Overview

Dataset statistics

Number of variables15
Number of observations216
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.3 KiB
Average record size in memory129.6 B

Variable types

Numeric7
Categorical8

Dataset

DescriptionSample
Author경기대학교 빅데이터센터
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KGUWETHERINFO

Alerts

공개 일 has constant value ""Constant
시군구명 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
광역시도명 is highly overall correlated with 시군구코드 and 2 other fieldsHigh correlation
순번 is highly overall correlated with 공개 시간 and 2 other fieldsHigh correlation
시군구코드 is highly overall correlated with 풍향카테고리명 and 2 other fieldsHigh correlation
풍속 is highly overall correlated with 기온High correlation
풍향값 is highly overall correlated with 풍향카테고리명High correlation
공개 시간 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
습도 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
기온 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
풍향카테고리명 is highly overall correlated with 시군구코드 and 2 other fieldsHigh correlation
바람강도 유형명 is highly imbalanced (95.7%)Imbalance
동쪽서쪽바람유형명 is highly imbalanced (84.1%)Imbalance
강수 유형명 is highly imbalanced (95.7%)Imbalance
1시간 강수량 값 is highly imbalanced (92.4%)Imbalance
순번 has unique valuesUnique
풍속 has 5 (2.3%) zerosZeros
공개 시간 has 9 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-10 06:32:18.973779
Analysis finished2023-12-10 06:32:29.334322
Duration10.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3836970.8
Minimum3834001
Maximum3839913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-10T15:32:29.468468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3834001
5-th percentile3834258
Q13835479
median3836957
Q33838435
95-th percentile3839643.5
Maximum3839913
Range5912
Interquartile range (IQR)2956

Descriptive statistics

Standard deviation1735.7903
Coefficient of variation (CV)0.0004523856
Kurtosis-1.2007502
Mean3836970.8
Median Absolute Deviation (MAD)1500
Skewness-3.356076 × 10-5
Sum8.2878569 × 108
Variance3012968.1
MonotonicityStrictly increasing
2023-12-10T15:32:29.728017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3834001 1
 
0.5%
3838135 1
 
0.5%
3837878 1
 
0.5%
3837879 1
 
0.5%
3837885 1
 
0.5%
3837891 1
 
0.5%
3837901 1
 
0.5%
3837913 1
 
0.5%
3838001 1
 
0.5%
3838005 1
 
0.5%
Other values (206) 206
95.4%
ValueCountFrequency (%)
3834001 1
0.5%
3834005 1
0.5%
3834009 1
0.5%
3834128 1
0.5%
3834129 1
0.5%
3834135 1
0.5%
3834141 1
0.5%
3834151 1
0.5%
3834163 1
0.5%
3834251 1
0.5%
ValueCountFrequency (%)
3839913 1
0.5%
3839901 1
0.5%
3839891 1
0.5%
3839885 1
0.5%
3839879 1
0.5%
3839878 1
0.5%
3839759 1
0.5%
3839755 1
0.5%
3839751 1
0.5%
3839663 1
0.5%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26567.778
Minimum11110
Maximum42210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-10T15:32:29.926317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q111680
median26170
Q342130
95-th percentile42210
Maximum42210
Range31100
Interquartile range (IQR)30450

Descriptive statistics

Standard deviation12616.367
Coefficient of variation (CV)0.47487474
Kurtosis-1.5062785
Mean26567.778
Median Absolute Deviation (MAD)14940
Skewness0.043650622
Sum5738640
Variance1.5917271 × 108
MonotonicityNot monotonic
2023-12-10T15:32:30.141279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
42150 24
11.1%
42210 24
11.1%
42130 24
11.1%
26290 24
11.1%
26170 24
11.1%
26140 24
11.1%
11680 24
11.1%
11230 24
11.1%
11110 24
11.1%
ValueCountFrequency (%)
11110 24
11.1%
11230 24
11.1%
11680 24
11.1%
26140 24
11.1%
26170 24
11.1%
26290 24
11.1%
42130 24
11.1%
42150 24
11.1%
42210 24
11.1%
ValueCountFrequency (%)
42210 24
11.1%
42150 24
11.1%
42130 24
11.1%
26290 24
11.1%
26170 24
11.1%
26140 24
11.1%
11680 24
11.1%
11230 24
11.1%
11110 24
11.1%

풍속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6402778
Minimum0
Maximum4
Zeros5
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-10T15:32:30.369262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.975
median1.5
Q32.2
95-th percentile3.5
Maximum4
Range4
Interquartile range (IQR)1.225

Descriptive statistics

Standard deviation0.96722952
Coefficient of variation (CV)0.58967422
Kurtosis-0.50934436
Mean1.6402778
Median Absolute Deviation (MAD)0.6
Skewness0.46844115
Sum354.3
Variance0.93553295
MonotonicityNot monotonic
2023-12-10T15:32:30.607523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1.0 15
 
6.9%
1.7 13
 
6.0%
1.3 10
 
4.6%
2.1 10
 
4.6%
1.4 9
 
4.2%
0.3 9
 
4.2%
1.8 9
 
4.2%
0.6 9
 
4.2%
1.5 9
 
4.2%
0.7 8
 
3.7%
Other values (29) 115
53.2%
ValueCountFrequency (%)
0.0 5
2.3%
0.1 3
 
1.4%
0.2 1
 
0.5%
0.3 9
4.2%
0.4 3
 
1.4%
0.5 5
2.3%
0.6 9
4.2%
0.7 8
3.7%
0.8 7
3.2%
0.9 4
1.9%
ValueCountFrequency (%)
4.0 1
 
0.5%
3.8 2
 
0.9%
3.7 3
 
1.4%
3.6 3
 
1.4%
3.5 4
1.9%
3.4 3
 
1.4%
3.3 3
 
1.4%
3.1 8
3.7%
3.0 2
 
0.9%
2.9 3
 
1.4%

바람강도 유형명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
약
215 
약간강
 
1

Length

Max length3
Median length1
Mean length1.0092593
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
약 215
99.5%
약간강 1
 
0.5%

Length

2023-12-10T15:32:30.888416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:31.113205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
약 215
99.5%
약간강 1
 
0.5%

동쪽서쪽바람유형명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
east
211 
none
 
5

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
east 211
97.7%
none 5
 
2.3%

Length

2023-12-10T15:32:31.844393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:32.015907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
east 211
97.7%
none 5
 
2.3%

풍향값
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.08796
Minimum0
Maximum349
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-10T15:32:32.280393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q192
median158
Q3217.25
95-th percentile285.25
Maximum349
Range349
Interquartile range (IQR)125.25

Descriptive statistics

Standard deviation82.099014
Coefficient of variation (CV)0.52937064
Kurtosis-0.6289845
Mean155.08796
Median Absolute Deviation (MAD)62
Skewness0.092188935
Sum33499
Variance6740.248
MonotonicityNot monotonic
2023-12-10T15:32:32.552020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103 5
 
2.3%
113 4
 
1.9%
216 4
 
1.9%
230 4
 
1.9%
3 4
 
1.9%
92 4
 
1.9%
66 4
 
1.9%
349 4
 
1.9%
43 4
 
1.9%
208 3
 
1.4%
Other values (127) 176
81.5%
ValueCountFrequency (%)
0 1
 
0.5%
3 4
1.9%
6 2
0.9%
7 2
0.9%
18 1
 
0.5%
33 2
0.9%
35 1
 
0.5%
37 1
 
0.5%
40 1
 
0.5%
41 1
 
0.5%
ValueCountFrequency (%)
349 4
1.9%
348 2
0.9%
320 1
 
0.5%
299 1
 
0.5%
294 1
 
0.5%
291 1
 
0.5%
289 1
 
0.5%
284 1
 
0.5%
271 1
 
0.5%
269 1
 
0.5%

풍향카테고리명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
SW
32 
ESE
30 
SSW
24 
S
19 
NE
19 
Other values (11)
92 

Length

Max length3
Median length2
Mean length2.2407407
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowS
2nd rowWNW
3rd rowNE
4th rowESE
5th rowESE

Common Values

ValueCountFrequency (%)
SW 32
14.8%
ESE 30
13.9%
SSW 24
11.1%
S 19
8.8%
NE 19
8.8%
E 14
6.5%
WSW 13
6.0%
ENE 13
6.0%
N 13
6.0%
SSE 12
 
5.6%
Other values (6) 27
12.5%

Length

2023-12-10T15:32:32.848983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sw 32
14.8%
ese 30
13.9%
ssw 24
11.1%
s 19
8.8%
ne 19
8.8%
e 14
6.5%
wsw 13
6.0%
ene 13
6.0%
n 13
6.0%
sse 12
 
5.6%
Other values (6) 27
12.5%

광역시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
강원도
72 
부산광역시
72 
서울특별시
72 

Length

Max length5
Median length5
Mean length4.3333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row강원도
3rd row강원도
4th row부산광역시
5th row부산광역시

Common Values

ValueCountFrequency (%)
강원도 72
33.3%
부산광역시 72
33.3%
서울특별시 72
33.3%

Length

2023-12-10T15:32:33.162561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:33.457158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 72
33.3%
부산광역시 72
33.3%
서울특별시 72
33.3%

시군구명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
강릉시
24 
속초시
24 
원주시
24 
남구
24 
동구
24 
Other values (4)
96 

Length

Max length4
Median length3
Mean length2.7777778
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강릉시
2nd row속초시
3rd row원주시
4th row남구
5th row동구

Common Values

ValueCountFrequency (%)
강릉시 24
11.1%
속초시 24
11.1%
원주시 24
11.1%
남구 24
11.1%
동구 24
11.1%
서구 24
11.1%
강남구 24
11.1%
동대문구 24
11.1%
종로구 24
11.1%

Length

2023-12-10T15:32:33.709124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:33.946478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강릉시 24
11.1%
속초시 24
11.1%
원주시 24
11.1%
남구 24
11.1%
동구 24
11.1%
서구 24
11.1%
강남구 24
11.1%
동대문구 24
11.1%
종로구 24
11.1%

공개 일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
20201001
216 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201001
2nd row20201001
3rd row20201001
4th row20201001
5th row20201001

Common Values

ValueCountFrequency (%)
20201001 216
100.0%

Length

2023-12-10T15:32:34.290833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:34.458380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201001 216
100.0%

공개 시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1150
Minimum0
Maximum2300
Zeros9
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-10T15:32:34.632077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q1575
median1150
Q31725
95-th percentile2200
Maximum2300
Range2300
Interquartile range (IQR)1150

Descriptive statistics

Standard deviation693.8266
Coefficient of variation (CV)0.60332748
Kurtosis-1.2042195
Mean1150
Median Absolute Deviation (MAD)600
Skewness0
Sum248400
Variance481395.35
MonotonicityIncreasing
2023-12-10T15:32:34.813946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9
 
4.2%
1300 9
 
4.2%
2300 9
 
4.2%
2200 9
 
4.2%
2100 9
 
4.2%
2000 9
 
4.2%
1900 9
 
4.2%
1800 9
 
4.2%
1700 9
 
4.2%
1600 9
 
4.2%
Other values (14) 126
58.3%
ValueCountFrequency (%)
0 9
4.2%
100 9
4.2%
200 9
4.2%
300 9
4.2%
400 9
4.2%
500 9
4.2%
600 9
4.2%
700 9
4.2%
800 9
4.2%
900 9
4.2%
ValueCountFrequency (%)
2300 9
4.2%
2200 9
4.2%
2100 9
4.2%
2000 9
4.2%
1900 9
4.2%
1800 9
4.2%
1700 9
4.2%
1600 9
4.2%
1500 9
4.2%
1400 9
4.2%

강수 유형명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
없음
215 
비
 
1

Length

Max length2
Median length2
Mean length1.9953704
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
없음 215
99.5%
비 1
 
0.5%

Length

2023-12-10T15:32:35.012978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:35.187656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 215
99.5%
비 1
 
0.5%

습도
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.694444
Minimum48
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-10T15:32:35.406973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile54.75
Q166
median75
Q382
95-th percentile88.25
Maximum92
Range44
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.200737
Coefficient of variation (CV)0.13841935
Kurtosis-0.53308708
Mean73.694444
Median Absolute Deviation (MAD)8
Skewness-0.37179017
Sum15918
Variance104.05504
MonotonicityNot monotonic
2023-12-10T15:32:35.647202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
83 14
 
6.5%
76 12
 
5.6%
86 12
 
5.6%
69 11
 
5.1%
71 11
 
5.1%
63 11
 
5.1%
64 10
 
4.6%
77 10
 
4.6%
78 9
 
4.2%
80 8
 
3.7%
Other values (34) 108
50.0%
ValueCountFrequency (%)
48 1
 
0.5%
49 1
 
0.5%
50 2
0.9%
51 1
 
0.5%
52 2
0.9%
53 3
1.4%
54 1
 
0.5%
55 1
 
0.5%
57 3
1.4%
58 2
0.9%
ValueCountFrequency (%)
92 2
 
0.9%
91 7
3.2%
90 1
 
0.5%
89 1
 
0.5%
88 2
 
0.9%
87 3
 
1.4%
86 12
5.6%
85 5
 
2.3%
84 4
 
1.9%
83 14
6.5%

1시간 강수량 값
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0.0
214 
0.3
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 214
99.1%
0.3 2
 
0.9%

Length

2023-12-10T15:32:35.994727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:32:36.226127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 214
99.1%
0.3 2
 
0.9%

기온
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.196296
Minimum15.5
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-10T15:32:36.460377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.5
5-th percentile16.075
Q117.375
median18.6
Q321.1
95-th percentile23.525
Maximum25
Range9.5
Interquartile range (IQR)3.725

Descriptive statistics

Standard deviation2.4010047
Coefficient of variation (CV)0.12507645
Kurtosis-0.62735167
Mean19.196296
Median Absolute Deviation (MAD)1.5
Skewness0.64215994
Sum4146.4
Variance5.7648234
MonotonicityNot monotonic
2023-12-10T15:32:36.923660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.2 9
 
4.2%
19.0 8
 
3.7%
17.0 7
 
3.2%
17.3 6
 
2.8%
16.5 6
 
2.8%
17.4 6
 
2.8%
18.6 6
 
2.8%
16.2 5
 
2.3%
18.5 5
 
2.3%
18.1 5
 
2.3%
Other values (67) 153
70.8%
ValueCountFrequency (%)
15.5 1
 
0.5%
15.6 1
 
0.5%
15.8 3
1.4%
15.9 2
 
0.9%
16.0 4
1.9%
16.1 2
 
0.9%
16.2 5
2.3%
16.4 2
 
0.9%
16.5 6
2.8%
16.6 2
 
0.9%
ValueCountFrequency (%)
25.0 2
0.9%
24.6 2
0.9%
24.5 1
0.5%
24.3 2
0.9%
23.9 1
0.5%
23.8 1
0.5%
23.6 2
0.9%
23.5 2
0.9%
23.4 1
0.5%
23.3 2
0.9%

Interactions

2023-12-10T15:32:27.091428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:20.348719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:21.633849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:23.175605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:24.213662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:25.218151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:26.100742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:27.292149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:20.531505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:21.797084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:23.362543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:24.361577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:25.355316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:26.251258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:27.472535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:20.762781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:21.941421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:23.514790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:24.500138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:25.508876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:26.379459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:27.680281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:20.972568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:22.474596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:23.671444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:24.640949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:25.616528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:26.524298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:27.896689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:21.120209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:22.617288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:23.833909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:24.796911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:25.744006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:26.705609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:28.124755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:21.268574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:22.837780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:23.956830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:24.920976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:25.873351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:26.825542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:28.388905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:21.440136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:23.023400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:24.094725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:25.058598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:25.983058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:26.948926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:32:37.121343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군구코드풍속바람강도 유형명동쪽서쪽바람유형명풍향값풍향카테고리명광역시도명시군구명공개 시간강수 유형명습도1시간 강수량 값기온
순번1.0000.0000.5700.1280.1980.5800.4410.0000.0000.9960.0480.6830.0000.757
시군구코드0.0001.0000.567NaN0.0370.6910.7461.0001.0000.0000.0000.5720.0510.329
풍속0.5700.5671.0000.3400.6170.5030.4550.5320.4670.5710.0000.5010.0000.509
바람강도 유형명0.128NaN0.3401.0000.0000.0000.0000.0040.0130.0000.0000.0000.0000.310
동쪽서쪽바람유형명0.1980.0370.6170.0001.0000.2410.2230.0000.1860.2560.0000.2660.0000.251
풍향값0.5800.6910.5030.0000.2411.0000.9670.6580.5390.5530.5210.5290.3440.565
풍향카테고리명0.4410.7460.4550.0000.2230.9671.0000.7160.6280.4450.4680.5260.2610.423
광역시도명0.0001.0000.5320.0040.0000.6580.7161.0001.0000.0000.0040.5580.0590.331
시군구명0.0001.0000.4670.0130.1860.5390.6281.0001.0000.0000.0130.4490.1980.298
공개 시간0.9960.0000.5710.0000.2560.5530.4450.0000.0001.0000.0000.6780.0000.807
강수 유형명0.0480.0000.0000.0000.0000.5210.4680.0040.0130.0001.0000.0000.5140.000
습도0.6830.5720.5010.0000.2660.5290.5260.5580.4490.6780.0001.0000.1250.847
1시간 강수량 값0.0000.0510.0000.0000.0000.3440.2610.0590.1980.0000.5140.1251.0000.000
기온0.7570.3290.5090.3100.2510.5650.4230.3310.2980.8070.0000.8470.0001.000
2023-12-10T15:32:37.386771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1시간 강수량 값시군구명광역시도명동쪽서쪽바람유형명풍향카테고리명강수 유형명바람강도 유형명
1시간 강수량 값1.0000.1940.0970.0000.1970.3440.000
시군구명0.1941.0000.9860.1820.3130.0000.000
광역시도명0.0970.9861.0000.0000.5130.0000.000
동쪽서쪽바람유형명0.0000.1820.0001.0000.1680.0000.000
풍향카테고리명0.1970.3130.5130.1681.0000.3570.000
강수 유형명0.3440.0000.0000.0000.3571.0000.000
바람강도 유형명0.0000.0000.0000.0000.0000.0001.000
2023-12-10T15:32:37.687821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군구코드풍속풍향값공개 시간습도기온바람강도 유형명동쪽서쪽바람유형명풍향카테고리명광역시도명시군구명강수 유형명1시간 강수량 값
순번1.000-0.041-0.2960.0190.9990.553-0.5820.0970.1490.1850.0000.0000.0370.000
시군구코드-0.0411.000-0.361-0.0660.0000.238-0.2170.0000.0000.5131.0000.9860.0000.097
풍속-0.296-0.3611.000-0.194-0.310-0.4430.5270.3490.4230.1800.3980.2330.0000.000
풍향값0.019-0.066-0.1941.0000.016-0.149-0.0300.0000.1810.8380.4990.2800.3940.259
공개 시간0.9990.000-0.3100.0161.0000.564-0.5920.0000.1920.1880.0000.0000.0000.000
습도0.5530.238-0.443-0.1490.5641.000-0.9050.0000.2000.2320.3950.2220.0000.093
기온-0.582-0.2170.527-0.030-0.592-0.9051.0000.1760.1470.1980.2110.1240.0000.000
바람강도 유형명0.0970.0000.3490.0000.0000.0000.1761.0000.0000.0000.0000.0000.0000.000
동쪽서쪽바람유형명0.1490.0000.4230.1810.1920.2000.1470.0001.0000.1680.0000.1820.0000.000
풍향카테고리명0.1850.5130.1800.8380.1880.2320.1980.0000.1681.0000.5130.3130.3570.197
광역시도명0.0001.0000.3980.4990.0000.3950.2110.0000.0000.5131.0000.9860.0000.097
시군구명0.0000.9860.2330.2800.0000.2220.1240.0000.1820.3130.9861.0000.0000.194
강수 유형명0.0370.0000.0000.3940.0000.0000.0000.0000.0000.3570.0000.0001.0000.344
1시간 강수량 값0.0000.0970.0000.2590.0000.0930.0000.0000.0000.1970.0970.1940.3441.000

Missing values

2023-12-10T15:32:28.704468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:32:29.177126image/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

순번시군구코드풍속바람강도 유형명동쪽서쪽바람유형명풍향값풍향카테고리명광역시도명시군구명공개 일공개 시간강수 유형명습도1시간 강수량 값기온
03834001421501.2약east183S강원도강릉시202010010없음850.017.6
13834005422100.3약east294WNW강원도속초시202010010없음820.017.1
23834009421301.1약east48NE강원도원주시202010010없음620.020.2
33834128262901.3약east109ESE부산광역시남구202010010없음610.024.3
43834129261701.3약east109ESE부산광역시동구202010010없음610.024.3
53834135261402.1약east83E부산광역시서구202010010없음610.023.5
63834141116802.3약east158SSE서울특별시강남구202010010없음660.019.8
73834151112302.1약east234SW서울특별시동대문구202010010없음620.020.0
83834163111101.9약east92E서울특별시종로구202010010없음740.019.6
93834251421501.1약east148SSE강원도강릉시20201001100없음760.018.2
순번시군구코드풍속바람강도 유형명동쪽서쪽바람유형명풍향값풍향카테고리명광역시도명시군구명공개 일공개 시간강수 유형명습도1시간 강수량 값기온
2063839663111101.5약east154SSE서울특별시종로구202010012200없음810.017.9
2073839751421502.8약east206SSW강원도강릉시202010012300없음500.022.2
2083839755422101.2약east33NNE강원도속초시202010012300없음500.022.3
2093839759421302.5약east194SSW강원도원주시202010012300없음640.020.2
2103839878262900.9약east349N부산광역시남구202010012300없음700.022.3
2113839879261700.9약east349N부산광역시동구202010012300없음700.022.3
2123839885261402.0약east103ESE부산광역시서구202010012300없음700.022.5
2133839891116802.4약east188S서울특별시강남구202010012300없음750.019.6
2143839901112301.4약east218SW서울특별시동대문구202010012300없음690.019.8
2153839913111102.7약east177S서울특별시종로구202010012300없음780.019.1