Overview

Dataset statistics

Number of variables21
Number of observations500
Missing cells207
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.1 KiB
Average record size in memory174.3 B

Variable types

Text8
Numeric2
Categorical10
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 보증고객창업기업유형정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092886/fileData.do

Alerts

업무구분코드 has constant value ""Constant
설립일자 has constant value ""Constant
접수일자 has constant value ""Constant
대표자생년월일 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
예비창업여부 is highly imbalanced (78.6%)Imbalance
청년창업구분코드 is highly imbalanced (72.3%)Imbalance
상담ID has 207 (41.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 14:15:21.067290
Analysis finished2023-12-12 14:15:21.467818
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct385
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:15:21.681584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)61.2%

Sample

1st row9dnMGg79h7
2nd row9dnSZOyTXg
3rd row9dnzFgC2IJ
4th row9dnOrSPU2w
5th row9crj06Lvju
ValueCountFrequency (%)
9cu3mo7pwm 16
 
3.2%
9dnsmszqf3 6
 
1.2%
9dnsoxlkbw 4
 
0.8%
9cendyiqe5 4
 
0.8%
9dnzb9b7i7 4
 
0.8%
9dnakedgxy 3
 
0.6%
9dnskfqaop 3
 
0.6%
9dnzfgc2ij 3
 
0.6%
9cq9mqfqlq 3
 
0.6%
9dnk4aunp2 3
 
0.6%
Other values (375) 451
90.2%
2023-12-12T23:15:22.131822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 563
 
11.3%
d 342
 
6.8%
n 293
 
5.9%
c 262
 
5.2%
S 229
 
4.6%
W 99
 
2.0%
v 96
 
1.9%
M 96
 
1.9%
u 88
 
1.8%
P 81
 
1.6%
Other values (52) 2851
57.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2245
44.9%
Uppercase Letter 1732
34.6%
Decimal Number 1023
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 342
15.2%
n 293
 
13.1%
c 262
 
11.7%
v 96
 
4.3%
u 88
 
3.9%
p 75
 
3.3%
z 75
 
3.3%
q 73
 
3.3%
s 68
 
3.0%
e 67
 
3.0%
Other values (16) 806
35.9%
Uppercase Letter
ValueCountFrequency (%)
S 229
 
13.2%
W 99
 
5.7%
M 96
 
5.5%
P 81
 
4.7%
N 79
 
4.6%
O 79
 
4.6%
X 70
 
4.0%
E 69
 
4.0%
U 60
 
3.5%
J 59
 
3.4%
Other values (16) 811
46.8%
Decimal Number
ValueCountFrequency (%)
9 563
55.0%
3 81
 
7.9%
4 64
 
6.3%
5 59
 
5.8%
7 57
 
5.6%
6 44
 
4.3%
2 42
 
4.1%
1 42
 
4.1%
8 36
 
3.5%
0 35
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 3977
79.5%
Common 1023
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 342
 
8.6%
n 293
 
7.4%
c 262
 
6.6%
S 229
 
5.8%
W 99
 
2.5%
v 96
 
2.4%
M 96
 
2.4%
u 88
 
2.2%
P 81
 
2.0%
N 79
 
2.0%
Other values (42) 2312
58.1%
Common
ValueCountFrequency (%)
9 563
55.0%
3 81
 
7.9%
4 64
 
6.3%
5 59
 
5.8%
7 57
 
5.6%
6 44
 
4.3%
2 42
 
4.1%
1 42
 
4.1%
8 36
 
3.5%
0 35
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 563
 
11.3%
d 342
 
6.8%
n 293
 
5.9%
c 262
 
5.2%
S 229
 
4.6%
W 99
 
2.0%
v 96
 
1.9%
M 96
 
1.9%
u 88
 
1.8%
P 81
 
1.6%
Other values (52) 2851
57.0%

이력일련번호
Real number (ℝ)

Distinct35
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.968
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:15:22.279838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile13.05
Maximum72
Range71
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.277648
Coefficient of variation (CV)2.8421492
Kurtosis24.003418
Mean3.968
Median Absolute Deviation (MAD)0
Skewness4.9255923
Sum1984
Variance127.18535
MonotonicityNot monotonic
2023-12-12T23:15:22.428135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 385
77.0%
3 33
 
6.6%
2 21
 
4.2%
4 11
 
2.2%
10 6
 
1.2%
6 4
 
0.8%
11 4
 
0.8%
12 3
 
0.6%
13 3
 
0.6%
5 3
 
0.6%
Other values (25) 27
 
5.4%
ValueCountFrequency (%)
1 385
77.0%
2 21
 
4.2%
3 33
 
6.6%
4 11
 
2.2%
5 3
 
0.6%
6 4
 
0.8%
7 1
 
0.2%
9 1
 
0.2%
10 6
 
1.2%
11 4
 
0.8%
ValueCountFrequency (%)
72 1
0.2%
71 1
0.2%
70 1
0.2%
69 1
0.2%
68 1
0.2%
67 1
0.2%
66 1
0.2%
65 1
0.2%
64 1
0.2%
63 1
0.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
278 
1
222 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 278
55.6%
1 222
44.4%

Length

2023-12-12T23:15:22.556979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:22.646840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 278
55.6%
1 222
44.4%

상담ID
Text

MISSING 

Distinct154
Distinct (%)52.6%
Missing207
Missing (%)41.4%
Memory size4.0 KiB
2023-12-12T23:15:22.888583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2930
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)39.6%

Sample

1st row9dnMGdWUWv
2nd row9dnzFaFVUF
3rd row
4th row9dnzFaFVUF
5th row
ValueCountFrequency (%)
9dnsmpa07i 6
 
3.0%
9dnzb44vuj 4
 
2.0%
9dnsojzoe4 4
 
2.0%
9dnsjt3ivw 3
 
1.5%
9dnzfafvuf 3
 
1.5%
9dnk371mks 3
 
1.5%
9dnakcd3fh 3
 
1.5%
9dndudnyxw 2
 
1.0%
9dnlcvtzll 2
 
1.0%
9dnsvqbwa4 2
 
1.0%
Other values (143) 170
84.2%
2023-12-12T23:15:23.302772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
910
31.1%
d 217
 
7.4%
9 217
 
7.4%
n 213
 
7.3%
S 115
 
3.9%
M 51
 
1.7%
O 37
 
1.3%
4 35
 
1.2%
Z 35
 
1.2%
L 33
 
1.1%
Other values (53) 1067
36.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 910
31.1%
Lowercase Letter 874
29.8%
Uppercase Letter 744
25.4%
Decimal Number 402
13.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 217
24.8%
n 213
24.4%
z 32
 
3.7%
v 31
 
3.5%
s 24
 
2.7%
m 24
 
2.7%
y 21
 
2.4%
h 21
 
2.4%
i 21
 
2.4%
j 21
 
2.4%
Other values (16) 249
28.5%
Uppercase Letter
ValueCountFrequency (%)
S 115
 
15.5%
M 51
 
6.9%
O 37
 
5.0%
Z 35
 
4.7%
L 33
 
4.4%
K 32
 
4.3%
W 31
 
4.2%
X 29
 
3.9%
J 29
 
3.9%
U 28
 
3.8%
Other values (16) 324
43.5%
Decimal Number
ValueCountFrequency (%)
9 217
54.0%
4 35
 
8.7%
3 29
 
7.2%
0 24
 
6.0%
2 22
 
5.5%
7 21
 
5.2%
1 19
 
4.7%
5 15
 
3.7%
6 12
 
3.0%
8 8
 
2.0%
Space Separator
ValueCountFrequency (%)
910
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1618
55.2%
Common 1312
44.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 217
 
13.4%
n 213
 
13.2%
S 115
 
7.1%
M 51
 
3.2%
O 37
 
2.3%
Z 35
 
2.2%
L 33
 
2.0%
K 32
 
2.0%
z 32
 
2.0%
W 31
 
1.9%
Other values (42) 822
50.8%
Common
ValueCountFrequency (%)
910
69.4%
9 217
 
16.5%
4 35
 
2.7%
3 29
 
2.2%
0 24
 
1.8%
2 22
 
1.7%
7 21
 
1.6%
1 19
 
1.4%
5 15
 
1.1%
6 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
910
31.1%
d 217
 
7.4%
9 217
 
7.4%
n 213
 
7.3%
S 115
 
3.9%
M 51
 
1.7%
O 37
 
1.3%
4 35
 
1.2%
Z 35
 
1.2%
L 33
 
1.1%
Other values (53) 1067
36.4%

업무구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
G
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
G 500
100.0%

Length

2023-12-12T23:15:23.448847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:23.558217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

설립일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T23:15:23.662096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:23.755504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

접수일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T23:15:23.857360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:23.942512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

대표자생년월일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T23:15:24.027990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:24.135168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

예비창업여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
483 
True
 
17
ValueCountFrequency (%)
False 483
96.6%
True 17
 
3.4%
2023-12-12T23:15:24.216841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
5
251 
2
121 
3
70 
4
45 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 251
50.2%
2 121
24.2%
3 70
 
14.0%
4 45
 
9.0%
1 13
 
2.6%

Length

2023-12-12T23:15:24.330426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:24.470399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 251
50.2%
2 121
24.2%
3 70
 
14.0%
4 45
 
9.0%
1 13
 
2.6%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
5
251 
4
130 
3
73 
1
 
24
2
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row2
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 251
50.2%
4 130
26.0%
3 73
 
14.6%
1 24
 
4.8%
2 22
 
4.4%

Length

2023-12-12T23:15:24.611280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:24.735877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 251
50.2%
4 130
26.0%
3 73
 
14.6%
1 24
 
4.8%
2 22
 
4.4%

청년창업구분코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3
464 
1
 
24
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 464
92.8%
1 24
 
4.8%
2 12
 
2.4%

Length

2023-12-12T23:15:24.863889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:24.978296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 464
92.8%
1 24
 
4.8%
2 12
 
2.4%
Distinct463
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:15:25.298854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique427 ?
Unique (%)85.4%

Sample

1st row45:34.8
2nd row33:27.5
3rd row32:50.8
4th row33:07.7
5th row32:57.0
ValueCountFrequency (%)
37:12.8 3
 
0.6%
55:36.7 2
 
0.4%
23:32.7 2
 
0.4%
49:15.7 2
 
0.4%
16:55.0 2
 
0.4%
28:44.1 2
 
0.4%
53:18.6 2
 
0.4%
26:36.4 2
 
0.4%
40:52.4 2
 
0.4%
30:11.2 2
 
0.4%
Other values (453) 479
95.8%
2023-12-12T23:15:25.789728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 362
10.3%
4 346
9.9%
3 321
9.2%
5 291
8.3%
0 279
8.0%
1 262
7.5%
6 177
 
5.1%
7 165
 
4.7%
Other values (2) 297
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 362
14.5%
4 346
13.8%
3 321
12.8%
5 291
11.6%
0 279
11.2%
1 262
10.5%
6 177
7.1%
7 165
6.6%
9 152
6.1%
8 145
5.8%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 362
10.3%
4 346
9.9%
3 321
9.2%
5 291
8.3%
0 279
8.0%
1 262
7.5%
6 177
 
5.1%
7 165
 
4.7%
Other values (2) 297
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 362
10.3%
4 346
9.9%
3 321
9.2%
5 291
8.3%
0 279
8.0%
1 262
7.5%
6 177
 
5.1%
7 165
 
4.7%
Other values (2) 297
8.5%
Distinct306
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:15:26.196491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.3
Min length4

Characters and Unicode

Total characters2150
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)37.8%

Sample

1st row4223
2nd row5601
3rd row4535
4th row9C768
5th row4963
ValueCountFrequency (%)
9c776 18
 
3.6%
5214 6
 
1.2%
4685 5
 
1.0%
9c780 5
 
1.0%
9c716 5
 
1.0%
5888 5
 
1.0%
4535 4
 
0.8%
4602 4
 
0.8%
5067 4
 
0.8%
9c713 4
 
0.8%
Other values (296) 440
88.0%
2023-12-12T23:15:26.734241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 266
12.4%
5 263
12.2%
4 244
11.3%
6 243
11.3%
7 237
11.0%
3 180
8.4%
0 173
8.0%
C 150
7.0%
2 136
6.3%
1 129
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
93.0%
Uppercase Letter 150
 
7.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 266
13.3%
5 263
13.2%
4 244
12.2%
6 243
12.2%
7 237
11.8%
3 180
9.0%
0 173
8.6%
2 136
6.8%
1 129
6.5%
8 129
6.5%
Uppercase Letter
ValueCountFrequency (%)
C 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
93.0%
Latin 150
 
7.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 266
13.3%
5 263
13.2%
4 244
12.2%
6 243
12.2%
7 237
11.8%
3 180
9.0%
0 173
8.6%
2 136
6.8%
1 129
6.5%
8 129
6.5%
Latin
ValueCountFrequency (%)
C 150
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 266
12.4%
5 263
12.2%
4 244
11.3%
6 243
11.3%
7 237
11.0%
3 180
8.4%
0 173
8.0%
C 150
7.0%
2 136
6.3%
1 129
6.0%

유효개시일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T23:15:26.891832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:26.992574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

유효종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T23:15:27.091432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:27.184303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

최종수정수
Real number (ℝ)

Distinct45
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.706
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:15:27.583991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q39
95-th percentile25
Maximum72
Range71
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.072741
Coefficient of variation (CV)1.5666676
Kurtosis13.911928
Mean7.706
Median Absolute Deviation (MAD)2
Skewness3.5532406
Sum3853
Variance145.75107
MonotonicityNot monotonic
2023-12-12T23:15:27.721020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 114
22.8%
2 101
20.2%
3 57
11.4%
4 29
 
5.8%
11 20
 
4.0%
9 18
 
3.6%
6 17
 
3.4%
8 15
 
3.0%
5 15
 
3.0%
10 12
 
2.4%
Other values (35) 102
20.4%
ValueCountFrequency (%)
1 114
22.8%
2 101
20.2%
3 57
11.4%
4 29
 
5.8%
5 15
 
3.0%
6 17
 
3.4%
7 11
 
2.2%
8 15
 
3.0%
9 18
 
3.6%
10 12
 
2.4%
ValueCountFrequency (%)
72 1
0.2%
71 1
0.2%
70 1
0.2%
69 1
0.2%
68 1
0.2%
67 1
0.2%
66 1
0.2%
65 1
0.2%
64 1
0.2%
63 1
0.2%
Distinct496
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:15:28.094329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique492 ?
Unique (%)98.4%

Sample

1st row33:29.1
2nd row33:27.5
3rd row33:18.0
4th row33:07.7
5th row32:57.0
ValueCountFrequency (%)
52:29.3 2
 
0.4%
29:16.4 2
 
0.4%
27:20.8 2
 
0.4%
04:19.3 2
 
0.4%
06:39.8 1
 
0.2%
58:43.6 1
 
0.2%
59:53.7 1
 
0.2%
01:03.4 1
 
0.2%
01:06.0 1
 
0.2%
01:48.3 1
 
0.2%
Other values (486) 486
97.2%
2023-12-12T23:15:28.589883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 365
10.4%
4 357
10.2%
3 324
9.3%
0 287
8.2%
5 280
8.0%
1 259
7.4%
6 170
 
4.9%
7 158
 
4.5%
Other values (2) 300
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 365
14.6%
4 357
14.3%
3 324
13.0%
0 287
11.5%
5 280
11.2%
1 259
10.4%
6 170
6.8%
7 158
6.3%
8 152
6.1%
9 148
5.9%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 365
10.4%
4 357
10.2%
3 324
9.3%
0 287
8.2%
5 280
8.0%
1 259
7.4%
6 170
 
4.9%
7 158
 
4.5%
Other values (2) 300
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 365
10.4%
4 357
10.2%
3 324
9.3%
0 287
8.2%
5 280
8.0%
1 259
7.4%
6 170
 
4.9%
7 158
 
4.5%
Other values (2) 300
8.6%
Distinct306
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:15:29.087819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.3
Min length4

Characters and Unicode

Total characters2150
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)38.2%

Sample

1st row5545
2nd row5601
3rd row4535
4th row9C768
5th row4963
ValueCountFrequency (%)
9c776 18
 
3.6%
5214 6
 
1.2%
9c780 5
 
1.0%
9c716 5
 
1.0%
4685 5
 
1.0%
5888 5
 
1.0%
4535 4
 
0.8%
9c718 4
 
0.8%
9c713 4
 
0.8%
5067 4
 
0.8%
Other values (296) 440
88.0%
2023-12-12T23:15:29.686213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 269
12.5%
9 268
12.5%
6 248
11.5%
4 241
11.2%
7 236
11.0%
0 177
8.2%
3 167
7.8%
C 150
7.0%
1 134
6.2%
2 132
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
93.0%
Uppercase Letter 150
 
7.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 269
13.5%
9 268
13.4%
6 248
12.4%
4 241
12.0%
7 236
11.8%
0 177
8.8%
3 167
8.3%
1 134
6.7%
2 132
6.6%
8 128
6.4%
Uppercase Letter
ValueCountFrequency (%)
C 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
93.0%
Latin 150
 
7.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 269
13.5%
9 268
13.4%
6 248
12.4%
4 241
12.0%
7 236
11.8%
0 177
8.8%
3 167
8.3%
1 134
6.7%
2 132
6.6%
8 128
6.4%
Latin
ValueCountFrequency (%)
C 150
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 269
12.5%
9 268
12.5%
6 248
11.5%
4 241
11.2%
7 236
11.0%
0 177
8.2%
3 167
7.8%
C 150
7.0%
1 134
6.2%
2 132
6.1%
Distinct384
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:15:30.095500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique304 ?
Unique (%)60.8%

Sample

1st row45:34.8
2nd row23:56.7
3rd row23:28.2
4th row39:10.4
5th row22:46.4
ValueCountFrequency (%)
28:50.8 16
 
3.2%
06:32.4 6
 
1.2%
38:09.1 4
 
0.8%
09:13.9 4
 
0.8%
35:56.1 4
 
0.8%
25:34.9 3
 
0.6%
48:35.5 3
 
0.6%
25:27.9 3
 
0.6%
14:50.6 3
 
0.6%
32:23.7 3
 
0.6%
Other values (374) 451
90.2%
2023-12-12T23:15:30.592161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 356
10.2%
2 324
9.3%
3 324
9.3%
0 292
8.3%
4 283
8.1%
1 270
7.7%
8 205
5.9%
6 166
 
4.7%
Other values (2) 280
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 356
14.2%
2 324
13.0%
3 324
13.0%
0 292
11.7%
4 283
11.3%
1 270
10.8%
8 205
8.2%
6 166
6.6%
9 145
5.8%
7 135
 
5.4%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 356
10.2%
2 324
9.3%
3 324
9.3%
0 292
8.3%
4 283
8.1%
1 270
7.7%
8 205
5.9%
6 166
 
4.7%
Other values (2) 280
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 356
10.2%
2 324
9.3%
3 324
9.3%
0 292
8.3%
4 283
8.1%
1 270
7.7%
8 205
5.9%
6 166
 
4.7%
Other values (2) 280
8.0%
Distinct332
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:15:30.984385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.196
Min length4

Characters and Unicode

Total characters2098
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique233 ?
Unique (%)46.6%

Sample

1st row4223
2nd row5601
3rd row4220
4th row5467
5th row4763
ValueCountFrequency (%)
4348 16
 
3.2%
3723 8
 
1.6%
5214 6
 
1.2%
4224 5
 
1.0%
4083 5
 
1.0%
3082 4
 
0.8%
4457 4
 
0.8%
4877 4
 
0.8%
5208 4
 
0.8%
4110 4
 
0.8%
Other values (322) 440
88.0%
2023-12-12T23:15:31.575345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 350
16.7%
3 272
13.0%
5 263
12.5%
9 207
9.9%
0 178
8.5%
7 173
8.2%
2 170
8.1%
8 161
7.7%
1 146
7.0%
6 125
 
6.0%
Other values (3) 53
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2045
97.5%
Uppercase Letter 53
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 350
17.1%
3 272
13.3%
5 263
12.9%
9 207
10.1%
0 178
8.7%
7 173
8.5%
2 170
8.3%
8 161
7.9%
1 146
7.1%
6 125
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
C 30
56.6%
B 20
37.7%
A 3
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2045
97.5%
Latin 53
 
2.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 350
17.1%
3 272
13.3%
5 263
12.9%
9 207
10.1%
0 178
8.7%
7 173
8.5%
2 170
8.3%
8 161
7.9%
1 146
7.1%
6 125
 
6.1%
Latin
ValueCountFrequency (%)
C 30
56.6%
B 20
37.7%
A 3
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 350
16.7%
3 272
13.0%
5 263
12.5%
9 207
9.9%
0 178
8.5%
7 173
8.2%
2 170
8.1%
8 161
7.7%
1 146
7.0%
6 125
 
6.0%
Other values (3) 53
 
2.5%

Sample

창업기업유형ID이력일련번호창업기업유형자료구분코드상담ID업무구분코드설립일자접수일자대표자생년월일예비창업여부창업단계코드창업유형코드청년창업구분코드입력일시입력직원번호유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnMGg79h7119dnMGdWUWvG00:00.000:00.000:00.0N55345:34.8422300:00.000:00.0233:29.1554545:34.84223
19dnSZOyTXg11<NA>G00:00.000:00.000:00.0N55333:27.5560100:00.000:00.0233:27.5560123:56.75601
29dnzFgC2IJ119dnzFaFVUFG00:00.000:00.000:00.0N32332:50.8453500:00.000:00.0433:18.0453523:28.24220
39dnOrSPU2w12G00:00.000:00.000:00.0N55333:07.79C76800:00.000:00.0233:07.79C76839:10.45467
49crj06Lvju12<NA>G00:00.000:00.000:00.0N55332:57.0496300:00.000:00.0532:57.0496322:46.44763
59dnzFgC2IJ419dnzFaFVUFG00:00.000:00.000:00.0N32332:50.8453500:00.000:00.0332:50.8453523:28.24220
69dfccUMOuk12G00:00.000:00.000:00.0N44332:10.39C75800:00.000:00.0432:10.39C75828:31.49C069
79dnS0jAIT2119dnSZr5kZzG00:00.000:00.000:00.0N55331:35.2397500:00.000:00.0131:35.2397531:35.23975
89dnAH5aYhL119dnAGrrMx7G00:00.000:00.000:00.0N55353:07.6296200:00.000:00.0231:29.5582053:07.62962
99cuEJGTzdS12<NA>G00:00.000:00.000:00.0N55331:21.39C66600:00.000:00.02731:21.39C66657:38.74870
창업기업유형ID이력일련번호창업기업유형자료구분코드상담ID업무구분코드설립일자접수일자대표자생년월일예비창업여부창업단계코드창업유형코드청년창업구분코드입력일시입력직원번호유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909c38zUJ53Q12G00:00.000:00.000:00.0N44326:15.49C67500:00.000:00.0326:15.49C67543:40.24942
4919dnSN8jAPH119dnSN90FgSG00:00.000:00.000:00.0N23325:42.7444500:00.000:00.0325:58.5444525:35.04445
4929dnSN9SRTB119dnSNZMh7aG00:00.000:00.000:00.0N55325:58.2449500:00.000:00.0125:58.2449525:58.24495
4939cvfBn8vrG12<NA>G00:00.000:00.000:00.0N55325:43.79C73400:00.000:00.0825:43.79C73437:41.15208
4949dnSN8jAPH31<NA>G00:00.000:00.000:00.0N23325:42.7444500:00.000:00.0225:42.7444525:35.04445
4959dnSN8jAPH21<NA>G00:00.000:00.000:00.0N23325:35.0444500:00.000:00.0125:35.0444525:35.04445
4969dnJTkPZyO119dnJThICQyG00:00.000:00.000:00.0N55344:58.6348400:00.000:00.0225:23.7610744:58.63484
4979dnSN6Srfe11<NA>G00:00.000:00.000:00.0N55325:13.8559600:00.000:00.0125:13.8559625:13.85596
4989dnSN6z1aD119dnSKRYTYoG00:00.000:00.000:00.0N44325:09.4378800:00.000:00.0125:09.4378825:09.43788
4999dnAWM1gBJ119dnAWJXNuzG00:00.000:00.000:00.0N23322:34.5504800:00.000:00.0324:49.3504837:41.33676