Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory68.3 B

Variable types

Categorical5
Numeric3

Alerts

분석일자 has constant value ""Constant
주소 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
관측소위치 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 관측소위치 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 관측소위치 and 1 other fieldsHigh correlation
분석값 is highly overall correlated with 분석결과High correlation
분석결과 is highly overall correlated with 분석값High correlation
분석결과 is highly imbalanced (50.0%)Imbalance

Reproduction

Analysis started2023-12-10 12:07:38.738915
Analysis finished2023-12-10 12:07:40.432854
Duration1.69 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분석일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019-04-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-04-01
2nd row2019-04-01
3rd row2019-04-01
4th row2019-04-01
5th row2019-04-01

Common Values

ValueCountFrequency (%)
2019-04-01 100
100.0%

Length

2023-12-10T21:07:40.509279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:40.616901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-04-01 100
100.0%

관측소위치
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서산(기)
 
5
대관령(기)
 
5
대구(기)
 
5
춘천(기)
 
5
강릉(기)
 
5
Other values (16)
75 

Length

Max length6
Median length5
Mean length5.15
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row속초(기)
2nd row대관령(기)
3rd row대관령(기)
4th row대관령(기)
5th row대관령(기)

Common Values

ValueCountFrequency (%)
서산(기) 5
 
5.0%
대관령(기) 5
 
5.0%
대구(기) 5
 
5.0%
춘천(기) 5
 
5.0%
강릉(기) 5
 
5.0%
서울(기) 5
 
5.0%
인천(기) 5
 
5.0%
원주(기) 5
 
5.0%
울릉도(기) 5
 
5.0%
수원(기) 5
 
5.0%
Other values (11) 50
50.0%

Length

2023-12-10T21:07:40.763248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서산(기 5
 
5.0%
대관령(기 5
 
5.0%
군산(기 5
 
5.0%
포항(기 5
 
5.0%
안동(기 5
 
5.0%
추풍령(기 5
 
5.0%
대전(기 5
 
5.0%
청주(기 5
 
5.0%
울진(기 5
 
5.0%
충주(기 5
 
5.0%
Other values (11) 50
50.0%

주소
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충청남도 서산시 수석동
 
5
(산간)강원도 평창군 대관령면 횡계리
 
5
대구광역시 동구 효목동
 
5
강원도 춘천시 우두동
 
5
강원도 강릉시 용강동
 
5
Other values (16)
75 

Length

Max length20
Median length15
Mean length13.56
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도 고성군 토성면 봉포리
2nd row(산간)강원도 평창군 대관령면 횡계리
3rd row(산간)강원도 평창군 대관령면 횡계리
4th row(산간)강원도 평창군 대관령면 횡계리
5th row(산간)강원도 평창군 대관령면 횡계리

Common Values

ValueCountFrequency (%)
충청남도 서산시 수석동 5
 
5.0%
(산간)강원도 평창군 대관령면 횡계리 5
 
5.0%
대구광역시 동구 효목동 5
 
5.0%
강원도 춘천시 우두동 5
 
5.0%
강원도 강릉시 용강동 5
 
5.0%
서울특별시 종로구 송월동 5
 
5.0%
인천광역시 중구 전동 5
 
5.0%
강원도 원주시 명륜동 5
 
5.0%
경상북도 울릉군 울릉읍 도동리 5
 
5.0%
경기도 수원시 권선구 서둔동 5
 
5.0%
Other values (11) 50
50.0%

Length

2023-12-10T21:07:40.918067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 20
 
6.1%
강원도 17
 
5.2%
충청북도 15
 
4.6%
전라북도 10
 
3.1%
중구 8
 
2.4%
충청남도 5
 
1.5%
복대동 5
 
1.5%
전주시완산구 5
 
1.5%
남노송동 5
 
1.5%
충주시 5
 
1.5%
Other values (49) 232
70.9%

SPI구분
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
SPI12
20 
SPI1
20 
SPI3
20 
SPI6
20 
SPI9
20 

Length

Max length5
Median length4
Mean length4.2
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSPI12
2nd rowSPI1
3rd rowSPI3
4th rowSPI6
5th rowSPI9

Common Values

ValueCountFrequency (%)
SPI12 20
20.0%
SPI1 20
20.0%
SPI3 20
20.0%
SPI6 20
20.0%
SPI9 20
20.0%

Length

2023-12-10T21:07:41.064717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:41.178533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
spi12 20
20.0%
spi1 20
20.0%
spi3 20
20.0%
spi6 20
20.0%
spi9 20
20.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.05453
Minimum126.49391
Maximum130.89864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:41.296402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.49391
5-th percentile126.61784
Q1127.11254
median127.94963
Q3128.7587
95-th percentile129.48707
Maximum130.89864
Range4.40473
Interquartile range (IQR)1.646153

Descriptive statistics

Standard deviation1.1268469
Coefficient of variation (CV)0.0087997424
Kurtosis-0.041497826
Mean128.05453
Median Absolute Deviation (MAD)0.809068
Skewness0.63135723
Sum12805.453
Variance1.2697839
MonotonicityNot monotonic
2023-12-10T21:07:41.444452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
129.41278 5
 
5.0%
128.758698 5
 
5.0%
128.6522 5
 
5.0%
126.705696 5
 
5.0%
129.37963 5
 
5.0%
128.70726 5
 
5.0%
127.99457 5
 
5.0%
127.37212 5
 
5.0%
127.44066 5
 
5.0%
126.49391 5
 
5.0%
Other values (11) 50
50.0%
ValueCountFrequency (%)
126.49391 5
5.0%
126.62436 5
5.0%
126.705696 5
5.0%
126.965792 5
5.0%
126.9853 5
5.0%
127.15496 5
5.0%
127.37212 5
5.0%
127.44066 5
5.0%
127.7357 5
5.0%
127.9466 5
5.0%
ValueCountFrequency (%)
130.89864 5
5.0%
129.41278 5
5.0%
129.37963 5
5.0%
129.32026 3
3.0%
128.89098 5
5.0%
128.758698 5
5.0%
128.70726 5
5.0%
128.6522 5
5.0%
128.56472 2
 
2.0%
127.99457 5
5.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.866792
Minimum35.56014
Maximum38.25085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:41.581950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.56014
5-th percentile35.8215
Q136.22023
median36.97038
Q337.48129
95-th percentile37.90256
Maximum38.25085
Range2.69071
Interquartile range (IQR)1.26106

Descriptive statistics

Standard deviation0.72539015
Coefficient of variation (CV)0.019675977
Kurtosis-1.2116058
Mean36.866792
Median Absolute Deviation (MAD)0.5997055
Skewness-0.11869353
Sum3686.6792
Variance0.52619086
MonotonicityNot monotonic
2023-12-10T21:07:41.735481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
36.99176 5
 
5.0%
37.686924 5
 
5.0%
35.828 5
 
5.0%
35.992958 5
 
5.0%
36.03259 5
 
5.0%
36.573044 5
 
5.0%
36.22023 5
 
5.0%
36.372 5
 
5.0%
36.63924 5
 
5.0%
36.77661 5
 
5.0%
Other values (11) 50
50.0%
ValueCountFrequency (%)
35.56014 3
3.0%
35.8215 5
5.0%
35.828 5
5.0%
35.992958 5
5.0%
36.03259 5
5.0%
36.22023 5
5.0%
36.372 5
5.0%
36.573044 5
5.0%
36.63924 5
5.0%
36.77661 5
5.0%
ValueCountFrequency (%)
38.25085 2
 
2.0%
37.90256 5
5.0%
37.75147 5
5.0%
37.686924 5
5.0%
37.571411 5
5.0%
37.48129 5
5.0%
37.47759 5
5.0%
37.33756 5
5.0%
37.2723 5
5.0%
36.99176 5
5.0%

분석결과
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정상
77 
보통가뭄
10 
보통습윤
9 
심한습윤
 
2
심한가뭄
 
2

Length

Max length4
Median length2
Mean length2.46
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row심한습윤
2nd row정상
3rd row보통가뭄
4th row정상
5th row정상

Common Values

ValueCountFrequency (%)
정상 77
77.0%
보통가뭄 10
 
10.0%
보통습윤 9
 
9.0%
심한습윤 2
 
2.0%
심한가뭄 2
 
2.0%

Length

2023-12-10T21:07:41.918889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:42.045662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 77
77.0%
보통가뭄 10
 
10.0%
보통습윤 9
 
9.0%
심한습윤 2
 
2.0%
심한가뭄 2
 
2.0%

분석값
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0821
Minimum-1.61
Maximum1.57
Zeros1
Zeros (%)1.0%
Negative59
Negative (%)59.0%
Memory size1.0 KiB
2023-12-10T21:07:42.185469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.61
5-th percentile-1.2605
Q1-0.6025
median-0.155
Q30.3925
95-th percentile1.2005
Maximum1.57
Range3.18
Interquartile range (IQR)0.995

Descriptive statistics

Standard deviation0.75575943
Coefficient of variation (CV)-9.2053524
Kurtosis-0.56492961
Mean-0.0821
Median Absolute Deviation (MAD)0.515
Skewness0.18446866
Sum-8.21
Variance0.57117231
MonotonicityNot monotonic
2023-12-10T21:07:42.355881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.16 3
 
3.0%
-0.5 2
 
2.0%
-1.04 2
 
2.0%
0.1 2
 
2.0%
0.74 2
 
2.0%
0.13 2
 
2.0%
-0.02 2
 
2.0%
1.21 2
 
2.0%
-0.87 2
 
2.0%
0.72 2
 
2.0%
Other values (76) 79
79.0%
ValueCountFrequency (%)
-1.61 1
1.0%
-1.6 1
1.0%
-1.48 1
1.0%
-1.41 1
1.0%
-1.27 1
1.0%
-1.26 1
1.0%
-1.23 1
1.0%
-1.14 1
1.0%
-1.1 1
1.0%
-1.04 2
2.0%
ValueCountFrequency (%)
1.57 1
1.0%
1.54 1
1.0%
1.3 1
1.0%
1.21 2
2.0%
1.2 2
2.0%
1.19 1
1.0%
1.18 1
1.0%
1.16 1
1.0%
1.02 1
1.0%
0.95 1
1.0%

Interactions

2023-12-10T21:07:39.823925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:39.109586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:39.406454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:39.915869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:39.205074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:39.502702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:40.014738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:39.295990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:39.651038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:07:42.474170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소위치주소SPI구분위도경도분석결과분석값
관측소위치1.0001.0000.0001.0001.0000.0000.000
주소1.0001.0000.0001.0001.0000.0000.000
SPI구분0.0000.0001.0000.0000.0000.6180.793
위도1.0001.0000.0001.0000.8100.0000.000
경도1.0001.0000.0000.8101.0000.4230.184
분석결과0.0000.0000.6180.0000.4231.0000.986
분석값0.0000.0000.7930.0000.1840.9861.000
2023-12-10T21:07:42.968519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소SPI구분관측소위치분석결과
주소1.0000.0001.0000.000
SPI구분0.0001.0000.0000.271
관측소위치1.0000.0001.0000.000
분석결과0.0000.2710.0001.000
2023-12-10T21:07:43.103008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도분석값관측소위치주소SPI구분분석결과
위도1.0000.0630.1310.9270.9270.0000.000
경도0.0631.0000.0940.9370.9370.0000.181
분석값0.1310.0941.0000.0000.0000.4360.810
관측소위치0.9270.9370.0001.0001.0000.0000.000
주소0.9270.9370.0001.0001.0000.0000.000
SPI구분0.0000.0000.4360.0000.0001.0000.271
분석결과0.0000.1810.8100.0000.0000.2711.000

Missing values

2023-12-10T21:07:40.197325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:07:40.370434image/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

분석일자관측소위치주소SPI구분위도경도분석결과분석값
02019-04-01속초(기)강원도 고성군 토성면 봉포리SPI12128.5647238.25085심한습윤1.57
12019-04-01대관령(기)(산간)강원도 평창군 대관령면 횡계리SPI1128.75869837.686924정상-0.23
22019-04-01대관령(기)(산간)강원도 평창군 대관령면 횡계리SPI3128.75869837.686924보통가뭄-1.14
32019-04-01대관령(기)(산간)강원도 평창군 대관령면 횡계리SPI6128.75869837.686924정상-0.41
42019-04-01대관령(기)(산간)강원도 평창군 대관령면 횡계리SPI9128.75869837.686924정상-0.26
52019-04-01대관령(기)(산간)강원도 평창군 대관령면 횡계리SPI12128.75869837.686924정상0.0
62019-04-01춘천(기)강원도 춘천시 우두동SPI1127.735737.90256정상0.3
72019-04-01춘천(기)강원도 춘천시 우두동SPI3127.735737.90256정상-0.3
82019-04-01춘천(기)강원도 춘천시 우두동SPI6127.735737.90256보통습윤1.3
92019-04-01춘천(기)강원도 춘천시 우두동SPI9127.735737.90256정상-0.63
분석일자관측소위치주소SPI구분위도경도분석결과분석값
902019-04-01대구(기)대구광역시 동구 효목동SPI12128.652235.828정상0.74
912019-04-01전주(기)전라북도 전주시완산구 남노송동SPI1127.1549635.8215정상-0.61
922019-04-01전주(기)전라북도 전주시완산구 남노송동SPI3127.1549635.8215정상-0.83
932019-04-01전주(기)전라북도 전주시완산구 남노송동SPI6127.1549635.8215정상0.13
942019-04-01전주(기)전라북도 전주시완산구 남노송동SPI9127.1549635.8215정상-0.6
952019-04-01전주(기)전라북도 전주시완산구 남노송동SPI12127.1549635.8215정상0.05
962019-04-01울산(기)울산광역시 중구 북정동SPI1129.3202635.56014정상-0.42
972019-04-01울산(기)울산광역시 중구 북정동SPI3129.3202635.56014정상-0.74
982019-04-01울산(기)울산광역시 중구 북정동SPI6129.3202635.56014정상0.69
992019-04-01속초(기)강원도 고성군 토성면 봉포리SPI9128.5647238.25085보통습윤1.21