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

Reproduction

Analysis started2023-12-10 12:07:27.221631
Analysis finished2023-12-10 12:07:28.867662
Duration1.65 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
2019-06-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-06-01
2nd row2019-06-01
3rd row2019-06-01
4th row2019-06-01
5th row2019-06-01

Common Values

ValueCountFrequency (%)
2019-06-01 100
100.0%

Length

2023-12-10T21:07:28.973307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:29.105629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-06-01 100
100.0%

관측소위치
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
이천(기)
 
5
완도(기)
 
5
순천(기)
 
5
제주(기)
 
5
성산(기)
 
5
Other values (19)
75 

Length

Max length6
Median length5
Mean length5.05
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row통영(기)
2nd row목포(기)
3rd row목포(기)
4th row목포(기)
5th row목포(기)

Common Values

ValueCountFrequency (%)
이천(기) 5
 
5.0%
완도(기) 5
 
5.0%
순천(기) 5
 
5.0%
제주(기) 5
 
5.0%
성산(기) 5
 
5.0%
서귀포(기) 5
 
5.0%
진주(기) 5
 
5.0%
강화(기) 5
 
5.0%
양평(기) 5
 
5.0%
목포(기) 5
 
5.0%
Other values (14) 50
50.0%

Length

2023-12-10T21:07:29.218833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이천(기 5
 
5.0%
순천(기 5
 
5.0%
제주(기 5
 
5.0%
성산(기 5
 
5.0%
서귀포(기 5
 
5.0%
진주(기 5
 
5.0%
강화(기 5
 
5.0%
양평(기 5
 
5.0%
목포(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
경기도 이천시 부발읍 신하리
 
5
전라남도 완도군 군외면 불목리
 
5
전라남도 순천시 승주읍 평중리
 
5
제주특별자치도 제주시 건입동
 
5
제주특별자치도 서귀포시 성산읍 신산리
 
5
Other values (19)
75 

Length

Max length20
Median length17
Mean length14.74
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도 통영시 정량동
2nd row전라남도 목포시 연산동
3rd row전라남도 목포시 연산동
4th row전라남도 목포시 연산동
5th row전라남도 목포시 연산동

Common Values

ValueCountFrequency (%)
경기도 이천시 부발읍 신하리 5
 
5.0%
전라남도 완도군 군외면 불목리 5
 
5.0%
전라남도 순천시 승주읍 평중리 5
 
5.0%
제주특별자치도 제주시 건입동 5
 
5.0%
제주특별자치도 서귀포시 성산읍 신산리 5
 
5.0%
제주특별자치도 서귀포시 서귀동 5
 
5.0%
경상남도 진주시 초전동 5
 
5.0%
인천광역시 강화군 불은면 삼성리 5
 
5.0%
경기도 양평군 양평읍 양근리 5
 
5.0%
전라남도 목포시 연산동 5
 
5.0%
Other values (14) 50
50.0%

Length

2023-12-10T21:07:29.381140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라남도 20
 
5.6%
충청남도 15
 
4.2%
제주특별자치도 15
 
4.2%
서귀포시 10
 
2.8%
경기도 10
 
2.8%
전라북도 9
 
2.5%
충청북도 8
 
2.2%
강원도 7
 
2.0%
경상남도 7
 
2.0%
완도군 5
 
1.4%
Other values (60) 250
70.2%

SPI구분
Categorical

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

Length

Max length5
Median length4
Mean length4.19
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T21:07:29.565906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:29.742110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
spi3 21
21.0%
spi9 21
21.0%
spi6 20
20.0%
spi12 19
19.0%
spi1 19
19.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.29643
Minimum126.38121
Maximum128.98928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:29.921280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.38121
5-th percentile126.44308
Q1126.70184
median127.32748
Q3127.74064
95-th percentile128.4356
Maximum128.98928
Range2.60807
Interquartile range (IQR)1.0388

Descriptive statistics

Standard deviation0.69106295
Coefficient of variation (CV)0.0054287694
Kurtosis-0.46498477
Mean127.29643
Median Absolute Deviation (MAD)0.61093
Skewness0.53223755
Sum12729.643
Variance0.47756799
MonotonicityNot monotonic
2023-12-10T21:07:30.116625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
127.4842 5
 
5.0%
127.74064 5
 
5.0%
126.70184 5
 
5.0%
127.3694 5
 
5.0%
126.52968 5
 
5.0%
126.880394 5
 
5.0%
126.565331 5
 
5.0%
128.11911 5
 
5.0%
126.44634 5
 
5.0%
127.49447 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%
126.71655 3
3.0%
126.86611 2
 
2.0%
126.880394 5
5.0%
126.92081 4
4.0%
ValueCountFrequency (%)
128.98928 4
4.0%
128.4356 2
 
2.0%
128.19431 4
4.0%
128.16713 4
4.0%
128.11911 5
5.0%
127.88042 3
3.0%
127.74064 5
5.0%
127.73412 4
4.0%
127.49447 5
5.0%
127.4842 5
5.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.78113
Minimum33.24612
Maximum38.05987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:30.275517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.24612
5-th percentile33.379787
Q134.79749
median35.72954
Q337.17031
95-th percentile37.70739
Maximum38.05987
Range4.81375
Interquartile range (IQR)2.37282

Descriptive statistics

Standard deviation1.4609356
Coefficient of variation (CV)0.040829777
Kurtosis-1.0861926
Mean35.78113
Median Absolute Deviation (MAD)1.191876
Skewness-0.2142538
Sum3578.113
Variance2.1343327
MonotonicityNot monotonic
2023-12-10T21:07:30.455164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
37.26398 5
 
5.0%
34.73929 5
 
5.0%
34.39587 5
 
5.0%
35.0204 5
 
5.0%
33.51411 5
 
5.0%
33.386822 5
 
5.0%
33.24612 5
 
5.0%
35.208447 5
 
5.0%
37.70739 5
 
5.0%
37.48857 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 2
 
2.0%
35.0204 5
5.0%
35.208447 5
5.0%
35.56317 2
 
2.0%
ValueCountFrequency (%)
38.05987 4
4.0%
37.70739 5
5.0%
37.6836 3
3.0%
37.48857 5
5.0%
37.26398 5
5.0%
37.17031 4
4.0%
37.15927 4
4.0%
36.779622 3
3.0%
36.48759 4
4.0%
36.32721 4
4.0%

분석결과
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정상
67 
보통가뭄
18 
심한가뭄
10 
보통습윤
 
3
극한가뭄
 
2

Length

Max length4
Median length2
Mean length2.66
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 67
67.0%
보통가뭄 18
 
18.0%
심한가뭄 10
 
10.0%
보통습윤 3
 
3.0%
극한가뭄 2
 
2.0%

Length

2023-12-10T21:07:30.664727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:30.822090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 67
67.0%
보통가뭄 18
 
18.0%
심한가뭄 10
 
10.0%
보통습윤 3
 
3.0%
극한가뭄 2
 
2.0%

분석값
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.568
Minimum-2.1
Maximum1.41
Zeros0
Zeros (%)0.0%
Negative76
Negative (%)76.0%
Memory size1.0 KiB
2023-12-10T21:07:30.981818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.1
5-th percentile-1.6715
Q1-1.1475
median-0.61
Q3-0.0325
95-th percentile0.7715
Maximum1.41
Range3.51
Interquartile range (IQR)1.115

Descriptive statistics

Standard deviation0.75355052
Coefficient of variation (CV)-1.3266734
Kurtosis-0.42055159
Mean-0.568
Median Absolute Deviation (MAD)0.57
Skewness0.23277368
Sum-56.8
Variance0.56783838
MonotonicityNot monotonic
2023-12-10T21:07:31.169561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.62 2
 
2.0%
-0.04 2
 
2.0%
-0.09 2
 
2.0%
0.01 2
 
2.0%
-0.89 2
 
2.0%
-0.45 2
 
2.0%
-0.63 2
 
2.0%
-0.51 2
 
2.0%
-0.94 2
 
2.0%
-1.43 2
 
2.0%
Other values (75) 80
80.0%
ValueCountFrequency (%)
-2.1 1
1.0%
-2.07 1
1.0%
-1.79 1
1.0%
-1.72 1
1.0%
-1.7 1
1.0%
-1.67 1
1.0%
-1.64 1
1.0%
-1.62 2
2.0%
-1.58 1
1.0%
-1.55 1
1.0%
ValueCountFrequency (%)
1.41 1
1.0%
1.04 1
1.0%
1.0 1
1.0%
0.84 1
1.0%
0.8 1
1.0%
0.77 1
1.0%
0.76 1
1.0%
0.56 1
1.0%
0.45 1
1.0%
0.44 1
1.0%

Interactions

2023-12-10T21:07:28.296019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:27.620883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:27.973565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:28.407944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:27.724050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:28.092570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:28.511145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:27.856579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:28.193547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:07:31.310405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소위치주소SPI구분위도경도분석결과분석값
관측소위치1.0001.0000.0001.0001.0000.5900.520
주소1.0001.0000.0001.0001.0000.5900.520
SPI구분0.0000.0001.0000.0000.0000.5820.560
위도1.0001.0000.0001.0000.8970.1880.380
경도1.0001.0000.0000.8971.0000.5580.498
분석결과0.5900.5900.5820.1880.5581.0000.973
분석값0.5200.5200.5600.3800.4980.9731.000
2023-12-10T21:07:31.445222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소SPI구분관측소위치분석결과
주소1.0000.0001.0000.294
SPI구분0.0001.0000.0000.250
관측소위치1.0000.0001.0000.294
분석결과0.2940.2500.2941.000
2023-12-10T21:07:31.599215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도분석값관측소위치주소SPI구분분석결과
위도1.0000.399-0.1500.9140.9140.0000.103
경도0.3991.000-0.7250.9140.9140.0000.355
분석값-0.150-0.7251.0000.1950.1950.2570.748
관측소위치0.9140.9140.1951.0001.0000.0000.294
주소0.9140.9140.1951.0001.0000.0000.294
SPI구분0.0000.0000.2570.0000.0001.0000.250
분석결과0.1030.3550.7480.2940.2940.2501.000

Missing values

2023-12-10T21:07:28.657233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:07:28.809789image/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
02019-06-01통영(기)경상남도 통영시 정량동SPI12128.435634.84546정상0.84
12019-06-01목포(기)전라남도 목포시 연산동SPI1126.3812134.81689정상0.76
22019-06-01목포(기)전라남도 목포시 연산동SPI3126.3812134.81689정상0.27
32019-06-01목포(기)전라남도 목포시 연산동SPI6126.3812134.81689정상-0.12
42019-06-01목포(기)전라남도 목포시 연산동SPI9126.3812134.81689정상0.26
52019-06-01목포(기)전라남도 목포시 연산동SPI12126.3812134.81689정상0.1
62019-06-01여수(기)전라남도 여수시 중앙동SPI1127.7406434.73929정상0.13
72019-06-01여수(기)전라남도 여수시 중앙동SPI3127.7406434.73929정상-0.34
82019-06-01여수(기)전라남도 여수시 중앙동SPI6127.7406434.73929정상-0.39
92019-06-01여수(기)전라남도 여수시 중앙동SPI9127.7406434.73929정상0.22
분석일자관측소위치주소SPI구분위도경도분석결과분석값
902019-06-01부안(기)전라북도 부안군 행안면 역리SPI1126.7165535.72954정상-0.36
912019-06-01부안(기)전라북도 부안군 행안면 역리SPI9126.7165535.72954정상-0.45
922019-06-01부안(기)전라북도 부안군 행안면 역리SPI12126.7165535.72954정상-0.09
932019-06-01임실(기)전라북도 임실군 임실읍 이도리SPI1127.2855635.61227정상-0.39
942019-06-01임실(기)전라북도 임실군 임실읍 이도리SPI3127.2855635.61227정상-0.63
952019-06-01임실(기)전라북도 임실군 임실읍 이도리SPI6127.2855635.61227보통가뭄-1.03
962019-06-01임실(기)전라북도 임실군 임실읍 이도리SPI9127.2855635.61227정상-0.94
972019-06-01정읍(기)전라북도 정읍시 상동SPI3126.8661135.56317정상-0.59
982019-06-01정읍(기)전라북도 정읍시 상동SPI6126.8661135.56317보통가뭄-1.06
992019-06-01통영(기)경상남도 통영시 정량동SPI9128.435634.84546정상0.8