Overview

Dataset statistics

Number of variables14
Number of observations87
Missing cells407
Missing cells (%)33.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory119.5 B

Variable types

Categorical3
Numeric3
Text4
DateTime2
Unsupported1
Boolean1

Dataset

Description회계년도,제안번호,평가번호,점수,사유,이미지url,이미지타입,이미지명,이미지사이즈,변환파일명,등록일,수정id,수정일,사용여부
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15714/S/1/datasetView.do

Alerts

제안번호 is highly overall correlated with 회계년도 and 1 other fieldsHigh correlation
평가번호 is highly overall correlated with 회계년도 and 2 other fieldsHigh correlation
이미지사이즈 is highly overall correlated with 회계년도 and 1 other fieldsHigh correlation
회계년도 is highly overall correlated with 제안번호 and 4 other fieldsHigh correlation
점수 is highly overall correlated with 변환파일명High correlation
변환파일명 is highly overall correlated with 평가번호 and 3 other fieldsHigh correlation
사용여부 is highly overall correlated with 제안번호 and 4 other fieldsHigh correlation
이미지url has 83 (95.4%) missing valuesMissing
이미지타입 has 83 (95.4%) missing valuesMissing
이미지명 has 77 (88.5%) missing valuesMissing
이미지사이즈 has 77 (88.5%) missing valuesMissing
수정id has 87 (100.0%) missing valuesMissing
평가번호 has unique valuesUnique
등록일 has unique valuesUnique
수정일 has unique valuesUnique
수정id is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-04 02:35:21.202757
Analysis finished2024-05-04 02:35:31.934643
Duration10.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size828.0 B
2018
35 
2014
21 
2017
14 
2015
10 
2016
7 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 35
40.2%
2014 21
24.1%
2017 14
 
16.1%
2015 10
 
11.5%
2016 7
 
8.0%

Length

2024-05-04T02:35:32.156177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:35:32.511349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 35
40.2%
2014 21
24.1%
2017 14
 
16.1%
2015 10
 
11.5%
2016 7
 
8.0%

제안번호
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1636.4713
Minimum109
Maximum4438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-05-04T02:35:32.890545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile495
Q11124
median1184
Q31722
95-th percentile3621.1
Maximum4438
Range4329
Interquartile range (IQR)598

Descriptive statistics

Standard deviation1041.999
Coefficient of variation (CV)0.6367353
Kurtosis0.41056044
Mean1636.4713
Median Absolute Deviation (MAD)310
Skewness1.1859242
Sum142373
Variance1085761.9
MonotonicityNot monotonic
2024-05-04T02:35:33.567845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1124 15
17.2%
1184 11
 
12.6%
572 6
 
6.9%
1481 6
 
6.9%
3096 4
 
4.6%
1309 3
 
3.4%
994 3
 
3.4%
4438 3
 
3.4%
1537 3
 
3.4%
1106 2
 
2.3%
Other values (28) 31
35.6%
ValueCountFrequency (%)
109 1
 
1.1%
442 1
 
1.1%
455 1
 
1.1%
462 2
 
2.3%
572 6
6.9%
606 1
 
1.1%
692 1
 
1.1%
701 1
 
1.1%
959 1
 
1.1%
994 3
3.4%
ValueCountFrequency (%)
4438 3
3.4%
3662 1
 
1.1%
3622 1
 
1.1%
3619 1
 
1.1%
3236 1
 
1.1%
3224 1
 
1.1%
3222 1
 
1.1%
3219 1
 
1.1%
3213 1
 
1.1%
3212 1
 
1.1%

평가번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7586081 × 1012
Minimum1.5070811 × 1012
Maximum1.9102114 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-05-04T02:35:34.167686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5070811 × 1012
5-th percentile1.507092 × 1012
Q11.6081017 × 1012
median1.8111623 × 1012
Q31.9050218 × 1012
95-th percentile1.9101284 × 1012
Maximum1.9102114 × 1012
Range4.0313036 × 1011
Interquartile range (IQR)2.9692001 × 1011

Descriptive statistics

Standard deviation1.6578447 × 1011
Coefficient of variation (CV)0.094270278
Kurtosis-1.4507176
Mean1.7586081 × 1012
Median Absolute Deviation (MAD)9.4138733 × 1010
Skewness-0.54572744
Sum1.529989 × 1014
Variance2.7484492 × 1022
MonotonicityNot monotonic
2024-05-04T02:35:35.300219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1910152032122 1
 
1.1%
1910121035119 1
 
1.1%
1605202010045 1
 
1.1%
1608101747053 1
 
1.1%
1608101748054 1
 
1.1%
1608101748055 1
 
1.1%
1608152142056 1
 
1.1%
1608152143057 1
 
1.1%
1608152143058 1
 
1.1%
1608152144059 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
1507081058023 1
1.1%
1507081103024 1
1.1%
1507081437026 1
1.1%
1507092021027 1
1.1%
1507092023028 1
1.1%
1507092026029 1
1.1%
1507092038030 1
1.1%
1507151510031 1
1.1%
1507151511032 1
1.1%
1507151638033 1
1.1%
ValueCountFrequency (%)
1910211415124 1
1.1%
1910211349123 1
1.1%
1910152032122 1
1.1%
1910131526121 1
1.1%
1910131524120 1
1.1%
1910121035119 1
1.1%
1910121035118 1
1.1%
1909231034117 1
1.1%
1909231034116 1
1.1%
1909231034115 1
1.1%

점수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size828.0 B
5
40 
4
21 
3
16 
1
6 
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 40
46.0%
4 21
24.1%
3 16
 
18.4%
1 6
 
6.9%
2 4
 
4.6%

Length

2024-05-04T02:35:35.720962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:35:36.045393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 40
46.0%
4 21
24.1%
3 16
 
18.4%
1 6
 
6.9%
2 4
 
4.6%

사유
Text

Distinct72
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-05-04T02:35:36.560294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length286
Median length113
Mean length29.804598
Min length2

Characters and Unicode

Total characters2593
Distinct characters304
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)73.6%

Sample

1st rowuyt
2nd rowㅌㅊ
3rd rowㄴㅁㅇ
4th rowㅑㅐㅔㅔ
5th rowㅑㅐㅔㅔ
ValueCountFrequency (%)
좋아요 7
 
1.2%
수 7
 
1.2%
옥상에 6
 
1.1%
것이 6
 
1.1%
좋은 5
 
0.9%
발전을 4
 
0.7%
해당되며 4
 
0.7%
전형적인 4
 
0.7%
할 4
 
0.7%
테스트 4
 
0.7%
Other values (328) 517
91.0%
2024-05-04T02:35:37.383418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
490
 
18.9%
이 71
 
2.7%
지 46
 
1.8%
다 42
 
1.6%
을 42
 
1.6%
에 41
 
1.6%
하 39
 
1.5%
. 35
 
1.3%
ㅁ 35
 
1.3%
사 33
 
1.3%
Other values (294) 1719
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1978
76.3%
Space Separator 490
 
18.9%
Other Punctuation 42
 
1.6%
Decimal Number 37
 
1.4%
Lowercase Letter 35
 
1.3%
Uppercase Letter 9
 
0.3%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 71
 
3.6%
지 46
 
2.3%
다 42
 
2.1%
을 42
 
2.1%
에 41
 
2.1%
하 39
 
2.0%
ㅁ 35
 
1.8%
사 33
 
1.7%
니 32
 
1.6%
아 29
 
1.5%
Other values (270) 1568
79.3%
Lowercase Letter
ValueCountFrequency (%)
a 8
22.9%
f 6
17.1%
d 5
14.3%
s 5
14.3%
k 3
 
8.6%
w 3
 
8.6%
t 2
 
5.7%
e 1
 
2.9%
u 1
 
2.9%
y 1
 
2.9%
Decimal Number
ValueCountFrequency (%)
1 14
37.8%
2 11
29.7%
3 6
16.2%
0 3
 
8.1%
4 3
 
8.1%
Other Punctuation
ValueCountFrequency (%)
. 35
83.3%
, 5
 
11.9%
! 2
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
C 4
44.4%
T 3
33.3%
V 2
22.2%
Space Separator
ValueCountFrequency (%)
490
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1978
76.3%
Common 571
 
22.0%
Latin 44
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 71
 
3.6%
지 46
 
2.3%
다 42
 
2.1%
을 42
 
2.1%
에 41
 
2.1%
하 39
 
2.0%
ㅁ 35
 
1.8%
사 33
 
1.7%
니 32
 
1.6%
아 29
 
1.5%
Other values (270) 1568
79.3%
Latin
ValueCountFrequency (%)
a 8
18.2%
f 6
13.6%
d 5
11.4%
s 5
11.4%
C 4
9.1%
T 3
 
6.8%
k 3
 
6.8%
w 3
 
6.8%
V 2
 
4.5%
t 2
 
4.5%
Other values (3) 3
 
6.8%
Common
ValueCountFrequency (%)
490
85.8%
. 35
 
6.1%
1 14
 
2.5%
2 11
 
1.9%
3 6
 
1.1%
, 5
 
0.9%
0 3
 
0.5%
4 3
 
0.5%
! 2
 
0.4%
( 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1853
71.5%
ASCII 615
 
23.7%
Compat Jamo 125
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
490
79.7%
. 35
 
5.7%
1 14
 
2.3%
2 11
 
1.8%
a 8
 
1.3%
f 6
 
1.0%
3 6
 
1.0%
, 5
 
0.8%
d 5
 
0.8%
s 5
 
0.8%
Other values (14) 30
 
4.9%
Hangul
ValueCountFrequency (%)
이 71
 
3.8%
지 46
 
2.5%
다 42
 
2.3%
을 42
 
2.3%
에 41
 
2.2%
하 39
 
2.1%
사 33
 
1.8%
니 32
 
1.7%
아 29
 
1.6%
는 28
 
1.5%
Other values (256) 1450
78.3%
Compat Jamo
ValueCountFrequency (%)
ㅁ 35
28.0%
ㄴ 27
21.6%
ㅇ 26
20.8%
ㄹ 17
13.6%
ㅔ 4
 
3.2%
ㅂ 3
 
2.4%
ㅀ 2
 
1.6%
ㄱ 2
 
1.6%
ㅛ 2
 
1.6%
ㅑ 2
 
1.6%
Other values (4) 5
 
4.0%

이미지url
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing83
Missing (%)95.4%
Memory size828.0 B
2024-05-04T02:35:37.695508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters88
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row1542377608290510712087
2nd row1523599566475169819988
3rd row1436945923716513318867
4th row1436940689431284711638
ValueCountFrequency (%)
1542377608290510712087 1
25.0%
1523599566475169819988 1
25.0%
1436945923716513318867 1
25.0%
1436940689431284711638 1
25.0%
2024-05-04T02:35:38.412269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
15.9%
6 10
11.4%
8 10
11.4%
9 10
11.4%
3 9
10.2%
5 8
9.1%
4 8
9.1%
7 8
9.1%
2 6
6.8%
0 5
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
15.9%
6 10
11.4%
8 10
11.4%
9 10
11.4%
3 9
10.2%
5 8
9.1%
4 8
9.1%
7 8
9.1%
2 6
6.8%
0 5
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
15.9%
6 10
11.4%
8 10
11.4%
9 10
11.4%
3 9
10.2%
5 8
9.1%
4 8
9.1%
7 8
9.1%
2 6
6.8%
0 5
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
15.9%
6 10
11.4%
8 10
11.4%
9 10
11.4%
3 9
10.2%
5 8
9.1%
4 8
9.1%
7 8
9.1%
2 6
6.8%
0 5
 
5.7%

이미지타입
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing83
Missing (%)95.4%
Memory size828.0 B
2024-05-04T02:35:38.865775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters88
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row1542377608290510712087
2nd row1523599566475169819988
3rd row1436945923716513318867
4th row1436940689431284711638
ValueCountFrequency (%)
1542377608290510712087 1
25.0%
1523599566475169819988 1
25.0%
1436945923716513318867 1
25.0%
1436940689431284711638 1
25.0%
2024-05-04T02:35:39.703015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
15.9%
6 10
11.4%
8 10
11.4%
9 10
11.4%
3 9
10.2%
5 8
9.1%
4 8
9.1%
7 8
9.1%
2 6
6.8%
0 5
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
15.9%
6 10
11.4%
8 10
11.4%
9 10
11.4%
3 9
10.2%
5 8
9.1%
4 8
9.1%
7 8
9.1%
2 6
6.8%
0 5
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
15.9%
6 10
11.4%
8 10
11.4%
9 10
11.4%
3 9
10.2%
5 8
9.1%
4 8
9.1%
7 8
9.1%
2 6
6.8%
0 5
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
15.9%
6 10
11.4%
8 10
11.4%
9 10
11.4%
3 9
10.2%
5 8
9.1%
4 8
9.1%
7 8
9.1%
2 6
6.8%
0 5
 
5.7%

이미지명
Text

MISSING 

Distinct8
Distinct (%)80.0%
Missing77
Missing (%)88.5%
Memory size828.0 B
2024-05-04T02:35:40.066885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length11.6
Min length7

Characters and Unicode

Total characters116
Distinct characters43
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

Unique7 ?
Unique (%)70.0%

Sample

1st rown07.PNG
2nd rown07.PNG
3rd rowexternalFile_Ok.jpg
4th rown07.PNG
5th row다운로드.jpg
ValueCountFrequency (%)
n07.png 3
30.0%
externalfile_ok.jpg 1
 
10.0%
다운로드.jpg 1
 
10.0%
new_star_on.png 1
 
10.0%
desert.jpg 1
 
10.0%
chrysanthemum.jpg 1
 
10.0%
20150715_163747.jpg 1
 
10.0%
캡처3.jpg 1
 
10.0%
2024-05-04T02:35:40.855119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 10
 
8.6%
n 8
 
6.9%
e 7
 
6.0%
7 6
 
5.2%
g 6
 
5.2%
p 6
 
5.2%
0 5
 
4.3%
j 5
 
4.3%
_ 4
 
3.4%
P 4
 
3.4%
Other values (33) 55
47.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59
50.9%
Decimal Number 21
 
18.1%
Uppercase Letter 16
 
13.8%
Other Punctuation 10
 
8.6%
Other Letter 6
 
5.2%
Connector Punctuation 4
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 8
13.6%
e 7
11.9%
g 6
10.2%
p 6
10.2%
j 5
8.5%
t 4
 
6.8%
r 4
 
6.8%
s 3
 
5.1%
a 3
 
5.1%
h 2
 
3.4%
Other values (9) 11
18.6%
Decimal Number
ValueCountFrequency (%)
7 6
28.6%
0 5
23.8%
1 3
14.3%
3 2
 
9.5%
5 2
 
9.5%
2 1
 
4.8%
4 1
 
4.8%
6 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
P 4
25.0%
G 4
25.0%
N 3
18.8%
C 1
 
6.2%
D 1
 
6.2%
O 1
 
6.2%
F 1
 
6.2%
J 1
 
6.2%
Other Letter
ValueCountFrequency (%)
캡 1
16.7%
처 1
16.7%
다 1
16.7%
드 1
16.7%
로 1
16.7%
운 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75
64.7%
Common 35
30.2%
Hangul 6
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 8
 
10.7%
e 7
 
9.3%
g 6
 
8.0%
p 6
 
8.0%
j 5
 
6.7%
P 4
 
5.3%
G 4
 
5.3%
t 4
 
5.3%
r 4
 
5.3%
s 3
 
4.0%
Other values (17) 24
32.0%
Common
ValueCountFrequency (%)
. 10
28.6%
7 6
17.1%
0 5
14.3%
_ 4
 
11.4%
1 3
 
8.6%
3 2
 
5.7%
5 2
 
5.7%
2 1
 
2.9%
4 1
 
2.9%
6 1
 
2.9%
Hangul
ValueCountFrequency (%)
캡 1
16.7%
처 1
16.7%
다 1
16.7%
드 1
16.7%
로 1
16.7%
운 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110
94.8%
Hangul 6
 
5.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 10
 
9.1%
n 8
 
7.3%
e 7
 
6.4%
7 6
 
5.5%
g 6
 
5.5%
p 6
 
5.5%
0 5
 
4.5%
j 5
 
4.5%
_ 4
 
3.6%
P 4
 
3.6%
Other values (27) 49
44.5%
Hangul
ValueCountFrequency (%)
캡 1
16.7%
처 1
16.7%
다 1
16.7%
드 1
16.7%
로 1
16.7%
운 1
16.7%

이미지사이즈
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)80.0%
Missing77
Missing (%)88.5%
Infinite0
Infinite (%)0.0%
Mean515019.3
Minimum523
Maximum2763542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-05-04T02:35:41.204728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum523
5-th percentile2881.9
Q197352.5
median107065
Q3694385.25
95-th percentile1915675.4
Maximum2763542
Range2763019
Interquartile range (IQR)597032.75

Descriptive statistics

Standard deviation854687.09
Coefficient of variation (CV)1.6595244
Kurtosis6.233137
Mean515019.3
Median Absolute Deviation (MAD)103921
Skewness2.421543
Sum5150193
Variance7.3049002 × 1011
MonotonicityNot monotonic
2024-05-04T02:35:41.862195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
107065 3
 
3.4%
239718 1
 
1.1%
5765 1
 
1.1%
523 1
 
1.1%
845941 1
 
1.1%
879394 1
 
1.1%
2763542 1
 
1.1%
94115 1
 
1.1%
(Missing) 77
88.5%
ValueCountFrequency (%)
523 1
 
1.1%
5765 1
 
1.1%
94115 1
 
1.1%
107065 3
3.4%
239718 1
 
1.1%
845941 1
 
1.1%
879394 1
 
1.1%
2763542 1
 
1.1%
ValueCountFrequency (%)
2763542 1
 
1.1%
879394 1
 
1.1%
845941 1
 
1.1%
239718 1
 
1.1%
107065 3
3.4%
94115 1
 
1.1%
5765 1
 
1.1%
523 1
 
1.1%

변환파일명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size828.0 B
유**
23 
김**
22 
이**
11 
전**
5 
홍**
5 
Other values (11)
21 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)6.9%

Sample

1st rowa**
2nd row전**
3rd row전**
4th row전**
5th row전**

Common Values

ValueCountFrequency (%)
유** 23
26.4%
김** 22
25.3%
이** 11
12.6%
전** 5
 
5.7%
홍** 5
 
5.7%
윤** 4
 
4.6%
최** 4
 
4.6%
박** 3
 
3.4%
남** 2
 
2.3%
장** 2
 
2.3%
Other values (6) 6
 
6.9%

Length

2024-05-04T02:35:42.245048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
유 23
26.4%
김 22
25.3%
이 11
12.6%
전 5
 
5.7%
홍 5
 
5.7%
윤 4
 
4.6%
최 4
 
4.6%
박 3
 
3.4%
남 2
 
2.3%
장 2
 
2.3%
Other values (6) 6
 
6.9%

등록일
Date

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size828.0 B
Minimum2015-07-08 10:58:52
Maximum2019-10-21 14:15:19
2024-05-04T02:35:42.624485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:43.117514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수정id
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing87
Missing (%)100.0%
Memory size915.0 B

수정일
Date

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size828.0 B
Minimum2015-07-08 11:03:56
Maximum2019-10-21 17:30:29
2024-05-04T02:35:43.541292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:43.965423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size219.0 B
False
61 
True
26 
ValueCountFrequency (%)
False 61
70.1%
True 26
29.9%
2024-05-04T02:35:44.301718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2024-05-04T02:35:28.759283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:26.914728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:27.948289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:29.020206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:27.269120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:28.195755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:29.391481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:27.616575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:35:28.501242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T02:35:44.524711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계년도제안번호평가번호점수사유이미지url이미지타입이미지명이미지사이즈변환파일명등록일수정일사용여부
회계년도1.0000.8180.9860.6340.8471.0001.0001.0000.7110.8331.0001.0000.523
제안번호0.8181.0000.7150.5290.8931.0001.0001.0000.2350.7941.0001.0000.768
평가번호0.9860.7151.0000.5850.8921.0001.0001.0000.9710.9471.0001.0000.579
점수0.6340.5290.5851.0000.8681.0001.0000.7600.0000.7851.0001.0000.323
사유0.8470.8930.8920.8681.0001.0001.0000.7820.6280.9711.0001.0000.543
이미지url1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN
이미지타입1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN
이미지명1.0001.0001.0000.7600.7821.0001.0001.0001.0001.0001.0001.000NaN
이미지사이즈0.7110.2350.9710.0000.6281.0001.0001.0001.0000.6831.0001.000NaN
변환파일명0.8330.7940.9470.7850.9711.0001.0001.0000.6831.0001.0001.0000.857
등록일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
수정일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사용여부0.5230.7680.5790.3230.543NaNNaNNaNNaN0.8571.0001.0001.000
2024-05-04T02:35:44.943564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계년도변환파일명점수사용여부
회계년도1.0000.5720.2810.622
변환파일명0.5721.0000.5090.647
점수0.2810.5091.0000.387
사용여부0.6220.6470.3871.000
2024-05-04T02:35:45.219854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제안번호평가번호이미지사이즈회계년도점수변환파일명사용여부
제안번호1.000-0.2590.4110.6720.3520.3860.571
평가번호-0.2591.000-0.4480.8320.2490.6530.681
이미지사이즈0.411-0.4481.0000.5320.0000.0001.000
회계년도0.6720.8320.5321.0000.2810.5720.622
점수0.3520.2490.0000.2811.0000.5090.387
변환파일명0.3860.6530.0000.5720.5091.0000.647
사용여부0.5710.6811.0000.6220.3870.6471.000

Missing values

2024-05-04T02:35:29.889312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T02:35:30.810024image/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.
2024-05-04T02:35:31.654435image/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

회계년도제안번호평가번호점수사유이미지url이미지타입이미지명이미지사이즈변환파일명등록일수정id수정일사용여부
02018118419101520321225uyt<NA><NA><NA><NA>a**2019-10-15 20:32:27.0<NA>2019-10-15 20:32:27.0Y
12018118419101210351194ㅌㅊ<NA><NA><NA><NA>전**2019-10-12 10:35:32.0<NA>2019-10-12 10:35:47.0N
22018118419101210351184ㄴㅁㅇ<NA><NA><NA><NA>전**2019-10-12 10:35:06.0<NA>2019-10-12 10:35:43.0N
32018118419092310341175ㅑㅐㅔㅔ<NA><NA><NA><NA>전**2019-09-23 10:34:55.0<NA>2019-09-23 10:34:59.0N
42018112419092310341165ㅑㅐㅔㅔ<NA><NA><NA><NA>전**2019-09-23 10:34:37.0<NA>2019-09-23 10:34:41.0N
52018112419092310341153ㅀㅎㅀ<NA><NA><NA><NA>전**2019-09-23 10:34:03.0<NA>2019-09-23 10:34:22.0N
6201810919053010461143aaa<NA><NA><NA><NA>김**2019-05-30 10:46:01.0<NA>2019-05-30 10:46:06.0N
72018110619050316331134ㅁㄴㅇㄹ<NA><NA><NA><NA>김**2019-05-03 16:33:40.0<NA>2019-05-03 16:33:44.0N
82018110619050316321123ㄱㄱ<NA><NA><NA><NA>김**2019-05-03 16:32:41.0<NA>2019-05-03 16:33:24.0N
92018112419050218061115123123<NA><NA>n07.PNG107065유**2019-05-02 18:06:15.0<NA>2019-08-22 15:16:52.0N
회계년도제안번호평가번호점수사유이미지url이미지타입이미지명이미지사이즈변환파일명등록일수정id수정일사용여부
77201499415071516380333좋아요<NA><NA><NA><NA>김**2015-07-15 16:38:01.0<NA>2015-07-15 16:38:55.0N
782014173315071515110323좋아요14369406894312847116381436940689431284711638캡처3.JPG94115장**2015-07-15 15:11:29.0<NA>2015-07-15 15:11:35.0N
792014173315071515100313좋아요<NA><NA><NA><NA>장**2015-07-15 15:10:47.0<NA>2015-07-15 15:10:57.0N
802014169915070920380301CCTV는 감시카메라로 범죄예방과는 상관이 적으며 효과 입증이 안되어 있는 상태에서 지속적인 설치는 계속사업으로 부적격 사업에 해당되며 전형적인 예산낭비임 도로에 CCTV를 설치하면 범죄인의 양심이 정화된다면 범죄예방효과가 있을 수 있다. 범죄예방은 인성교육을 강화와 시민 신고정신향상과 순찰활동이 저비용이며 더 효과적임<NA><NA><NA><NA>최**2015-07-09 20:38:15.0<NA>2015-07-09 20:38:15.0Y
812014130915070920260291사업내용과 같이 서울시주민참여예산으로 성북 구의회건물 옥상에 에너지교육과 전기판매 수익을 복지에 사용하겠다고 예산 받아 설치하였습니다. 상기 사업은 중복사업에 해당되며 전형적인 예산낭비 입니다. 서울에 얼마 남지않은 산정상을 훼손하여 구의회등 공공시설을 건축하는 것도 이해가 안되지만 옥상에 인공구조물을 추가로 설치 할 것이 아니라 녹지 공간으로 조성하여 공기질을 개선하고 여름에 냉방효율을 높이고 거울에 난방비를 줄이는 것이 더이익입니다. 태양광발전기 10kw정도로는 수익성이 없는 것은 이미 판명되었습니다.<NA><NA><NA><NA>최**2015-07-09 20:26:23.0<NA>2015-07-09 20:26:23.0Y
822014130915070920230281사업내용과 같이 서울시주민참여예산으로 성북 구의회건물 옥상에 에너지교육과 전기판매 수익을 복지에 사용하겠다고 예산 받아 설치하였습니다. 상기 사업은 중복사업에 해당되며 전형적인 예산낭비 입니다. 서울에 얼마 남지않은 산정상을 훼손하여 구의회등 공공시설을 건축하는 것도 이해가 안되지만 옥상에 인공구조물을 추가로 설치 할 것이 아니라 녹지 공간으로 조성하여 공기질을 개선하고 여름에 냉방효율을 높이고 거울에 난방비를 줄이는 것이 더이익입니다. 태양광발전기 10kw정도로는 수익성이 없는 것은 이미 판명되었습니다.<NA><NA><NA><NA>최**2015-07-09 20:23:38.0<NA>2015-07-09 20:24:04.0N
832014130915070920210275사업내용과 같이 서울시주민참여예산으로 성북 구의회건물 옥상에 에너지교육과 전기판매 수익을 복지에 사용하겠다고 예산 받아 설치하였습니다. 상기 사업은 중복사업에 해당되며 전형적인 예산낭비 입니다. 서울에 얼마 남지않은 산정상을 훼손하여 구의회등 공공시설을 건축하는 것도 이해가 안되지만 옥상에 인공구조물을 추가로 설치 할 것이 아니라 녹지 공간으로 조성하여 공기질을 개선하고 여름에 냉방효율을 높이고 거울에 난방비를 줄이는 것이 더이익입니다. 태양광발전기 10kw정도로는 수익성이 없는 것은 이미 판명되었습니다.<NA><NA><NA><NA>최**2015-07-09 20:21:40.0<NA>2015-07-09 20:23:22.0N
84201499415070814370265꿈과희망...<NA><NA><NA><NA>이**2015-07-08 14:37:30.0<NA>2015-07-08 14:37:30.0N
852014153715070811030243아이들을 위한 제안 좋습니다.<NA><NA><NA><NA>r**2015-07-08 11:03:56.0<NA>2015-07-08 11:03:56.0N
862014153715070810580233아이들을 위한거라니 좋은 제안입니다<NA><NA><NA><NA>곽**2015-07-08 10:58:52.0<NA>2016-01-05 13:49:38.0N