Overview

Dataset statistics

Number of variables7
Number of observations156
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory61.8 B

Variable types

Categorical4
DateTime1
Numeric2

Dataset

Description한국지역난방공사에서 제공하는 기상청 포인트3일 예보 정보 입니다(관측소, 예측일, 예측시간, 기온, 강수량, 풍속, 습도 정보 포함)
Author한국지역난방공사
URLhttps://www.data.go.kr/data/15124153/fileData.do

Alerts

예측일 has constant value ""Constant
강수량 has constant value ""Constant
풍속 has constant value ""Constant
습도 has constant value ""Constant
예측시간 is highly overall correlated with 기온High correlation
기온 is highly overall correlated with 예측시간High correlation

Reproduction

Analysis started2023-12-12 20:11:13.604134
Analysis finished2023-12-12 20:11:14.176373
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소
Categorical

Distinct13
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
수원
12 
대구
12 
청주
12 
문산
12 
대전
12 
Other values (8)
96 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수원 12
 
7.7%
대구 12
 
7.7%
청주 12
 
7.7%
문산 12
 
7.7%
대전 12
 
7.7%
강남 12
 
7.7%
상암 12
 
7.7%
용인 12
 
7.7%
화성 12
 
7.7%
분당 12
 
7.7%
Other values (3) 36
23.1%

Length

2023-12-13T05:11:14.230890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원 12
 
7.7%
대구 12
 
7.7%
청주 12
 
7.7%
문산 12
 
7.7%
대전 12
 
7.7%
강남 12
 
7.7%
상암 12
 
7.7%
용인 12
 
7.7%
화성 12
 
7.7%
분당 12
 
7.7%
Other values (3) 36
23.1%

예측일
Date

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-07-07 00:00:00
Maximum2023-07-07 00:00:00
2023-12-13T05:11:14.312623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:14.401147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

예측시간
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T05:11:14.515621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4631703
Coefficient of variation (CV)0.53279543
Kurtosis-1.217232
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum1014
Variance11.993548
MonotonicityNot monotonic
2023-12-13T05:11:14.620800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 13
8.3%
2 13
8.3%
3 13
8.3%
4 13
8.3%
5 13
8.3%
6 13
8.3%
7 13
8.3%
8 13
8.3%
9 13
8.3%
10 13
8.3%
Other values (2) 26
16.7%
ValueCountFrequency (%)
1 13
8.3%
2 13
8.3%
3 13
8.3%
4 13
8.3%
5 13
8.3%
6 13
8.3%
7 13
8.3%
8 13
8.3%
9 13
8.3%
10 13
8.3%
ValueCountFrequency (%)
12 13
8.3%
11 13
8.3%
10 13
8.3%
9 13
8.3%
8 13
8.3%
7 13
8.3%
6 13
8.3%
5 13
8.3%
4 13
8.3%
3 13
8.3%

기온
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.897436
Minimum22
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T05:11:14.722473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q123
median24
Q326
95-th percentile31
Maximum32
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6351948
Coefficient of variation (CV)0.10584201
Kurtosis0.54169405
Mean24.897436
Median Absolute Deviation (MAD)1
Skewness1.2161762
Sum3884
Variance6.9442514
MonotonicityNot monotonic
2023-12-13T05:11:14.822405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
23 41
26.3%
24 37
23.7%
25 17
10.9%
22 17
10.9%
26 12
 
7.7%
30 7
 
4.5%
29 6
 
3.8%
28 5
 
3.2%
31 5
 
3.2%
27 5
 
3.2%
ValueCountFrequency (%)
22 17
10.9%
23 41
26.3%
24 37
23.7%
25 17
10.9%
26 12
 
7.7%
27 5
 
3.2%
28 5
 
3.2%
29 6
 
3.8%
30 7
 
4.5%
31 5
 
3.2%
ValueCountFrequency (%)
32 4
 
2.6%
31 5
 
3.2%
30 7
 
4.5%
29 6
 
3.8%
28 5
 
3.2%
27 5
 
3.2%
26 12
 
7.7%
25 17
10.9%
24 37
23.7%
23 41
26.3%

강수량
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0
156 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 156
100.0%

Length

2023-12-13T05:11:14.928886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:11:15.038618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 156
100.0%

풍속
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0
156 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 156
100.0%

Length

2023-12-13T05:11:15.145715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:11:15.233018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 156
100.0%

습도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0
156 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 156
100.0%

Length

2023-12-13T05:11:15.320864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:11:15.417094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 156
100.0%

Interactions

2023-12-13T05:11:13.860478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:13.712482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:13.931148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:11:13.782215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:11:15.482823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소예측시간기온
관측소1.0000.0000.430
예측시간0.0001.0000.820
기온0.4300.8201.000
2023-12-13T05:11:15.580632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예측시간기온관측소
예측시간1.0000.6790.000
기온0.6791.0000.058
관측소0.0000.0581.000

Missing values

2023-12-13T05:11:14.040297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:11:14.134131image/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

관측소예측일예측시간기온강수량풍속습도
0수원2023-07-07124000
1수원2023-07-07223000
2수원2023-07-07323000
3수원2023-07-07423000
4수원2023-07-07523000
5수원2023-07-07623000
6수원2023-07-07725000
7수원2023-07-07826000
8수원2023-07-07928000
9수원2023-07-071029000
관측소예측일예측시간기온강수량풍속습도
146김해2023-07-07323000
147김해2023-07-07423000
148김해2023-07-07523000
149김해2023-07-07622000
150김해2023-07-07723000
151김해2023-07-07823000
152김해2023-07-07924000
153김해2023-07-071024000
154김해2023-07-071123000
155김해2023-07-071223000