Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells9737
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory124.0 B

Variable types

Numeric4
Categorical7
Text3

Dataset

Description해당 자료는 제주특별자치도 내 등록되어 있는 자연재해 중 2018년 태풍콩레이에 의한 피해 현황을 담고 있으며, 세부적으로는 행정동 정보 등 포함하고 있습니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15110871/fileData.do

Alerts

일반정보(피해구분) has constant value ""Constant
데이터기준일자 has constant value ""Constant
일반정보(피해종류2) is highly overall correlated with 일반정보(피해종류1) and 2 other fieldsHigh correlation
일반정보(피해종류3) is highly overall correlated with 일반정보(피해종류1) and 2 other fieldsHigh correlation
일반정보(피해종류1) is highly overall correlated with 일반정보(피해종류2) and 2 other fieldsHigh correlation
번호 is highly overall correlated with 위도High correlation
행정동코드 is highly overall correlated with 일반정보(피해요인) and 1 other fieldsHigh correlation
위도 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 비고 외High correlation
일반정보(피해요인) is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
비고 외 is highly overall correlated with 행정동코드 and 6 other fieldsHigh correlation
일반정보(피해종류1) is highly imbalanced (99.1%)Imbalance
일반정보(피해종류2) is highly imbalanced (79.8%)Imbalance
일반정보(피해종류3) is highly imbalanced (63.3%)Imbalance
비고 외 is highly imbalanced (99.1%)Imbalance
소재지 도로명주소 has 9737 (97.4%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:45:01.520663
Analysis finished2023-12-12 16:45:05.555337
Duration4.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8582.9348
Minimum1
Maximum17247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:45:05.656114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile820.95
Q14272.25
median8588.5
Q312900.25
95-th percentile16335.05
Maximum17247
Range17246
Interquartile range (IQR)8628

Descriptive statistics

Standard deviation4971.9525
Coefficient of variation (CV)0.57928349
Kurtosis-1.1958171
Mean8582.9348
Median Absolute Deviation (MAD)4313.5
Skewness0.0018112626
Sum85829348
Variance24720311
MonotonicityNot monotonic
2023-12-13T01:45:05.820723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14522 1
 
< 0.1%
16606 1
 
< 0.1%
3503 1
 
< 0.1%
7399 1
 
< 0.1%
13126 1
 
< 0.1%
13593 1
 
< 0.1%
14308 1
 
< 0.1%
16108 1
 
< 0.1%
7662 1
 
< 0.1%
5656 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
22 1
< 0.1%
ValueCountFrequency (%)
17247 1
< 0.1%
17246 1
< 0.1%
17245 1
< 0.1%
17244 1
< 0.1%
17243 1
< 0.1%
17241 1
< 0.1%
17239 1
< 0.1%
17237 1
< 0.1%
17236 1
< 0.1%
17232 1
< 0.1%

일반정보(피해종류1)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농작물
9988 
농경지
 
10
농림시설
 
2

Length

Max length4
Median length3
Mean length3.0002
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농작물 9988
99.9%
농경지 10
 
0.1%
농림시설 2
 
< 0.1%

Length

2023-12-13T01:45:06.000750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:45:06.134417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농작물 9988
99.9%
농경지 10
 
0.1%
농림시설 2
 
< 0.1%

일반정보(피해종류2)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농약대
9035 
대파대
953 
농경지
 
10
비닐하우스
 
1
과수재배시설
 
1

Length

Max length6
Median length3
Mean length3.0005
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
농약대 9035
90.3%
대파대 953
 
9.5%
농경지 10
 
0.1%
비닐하우스 1
 
< 0.1%
과수재배시설 1
 
< 0.1%

Length

2023-12-13T01:45:06.295946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:45:06.427440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농약대 9035
90.3%
대파대 953
 
9.5%
농경지 10
 
0.1%
비닐하우스 1
 
< 0.1%
과수재배시설 1
 
< 0.1%

일반정보(피해종류3)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
병해충방제(채소류)
6627 
병해충방제(일반작물-수도작기준)
2319 
시설채소(엽채류)
726 
일반작물(무,배추기준)
 
224
병해충방제(과수류)
 
85
Other values (8)
 
19

Length

Max length17
Median length10
Mean length11.592
Min length5

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row병해충방제(일반작물-수도작기준)
2nd row병해충방제(채소류)
3rd row병해충방제(채소류)
4th row병해충방제(채소류)
5th row병해충방제(채소류)

Common Values

ValueCountFrequency (%)
병해충방제(채소류) 6627
66.3%
병해충방제(일반작물-수도작기준) 2319
 
23.2%
시설채소(엽채류) 726
 
7.3%
일반작물(무,배추기준) 224
 
2.2%
병해충방제(과수류) 85
 
0.9%
농경지 유실 10
 
0.1%
병해충방제(약용류) 3
 
< 0.1%
시설채소(과채류) 1
 
< 0.1%
철재파이프하우스(H-K형) 1
 
< 0.1%
방풍망시설 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2023-12-13T01:45:06.572972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
병해충방제(채소류 6627
66.2%
병해충방제(일반작물-수도작기준 2319
 
23.2%
시설채소(엽채류 726
 
7.3%
일반작물(무,배추기준 224
 
2.2%
병해충방제(과수류 85
 
0.8%
농경지 10
 
0.1%
유실 10
 
0.1%
병해충방제(약용류 3
 
< 0.1%
시설채소(과채류 1
 
< 0.1%
철재파이프하우스(h-k형 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

일반정보(피해구분)
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농작물피해
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농작물피해 10000
100.0%

Length

2023-12-13T01:45:06.721662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:45:06.809499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농작물피해 10000
100.0%

일반정보(피해요인)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
침수
6033 
기타
3442 
침수(기타)
 
523
건물붕괴
 
1
축대붕괴
 
1

Length

Max length6
Median length2
Mean length2.2096
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row침수
2nd row기타
3rd row침수
4th row침수
5th row침수

Common Values

ValueCountFrequency (%)
침수 6033
60.3%
기타 3442
34.4%
침수(기타) 523
 
5.2%
건물붕괴 1
 
< 0.1%
축대붕괴 1
 
< 0.1%

Length

2023-12-13T01:45:06.916581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:45:07.054278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
침수 6033
60.3%
기타 3442
34.4%
침수(기타 523
 
5.2%
건물붕괴 1
 
< 0.1%
축대붕괴 1
 
< 0.1%

일반정보(피해대상)
Text

Distinct74
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:45:07.239214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length1.8898
Min length1

Characters and Unicode

Total characters18898
Distinct characters94
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

Unique26 ?
Unique (%)0.3%

Sample

1st row콩
2nd row무
3rd row무
4th row무
5th row마늘
ValueCountFrequency (%)
무 3084
30.8%
월동무 1480
14.8%
콩 1176
 
11.7%
당근 1135
 
11.3%
감자 918
 
9.2%
브로콜리 453
 
4.5%
양배추 359
 
3.6%
메밀 343
 
3.4%
콜라비 191
 
1.9%
쪽파 181
 
1.8%
Other values (48) 699
 
7.0%
2023-12-13T01:45:07.583409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
무 4569
24.2%
동 1480
 
7.8%
월 1480
 
7.8%
콩 1179
 
6.2%
당 1135
 
6.0%
근 1135
 
6.0%
감 994
 
5.3%
자 918
 
4.9%
콜 669
 
3.5%
리 503
 
2.7%
Other values (84) 4836
25.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18858
99.8%
Space Separator 21
 
0.1%
Other Punctuation 19
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
무 4569
24.2%
동 1480
 
7.8%
월 1480
 
7.8%
콩 1179
 
6.3%
당 1135
 
6.0%
근 1135
 
6.0%
감 994
 
5.3%
자 918
 
4.9%
콜 669
 
3.5%
리 503
 
2.7%
Other values (82) 4796
25.4%
Space Separator
ValueCountFrequency (%)
21
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18858
99.8%
Common 40
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
무 4569
24.2%
동 1480
 
7.8%
월 1480
 
7.8%
콩 1179
 
6.3%
당 1135
 
6.0%
근 1135
 
6.0%
감 994
 
5.3%
자 918
 
4.9%
콜 669
 
3.5%
리 503
 
2.7%
Other values (82) 4796
25.4%
Common
ValueCountFrequency (%)
21
52.5%
, 19
47.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18858
99.8%
ASCII 40
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
무 4569
24.2%
동 1480
 
7.8%
월 1480
 
7.8%
콩 1179
 
6.3%
당 1135
 
6.0%
근 1135
 
6.0%
감 994
 
5.3%
자 918
 
4.9%
콜 669
 
3.5%
리 503
 
2.7%
Other values (82) 4796
25.4%
ASCII
ValueCountFrequency (%)
21
52.5%
, 19
47.5%

소재지 도로명주소
Text

MISSING 

Distinct183
Distinct (%)69.6%
Missing9737
Missing (%)97.4%
Memory size156.2 KiB
2023-12-13T01:45:08.038021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length20.863118
Min length15

Characters and Unicode

Total characters5487
Distinct characters162
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

Unique139 ?
Unique (%)52.9%

Sample

1st row제주특별자치도 서귀포시 성산읍 시흥상동로53번길
2nd row제주특별자치도 제주시 구좌읍 상하도길
3rd row제주특별자치도 제주시 구좌읍 종달로1길
4th row제주특별자치도 제주시 구좌읍 충렬로
5th row제주특별자치도 제주시 도련3길
ValueCountFrequency (%)
제주특별자치도 263
25.6%
제주시 153
14.9%
서귀포시 110
 
10.7%
구좌읍 76
 
7.4%
성산읍 57
 
5.5%
표선면 33
 
3.2%
애월읍 20
 
1.9%
한림읍 14
 
1.4%
일주동로 13
 
1.3%
대정읍 12
 
1.2%
Other values (178) 277
26.9%
2023-12-13T01:45:08.604435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
765
 
13.9%
주 439
 
8.0%
제 416
 
7.6%
도 282
 
5.1%
시 273
 
5.0%
자 268
 
4.9%
치 264
 
4.8%
별 263
 
4.8%
특 263
 
4.8%
로 197
 
3.6%
Other values (152) 2057
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4541
82.8%
Space Separator 765
 
13.9%
Decimal Number 181
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 439
 
9.7%
제 416
 
9.2%
도 282
 
6.2%
시 273
 
6.0%
자 268
 
5.9%
치 264
 
5.8%
별 263
 
5.8%
특 263
 
5.8%
로 197
 
4.3%
읍 193
 
4.3%
Other values (141) 1683
37.1%
Decimal Number
ValueCountFrequency (%)
1 35
19.3%
7 23
12.7%
4 21
11.6%
3 18
9.9%
2 16
8.8%
0 16
8.8%
9 14
 
7.7%
8 13
 
7.2%
5 13
 
7.2%
6 12
 
6.6%
Space Separator
ValueCountFrequency (%)
765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4541
82.8%
Common 946
 
17.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 439
 
9.7%
제 416
 
9.2%
도 282
 
6.2%
시 273
 
6.0%
자 268
 
5.9%
치 264
 
5.8%
별 263
 
5.8%
특 263
 
5.8%
로 197
 
4.3%
읍 193
 
4.3%
Other values (141) 1683
37.1%
Common
ValueCountFrequency (%)
765
80.9%
1 35
 
3.7%
7 23
 
2.4%
4 21
 
2.2%
3 18
 
1.9%
2 16
 
1.7%
0 16
 
1.7%
9 14
 
1.5%
8 13
 
1.4%
5 13
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4541
82.8%
ASCII 946
 
17.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
765
80.9%
1 35
 
3.7%
7 23
 
2.4%
4 21
 
2.2%
3 18
 
1.9%
2 16
 
1.7%
0 16
 
1.7%
9 14
 
1.5%
8 13
 
1.4%
5 13
 
1.4%
Hangul
ValueCountFrequency (%)
주 439
 
9.7%
제 416
 
9.2%
도 282
 
6.2%
시 273
 
6.0%
자 268
 
5.9%
치 264
 
5.8%
별 263
 
5.8%
특 263
 
5.8%
로 197
 
4.3%
읍 193
 
4.3%
Other values (141) 1683
37.1%

소재지 지번주소
Text

Distinct142
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:45:08.940931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length19.2179
Min length14

Characters and Unicode

Total characters192179
Distinct characters133
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

Unique11 ?
Unique (%)0.1%

Sample

1st row제주특별자치도 제주시 구좌읍 행원리
2nd row제주특별자치도 서귀포시 성산읍 신산리
3rd row제주특별자치도 제주시 구좌읍 평대리
4th row제주특별자치도 제주시 구좌읍 송당리
5th row제주특별자치도 제주시 구좌읍 행원리
ValueCountFrequency (%)
제주특별자치도 10000
25.2%
제주시 6805
17.2%
구좌읍 4680
11.8%
서귀포시 3195
 
8.1%
성산읍 2239
 
5.6%
한동리 1016
 
2.6%
종달리 726
 
1.8%
애월읍 671
 
1.7%
하도리 520
 
1.3%
표선면 513
 
1.3%
Other values (142) 9309
23.5%
2023-12-13T01:45:09.419492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29674
15.4%
제 16805
 
8.7%
주 16805
 
8.7%
도 10924
 
5.7%
시 10417
 
5.4%
특 10000
 
5.2%
별 10000
 
5.2%
자 10000
 
5.2%
치 10000
 
5.2%
리 9674
 
5.0%
Other values (123) 57880
30.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 162424
84.5%
Space Separator 29674
 
15.4%
Decimal Number 81
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 16805
 
10.3%
주 16805
 
10.3%
도 10924
 
6.7%
시 10417
 
6.4%
특 10000
 
6.2%
별 10000
 
6.2%
자 10000
 
6.2%
치 10000
 
6.2%
리 9674
 
6.0%
읍 8626
 
5.3%
Other values (120) 49173
30.3%
Decimal Number
ValueCountFrequency (%)
2 52
64.2%
1 29
35.8%
Space Separator
ValueCountFrequency (%)
29674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 162424
84.5%
Common 29755
 
15.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 16805
 
10.3%
주 16805
 
10.3%
도 10924
 
6.7%
시 10417
 
6.4%
특 10000
 
6.2%
별 10000
 
6.2%
자 10000
 
6.2%
치 10000
 
6.2%
리 9674
 
6.0%
읍 8626
 
5.3%
Other values (120) 49173
30.3%
Common
ValueCountFrequency (%)
29674
99.7%
2 52
 
0.2%
1 29
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 162424
84.5%
ASCII 29755
 
15.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29674
99.7%
2 52
 
0.2%
1 29
 
0.1%
Hangul
ValueCountFrequency (%)
제 16805
 
10.3%
주 16805
 
10.3%
도 10924
 
6.7%
시 10417
 
6.4%
특 10000
 
6.2%
별 10000
 
6.2%
자 10000
 
6.2%
치 10000
 
6.2%
리 9674
 
6.0%
읍 8626
 
5.3%
Other values (120) 49173
30.3%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0116649 × 109
Minimum5.0110109 × 109
Maximum5.013032 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:45:09.586917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.0110109 × 109
5-th percentile5.011025 × 109
Q15.0110256 × 109
median5.0110256 × 109
Q35.0130259 × 109
95-th percentile5.013032 × 109
Maximum5.013032 × 109
Range2021126
Interquartile range (IQR)2000296

Descriptive statistics

Standard deviation933365.16
Coefficient of variation (CV)0.00018623854
Kurtosis-1.4006825
Mean5.0116649 × 109
Median Absolute Deviation (MAD)294.5
Skewness0.7743045
Sum5.0116649 × 1013
Variance8.7117053 × 1011
MonotonicityNot monotonic
2023-12-13T01:45:09.778325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5011025630 1016
 
10.2%
5011025632 726
 
7.3%
5011025631 520
 
5.2%
5011025627 434
 
4.3%
5011025625 430
 
4.3%
5011025633 429
 
4.3%
5011025628 409
 
4.1%
5013025925 399
 
4.0%
5013025923 295
 
2.9%
5013025931 261
 
2.6%
Other values (131) 5081
50.8%
ValueCountFrequency (%)
5011010900 1
 
< 0.1%
5011011000 31
0.3%
5011011200 17
0.2%
5011011300 22
0.2%
5011011400 1
 
< 0.1%
5011011500 1
 
< 0.1%
5011011600 9
 
0.1%
5011011700 7
 
0.1%
5011011800 6
 
0.1%
5011012000 24
0.2%
ValueCountFrequency (%)
5013032026 31
 
0.3%
5013032025 39
 
0.4%
5013032024 122
1.2%
5013032023 134
1.3%
5013032022 116
1.2%
5013032021 71
0.7%
5013031030 11
 
0.1%
5013031029 5
 
0.1%
5013031028 70
0.7%
5013031027 21
 
0.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct9860
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.454664
Minimum33.197842
Maximum33.562418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:45:09.995033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.197842
5-th percentile33.31174
Q133.408125
median33.475391
Q333.512389
95-th percentile33.541932
Maximum33.562418
Range0.36457573
Interquartile range (IQR)0.10426376

Descriptive statistics

Standard deviation0.074230506
Coefficient of variation (CV)0.0022188388
Kurtosis0.43445328
Mean33.454664
Median Absolute Deviation (MAD)0.043688905
Skewness-0.99846717
Sum334546.64
Variance0.005510168
MonotonicityNot monotonic
2023-12-13T01:45:10.184285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.20445078 5
 
0.1%
33.5455501 3
 
< 0.1%
33.49741824 3
 
< 0.1%
33.50655845 3
 
< 0.1%
33.45575527 3
 
< 0.1%
33.4685711 3
 
< 0.1%
33.51532826 2
 
< 0.1%
33.45730298 2
 
< 0.1%
33.40055035 2
 
< 0.1%
33.46662146 2
 
< 0.1%
Other values (9850) 9972
99.7%
ValueCountFrequency (%)
33.19784204 1
 
< 0.1%
33.19908349 1
 
< 0.1%
33.20014454 2
 
< 0.1%
33.20199043 1
 
< 0.1%
33.20271884 1
 
< 0.1%
33.20304809 1
 
< 0.1%
33.20366686 1
 
< 0.1%
33.20432547 1
 
< 0.1%
33.20445078 5
0.1%
33.20481873 1
 
< 0.1%
ValueCountFrequency (%)
33.56241777 1
< 0.1%
33.56196616 1
< 0.1%
33.56135236 1
< 0.1%
33.56116475 1
< 0.1%
33.56113638 1
< 0.1%
33.56108431 1
< 0.1%
33.56086663 1
< 0.1%
33.56079783 1
< 0.1%
33.56069143 1
< 0.1%
33.56059111 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct9847
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.72439
Minimum126.16392
Maximum126.96868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:45:10.686328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16392
5-th percentile126.24777
Q1126.73056
median126.82194
Q3126.86819
95-th percentile126.90525
Maximum126.96868
Range0.8047544
Interquartile range (IQR)0.13762512

Descriptive statistics

Standard deviation0.2218488
Coefficient of variation (CV)0.00175064
Kurtosis0.27663836
Mean126.72439
Median Absolute Deviation (MAD)0.05353525
Skewness-1.3573897
Sum1267243.9
Variance0.049216889
MonotonicityNot monotonic
2023-12-13T01:45:10.826756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.2726436 5
 
0.1%
126.7896715 3
 
< 0.1%
126.8101232 3
 
< 0.1%
126.8102643 3
 
< 0.1%
126.8498567 3
 
< 0.1%
126.7650866 3
 
< 0.1%
126.3871263 2
 
< 0.1%
126.3919885 2
 
< 0.1%
126.8738381 2
 
< 0.1%
126.8253826 2
 
< 0.1%
Other values (9837) 9972
99.7%
ValueCountFrequency (%)
126.1639206 1
< 0.1%
126.1641271 1
< 0.1%
126.1647447 1
< 0.1%
126.1651575 1
< 0.1%
126.165503 1
< 0.1%
126.1658248 1
< 0.1%
126.1669713 1
< 0.1%
126.1670372 1
< 0.1%
126.1675512 1
< 0.1%
126.1678928 1
< 0.1%
ValueCountFrequency (%)
126.968675 1
< 0.1%
126.9686531 1
< 0.1%
126.9686072 1
< 0.1%
126.9674304 1
< 0.1%
126.9673915 1
< 0.1%
126.9668532 1
< 0.1%
126.9654805 1
< 0.1%
126.9654474 1
< 0.1%
126.9652955 1
< 0.1%
126.9641779 1
< 0.1%

비고 외
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9975 
노지
 
8
유실
 
8
종자
 
2
농경지 일부 유실
 
1
Other values (6)
 
6

Length

Max length34
Median length4
Mean length4.0007
Min length1

Unique

Unique7 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9975
99.8%
노지 8
 
0.1%
유실 8
 
0.1%
종자 2
 
< 0.1%
농경지 일부 유실 1
 
< 0.1%
1
 
< 0.1%
방풍망 1
 
< 0.1%
1
 
< 0.1%
(절반은 거북농산 재배) 1
 
< 0.1%
연구본관동 1
 
< 0.1%

Length

2023-12-13T01:45:11.008297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9975
99.7%
유실 9
 
0.1%
노지 8
 
0.1%
종자 2
 
< 0.1%
농경지 1
 
< 0.1%
일부 1
 
< 0.1%
방풍망 1
 
< 0.1%
절반은 1
 
< 0.1%
거북농산 1
 
< 0.1%
재배 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-11-03
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-11-03
2nd row2022-11-03
3rd row2022-11-03
4th row2022-11-03
5th row2022-11-03

Common Values

ValueCountFrequency (%)
2022-11-03 10000
100.0%

Length

2023-12-13T01:45:11.157346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:45:11.259459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-11-03 10000
100.0%

Interactions

2023-12-13T01:45:04.494923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:02.861338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:03.312989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:03.908000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:04.630031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:02.962057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:03.445190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:04.039231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:04.789833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:03.083508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:03.591291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:04.201063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:04.937092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:03.199494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:03.753802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:45:04.342719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:45:11.331944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호일반정보(피해종류1)일반정보(피해종류2)일반정보(피해종류3)일반정보(피해요인)일반정보(피해대상)행정동코드위도경도비고 외
번호1.0000.0440.1130.3020.7260.6310.9340.6580.5520.327
일반정보(피해종류1)0.0441.0001.0001.0000.0300.8560.0160.0910.1381.000
일반정보(피해종류2)0.1131.0001.0001.0000.0940.8620.0180.1770.2711.000
일반정보(피해종류3)0.3021.0001.0001.0000.2180.9650.2510.2840.3731.000
일반정보(피해요인)0.7260.0300.0940.2181.0000.6860.6250.7710.5720.886
일반정보(피해대상)0.6310.8560.8620.9650.6861.0000.7950.6770.7741.000
행정동코드0.9340.0160.0180.2510.6250.7951.0000.8530.3971.000
위도0.6580.0910.1770.2840.7710.6770.8531.0000.7420.903
경도0.5520.1380.2710.3730.5720.7740.3970.7421.0000.890
비고 외0.3271.0001.0001.0000.8861.0001.0000.9030.8901.000
2023-12-13T01:45:11.476413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반정보(피해요인)일반정보(피해종류2)일반정보(피해종류3)비고 외일반정보(피해종류1)
일반정보(피해요인)1.0000.0350.1190.6800.023
일반정보(피해종류2)0.0351.0001.0000.8451.000
일반정보(피해종류3)0.1191.0001.0000.8890.999
비고 외0.6800.8450.8891.0000.826
일반정보(피해종류1)0.0231.0000.9990.8261.000
2023-12-13T01:45:11.658945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동코드위도경도일반정보(피해종류1)일반정보(피해종류2)일반정보(피해종류3)일반정보(피해요인)비고 외
번호1.000-0.4740.526-0.1830.0260.0470.1290.3850.052
행정동코드-0.4741.000-0.4990.4000.0260.0220.2330.7520.808
위도0.526-0.4991.0000.2330.0540.0750.1210.4270.684
경도-0.1830.4000.2331.0000.0830.1160.1640.2730.653
일반정보(피해종류1)0.0260.0260.0540.0831.0001.0000.9990.0230.826
일반정보(피해종류2)0.0470.0220.0750.1161.0001.0001.0000.0350.845
일반정보(피해종류3)0.1290.2330.1210.1640.9991.0001.0000.1190.889
일반정보(피해요인)0.3850.7520.4270.2730.0230.0350.1191.0000.680
비고 외0.0520.8080.6840.6530.8260.8450.8890.6801.000

Missing values

2023-12-13T01:45:05.140878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:45:05.427135image/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

번호일반정보(피해종류1)일반정보(피해종류2)일반정보(피해종류3)일반정보(피해구분)일반정보(피해요인)일반정보(피해대상)소재지 도로명주소소재지 지번주소행정동코드위도경도비고 외데이터기준일자
1452114522농작물농약대병해충방제(일반작물-수도작기준)농작물피해침수콩<NA>제주특별자치도 제주시 구좌읍 행원리501102562733.508983126.771507<NA>2022-11-03
694695농작물농약대병해충방제(채소류)농작물피해기타무<NA>제주특별자치도 서귀포시 성산읍 신산리501302592733.389483126.891092<NA>2022-11-03
86808681농작물농약대병해충방제(채소류)농작물피해침수무<NA>제주특별자치도 제주시 구좌읍 평대리501102563333.533828126.836996<NA>2022-11-03
129130농작물농약대병해충방제(채소류)농작물피해침수무<NA>제주특별자치도 제주시 구좌읍 송당리501102562533.458671126.815279<NA>2022-11-03
1466014661농작물농약대병해충방제(채소류)농작물피해침수마늘<NA>제주특별자치도 제주시 구좌읍 행원리501102562733.547399126.803892<NA>2022-11-03
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