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 (52.2%)Imbalance

Reproduction

Analysis started2023-12-10 12:07:33.065922
Analysis finished2023-12-10 12:07:34.990519
Duration1.92 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
2019-05-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-05-01
2nd row2019-05-01
3rd row2019-05-01
4th row2019-05-01
5th row2019-05-01

Common Values

ValueCountFrequency (%)
2019-05-01 100
100.0%

Length

2023-12-10T21:07:35.059395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:35.178657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-05-01 100
100.0%

관측소위치
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
청주(기)
 
5
창원(기)
 
5
원주(기)
 
5
춘천(기)
 
5
인천(기)
 
5
Other values (17)
75 

Length

Max length6
Median length5
Mean length5.13
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row청주(기)
2nd row창원(기)
3rd row원주(기)
4th row청주(기)
5th row대구(기)

Common Values

ValueCountFrequency (%)
청주(기) 5
 
5.0%
창원(기) 5
 
5.0%
원주(기) 5
 
5.0%
춘천(기) 5
 
5.0%
인천(기) 5
 
5.0%
대관령(기) 5
 
5.0%
전주(기) 5
 
5.0%
서울(기) 5
 
5.0%
충주(기) 5
 
5.0%
울릉도(기) 5
 
5.0%
Other values (12) 50
50.0%

Length

2023-12-10T21:07:35.282418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청주(기 5
 
5.0%
충주(기 5
 
5.0%
강릉(기 5
 
5.0%
속초(기 5
 
5.0%
울산(기 5
 
5.0%
창원(기 5
 
5.0%
수원(기 5
 
5.0%
울릉도(기 5
 
5.0%
서산(기 5
 
5.0%
서울(기 5
 
5.0%
Other values (12) 50
50.0%

주소
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충청북도 청주시흥덕구 복대동
 
5
경상남도 마산시 합포구 월포동
 
5
강원도 원주시 명륜동
 
5
강원도 춘천시 우두동
 
5
인천광역시 중구 전동
 
5
Other values (17)
75 

Length

Max length20
Median length15
Mean length13.66
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청북도 청주시흥덕구 복대동
2nd row경상남도 마산시 합포구 월포동
3rd row강원도 원주시 명륜동
4th row충청북도 청주시흥덕구 복대동
5th row대구광역시 동구 효목동

Common Values

ValueCountFrequency (%)
충청북도 청주시흥덕구 복대동 5
 
5.0%
경상남도 마산시 합포구 월포동 5
 
5.0%
강원도 원주시 명륜동 5
 
5.0%
강원도 춘천시 우두동 5
 
5.0%
인천광역시 중구 전동 5
 
5.0%
(산간)강원도 평창군 대관령면 횡계리 5
 
5.0%
전라북도 전주시완산구 남노송동 5
 
5.0%
서울특별시 종로구 송월동 5
 
5.0%
충청북도 충주시 안림동 5
 
5.0%
경상북도 울릉군 울릉읍 도동리 5
 
5.0%
Other values (12) 50
50.0%

Length

2023-12-10T21:07:35.403802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원도 20
 
6.0%
경상북도 16
 
4.8%
충청북도 13
 
3.9%
중구 10
 
3.0%
전라북도 8
 
2.4%
울릉군 5
 
1.5%
울릉읍 5
 
1.5%
도동리 5
 
1.5%
청주시흥덕구 5
 
1.5%
수원시 5
 
1.5%
Other values (53) 239
72.2%

SPI구분
Categorical

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

Length

Max length5
Median length4
Mean length4.2
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T21:07:35.534280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:35.649027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
spi6 21
21.0%
spi9 21
21.0%
spi12 20
20.0%
spi1 19
19.0%
spi3 19
19.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.10276
Minimum126.49391
Maximum130.89864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:35.788121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.49391
5-th percentile126.61784
Q1127.15496
median127.95266
Q3128.7587
95-th percentile129.48707
Maximum130.89864
Range4.40473
Interquartile range (IQR)1.603738

Descriptive statistics

Standard deviation1.1029087
Coefficient of variation (CV)0.0086095625
Kurtosis0.03582916
Mean128.10276
Median Absolute Deviation (MAD)0.801869
Skewness0.5587373
Sum12810.276
Variance1.2164076
MonotonicityNot monotonic
2023-12-10T21:07:35.917870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
127.44066 5
 
5.0%
128.758698 5
 
5.0%
128.89098 5
 
5.0%
128.56472 5
 
5.0%
129.32026 5
 
5.0%
126.49391 5
 
5.0%
126.9853 5
 
5.0%
130.89864 5
 
5.0%
128.56517 5
 
5.0%
126.965792 5
 
5.0%
Other values (12) 50
50.0%
ValueCountFrequency (%)
126.49391 5
5.0%
126.62436 5
5.0%
126.705696 3
3.0%
126.965792 5
5.0%
126.9853 5
5.0%
127.15496 5
5.0%
127.37212 4
4.0%
127.44066 5
5.0%
127.7357 5
5.0%
127.9466 5
5.0%
ValueCountFrequency (%)
130.89864 5
5.0%
129.41278 3
3.0%
129.37963 4
4.0%
129.32026 5
5.0%
128.89098 5
5.0%
128.758698 5
5.0%
128.70726 4
4.0%
128.6522 4
4.0%
128.56517 5
5.0%
128.56472 5
5.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.852879
Minimum35.190289
Maximum38.25085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:36.045627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.190289
5-th percentile35.541647
Q136.03259
median36.97038
Q337.50382
95-th percentile37.919975
Maximum38.25085
Range3.060561
Interquartile range (IQR)1.4712303

Descriptive statistics

Standard deviation0.84559345
Coefficient of variation (CV)0.022945112
Kurtosis-0.98191618
Mean36.852879
Median Absolute Deviation (MAD)0.601031
Skewness-0.30326672
Sum3685.2879
Variance0.71502829
MonotonicityNot monotonic
2023-12-10T21:07:36.172685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
36.63924 5
 
5.0%
37.686924 5
 
5.0%
37.75147 5
 
5.0%
38.25085 5
 
5.0%
35.56014 5
 
5.0%
36.77661 5
 
5.0%
37.2723 5
 
5.0%
37.48129 5
 
5.0%
35.190289 5
 
5.0%
37.571411 5
 
5.0%
Other values (12) 50
50.0%
ValueCountFrequency (%)
35.190289 5
5.0%
35.56014 5
5.0%
35.8215 5
5.0%
35.828 4
4.0%
35.992958 3
3.0%
36.03259 4
4.0%
36.22023 3
3.0%
36.372 4
4.0%
36.573044 4
4.0%
36.63924 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 3
3.0%

분석결과
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정상
77 
보통습윤
14 
심한습윤
 
6
보통가뭄
 
2
극한습윤
 
1

Length

Max length4
Median length2
Mean length2.46
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 77
77.0%
보통습윤 14
 
14.0%
심한습윤 6
 
6.0%
보통가뭄 2
 
2.0%
극한습윤 1
 
1.0%

Length

2023-12-10T21:07:36.353861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:36.518263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 77
77.0%
보통습윤 14
 
14.0%
심한습윤 6
 
6.0%
보통가뭄 2
 
2.0%
극한습윤 1
 
1.0%

분석값
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3642
Minimum-1
Maximum2.07
Zeros0
Zeros (%)0.0%
Negative33
Negative (%)33.0%
Memory size1.0 KiB
2023-12-10T21:07:36.683043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.4935
Q1-0.12
median0.285
Q30.845
95-th percentile1.566
Maximum2.07
Range3.07
Interquartile range (IQR)0.965

Descriptive statistics

Standard deviation0.67496978
Coefficient of variation (CV)1.8532943
Kurtosis-0.23387029
Mean0.3642
Median Absolute Deviation (MAD)0.465
Skewness0.46090056
Sum36.42
Variance0.4555842
MonotonicityNot monotonic
2023-12-10T21:07:36.884229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 3
 
3.0%
0.34 3
 
3.0%
-0.31 3
 
3.0%
-0.12 3
 
3.0%
0.09 3
 
3.0%
0.08 2
 
2.0%
0.96 2
 
2.0%
0.51 2
 
2.0%
-0.32 2
 
2.0%
0.52 2
 
2.0%
Other values (69) 75
75.0%
ValueCountFrequency (%)
-1.0 2
2.0%
-0.85 1
1.0%
-0.59 1
1.0%
-0.56 1
1.0%
-0.49 1
1.0%
-0.47 2
2.0%
-0.41 1
1.0%
-0.39 1
1.0%
-0.38 1
1.0%
-0.36 1
1.0%
ValueCountFrequency (%)
2.07 1
1.0%
1.97 1
1.0%
1.94 1
1.0%
1.75 1
1.0%
1.68 1
1.0%
1.56 1
1.0%
1.53 1
1.0%
1.44 1
1.0%
1.41 1
1.0%
1.28 1
1.0%

Interactions

2023-12-10T21:07:34.041782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:33.452263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:33.772130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:34.246220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:33.563724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:33.857358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:34.347847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:33.673125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:33.941422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:07:37.015886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소위치주소SPI구분위도경도분석결과분석값
관측소위치1.0001.0000.0001.0001.0000.5240.627
주소1.0001.0000.0001.0001.0000.5240.627
SPI구분0.0000.0001.0000.0000.0000.2810.315
위도1.0001.0000.0001.0000.8230.1650.385
경도1.0001.0000.0000.8231.0000.7170.527
분석결과0.5240.5240.2810.1650.7171.0000.979
분석값0.6270.6270.3150.3850.5270.9791.000
2023-12-10T21:07:37.168905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소SPI구분관측소위치분석결과
주소1.0000.0001.0000.260
SPI구분0.0001.0000.0000.106
관측소위치1.0000.0001.0000.260
분석결과0.2600.1060.2601.000
2023-12-10T21:07:37.277547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도분석값관측소위치주소SPI구분분석결과
위도1.000-0.0180.3170.9210.9210.0000.096
경도-0.0181.000-0.0110.9310.9310.0000.367
분석값0.317-0.0111.0000.2640.2640.1290.773
관측소위치0.9210.9310.2641.0001.0000.0000.260
주소0.9210.9310.2641.0001.0000.0000.260
SPI구분0.0000.0000.1290.0000.0001.0000.106
분석결과0.0960.3670.7730.2600.2600.1061.000

Missing values

2023-12-10T21:07:34.759063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:07:34.936249image/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
02019-05-01청주(기)충청북도 청주시흥덕구 복대동SPI12127.4406636.63924정상0.08
12019-05-01창원(기)경상남도 마산시 합포구 월포동SPI6128.5651735.190289정상-0.49
22019-05-01원주(기)강원도 원주시 명륜동SPI1127.946637.33756정상0.29
32019-05-01청주(기)충청북도 청주시흥덕구 복대동SPI9127.4406636.63924정상0.49
42019-05-01대구(기)대구광역시 동구 효목동SPI1128.652235.828보통습윤1.04
52019-05-01창원(기)경상남도 마산시 합포구 월포동SPI3128.5651735.190289정상-0.38
62019-05-01춘천(기)강원도 춘천시 우두동SPI12127.735737.90256정상0.34
72019-05-01인천(기)인천광역시 중구 전동SPI12126.6243637.47759정상-0.34
82019-05-01충주(기)충청북도 충주시 안림동SPI1127.9526636.97038정상0.54
92019-05-01청주(기)충청북도 청주시흥덕구 복대동SPI6127.4406636.63924정상-0.31
분석일자관측소위치주소SPI구분위도경도분석결과분석값
902019-05-01서산(기)충청남도 서산시 수석동SPI9126.4939136.77661정상-0.12
912019-05-01서산(기)충청남도 서산시 수석동SPI12126.4939136.77661정상-0.36
922019-05-01울진(기)경상북도 울진군 울진읍 연지리SPI3129.4127836.99176정상0.84
932019-05-01울진(기)경상북도 울진군 울진읍 연지리SPI6129.4127836.99176정상0.51
942019-05-01대전(기)대전광역시 유성구 구성동SPI1127.3721236.372보통습윤1.08
952019-05-01대전(기)대전광역시 유성구 구성동SPI6127.3721236.372정상0.12
962019-05-01대전(기)대전광역시 유성구 구성동SPI12127.3721236.372정상0.37
972019-05-01추풍령(기)충청북도 영동군 추풍령면 관리SPI3127.9945736.22023정상0.46
982019-05-01창원(기)경상남도 마산시 합포구 월포동SPI12128.5651735.190289보통가뭄-1.0
992019-05-01창원(기)경상남도 마산시 합포구 월포동SPI9128.5651735.190289보통가뭄-1.0