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

Reproduction

Analysis started2023-12-10 12:07:50.996758
Analysis finished2023-12-10 12:07:52.597195
Duration1.6 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
2019-01-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T21:07:52.680198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:52.784627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-01-01 100
100.0%

관측소위치
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
청주(기)
 
5
전주(기)
 
5
원주(기)
 
5
포항(기)
 
5
춘천(기)
 
5
Other values (15)
75 

Length

Max length6
Median length5
Mean length5.15
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row청주(기)
2nd row전주(기)
3rd row원주(기)
4th row청주(기)
5th row포항(기)

Common Values

ValueCountFrequency (%)
청주(기) 5
 
5.0%
전주(기) 5
 
5.0%
원주(기) 5
 
5.0%
포항(기) 5
 
5.0%
춘천(기) 5
 
5.0%
인천(기) 5
 
5.0%
충주(기) 5
 
5.0%
추풍령(기) 5
 
5.0%
군산(기) 5
 
5.0%
대관령(기) 5
 
5.0%
Other values (10) 50
50.0%

Length

2023-12-10T21:07:52.907203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청주(기 5
 
5.0%
전주(기 5
 
5.0%
속초(기 5
 
5.0%
대구(기 5
 
5.0%
안동(기 5
 
5.0%
대전(기 5
 
5.0%
울진(기 5
 
5.0%
서산(기 5
 
5.0%
수원(기 5
 
5.0%
울릉도(기 5
 
5.0%
Other values (10) 50
50.0%

주소
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충청북도 청주시흥덕구 복대동
 
5
전라북도 전주시완산구 남노송동
 
5
강원도 원주시 명륜동
 
5
경상북도 포항시남구 송도동
 
5
강원도 춘천시 우두동
 
5
Other values (15)
75 

Length

Max length20
Median length15
Mean length13.65
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청북도 청주시흥덕구 복대동
2nd row전라북도 전주시완산구 남노송동
3rd row강원도 원주시 명륜동
4th row충청북도 청주시흥덕구 복대동
5th row경상북도 포항시남구 송도동

Common Values

ValueCountFrequency (%)
충청북도 청주시흥덕구 복대동 5
 
5.0%
전라북도 전주시완산구 남노송동 5
 
5.0%
강원도 원주시 명륜동 5
 
5.0%
경상북도 포항시남구 송도동 5
 
5.0%
강원도 춘천시 우두동 5
 
5.0%
인천광역시 중구 전동 5
 
5.0%
충청북도 충주시 안림동 5
 
5.0%
충청북도 영동군 추풍령면 관리 5
 
5.0%
전라북도 군산시 내흥동 5
 
5.0%
(산간)강원도 평창군 대관령면 횡계리 5
 
5.0%
Other values (10) 50
50.0%

Length

2023-12-10T21:07:53.061649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원도 20
 
6.1%
경상북도 20
 
6.1%
충청북도 15
 
4.5%
전라북도 10
 
3.0%
연지리 5
 
1.5%
울릉읍 5
 
1.5%
도동리 5
 
1.5%
경기도 5
 
1.5%
수원시 5
 
1.5%
권선구 5
 
1.5%
Other values (47) 235
71.2%

SPI구분
Categorical

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

Length

Max length5
Median length4
Mean length4.2
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T21:07:53.223652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:53.381744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
spi6 20
20.0%
spi1 20
20.0%
spi3 20
20.0%
spi12 20
20.0%
spi9 20
20.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.03186
Minimum126.49391
Maximum130.89864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:53.515488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.49391
5-th percentile126.61784
Q1127.11254
median127.94963
Q3128.72012
95-th percentile129.48707
Maximum130.89864
Range4.40473
Interquartile range (IQR)1.6075745

Descriptive statistics

Standard deviation1.1084245
Coefficient of variation (CV)0.0086574114
Kurtosis0.16868951
Mean128.03186
Median Absolute Deviation (MAD)0.801869
Skewness0.68294332
Sum12803.186
Variance1.2286049
MonotonicityNot monotonic
2023-12-10T21:07:53.660616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
127.44066 5
 
5.0%
130.89864 5
 
5.0%
128.89098 5
 
5.0%
128.56472 5
 
5.0%
128.6522 5
 
5.0%
128.70726 5
 
5.0%
127.37212 5
 
5.0%
129.41278 5
 
5.0%
126.49391 5
 
5.0%
126.9853 5
 
5.0%
Other values (10) 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%
128.89098 5
5.0%
128.758698 5
5.0%
128.70726 5
5.0%
128.6522 5
5.0%
128.56472 5
5.0%
127.99457 5
5.0%
127.95266 5
5.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.947513
Minimum35.8215
Maximum38.25085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:53.860753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.8215
5-th percentile35.827675
Q136.334058
median36.98107
Q337.50382
95-th percentile37.919975
Maximum38.25085
Range2.42935
Interquartile range (IQR)1.1697628

Descriptive statistics

Standard deviation0.72520379
Coefficient of variation (CV)0.019627946
Kurtosis-1.1707897
Mean36.947513
Median Absolute Deviation (MAD)0.5997055
Skewness-0.0651269
Sum3694.7513
Variance0.52592054
MonotonicityNot monotonic
2023-12-10T21:07:54.042423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
36.63924 5
 
5.0%
37.48129 5
 
5.0%
37.75147 5
 
5.0%
38.25085 5
 
5.0%
35.828 5
 
5.0%
36.573044 5
 
5.0%
36.372 5
 
5.0%
36.99176 5
 
5.0%
36.77661 5
 
5.0%
37.2723 5
 
5.0%
Other values (10) 50
50.0%
ValueCountFrequency (%)
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%
36.97038 5
5.0%
ValueCountFrequency (%)
38.25085 5
5.0%
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 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정상
70 
보통습윤
12 
심한습윤
10 
보통가뭄
 
5
극한습윤
 
3

Length

Max length4
Median length2
Mean length2.6
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 70
70.0%
보통습윤 12
 
12.0%
심한습윤 10
 
10.0%
보통가뭄 5
 
5.0%
극한습윤 3
 
3.0%

Length

2023-12-10T21:07:54.500683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:54.669986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 70
70.0%
보통습윤 12
 
12.0%
심한습윤 10
 
10.0%
보통가뭄 5
 
5.0%
극한습윤 3
 
3.0%

분석값
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4727
Minimum-1.48
Maximum2.05
Zeros0
Zeros (%)0.0%
Negative25
Negative (%)25.0%
Memory size1.0 KiB
2023-12-10T21:07:54.880167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.48
5-th percentile-0.6545
Q10.0025
median0.385
Q30.995
95-th percentile1.751
Maximum2.05
Range3.53
Interquartile range (IQR)0.9925

Descriptive statistics

Standard deviation0.78198124
Coefficient of variation (CV)1.6542865
Kurtosis-0.064726357
Mean0.4727
Median Absolute Deviation (MAD)0.48
Skewness-0.023297609
Sum47.27
Variance0.61149466
MonotonicityNot monotonic
2023-12-10T21:07:55.083218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.18 3
 
3.0%
0.28 3
 
3.0%
0.43 2
 
2.0%
0.73 2
 
2.0%
0.01 2
 
2.0%
1.75 2
 
2.0%
0.74 2
 
2.0%
0.06 2
 
2.0%
1.68 2
 
2.0%
0.2 2
 
2.0%
Other values (77) 78
78.0%
ValueCountFrequency (%)
-1.48 1
1.0%
-1.44 1
1.0%
-1.31 1
1.0%
-1.17 1
1.0%
-1.12 1
1.0%
-0.63 1
1.0%
-0.58 1
1.0%
-0.43 1
1.0%
-0.36 1
1.0%
-0.32 1
1.0%
ValueCountFrequency (%)
2.05 1
1.0%
2.02 1
1.0%
2.0 1
1.0%
1.91 1
1.0%
1.77 1
1.0%
1.75 2
2.0%
1.69 1
1.0%
1.68 2
2.0%
1.6 1
1.0%
1.59 1
1.0%

Interactions

2023-12-10T21:07:52.007121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:51.439876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:51.735584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:52.141892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:51.529849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:51.824646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:52.241962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:51.625412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:51.905784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:07:55.227091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소위치주소SPI구분위도경도분석결과분석값
관측소위치1.0001.0000.0001.0001.0000.4470.471
주소1.0001.0000.0001.0001.0000.4470.471
SPI구분0.0000.0001.0000.0000.0000.7320.691
위도1.0001.0000.0001.0000.8480.3830.163
경도1.0001.0000.0000.8481.0000.5100.241
분석결과0.4470.4470.7320.3830.5101.0000.984
분석값0.4710.4710.6910.1630.2410.9841.000
2023-12-10T21:07:55.387507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소SPI구분관측소위치분석결과
주소1.0000.0001.0000.186
SPI구분0.0001.0000.0000.356
관측소위치1.0000.0001.0000.186
분석결과0.1860.3560.1861.000
2023-12-10T21:07:55.515662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도분석값관측소위치주소SPI구분분석결과
위도1.0000.1500.2500.9330.9330.0000.241
경도0.1501.000-0.1200.9430.9430.0000.227
분석값0.250-0.1201.0000.1510.1510.3460.800
관측소위치0.9330.9430.1511.0001.0000.0000.186
주소0.9330.9430.1511.0001.0000.0000.186
SPI구분0.0000.0000.3460.0000.0001.0000.356
분석결과0.2410.2270.8000.1860.1860.3561.000

Missing values

2023-12-10T21:07:52.394298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:07:52.545619image/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
02019-01-01청주(기)충청북도 청주시흥덕구 복대동SPI6127.4406636.63924정상-0.18
12019-01-01전주(기)전라북도 전주시완산구 남노송동SPI6127.1549635.8215정상-0.36
22019-01-01원주(기)강원도 원주시 명륜동SPI1127.946637.33756정상0.28
32019-01-01청주(기)충청북도 청주시흥덕구 복대동SPI3127.4406636.63924보통습윤1.08
42019-01-01포항(기)경상북도 포항시남구 송도동SPI3129.3796336.03259극한습윤2.02
52019-01-01전주(기)전라북도 전주시완산구 남노송동SPI3127.1549635.8215정상0.86
62019-01-01춘천(기)강원도 춘천시 우두동SPI12127.735737.90256정상0.38
72019-01-01인천(기)인천광역시 중구 전동SPI12126.6243637.47759정상-0.1
82019-01-01충주(기)충청북도 충주시 안림동SPI1127.9526636.97038보통습윤1.04
92019-01-01청주(기)충청북도 청주시흥덕구 복대동SPI1127.4406636.63924정상0.43
분석일자관측소위치주소SPI구분위도경도분석결과분석값
902019-01-01서산(기)충청남도 서산시 수석동SPI9126.4939136.77661정상-0.18
912019-01-01서산(기)충청남도 서산시 수석동SPI12126.4939136.77661정상0.06
922019-01-01울진(기)경상북도 울진군 울진읍 연지리SPI3129.4127836.99176극한습윤2.05
932019-01-01울진(기)경상북도 울진군 울진읍 연지리SPI6129.4127836.99176정상0.73
942019-01-01청주(기)충청북도 청주시흥덕구 복대동SPI9127.4406636.63924정상0.46
952019-01-01청주(기)충청북도 청주시흥덕구 복대동SPI12127.4406636.63924정상0.64
962019-01-01대전(기)대전광역시 유성구 구성동SPI3127.3721236.372보통습윤1.4
972019-01-01대전(기)대전광역시 유성구 구성동SPI6127.3721236.372정상0.01
982019-01-01전주(기)전라북도 전주시완산구 남노송동SPI12127.1549635.8215정상0.28
992019-01-01전주(기)전라북도 전주시완산구 남노송동SPI9127.1549635.8215정상0.08