼라이트
85중구2태마마㈜
86중구3휠버그게임즈㈜
87중구4팀에이치씨(Team HC)
88동구1프로그게임즈
89<NA><NA><NA>