Overview

Dataset statistics

Number of variables17
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory145.3 B

Variable types

Numeric3
Categorical7
Text4
Boolean2
DateTime1

Dataset

Description경상북도 상주시 계약서식 테이블
Author경상북도 상주시
URLhttps://www.data.go.kr/data/15063669/fileData.do

Alerts

SEQ_ORDER has constant value ""Constant
THREAD has constant value ""Constant
SECRET has constant value ""Constant
GROUPING has constant value ""Constant
GONGJI_USE is highly overall correlated with CONTRACT_DATA_SEQ and 4 other fieldsHigh correlation
ID is highly overall correlated with CONTRACT_DATA_SEQ and 4 other fieldsHigh correlation
PW is highly overall correlated with CONTRACT_DATA_SEQ and 5 other fieldsHigh correlation
NAME is highly overall correlated with CONTRACT_DATA_SEQ and 6 other fieldsHigh correlation
IP is highly overall correlated with CONTRACT_DATA_SEQ and 6 other fieldsHigh correlation
CONTRACT_DATA_SEQ is highly overall correlated with SEQ_GROUP and 6 other fieldsHigh correlation
SEQ_GROUP is highly overall correlated with CONTRACT_DATA_SEQ and 6 other fieldsHigh correlation
REF is highly overall correlated with CONTRACT_DATA_SEQ and 3 other fieldsHigh correlation
CONTRACT_DATA_SEQ has unique valuesUnique
SEQ_GROUP has unique valuesUnique
TITLE has unique valuesUnique
FILE_PATH has unique valuesUnique
CONTENT has unique valuesUnique
REF has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:52:51.859726
Analysis finished2023-12-12 03:52:54.618173
Duration2.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CONTRACT_DATA_SEQ
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5
Minimum2
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:52:54.713976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.95
Q111.75
median21.5
Q331.25
95-th percentile39.05
Maximum41
Range39
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.690452
Coefficient of variation (CV)0.54374195
Kurtosis-1.2
Mean21.5
Median Absolute Deviation (MAD)10
Skewness0
Sum860
Variance136.66667
MonotonicityNot monotonic
2023-12-12T12:52:54.938960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 1
 
2.5%
19 1
 
2.5%
21 1
 
2.5%
22 1
 
2.5%
23 1
 
2.5%
24 1
 
2.5%
25 1
 
2.5%
26 1
 
2.5%
27 1
 
2.5%
28 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
2 1
2.5%
3 1
2.5%
4 1
2.5%
5 1
2.5%
6 1
2.5%
7 1
2.5%
8 1
2.5%
9 1
2.5%
10 1
2.5%
11 1
2.5%
ValueCountFrequency (%)
41 1
2.5%
40 1
2.5%
39 1
2.5%
38 1
2.5%
37 1
2.5%
36 1
2.5%
35 1
2.5%
34 1
2.5%
33 1
2.5%
32 1
2.5%

SEQ_GROUP
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.5
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:52:55.116754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.95
Q110.75
median20.5
Q330.25
95-th percentile38.05
Maximum40
Range39
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.690452
Coefficient of variation (CV)0.57026595
Kurtosis-1.2
Mean20.5
Median Absolute Deviation (MAD)10
Skewness0
Sum820
Variance136.66667
MonotonicityNot monotonic
2023-12-12T12:52:55.309629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
9 1
 
2.5%
18 1
 
2.5%
20 1
 
2.5%
21 1
 
2.5%
22 1
 
2.5%
23 1
 
2.5%
24 1
 
2.5%
25 1
 
2.5%
26 1
 
2.5%
27 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
1 1
2.5%
2 1
2.5%
3 1
2.5%
4 1
2.5%
5 1
2.5%
6 1
2.5%
7 1
2.5%
8 1
2.5%
9 1
2.5%
10 1
2.5%
ValueCountFrequency (%)
40 1
2.5%
39 1
2.5%
38 1
2.5%
37 1
2.5%
36 1
2.5%
35 1
2.5%
34 1
2.5%
33 1
2.5%
32 1
2.5%
31 1
2.5%

SEQ_ORDER
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
100000000
40 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100000000
2nd row100000000
3rd row100000000
4th row100000000
5th row100000000

Common Values

ValueCountFrequency (%)
100000000 40
100.0%

Length

2023-12-12T12:52:55.476560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:52:55.605569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100000000 40
100.0%

THREAD
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
0
40 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40
100.0%

Length

2023-12-12T12:52:55.769281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:52:55.934087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 40
100.0%

ID
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
post1814
28 
contract
12 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpost1814
2nd rowpost1814
3rd rowpost1814
4th rowpost1814
5th rowpost1814

Common Values

ValueCountFrequency (%)
post1814 28
70.0%
contract 12
30.0%

Length

2023-12-12T12:52:56.108827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:52:56.271814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
post1814 28
70.0%
contract 12
30.0%

NAME
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
관리자
28 
박일룡
5 
곽재준
5 
정해광
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관리자 28
70.0%
박일룡 5
 
12.5%
곽재준 5
 
12.5%
정해광 2
 
5.0%

Length

2023-12-12T12:52:56.431925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:52:56.599916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관리자 28
70.0%
박일룡 5
 
12.5%
곽재준 5
 
12.5%
정해광 2
 
5.0%

PW
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
5425
28 
7283
6 
9237
5 
7281
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)2.5%

Sample

1st row5425
2nd row5425
3rd row5425
4th row5425
5th row5425

Common Values

ValueCountFrequency (%)
5425 28
70.0%
7283 6
 
15.0%
9237 5
 
12.5%
7281 1
 
2.5%

Length

2023-12-12T12:52:56.799317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:52:56.966538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5425 28
70.0%
7283 6
 
15.0%
9237 5
 
12.5%
7281 1
 
2.5%

TITLE
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T12:52:57.348720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length30.5
Mean length23.375
Min length3

Characters and Unicode

Total characters935
Distinct characters139
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row사용인감신고서
2nd row준공검사원
3rd row준공계
4th row물품이행실적증명서
5th row계약보증지급각서
ValueCountFrequency (%)
지방자치단체 17
 
9.8%
8
 
4.6%
입찰및계약집행기준 7
 
4.0%
낙찰자 5
 
2.9%
입찰 5
 
2.9%
및 5
 
2.9%
결정기준(예규 4
 
2.3%
안전행정부 4
 
2.3%
입찰시 4
 
2.3%
수의계약 3
 
1.7%
Other values (96) 112
64.4%
2023-12-12T12:52:57.969572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
135
 
14.4%
계 31
 
3.3%
자 30
 
3.2%
약 28
 
3.0%
준 26
 
2.8%
찰 26
 
2.8%
지 25
 
2.7%
기 21
 
2.2%
방 21
 
2.2%
행 21
 
2.2%
Other values (129) 571
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 658
70.4%
Space Separator 135
 
14.4%
Decimal Number 87
 
9.3%
Other Punctuation 23
 
2.5%
Open Punctuation 11
 
1.2%
Close Punctuation 10
 
1.1%
Dash Punctuation 8
 
0.9%
Connector Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 31
 
4.7%
자 30
 
4.6%
약 28
 
4.3%
준 26
 
4.0%
찰 26
 
4.0%
지 25
 
3.8%
기 21
 
3.2%
방 21
 
3.2%
행 21
 
3.2%
제 20
 
3.0%
Other values (111) 409
62.2%
Decimal Number
ValueCountFrequency (%)
1 20
23.0%
2 15
17.2%
3 12
13.8%
0 12
13.8%
4 9
10.3%
5 8
 
9.2%
7 5
 
5.7%
6 2
 
2.3%
8 2
 
2.3%
9 2
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 16
69.6%
, 6
 
26.1%
/ 1
 
4.3%
Space Separator
ValueCountFrequency (%)
135
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 658
70.4%
Common 277
29.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 31
 
4.7%
자 30
 
4.6%
약 28
 
4.3%
준 26
 
4.0%
찰 26
 
4.0%
지 25
 
3.8%
기 21
 
3.2%
방 21
 
3.2%
행 21
 
3.2%
제 20
 
3.0%
Other values (111) 409
62.2%
Common
ValueCountFrequency (%)
135
48.7%
1 20
 
7.2%
. 16
 
5.8%
2 15
 
5.4%
3 12
 
4.3%
0 12
 
4.3%
( 11
 
4.0%
) 10
 
3.6%
4 9
 
3.2%
5 8
 
2.9%
Other values (8) 29
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 657
70.3%
ASCII 277
29.6%
Compat Jamo 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
135
48.7%
1 20
 
7.2%
. 16
 
5.8%
2 15
 
5.4%
3 12
 
4.3%
0 12
 
4.3%
( 11
 
4.0%
) 10
 
3.6%
4 9
 
3.2%
5 8
 
2.9%
Other values (8) 29
 
10.5%
Hangul
ValueCountFrequency (%)
계 31
 
4.7%
자 30
 
4.6%
약 28
 
4.3%
준 26
 
4.0%
찰 26
 
4.0%
지 25
 
3.8%
기 21
 
3.2%
방 21
 
3.2%
행 21
 
3.2%
제 20
 
3.0%
Other values (110) 408
62.1%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

FILE_NAME
Text

Distinct37
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T12:52:58.228836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length84
Median length35.5
Mean length28.825
Min length7

Characters and Unicode

Total characters1153
Distinct characters143
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)85.0%

Sample

1st row사용인감신고서.hwp
2nd row준공검사원.hwp
3rd row준공계.hwp
4th row물품이행실적증명서.hwp
5th row계약보증금지급각서 양식(배포용).hwp
ValueCountFrequency (%)
지방자치단체 14
 
10.8%
8
 
6.2%
입찰및계약집행기준 7
 
5.4%
입찰 6
 
4.6%
및 5
 
3.8%
낙찰자 3
 
2.3%
운영요령.hwp 3
 
2.3%
일반조건.hwp 3
 
2.3%
계약 3
 
2.3%
시 3
 
2.3%
Other values (71) 75
57.7%
2023-12-12T12:52:58.673803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
 
7.8%
. 48
 
4.2%
_ 45
 
3.9%
p 44
 
3.8%
w 44
 
3.8%
h 44
 
3.8%
자 33
 
2.9%
계 33
 
2.9%
약 31
 
2.7%
찰 28
 
2.4%
Other values (133) 713
61.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 725
62.9%
Lowercase Letter 132
 
11.4%
Space Separator 90
 
7.8%
Decimal Number 56
 
4.9%
Other Punctuation 52
 
4.5%
Connector Punctuation 45
 
3.9%
Close Punctuation 21
 
1.8%
Open Punctuation 21
 
1.8%
Dash Punctuation 8
 
0.7%
Math Symbol 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 33
 
4.6%
계 33
 
4.6%
약 31
 
4.3%
찰 28
 
3.9%
준 28
 
3.9%
지 27
 
3.7%
기 23
 
3.2%
체 23
 
3.2%
단 22
 
3.0%
제 22
 
3.0%
Other values (113) 455
62.8%
Decimal Number
ValueCountFrequency (%)
1 13
23.2%
3 10
17.9%
0 9
16.1%
2 7
12.5%
4 6
10.7%
5 6
10.7%
7 2
 
3.6%
9 2
 
3.6%
8 1
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
p 44
33.3%
w 44
33.3%
h 44
33.3%
Other Punctuation
ValueCountFrequency (%)
. 48
92.3%
, 4
 
7.7%
Space Separator
ValueCountFrequency (%)
90
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 725
62.9%
Common 296
25.7%
Latin 132
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 33
 
4.6%
계 33
 
4.6%
약 31
 
4.3%
찰 28
 
3.9%
준 28
 
3.9%
지 27
 
3.7%
기 23
 
3.2%
체 23
 
3.2%
단 22
 
3.0%
제 22
 
3.0%
Other values (113) 455
62.8%
Common
ValueCountFrequency (%)
90
30.4%
. 48
16.2%
_ 45
15.2%
) 21
 
7.1%
( 21
 
7.1%
1 13
 
4.4%
3 10
 
3.4%
0 9
 
3.0%
- 8
 
2.7%
2 7
 
2.4%
Other values (7) 24
 
8.1%
Latin
ValueCountFrequency (%)
p 44
33.3%
w 44
33.3%
h 44
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 724
62.8%
ASCII 428
37.1%
Compat Jamo 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
21.0%
. 48
11.2%
_ 45
10.5%
p 44
10.3%
w 44
10.3%
h 44
10.3%
) 21
 
4.9%
( 21
 
4.9%
1 13
 
3.0%
3 10
 
2.3%
Other values (10) 48
11.2%
Hangul
ValueCountFrequency (%)
자 33
 
4.6%
계 33
 
4.6%
약 31
 
4.3%
찰 28
 
3.9%
준 28
 
3.9%
지 27
 
3.7%
기 23
 
3.2%
체 23
 
3.2%
단 22
 
3.0%
제 22
 
3.0%
Other values (112) 454
62.7%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

FILE_PATH
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T12:52:58.927824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length35
Mean length38.6
Min length35

Characters and Unicode

Total characters1544
Distinct characters26
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

Unique40 ?
Unique (%)100.0%

Sample

1st row/fileUpload/board/1418824135453.hwp
2nd row/fileUpload/board/1418824165987.hwp
3rd row/fileUpload/board/1418824191582.hwp
4th row/fileUpload/board/1418824217988.hwp
5th row/fileUpload/board/1418918525027.hwp
ValueCountFrequency (%)
fileupload/board/1418824135453.hwp 1
 
2.5%
fileupload/board/1418824165987.hwp 1
 
2.5%
fileupload/board/1418823659118.hwp 1
 
2.5%
fileupload/board/1418823401325.hwp 1
 
2.5%
fileupload/board/1418823435361.hwp 1
 
2.5%
fileupload/board/1418823476298.hwp 1
 
2.5%
fileupload/board/1418823503645.hwp 1
 
2.5%
fileupload/board/1418823537641.hwp 1
 
2.5%
fileupload/board/1418823579896.hwp 1
 
2.5%
fileupload/board/1418823616937.hwp 1
 
2.5%
Other values (30) 30
75.0%
2023-12-12T12:52:59.381672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 132
 
8.5%
1 122
 
7.9%
a 88
 
5.7%
l 88
 
5.7%
d 88
 
5.7%
p 88
 
5.7%
o 88
 
5.7%
8 85
 
5.5%
4 75
 
4.9%
2 63
 
4.1%
Other values (16) 627
40.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 748
48.4%
Decimal Number 572
37.0%
Other Punctuation 180
 
11.7%
Uppercase Letter 44
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 88
11.8%
l 88
11.8%
d 88
11.8%
p 88
11.8%
o 88
11.8%
r 44
5.9%
e 44
5.9%
f 44
5.9%
i 44
5.9%
b 44
5.9%
Other values (2) 88
11.8%
Decimal Number
ValueCountFrequency (%)
1 122
21.3%
8 85
14.9%
4 75
13.1%
2 63
11.0%
3 57
10.0%
9 39
 
6.8%
7 39
 
6.8%
0 34
 
5.9%
6 31
 
5.4%
5 27
 
4.7%
Other Punctuation
ValueCountFrequency (%)
/ 132
73.3%
. 44
 
24.4%
, 4
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
U 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 792
51.3%
Common 752
48.7%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 132
17.6%
1 122
16.2%
8 85
11.3%
4 75
10.0%
2 63
8.4%
3 57
7.6%
. 44
 
5.9%
9 39
 
5.2%
7 39
 
5.2%
0 34
 
4.5%
Other values (3) 62
8.2%
Latin
ValueCountFrequency (%)
a 88
11.1%
l 88
11.1%
d 88
11.1%
p 88
11.1%
o 88
11.1%
U 44
 
5.6%
r 44
 
5.6%
e 44
 
5.6%
f 44
 
5.6%
i 44
 
5.6%
Other values (3) 132
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 132
 
8.5%
1 122
 
7.9%
a 88
 
5.7%
l 88
 
5.7%
d 88
 
5.7%
p 88
 
5.7%
o 88
 
5.7%
8 85
 
5.5%
4 75
 
4.9%
2 63
 
4.1%
Other values (16) 627
40.6%

GONGJI_USE
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size172.0 B
False
35 
True
5 
ValueCountFrequency (%)
False 35
87.5%
True 5
 
12.5%
2023-12-12T12:52:59.560936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

CONTENT
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T12:52:59.900474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length363
Median length46
Mean length52.975
Min length8

Characters and Unicode

Total characters2119
Distinct characters184
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row서식 - 사용인감신고서
2nd row서식 - 준공검사원
3rd row서식 - 준공계
4th row물품이행실적증명서
5th row계약보증지급각서
ValueCountFrequency (%)
37
 
8.5%
지방자치단체 17
 
3.9%
시 16
 
3.7%
서식 12
 
2.8%
및 11
 
2.5%
법령 11
 
2.5%
입찰 9
 
2.1%
바랍니다 7
 
1.6%
적격심사 7
 
1.6%
입찰및계약집행기준 7
 
1.6%
Other values (181) 302
69.3%
2023-12-12T12:53:00.639571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
404
 
19.1%
. 77
 
3.6%
계 53
 
2.5%
시 51
 
2.4%
약 47
 
2.2%
1 42
 
2.0%
사 38
 
1.8%
0 38
 
1.8%
기 33
 
1.6%
- 33
 
1.6%
Other values (174) 1303
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1333
62.9%
Space Separator 404
 
19.1%
Decimal Number 198
 
9.3%
Other Punctuation 99
 
4.7%
Dash Punctuation 33
 
1.6%
Close Punctuation 24
 
1.1%
Open Punctuation 23
 
1.1%
Connector Punctuation 3
 
0.1%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 53
 
4.0%
시 51
 
3.8%
약 47
 
3.5%
사 38
 
2.9%
기 33
 
2.5%
입 32
 
2.4%
행 31
 
2.3%
자 31
 
2.3%
공 30
 
2.3%
찰 30
 
2.3%
Other values (150) 957
71.8%
Decimal Number
ValueCountFrequency (%)
1 42
21.2%
0 38
19.2%
2 30
15.2%
5 24
12.1%
3 20
10.1%
4 19
9.6%
7 10
 
5.1%
9 6
 
3.0%
8 5
 
2.5%
6 4
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 77
77.8%
, 16
 
16.2%
: 4
 
4.0%
※ 2
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 14
58.3%
] 8
33.3%
】 2
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 13
56.5%
[ 8
34.8%
【 2
 
8.7%
Space Separator
ValueCountFrequency (%)
404
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Math Symbol
ValueCountFrequency (%)
→ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1333
62.9%
Common 786
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 53
 
4.0%
시 51
 
3.8%
약 47
 
3.5%
사 38
 
2.9%
기 33
 
2.5%
입 32
 
2.4%
행 31
 
2.3%
자 31
 
2.3%
공 30
 
2.3%
찰 30
 
2.3%
Other values (150) 957
71.8%
Common
ValueCountFrequency (%)
404
51.4%
. 77
 
9.8%
1 42
 
5.3%
0 38
 
4.8%
- 33
 
4.2%
2 30
 
3.8%
5 24
 
3.1%
3 20
 
2.5%
4 19
 
2.4%
, 16
 
2.0%
Other values (14) 83
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1332
62.9%
ASCII 778
36.7%
None 4
 
0.2%
Arrows 2
 
0.1%
Punctuation 2
 
0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
404
51.9%
. 77
 
9.9%
1 42
 
5.4%
0 38
 
4.9%
- 33
 
4.2%
2 30
 
3.9%
5 24
 
3.1%
3 20
 
2.6%
4 19
 
2.4%
, 16
 
2.1%
Other values (10) 75
 
9.6%
Hangul
ValueCountFrequency (%)
계 53
 
4.0%
시 51
 
3.8%
약 47
 
3.5%
사 38
 
2.9%
기 33
 
2.5%
입 32
 
2.4%
행 31
 
2.3%
자 31
 
2.3%
공 30
 
2.3%
찰 30
 
2.3%
Other values (149) 956
71.8%
Arrows
ValueCountFrequency (%)
→ 2
100.0%
Punctuation
ValueCountFrequency (%)
※ 2
100.0%
None
ValueCountFrequency (%)
【 2
50.0%
】 2
50.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

SECRET
Boolean

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size172.0 B
False
40 
ValueCountFrequency (%)
False 40
100.0%
2023-12-12T12:53:00.817374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

IP
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
202.31.195.89
28 
210.178.101.174
7 
210.178.101.206
5 

Length

Max length15
Median length13
Mean length13.6
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202.31.195.89
2nd row202.31.195.89
3rd row202.31.195.89
4th row202.31.195.89
5th row202.31.195.89

Common Values

ValueCountFrequency (%)
202.31.195.89 28
70.0%
210.178.101.174 7
 
17.5%
210.178.101.206 5
 
12.5%

Length

2023-12-12T12:53:00.985670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:53:01.166534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202.31.195.89 28
70.0%
210.178.101.174 7
 
17.5%
210.178.101.206 5
 
12.5%

REG_DATE
Date

Distinct6
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
Minimum2014-12-17 00:00:00
Maximum2016-08-23 00:00:00
2023-12-12T12:53:01.298822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:53:01.478131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

REF
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1194.925
Minimum447
Maximum4225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:53:02.103255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum447
5-th percentile501.65
Q1686.75
median898
Q31288.25
95-th percentile2780.45
Maximum4225
Range3778
Interquartile range (IQR)601.5

Descriptive statistics

Standard deviation814.15586
Coefficient of variation (CV)0.68134474
Kurtosis4.074594
Mean1194.925
Median Absolute Deviation (MAD)250.5
Skewness1.9424909
Sum47797
Variance662849.76
MonotonicityNot monotonic
2023-12-12T12:53:02.361234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
654 1
 
2.5%
696 1
 
2.5%
953 1
 
2.5%
928 1
 
2.5%
1069 1
 
2.5%
862 1
 
2.5%
995 1
 
2.5%
1258 1
 
2.5%
1379 1
 
2.5%
2778 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
447 1
2.5%
476 1
2.5%
503 1
2.5%
516 1
2.5%
522 1
2.5%
568 1
2.5%
640 1
2.5%
641 1
2.5%
654 1
2.5%
659 1
2.5%
ValueCountFrequency (%)
4225 1
2.5%
2827 1
2.5%
2778 1
2.5%
2710 1
2.5%
2213 1
2.5%
2200 1
2.5%
1897 1
2.5%
1755 1
2.5%
1668 1
2.5%
1379 1
2.5%

GROUPING
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
40 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
40
100.0%

Length

2023-12-12T12:53:02.584280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:53:02.724450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Interactions

2023-12-12T12:52:53.662770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:52.775212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:53.207657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:53.845887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:52.909748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:53.333069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:54.011851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:53.060276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:53.496407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:53:02.821505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CONTRACT_DATA_SEQSEQ_GROUPIDNAMEPWTITLEFILE_NAMEFILE_PATHGONGJI_USECONTENTIPREG_DATEREF
CONTRACT_DATA_SEQ1.0001.0001.0000.8850.8591.0000.9781.0000.9821.0000.9340.7670.493
SEQ_GROUP1.0001.0001.0000.8850.8591.0000.9781.0000.9821.0000.9340.7670.493
ID1.0001.0001.0001.0001.0001.0001.0001.0000.6791.0001.0001.0000.675
NAME0.8850.8851.0001.0000.9811.0000.0001.0001.0001.0001.0001.0000.894
PW0.8590.8591.0000.9811.0001.0001.0001.0001.0001.0001.0001.0000.793
TITLE1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
FILE_NAME0.9780.9781.0000.0001.0001.0001.0001.0001.0001.0001.0000.0000.000
FILE_PATH1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
GONGJI_USE0.9820.9820.6791.0001.0001.0001.0001.0001.0001.0001.0001.0000.532
CONTENT1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
IP0.9340.9341.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.681
REG_DATE0.7670.7671.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.743
REF0.4930.4930.6750.8940.7931.0000.0001.0000.5321.0000.6810.7431.000
2023-12-12T12:53:02.986496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GONGJI_USEIDPWNAMEIP
GONGJI_USE1.0000.4740.9730.9730.987
ID0.4741.0000.9730.9730.987
PW0.9730.9731.0000.8140.986
NAME0.9730.9730.8141.0000.986
IP0.9870.9870.9860.9861.000
2023-12-12T12:53:03.138051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CONTRACT_DATA_SEQSEQ_GROUPREFIDNAMEPWGONGJI_USEIP
CONTRACT_DATA_SEQ1.0001.0000.7890.8890.6870.6470.7830.822
SEQ_GROUP1.0001.0000.7890.8890.6870.6470.7830.822
REF0.7890.7891.0000.4780.5580.4360.3720.526
ID0.8890.8890.4781.0000.9730.9730.4740.987
NAME0.6870.6870.5580.9731.0000.8140.9730.986
PW0.6470.6470.4360.9730.8141.0000.9730.986
GONGJI_USE0.7830.7830.3720.4740.9730.9731.0000.987
IP0.8220.8220.5260.9870.9860.9860.9871.000

Missing values

2023-12-12T12:52:54.255725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:52:54.518651image/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

CONTRACT_DATA_SEQSEQ_GROUPSEQ_ORDERTHREADIDNAMEPWTITLEFILE_NAMEFILE_PATHGONGJI_USECONTENTSECRETIPREG_DATEREFGROUPING
01091000000000post1814관리자5425사용인감신고서사용인감신고서.hwp/fileUpload/board/1418824135453.hwpN서식 - 사용인감신고서N202.31.195.892014-12-17654
111101000000000post1814관리자5425준공검사원준공검사원.hwp/fileUpload/board/1418824165987.hwpN서식 - 준공검사원N202.31.195.892014-12-17834
212111000000000post1814관리자5425준공계준공계.hwp/fileUpload/board/1418824191582.hwpN서식 - 준공계N202.31.195.892014-12-17778
313121000000000post1814관리자5425물품이행실적증명서물품이행실적증명서.hwp/fileUpload/board/1418824217988.hwpN물품이행실적증명서N202.31.195.892014-12-17568
414131000000000post1814관리자5425계약보증지급각서계약보증금지급각서 양식(배포용).hwp/fileUpload/board/1418918525027.hwpN계약보증지급각서N202.31.195.892014-12-171085
530291000000000contract박일룡7281하자보수보증금 지급각서하자보수보증금 지급각서.hwp/fileUpload/board/1421392777983.hwpN계약금액이 3천만원을 초과하지 아니하는 경우(조경공사는 제외입니다)N210.178.101.1742015-01-161041
636351000000000contract박일룡7283수의계약 각서 등 각종 서식수의계약각서.hwp,용역계약 일반조건 및 청렴서약서(2015.01.05).hwp/fileUpload/board/1435307403160.hwp,/fileUpload/board/1435307403159.hwpN0. 수의계약 체결시 필요한 수의계약각서0. 용역계약 체결시 필요한 용역계약 일반조건0. 공사, 용역, 물품 계약 체결시 활용할 수 있는 청렴서약서 서식 첨부합니다.N210.178.101.1742015-06-262200
739381000000000contract곽재준9237적격심사 신청서적격심사신청서.hwp/fileUpload/board/1471912480795.hwpY지방자치단체 입찰 시 낙찰자 결정기준의 적격심사 신청서를 게시 하오니활용 하기기 바랍니다.N210.178.101.2062016-08-231082
835341000000000contract박일룡7283소프트웨어 사업의 협상계약 제도 개선소프트웨어 사업의 협상계약 제도 개선 내용.hwp/fileUpload/board/1428994494634.hwpN소프트웨어 사업의 협상계약 제도 개선 내용입니다.업무에 참고하시기 바랍니다.N210.178.101.1742015-04-14738
940391000000000contract곽재준9237건강보험료 납부증명제도 시행 안내(2016. 8. 4)건강보험료+납부증명제도시행+홍보+안내문.hwp/fileUpload/board/1471912609184.hwpY건강보험료 납부증명제도 시행 안내문 입니다.N210.178.101.2062016-08-231135
CONTRACT_DATA_SEQSEQ_GROUPSEQ_ORDERTHREADIDNAMEPWTITLEFILE_NAMEFILE_PATHGONGJI_USECONTENTSECRETIPREG_DATEREFGROUPING
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