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 (67.7%)Imbalance

Reproduction

Analysis started2023-12-10 12:07:44.841763
Analysis finished2023-12-10 12:07:46.575501
Duration1.73 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
2019-03-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-03-01
2nd row2019-03-01
3rd row2019-03-01
4th row2019-03-01
5th row2019-03-01

Common Values

ValueCountFrequency (%)
2019-03-01 100
100.0%

Length

2023-12-10T21:07:46.652056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:46.756934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-03-01 100
100.0%

관측소위치
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
대전(기)
 
5
전주(기)
 
5
서산(기)
 
5
포항(기)
 
5
울릉도(기)
 
5
Other values (15)
75 

Length

Max length6
Median length5
Mean length5.15
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전(기)
2nd row전주(기)
3rd row서산(기)
4th row대전(기)
5th row포항(기)

Common Values

ValueCountFrequency (%)
대전(기) 5
 
5.0%
전주(기) 5
 
5.0%
서산(기) 5
 
5.0%
포항(기) 5
 
5.0%
울릉도(기) 5
 
5.0%
울진(기) 5
 
5.0%
추풍령(기) 5
 
5.0%
군산(기) 5
 
5.0%
인천(기) 5
 
5.0%
수원(기) 5
 
5.0%
Other values (10) 50
50.0%

Length

2023-12-10T21:07:46.864986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대전(기 5
 
5.0%
전주(기 5
 
5.0%
대관령(기 5
 
5.0%
속초(기 5
 
5.0%
서울(기 5
 
5.0%
강릉(기 5
 
5.0%
원주(기 5
 
5.0%
대구(기 5
 
5.0%
안동(기 5
 
5.0%
청주(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
대전광역시 유성구 구성동
 
5
전라북도 전주시완산구 남노송동
 
5
충청남도 서산시 수석동
 
5
경상북도 포항시남구 송도동
 
5
경상북도 울릉군 울릉읍 도동리
 
5
Other values (15)
75 

Length

Max length20
Median length15
Mean length13.65
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시 유성구 구성동
2nd row전라북도 전주시완산구 남노송동
3rd row충청남도 서산시 수석동
4th row대전광역시 유성구 구성동
5th row경상북도 포항시남구 송도동

Common Values

ValueCountFrequency (%)
대전광역시 유성구 구성동 5
 
5.0%
전라북도 전주시완산구 남노송동 5
 
5.0%
충청남도 서산시 수석동 5
 
5.0%
경상북도 포항시남구 송도동 5
 
5.0%
경상북도 울릉군 울릉읍 도동리 5
 
5.0%
경상북도 울진군 울진읍 연지리 5
 
5.0%
충청북도 영동군 추풍령면 관리 5
 
5.0%
전라북도 군산시 내흥동 5
 
5.0%
인천광역시 중구 전동 5
 
5.0%
경기도 수원시 권선구 서둔동 5
 
5.0%
Other values (10) 50
50.0%

Length

2023-12-10T21:07:46.999403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 20
 
6.1%
강원도 20
 
6.1%
충청북도 15
 
4.5%
전라북도 10
 
3.0%
대전광역시 5
 
1.5%
강릉시 5
 
1.5%
안림동 5
 
1.5%
청주시흥덕구 5
 
1.5%
복대동 5
 
1.5%
안동시 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 
SPI3
20 
SPI1
20 
SPI9
20 
SPI12
20 

Length

Max length5
Median length4
Mean length4.2
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T21:07:47.163120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:47.302031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
spi6 20
20.0%
spi3 20
20.0%
spi1 20
20.0%
spi9 20
20.0%
spi12 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
2023-12-10T21:07:47.468469image/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
2023-12-10T21:07:47.645328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
127.37212 5
 
5.0%
127.44066 5
 
5.0%
127.7357 5
 
5.0%
128.758698 5
 
5.0%
128.56472 5
 
5.0%
126.965792 5
 
5.0%
128.89098 5
 
5.0%
127.9466 5
 
5.0%
128.6522 5
 
5.0%
128.70726 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
2023-12-10T21:07:47.796734image/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
2023-12-10T21:07:47.973788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
36.372 5
 
5.0%
36.63924 5
 
5.0%
37.90256 5
 
5.0%
37.686924 5
 
5.0%
38.25085 5
 
5.0%
37.571411 5
 
5.0%
37.75147 5
 
5.0%
37.33756 5
 
5.0%
35.828 5
 
5.0%
36.573044 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  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정상
89 
보통습윤
 
6
보통가뭄
 
4
심한습윤
 
1

Length

Max length4
Median length2
Mean length2.22
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 89
89.0%
보통습윤 6
 
6.0%
보통가뭄 4
 
4.0%
심한습윤 1
 
1.0%

Length

2023-12-10T21:07:48.203295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:48.355025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 89
89.0%
보통습윤 6
 
6.0%
보통가뭄 4
 
4.0%
심한습윤 1
 
1.0%

분석값
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1329
Minimum-1.39
Maximum1.64
Zeros1
Zeros (%)1.0%
Negative35
Negative (%)35.0%
Memory size1.0 KiB
2023-12-10T21:07:48.526647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.39
5-th percentile-0.812
Q1-0.2475
median0.185
Q30.4525
95-th percentile1.081
Maximum1.64
Range3.03
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.58309397
Coefficient of variation (CV)4.387464
Kurtosis0.18225594
Mean0.1329
Median Absolute Deviation (MAD)0.345
Skewness-0.20046893
Sum13.29
Variance0.33999858
MonotonicityNot monotonic
2023-12-10T21:07:48.731462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.39 4
 
4.0%
0.38 2
 
2.0%
0.42 2
 
2.0%
0.01 2
 
2.0%
0.49 2
 
2.0%
0.23 2
 
2.0%
0.13 2
 
2.0%
-0.27 2
 
2.0%
0.64 2
 
2.0%
0.36 2
 
2.0%
Other values (71) 78
78.0%
ValueCountFrequency (%)
-1.39 1
1.0%
-1.35 1
1.0%
-1.14 1
1.0%
-1.0 1
1.0%
-0.85 1
1.0%
-0.81 1
1.0%
-0.79 1
1.0%
-0.75 1
1.0%
-0.72 1
1.0%
-0.71 1
1.0%
ValueCountFrequency (%)
1.64 1
1.0%
1.35 1
1.0%
1.26 1
1.0%
1.13 1
1.0%
1.1 1
1.0%
1.08 1
1.0%
1.01 1
1.0%
0.95 1
1.0%
0.92 1
1.0%
0.91 1
1.0%

Interactions

2023-12-10T21:07:45.890585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:45.270994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:45.583999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:45.994610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:45.379395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:45.665025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:46.131405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:45.491686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:45.791305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:07:48.895283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소위치주소SPI구분위도경도분석결과분석값
관측소위치1.0001.0000.0001.0001.0000.4000.372
주소1.0001.0000.0001.0001.0000.4000.372
SPI구분0.0000.0001.0000.0000.0000.2700.717
위도1.0001.0000.0001.0000.8480.3940.219
경도1.0001.0000.0000.8481.0000.3910.420
분석결과0.4000.4000.2700.3940.3911.0000.916
분석값0.3720.3720.7170.2190.4200.9161.000
2023-12-10T21:07:49.046046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소SPI구분관측소위치분석결과
주소1.0000.0001.0000.176
SPI구분0.0001.0000.0000.221
관측소위치1.0000.0001.0000.176
분석결과0.1760.2210.1761.000
2023-12-10T21:07:49.173736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도분석값관측소위치주소SPI구분분석결과
위도1.0000.1500.1380.9330.9330.0000.179
경도0.1501.000-0.2350.9430.9430.0000.234
분석값0.138-0.2351.0000.1100.1100.3670.786
관측소위치0.9330.9430.1101.0001.0000.0000.176
주소0.9330.9430.1101.0001.0000.0000.176
SPI구분0.0000.0000.3670.0000.0001.0000.221
분석결과0.1790.2340.7860.1760.1760.2211.000

Missing values

2023-12-10T21:07:46.299310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:07:46.503260image/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
02019-03-01대전(기)대전광역시 유성구 구성동SPI6127.3721236.372정상0.72
12019-03-01전주(기)전라북도 전주시완산구 남노송동SPI6127.1549635.8215정상0.18
22019-03-01서산(기)충청남도 서산시 수석동SPI3126.4939136.77661정상-0.63
32019-03-01대전(기)대전광역시 유성구 구성동SPI3127.3721236.372정상-0.13
42019-03-01포항(기)경상북도 포항시남구 송도동SPI3129.3796336.03259정상-0.4
52019-03-01전주(기)전라북도 전주시완산구 남노송동SPI3127.1549635.8215정상-0.35
62019-03-01울릉도(기)경상북도 울릉군 울릉읍 도동리SPI6130.8986437.48129정상0.56
72019-03-01서산(기)충청남도 서산시 수석동SPI1126.4939136.77661정상0.19
82019-03-01울진(기)경상북도 울진군 울진읍 연지리SPI9129.4127836.99176정상0.79
92019-03-01대전(기)대전광역시 유성구 구성동SPI1127.3721236.372정상0.44
분석일자관측소위치주소SPI구분위도경도분석결과분석값
902019-03-01춘천(기)강원도 춘천시 우두동SPI9127.735737.90256정상-0.19
912019-03-01강릉(기)강원도 강릉시 용강동SPI3128.8909837.75147보통가뭄-1.0
922019-03-01강릉(기)강원도 강릉시 용강동SPI1128.8909837.75147정상-0.34
932019-03-01춘천(기)강원도 춘천시 우두동SPI12127.735737.90256정상0.34
942019-03-01서울(기)서울특별시 종로구 송월동SPI1126.96579237.571411정상0.1
952019-03-01서울(기)서울특별시 종로구 송월동SPI6126.96579237.571411정상0.23
962019-03-01서울(기)서울특별시 종로구 송월동SPI12126.96579237.571411정상-0.27
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982019-03-01전주(기)전라북도 전주시완산구 남노송동SPI12127.1549635.8215정상0.3
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