Overview

Dataset statistics

Number of variables11
Number of observations186
Missing cells432
Missing cells (%)21.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 KiB
Average record size in memory92.7 B

Variable types

Numeric2
Text4
Unsupported2
Categorical2
DateTime1

Alerts

region has constant value ""Constant
last_load_dttm has constant value ""Constant
skey is highly overall correlated with reg_no and 1 other fieldsHigh correlation
reg_no is highly overall correlated with skey and 1 other fieldsHigh correlation
gubun is highly overall correlated with skey and 1 other fieldsHigh correlation
insti_gubun has 186 (100.0%) missing valuesMissing
company_reg_no has 186 (100.0%) missing valuesMissing
target_country has 60 (32.3%) missing valuesMissing
skey has unique valuesUnique
reg_no has unique valuesUnique
insti_gubun is an unsupported type, check if it needs cleaning or further analysisUnsupported
company_reg_no is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-17 16:37:14.770511
Analysis finished2024-04-17 16:37:15.727853
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2048.5
Minimum1956
Maximum2141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T01:37:15.783138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1956
5-th percentile1965.25
Q12002.25
median2048.5
Q32094.75
95-th percentile2131.75
Maximum2141
Range185
Interquartile range (IQR)92.5

Descriptive statistics

Standard deviation53.837719
Coefficient of variation (CV)0.026281532
Kurtosis-1.2
Mean2048.5
Median Absolute Deviation (MAD)46.5
Skewness0
Sum381021
Variance2898.5
MonotonicityNot monotonic
2024-04-18T01:37:15.917733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2089 1
 
0.5%
1966 1
 
0.5%
1957 1
 
0.5%
1958 1
 
0.5%
1959 1
 
0.5%
1960 1
 
0.5%
1961 1
 
0.5%
1962 1
 
0.5%
1963 1
 
0.5%
1964 1
 
0.5%
Other values (176) 176
94.6%
ValueCountFrequency (%)
1956 1
0.5%
1957 1
0.5%
1958 1
0.5%
1959 1
0.5%
1960 1
0.5%
1961 1
0.5%
1962 1
0.5%
1963 1
0.5%
1964 1
0.5%
1965 1
0.5%
ValueCountFrequency (%)
2141 1
0.5%
2140 1
0.5%
2139 1
0.5%
2138 1
0.5%
2137 1
0.5%
2136 1
0.5%
2135 1
0.5%
2134 1
0.5%
2133 1
0.5%
2132 1
0.5%

reg_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.5
Minimum1
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T01:37:16.051494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.25
Q147.25
median93.5
Q3139.75
95-th percentile176.75
Maximum186
Range185
Interquartile range (IQR)92.5

Descriptive statistics

Standard deviation53.837719
Coefficient of variation (CV)0.57580448
Kurtosis-1.2
Mean93.5
Median Absolute Deviation (MAD)46.5
Skewness0
Sum17391
Variance2898.5
MonotonicityNot monotonic
2024-04-18T01:37:16.154425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134 1
 
0.5%
11 1
 
0.5%
2 1
 
0.5%
3 1
 
0.5%
4 1
 
0.5%
5 1
 
0.5%
6 1
 
0.5%
7 1
 
0.5%
8 1
 
0.5%
9 1
 
0.5%
Other values (176) 176
94.6%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%
181 1
0.5%
180 1
0.5%
179 1
0.5%
178 1
0.5%
177 1
0.5%

business_nm
Text

Distinct185
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-18T01:37:16.368356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length18
Mean length8.327957
Min length3

Characters and Unicode

Total characters1549
Distinct characters293
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique184 ?
Unique (%)98.9%

Sample

1st row보스톡코리아
2nd row오토투어
3rd row(주)나무투어
4th row주식회사 비에스펀투어
5th row인피니트(INFINITE)
ValueCountFrequency (%)
주식회사 21
 
8.4%
㈜ 8
 
3.2%
예쁜미소바른이치과 2
 
0.8%
인제대학교 2
 
0.8%
의원 2
 
0.8%
성형외과의원 2
 
0.8%
우리원병원 1
 
0.4%
펀펀투어 1
 
0.4%
부산대학교병원 1
 
0.4%
루덴스 1
 
0.4%
Other values (209) 209
83.6%
2024-04-18T01:37:16.661643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 108
 
7.0%
의 71
 
4.6%
67
 
4.3%
과 49
 
3.2%
이 41
 
2.6%
병 40
 
2.6%
부 36
 
2.3%
주 35
 
2.3%
사 32
 
2.1%
스 31
 
2.0%
Other values (283) 1039
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1325
85.5%
Space Separator 67
 
4.3%
Uppercase Letter 53
 
3.4%
Lowercase Letter 34
 
2.2%
Open Punctuation 22
 
1.4%
Close Punctuation 22
 
1.4%
Other Symbol 15
 
1.0%
Decimal Number 7
 
0.5%
Other Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 108
 
8.2%
의 71
 
5.4%
과 49
 
3.7%
이 41
 
3.1%
병 40
 
3.0%
부 36
 
2.7%
주 35
 
2.6%
사 32
 
2.4%
스 31
 
2.3%
회 27
 
2.0%
Other values (239) 855
64.5%
Uppercase Letter
ValueCountFrequency (%)
S 6
11.3%
N 5
9.4%
A 5
9.4%
E 4
 
7.5%
B 4
 
7.5%
M 4
 
7.5%
I 4
 
7.5%
D 3
 
5.7%
C 3
 
5.7%
T 3
 
5.7%
Other values (10) 12
22.6%
Lowercase Letter
ValueCountFrequency (%)
a 5
14.7%
e 4
11.8%
i 4
11.8%
d 3
8.8%
n 3
8.8%
s 3
8.8%
t 2
 
5.9%
m 2
 
5.9%
c 2
 
5.9%
v 2
 
5.9%
Other values (4) 4
11.8%
Decimal Number
ValueCountFrequency (%)
3 2
28.6%
6 2
28.6%
5 2
28.6%
2 1
14.3%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
, 2
50.0%
Space Separator
ValueCountFrequency (%)
67
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1340
86.5%
Common 122
 
7.9%
Latin 87
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 108
 
8.1%
의 71
 
5.3%
과 49
 
3.7%
이 41
 
3.1%
병 40
 
3.0%
부 36
 
2.7%
주 35
 
2.6%
사 32
 
2.4%
스 31
 
2.3%
회 27
 
2.0%
Other values (240) 870
64.9%
Latin
ValueCountFrequency (%)
S 6
 
6.9%
a 5
 
5.7%
N 5
 
5.7%
A 5
 
5.7%
E 4
 
4.6%
B 4
 
4.6%
e 4
 
4.6%
M 4
 
4.6%
I 4
 
4.6%
i 4
 
4.6%
Other values (24) 42
48.3%
Common
ValueCountFrequency (%)
67
54.9%
( 22
 
18.0%
) 22
 
18.0%
3 2
 
1.6%
6 2
 
1.6%
5 2
 
1.6%
. 2
 
1.6%
, 2
 
1.6%
2 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1325
85.5%
ASCII 209
 
13.5%
None 15
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 108
 
8.2%
의 71
 
5.4%
과 49
 
3.7%
이 41
 
3.1%
병 40
 
3.0%
부 36
 
2.7%
주 35
 
2.6%
사 32
 
2.4%
스 31
 
2.3%
회 27
 
2.0%
Other values (239) 855
64.5%
ASCII
ValueCountFrequency (%)
67
32.1%
( 22
 
10.5%
) 22
 
10.5%
S 6
 
2.9%
a 5
 
2.4%
N 5
 
2.4%
A 5
 
2.4%
E 4
 
1.9%
B 4
 
1.9%
e 4
 
1.9%
Other values (33) 65
31.1%
None
ValueCountFrequency (%)
㈜ 15
100.0%

insti_gubun
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing186
Missing (%)100.0%
Memory size1.8 KiB

region
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
부산
186 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산 186
100.0%

Length

2024-04-18T01:37:16.768502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:37:16.839944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산 186
100.0%

representative
Text

Distinct181
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-18T01:37:17.053712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length3
Mean length3.9677419
Min length2

Characters and Unicode

Total characters738
Distinct characters164
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

Unique176 ?
Unique (%)94.6%

Sample

1st row김춘웅
2nd row리옥연
3rd row천정화
4th row최효섭
5th rowPARK SVETLANA
ValueCountFrequency (%)
1명 10
 
4.5%
외 8
 
3.6%
이순형 2
 
0.9%
정선윤 2
 
0.9%
정흥태 2
 
0.9%
구영수 2
 
0.9%
이현주 2
 
0.9%
박희두 1
 
0.5%
김문찬 1
 
0.5%
하얀 1
 
0.5%
Other values (191) 191
86.0%
2024-04-18T01:37:17.452940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38
 
5.1%
이 29
 
3.9%
김 29
 
3.9%
정 25
 
3.4%
박 23
 
3.1%
A 20
 
2.7%
명 17
 
2.3%
영 16
 
2.2%
N 15
 
2.0%
성 15
 
2.0%
Other values (154) 511
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 573
77.6%
Uppercase Letter 108
 
14.6%
Space Separator 38
 
5.1%
Decimal Number 13
 
1.8%
Close Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 29
 
5.1%
김 29
 
5.1%
정 25
 
4.4%
박 23
 
4.0%
명 17
 
3.0%
영 16
 
2.8%
성 15
 
2.6%
현 13
 
2.3%
외 13
 
2.3%
진 12
 
2.1%
Other values (128) 381
66.5%
Uppercase Letter
ValueCountFrequency (%)
A 20
18.5%
N 15
13.9%
E 9
 
8.3%
L 8
 
7.4%
I 7
 
6.5%
O 7
 
6.5%
M 5
 
4.6%
S 4
 
3.7%
V 4
 
3.7%
K 4
 
3.7%
Other values (11) 25
23.1%
Decimal Number
ValueCountFrequency (%)
1 12
92.3%
4 1
 
7.7%
Space Separator
ValueCountFrequency (%)
38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 573
77.6%
Latin 108
 
14.6%
Common 57
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 29
 
5.1%
김 29
 
5.1%
정 25
 
4.4%
박 23
 
4.0%
명 17
 
3.0%
영 16
 
2.8%
성 15
 
2.6%
현 13
 
2.3%
외 13
 
2.3%
진 12
 
2.1%
Other values (128) 381
66.5%
Latin
ValueCountFrequency (%)
A 20
18.5%
N 15
13.9%
E 9
 
8.3%
L 8
 
7.4%
I 7
 
6.5%
O 7
 
6.5%
M 5
 
4.6%
S 4
 
3.7%
V 4
 
3.7%
K 4
 
3.7%
Other values (11) 25
23.1%
Common
ValueCountFrequency (%)
38
66.7%
1 12
 
21.1%
) 3
 
5.3%
( 3
 
5.3%
4 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 573
77.6%
ASCII 165
 
22.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38
23.0%
A 20
12.1%
N 15
 
9.1%
1 12
 
7.3%
E 9
 
5.5%
L 8
 
4.8%
I 7
 
4.2%
O 7
 
4.2%
M 5
 
3.0%
S 4
 
2.4%
Other values (16) 40
24.2%
Hangul
ValueCountFrequency (%)
이 29
 
5.1%
김 29
 
5.1%
정 25
 
4.4%
박 23
 
4.0%
명 17
 
3.0%
영 16
 
2.8%
성 15
 
2.6%
현 13
 
2.3%
외 13
 
2.3%
진 12
 
2.1%
Other values (128) 381
66.5%

company_reg_no
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing186
Missing (%)100.0%
Memory size1.8 KiB

addr
Text

Distinct185
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-18T01:37:17.678805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length46
Mean length35.629032
Min length22

Characters and Unicode

Total characters6627
Distinct characters276
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique184 ?
Unique (%)98.9%

Sample

1st row부산 수영구 수영로680번길 30 (광안동) 501호
2nd row부산 남구 수영로325번길 61 (대연동, 대연 롯데캐슬) 102동303호
3rd row부산 동구 중앙대로274번길 7-7 (초량동, 부산역 유림 로미오) 상가 1층 104호
4th row부산 해운대구 마린시티2로 33 (우동, 해운대두산위브더제니스) 해운대 두산 제니스스퀘어 A동401호
5th row부산광역시 해운대구 해운대로 564, A동 2303호(우동)
ValueCountFrequency (%)
부산광역시 125
 
10.7%
부산 61
 
5.2%
부산진구 53
 
4.6%
해운대구 43
 
3.7%
동구 24
 
2.1%
중앙대로 19
 
1.6%
부전동 17
 
1.5%
가야대로 16
 
1.4%
동래구 13
 
1.1%
서면로 12
 
1.0%
Other values (555) 780
67.1%
2024-04-18T01:37:18.018100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1232
 
18.6%
부 308
 
4.6%
동 260
 
3.9%
산 256
 
3.9%
1 221
 
3.3%
, 207
 
3.1%
) 189
 
2.9%
( 189
 
2.9%
구 188
 
2.8%
로 186
 
2.8%
Other values (266) 3391
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3636
54.9%
Space Separator 1232
 
18.6%
Decimal Number 1087
 
16.4%
Other Punctuation 209
 
3.2%
Close Punctuation 189
 
2.9%
Open Punctuation 189
 
2.9%
Dash Punctuation 34
 
0.5%
Uppercase Letter 23
 
0.3%
Lowercase Letter 15
 
0.2%
Math Symbol 13
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 308
 
8.5%
동 260
 
7.2%
산 256
 
7.0%
구 188
 
5.2%
로 186
 
5.1%
대 166
 
4.6%
시 136
 
3.7%
광 131
 
3.6%
역 126
 
3.5%
층 99
 
2.7%
Other values (230) 1780
49.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
26.7%
o 2
13.3%
d 1
 
6.7%
x 1
 
6.7%
l 1
 
6.7%
p 1
 
6.7%
m 1
 
6.7%
a 1
 
6.7%
i 1
 
6.7%
w 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 221
20.3%
0 142
13.1%
2 141
13.0%
4 109
10.0%
3 108
9.9%
7 90
8.3%
5 85
 
7.8%
6 72
 
6.6%
8 64
 
5.9%
9 55
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
A 7
30.4%
C 4
17.4%
B 4
17.4%
F 3
13.0%
S 2
 
8.7%
T 1
 
4.3%
K 1
 
4.3%
M 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 207
99.0%
. 2
 
1.0%
Space Separator
ValueCountFrequency (%)
1232
100.0%
Close Punctuation
ValueCountFrequency (%)
) 189
100.0%
Open Punctuation
ValueCountFrequency (%)
( 189
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3636
54.9%
Common 2953
44.6%
Latin 38
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 308
 
8.5%
동 260
 
7.2%
산 256
 
7.0%
구 188
 
5.2%
로 186
 
5.1%
대 166
 
4.6%
시 136
 
3.7%
광 131
 
3.6%
역 126
 
3.5%
층 99
 
2.7%
Other values (230) 1780
49.0%
Latin
ValueCountFrequency (%)
A 7
18.4%
C 4
10.5%
e 4
10.5%
B 4
10.5%
F 3
 
7.9%
S 2
 
5.3%
o 2
 
5.3%
d 1
 
2.6%
x 1
 
2.6%
l 1
 
2.6%
Other values (9) 9
23.7%
Common
ValueCountFrequency (%)
1232
41.7%
1 221
 
7.5%
, 207
 
7.0%
) 189
 
6.4%
( 189
 
6.4%
0 142
 
4.8%
2 141
 
4.8%
4 109
 
3.7%
3 108
 
3.7%
7 90
 
3.0%
Other values (7) 325
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3636
54.9%
ASCII 2991
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1232
41.2%
1 221
 
7.4%
, 207
 
6.9%
) 189
 
6.3%
( 189
 
6.3%
0 142
 
4.7%
2 141
 
4.7%
4 109
 
3.6%
3 108
 
3.6%
7 90
 
3.0%
Other values (26) 363
 
12.1%
Hangul
ValueCountFrequency (%)
부 308
 
8.5%
동 260
 
7.2%
산 256
 
7.0%
구 188
 
5.2%
로 186
 
5.1%
대 166
 
4.6%
시 136
 
3.7%
광 131
 
3.6%
역 126
 
3.5%
층 99
 
2.7%
Other values (230) 1780
49.0%

target_country
Text

MISSING 

Distinct62
Distinct (%)49.2%
Missing60
Missing (%)32.3%
Memory size1.6 KiB
2024-04-18T01:37:18.207041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length21
Mean length10.357143
Min length1

Characters and Unicode

Total characters1305
Distinct characters49
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)34.1%

Sample

1st row러시아
2nd row베트남/기타
3rd row중국/러시아/베트남
4th row러시아
5th row중국, 러시아, 몽골, 베트남
ValueCountFrequency (%)
러시아 34
18.1%
중국 28
14.9%
일본 16
 
8.5%
베트남 13
 
6.9%
몽골 10
 
5.3%
미국/일본/중국 7
 
3.7%
미국/일본/중국/몽골/러시아 5
 
2.7%
미국/일본/중국/러시아/베트남 4
 
2.1%
러시아(cis연합 4
 
2.1%
미국/일본/중국/러시아 4
 
2.1%
Other values (46) 63
33.5%
2024-04-18T01:37:18.498166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 190
14.6%
국 127
 
9.7%
중 95
 
7.3%
아 91
 
7.0%
시 87
 
6.7%
러 86
 
6.6%
일 66
 
5.1%
본 65
 
5.0%
62
 
4.8%
, 58
 
4.4%
Other values (39) 378
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 928
71.1%
Other Punctuation 248
 
19.0%
Space Separator 62
 
4.8%
Uppercase Letter 33
 
2.5%
Close Punctuation 16
 
1.2%
Open Punctuation 16
 
1.2%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 127
13.7%
중 95
10.2%
아 91
9.8%
시 87
9.4%
러 86
9.3%
일 66
 
7.1%
본 65
 
7.0%
미 47
 
5.1%
남 41
 
4.4%
베 37
 
4.0%
Other values (30) 186
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 11
33.3%
I 11
33.3%
C 11
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 190
76.6%
, 58
 
23.4%
Space Separator
ValueCountFrequency (%)
62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 928
71.1%
Common 344
 
26.4%
Latin 33
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 127
13.7%
중 95
10.2%
아 91
9.8%
시 87
9.4%
러 86
9.3%
일 66
 
7.1%
본 65
 
7.0%
미 47
 
5.1%
남 41
 
4.4%
베 37
 
4.0%
Other values (30) 186
20.0%
Common
ValueCountFrequency (%)
/ 190
55.2%
62
 
18.0%
, 58
 
16.9%
) 16
 
4.7%
( 16
 
4.7%
- 2
 
0.6%
Latin
ValueCountFrequency (%)
S 11
33.3%
I 11
33.3%
C 11
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 928
71.1%
ASCII 377
28.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 190
50.4%
62
 
16.4%
, 58
 
15.4%
) 16
 
4.2%
( 16
 
4.2%
S 11
 
2.9%
I 11
 
2.9%
C 11
 
2.9%
- 2
 
0.5%
Hangul
ValueCountFrequency (%)
국 127
13.7%
중 95
10.2%
아 91
9.8%
시 87
9.4%
러 86
9.3%
일 66
 
7.1%
본 65
 
7.0%
미 47
 
5.1%
남 41
 
4.4%
베 37
 
4.0%
Other values (30) 186
20.0%

gubun
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
71 
의원
52 
병원
19 
종합병원
14 
치과의원
12 
Other values (4)
18 

Length

Max length6
Median length4
Mean length3.2204301
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 71
38.2%
의원 52
28.0%
병원 19
 
10.2%
종합병원 14
 
7.5%
치과의원 12
 
6.5%
한의원 11
 
5.9%
상급종합병원 4
 
2.2%
치과병원 2
 
1.1%
한방병원 1
 
0.5%

Length

2024-04-18T01:37:18.609383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:37:18.704026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 71
38.2%
의원 52
28.0%
병원 19
 
10.2%
종합병원 14
 
7.5%
치과의원 12
 
6.5%
한의원 11
 
5.9%
상급종합병원 4
 
2.2%
치과병원 2
 
1.1%
한방병원 1
 
0.5%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2021-03-01 05:02:03
Maximum2021-03-01 05:02:03
2024-04-18T01:37:18.805910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:37:19.111660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-18T01:37:15.343385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:37:15.197817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:37:15.418793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:37:15.271853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T01:37:19.164472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyreg_notarget_countrygubun
skey1.0001.0000.8210.868
reg_no1.0001.0000.7880.869
target_country0.8210.7881.0000.418
gubun0.8680.8690.4181.000
2024-04-18T01:37:19.234651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyreg_nogubun
skey1.0001.0000.697
reg_no1.0001.0000.697
gubun0.6970.6971.000

Missing values

2024-04-18T01:37:15.530315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T01:37:15.682405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

skeyreg_nobusiness_nminsti_gubunregionrepresentativecompany_reg_noaddrtarget_countrygubunlast_load_dttm
02089134보스톡코리아<NA>부산김춘웅<NA>부산 수영구 수영로680번길 30 (광안동) 501호러시아<NA>2021-03-01 05:02:03
12090135오토투어<NA>부산리옥연<NA>부산 남구 수영로325번길 61 (대연동, 대연 롯데캐슬) 102동303호<NA><NA>2021-03-01 05:02:03
22091136(주)나무투어<NA>부산천정화<NA>부산 동구 중앙대로274번길 7-7 (초량동, 부산역 유림 로미오) 상가 1층 104호베트남/기타<NA>2021-03-01 05:02:03
32092137주식회사 비에스펀투어<NA>부산최효섭<NA>부산 해운대구 마린시티2로 33 (우동, 해운대두산위브더제니스) 해운대 두산 제니스스퀘어 A동401호중국/러시아/베트남<NA>2021-03-01 05:02:03
42093138인피니트(INFINITE)<NA>부산PARK SVETLANA<NA>부산광역시 해운대구 해운대로 564, A동 2303호(우동)<NA><NA>2021-03-01 05:02:03
52094139㈜ 하이신<NA>부산CHUB NATALIYA<NA>부산광역시 해운대구 양운로 45, 비동 1409호(좌동, 베르나움 오피스텔)러시아<NA>2021-03-01 05:02:03
62095140주식회사 호비컴퍼니<NA>부산최기석<NA>부산 해운대구 센텀중앙로 48 (우동, 에이스하이테크21) 401호중국, 러시아, 몽골, 베트남<NA>2021-03-01 05:02:03
72096141㈜ 마리안느 마이스 앤 투어<NA>부산이종근<NA>부산광역시 해운대구 해운대해변로 310(중동, 2층)중국, 러시아, 중동<NA>2021-03-01 05:02:03
82097142유이수 주식회사<NA>부산이창헌<NA>부산광역시 해운대구 센텀중앙로 48, 2005호(우동, 에이스하이테크21)중국, 베트남<NA>2021-03-01 05:02:03
92098143케이에스(K.S)<NA>부산TEN SU NE<NA>부산광역시 해운대구 해운대해변로 203, 1227호(우동, 오션타워)<NA><NA>2021-03-01 05:02:03
skeyreg_nobusiness_nminsti_gubunregionrepresentativecompany_reg_noaddrtarget_countrygubunlast_load_dttm
176201459삼성뉴방외과의원<NA>부산정정필외 1명<NA>부산광역시 해운대구 해운대로 794(좌동, 엘리움 6층 603호)미국의원2021-03-01 05:02:03
177201560케이엔젤성형외과의원<NA>부산권용석<NA>부산 부산진구 부전로66번길 28 (부전동) 3-6층<NA>의원2021-03-01 05:02:03
178201661정성훈 성형외과의원<NA>부산정성훈<NA>부산광역시 부산진구 서면로 64(부전동)미국/일본/중국/러시아/베트남의원2021-03-01 05:02:03
179201762굿모닝백이안과의원<NA>부산백태민<NA>부산광역시 부산진구 중앙대로 724(부전동,하나금융프라자3,6,7,8층)미국/일본/중국의원2021-03-01 05:02:03
180201863뉴라인성형외과의원<NA>부산손희동<NA>부산광역시 부산진구 부전로66번길 40 (부전동)일본/중국/러시아(CIS연합)의원2021-03-01 05:02:03
181201964스마일누네빛안과의원<NA>부산박효순외 1명<NA>부산광역시 부산진구 가야대로 772(부전동)일본/중국/러시아의원2021-03-01 05:02:03
182202065김병준 레다스 흉부외과의원<NA>부산김병준<NA>부산광역시 부산진구 서면문화로 10(부전동, 11~12, 13층 일부)일본/러시아의원2021-03-01 05:02:03
183202166소중한눈안과의원<NA>부산김승기<NA>부산광역시 해운대구 센텀남대로 50 (우동, 센텀임페리얼타워 1402호)미국/일본/중국의원2021-03-01 05:02:03
184202267마리아의원<NA>부산임진호<NA>부산광역시 연제구 월드컵대로 125(연산동, 더웰타워 7층~8층)러시아의원2021-03-01 05:02:03
185202368아이사랑산부인과의원<NA>부산승희진 외 1명<NA>부산광역시 부산진구 가야대로 470(개금동)일본의원2021-03-01 05:02:03