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 분석값 and 2 other fieldsHigh correlation
분석값 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
분석결과 is highly overall correlated with 분석값High correlation
분석결과 is highly imbalanced (51.9%)Imbalance

Reproduction

Analysis started2023-12-10 12:07:21.124514
Analysis finished2023-12-10 12:07:22.885437
Duration1.76 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
2019-07-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-07-01
2nd row2019-07-01
3rd row2019-07-01
4th row2019-07-01
5th row2019-07-01

Common Values

ValueCountFrequency (%)
2019-07-01 100
100.0%

Length

2023-12-10T21:07:22.971916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:23.090597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-07-01 100
100.0%

관측소위치
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강화(기)
 
5
서귀포(기)
 
5
순천(기)
 
5
목포(기)
 
5
광주(기)
 
5
Other values (19)
75 

Length

Max length6
Median length5
Mean length5.05
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row강화(기)
2nd row보령(기)
3rd row여수(기)
4th row강화(기)
5th row태백(기)

Common Values

ValueCountFrequency (%)
강화(기) 5
 
5.0%
서귀포(기) 5
 
5.0%
순천(기) 5
 
5.0%
목포(기) 5
 
5.0%
광주(기) 5
 
5.0%
완도(기) 5
 
5.0%
통영(기) 5
 
5.0%
성산(기) 5
 
5.0%
제주(기) 5
 
5.0%
부산(기) 5
 
5.0%
Other values (14) 50
50.0%

Length

2023-12-10T21:07:23.219403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강화(기 5
 
5.0%
순천(기 5
 
5.0%
목포(기 5
 
5.0%
광주(기 5
 
5.0%
완도(기 5
 
5.0%
통영(기 5
 
5.0%
성산(기 5
 
5.0%
제주(기 5
 
5.0%
부산(기 5
 
5.0%
여수(기 5
 
5.0%
Other values (14) 50
50.0%

주소
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
인천광역시 강화군 불은면 삼성리
 
5
제주특별자치도 서귀포시 서귀동
 
5
전라남도 순천시 승주읍 평중리
 
5
전라남도 목포시 연산동
 
5
광주광역시 남구 서동 대성초등학교
 
5
Other values (19)
75 

Length

Max length20
Median length18
Mean length14.7
Min length12

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row인천광역시 강화군 불은면 삼성리
2nd row충청남도 보령시 요암동
3rd row전라남도 여수시 중앙동
4th row인천광역시 강화군 불은면 삼성리
5th row(산간)강원도 태백시 황지동

Common Values

ValueCountFrequency (%)
인천광역시 강화군 불은면 삼성리 5
 
5.0%
제주특별자치도 서귀포시 서귀동 5
 
5.0%
전라남도 순천시 승주읍 평중리 5
 
5.0%
전라남도 목포시 연산동 5
 
5.0%
광주광역시 남구 서동 대성초등학교 5
 
5.0%
전라남도 완도군 군외면 불목리 5
 
5.0%
경상남도 통영시 정량동 5
 
5.0%
제주특별자치도 서귀포시 성산읍 신산리 5
 
5.0%
제주특별자치도 제주시 건입동 5
 
5.0%
부산광역시 중구 대청동1가 5
 
5.0%
Other values (14) 50
50.0%

Length

2023-12-10T21:07:23.374812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라남도 20
 
5.7%
제주특별자치도 15
 
4.3%
경상남도 13
 
3.7%
서귀포시 10
 
2.9%
충청남도 9
 
2.6%
중구 8
 
2.3%
충청북도 7
 
2.0%
경기도 7
 
2.0%
강원도 7
 
2.0%
대청동1가 5
 
1.4%
Other values (60) 247
71.0%

SPI구분
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
SPI1
21 
SPI12
21 
SPI9
20 
SPI3
19 
SPI6
19 

Length

Max length5
Median length4
Mean length4.21
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
SPI1 21
21.0%
SPI12 21
21.0%
SPI9 20
20.0%
SPI3 19
19.0%
SPI6 19
19.0%

Length

2023-12-10T21:07:23.551207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:23.685085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
spi1 21
21.0%
spi12 21
21.0%
spi9 20
20.0%
spi3 19
19.0%
spi6 19
19.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.52382
Minimum126.38121
Maximum129.32026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:23.807461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.38121
5-th percentile126.44308
Q1126.70184
median127.4842
Q3128.19431
95-th percentile129.03203
Maximum129.32026
Range2.93905
Interquartile range (IQR)1.49247

Descriptive statistics

Standard deviation0.89102209
Coefficient of variation (CV)0.0069871031
Kurtosis-1.0702499
Mean127.52382
Median Absolute Deviation (MAD)0.78236
Skewness0.39713139
Sum12752.382
Variance0.79392036
MonotonicityNot monotonic
2023-12-10T21:07:23.987769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
126.44634 5
 
5.0%
128.56517 5
 
5.0%
126.565331 5
 
5.0%
127.3694 5
 
5.0%
126.38121 5
 
5.0%
126.904722 5
 
5.0%
126.70184 5
 
5.0%
128.4356 5
 
5.0%
126.880394 5
 
5.0%
126.52968 5
 
5.0%
Other values (14) 50
50.0%
ValueCountFrequency (%)
126.38121 5
5.0%
126.44634 5
5.0%
126.52968 5
5.0%
126.55741 4
4.0%
126.565331 5
5.0%
126.70184 5
5.0%
126.880394 5
5.0%
126.904722 5
5.0%
126.92081 1
 
1.0%
127.121268 4
4.0%
ValueCountFrequency (%)
129.32026 3
3.0%
129.03203 5
5.0%
128.98928 4
4.0%
128.56517 5
5.0%
128.4356 5
5.0%
128.19431 4
4.0%
128.16713 4
4.0%
128.11911 3
3.0%
127.88042 3
3.0%
127.74064 5
5.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.601531
Minimum33.24612
Maximum38.05987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:24.168723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.24612
5-th percentile33.379787
Q134.79749
median35.16695
Q337.15927
95-th percentile37.70739
Maximum38.05987
Range4.81375
Interquartile range (IQR)2.36178

Descriptive statistics

Standard deviation1.4472619
Coefficient of variation (CV)0.040651675
Kurtosis-1.0986054
Mean35.601531
Median Absolute Deviation (MAD)1.16026
Skewness0.096820255
Sum3560.1531
Variance2.0945669
MonotonicityNot monotonic
2023-12-10T21:07:24.313320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
37.70739 5
 
5.0%
35.190289 5
 
5.0%
33.24612 5
 
5.0%
35.0204 5
 
5.0%
34.81689 5
 
5.0%
35.143611 5
 
5.0%
34.39587 5
 
5.0%
34.84546 5
 
5.0%
33.386822 5
 
5.0%
33.51411 5
 
5.0%
Other values (14) 50
50.0%
ValueCountFrequency (%)
33.24612 5
5.0%
33.386822 5
5.0%
33.51411 5
5.0%
34.39587 5
5.0%
34.73929 5
5.0%
34.81689 5
5.0%
34.84546 5
5.0%
35.0204 5
5.0%
35.10468 5
5.0%
35.143611 5
5.0%
ValueCountFrequency (%)
38.05987 4
4.0%
37.70739 5
5.0%
37.6836 3
3.0%
37.48857 3
3.0%
37.26398 4
4.0%
37.17031 4
4.0%
37.15927 4
4.0%
36.779622 4
4.0%
36.48759 3
3.0%
36.32721 4
4.0%

분석결과
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정상
76 
보통가뭄
10 
심한가뭄
8 
보통습윤
 
4
극한가뭄
 
1

Length

Max length4
Median length2
Mean length2.48
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 76
76.0%
보통가뭄 10
 
10.0%
심한가뭄 8
 
8.0%
보통습윤 4
 
4.0%
극한가뭄 1
 
1.0%
심한습윤 1
 
1.0%

Length

2023-12-10T21:07:24.481330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:24.639858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 76
76.0%
보통가뭄 10
 
10.0%
심한가뭄 8
 
8.0%
보통습윤 4
 
4.0%
극한가뭄 1
 
1.0%
심한습윤 1
 
1.0%

분석값
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3098
Minimum-2.02
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative64
Negative (%)64.0%
Memory size1.0 KiB
2023-12-10T21:07:25.105206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.02
5-th percentile-1.7405
Q1-0.8325
median-0.25
Q30.29
95-th percentile0.991
Maximum1.58
Range3.6
Interquartile range (IQR)1.1225

Descriptive statistics

Standard deviation0.79862885
Coefficient of variation (CV)-2.5778852
Kurtosis-0.57071991
Mean-0.3098
Median Absolute Deviation (MAD)0.585
Skewness-0.17056402
Sum-30.98
Variance0.63780804
MonotonicityNot monotonic
2023-12-10T21:07:25.288103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.75 2
 
2.0%
-0.27 2
 
2.0%
-0.37 2
 
2.0%
-0.56 2
 
2.0%
-0.83 2
 
2.0%
-0.04 2
 
2.0%
0.68 2
 
2.0%
1.04 2
 
2.0%
1.01 2
 
2.0%
0.16 2
 
2.0%
Other values (76) 80
80.0%
ValueCountFrequency (%)
-2.02 1
1.0%
-1.97 1
1.0%
-1.82 1
1.0%
-1.75 2
2.0%
-1.74 1
1.0%
-1.68 1
1.0%
-1.57 1
1.0%
-1.55 1
1.0%
-1.49 1
1.0%
-1.48 1
1.0%
ValueCountFrequency (%)
1.58 1
1.0%
1.04 2
2.0%
1.01 2
2.0%
0.99 1
1.0%
0.79 1
1.0%
0.74 1
1.0%
0.73 1
1.0%
0.68 2
2.0%
0.64 1
1.0%
0.61 1
1.0%

Interactions

2023-12-10T21:07:22.240103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:21.532899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:21.901186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:22.364842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:21.650944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:22.016664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:22.485697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:21.774749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:22.127858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:07:25.442421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소위치주소SPI구분위도경도분석결과분석값
관측소위치1.0001.0000.0001.0001.0000.4990.656
주소1.0001.0000.0001.0001.0000.4990.656
SPI구분0.0000.0001.0000.0000.0000.2380.492
위도1.0001.0000.0001.0000.8380.0000.484
경도1.0001.0000.0000.8381.0000.3370.568
분석결과0.4990.4990.2380.0000.3371.0000.919
분석값0.6560.6560.4920.4840.5680.9191.000
2023-12-10T21:07:25.578278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소SPI구분관측소위치분석결과
주소1.0000.0001.0000.195
SPI구분0.0001.0000.0000.161
관측소위치1.0000.0001.0000.195
분석결과0.1950.1610.1951.000
2023-12-10T21:07:25.706525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도분석값관측소위치주소SPI구분분석결과
위도1.0000.3120.2260.9190.9190.0000.000
경도0.3121.000-0.5670.9090.9090.0000.191
분석값0.226-0.5671.0000.2780.2780.2180.780
관측소위치0.9190.9090.2781.0001.0000.0000.195
주소0.9190.9090.2781.0001.0000.0000.195
SPI구분0.0000.0000.2180.0000.0001.0000.161
분석결과0.0000.1910.7800.1950.1950.1611.000

Missing values

2023-12-10T21:07:22.647371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:07:22.820011image/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
02019-07-01강화(기)인천광역시 강화군 불은면 삼성리SPI3126.4463437.70739심한가뭄-1.75
12019-07-01보령(기)충청남도 보령시 요암동SPI9126.5574136.32721정상-0.82
22019-07-01여수(기)전라남도 여수시 중앙동SPI6127.7406434.73929정상0.15
32019-07-01강화(기)인천광역시 강화군 불은면 삼성리SPI1126.4463437.70739정상-0.8
42019-07-01태백(기)(산간)강원도 태백시 황지동SPI6128.9892837.17031보통가뭄-1.27
52019-07-01보령(기)충청남도 보령시 요암동SPI6126.5574136.32721심한가뭄-1.97
62019-07-01부산(기)부산광역시 중구 대청동1가SPI3129.0320335.10468정상0.02
72019-07-01여수(기)전라남도 여수시 중앙동SPI3127.7406434.73929정상0.35
82019-07-01제주(기)제주특별자치도 제주시 건입동SPI6126.5296833.51411보통가뭄-1.39
92019-07-01진주(기)경상남도 진주시 초전동SPI6128.1191135.208447정상-0.14
분석일자관측소위치주소SPI구분위도경도분석결과분석값
902019-07-01서귀포(기)제주특별자치도 서귀포시 서귀동SPI1126.56533133.24612정상-0.51
912019-07-01서귀포(기)제주특별자치도 서귀포시 서귀동SPI3126.56533133.24612정상-0.04
922019-07-01서귀포(기)제주특별자치도 서귀포시 서귀동SPI9126.56533133.24612정상-0.22
932019-07-01서귀포(기)제주특별자치도 서귀포시 서귀동SPI12126.56533133.24612정상-0.36
942019-07-01강화(기)인천광역시 강화군 불은면 삼성리SPI6126.4463437.70739심한가뭄-1.82
952019-07-01강화(기)인천광역시 강화군 불은면 삼성리SPI9126.4463437.70739보통가뭄-1.29
962019-07-01양평(기)경기도 양평군 양평읍 양근리SPI3127.4944737.48857심한가뭄-1.74
972019-07-01양평(기)경기도 양평군 양평읍 양근리SPI6127.4944737.48857심한가뭄-1.68
982019-07-01부여(기)충청남도 부여군 부여읍 가탑리SPI3126.9208136.27236심한가뭄-1.55
992019-07-01보령(기)충청남도 보령시 요암동SPI12126.5574136.32721보통가뭄-1.17