Overview

Dataset statistics

Number of variables17
Number of observations93
Missing cells94
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.7 KiB
Average record size in memory139.4 B

Variable types

Categorical7
Text8
Numeric2

Dataset

Description1. 아프리카혁신스타트업디렉터리 목록 조회: 한글 국가명 또는 ISO국가코드(다.참고 1 ISO국가코드 이용 )를 이용하여 아프리카혁신스타트업디렉터리 목록 조회
Author한아프리카재단
URLhttps://www.data.go.kr/data/15099247/fileData.do

Alerts

iso 2자리코드 is highly overall correlated with 국가명 and 2 other fieldsHigh correlation
국가영문명 is highly overall correlated with 국가명 and 2 other fieldsHigh correlation
국가명 is highly overall correlated with 국가영문명 and 2 other fieldsHigh correlation
창업연도 is highly overall correlated with 직원 수 and 1 other fieldsHigh correlation
투자유치액(달러) is highly overall correlated with 직원 수High correlation
본사 is highly overall correlated with 국가명 and 2 other fieldsHigh correlation
직원 수 is highly overall correlated with 창업연도 and 2 other fieldsHigh correlation
최종 투자 단계 is highly overall correlated with 창업연도 and 1 other fieldsHigh correlation
창업가 has 4 (4.3%) missing valuesMissing
투자유치액(달러) has 28 (30.1%) missing valuesMissing
대표전화 has 15 (16.1%) missing valuesMissing
주소 has 13 (14.0%) missing valuesMissing
이메일 has 5 (5.4%) missing valuesMissing
SNS 링크 has 4 (4.3%) missing valuesMissing
최대 투자자/기업 has 25 (26.9%) missing valuesMissing
기업명 has unique valuesUnique
홈페이지 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:55:48.070306
Analysis finished2023-12-12 21:55:50.736747
Duration2.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국가명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size876.0 B
나이지리아
35 
케냐
14 
남아프리카공화국
13 
가나
5 
세네갈
5 
Other values (12)
21 

Length

Max length8
Median length6
Mean length4.3333333
Min length2

Unique

Unique5 ?
Unique (%)5.4%

Sample

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

Common Values

ValueCountFrequency (%)
나이지리아 35
37.6%
케냐 14
 
15.1%
남아프리카공화국 13
 
14.0%
가나 5
 
5.4%
세네갈 5
 
5.4%
르완다 3
 
3.2%
이집트 3
 
3.2%
우간다 2
 
2.2%
프랑스 2
 
2.2%
탄자니아 2
 
2.2%
Other values (7) 9
 
9.7%

Length

2023-12-13T06:55:50.808208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
나이지리아 35
37.6%
케냐 14
 
15.1%
남아프리카공화국 13
 
14.0%
가나 5
 
5.4%
세네갈 5
 
5.4%
르완다 3
 
3.2%
이집트 3
 
3.2%
코트디부아르 2
 
2.2%
카메룬 2
 
2.2%
프랑스 2
 
2.2%
Other values (7) 9
 
9.7%

국가영문명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size876.0 B
Nigeria
35 
Kenya
14 
South Africa
13 
Ghana
5 
Senegal
5 
Other values (12)
21 

Length

Max length24
Median length13
Mean length7.4946237
Min length5

Unique

Unique5 ?
Unique (%)5.4%

Sample

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

Common Values

ValueCountFrequency (%)
Nigeria 35
37.6%
Kenya 14
 
15.1%
South Africa 13
 
14.0%
Ghana 5
 
5.4%
Senegal 5
 
5.4%
Rwanda 3
 
3.2%
Egypt 3
 
3.2%
Uganda 2
 
2.2%
France 2
 
2.2%
Tanzania 2
 
2.2%
Other values (7) 9
 
9.7%

Length

2023-12-13T06:55:50.973738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nigeria 35
31.5%
kenya 14
 
12.6%
south 13
 
11.7%
africa 13
 
11.7%
ghana 5
 
4.5%
senegal 5
 
4.5%
rwanda 3
 
2.7%
egypt 3
 
2.7%
cameroon 2
 
1.8%
d'ivoire 2
 
1.8%
Other values (12) 16
14.4%

iso 2자리코드
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size876.0 B
NG
35 
KE
14 
ZA
13 
GH
5 
SN
5 
Other values (12)
21 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique5 ?
Unique (%)5.4%

Sample

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

Common Values

ValueCountFrequency (%)
NG 35
37.6%
KE 14
 
15.1%
ZA 13
 
14.0%
GH 5
 
5.4%
SN 5
 
5.4%
RW 3
 
3.2%
EG 3
 
3.2%
UG 2
 
2.2%
FR 2
 
2.2%
TZ 2
 
2.2%
Other values (7) 9
 
9.7%

Length

2023-12-13T06:55:51.100807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ng 35
37.6%
ke 14
 
15.1%
za 13
 
14.0%
gh 5
 
5.4%
sn 5
 
5.4%
rw 3
 
3.2%
eg 3
 
3.2%
ci 2
 
2.2%
cm 2
 
2.2%
fr 2
 
2.2%
Other values (7) 9
 
9.7%

분야
Categorical

Distinct6
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size876.0 B
금융&핀테크
46 
헬스케어
22 
에듀테크
11 
에너지
7 
보험
5 

Length

Max length6
Median length4
Mean length4.7849462
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부동산
2nd row헬스케어
3rd row보험
4th row금융&핀테크
5th row금융&핀테크

Common Values

ValueCountFrequency (%)
금융&핀테크 46
49.5%
헬스케어 22
23.7%
에듀테크 11
 
11.8%
에너지 7
 
7.5%
보험 5
 
5.4%
부동산 2
 
2.2%

Length

2023-12-13T06:55:51.226245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:55:51.336154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금융&핀테크 46
49.5%
헬스케어 22
23.7%
에듀테크 11
 
11.8%
에너지 7
 
7.5%
보험 5
 
5.4%
부동산 2
 
2.2%

기업명
Text

UNIQUE 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size876.0 B
2023-12-13T06:55:51.621450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length4.4623656
Min length2

Characters and Unicode

Total characters415
Distinct characters157
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)100.0%

Sample

1st row미까사
2nd row엠파마
3rd row월드커버
4th row치퍼 캐시
5th row쿠디고
ValueCountFrequency (%)
그룹 3
 
2.7%
미까사 1
 
0.9%
마톤틴 1
 
0.9%
셰즈롱 1
 
0.9%
아카데미 1
 
0.9%
눈 1
 
0.9%
테헤카 1
 
0.9%
누미다 1
 
0.9%
사우데 1
 
0.9%
애피 1
 
0.9%
Other values (101) 101
89.4%
2023-12-13T06:55:52.111619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 29
 
7.0%
24
 
5.8%
스 15
 
3.6%
리 15
 
3.6%
라 15
 
3.6%
트 11
 
2.7%
에 11
 
2.7%
아 11
 
2.7%
페 10
 
2.4%
프 8
 
1.9%
Other values (147) 266
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 387
93.3%
Space Separator 24
 
5.8%
Uppercase Letter 2
 
0.5%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 29
 
7.5%
스 15
 
3.9%
리 15
 
3.9%
라 15
 
3.9%
트 11
 
2.8%
에 11
 
2.8%
아 11
 
2.8%
페 10
 
2.6%
프 8
 
2.1%
드 7
 
1.8%
Other values (142) 255
65.9%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
A 1
50.0%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
5 1
50.0%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 387
93.3%
Common 26
 
6.3%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 29
 
7.5%
스 15
 
3.9%
리 15
 
3.9%
라 15
 
3.9%
트 11
 
2.8%
에 11
 
2.8%
아 11
 
2.8%
페 10
 
2.6%
프 8
 
2.1%
드 7
 
1.8%
Other values (142) 255
65.9%
Common
ValueCountFrequency (%)
24
92.3%
4 1
 
3.8%
5 1
 
3.8%
Latin
ValueCountFrequency (%)
C 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 387
93.3%
ASCII 28
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 29
 
7.5%
스 15
 
3.9%
리 15
 
3.9%
라 15
 
3.9%
트 11
 
2.8%
에 11
 
2.8%
아 11
 
2.8%
페 10
 
2.6%
프 8
 
2.1%
드 7
 
1.8%
Other values (142) 255
65.9%
ASCII
ValueCountFrequency (%)
24
85.7%
C 1
 
3.6%
A 1
 
3.6%
4 1
 
3.6%
5 1
 
3.6%

창업연도
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.8387
Minimum2002
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-13T06:55:52.251495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2007.6
Q12014
median2015
Q32017
95-th percentile2019
Maximum2019
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.3175761
Coefficient of variation (CV)0.0016465716
Kurtosis4.1062399
Mean2014.8387
Median Absolute Deviation (MAD)2
Skewness-1.7938928
Sum187380
Variance11.006311
MonotonicityNot monotonic
2023-12-13T06:55:52.392503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2015 19
20.4%
2017 16
17.2%
2016 13
14.0%
2014 9
9.7%
2018 9
9.7%
2013 8
8.6%
2019 6
 
6.5%
2012 3
 
3.2%
2011 2
 
2.2%
2010 2
 
2.2%
Other values (5) 6
 
6.5%
ValueCountFrequency (%)
2002 1
 
1.1%
2003 1
 
1.1%
2004 1
 
1.1%
2007 2
 
2.2%
2008 1
 
1.1%
2010 2
 
2.2%
2011 2
 
2.2%
2012 3
 
3.2%
2013 8
8.6%
2014 9
9.7%
ValueCountFrequency (%)
2019 6
 
6.5%
2018 9
9.7%
2017 16
17.2%
2016 13
14.0%
2015 19
20.4%
2014 9
9.7%
2013 8
8.6%
2012 3
 
3.2%
2011 2
 
2.2%
2010 2
 
2.2%

본사
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size876.0 B
나이지리아 라고스
29 
케냐 나이로비
7 
남아프리카공화국 요하네스버그
6 
미국 샌프란시스코
5 
세네갈 다카르
 
4
Other values (27)
42 

Length

Max length15
Median length14
Mean length8.8709677
Min length4

Unique

Unique17 ?
Unique (%)18.3%

Sample

1st row가나 아크라
2nd row가나 아크라
3rd row미국 뉴욕
4th row미국 샌프란시스코
5th row가나 아크라

Common Values

ValueCountFrequency (%)
나이지리아 라고스 29
31.2%
케냐 나이로비 7
 
7.5%
남아프리카공화국 요하네스버그 6
 
6.5%
미국 샌프란시스코 5
 
5.4%
세네갈 다카르 4
 
4.3%
남아프리카공화국 케이프타운 4
 
4.3%
프랑스 파리 3
 
3.2%
가나 아크라 3
 
3.2%
이집트 기자 3
 
3.2%
르완다 키갈리 2
 
2.2%
Other values (22) 27
29.0%

Length

2023-12-13T06:55:52.532189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
나이지리아 30
16.0%
라고스 29
15.5%
남아프리카공화국 13
 
7.0%
미국 10
 
5.3%
케냐 7
 
3.7%
나이로비 7
 
3.7%
요하네스버그 6
 
3.2%
프랑스 5
 
2.7%
샌프란시스코 5
 
2.7%
세네갈 4
 
2.1%
Other values (42) 71
38.0%

창업가
Text

MISSING 

Distinct89
Distinct (%)100.0%
Missing4
Missing (%)4.3%
Memory size876.0 B
2023-12-13T06:55:52.849282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length104
Median length40
Mean length28.247191
Min length5

Characters and Unicode

Total characters2514
Distinct characters63
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

Unique89 ?
Unique (%)100.0%

Sample

1st rowKelvin Nyame, Rashad Seini, Kofi Amuasi
2nd rowDaniel Shoukimas, Gregory Rockson, James Finucane
3rd rowChristopher Sheehan, Shiliang Tang
4th rowHam Serunjogi, Maijid Moujaled
5th rowBright Ahedor, Gideon Boateng, Kingsley Abrokwah
ValueCountFrequency (%)
daniel 4
 
1.2%
ahmed 3
 
0.9%
de 2
 
0.6%
emmanuel 2
 
0.6%
oshinaga 2
 
0.6%
boye 2
 
0.6%
christopher 2
 
0.6%
kehinde 2
 
0.6%
elizabeth 2
 
0.6%
rossiello 2
 
0.6%
Other values (316) 323
93.4%
2023-12-13T06:55:53.375918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
277
 
11.0%
a 239
 
9.5%
e 226
 
9.0%
n 160
 
6.4%
i 150
 
6.0%
o 127
 
5.1%
r 109
 
4.3%
l 107
 
4.3%
h 80
 
3.2%
u 77
 
3.1%
Other values (53) 962
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1794
71.4%
Uppercase Letter 349
 
13.9%
Space Separator 277
 
11.0%
Other Punctuation 77
 
3.1%
Other Letter 8
 
0.3%
Dash Punctuation 5
 
0.2%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 239
13.3%
e 226
12.6%
n 160
 
8.9%
i 150
 
8.4%
o 127
 
7.1%
r 109
 
6.1%
l 107
 
6.0%
h 80
 
4.5%
u 77
 
4.3%
s 76
 
4.2%
Other values (16) 443
24.7%
Uppercase Letter
ValueCountFrequency (%)
A 42
 
12.0%
S 29
 
8.3%
B 23
 
6.6%
M 22
 
6.3%
C 22
 
6.3%
E 21
 
6.0%
T 20
 
5.7%
K 19
 
5.4%
D 18
 
5.2%
P 14
 
4.0%
Other values (13) 119
34.1%
Other Letter
ValueCountFrequency (%)
다 1
12.5%
모 1
12.5%
로 1
12.5%
코 1
12.5%
중 1
12.5%
앙 1
12.5%
은 1
12.5%
행 1
12.5%
Other Punctuation
ValueCountFrequency (%)
, 71
92.2%
. 6
 
7.8%
Space Separator
ValueCountFrequency (%)
277
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2143
85.2%
Common 363
 
14.4%
Hangul 8
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 239
 
11.2%
e 226
 
10.5%
n 160
 
7.5%
i 150
 
7.0%
o 127
 
5.9%
r 109
 
5.1%
l 107
 
5.0%
h 80
 
3.7%
u 77
 
3.6%
s 76
 
3.5%
Other values (39) 792
37.0%
Hangul
ValueCountFrequency (%)
다 1
12.5%
모 1
12.5%
로 1
12.5%
코 1
12.5%
중 1
12.5%
앙 1
12.5%
은 1
12.5%
행 1
12.5%
Common
ValueCountFrequency (%)
277
76.3%
, 71
 
19.6%
. 6
 
1.7%
- 5
 
1.4%
( 2
 
0.6%
) 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2506
99.7%
Hangul 8
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
277
 
11.1%
a 239
 
9.5%
e 226
 
9.0%
n 160
 
6.4%
i 150
 
6.0%
o 127
 
5.1%
r 109
 
4.3%
l 107
 
4.3%
h 80
 
3.2%
u 77
 
3.1%
Other values (45) 954
38.1%
Hangul
ValueCountFrequency (%)
다 1
12.5%
모 1
12.5%
로 1
12.5%
코 1
12.5%
중 1
12.5%
앙 1
12.5%
은 1
12.5%
행 1
12.5%

투자유치액(달러)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)92.3%
Missing28
Missing (%)30.1%
Infinite0
Infinite (%)0.0%
Mean40216277
Minimum5000
Maximum3.65 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-13T06:55:53.592166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile31000
Q1300000
median6100000
Q333300000
95-th percentile2.0928 × 108
Maximum3.65 × 108
Range3.64995 × 108
Interquartile range (IQR)33000000

Descriptive statistics

Standard deviation76724509
Coefficient of variation (CV)1.9077974
Kurtosis5.781184
Mean40216277
Median Absolute Deviation (MAD)6045000
Skewness2.4719665
Sum2.614058 × 109
Variance5.8866502 × 1015
MonotonicityNot monotonic
2023-12-13T06:55:53.781830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500000 3
 
3.2%
220000 2
 
2.2%
300000 2
 
2.2%
1100000 2
 
2.2%
2100000 1
 
1.1%
23000000 1
 
1.1%
6200000 1
 
1.1%
33300000 1
 
1.1%
1660000 1
 
1.1%
233000000 1
 
1.1%
Other values (50) 50
53.8%
(Missing) 28
30.1%
ValueCountFrequency (%)
5000 1
1.1%
10000 1
1.1%
15000 1
1.1%
30000 1
1.1%
35000 1
1.1%
49000 1
1.1%
55000 1
1.1%
65000 1
1.1%
100000 1
1.1%
115000 1
1.1%
ValueCountFrequency (%)
365000000 1
1.1%
264700000 1
1.1%
233000000 1
1.1%
210500000 1
1.1%
204400000 1
1.1%
197000000 1
1.1%
181000000 1
1.1%
170000000 1
1.1%
122000000 1
1.1%
55500000 1
1.1%

직원 수
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size876.0 B
1~10명
30 
11~50명
30 
101~250명
9 
<NA>
7 
51~100명
4 
Other values (10)
13 

Length

Max length12
Median length11
Mean length6.1290323
Min length4

Unique

Unique9 ?
Unique (%)9.7%

Sample

1st row1~10명
2nd row11~50명
3rd row11~50명
4th row51~100명
5th row1~10명

Common Values

ValueCountFrequency (%)
1~10명 30
32.3%
11~50명 30
32.3%
101~250명 9
 
9.7%
<NA> 7
 
7.5%
51~100명 4
 
4.3%
251~500명 4
 
4.3%
1,000~5,000명 1
 
1.1%
50~100명 1
 
1.1%
11~50명 1
 
1.1%
2,000명 1
 
1.1%
Other values (5) 5
 
5.4%

Length

2023-12-13T06:55:53.929604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11~50명 31
33.3%
1~10명 30
32.3%
101~250명 10
 
10.8%
na 7
 
7.5%
51~100명 4
 
4.3%
251~500명 4
 
4.3%
501~1,000명 2
 
2.2%
1,000~5,000명 1
 
1.1%
50~100명 1
 
1.1%
2,000명 1
 
1.1%
Other values (2) 2
 
2.2%

대표전화
Text

MISSING 

Distinct78
Distinct (%)100.0%
Missing15
Missing (%)16.1%
Memory size876.0 B
2023-12-13T06:55:54.319193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length16.551282
Min length14

Characters and Unicode

Total characters1291
Distinct characters24
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)100.0%

Sample

1st row+233 506 866 060
2nd row+972 543 809 601
3rd row+1 646 854 3012 (미국)
4th row+254 794 586 269
5th row+233 307 074 353
ValueCountFrequency (%)
234 32
 
9.8%
27 10
 
3.1%
254 10
 
3.1%
33 7
 
2.1%
1 5
 
1.5%
700 5
 
1.5%
77 4
 
1.2%
250 4
 
1.2%
353 4
 
1.2%
906 4
 
1.2%
Other values (216) 242
74.0%
2023-12-13T06:55:54.892027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
252
19.5%
2 153
11.9%
0 124
9.6%
3 111
8.6%
7 92
 
7.1%
4 90
 
7.0%
5 79
 
6.1%
+ 78
 
6.0%
8 78
 
6.0%
1 76
 
5.9%
Other values (14) 158
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 944
73.1%
Space Separator 252
 
19.5%
Math Symbol 78
 
6.0%
Lowercase Letter 6
 
0.5%
Other Punctuation 3
 
0.2%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%
Other Letter 2
 
0.2%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 153
16.2%
0 124
13.1%
3 111
11.8%
7 92
9.7%
4 90
9.5%
5 79
8.4%
8 78
8.3%
1 76
8.1%
6 71
7.5%
9 70
7.4%
Lowercase Letter
ValueCountFrequency (%)
p 2
33.3%
h 1
16.7%
a 1
16.7%
t 1
16.7%
s 1
16.7%
Other Letter
ValueCountFrequency (%)
미 1
50.0%
국 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
W 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
252
100.0%
Math Symbol
ValueCountFrequency (%)
+ 78
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1281
99.2%
Latin 8
 
0.6%
Hangul 2
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
252
19.7%
2 153
11.9%
0 124
9.7%
3 111
8.7%
7 92
 
7.2%
4 90
 
7.0%
5 79
 
6.2%
+ 78
 
6.1%
8 78
 
6.1%
1 76
 
5.9%
Other values (5) 148
11.6%
Latin
ValueCountFrequency (%)
p 2
25.0%
W 1
12.5%
h 1
12.5%
a 1
12.5%
t 1
12.5%
s 1
12.5%
A 1
12.5%
Hangul
ValueCountFrequency (%)
미 1
50.0%
국 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1289
99.8%
Hangul 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
252
19.6%
2 153
11.9%
0 124
9.6%
3 111
8.6%
7 92
 
7.1%
4 90
 
7.0%
5 79
 
6.1%
+ 78
 
6.1%
8 78
 
6.1%
1 76
 
5.9%
Other values (12) 156
12.1%
Hangul
ValueCountFrequency (%)
미 1
50.0%
국 1
50.0%

주소
Text

MISSING 

Distinct80
Distinct (%)100.0%
Missing13
Missing (%)14.0%
Memory size876.0 B
2023-12-13T06:55:55.226552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length132
Median length75
Mean length57.9
Min length22

Characters and Unicode

Total characters4632
Distinct characters74
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st row23 Kofi Annan Ave, North Legon, Accra, Ghana
2nd rowAutowinghltd, Opposite Galaxy International School, Boundary Rd, Accra, Ghana
3rd rowno. 840, Mash Plaza, Rice City, Gumani, Northem Tamale Ghana
4th rowBlock A, Accra Digital Center, Ring Road West, Accra, Ghana
5th row17b Sybil Iroche St, Lekki Phase I, Lagos, Nigeria
ValueCountFrequency (%)
lagos 32
 
4.5%
nigeria 27
 
3.8%
road 22
 
3.1%
south 11
 
1.6%
africa 11
 
1.6%
st 9
 
1.3%
kenya 9
 
1.3%
nairobi 9
 
1.3%
rd 9
 
1.3%
street 7
 
1.0%
Other values (387) 560
79.3%
2023-12-13T06:55:55.798998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
644
 
13.9%
a 412
 
8.9%
e 309
 
6.7%
, 264
 
5.7%
o 253
 
5.5%
i 229
 
4.9%
r 218
 
4.7%
t 174
 
3.8%
n 171
 
3.7%
g 129
 
2.8%
Other values (64) 1829
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2791
60.3%
Space Separator 644
 
13.9%
Uppercase Letter 597
 
12.9%
Other Punctuation 297
 
6.4%
Decimal Number 285
 
6.2%
Dash Punctuation 11
 
0.2%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Other Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 412
14.8%
e 309
11.1%
o 253
 
9.1%
i 229
 
8.2%
r 218
 
7.8%
t 174
 
6.2%
n 171
 
6.1%
g 129
 
4.6%
s 122
 
4.4%
l 119
 
4.3%
Other values (17) 655
23.5%
Uppercase Letter
ValueCountFrequency (%)
A 64
 
10.7%
S 58
 
9.7%
R 50
 
8.4%
N 50
 
8.4%
L 43
 
7.2%
P 32
 
5.4%
C 31
 
5.2%
I 30
 
5.0%
G 29
 
4.9%
B 28
 
4.7%
Other values (15) 182
30.5%
Decimal Number
ValueCountFrequency (%)
1 67
23.5%
0 54
18.9%
2 43
15.1%
5 23
 
8.1%
4 22
 
7.7%
6 18
 
6.3%
8 17
 
6.0%
9 15
 
5.3%
3 15
 
5.3%
7 11
 
3.9%
Other Punctuation
ValueCountFrequency (%)
, 264
88.9%
. 24
 
8.1%
' 6
 
2.0%
/ 2
 
0.7%
@ 1
 
0.3%
Space Separator
ValueCountFrequency (%)
644
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Letter
ValueCountFrequency (%)
다 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Symbol
ValueCountFrequency (%)
№ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3388
73.1%
Common 1243
 
26.8%
Hangul 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 412
 
12.2%
e 309
 
9.1%
o 253
 
7.5%
i 229
 
6.8%
r 218
 
6.4%
t 174
 
5.1%
n 171
 
5.0%
g 129
 
3.8%
s 122
 
3.6%
l 119
 
3.5%
Other values (42) 1252
37.0%
Common
ValueCountFrequency (%)
644
51.8%
, 264
21.2%
1 67
 
5.4%
0 54
 
4.3%
2 43
 
3.5%
. 24
 
1.9%
5 23
 
1.9%
4 22
 
1.8%
6 18
 
1.4%
8 17
 
1.4%
Other values (11) 67
 
5.4%
Hangul
ValueCountFrequency (%)
다 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4629
99.9%
Hangul 1
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
644
 
13.9%
a 412
 
8.9%
e 309
 
6.7%
, 264
 
5.7%
o 253
 
5.5%
i 229
 
4.9%
r 218
 
4.7%
t 174
 
3.8%
n 171
 
3.7%
g 129
 
2.8%
Other values (61) 1826
39.4%
Hangul
ValueCountFrequency (%)
다 1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
№ 1
100.0%
None
ValueCountFrequency (%)
é 1
100.0%

이메일
Text

MISSING 

Distinct88
Distinct (%)100.0%
Missing5
Missing (%)5.4%
Memory size876.0 B
2023-12-13T06:55:56.068135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length28
Mean length19.352273
Min length12

Characters and Unicode

Total characters1703
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)100.0%

Sample

1st rowinfo@meqasa.com
2nd rowSupport@mpharma.com
3rd rowinfo@worldcovr.com
4th rowteam@chippercash.com
5th rowkingsley@kudigo.com
ValueCountFrequency (%)
info@getslideapp.com 1
 
1.1%
info@worldcovr.com 1
 
1.1%
info@matontine.com 1
 
1.1%
info@fawry.com 1
 
1.1%
operator@shezlong.com 1
 
1.1%
master.noonacademy@gmail.com 1
 
1.1%
info@techca.com 1
 
1.1%
info@numidatech.com 1
 
1.1%
feedback@appy.co.ao 1
 
1.1%
contact@eyone.net 1
 
1.1%
Other values (78) 78
88.6%
2023-12-13T06:55:56.527043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 194
 
11.4%
c 139
 
8.2%
a 125
 
7.3%
i 114
 
6.7%
n 111
 
6.5%
. 102
 
6.0%
e 95
 
5.6%
m 92
 
5.4%
t 90
 
5.3%
@ 88
 
5.2%
Other values (25) 553
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1496
87.8%
Other Punctuation 194
 
11.4%
Dash Punctuation 8
 
0.5%
Uppercase Letter 3
 
0.2%
Decimal Number 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 194
13.0%
c 139
 
9.3%
a 125
 
8.4%
i 114
 
7.6%
n 111
 
7.4%
e 95
 
6.4%
m 92
 
6.1%
t 90
 
6.0%
r 72
 
4.8%
p 71
 
4.7%
Other values (16) 393
26.3%
Other Punctuation
ValueCountFrequency (%)
. 102
52.6%
@ 88
45.4%
/ 4
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
A 1
33.3%
S 1
33.3%
C 1
33.3%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
5 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1499
88.0%
Common 204
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 194
12.9%
c 139
 
9.3%
a 125
 
8.3%
i 114
 
7.6%
n 111
 
7.4%
e 95
 
6.3%
m 92
 
6.1%
t 90
 
6.0%
r 72
 
4.8%
p 71
 
4.7%
Other values (19) 396
26.4%
Common
ValueCountFrequency (%)
. 102
50.0%
@ 88
43.1%
- 8
 
3.9%
/ 4
 
2.0%
4 1
 
0.5%
5 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 194
 
11.4%
c 139
 
8.2%
a 125
 
7.3%
i 114
 
6.7%
n 111
 
6.5%
. 102
 
6.0%
e 95
 
5.6%
m 92
 
5.4%
t 90
 
5.3%
@ 88
 
5.2%
Other values (25) 553
32.5%

홈페이지
Text

UNIQUE 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size876.0 B
2023-12-13T06:55:56.813132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length22.569892
Min length9

Characters and Unicode

Total characters2099
Distinct characters46
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)100.0%

Sample

1st rowhttp://Meqasa.com/
2nd rowhttp://mpharma.com
3rd rowhttp://worldcovr.com/
4th rowhttp://chippercash.com/
5th rowhttp://www.kudigo.com/
ValueCountFrequency (%)
http://meqasa.com 1
 
1.1%
http://matontine.com 1
 
1.1%
http://www.shezlong.com 1
 
1.1%
http://www.noonacademy.com/eg-en 1
 
1.1%
http://techca.com 1
 
1.1%
http://www.numida.co 1
 
1.1%
http://appysaude.co.al 1
 
1.1%
http://www.ama.finance 1
 
1.1%
http://www.eyone.net 1
 
1.1%
http://www.xaalys.com 1
 
1.1%
Other values (84) 84
89.4%
2023-12-13T06:55:57.271830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 241
 
11.5%
t 212
 
10.1%
. 160
 
7.6%
w 158
 
7.5%
p 129
 
6.1%
o 125
 
6.0%
c 118
 
5.6%
a 118
 
5.6%
h 107
 
5.1%
m 86
 
4.1%
Other values (36) 645
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1579
75.2%
Other Punctuation 487
 
23.2%
Other Letter 12
 
0.6%
Dash Punctuation 7
 
0.3%
Uppercase Letter 4
 
0.2%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Space Separator 2
 
0.1%
Decimal Number 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 212
13.4%
w 158
10.0%
p 129
 
8.2%
o 125
 
7.9%
c 118
 
7.5%
a 118
 
7.5%
h 107
 
6.8%
m 86
 
5.4%
e 82
 
5.2%
i 60
 
3.8%
Other values (16) 384
24.3%
Other Letter
ValueCountFrequency (%)
불 3
25.0%
가 3
25.0%
접 2
16.7%
속 2
16.7%
결 1
 
8.3%
연 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 241
49.5%
. 160
32.9%
: 83
 
17.0%
# 3
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
L 1
25.0%
M 1
25.0%
S 1
25.0%
T 1
25.0%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
4 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1583
75.4%
Common 504
 
24.0%
Hangul 12
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 212
13.4%
w 158
10.0%
p 129
 
8.1%
o 125
 
7.9%
c 118
 
7.5%
a 118
 
7.5%
h 107
 
6.8%
m 86
 
5.4%
e 82
 
5.2%
i 60
 
3.8%
Other values (20) 388
24.5%
Common
ValueCountFrequency (%)
/ 241
47.8%
. 160
31.7%
: 83
 
16.5%
- 7
 
1.4%
( 3
 
0.6%
) 3
 
0.6%
# 3
 
0.6%
2
 
0.4%
5 1
 
0.2%
4 1
 
0.2%
Hangul
ValueCountFrequency (%)
불 3
25.0%
가 3
25.0%
접 2
16.7%
속 2
16.7%
결 1
 
8.3%
연 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2087
99.4%
Hangul 12
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 241
 
11.5%
t 212
 
10.2%
. 160
 
7.7%
w 158
 
7.6%
p 129
 
6.2%
o 125
 
6.0%
c 118
 
5.7%
a 118
 
5.7%
h 107
 
5.1%
m 86
 
4.1%
Other values (30) 633
30.3%
Hangul
ValueCountFrequency (%)
불 3
25.0%
가 3
25.0%
접 2
16.7%
속 2
16.7%
결 1
 
8.3%
연 1
 
8.3%

SNS 링크
Text

MISSING 

Distinct88
Distinct (%)98.9%
Missing4
Missing (%)4.3%
Memory size876.0 B
2023-12-13T06:55:57.551007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length88
Median length53
Mean length37.707865
Min length28

Characters and Unicode

Total characters3356
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)97.8%

Sample

1st rowhttp://www.facebook.com/meqasa
2nd rowhttp://www.facebook.com/mpharmaGH
3rd rowhttp://www.facebook.com/worldcovr
4th rowhttp://www.facebook.com/cchippercashapp/
5th rowhttp://www.facebook.com/kudigoinc/
ValueCountFrequency (%)
http://www.facebook.com/eyoneentreprise 2
 
2.2%
http://www.facebook.com/getslideapp 1
 
1.1%
http://www.facebook.com/nooneduegypt 1
 
1.1%
http://www.facebook.com/thcareug 1
 
1.1%
http://www.facebook.com/trackapp.numidatech 1
 
1.1%
http://www.facebook.com/appysaudeangola 1
 
1.1%
http://www.linkedin.com/company/amafinance 1
 
1.1%
http://www.facebook.com/xaalys 1
 
1.1%
http://www.facebook.com/sudpaysa 1
 
1.1%
http://www.facebook.com/matontinesn 1
 
1.1%
Other values (79) 79
87.8%
2023-12-13T06:55:58.060329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 336
 
10.0%
o 305
 
9.1%
w 260
 
7.7%
t 258
 
7.7%
c 215
 
6.4%
a 200
 
6.0%
. 182
 
5.4%
e 178
 
5.3%
p 159
 
4.7%
m 134
 
4.0%
Other values (59) 1129
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2626
78.2%
Other Punctuation 613
 
18.3%
Uppercase Letter 62
 
1.8%
Decimal Number 38
 
1.1%
Dash Punctuation 8
 
0.2%
Connector Punctuation 3
 
0.1%
Space Separator 3
 
0.1%
Math Symbol 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 305
11.6%
w 260
 
9.9%
t 258
 
9.8%
c 215
 
8.2%
a 200
 
7.6%
e 178
 
6.8%
p 159
 
6.1%
m 134
 
5.1%
h 112
 
4.3%
i 100
 
3.8%
Other values (16) 705
26.8%
Uppercase Letter
ValueCountFrequency (%)
S 11
17.7%
A 10
16.1%
H 4
 
6.5%
F 3
 
4.8%
C 3
 
4.8%
D 3
 
4.8%
N 3
 
4.8%
X 2
 
3.2%
Q 2
 
3.2%
B 2
 
3.2%
Other values (14) 19
30.6%
Decimal Number
ValueCountFrequency (%)
1 8
21.1%
9 5
13.2%
7 5
13.2%
8 4
10.5%
2 4
10.5%
5 4
10.5%
4 3
 
7.9%
0 3
 
7.9%
3 2
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/ 336
54.8%
. 182
29.7%
: 90
 
14.7%
? 3
 
0.5%
& 1
 
0.2%
, 1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
= 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2688
80.1%
Common 668
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 305
 
11.3%
w 260
 
9.7%
t 258
 
9.6%
c 215
 
8.0%
a 200
 
7.4%
e 178
 
6.6%
p 159
 
5.9%
m 134
 
5.0%
h 112
 
4.2%
i 100
 
3.7%
Other values (40) 767
28.5%
Common
ValueCountFrequency (%)
/ 336
50.3%
. 182
27.2%
: 90
 
13.5%
1 8
 
1.2%
- 8
 
1.2%
9 5
 
0.7%
7 5
 
0.7%
8 4
 
0.6%
2 4
 
0.6%
5 4
 
0.6%
Other values (9) 22
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 336
 
10.0%
o 305
 
9.1%
w 260
 
7.7%
t 258
 
7.7%
c 215
 
6.4%
a 200
 
6.0%
. 182
 
5.4%
e 178
 
5.3%
p 159
 
4.7%
m 134
 
4.0%
Other values (59) 1129
33.6%

최대 투자자/기업
Text

MISSING 

Distinct66
Distinct (%)97.1%
Missing25
Missing (%)26.9%
Memory size876.0 B
2023-12-13T06:55:58.363282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length111
Median length38.5
Mean length27.647059
Min length4

Characters and Unicode

Total characters1880
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)95.6%

Sample

1st rowFrontier Digital Venture, Meltwater Entrepreneurial School of Technology (MEST)
2nd rowGolden palm Investments
3rd rowMS&AD Venture
4th rowRibbit Capital
5th rowFounders Factory Africa
ValueCountFrequency (%)
capital 20
 
8.5%
venture 7
 
3.0%
co-creation 5
 
2.1%
fund 5
 
2.1%
nigeria 5
 
2.1%
hub 5
 
2.1%
ventures 5
 
2.1%
management 4
 
1.7%
foundation 4
 
1.7%
group 4
 
1.7%
Other values (145) 171
72.8%
2023-12-13T06:55:58.796945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
185
 
9.8%
a 169
 
9.0%
e 149
 
7.9%
t 126
 
6.7%
n 126
 
6.7%
r 120
 
6.4%
i 113
 
6.0%
o 104
 
5.5%
s 60
 
3.2%
u 60
 
3.2%
Other values (45) 668
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1371
72.9%
Uppercase Letter 275
 
14.6%
Space Separator 185
 
9.8%
Other Punctuation 36
 
1.9%
Dash Punctuation 7
 
0.4%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 169
12.3%
e 149
10.9%
t 126
9.2%
n 126
9.2%
r 120
8.8%
i 113
 
8.2%
o 104
 
7.6%
s 60
 
4.4%
u 60
 
4.4%
l 60
 
4.4%
Other values (15) 284
20.7%
Uppercase Letter
ValueCountFrequency (%)
C 48
17.5%
V 22
 
8.0%
A 22
 
8.0%
F 21
 
7.6%
M 20
 
7.3%
S 19
 
6.9%
G 15
 
5.5%
E 14
 
5.1%
I 14
 
5.1%
P 12
 
4.4%
Other values (13) 68
24.7%
Other Punctuation
ValueCountFrequency (%)
, 33
91.7%
& 2
 
5.6%
. 1
 
2.8%
Space Separator
ValueCountFrequency (%)
185
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1646
87.6%
Common 234
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 169
 
10.3%
e 149
 
9.1%
t 126
 
7.7%
n 126
 
7.7%
r 120
 
7.3%
i 113
 
6.9%
o 104
 
6.3%
s 60
 
3.6%
u 60
 
3.6%
l 60
 
3.6%
Other values (38) 559
34.0%
Common
ValueCountFrequency (%)
185
79.1%
, 33
 
14.1%
- 7
 
3.0%
) 3
 
1.3%
( 3
 
1.3%
& 2
 
0.9%
. 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
185
 
9.8%
a 169
 
9.0%
e 149
 
7.9%
t 126
 
6.7%
n 126
 
6.7%
r 120
 
6.4%
i 113
 
6.0%
o 104
 
5.5%
s 60
 
3.2%
u 60
 
3.2%
Other values (45) 668
35.5%

최종 투자 단계
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size876.0 B
Seed
42 
<NA>
19 
Series A
17 
Series B
8 
Series D
 
4
Other values (2)
 
3

Length

Max length8
Median length4
Mean length5.3763441
Min length4

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st rowSeed
2nd row<NA>
3rd rowSeries A
4th rowSeries B
5th rowSeed

Common Values

ValueCountFrequency (%)
Seed 42
45.2%
<NA> 19
20.4%
Series A 17
18.3%
Series B 8
 
8.6%
Series D 4
 
4.3%
Series C 2
 
2.2%
Series E 1
 
1.1%

Length

2023-12-13T06:55:58.963858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:55:59.064886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seed 42
33.6%
series 32
25.6%
na 19
15.2%
a 17
13.6%
b 8
 
6.4%
d 4
 
3.2%
c 2
 
1.6%
e 1
 
0.8%

Interactions

2023-12-13T06:55:49.593093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:49.407576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:49.722013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:55:49.502011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:55:59.157985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가명국가영문명iso 2자리코드분야기업명창업연도본사창업가투자유치액(달러)직원 수대표전화주소이메일홈페이지SNS 링크최대 투자자/기업최종 투자 단계
국가명1.0001.0001.0000.0001.0000.7070.9921.0000.4740.3041.0001.0001.0001.0000.9871.0000.000
국가영문명1.0001.0001.0000.0001.0000.7070.9921.0000.4740.3041.0001.0001.0001.0000.9871.0000.000
iso 2자리코드1.0001.0001.0000.0001.0000.7070.9921.0000.4740.3041.0001.0001.0001.0000.9871.0000.000
분야0.0000.0000.0001.0001.0000.1620.1481.0000.0000.0001.0001.0001.0001.0000.9710.8840.424
기업명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
창업연도0.7070.7070.7070.1621.0001.0000.7741.0000.6470.8241.0001.0001.0001.0001.0000.9930.720
본사0.9920.9920.9920.1481.0000.7741.0001.0000.0000.7521.0001.0001.0001.0000.9971.0000.542
창업가1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
투자유치액(달러)0.4740.4740.4740.0001.0000.6470.0001.0001.0000.8281.0001.0001.0001.0001.0001.0000.648
직원 수0.3040.3040.3040.0001.0000.8240.7521.0000.8281.0001.0001.0001.0001.0001.0000.9990.947
대표전화1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
이메일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
홈페이지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
SNS 링크0.9870.9870.9870.9711.0001.0000.9971.0001.0001.0001.0001.0001.0001.0001.0001.0000.973
최대 투자자/기업1.0001.0001.0000.8841.0000.9931.0001.0001.0000.9991.0001.0001.0001.0001.0001.0000.976
최종 투자 단계0.0000.0000.0000.4241.0000.7200.5421.0000.6480.9471.0001.0001.0001.0000.9730.9761.000
2023-12-13T06:55:59.321038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
iso 2자리코드분야최종 투자 단계국가영문명직원 수본사국가명
iso 2자리코드1.0000.0000.0001.0000.0990.8171.000
분야0.0001.0000.1620.0000.0000.0000.000
최종 투자 단계0.0000.1621.0000.0000.6430.2140.000
국가영문명1.0000.0000.0001.0000.0990.8171.000
직원 수0.0990.0000.6430.0991.0000.2890.099
본사0.8170.0000.2140.8170.2891.0000.817
국가명1.0000.0000.0001.0000.0990.8171.000
2023-12-13T06:55:59.433233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
창업연도투자유치액(달러)국가명국가영문명iso 2자리코드분야본사직원 수최종 투자 단계
창업연도1.000-0.3670.3550.3550.3550.0000.3500.5040.508
투자유치액(달러)-0.3671.0000.2260.2260.2260.0000.0000.5440.453
국가명0.3550.2261.0001.0001.0000.0000.8170.0990.000
국가영문명0.3550.2261.0001.0001.0000.0000.8170.0990.000
iso 2자리코드0.3550.2261.0001.0001.0000.0000.8170.0990.000
분야0.0000.0000.0000.0000.0001.0000.0000.0000.162
본사0.3500.0000.8170.8170.8170.0001.0000.2890.214
직원 수0.5040.5440.0990.0990.0990.0000.2891.0000.643
최종 투자 단계0.5080.4530.0000.0000.0000.1620.2140.6431.000

Missing values

2023-12-13T06:55:49.847261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:55:50.074493image/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.
2023-12-13T06:55:50.626722image/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

국가명국가영문명iso 2자리코드분야기업명창업연도본사창업가투자유치액(달러)직원 수대표전화주소이메일홈페이지SNS 링크최대 투자자/기업최종 투자 단계
0가나GhanaGH부동산미까사2013가나 아크라Kelvin Nyame, Rashad Seini, Kofi Amuasi5000001~10명+233 506 866 06023 Kofi Annan Ave, North Legon, Accra, Ghanainfo@meqasa.comhttp://Meqasa.com/http://www.facebook.com/meqasaFrontier Digital Venture, Meltwater Entrepreneurial School of Technology (MEST)Seed
1가나GhanaGH헬스케어엠파마2013가나 아크라Daniel Shoukimas, Gregory Rockson, James Finucane5550000011~50명+972 543 809 601Autowinghltd, Opposite Galaxy International School, Boundary Rd, Accra, GhanaSupport@mpharma.comhttp://mpharma.comhttp://www.facebook.com/mpharmaGHGolden palm Investments<NA>
2가나GhanaGH보험월드커버2015미국 뉴욕Christopher Sheehan, Shiliang Tang610000011~50명+1 646 854 3012 (미국)no. 840, Mash Plaza, Rice City, Gumani, Northem Tamale Ghanainfo@worldcovr.comhttp://worldcovr.com/http://www.facebook.com/worldcovrMS&AD VentureSeries A
3가나GhanaGH금융&핀테크치퍼 캐시2017미국 샌프란시스코Ham Serunjogi, Maijid Moujaled5220000051~100명+254 794 586 269<NA>team@chippercash.comhttp://chippercash.com/http://www.facebook.com/cchippercashapp/Ribbit CapitalSeries B
4가나GhanaGH금융&핀테크쿠디고2017가나 아크라Bright Ahedor, Gideon Boateng, Kingsley Abrokwah3000001~10명+233 307 074 353Block A, Accra Digital Center, Ring Road West, Accra, Ghanakingsley@kudigo.comhttp://www.kudigo.com/http://www.facebook.com/kudigoinc/Founders Factory AfricaSeed
5나이지리아NigeriaNG헬스케어54 진2019미국 워싱턴 D.CAbasi Ene-Obong1970000011~50명+234 906 281 567717b Sybil Iroche St, Lekki Phase I, Lagos, Nigeriahello@54gene.comhttp://www.54gene.com/http://www.facebook.com/54gene/Adjuvant CapitalSeries A
6나이지리아NigeriaNG에듀테크게잘2019나이지리아 라고스Yewande Akinjewe<NA><NA><NA><NA><NA>http://thegesal.com/(접속불가)http://twitter.com/gesalnigeria<NA><NA>
7나이지리아NigeriaNG에듀테크그레이들리2019나이지리아 라고스Boye Oshinaga350001~10명+234 810 059 82688, Montgomery Street, Yaba, Lagos, Nigeriasupport@gradely.nghttp://www.gradely.ng/http://www.facebook.com/gradelyng/Microtraction, FacebookSeed
8나이지리아NigeriaNG헬스케어닥투라2016나이지리아 라고스Alecia Esson, Debo Odulana4900011~50명+234 810 509 472911, Criag Street, Ogudu Rd, Lagos, Nogeriasupport@doctoora.com.nghttp://www.doctoora.com/http://www.facebook.com/DoctooraHealthMarket/Co-Creation Hub Nigeria<NA>
9나이지리아NigeriaNG에너지데이스타 파워 아프리카2017나이지리아 라고스Christian Wessels, Jasper Graf Von Hardenberg3060000051~100명<NA>1 Adeyemi, Bero Cres, llupeju, Lagos, Nigeriainfo@daystar-power.comhttp://www.daystar-power.com/http://www.linkedin.com/company/daystar-power-group/Verod Capital ManagementSeries A
국가명국가영문명iso 2자리코드분야기업명창업연도본사창업가투자유치액(달러)직원 수대표전화주소이메일홈페이지SNS 링크최대 투자자/기업최종 투자 단계
83케냐KenyaKE헬스케어케어페이2015네델란드 암스테르담Michiel Slootweg45200000101~250명+254 726 473 002P.O.Box 52887-00100, Manyani East Road, House 114, Valley Arcade Nairobi, Kenyaoffice@carepay.co.kehttp://www.carepay.com/http://www.linkedin.com/company/carepayinternational/ELMA PhilanthropiesSeries A
84케냐KenyaKE금융&핀테크탈라2011미국 산타모니카Shivani Siroya204400000501~1,000명<NA><NA>info@tala.cohttp://tala.co/http://www.facebook.com/talamobile/RPS VenturesSeries D
85케냐KenyaKE금융&핀테크튜라코2018케냐 나이로비Peter Gross, Ted Pantone33000001~10명+254 725 525 561163 Shanzu Road, Spring Valley, Nairobi Kenyainfo@myturaco.comhttp://www.myTuraco.com/http://www.facebook.com/myturaco/Novastar Ventures, Catalyst Fund, VillgroSeed
86케냐KenyaKE보험페사바자르2014케냐 나이로비Calleb Karegyesa, Prashanth Srinivas5000001~10명+254 205 250 9961st Floor, Rhapta Heights, Rhapta Road, Westlands, Nairobi, Kenyainfo@pesabazaar.comhttp://www.pesabazaar.com/http://www.facebook.com/pesabazaar/Novastar Venture, SafaricomSeed
87코트디부아르Côte D'IvoireCI금융&핀테크시넷페이2016코트디부아르 아비장Idriss Marcial Monthe et Daniel Dindji<NA>11~50명+225 72 50 05 03Immeuble Toronto Tower en face de I'entrée principale du CHU d'Angre - 3e etage, porte a droite. Cocody Angre, Abidjan Cote d'ivoireinfos@cinetpay.comwww.cinetpay.comhttps://www.facebook.com/cinetpay/<NA>Seed
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