Overview

Dataset statistics

Number of variables3
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory28.3 B

Variable types

Numeric2
Categorical1

Dataset

Description샘플 데이터
Author한국기상산업기술원
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=bf3f0840-301f-11ea-94b6-73a02796bba4

Alerts

위도 has constant value ""Constant
경도 is highly overall correlated with 동서바람High correlation
동서바람 is highly overall correlated with 경도High correlation
경도 has unique valuesUnique
동서바람 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:17:21.917922
Analysis finished2023-12-10 13:17:23.128837
Duration1.21 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.25
Minimum0
Maximum82.5
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:17:23.252846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.125003
Q120.624975
median41.25
Q361.875025
95-th percentile78.37497
Maximum82.5
Range82.5
Interquartile range (IQR)41.25005

Descriptive statistics

Standard deviation24.176244
Coefficient of variation (CV)0.58609075
Kurtosis-1.2
Mean41.25
Median Absolute Deviation (MAD)20.83335
Skewness2.2790231 × 10-8
Sum4125
Variance584.49075
MonotonicityStrictly increasing
2023-12-10T22:17:23.466852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1
 
1.0%
53.3333 1
 
1.0%
61.6667 1
 
1.0%
60.8333 1
 
1.0%
60.0 1
 
1.0%
59.1667 1
 
1.0%
58.3333 1
 
1.0%
57.5 1
 
1.0%
56.6667 1
 
1.0%
55.8333 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.833333 1
1.0%
1.66667 1
1.0%
2.5 1
1.0%
3.33333 1
1.0%
4.16667 1
1.0%
5.0 1
1.0%
5.83333 1
1.0%
6.66667 1
1.0%
7.5 1
1.0%
ValueCountFrequency (%)
82.5 1
1.0%
81.6667 1
1.0%
80.8333 1
1.0%
80.0 1
1.0%
79.1667 1
1.0%
78.3333 1
1.0%
77.5 1
1.0%
76.6667 1
1.0%
75.8333 1
1.0%
75.0 1
1.0%

위도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-90
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-90
2nd row-90
3rd row-90
4th row-90
5th row-90

Common Values

ValueCountFrequency (%)
-90 100
100.0%

Length

2023-12-10T22:17:23.667435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:17:23.808710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
90 100
100.0%

동서바람
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0119
Minimum-0.249
Maximum0.269
Zeros0
Zeros (%)0.0%
Negative48
Negative (%)48.0%
Memory size1.0 KiB
2023-12-10T22:17:23.979615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.249
5-th percentile-0.2272
Q1-0.12725
median0.013
Q30.15125
95-th percentile0.2482
Maximum0.269
Range0.518
Interquartile range (IQR)0.2785

Descriptive statistics

Standard deviation0.15763764
Coefficient of variation (CV)13.246861
Kurtosis-1.2780081
Mean0.0119
Median Absolute Deviation (MAD)0.1405
Skewness-0.014982954
Sum1.19
Variance0.024849626
MonotonicityStrictly increasing
2023-12-10T22:17:24.332940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.249 1
 
1.0%
0.095 1
 
1.0%
0.15 1
 
1.0%
0.144 1
 
1.0%
0.14 1
 
1.0%
0.134 1
 
1.0%
0.128 1
 
1.0%
0.123 1
 
1.0%
0.117 1
 
1.0%
0.112 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
-0.249 1
1.0%
-0.245 1
1.0%
-0.241 1
1.0%
-0.236 1
1.0%
-0.231 1
1.0%
-0.227 1
1.0%
-0.222 1
1.0%
-0.218 1
1.0%
-0.212 1
1.0%
-0.208 1
1.0%
ValueCountFrequency (%)
0.269 1
1.0%
0.265 1
1.0%
0.261 1
1.0%
0.256 1
1.0%
0.252 1
1.0%
0.248 1
1.0%
0.243 1
1.0%
0.239 1
1.0%
0.235 1
1.0%
0.23 1
1.0%

Interactions

2023-12-10T22:17:22.593973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:22.220976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:22.777603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:17:22.453377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:17:24.548626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도동서바람
경도1.0000.992
동서바람0.9921.000
2023-12-10T22:17:24.669377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도동서바람
경도1.0001.000
동서바람1.0001.000

Missing values

2023-12-10T22:17:22.943753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:17:23.081719image/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

경도위도동서바람
00.0-90-0.249
10.833333-90-0.245
21.66667-90-0.241
32.5-90-0.236
43.33333-90-0.231
54.16667-90-0.227
65.0-90-0.222
75.83333-90-0.218
86.66667-90-0.212
97.5-90-0.208
경도위도동서바람
9075.0-900.23
9175.8333-900.235
9276.6667-900.239
9377.5-900.243
9478.3333-900.248
9579.1667-900.252
9680.0-900.256
9780.8333-900.261
9881.6667-900.265
9982.5-900.269