Overview

Dataset statistics

Number of variables9
Number of observations59
Missing cells300
Missing cells (%)56.5%
Duplicate rows1
Duplicate rows (%)1.7%
Total size in memory4.3 KiB
Average record size in memory75.2 B

Variable types

Unsupported3
Text5
Categorical1

Dataset

Description웹기반 산불위험예보시스템이란 지리정보시스템을 이용하여 전국 각 지역별 지형조건, 산림의 상황과 기상청에서 예보하는 온도, 습도, 풍속 등 기상조건을 실시간으로 종합분석, 산불위험도가 높은 지역을 예측하여 4가지 등급의 경보 기준을 예보하는 운영 시스템
Author국립산림과학원
URLhttps://www.vworld.kr/dtmk/dtmk_ntads_s002.do?dsId=30560

Alerts

Unnamed: 8 has constant value ""Constant
Dataset has 1 (1.7%) duplicate rowsDuplicates
테이블정의서 has 1 (1.7%) missing valuesMissing
Unnamed: 1 has 6 (10.2%) missing valuesMissing
Unnamed: 2 has 57 (96.6%) missing valuesMissing
Unnamed: 4 has 6 (10.2%) missing valuesMissing
Unnamed: 5 has 59 (100.0%) missing valuesMissing
Unnamed: 6 has 56 (94.9%) missing valuesMissing
Unnamed: 7 has 57 (96.6%) missing valuesMissing
Unnamed: 8 has 58 (98.3%) missing valuesMissing
테이블정의서 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-21 14:00:51.793517
Analysis finished2024-04-21 14:00:52.796635
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

테이블정의서
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)1.7%
Memory size600.0 B

Unnamed: 1
Text

MISSING 

Distinct53
Distinct (%)100.0%
Missing6
Missing (%)10.2%
Memory size600.0 B
2024-04-21T23:00:53.496447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.5660377
Min length2

Characters and Unicode

Total characters401
Distinct characters27
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)100.0%

Sample

1st row컬럼ID
2nd rowXID
3rd rowID
4th rowID_NM
5th rowYMD
ValueCountFrequency (%)
value03h 1
 
1.9%
value11h 1
 
1.9%
class11h 1
 
1.9%
value12h 1
 
1.9%
class12h 1
 
1.9%
value13h 1
 
1.9%
class13h 1
 
1.9%
value14h 1
 
1.9%
class14h 1
 
1.9%
value15h 1
 
1.9%
Other values (43) 43
81.1%
2024-04-21T23:00:54.522388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 48
12.0%
L 48
12.0%
H 48
12.0%
S 48
12.0%
0 26
6.5%
1 26
6.5%
V 24
 
6.0%
U 24
 
6.0%
E 24
 
6.0%
C 24
 
6.0%
Other values (17) 61
15.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 302
75.3%
Decimal Number 96
 
23.9%
Other Letter 2
 
0.5%
Connector Punctuation 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 48
15.9%
L 48
15.9%
H 48
15.9%
S 48
15.9%
V 24
7.9%
U 24
7.9%
E 24
7.9%
C 24
7.9%
D 5
 
1.7%
I 4
 
1.3%
Other values (4) 5
 
1.7%
Decimal Number
ValueCountFrequency (%)
0 26
27.1%
1 26
27.1%
2 14
14.6%
3 6
 
6.2%
9 4
 
4.2%
7 4
 
4.2%
4 4
 
4.2%
5 4
 
4.2%
6 4
 
4.2%
8 4
 
4.2%
Other Letter
ValueCountFrequency (%)
컬 1
50.0%
럼 1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 302
75.3%
Common 97
 
24.2%
Hangul 2
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 48
15.9%
L 48
15.9%
H 48
15.9%
S 48
15.9%
V 24
7.9%
U 24
7.9%
E 24
7.9%
C 24
7.9%
D 5
 
1.7%
I 4
 
1.3%
Other values (4) 5
 
1.7%
Common
ValueCountFrequency (%)
0 26
26.8%
1 26
26.8%
2 14
14.4%
3 6
 
6.2%
9 4
 
4.1%
7 4
 
4.1%
4 4
 
4.1%
5 4
 
4.1%
6 4
 
4.1%
8 4
 
4.1%
Hangul
ValueCountFrequency (%)
컬 1
50.0%
럼 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399
99.5%
Hangul 2
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 48
12.0%
L 48
12.0%
H 48
12.0%
S 48
12.0%
0 26
6.5%
1 26
6.5%
V 24
6.0%
U 24
6.0%
E 24
6.0%
C 24
6.0%
Other values (15) 59
14.8%
Hangul
ValueCountFrequency (%)
컬 1
50.0%
럼 1
50.0%

Unnamed: 2
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing57
Missing (%)96.6%
Memory size600.0 B
2024-04-21T23:00:54.931115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters7
Distinct characters7
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

Unique2 ?
Unique (%)100.0%

Sample

1st row컬럼명
2nd row인덱스키
ValueCountFrequency (%)
컬럼명 1
50.0%
인덱스키 1
50.0%
2024-04-21T23:00:55.529300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
컬 1
14.3%
럼 1
14.3%
명 1
14.3%
인 1
14.3%
덱 1
14.3%
스 1
14.3%
키 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
컬 1
14.3%
럼 1
14.3%
명 1
14.3%
인 1
14.3%
덱 1
14.3%
스 1
14.3%
키 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
컬 1
14.3%
럼 1
14.3%
명 1
14.3%
인 1
14.3%
덱 1
14.3%
스 1
14.3%
키 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
컬 1
14.3%
럼 1
14.3%
명 1
14.3%
인 1
14.3%
덱 1
14.3%
스 1
14.3%
키 1
14.3%

Unnamed: 3
Categorical

Distinct6
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size600.0 B
VARCHAR
27 
NUMERIC
24 
<NA>
5 
테이블ID
 
1
타입
 
1

Length

Max length7
Median length7
Mean length6.6271186
Min length2

Unique

Unique3 ?
Unique (%)5.1%

Sample

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

Common Values

ValueCountFrequency (%)
VARCHAR 27
45.8%
NUMERIC 24
40.7%
<NA> 5
 
8.5%
테이블ID 1
 
1.7%
타입 1
 
1.7%
INTEGER 1
 
1.7%

Length

2024-04-21T23:00:55.740448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T23:00:55.931237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
varchar 27
45.8%
numeric 24
40.7%
na 5
 
8.5%
테이블id 1
 
1.7%
타입 1
 
1.7%
integer 1
 
1.7%

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6
Missing (%)10.2%
Memory size600.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing59
Missing (%)100.0%
Memory size659.0 B

Unnamed: 6
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing56
Missing (%)94.9%
Memory size600.0 B
2024-04-21T23:00:56.373436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4
Min length3

Characters and Unicode

Total characters12
Distinct characters11
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row작성일
2nd row테이블명
3rd rowPK/FK
ValueCountFrequency (%)
작성일 1
33.3%
테이블명 1
33.3%
pk/fk 1
33.3%
2024-04-21T23:00:57.041054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 2
16.7%
작 1
8.3%
성 1
8.3%
일 1
8.3%
테 1
8.3%
이 1
8.3%
블 1
8.3%
명 1
8.3%
P 1
8.3%
/ 1
8.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7
58.3%
Uppercase Letter 4
33.3%
Other Punctuation 1
 
8.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
작 1
14.3%
성 1
14.3%
일 1
14.3%
테 1
14.3%
이 1
14.3%
블 1
14.3%
명 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
K 2
50.0%
P 1
25.0%
F 1
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7
58.3%
Latin 4
33.3%
Common 1
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
작 1
14.3%
성 1
14.3%
일 1
14.3%
테 1
14.3%
이 1
14.3%
블 1
14.3%
명 1
14.3%
Latin
ValueCountFrequency (%)
K 2
50.0%
P 1
25.0%
F 1
25.0%
Common
ValueCountFrequency (%)
/ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7
58.3%
ASCII 5
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 2
40.0%
P 1
20.0%
/ 1
20.0%
F 1
20.0%
Hangul
ValueCountFrequency (%)
작 1
14.3%
성 1
14.3%
일 1
14.3%
테 1
14.3%
이 1
14.3%
블 1
14.3%
명 1
14.3%

Unnamed: 7
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing57
Missing (%)96.6%
Memory size600.0 B
2024-04-21T23:00:57.477645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length7.5
Min length7

Characters and Unicode

Total characters15
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row산불위험예측지도
2nd rowDefault
ValueCountFrequency (%)
산불위험예측지도 1
50.0%
default 1
50.0%
2024-04-21T23:00:58.111381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 1
 
6.7%
불 1
 
6.7%
위 1
 
6.7%
험 1
 
6.7%
예 1
 
6.7%
측 1
 
6.7%
지 1
 
6.7%
도 1
 
6.7%
D 1
 
6.7%
e 1
 
6.7%
Other values (5) 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8
53.3%
Lowercase Letter 6
40.0%
Uppercase Letter 1
 
6.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1
12.5%
불 1
12.5%
위 1
12.5%
험 1
12.5%
예 1
12.5%
측 1
12.5%
지 1
12.5%
도 1
12.5%
Lowercase Letter
ValueCountFrequency (%)
e 1
16.7%
f 1
16.7%
a 1
16.7%
u 1
16.7%
l 1
16.7%
t 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
D 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8
53.3%
Latin 7
46.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1
12.5%
불 1
12.5%
위 1
12.5%
험 1
12.5%
예 1
12.5%
측 1
12.5%
지 1
12.5%
도 1
12.5%
Latin
ValueCountFrequency (%)
D 1
14.3%
e 1
14.3%
f 1
14.3%
a 1
14.3%
u 1
14.3%
l 1
14.3%
t 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8
53.3%
ASCII 7
46.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 1
12.5%
불 1
12.5%
위 1
12.5%
험 1
12.5%
예 1
12.5%
측 1
12.5%
지 1
12.5%
도 1
12.5%
ASCII
ValueCountFrequency (%)
D 1
14.3%
e 1
14.3%
f 1
14.3%
a 1
14.3%
u 1
14.3%
l 1
14.3%
t 1
14.3%

Unnamed: 8
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing58
Missing (%)98.3%
Memory size600.0 B
2024-04-21T23:00:58.530320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row참조테이블명/비고
ValueCountFrequency (%)
참조테이블명/비고 1
100.0%
2024-04-21T23:00:59.409981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
참 1
11.1%
조 1
11.1%
테 1
11.1%
이 1
11.1%
블 1
11.1%
명 1
11.1%
/ 1
11.1%
비 1
11.1%
고 1
11.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8
88.9%
Other Punctuation 1
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
참 1
12.5%
조 1
12.5%
테 1
12.5%
이 1
12.5%
블 1
12.5%
명 1
12.5%
비 1
12.5%
고 1
12.5%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8
88.9%
Common 1
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
참 1
12.5%
조 1
12.5%
테 1
12.5%
이 1
12.5%
블 1
12.5%
명 1
12.5%
비 1
12.5%
고 1
12.5%
Common
ValueCountFrequency (%)
/ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8
88.9%
ASCII 1
 
11.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
참 1
12.5%
조 1
12.5%
테 1
12.5%
이 1
12.5%
블 1
12.5%
명 1
12.5%
비 1
12.5%
고 1
12.5%
ASCII
ValueCountFrequency (%)
/ 1
100.0%

Correlations

2024-04-21T23:00:59.653820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 6Unnamed: 7
Unnamed: 11.000NaN1.000NaNNaN
Unnamed: 2NaN1.000NaNNaNNaN
Unnamed: 31.000NaN1.0000.0000.000
Unnamed: 6NaNNaN0.0001.0000.000
Unnamed: 7NaNNaN0.0000.0001.000

Missing values

2024-04-21T23:00:52.168128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T23:00:52.404798image/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.
2024-04-21T23:00:52.632074image/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

테이블정의서Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0작성자<NA><NA><NA>NaN<NA>작성일<NA><NA>
1주제영역명<NA><NA>테이블IDZ_KFDRS_SIGU_GRADE_NEW<NA>테이블명산불위험예측지도<NA>
2테이블설명<NA><NA><NA>NaN<NA><NA><NA><NA>
3No컬럼ID컬럼명타입길이(Byte)<NA>PK/FKDefault참조테이블명/비고
41XID<NA>INTEGERNaN<NA><NA><NA><NA>
52ID<NA>VARCHAR12<NA><NA><NA><NA>
63ID_NM<NA>VARCHAR52<NA><NA><NA><NA>
74YMD<NA>VARCHAR10<NA><NA><NA><NA>
85VALUE00H<NA>NUMERIC23<NA><NA><NA><NA>
96CLASS00H<NA>VARCHAR12<NA><NA><NA><NA>
테이블정의서Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
4946CLASS20H<NA>VARCHAR12<NA><NA><NA><NA>
5047VALUE21H<NA>NUMERIC23<NA><NA><NA><NA>
5148CLASS21H<NA>VARCHAR12<NA><NA><NA><NA>
5249VALUE22H<NA>NUMERIC23<NA><NA><NA><NA>
5350CLASS22H<NA>VARCHAR12<NA><NA><NA><NA>
5451VALUE23H<NA>NUMERIC23<NA><NA><NA><NA>
5552CLASS23H<NA>VARCHAR12<NA><NA><NA><NA>
56인덱스명<NA>인덱스키<NA>NaN<NA><NA><NA><NA>
57NaN<NA><NA><NA>NaN<NA><NA><NA><NA>
58업무규칙<NA><NA><NA>NaN<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 6Unnamed: 7Unnamed: 8# duplicates
0<NA><NA><NA><NA><NA><NA>3