Overview

Dataset statistics

Number of variables27
Number of observations165
Missing cells1034
Missing cells (%)23.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.7 KiB
Average record size in memory221.8 B

Variable types

Numeric5
Text8
Categorical11
DateTime3

Dataset

Description해외에 거점을 둔 컨설팅, 마케팅 전문 업체인 해외민간네트워크 기업 정보에 대한 데이터로 기관명, 설립일, 대표자명, 상시종업원, 주소, 지원분야, 지원업종 등을 제공함.
URLhttps://www.data.go.kr/data/15052846/fileData.do

Alerts

해외_설치형태 is highly imbalanced (60.5%)Imbalance
지원분야 1순위 is highly imbalanced (61.2%)Imbalance
설립일 has 7 (4.2%) missing valuesMissing
주소 has 7 (4.2%) missing valuesMissing
해외_조직명 has 124 (75.2%) missing valuesMissing
해외_설립일 has 137 (83.0%) missing valuesMissing
해외_상시종업원수 has 136 (82.4%) missing valuesMissing
해외_주소 has 136 (82.4%) missing valuesMissing
국내_부서명 has 121 (73.3%) missing valuesMissing
국내_설립일 has 123 (74.5%) missing valuesMissing
국내_상시종업원수 has 121 (73.3%) missing valuesMissing
국내_주소 has 121 (73.3%) missing valuesMissing
연번 has unique valuesUnique
민간네트워크 명 has unique valuesUnique
대표자명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:05:32.405610
Analysis finished2023-12-12 09:05:33.750425
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83
Minimum1
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T18:05:34.169277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.2
Q142
median83
Q3124
95-th percentile156.8
Maximum165
Range164
Interquartile range (IQR)82

Descriptive statistics

Standard deviation47.775517
Coefficient of variation (CV)0.57560864
Kurtosis-1.2
Mean83
Median Absolute Deviation (MAD)41
Skewness0
Sum13695
Variance2282.5
MonotonicityStrictly increasing
2023-12-12T18:05:34.353612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
105 1
 
0.6%
107 1
 
0.6%
108 1
 
0.6%
109 1
 
0.6%
110 1
 
0.6%
111 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
Other values (155) 155
93.9%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
165 1
0.6%
164 1
0.6%
163 1
0.6%
162 1
0.6%
161 1
0.6%
160 1
0.6%
159 1
0.6%
158 1
0.6%
157 1
0.6%
156 1
0.6%

민간네트워크 명
Text

UNIQUE 

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T18:05:34.567924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length31
Mean length16.393939
Min length2

Characters and Unicode

Total characters2705
Distinct characters237
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)100.0%

Sample

1st rowEureka Consultrade LTD
2nd rowUPCYCLE LTD.
3rd rowAQUA SPACE CONSULTING SDN. BHD.
4th rowPDS Consulting SDN. BHD.
5th rowKMTC CO. LTD.
ValueCountFrequency (%)
ltd 37
 
9.1%
co 14
 
3.4%
inc 10
 
2.5%
pvt 9
 
2.2%
llc 7
 
1.7%
global 6
 
1.5%
consulting 6
 
1.5%
trading 6
 
1.5%
limited 4
 
1.0%
pt 4
 
1.0%
Other values (265) 304
74.7%
2023-12-12T18:05:35.032278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
245
 
9.1%
T 124
 
4.6%
L 109
 
4.0%
A 99
 
3.7%
I 97
 
3.6%
N 92
 
3.4%
C 91
 
3.4%
E 83
 
3.1%
. 82
 
3.0%
O 74
 
2.7%
Other values (227) 1609
59.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1230
45.5%
Other Letter 554
20.5%
Lowercase Letter 494
18.3%
Space Separator 245
 
9.1%
Other Punctuation 82
 
3.0%
Open Punctuation 49
 
1.8%
Close Punctuation 49
 
1.8%
Decimal Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 39
 
7.0%
스 22
 
4.0%
한 21
 
3.8%
유 20
 
3.6%
사 18
 
3.2%
이 17
 
3.1%
공 16
 
2.9%
코 15
 
2.7%
리 13
 
2.3%
아 12
 
2.2%
Other values (170) 361
65.2%
Uppercase Letter
ValueCountFrequency (%)
T 124
 
10.1%
L 109
 
8.9%
A 99
 
8.0%
I 97
 
7.9%
N 92
 
7.5%
C 91
 
7.4%
E 83
 
6.7%
O 74
 
6.0%
D 72
 
5.9%
S 69
 
5.6%
Other values (16) 320
26.0%
Lowercase Letter
ValueCountFrequency (%)
i 55
11.1%
n 51
10.3%
a 48
9.7%
e 47
9.5%
o 45
9.1%
t 35
 
7.1%
r 35
 
7.1%
d 30
 
6.1%
l 29
 
5.9%
s 18
 
3.6%
Other values (15) 101
20.4%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
245
100.0%
Other Punctuation
ValueCountFrequency (%)
. 82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1724
63.7%
Hangul 554
 
20.5%
Common 427
 
15.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 39
 
7.0%
스 22
 
4.0%
한 21
 
3.8%
유 20
 
3.6%
사 18
 
3.2%
이 17
 
3.1%
공 16
 
2.9%
코 15
 
2.7%
리 13
 
2.3%
아 12
 
2.2%
Other values (170) 361
65.2%
Latin
ValueCountFrequency (%)
T 124
 
7.2%
L 109
 
6.3%
A 99
 
5.7%
I 97
 
5.6%
N 92
 
5.3%
C 91
 
5.3%
E 83
 
4.8%
O 74
 
4.3%
D 72
 
4.2%
S 69
 
4.0%
Other values (41) 814
47.2%
Common
ValueCountFrequency (%)
245
57.4%
. 82
 
19.2%
( 49
 
11.5%
) 49
 
11.5%
4 1
 
0.2%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2151
79.5%
Hangul 554
 
20.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
245
 
11.4%
T 124
 
5.8%
L 109
 
5.1%
A 99
 
4.6%
I 97
 
4.5%
N 92
 
4.3%
C 91
 
4.2%
E 83
 
3.9%
. 82
 
3.8%
O 74
 
3.4%
Other values (47) 1055
49.0%
Hangul
ValueCountFrequency (%)
주 39
 
7.0%
스 22
 
4.0%
한 21
 
3.8%
유 20
 
3.6%
사 18
 
3.2%
이 17
 
3.1%
공 16
 
2.9%
코 15
 
2.7%
리 13
 
2.3%
아 12
 
2.2%
Other values (170) 361
65.2%

지정연도
Real number (ℝ)

Distinct18
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.7636
Minimum2005
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T18:05:35.215334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006.2
Q12016
median2022
Q32023
95-th percentile2023
Maximum2023
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.3714363
Coefficient of variation (CV)0.0026607554
Kurtosis0.38552121
Mean2018.7636
Median Absolute Deviation (MAD)1
Skewness-1.2724656
Sum333096
Variance28.852328
MonotonicityNot monotonic
2023-12-12T18:05:35.413118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2023 51
30.9%
2022 33
20.0%
2020 14
 
8.5%
2021 8
 
4.8%
2018 8
 
4.8%
2019 6
 
3.6%
2016 6
 
3.6%
2015 5
 
3.0%
2005 5
 
3.0%
2013 5
 
3.0%
Other values (8) 24
14.5%
ValueCountFrequency (%)
2005 5
3.0%
2006 4
2.4%
2007 2
 
1.2%
2008 2
 
1.2%
2009 4
2.4%
2010 4
2.4%
2011 2
 
1.2%
2012 3
1.8%
2013 5
3.0%
2014 3
1.8%
ValueCountFrequency (%)
2023 51
30.9%
2022 33
20.0%
2021 8
 
4.8%
2020 14
 
8.5%
2019 6
 
3.6%
2018 8
 
4.8%
2016 6
 
3.6%
2015 5
 
3.0%
2014 3
 
1.8%
2013 5
 
3.0%

권역
Categorical

Distinct10
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
동남아_대양주
49 
중국
37 
북미
16 
일본
16 
서남아
13 
Other values (5)
34 

Length

Max length7
Median length2
Mean length3.9636364
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
동남아_대양주 49
29.7%
중국 37
22.4%
북미 16
 
9.7%
일본 16
 
9.7%
서남아 13
 
7.9%
러시아_CIS 12
 
7.3%
유럽 12
 
7.3%
중동 5
 
3.0%
중남미 4
 
2.4%
아프리카 1
 
0.6%

Length

2023-12-12T18:05:35.578531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:35.780253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동남아_대양주 49
29.7%
중국 37
22.4%
북미 16
 
9.7%
일본 16
 
9.7%
서남아 13
 
7.9%
러시아_cis 12
 
7.3%
유럽 12
 
7.3%
중동 5
 
3.0%
중남미 4
 
2.4%
아프리카 1
 
0.6%

해외거점국가
Categorical

Distinct36
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
중국
34 
베트남
19 
일본
16 
미국
14 
인도
13 
Other values (31)
69 

Length

Max length6
Median length2
Mean length2.769697
Min length2

Unique

Unique15 ?
Unique (%)9.1%

Sample

1st row뉴질랜드
2nd row뉴질랜드
3rd row말레이시아
4th row말레이시아
5th row미얀마

Common Values

ValueCountFrequency (%)
중국 34
20.6%
베트남 19
11.5%
일본 16
 
9.7%
미국 14
 
8.5%
인도 13
 
7.9%
인도네시아 6
 
3.6%
러시아 5
 
3.0%
호주 5
 
3.0%
아랍에미리트 4
 
2.4%
싱가포르 4
 
2.4%
Other values (26) 45
27.3%

Length

2023-12-12T18:05:36.022800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중국 34
20.6%
베트남 19
11.5%
일본 16
 
9.7%
미국 14
 
8.5%
인도 13
 
7.9%
인도네시아 6
 
3.6%
러시아 5
 
3.0%
호주 5
 
3.0%
태국 4
 
2.4%
필리핀 4
 
2.4%
Other values (26) 45
27.3%

도시
Text

Distinct65
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T18:05:36.347584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.4060606
Min length2

Characters and Unicode

Total characters562
Distinct characters132
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)21.2%

Sample

1st row오클랜드
2nd row오클랜드
3rd row쿠알라룸푸르
4th row쿠알라룸푸르
5th row양곤
ValueCountFrequency (%)
도쿄 14
 
8.5%
베이징 11
 
6.7%
하노이 10
 
6.1%
호치민 9
 
5.5%
상하이 8
 
4.8%
뉴델리 7
 
4.2%
자카르타 6
 
3.6%
로스앤젤레스 5
 
3.0%
싱가포르 4
 
2.4%
방콕 4
 
2.4%
Other values (55) 87
52.7%
2023-12-12T18:05:36.786609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 47
 
8.4%
르 22
 
3.9%
하 21
 
3.7%
도 18
 
3.2%
스 18
 
3.2%
베 16
 
2.8%
바 14
 
2.5%
쿄 14
 
2.5%
타 11
 
2.0%
징 11
 
2.0%
Other values (122) 370
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 554
98.6%
Open Punctuation 4
 
0.7%
Close Punctuation 4
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 47
 
8.5%
르 22
 
4.0%
하 21
 
3.8%
도 18
 
3.2%
스 18
 
3.2%
베 16
 
2.9%
바 14
 
2.5%
쿄 14
 
2.5%
타 11
 
2.0%
징 11
 
2.0%
Other values (120) 362
65.3%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 554
98.6%
Common 8
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 47
 
8.5%
르 22
 
4.0%
하 21
 
3.8%
도 18
 
3.2%
스 18
 
3.2%
베 16
 
2.9%
바 14
 
2.5%
쿄 14
 
2.5%
타 11
 
2.0%
징 11
 
2.0%
Other values (120) 362
65.3%
Common
ValueCountFrequency (%)
( 4
50.0%
) 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 554
98.6%
ASCII 8
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 47
 
8.5%
르 22
 
4.0%
하 21
 
3.8%
도 18
 
3.2%
스 18
 
3.2%
베 16
 
2.9%
바 14
 
2.5%
쿄 14
 
2.5%
타 11
 
2.0%
징 11
 
2.0%
Other values (120) 362
65.3%
ASCII
ValueCountFrequency (%)
( 4
50.0%
) 4
50.0%

설립일
Date

MISSING 

Distinct155
Distinct (%)98.1%
Missing7
Missing (%)4.2%
Memory size1.4 KiB
Minimum1973-07-07 00:00:00
Maximum2020-02-21 00:00:00
2023-12-12T18:05:36.918773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:37.069796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

상시종업원수
Real number (ℝ)

Distinct45
Distinct (%)27.4%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean54.573171
Minimum2
Maximum3800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T18:05:37.210562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median10
Q320
95-th percentile86.05
Maximum3800
Range3798
Interquartile range (IQR)15

Descriptive statistics

Standard deviation310.88917
Coefficient of variation (CV)5.69674
Kurtosis131.63816
Mean54.573171
Median Absolute Deviation (MAD)5
Skewness11.076906
Sum8950
Variance96652.074
MonotonicityNot monotonic
2023-12-12T18:05:37.339126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5 23
 
13.9%
10 18
 
10.9%
4 12
 
7.3%
3 11
 
6.7%
8 9
 
5.5%
6 9
 
5.5%
7 8
 
4.8%
9 6
 
3.6%
12 5
 
3.0%
20 5
 
3.0%
Other values (35) 58
35.2%
ValueCountFrequency (%)
2 1
 
0.6%
3 11
6.7%
4 12
7.3%
5 23
13.9%
6 9
 
5.5%
7 8
 
4.8%
8 9
 
5.5%
9 6
 
3.6%
10 18
10.9%
11 3
 
1.8%
ValueCountFrequency (%)
3800 1
0.6%
1000 1
0.6%
550 1
0.6%
447 1
0.6%
358 1
0.6%
225 1
0.6%
182 1
0.6%
140 1
0.6%
88 1
0.6%
75 2
1.2%

주소
Text

MISSING 

Distinct157
Distinct (%)99.4%
Missing7
Missing (%)4.2%
Memory size1.4 KiB
2023-12-12T18:05:37.641105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length121
Median length52
Mean length34.613924
Min length3

Characters and Unicode

Total characters5469
Distinct characters469
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)98.7%

Sample

1st row9층 17 알버트 스트릿 오클랜드뉴질랜드
2nd row유닛1 321 넬슨 스트릿 오네헝가 오클랜드 뉴질랜드
3rd row 23 2층 엠파이어 쇼핑몰 수방자야 슬렝고 47500 말레이시아
4th row서울시 마포구 마포대로 127 풍림VIP 빌딩 302호
5th row베트남 하노이시 Cau Giay구 Trung Hoa동 Le Van Luong길 63번 319빌딩 15층
ValueCountFrequency (%)
서울시 17
 
1.5%
서울특별시 10
 
0.9%
베트남 10
 
0.9%
중국 10
 
0.9%
강남구 8
 
0.7%
3층 6
 
0.5%
도쿄도 6
 
0.5%
미국 5
 
0.4%
a동 5
 
0.4%
빌딩 5
 
0.4%
Other values (880) 1059
92.8%
2023-12-12T18:05:38.233346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
997
 
18.2%
1 225
 
4.1%
0 142
 
2.6%
2 138
 
2.5%
3 101
 
1.8%
시 96
 
1.8%
구 90
 
1.6%
6 77
 
1.4%
4 76
 
1.4%
a 74
 
1.4%
Other values (459) 3453
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2587
47.3%
Space Separator 997
 
18.2%
Decimal Number 976
 
17.8%
Lowercase Letter 583
 
10.7%
Uppercase Letter 258
 
4.7%
Other Punctuation 26
 
0.5%
Open Punctuation 21
 
0.4%
Close Punctuation 21
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 96
 
3.7%
구 90
 
3.5%
로 68
 
2.6%
이 65
 
2.5%
호 65
 
2.5%
스 62
 
2.4%
서 49
 
1.9%
트 49
 
1.9%
동 46
 
1.8%
도 42
 
1.6%
Other values (395) 1955
75.6%
Uppercase Letter
ValueCountFrequency (%)
S 31
 
12.0%
B 24
 
9.3%
C 22
 
8.5%
A 19
 
7.4%
D 17
 
6.6%
H 15
 
5.8%
K 13
 
5.0%
T 13
 
5.0%
G 12
 
4.7%
N 10
 
3.9%
Other values (15) 82
31.8%
Lowercase Letter
ValueCountFrequency (%)
a 74
12.7%
n 67
11.5%
i 56
9.6%
o 49
8.4%
e 49
8.4%
t 46
7.9%
r 44
 
7.5%
u 34
 
5.8%
h 29
 
5.0%
s 25
 
4.3%
Other values (14) 110
18.9%
Decimal Number
ValueCountFrequency (%)
1 225
23.1%
0 142
14.5%
2 138
14.1%
3 101
10.3%
6 77
 
7.9%
4 76
 
7.8%
5 62
 
6.4%
9 55
 
5.6%
7 52
 
5.3%
8 48
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 14
53.8%
/ 12
46.2%
Space Separator
ValueCountFrequency (%)
997
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2587
47.3%
Common 2041
37.3%
Latin 841
 
15.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 96
 
3.7%
구 90
 
3.5%
로 68
 
2.6%
이 65
 
2.5%
호 65
 
2.5%
스 62
 
2.4%
서 49
 
1.9%
트 49
 
1.9%
동 46
 
1.8%
도 42
 
1.6%
Other values (395) 1955
75.6%
Latin
ValueCountFrequency (%)
a 74
 
8.8%
n 67
 
8.0%
i 56
 
6.7%
o 49
 
5.8%
e 49
 
5.8%
t 46
 
5.5%
r 44
 
5.2%
u 34
 
4.0%
S 31
 
3.7%
h 29
 
3.4%
Other values (39) 362
43.0%
Common
ValueCountFrequency (%)
997
48.8%
1 225
 
11.0%
0 142
 
7.0%
2 138
 
6.8%
3 101
 
4.9%
6 77
 
3.8%
4 76
 
3.7%
5 62
 
3.0%
9 55
 
2.7%
7 52
 
2.5%
Other values (5) 116
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2882
52.7%
Hangul 2587
47.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
997
34.6%
1 225
 
7.8%
0 142
 
4.9%
2 138
 
4.8%
3 101
 
3.5%
6 77
 
2.7%
4 76
 
2.6%
a 74
 
2.6%
n 67
 
2.3%
5 62
 
2.2%
Other values (54) 923
32.0%
Hangul
ValueCountFrequency (%)
시 96
 
3.7%
구 90
 
3.5%
로 68
 
2.6%
이 65
 
2.5%
호 65
 
2.5%
스 62
 
2.4%
서 49
 
1.9%
트 49
 
1.9%
동 46
 
1.8%
도 42
 
1.6%
Other values (395) 1955
75.6%

대표자명
Text

UNIQUE 

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T18:05:38.618461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length3
Mean length3.4666667
Min length2

Characters and Unicode

Total characters572
Distinct characters203
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)100.0%

Sample

1st row정광수
2nd row알렉스태원홍
3rd row한종훈
4th row홍병철
5th row눈젖
ValueCountFrequency (%)
정광수 1
 
0.5%
장지앤밍 1
 
0.5%
홍건택 1
 
0.5%
김기호 1
 
0.5%
윤홍찬 1
 
0.5%
나건용 1
 
0.5%
고현승 1
 
0.5%
이재일 1
 
0.5%
채욱 1
 
0.5%
김진 1
 
0.5%
Other values (172) 172
94.5%
2023-12-12T18:05:39.315392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 31
 
5.4%
김 26
 
4.5%
18
 
3.1%
박 15
 
2.6%
정 13
 
2.3%
종 11
 
1.9%
진 11
 
1.9%
현 11
 
1.9%
홍 10
 
1.7%
승 10
 
1.7%
Other values (193) 416
72.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 543
94.9%
Space Separator 18
 
3.1%
Lowercase Letter 7
 
1.2%
Uppercase Letter 2
 
0.3%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 31
 
5.7%
김 26
 
4.8%
박 15
 
2.8%
정 13
 
2.4%
종 11
 
2.0%
진 11
 
2.0%
현 11
 
2.0%
홍 10
 
1.8%
승 10
 
1.8%
영 9
 
1.7%
Other values (181) 396
72.9%
Lowercase Letter
ValueCountFrequency (%)
a 1
14.3%
v 1
14.3%
i 1
14.3%
d 1
14.3%
n 1
14.3%
u 1
14.3%
y 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
D 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 543
94.9%
Common 20
 
3.5%
Latin 9
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 31
 
5.7%
김 26
 
4.8%
박 15
 
2.8%
정 13
 
2.4%
종 11
 
2.0%
진 11
 
2.0%
현 11
 
2.0%
홍 10
 
1.8%
승 10
 
1.8%
영 9
 
1.7%
Other values (181) 396
72.9%
Latin
ValueCountFrequency (%)
D 1
11.1%
a 1
11.1%
v 1
11.1%
i 1
11.1%
d 1
11.1%
n 1
11.1%
u 1
11.1%
y 1
11.1%
B 1
11.1%
Common
ValueCountFrequency (%)
18
90.0%
) 1
 
5.0%
( 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 543
94.9%
ASCII 29
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 31
 
5.7%
김 26
 
4.8%
박 15
 
2.8%
정 13
 
2.4%
종 11
 
2.0%
진 11
 
2.0%
현 11
 
2.0%
홍 10
 
1.8%
승 10
 
1.8%
영 9
 
1.7%
Other values (181) 396
72.9%
ASCII
ValueCountFrequency (%)
18
62.1%
D 1
 
3.4%
) 1
 
3.4%
( 1
 
3.4%
a 1
 
3.4%
v 1
 
3.4%
i 1
 
3.4%
d 1
 
3.4%
n 1
 
3.4%
u 1
 
3.4%
Other values (2) 2
 
6.9%

국내_국외
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
국외
122 
국내
43 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국외 122
73.9%
국내 43
 
26.1%

Length

2023-12-12T18:05:39.511222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:39.617814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국외 122
73.9%
국내 43
 
26.1%

해외_조직명
Text

MISSING 

Distinct40
Distinct (%)97.6%
Missing124
Missing (%)75.2%
Memory size1.4 KiB
2023-12-12T18:05:39.880109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length37
Mean length25.292683
Min length1

Characters and Unicode

Total characters1037
Distinct characters218
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)95.1%

Sample

1st rowPIKOR ASEAN
2nd row베트남 호치민 법인 소유 베트남 하노이 지사 소유
3rd row베트남지사
4th row베트남 인도네시아 중국 지사 보유
5th row베트남 인도네시아 중국 지사 보유
ValueCountFrequency (%)
지사 10
 
5.4%
보유 9
 
4.9%
중국 6
 
3.3%
베트남 5
 
2.7%
법인 3
 
1.6%
co.ltd 3
 
1.6%
대만 3
 
1.6%
우즈베키스탄 3
 
1.6%
러시아 3
 
1.6%
미국 3
 
1.6%
Other values (125) 136
73.9%
2023-12-12T18:05:40.292013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
147
 
14.2%
사 27
 
2.6%
a 24
 
2.3%
지 22
 
2.1%
n 20
 
1.9%
A 20
 
1.9%
유 19
 
1.8%
e 19
 
1.8%
r 18
 
1.7%
i 17
 
1.6%
Other values (208) 704
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 486
46.9%
Lowercase Letter 180
 
17.4%
Uppercase Letter 175
 
16.9%
Space Separator 147
 
14.2%
Open Punctuation 15
 
1.4%
Close Punctuation 15
 
1.4%
Decimal Number 12
 
1.2%
Other Punctuation 7
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 27
 
5.6%
지 22
 
4.5%
유 19
 
3.9%
국 15
 
3.1%
인 14
 
2.9%
베 12
 
2.5%
이 11
 
2.3%
보 11
 
2.3%
법 10
 
2.1%
트 10
 
2.1%
Other values (149) 335
68.9%
Uppercase Letter
ValueCountFrequency (%)
A 20
11.4%
T 16
 
9.1%
N 14
 
8.0%
I 13
 
7.4%
S 13
 
7.4%
C 12
 
6.9%
O 12
 
6.9%
R 10
 
5.7%
L 9
 
5.1%
E 8
 
4.6%
Other values (15) 48
27.4%
Lowercase Letter
ValueCountFrequency (%)
a 24
13.3%
n 20
11.1%
e 19
10.6%
r 18
10.0%
i 17
9.4%
t 13
 
7.2%
s 11
 
6.1%
o 10
 
5.6%
c 7
 
3.9%
h 7
 
3.9%
Other values (11) 34
18.9%
Decimal Number
ValueCountFrequency (%)
1 3
25.0%
2 2
16.7%
7 2
16.7%
3 2
16.7%
5 1
 
8.3%
8 1
 
8.3%
6 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 5
71.4%
? 1
 
14.3%
/ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
147
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 465
44.8%
Latin 355
34.2%
Common 196
18.9%
Han 21
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 27
 
5.8%
지 22
 
4.7%
유 19
 
4.1%
국 15
 
3.2%
인 14
 
3.0%
베 12
 
2.6%
이 11
 
2.4%
보 11
 
2.4%
법 10
 
2.2%
트 10
 
2.2%
Other values (129) 314
67.5%
Latin
ValueCountFrequency (%)
a 24
 
6.8%
n 20
 
5.6%
A 20
 
5.6%
e 19
 
5.4%
r 18
 
5.1%
i 17
 
4.8%
T 16
 
4.5%
N 14
 
3.9%
t 13
 
3.7%
I 13
 
3.7%
Other values (36) 181
51.0%
Han
ValueCountFrequency (%)
北 2
 
9.5%
咨 1
 
4.8%
京 1
 
4.8%
初 1
 
4.8%
那 1
 
4.8%
有 1
 
4.8%
之 1
 
4.8%
司 1
 
4.8%
公 1
 
4.8%
限 1
 
4.8%
Other values (10) 10
47.6%
Common
ValueCountFrequency (%)
147
75.0%
( 15
 
7.7%
) 15
 
7.7%
. 5
 
2.6%
1 3
 
1.5%
2 2
 
1.0%
7 2
 
1.0%
3 2
 
1.0%
? 1
 
0.5%
5 1
 
0.5%
Other values (3) 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 551
53.1%
Hangul 465
44.8%
CJK 21
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147
26.7%
a 24
 
4.4%
n 20
 
3.6%
A 20
 
3.6%
e 19
 
3.4%
r 18
 
3.3%
i 17
 
3.1%
T 16
 
2.9%
( 15
 
2.7%
) 15
 
2.7%
Other values (49) 240
43.6%
Hangul
ValueCountFrequency (%)
사 27
 
5.8%
지 22
 
4.7%
유 19
 
4.1%
국 15
 
3.2%
인 14
 
3.0%
베 12
 
2.6%
이 11
 
2.4%
보 11
 
2.4%
법 10
 
2.2%
트 10
 
2.2%
Other values (129) 314
67.5%
CJK
ValueCountFrequency (%)
北 2
 
9.5%
咨 1
 
4.8%
京 1
 
4.8%
初 1
 
4.8%
那 1
 
4.8%
有 1
 
4.8%
之 1
 
4.8%
司 1
 
4.8%
公 1
 
4.8%
限 1
 
4.8%
Other values (10) 10
47.6%

해외_설립일
Date

MISSING 

Distinct28
Distinct (%)100.0%
Missing137
Missing (%)83.0%
Memory size1.4 KiB
Minimum2003-08-03 00:00:00
Maximum2022-09-07 00:00:00
2023-12-12T18:05:40.410572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:40.520507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

해외_상시종업원수
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)58.6%
Missing136
Missing (%)82.4%
Infinite0
Infinite (%)0.0%
Mean12
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T18:05:40.644964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median8
Q315
95-th percentile38.2
Maximum60
Range59
Interquartile range (IQR)11

Descriptive statistics

Standard deviation13.535667
Coefficient of variation (CV)1.1279723
Kurtosis5.4753216
Mean12
Median Absolute Deviation (MAD)4
Skewness2.2605403
Sum348
Variance183.21429
MonotonicityNot monotonic
2023-12-12T18:05:40.770100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 4
 
2.4%
8 3
 
1.8%
3 3
 
1.8%
5 3
 
1.8%
2 2
 
1.2%
9 2
 
1.2%
15 2
 
1.2%
10 1
 
0.6%
1 1
 
0.6%
6 1
 
0.6%
Other values (7) 7
 
4.2%
(Missing) 136
82.4%
ValueCountFrequency (%)
1 1
 
0.6%
2 2
1.2%
3 3
1.8%
4 4
2.4%
5 3
1.8%
6 1
 
0.6%
8 3
1.8%
9 2
1.2%
10 1
 
0.6%
12 1
 
0.6%
ValueCountFrequency (%)
60 1
0.6%
45 1
0.6%
28 1
0.6%
25 1
0.6%
23 1
0.6%
22 1
0.6%
15 2
1.2%
12 1
0.6%
10 1
0.6%
9 2
1.2%

해외_설치형태
Categorical

IMBALANCE 

Distinct6
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
136 
단독법인
 
12
지사
 
7
연락사무소
 
6
합작법인
 
3

Length

Max length6
Median length4
Mean length3.9636364
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 136
82.4%
단독법인 12
 
7.3%
지사 7
 
4.2%
연락사무소 6
 
3.6%
합작법인 3
 
1.8%
장기협력약정 1
 
0.6%

Length

2023-12-12T18:05:40.942292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:41.107891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 136
82.4%
단독법인 12
 
7.3%
지사 7
 
4.2%
연락사무소 6
 
3.6%
합작법인 3
 
1.8%
장기협력약정 1
 
0.6%

해외_주소
Text

MISSING 

Distinct29
Distinct (%)100.0%
Missing136
Missing (%)82.4%
Memory size1.4 KiB
2023-12-12T18:05:41.332494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length182
Median length69
Mean length63.862069
Min length17

Characters and Unicode

Total characters1852
Distinct characters132
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st rowNo.A03 Jalan 2/142a Taman Len Seng 56000 Kuala Lumpur
2nd rowL448.OT12 Landmark 4 Vinhomes Central Park 208 Nguyen Huu Canh Ward 22 Binh Thanh District Ho Chi Minh City Viet Nam
3rd row10F(Room 106) Saigon Paragon Bldg. 03 Nguyen Luong Bang Str. Tan Phu Ward District 7 HCMC Vietnam
4th rowMenara Prima 27F Suite D Jl. DR. Ide Anak Agung Gde Agung Blok 6.2 Kawasan Mega Kuningan Jakarta Selatan Indonesia
5th rowNO.J05STJADESANGKAT TOMNOP TEUK KHAN CHAMKARMON P.P CAMBODIA
ValueCountFrequency (%)
district 6
 
2.1%
china 4
 
1.4%
city 3
 
1.0%
rd 3
 
1.0%
plaza 3
 
1.0%
no 3
 
1.0%
2 2
 
0.7%
19 2
 
0.7%
new 2
 
0.7%
ward 2
 
0.7%
Other values (251) 261
89.7%
2023-12-12T18:05:41.868165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
266
 
14.4%
a 129
 
7.0%
i 97
 
5.2%
n 95
 
5.1%
o 68
 
3.7%
e 67
 
3.6%
t 57
 
3.1%
r 48
 
2.6%
1 48
 
2.6%
h 47
 
2.5%
Other values (122) 930
50.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 916
49.5%
Uppercase Letter 301
 
16.3%
Space Separator 266
 
14.4%
Decimal Number 225
 
12.1%
Other Letter 96
 
5.2%
Other Punctuation 42
 
2.3%
Open Punctuation 2
 
0.1%
Final Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 6
 
6.2%
구 4
 
4.2%
시 4
 
4.2%
북 3
 
3.1%
北 3
 
3.1%
국 3
 
3.1%
台 2
 
2.1%
市 2
 
2.1%
區 2
 
2.1%
흥 2
 
2.1%
Other values (56) 65
67.7%
Lowercase Letter
ValueCountFrequency (%)
a 129
14.1%
i 97
10.6%
n 95
10.4%
o 68
 
7.4%
e 67
 
7.3%
t 57
 
6.2%
r 48
 
5.2%
h 47
 
5.1%
u 46
 
5.0%
g 41
 
4.5%
Other values (16) 221
24.1%
Uppercase Letter
ValueCountFrequency (%)
A 31
 
10.3%
S 24
 
8.0%
N 24
 
8.0%
P 22
 
7.3%
C 21
 
7.0%
D 18
 
6.0%
R 17
 
5.6%
H 15
 
5.0%
B 14
 
4.7%
T 12
 
4.0%
Other values (14) 103
34.2%
Decimal Number
ValueCountFrequency (%)
1 48
21.3%
0 41
18.2%
2 39
17.3%
3 25
11.1%
4 16
 
7.1%
6 14
 
6.2%
5 13
 
5.8%
8 12
 
5.3%
7 9
 
4.0%
9 8
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 38
90.5%
/ 4
 
9.5%
Space Separator
ValueCountFrequency (%)
266
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1217
65.7%
Common 539
29.1%
Hangul 71
 
3.8%
Han 25
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 129
 
10.6%
i 97
 
8.0%
n 95
 
7.8%
o 68
 
5.6%
e 67
 
5.5%
t 57
 
4.7%
r 48
 
3.9%
h 47
 
3.9%
u 46
 
3.8%
g 41
 
3.4%
Other values (40) 522
42.9%
Hangul
ValueCountFrequency (%)
호 6
 
8.5%
구 4
 
5.6%
시 4
 
5.6%
북 3
 
4.2%
국 3
 
4.2%
흥 2
 
2.8%
경 2
 
2.8%
중 2
 
2.8%
로 2
 
2.8%
대 2
 
2.8%
Other values (39) 41
57.7%
Han
ValueCountFrequency (%)
北 3
12.0%
台 2
 
8.0%
市 2
 
8.0%
區 2
 
8.0%
路 2
 
8.0%
樓 2
 
8.0%
號 2
 
8.0%
內 1
 
4.0%
湖 1
 
4.0%
瑞 1
 
4.0%
Other values (7) 7
28.0%
Common
ValueCountFrequency (%)
266
49.4%
1 48
 
8.9%
0 41
 
7.6%
2 39
 
7.2%
. 38
 
7.1%
3 25
 
4.6%
4 16
 
3.0%
6 14
 
2.6%
5 13
 
2.4%
8 12
 
2.2%
Other values (6) 27
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1754
94.7%
Hangul 71
 
3.8%
CJK 25
 
1.3%
Punctuation 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
266
 
15.2%
a 129
 
7.4%
i 97
 
5.5%
n 95
 
5.4%
o 68
 
3.9%
e 67
 
3.8%
t 57
 
3.2%
r 48
 
2.7%
1 48
 
2.7%
h 47
 
2.7%
Other values (55) 832
47.4%
Hangul
ValueCountFrequency (%)
호 6
 
8.5%
구 4
 
5.6%
시 4
 
5.6%
북 3
 
4.2%
국 3
 
4.2%
흥 2
 
2.8%
경 2
 
2.8%
중 2
 
2.8%
로 2
 
2.8%
대 2
 
2.8%
Other values (39) 41
57.7%
CJK
ValueCountFrequency (%)
北 3
12.0%
台 2
 
8.0%
市 2
 
8.0%
區 2
 
8.0%
路 2
 
8.0%
樓 2
 
8.0%
號 2
 
8.0%
內 1
 
4.0%
湖 1
 
4.0%
瑞 1
 
4.0%
Other values (7) 7
28.0%
Punctuation
ValueCountFrequency (%)
’ 2
100.0%

국내_부서명
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing121
Missing (%)73.3%
Memory size1.4 KiB
2023-12-12T18:05:42.182432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11.5
Mean length8.2727273
Min length4

Characters and Unicode

Total characters364
Distinct characters129
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row송림글로벌
2nd row할랄코리아(주)
3rd row엔젤컴퍼니
4th row한텍파트너스
5th row해피글로벌
ValueCountFrequency (%)
주식회사 7
 
12.1%
송림글로벌 1
 
1.7%
글로벌비즈센터 1
 
1.7%
제이앤아이글로벌 1
 
1.7%
브릭스코리아(주 1
 
1.7%
데타에이아이컨설팅코리아 1
 
1.7%
까마코리아 1
 
1.7%
주)에이디엘 1
 
1.7%
주)웨비오 1
 
1.7%
마카글로벌 1
 
1.7%
Other values (42) 42
72.4%
2023-12-12T18:05:42.741438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 23
 
6.3%
아 18
 
4.9%
코 18
 
4.9%
리 16
 
4.4%
14
 
3.8%
) 14
 
3.8%
( 14
 
3.8%
이 14
 
3.8%
사 12
 
3.3%
스 11
 
3.0%
Other values (119) 210
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 313
86.0%
Space Separator 14
 
3.8%
Close Punctuation 14
 
3.8%
Open Punctuation 14
 
3.8%
Uppercase Letter 9
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 23
 
7.3%
아 18
 
5.8%
코 18
 
5.8%
리 16
 
5.1%
이 14
 
4.5%
사 12
 
3.8%
스 11
 
3.5%
회 10
 
3.2%
로 10
 
3.2%
벌 9
 
2.9%
Other values (108) 172
55.0%
Uppercase Letter
ValueCountFrequency (%)
E 2
22.2%
A 1
11.1%
R 1
11.1%
O 1
11.1%
K 1
11.1%
T 1
11.1%
X 1
11.1%
N 1
11.1%
Space Separator
ValueCountFrequency (%)
14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 313
86.0%
Common 42
 
11.5%
Latin 9
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 23
 
7.3%
아 18
 
5.8%
코 18
 
5.8%
리 16
 
5.1%
이 14
 
4.5%
사 12
 
3.8%
스 11
 
3.5%
회 10
 
3.2%
로 10
 
3.2%
벌 9
 
2.9%
Other values (108) 172
55.0%
Latin
ValueCountFrequency (%)
E 2
22.2%
A 1
11.1%
R 1
11.1%
O 1
11.1%
K 1
11.1%
T 1
11.1%
X 1
11.1%
N 1
11.1%
Common
ValueCountFrequency (%)
14
33.3%
) 14
33.3%
( 14
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 313
86.0%
ASCII 51
 
14.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 23
 
7.3%
아 18
 
5.8%
코 18
 
5.8%
리 16
 
5.1%
이 14
 
4.5%
사 12
 
3.8%
스 11
 
3.5%
회 10
 
3.2%
로 10
 
3.2%
벌 9
 
2.9%
Other values (108) 172
55.0%
ASCII
ValueCountFrequency (%)
14
27.5%
) 14
27.5%
( 14
27.5%
E 2
 
3.9%
A 1
 
2.0%
R 1
 
2.0%
O 1
 
2.0%
K 1
 
2.0%
T 1
 
2.0%
X 1
 
2.0%

국내_설립일
Date

MISSING 

Distinct41
Distinct (%)97.6%
Missing123
Missing (%)74.5%
Memory size1.4 KiB
Minimum1962-04-24 00:00:00
Maximum2021-10-18 00:00:00
2023-12-12T18:05:42.914694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:05:43.120496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)

국내_상시종업원수
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)29.5%
Missing121
Missing (%)73.3%
Infinite0
Infinite (%)0.0%
Mean7.1818182
Minimum0
Maximum120
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T18:05:43.266809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34.25
95-th percentile24.1
Maximum120
Range120
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation18.325573
Coefficient of variation (CV)2.5516621
Kurtosis35.248477
Mean7.1818182
Median Absolute Deviation (MAD)1
Skewness5.7186948
Sum316
Variance335.82664
MonotonicityNot monotonic
2023-12-12T18:05:43.426446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 11
 
6.7%
1 9
 
5.5%
3 7
 
4.2%
4 5
 
3.0%
25 2
 
1.2%
5 2
 
1.2%
6 2
 
1.2%
19 1
 
0.6%
8 1
 
0.6%
0 1
 
0.6%
Other values (3) 3
 
1.8%
(Missing) 121
73.3%
ValueCountFrequency (%)
0 1
 
0.6%
1 9
5.5%
2 11
6.7%
3 7
4.2%
4 5
3.0%
5 2
 
1.2%
6 2
 
1.2%
8 1
 
0.6%
11 1
 
0.6%
14 1
 
0.6%
ValueCountFrequency (%)
120 1
 
0.6%
25 2
 
1.2%
19 1
 
0.6%
14 1
 
0.6%
11 1
 
0.6%
8 1
 
0.6%
6 2
 
1.2%
5 2
 
1.2%
4 5
3.0%
3 7
4.2%

국내_설치형태
Categorical

Distinct5
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
121 
단독법인
21 
연락사무소
 
12
지사
 
10
합작법인
 
1

Length

Max length5
Median length4
Mean length3.9515152
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row<NA>
2nd row<NA>
3rd row연락사무소
4th row<NA>
5th row단독법인

Common Values

ValueCountFrequency (%)
<NA> 121
73.3%
단독법인 21
 
12.7%
연락사무소 12
 
7.3%
지사 10
 
6.1%
합작법인 1
 
0.6%

Length

2023-12-12T18:05:43.620993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:43.786127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 121
73.3%
단독법인 21
 
12.7%
연락사무소 12
 
7.3%
지사 10
 
6.1%
합작법인 1
 
0.6%

국내_주소
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing121
Missing (%)73.3%
Memory size1.4 KiB
2023-12-12T18:05:44.205381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length35
Mean length29.840909
Min length16

Characters and Unicode

Total characters1313
Distinct characters205
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row강남구 삼성동 영동대로 511트레이드 타워 2층 벤처플라자
2nd row서울시 마포구 마포대로 127 풍림VIP 빌딩 302호
3rd row경기도 화성시 봉담읍 와우안길 17 고운첨단관 902
4th row서울특별시 마포구 백범로 212 102동 205호
5th row경기도 용인시 기흥구 강남로 9 705층 103호(구갈동 태평양프라자)
ValueCountFrequency (%)
강남구 11
 
3.9%
서울특별시 11
 
3.9%
경기도 9
 
3.2%
서울시 7
 
2.5%
중구 5
 
1.8%
10 4
 
1.4%
서울 4
 
1.4%
영동대로 3
 
1.1%
마포구 3
 
1.1%
6층 3
 
1.1%
Other values (208) 224
78.9%
2023-12-12T18:05:44.828558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
242
 
18.4%
1 70
 
5.3%
2 41
 
3.1%
시 40
 
3.0%
구 39
 
3.0%
로 39
 
3.0%
0 37
 
2.8%
서 32
 
2.4%
3 27
 
2.1%
동 26
 
2.0%
Other values (195) 720
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 732
55.8%
Decimal Number 285
 
21.7%
Space Separator 242
 
18.4%
Lowercase Letter 21
 
1.6%
Uppercase Letter 12
 
0.9%
Open Punctuation 10
 
0.8%
Close Punctuation 10
 
0.8%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 40
 
5.5%
구 39
 
5.3%
로 39
 
5.3%
서 32
 
4.4%
동 26
 
3.6%
울 25
 
3.4%
호 24
 
3.3%
길 20
 
2.7%
남 17
 
2.3%
대 17
 
2.3%
Other values (164) 453
61.9%
Decimal Number
ValueCountFrequency (%)
1 70
24.6%
2 41
14.4%
0 37
13.0%
3 27
 
9.5%
6 23
 
8.1%
4 22
 
7.7%
7 20
 
7.0%
8 19
 
6.7%
5 14
 
4.9%
9 12
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
o 6
28.6%
e 4
19.0%
c 2
 
9.5%
u 2
 
9.5%
h 2
 
9.5%
d 1
 
4.8%
a 1
 
4.8%
r 1
 
4.8%
g 1
 
4.8%
l 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
S 4
33.3%
F 2
16.7%
I 2
16.7%
V 1
 
8.3%
P 1
 
8.3%
O 1
 
8.3%
B 1
 
8.3%
Space Separator
ValueCountFrequency (%)
242
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 732
55.8%
Common 548
41.7%
Latin 33
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 40
 
5.5%
구 39
 
5.3%
로 39
 
5.3%
서 32
 
4.4%
동 26
 
3.6%
울 25
 
3.4%
호 24
 
3.3%
길 20
 
2.7%
남 17
 
2.3%
대 17
 
2.3%
Other values (164) 453
61.9%
Latin
ValueCountFrequency (%)
o 6
18.2%
S 4
12.1%
e 4
12.1%
c 2
 
6.1%
F 2
 
6.1%
u 2
 
6.1%
h 2
 
6.1%
I 2
 
6.1%
V 1
 
3.0%
d 1
 
3.0%
Other values (7) 7
21.2%
Common
ValueCountFrequency (%)
242
44.2%
1 70
 
12.8%
2 41
 
7.5%
0 37
 
6.8%
3 27
 
4.9%
6 23
 
4.2%
4 22
 
4.0%
7 20
 
3.6%
8 19
 
3.5%
5 14
 
2.6%
Other values (4) 33
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 732
55.8%
ASCII 581
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
242
41.7%
1 70
 
12.0%
2 41
 
7.1%
0 37
 
6.4%
3 27
 
4.6%
6 23
 
4.0%
4 22
 
3.8%
7 20
 
3.4%
8 19
 
3.3%
5 14
 
2.4%
Other values (21) 66
 
11.4%
Hangul
ValueCountFrequency (%)
시 40
 
5.5%
구 39
 
5.3%
로 39
 
5.3%
서 32
 
4.4%
동 26
 
3.6%
울 25
 
3.4%
호 24
 
3.3%
길 20
 
2.7%
남 17
 
2.3%
대 17
 
2.3%
Other values (164) 453
61.9%

지원분야 1순위
Categorical

IMBALANCE 

Distinct8
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
수출지원
133 
해외유통망진출
 
13
인큐베이팅
 
6
품목별 타겟진출
 
5
기술수출(제휴)
 
4
Other values (3)
 
4

Length

Max length17
Median length4
Mean length4.6242424
Min length4

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row수출지원
2nd row수출지원
3rd row수출지원
4th row수출지원
5th row품목별 타겟진출

Common Values

ValueCountFrequency (%)
수출지원 133
80.6%
해외유통망진출 13
 
7.9%
인큐베이팅 6
 
3.6%
품목별 타겟진출 5
 
3.0%
기술수출(제휴) 4
 
2.4%
액셀러레이팅 2
 
1.2%
현지투자지원(투자타당성검토 등) 1
 
0.6%
O2O 방식 지원 1
 
0.6%

Length

2023-12-12T18:05:45.053470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:45.246446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수출지원 133
76.9%
해외유통망진출 13
 
7.5%
인큐베이팅 6
 
3.5%
품목별 5
 
2.9%
타겟진출 5
 
2.9%
기술수출(제휴 4
 
2.3%
액셀러레이팅 2
 
1.2%
현지투자지원(투자타당성검토 1
 
0.6%
등 1
 
0.6%
o2o 1
 
0.6%
Other values (2) 2
 
1.2%

지원분야 2순위
Categorical

Distinct11
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
해외유통망진출
81 
품목별 타겟진출
34 
기술수출(제휴)
15 
액셀러레이팅
14 
현지투자지원(투자타당성검토 등)
 
8
Other values (6)
13 

Length

Max length17
Median length11
Mean length7.6424242
Min length4

Unique

Unique3 ?
Unique (%)1.8%

Sample

1st row품목별 타겟진출
2nd row품목별 타겟진출
3rd row해외유통망진출
4th row해외유통망진출
5th row기술수출(제휴)

Common Values

ValueCountFrequency (%)
해외유통망진출 81
49.1%
품목별 타겟진출 34
20.6%
기술수출(제휴) 15
 
9.1%
액셀러레이팅 14
 
8.5%
현지투자지원(투자타당성검토 등) 8
 
4.8%
인큐베이팅 6
 
3.6%
수출지원 2
 
1.2%
데이터사이언스 컨설팅 2
 
1.2%
조달진출 1
 
0.6%
O2O 방식 지원 1
 
0.6%

Length

2023-12-12T18:05:45.436702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해외유통망진출 81
38.4%
품목별 34
16.1%
타겟진출 34
16.1%
기술수출(제휴 16
 
7.6%
액셀러레이팅 14
 
6.6%
현지투자지원(투자타당성검토 8
 
3.8%
등 8
 
3.8%
인큐베이팅 6
 
2.8%
수출지원 2
 
0.9%
데이터사이언스 2
 
0.9%
Other values (5) 6
 
2.8%

지원분야 3순위
Categorical

Distinct28
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
품목별 타겟진출
49 
현지투자지원(투자타당성검토 등)
21 
액셀러레이팅
17 
데이터사이언스 컨설팅
15 
기술수출(제휴)
15 
Other values (23)
48 

Length

Max length19
Median length17
Mean length9.2545455
Min length4

Unique

Unique20 ?
Unique (%)12.1%

Sample

1st row액셀러레이팅
2nd row액셀러레이팅
3rd row품목별 타겟진출
4th row품목별 타겟진출
5th row현지투자지원(투자타당성검토 등)

Common Values

ValueCountFrequency (%)
품목별 타겟진출 49
29.7%
현지투자지원(투자타당성검토 등) 21
12.7%
액셀러레이팅 17
 
10.3%
데이터사이언스 컨설팅 15
 
9.1%
기술수출(제휴) 15
 
9.1%
O2O 방식 지원 12
 
7.3%
인큐베이팅 9
 
5.5%
조달진출 7
 
4.2%
기타(글로벌 디지털 마케팅) 1
 
0.6%
기타(홍보 마케팅) 1
 
0.6%
Other values (18) 18
 
10.9%

Length

2023-12-12T18:05:45.632300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
품목별 49
16.8%
타겟진출 49
16.8%
현지투자지원(투자타당성검토 21
 
7.2%
등 21
 
7.2%
액셀러레이팅 18
 
6.2%
컨설팅 17
 
5.8%
데이터사이언스 15
 
5.1%
기술수출(제휴 15
 
5.1%
지원 14
 
4.8%
방식 13
 
4.5%
Other values (32) 60
20.5%

지원업종 1순위
Categorical

Distinct10
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
전기전자
43 
기계금속
40 
정보통신(SW)
26 
바이오
24 
의료기기
18 
Other values (5)
14 

Length

Max length8
Median length4
Mean length4.369697
Min length2

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row건설
2nd row전기전자
3rd row의료기기
4th row기계금속
5th row바이오

Common Values

ValueCountFrequency (%)
전기전자 43
26.1%
기계금속 40
24.2%
정보통신(SW) 26
15.8%
바이오 24
14.5%
의료기기 18
10.9%
건설 7
 
4.2%
섬유화공 3
 
1.8%
식품 2
 
1.2%
화장품 1
 
0.6%
생활용품 1
 
0.6%

Length

2023-12-12T18:05:46.193912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:05:46.399530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전기전자 43
26.1%
기계금속 40
24.2%
정보통신(sw 26
15.8%
바이오 24
14.5%
의료기기 18
10.9%
건설 7
 
4.2%
섬유화공 3
 
1.8%
식품 2
 
1.2%
화장품 1
 
0.6%
생활용품 1
 
0.6%

지원업종 2순위
Categorical

Distinct13
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
생활용품
51 
의료기기
26 
건설
24 
정보통신(SW)
20 
전기전자
15 
Other values (8)
29 

Length

Max length8
Median length4
Mean length4.0242424
Min length2

Unique

Unique5 ?
Unique (%)3.0%

Sample

1st row환경
2nd row바이오
3rd row건설
4th row전기전자
5th row생활용품

Common Values

ValueCountFrequency (%)
생활용품 51
30.9%
의료기기 26
15.8%
건설 24
14.5%
정보통신(SW) 20
 
12.1%
전기전자 15
 
9.1%
바이오 13
 
7.9%
환경 7
 
4.2%
섬유화공 4
 
2.4%
생황용품 1
 
0.6%
기계금속 1
 
0.6%
Other values (3) 3
 
1.8%

Length

2023-12-12T18:05:46.582317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
생활용품 51
30.9%
의료기기 27
16.4%
건설 24
14.5%
정보통신(sw 20
 
12.1%
전기전자 15
 
9.1%
바이오 13
 
7.9%
환경 7
 
4.2%
섬유화공 4
 
2.4%
생황용품 1
 
0.6%
기계금속 1
 
0.6%
Other values (2) 2
 
1.2%

지원업종 3순위
Categorical

Distinct44
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
생활용품
34 
건설
25 
환경
24 
기타(화장품)
13 
기타(식품)
9 
Other values (39)
60 

Length

Max length20
Median length16
Mean length4.9575758
Min length2

Unique

Unique32 ?
Unique (%)19.4%

Sample

1st row에너지
2nd row건설
3rd row환경
4th row섬유화공
5th row기타(농축산)

Common Values

ValueCountFrequency (%)
생활용품 34
20.6%
건설 25
15.2%
환경 24
14.5%
기타(화장품) 13
 
7.9%
기타(식품) 9
 
5.5%
에너지 7
 
4.2%
의료기기 7
 
4.2%
섬유화공 4
 
2.4%
기타(화장품 3
 
1.8%
정보통신(SW) 3
 
1.8%
Other values (34) 36
21.8%

Length

2023-12-12T18:05:46.744420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
생활용품 34
18.6%
건설 25
13.7%
환경 24
13.1%
기타(화장품 18
 
9.8%
기타(식품 11
 
6.0%
에너지 7
 
3.8%
의료기기 7
 
3.8%
섬유화공 4
 
2.2%
정보통신(sw 3
 
1.6%
및 3
 
1.6%
Other values (46) 47
25.7%

Sample

연번민간네트워크 명지정연도권역해외거점국가도시설립일상시종업원수주소대표자명국내_국외해외_조직명해외_설립일해외_상시종업원수해외_설치형태해외_주소국내_부서명국내_설립일국내_상시종업원수국내_설치형태국내_주소지원분야 1순위지원분야 2순위지원분야 3순위지원업종 1순위지원업종 2순위지원업종 3순위
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