Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells19551
Missing cells (%)14.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory124.0 B

Variable types

Numeric4
Categorical6
Text4

Dataset

Description해당 자료는 제주특별자치도 내 등록되어 있는 자연재해 중 2019년 태풍타파에 의한 피해 현황을 담고 있으며, 세부적으로는 행정동 정보 등 포함하고 있습니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15110896/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
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
일반정보(피해종류2) is highly overall correlated with 일반정보(피해종류1) and 2 other fieldsHigh correlation
일반정보(피해구분) is highly overall correlated with 일반정보(피해종류1) and 2 other fieldsHigh correlation
행정동코드 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 행정동코드High correlation
일반정보(피해요인) is highly overall correlated with 행정동코드High correlation
일반정보(피해종류1) is highly imbalanced (98.3%)Imbalance
일반정보(피해종류2) is highly imbalanced (80.3%)Imbalance
일반정보(피해종류3) is highly imbalanced (60.7%)Imbalance
일반정보(피해구분) is highly imbalanced (98.9%)Imbalance
일반정보(피해요인) is highly imbalanced (59.7%)Imbalance
소재지 도로명주소 has 9726 (97.3%) missing valuesMissing
비고 외 has 9825 (98.2%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:15:50.633428
Analysis finished2023-12-11 23:15:53.840030
Duration3.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10371.397
Minimum1
Maximum20822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:15:53.912304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1023.9
Q15110.75
median10395.5
Q315531.25
95-th percentile19808.05
Maximum20822
Range20821
Interquartile range (IQR)10420.5

Descriptive statistics

Standard deviation6018.5977
Coefficient of variation (CV)0.58030732
Kurtosis-1.2012481
Mean10371.397
Median Absolute Deviation (MAD)5213.5
Skewness0.0085454251
Sum1.0371397 × 108
Variance36223518
MonotonicityNot monotonic
2023-12-12T08:15:54.038123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14907 1
 
< 0.1%
6118 1
 
< 0.1%
4464 1
 
< 0.1%
13595 1
 
< 0.1%
672 1
 
< 0.1%
631 1
 
< 0.1%
10658 1
 
< 0.1%
12642 1
 
< 0.1%
2176 1
 
< 0.1%
2770 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
20822 1
< 0.1%
20820 1
< 0.1%
20818 1
< 0.1%
20816 1
< 0.1%
20814 1
< 0.1%
20808 1
< 0.1%
20807 1
< 0.1%
20806 1
< 0.1%
20801 1
< 0.1%
20799 1
< 0.1%

일반정보(피해종류1)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농작물
9976 
농림시설
 
15
농경지
 
9

Length

Max length4
Median length3
Mean length3.0015
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농작물 9976
99.8%
농림시설 15
 
0.1%
농경지 9
 
0.1%

Length

2023-12-12T08:15:54.144925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:15:54.225630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농작물 9976
99.8%
농림시설 15
 
0.1%
농경지 9
 
0.1%

일반정보(피해종류2)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농약대
8788 
대파대
1188 
창고등부대시설
 
10
농경지
 
9
과수재배시설
 
3
Other values (2)
 
2

Length

Max length7
Median length3
Mean length3.0053
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
농약대 8788
87.9%
대파대 1188
 
11.9%
창고등부대시설 10
 
0.1%
농경지 9
 
0.1%
과수재배시설 3
 
< 0.1%
버섯재배사 1
 
< 0.1%
비닐하우스 1
 
< 0.1%

Length

2023-12-12T08:15:54.319425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:15:54.426570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농약대 8788
87.9%
대파대 1188
 
11.9%
창고등부대시설 10
 
0.1%
농경지 9
 
0.1%
과수재배시설 3
 
< 0.1%
버섯재배사 1
 
< 0.1%
비닐하우스 1
 
< 0.1%

일반정보(피해종류3)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
병해충방제(채소류)
6020 
병해충방제(일반작물-수도작기준)
2491 
채소(엽채류)
937 
병해충방제(과수류)
 
273
일반작물(무,배추기준)
 
244
Other values (11)
 
35

Length

Max length17
Median length10
Mean length11.5103
Min length5

Unique

Unique6 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
병해충방제(채소류) 6020
60.2%
병해충방제(일반작물-수도작기준) 2491
24.9%
채소(엽채류) 937
 
9.4%
병해충방제(과수류) 273
 
2.7%
일반작물(무,배추기준) 244
 
2.4%
농산물저장창고(일반) 9
 
0.1%
농경지 유실 8
 
0.1%
채소(토마토,풋고추,가지) 6
 
0.1%
방풍망시설 3
 
< 0.1%
병해충방제(약용류) 3
 
< 0.1%
Other values (6) 6
 
0.1%

Length

2023-12-12T08:15:54.543731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
병해충방제(채소류 6020
60.1%
병해충방제(일반작물-수도작기준 2491
24.9%
채소(엽채류 937
 
9.4%
병해충방제(과수류 273
 
2.7%
일반작물(무,배추기준 244
 
2.4%
농산물저장창고(일반 9
 
0.1%
농경지 9
 
0.1%
유실 8
 
0.1%
채소(토마토,풋고추,가지 6
 
0.1%
방풍망시설 3
 
< 0.1%
Other values (7) 9
 
0.1%

일반정보(피해구분)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농작물피해
9985 
반파
 
12
전파
 
3

Length

Max length5
Median length5
Mean length4.9955
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농작물피해 9985
99.9%
반파 12
 
0.1%
전파 3
 
< 0.1%

Length

2023-12-12T08:15:54.663270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:15:54.753768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농작물피해 9985
99.9%
반파 12
 
0.1%
전파 3
 
< 0.1%

일반정보(피해요인)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
침수
8341 
기타
1057 
침수(기타)
 
601
축대붕괴
 
1

Length

Max length6
Median length2
Mean length2.2406
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row침수
2nd row침수
3rd row침수(기타)
4th row침수
5th row기타

Common Values

ValueCountFrequency (%)
침수 8341
83.4%
기타 1057
 
10.6%
침수(기타) 601
 
6.0%
축대붕괴 1
 
< 0.1%

Length

2023-12-12T08:15:54.874396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:15:54.994445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
침수 8341
83.4%
기타 1057
 
10.6%
침수(기타 601
 
6.0%
축대붕괴 1
 
< 0.1%

일반정보(피해대상)
Text

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:15:55.139556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length1.7504
Min length1

Characters and Unicode

Total characters17504
Distinct characters102
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

Unique31 ?
Unique (%)0.3%

Sample

1st row무
2nd row감자
3rd row무
4th row무
5th row콩
ValueCountFrequency (%)
무 3261
32.5%
콩 1612
16.1%
당근 1472
14.7%
감자 899
 
9.0%
양배추 635
 
6.3%
브로콜리 513
 
5.1%
월동무 326
 
3.3%
감귤 221
 
2.2%
메밀 183
 
1.8%
콜라비 142
 
1.4%
Other values (58) 759
 
7.6%
2023-12-12T08:15:55.518229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
무 3589
20.5%
콩 1625
 
9.3%
당 1472
 
8.4%
근 1472
 
8.4%
감 1132
 
6.5%
자 902
 
5.2%
양 708
 
4.0%
추 693
 
4.0%
배 680
 
3.9%
콜 666
 
3.8%
Other values (92) 4565
26.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17449
99.7%
Space Separator 30
 
0.2%
Other Punctuation 25
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
무 3589
20.6%
콩 1625
 
9.3%
당 1472
 
8.4%
근 1472
 
8.4%
감 1132
 
6.5%
자 902
 
5.2%
양 708
 
4.1%
추 693
 
4.0%
배 680
 
3.9%
콜 666
 
3.8%
Other values (90) 4510
25.8%
Space Separator
ValueCountFrequency (%)
30
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17449
99.7%
Common 55
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
무 3589
20.6%
콩 1625
 
9.3%
당 1472
 
8.4%
근 1472
 
8.4%
감 1132
 
6.5%
자 902
 
5.2%
양 708
 
4.1%
추 693
 
4.0%
배 680
 
3.9%
콜 666
 
3.8%
Other values (90) 4510
25.8%
Common
ValueCountFrequency (%)
30
54.5%
, 25
45.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17449
99.7%
ASCII 55
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
무 3589
20.6%
콩 1625
 
9.3%
당 1472
 
8.4%
근 1472
 
8.4%
감 1132
 
6.5%
자 902
 
5.2%
양 708
 
4.1%
추 693
 
4.0%
배 680
 
3.9%
콜 666
 
3.8%
Other values (90) 4510
25.8%
ASCII
ValueCountFrequency (%)
30
54.5%
, 25
45.5%

소재지 도로명주소
Text

MISSING 

Distinct201
Distinct (%)73.4%
Missing9726
Missing (%)97.3%
Memory size156.2 KiB
2023-12-12T08:15:55.719239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length20.686131
Min length15

Characters and Unicode

Total characters5668
Distinct characters169
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

Unique154 ?
Unique (%)56.2%

Sample

1st row제주특별자치도 제주시 구좌읍 용눈이오름로
2nd row제주특별자치도 서귀포시 성산읍 신풍하동로
3rd row제주특별자치도 서귀포시 표선면 토산중앙로287번길
4th row제주특별자치도 제주시 구좌읍 비자림로
5th row제주특별자치도 서귀포시 남원읍 신례로
ValueCountFrequency (%)
제주특별자치도 274
25.8%
제주시 152
14.3%
서귀포시 122
11.5%
구좌읍 68
 
6.4%
성산읍 61
 
5.8%
애월읍 30
 
2.8%
표선면 25
 
2.4%
한림읍 17
 
1.6%
중산간동로 14
 
1.3%
조천읍 11
 
1.0%
Other values (198) 286
27.0%
2023-12-12T08:15:56.034926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
786
 
13.9%
주 440
 
7.8%
제 428
 
7.6%
도 285
 
5.0%
시 281
 
5.0%
자 280
 
4.9%
치 275
 
4.9%
특 274
 
4.8%
별 274
 
4.8%
로 207
 
3.7%
Other values (159) 2138
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4704
83.0%
Space Separator 786
 
13.9%
Decimal Number 178
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 440
 
9.4%
제 428
 
9.1%
도 285
 
6.1%
시 281
 
6.0%
자 280
 
6.0%
치 275
 
5.8%
특 274
 
5.8%
별 274
 
5.8%
로 207
 
4.4%
읍 203
 
4.3%
Other values (148) 1757
37.4%
Decimal Number
ValueCountFrequency (%)
1 31
17.4%
4 27
15.2%
2 22
12.4%
7 21
11.8%
3 18
10.1%
5 14
7.9%
8 13
7.3%
6 13
7.3%
0 11
 
6.2%
9 8
 
4.5%
Space Separator
ValueCountFrequency (%)
786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4704
83.0%
Common 964
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 440
 
9.4%
제 428
 
9.1%
도 285
 
6.1%
시 281
 
6.0%
자 280
 
6.0%
치 275
 
5.8%
특 274
 
5.8%
별 274
 
5.8%
로 207
 
4.4%
읍 203
 
4.3%
Other values (148) 1757
37.4%
Common
ValueCountFrequency (%)
786
81.5%
1 31
 
3.2%
4 27
 
2.8%
2 22
 
2.3%
7 21
 
2.2%
3 18
 
1.9%
5 14
 
1.5%
8 13
 
1.3%
6 13
 
1.3%
0 11
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4704
83.0%
ASCII 964
 
17.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
786
81.5%
1 31
 
3.2%
4 27
 
2.8%
2 22
 
2.3%
7 21
 
2.2%
3 18
 
1.9%
5 14
 
1.5%
8 13
 
1.3%
6 13
 
1.3%
0 11
 
1.1%
Hangul
ValueCountFrequency (%)
주 440
 
9.4%
제 428
 
9.1%
도 285
 
6.1%
시 281
 
6.0%
자 280
 
6.0%
치 275
 
5.8%
특 274
 
5.8%
별 274
 
5.8%
로 207
 
4.4%
읍 203
 
4.3%
Other values (148) 1757
37.4%

소재지 지번주소
Text

Distinct152
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:15:56.363174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length19.1714
Min length14

Characters and Unicode

Total characters191714
Distinct characters143
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

Unique10 ?
Unique (%)0.1%

Sample

1st row제주특별자치도 제주시 구좌읍 종달리
2nd row제주특별자치도 제주시 조천읍 와산리
3rd row제주특별자치도 서귀포시 성산읍 신풍리
4th row제주특별자치도 제주시 봉개동
5th row제주특별자치도 서귀포시 안덕면 동광리
ValueCountFrequency (%)
제주특별자치도 10000
25.2%
제주시 7318
18.4%
구좌읍 4917
12.4%
서귀포시 2682
 
6.8%
성산읍 1891
 
4.8%
애월읍 1165
 
2.9%
한동리 872
 
2.2%
종달리 714
 
1.8%
세화리 587
 
1.5%
한림읍 532
 
1.3%
Other values (152) 9042
22.8%
2023-12-12T08:15:56.894471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29720
15.5%
제 17318
 
9.0%
주 17318
 
9.0%
도 10755
 
5.6%
시 10341
 
5.4%
치 10000
 
5.2%
자 10000
 
5.2%
별 10000
 
5.2%
특 10000
 
5.2%
리 9720
 
5.1%
Other values (133) 56542
29.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 161965
84.5%
Space Separator 29720
 
15.5%
Decimal Number 29
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 17318
 
10.7%
주 17318
 
10.7%
도 10755
 
6.6%
시 10341
 
6.4%
치 10000
 
6.2%
자 10000
 
6.2%
별 10000
 
6.2%
특 10000
 
6.2%
리 9720
 
6.0%
읍 9059
 
5.6%
Other values (130) 47454
29.3%
Decimal Number
ValueCountFrequency (%)
2 23
79.3%
1 6
 
20.7%
Space Separator
ValueCountFrequency (%)
29720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 161965
84.5%
Common 29749
 
15.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 17318
 
10.7%
주 17318
 
10.7%
도 10755
 
6.6%
시 10341
 
6.4%
치 10000
 
6.2%
자 10000
 
6.2%
별 10000
 
6.2%
특 10000
 
6.2%
리 9720
 
6.0%
읍 9059
 
5.6%
Other values (130) 47454
29.3%
Common
ValueCountFrequency (%)
29720
99.9%
2 23
 
0.1%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 161965
84.5%
ASCII 29749
 
15.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29720
99.9%
2 23
 
0.1%
1 6
 
< 0.1%
Hangul
ValueCountFrequency (%)
제 17318
 
10.7%
주 17318
 
10.7%
도 10755
 
6.6%
시 10341
 
6.4%
치 10000
 
6.2%
자 10000
 
6.2%
별 10000
 
6.2%
특 10000
 
6.2%
리 9720
 
6.0%
읍 9059
 
5.6%
Other values (130) 47454
29.3%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct152
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0115622 × 109
Minimum5.0110104 × 109
Maximum5.013032 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:15:57.051861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.0110104 × 109
5-th percentile5.011025 × 109
Q15.0110256 × 109
median5.0110256 × 109
Q35.0130259 × 109
95-th percentile5.013031 × 109
Maximum5.013032 × 109
Range2021626
Interquartile range (IQR)2000297

Descriptive statistics

Standard deviation886767.82
Coefficient of variation (CV)0.00017694439
Kurtosis-0.90475721
Mean5.0115622 × 109
Median Absolute Deviation (MAD)291
Skewness1.0466012
Sum5.0115622 × 1013
Variance7.8635716 × 1011
MonotonicityNot monotonic
2023-12-12T08:15:57.243324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5011025630 872
 
8.7%
5011025632 714
 
7.1%
5011025628 561
 
5.6%
5011025633 510
 
5.1%
5011025627 488
 
4.9%
5011025625 415
 
4.2%
5011025629 372
 
3.7%
5013025925 354
 
3.5%
5011025631 305
 
3.0%
5013025931 279
 
2.8%
Other values (142) 5130
51.3%
ValueCountFrequency (%)
5011010400 2
 
< 0.1%
5011010700 1
 
< 0.1%
5011010900 4
 
< 0.1%
5011011100 1
 
< 0.1%
5011011200 10
0.1%
5011011300 7
 
0.1%
5011011400 5
 
0.1%
5011011500 4
 
< 0.1%
5011011600 18
0.2%
5011011700 11
0.1%
ValueCountFrequency (%)
5013032026 36
 
0.4%
5013032025 26
 
0.3%
5013032024 146
1.5%
5013032023 117
1.2%
5013032022 85
0.9%
5013032021 49
 
0.5%
5013031030 10
 
0.1%
5013031029 8
 
0.1%
5013031028 82
0.8%
5013031027 25
 
0.2%

위도
Real number (ℝ)

Distinct9889
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.460294
Minimum33.196853
Maximum33.562313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:15:57.429608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.196853
5-th percentile33.334676
Q133.415885
median33.475322
Q333.513025
95-th percentile33.547013
Maximum33.562313
Range0.36546054
Interquartile range (IQR)0.097140497

Descriptive statistics

Standard deviation0.067247131
Coefficient of variation (CV)0.0020097591
Kurtosis0.20590566
Mean33.460294
Median Absolute Deviation (MAD)0.0438996
Skewness-0.83319184
Sum334602.94
Variance0.0045221766
MonotonicityNot monotonic
2023-12-12T08:15:57.572251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.5455501 5
 
0.1%
33.5494122 3
 
< 0.1%
33.54155037 3
 
< 0.1%
33.54513805 3
 
< 0.1%
33.49046405 3
 
< 0.1%
33.54904768 3
 
< 0.1%
33.45575527 3
 
< 0.1%
33.50341644 2
 
< 0.1%
33.54241339 2
 
< 0.1%
33.53526201 2
 
< 0.1%
Other values (9879) 9971
99.7%
ValueCountFrequency (%)
33.19685273 1
< 0.1%
33.19945726 1
< 0.1%
33.21201999 1
< 0.1%
33.21302721 1
< 0.1%
33.21421742 1
< 0.1%
33.21736766 1
< 0.1%
33.22095537 1
< 0.1%
33.22170586 1
< 0.1%
33.22192451 1
< 0.1%
33.22437934 1
< 0.1%
ValueCountFrequency (%)
33.56231327 1
< 0.1%
33.56183766 1
< 0.1%
33.56116475 1
< 0.1%
33.56103473 1
< 0.1%
33.56086663 1
< 0.1%
33.5606232 1
< 0.1%
33.56047463 1
< 0.1%
33.56004156 1
< 0.1%
33.5599109 1
< 0.1%
33.55969701 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct9871
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.7109
Minimum126.16512
Maximum126.96551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:15:57.750471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16512
5-th percentile126.28201
Q1126.65944
median126.81353
Q3126.85954
95-th percentile126.89868
Maximum126.96551
Range0.8003968
Interquartile range (IQR)0.20009485

Descriptive statistics

Standard deviation0.21956973
Coefficient of variation (CV)0.00173284
Kurtosis-0.3008884
Mean126.7109
Median Absolute Deviation (MAD)0.05600305
Skewness-1.1694185
Sum1267109
Variance0.048210864
MonotonicityNot monotonic
2023-12-12T08:15:57.902651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8102643 5
 
0.1%
126.813215 3
 
< 0.1%
126.7629292 3
 
< 0.1%
126.7958785 3
 
< 0.1%
126.7650866 3
 
< 0.1%
126.8073043 3
 
< 0.1%
126.8056304 3
 
< 0.1%
126.7649436 2
 
< 0.1%
126.7040895 2
 
< 0.1%
126.8134308 2
 
< 0.1%
Other values (9861) 9971
99.7%
ValueCountFrequency (%)
126.1651169 1
< 0.1%
126.1652072 1
< 0.1%
126.1656257 1
< 0.1%
126.1676421 1
< 0.1%
126.1678126 1
< 0.1%
126.1679391 1
< 0.1%
126.1691789 1
< 0.1%
126.1703715 1
< 0.1%
126.1710688 1
< 0.1%
126.1715064 1
< 0.1%
ValueCountFrequency (%)
126.9655137 1
< 0.1%
126.9649867 1
< 0.1%
126.9645473 1
< 0.1%
126.9637768 1
< 0.1%
126.9630492 1
< 0.1%
126.9626981 1
< 0.1%
126.962537 1
< 0.1%
126.9623464 1
< 0.1%
126.962004 1
< 0.1%
126.9616968 1
< 0.1%

비고 외
Text

MISSING 

Distinct124
Distinct (%)70.9%
Missing9825
Missing (%)98.2%
Memory size156.2 KiB
2023-12-12T08:15:58.193471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length17
Mean length8.6742857
Min length2

Characters and Unicode

Total characters1518
Distinct characters116
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

Unique108 ?
Unique (%)61.7%

Sample

1st row극조생
2nd row1300-집중호우중복
3rd row하우스
4th row4800-링링중복
5th row노지
ValueCountFrequency (%)
링링중복 13
 
5.7%
노지 12
 
5.2%
극조생 9
 
3.9%
제13호 8
 
3.5%
링링 8
 
3.5%
피해신고 8
 
3.5%
필지 7
 
3.0%
그외 6
 
2.6%
창고지붕파손 4
 
1.7%
1100-링링중복 3
 
1.3%
Other values (136) 152
66.1%
2023-12-12T08:15:58.620398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 174
 
11.5%
중 158
 
10.4%
링 146
 
9.6%
복 113
 
7.4%
- 98
 
6.5%
60
 
4.0%
호 53
 
3.5%
우 50
 
3.3%
1 48
 
3.2%
집 45
 
3.0%
Other values (106) 573
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 877
57.8%
Decimal Number 423
27.9%
Dash Punctuation 98
 
6.5%
Space Separator 60
 
4.0%
Open Punctuation 26
 
1.7%
Close Punctuation 26
 
1.7%
Other Punctuation 6
 
0.4%
Other Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 158
18.0%
링 146
16.6%
복 113
12.9%
호 53
 
6.0%
우 50
 
5.7%
집 45
 
5.1%
지 36
 
4.1%
고 16
 
1.8%
필 14
 
1.6%
노 13
 
1.5%
Other values (88) 233
26.6%
Decimal Number
ValueCountFrequency (%)
0 174
41.1%
1 48
 
11.3%
3 41
 
9.7%
2 37
 
8.7%
5 29
 
6.9%
7 21
 
5.0%
6 20
 
4.7%
4 19
 
4.5%
9 18
 
4.3%
8 16
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 2
33.3%
. 2
33.3%
* 2
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%
Space Separator
ValueCountFrequency (%)
60
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 877
57.8%
Common 641
42.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 158
18.0%
링 146
16.6%
복 113
12.9%
호 53
 
6.0%
우 50
 
5.7%
집 45
 
5.1%
지 36
 
4.1%
고 16
 
1.8%
필 14
 
1.6%
노 13
 
1.5%
Other values (88) 233
26.6%
Common
ValueCountFrequency (%)
0 174
27.1%
- 98
15.3%
60
 
9.4%
1 48
 
7.5%
3 41
 
6.4%
2 37
 
5.8%
5 29
 
4.5%
( 26
 
4.1%
) 26
 
4.1%
7 21
 
3.3%
Other values (8) 81
12.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 877
57.8%
ASCII 639
42.1%
CJK Compat 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
27.2%
- 98
15.3%
60
 
9.4%
1 48
 
7.5%
3 41
 
6.4%
2 37
 
5.8%
5 29
 
4.5%
( 26
 
4.1%
) 26
 
4.1%
7 21
 
3.3%
Other values (7) 79
12.4%
Hangul
ValueCountFrequency (%)
중 158
18.0%
링 146
16.6%
복 113
12.9%
호 53
 
6.0%
우 50
 
5.7%
집 45
 
5.1%
지 36
 
4.1%
고 16
 
1.8%
필 14
 
1.6%
노 13
 
1.5%
Other values (88) 233
26.6%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%

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

2023-12-12T08:15:59.022897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:15:59.117712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-11-03 10000
100.0%

Interactions

2023-12-12T08:15:53.088530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:51.764843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:52.362414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:52.734253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:53.167069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:51.832925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:52.440757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:52.815030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:53.266278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:51.921322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:52.535175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:52.908729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:53.364895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:52.016955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:52.623959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:52.994620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:15:59.188715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호일반정보(피해종류1)일반정보(피해종류2)일반정보(피해종류3)일반정보(피해구분)일반정보(피해요인)일반정보(피해대상)행정동코드위도경도
번호1.0000.1050.1950.3360.0840.5340.6350.9040.5680.562
일반정보(피해종류1)0.1051.0001.0001.0000.9430.0720.7620.0390.2830.215
일반정보(피해종류2)0.1951.0001.0001.0001.0000.1050.8360.0980.2350.229
일반정보(피해종류3)0.3361.0001.0001.0001.0000.2850.9560.3240.3970.462
일반정보(피해구분)0.0840.9431.0001.0001.0000.0730.7540.0380.2860.220
일반정보(피해요인)0.5340.0720.1050.2850.0731.0000.6050.9020.4610.306
일반정보(피해대상)0.6350.7620.8360.9560.7540.6051.0000.6930.6810.772
행정동코드0.9040.0390.0980.3240.0380.9020.6931.0000.7980.410
위도0.5680.2830.2350.3970.2860.4610.6810.7981.0000.698
경도0.5620.2150.2290.4620.2200.3060.7720.4100.6981.000
2023-12-12T08:15:59.310844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반정보(피해종류3)일반정보(피해종류1)일반정보(피해요인)일반정보(피해종류2)일반정보(피해구분)
일반정보(피해종류3)1.0000.9990.1371.0000.999
일반정보(피해종류1)0.9991.0000.0681.0000.707
일반정보(피해요인)0.1370.0681.0000.0720.069
일반정보(피해종류2)1.0001.0000.0721.0001.000
일반정보(피해구분)0.9990.7070.0691.0001.000
2023-12-12T08:15:59.426025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동코드위도경도일반정보(피해종류1)일반정보(피해종류2)일반정보(피해종류3)일반정보(피해구분)일반정보(피해요인)
번호1.000-0.265-0.005-0.3150.0630.1000.1380.0490.349
행정동코드-0.2651.000-0.2780.5130.0650.1050.2540.0620.716
위도-0.005-0.2781.0000.2170.1760.1210.1670.1780.293
경도-0.3150.5130.2171.0000.1310.1170.2010.1340.187
일반정보(피해종류1)0.0630.0650.1760.1311.0001.0000.9990.7070.068
일반정보(피해종류2)0.1000.1050.1210.1171.0001.0001.0001.0000.072
일반정보(피해종류3)0.1380.2540.1670.2010.9991.0001.0000.9990.137
일반정보(피해구분)0.0490.0620.1780.1340.7071.0000.9991.0000.069
일반정보(피해요인)0.3490.7160.2930.1870.0680.0720.1370.0691.000

Missing values

2023-12-12T08:15:53.493173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:15:53.665873image/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.
2023-12-12T08:15:53.793281image/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

번호일반정보(피해종류1)일반정보(피해종류2)일반정보(피해종류3)일반정보(피해구분)일반정보(피해요인)일반정보(피해대상)소재지 도로명주소소재지 지번주소행정동코드위도경도비고 외데이터기준일자
1490614907농작물농약대병해충방제(채소류)농작물피해침수무제주특별자치도 제주시 구좌읍 용눈이오름로제주특별자치도 제주시 구좌읍 종달리501102563233.491664126.887738<NA>2022-11-03
1172011721농작물농약대병해충방제(일반작물-수도작기준)농작물피해침수감자<NA>제주특별자치도 제주시 조천읍 와산리501102592833.474175126.677311<NA>2022-11-03
1112411125농작물농약대병해충방제(채소류)농작물피해침수(기타)무제주특별자치도 서귀포시 성산읍 신풍하동로제주특별자치도 서귀포시 성산읍 신풍리501302592933.362801126.86319<NA>2022-11-03
1387013871농작물농약대병해충방제(채소류)농작물피해침수무<NA>제주특별자치도 제주시 봉개동501101160033.476141126.617587<NA>2022-11-03
1808918090농작물농약대병해충방제(일반작물-수도작기준)농작물피해기타콩<NA>제주특별자치도 서귀포시 안덕면 동광리501303102833.292362126.333159<NA>2022-11-03
1103011031농작물농약대병해충방제(과수류)농작물피해침수조생귤<NA>제주특별자치도 제주시 애월읍 소길리501102532833.441545126.375784<NA>2022-11-03
25162517농작물농약대병해충방제(채소류)농작물피해기타무<NA>제주특별자치도 서귀포시 성산읍 시흥리501302592333.464618126.87051<NA>2022-11-03
86558656농작물농약대병해충방제(채소류)농작물피해침수양배추<NA>제주특별자치도 제주시 한림읍 한림리501102502433.414091126.275018<NA>2022-11-03
38293830농작물농약대병해충방제(일반작물-수도작기준)농작물피해침수콩<NA>제주특별자치도 제주시 구좌읍 덕천리501102562433.502436126.750506<NA>2022-11-03
1345313454농작물농약대병해충방제(일반작물-수도작기준)농작물피해침수콩<NA>제주특별자치도 제주시 한경면 조수리501103102633.333352126.213043<NA>2022-11-03
번호일반정보(피해종류1)일반정보(피해종류2)일반정보(피해종류3)일반정보(피해구분)일반정보(피해요인)일반정보(피해대상)소재지 도로명주소소재지 지번주소행정동코드위도경도비고 외데이터기준일자
56855686농작물농약대병해충방제(채소류)농작물피해기타무<NA>제주특별자치도 서귀포시 성산읍 시흥리501302592333.456658126.858227<NA>2022-11-03
1340113402농작물농약대병해충방제(일반작물-수도작기준)농작물피해침수감자<NA>제주특별자치도 제주시 구좌읍 덕천리501102562433.500417126.782804<NA>2022-11-03
74747475농작물농약대병해충방제(채소류)농작물피해기타무<NA>제주특별자치도 서귀포시 성산읍 신풍리501302592933.361443126.852325<NA>2022-11-03
1923819239농작물농약대병해충방제(채소류)농작물피해침수무<NA>제주특별자치도 제주시 구좌읍 한동리501102563033.504613126.788972<NA>2022-11-03
87378738농작물대파대채소(엽채류)농작물피해침수무<NA>제주특별자치도 서귀포시 표선면 성읍리501303202333.386319126.802682<NA>2022-11-03
1306613067농작물농약대병해충방제(채소류)농작물피해침수쪽파<NA>제주특별자치도 제주시 구좌읍 행원리501102562733.532756126.794696<NA>2022-11-03
1340613407농작물대파대채소(엽채류)농작물피해침수무<NA>제주특별자치도 제주시 구좌읍 하도리501102563133.523614126.888853<NA>2022-11-03
94239424농작물농약대병해충방제(채소류)농작물피해침수브로콜리<NA>제주특별자치도 제주시 한림읍 귀덕리501102502133.429732126.284916<NA>2022-11-03
1030910310농작물농약대병해충방제(채소류)농작물피해침수브로콜리<NA>제주특별자치도 제주시 애월읍 곽지리501102533733.440158126.307522<NA>2022-11-03
2010620107농작물농약대병해충방제(채소류)농작물피해침수무<NA>제주특별자치도 제주시 애월읍 고성리501102532933.448888126.415314<NA>2022-11-03