Overview

Dataset statistics

Number of variables9
Number of observations428
Missing cells49
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.1 KiB
Average record size in memory79.3 B

Variable types

Numeric7
Categorical1
DateTime1

Dataset

Description국립산림치유원 내 산림에서 측정한 음이온 및 기상측정 정보의 원천자료로 월 1회, 일 3회 측정한 정보입니다.측정단위는 음이온의 평방센티미터당 이온 수, 기온은 섭씨, 습도는 퍼센트, 풍속은 m/s 입니다.
Author한국산림복지진흥원
URLhttps://www.data.go.kr/data/15087158/fileData.do

Alerts

연번 is highly overall correlated with 임상코드High correlation
위도(도) is highly overall correlated with 임상코드High correlation
경도(도) is highly overall correlated with 임상코드High correlation
습도(퍼센트) is highly overall correlated with 풍속(meter per sec)High correlation
풍속(meter per sec) is highly overall correlated with 습도(퍼센트)High correlation
임상코드 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
습도(퍼센트) has 36 (8.4%) missing valuesMissing
음이온 has 13 (3.0%) missing valuesMissing
풍속(meter per sec) is highly skewed (γ1 = 20.68730652)Skewed
연번 has unique valuesUnique
풍속(meter per sec) has 87 (20.3%) zerosZeros

Reproduction

Analysis started2024-04-29 22:51:55.687199
Analysis finished2024-04-29 22:52:02.407435
Duration6.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.5
Minimum1
Maximum428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-04-30T07:52:02.481495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.35
Q1107.75
median214.5
Q3321.25
95-th percentile406.65
Maximum428
Range427
Interquartile range (IQR)213.5

Descriptive statistics

Standard deviation123.69721
Coefficient of variation (CV)0.57667697
Kurtosis-1.2
Mean214.5
Median Absolute Deviation (MAD)107
Skewness0
Sum91806
Variance15301
MonotonicityStrictly increasing
2024-04-30T07:52:02.618846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
296 1
 
0.2%
294 1
 
0.2%
293 1
 
0.2%
292 1
 
0.2%
291 1
 
0.2%
290 1
 
0.2%
289 1
 
0.2%
288 1
 
0.2%
287 1
 
0.2%
Other values (418) 418
97.7%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
428 1
0.2%
427 1
0.2%
426 1
0.2%
425 1
0.2%
424 1
0.2%
423 1
0.2%
422 1
0.2%
421 1
0.2%
420 1
0.2%
419 1
0.2%

위도(도)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.847526
Minimum36.833054
Maximum36.85636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-04-30T07:52:02.735003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.833054
5-th percentile36.833054
Q136.845947
median36.850182
Q336.852885
95-th percentile36.85636
Maximum36.85636
Range0.023306
Interquartile range (IQR)0.0069384725

Descriptive statistics

Standard deviation0.0085887265
Coefficient of variation (CV)0.00023308828
Kurtosis-0.77885664
Mean36.847526
Median Absolute Deviation (MAD)0.002703
Skewness-0.94790614
Sum15770.741
Variance7.3766222 × 10-5
MonotonicityNot monotonic
2024-04-30T07:52:02.853721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
36.850182 131
30.6%
36.833054 96
22.4%
36.85636 72
16.8%
36.852228 65
15.2%
36.852885 42
 
9.8%
36.83359444 11
 
2.6%
36.85006389 10
 
2.3%
36.850064 1
 
0.2%
ValueCountFrequency (%)
36.833054 96
22.4%
36.83359444 11
 
2.6%
36.85006389 10
 
2.3%
36.850064 1
 
0.2%
36.850182 131
30.6%
36.852228 65
15.2%
36.852885 42
 
9.8%
36.85636 72
16.8%
ValueCountFrequency (%)
36.85636 72
16.8%
36.852885 42
 
9.8%
36.852228 65
15.2%
36.850182 131
30.6%
36.850064 1
 
0.2%
36.85006389 10
 
2.3%
36.83359444 11
 
2.6%
36.833054 96
22.4%

경도(도)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.46985
Minimum128.46644
Maximum128.47223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-04-30T07:52:02.948076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.46644
5-th percentile128.46647
Q1128.46691
median128.47083
Q3128.47222
95-th percentile128.47222
Maximum128.47223
Range0.0057833
Interquartile range (IQR)0.00531325

Descriptive statistics

Standard deviation0.0025044858
Coefficient of variation (CV)1.9494736 × 10-5
Kurtosis-1.6627807
Mean128.46985
Median Absolute Deviation (MAD)0.0013895
Skewness-0.36813089
Sum54985.096
Variance6.2724492 × 10-6
MonotonicityNot monotonic
2024-04-30T07:52:03.069133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
128.47222 131
30.6%
128.466474 96
22.4%
128.471983 72
16.8%
128.469678 65
15.2%
128.467051 42
 
9.8%
128.472225 11
 
2.6%
128.4664417 11
 
2.6%
ValueCountFrequency (%)
128.4664417 11
 
2.6%
128.466474 96
22.4%
128.467051 42
 
9.8%
128.469678 65
15.2%
128.471983 72
16.8%
128.47222 131
30.6%
128.472225 11
 
2.6%
ValueCountFrequency (%)
128.472225 11
 
2.6%
128.47222 131
30.6%
128.471983 72
16.8%
128.469678 65
15.2%
128.467051 42
 
9.8%
128.466474 96
22.4%
128.4664417 11
 
2.6%

임상코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
소나무
214 
잣나무
149 
참나무
54 
기타활엽수
 
11

Length

Max length5
Median length3
Mean length3.0514019
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소나무 214
50.0%
잣나무 149
34.8%
참나무 54
 
12.6%
기타활엽수 11
 
2.6%

Length

2024-04-30T07:52:03.201050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:52:03.323381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소나무 214
50.0%
잣나무 149
34.8%
참나무 54
 
12.6%
기타활엽수 11
 
2.6%

측정일시(24H)
Date

Distinct161
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum2019-01-10 08:00:00
Maximum2023-10-18 17:00:00
2024-04-30T07:52:03.454816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:03.619044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

온도(섭씨)
Real number (ℝ)

Distinct391
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.499416
Minimum-10.83
Maximum67.42
Zeros0
Zeros (%)0.0%
Negative61
Negative (%)14.3%
Memory size3.9 KiB
2024-04-30T07:52:03.770202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10.83
5-th percentile-3.8765
Q14.96
median13.86
Q319.7575
95-th percentile25.136
Maximum67.42
Range78.25
Interquartile range (IQR)14.7975

Descriptive statistics

Standard deviation9.7275067
Coefficient of variation (CV)0.7782369
Kurtosis1.3087733
Mean12.499416
Median Absolute Deviation (MAD)6.595
Skewness-0.0031098372
Sum5349.75
Variance94.624387
MonotonicityNot monotonic
2024-04-30T07:52:03.908360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.1 3
 
0.7%
19.09 3
 
0.7%
-7.49 3
 
0.7%
18.05 3
 
0.7%
-2.27 2
 
0.5%
4.96 2
 
0.5%
18.54 2
 
0.5%
15.99 2
 
0.5%
16.66 2
 
0.5%
23.65 2
 
0.5%
Other values (381) 404
94.4%
ValueCountFrequency (%)
-10.83 1
 
0.2%
-9.57 1
 
0.2%
-8.75 1
 
0.2%
-8.74 1
 
0.2%
-7.52 1
 
0.2%
-7.49 3
0.7%
-7.1 1
 
0.2%
-6.94 1
 
0.2%
-6.65 1
 
0.2%
-6.56 1
 
0.2%
ValueCountFrequency (%)
67.42 1
0.2%
29.36 1
0.2%
29.28 1
0.2%
28.32 1
0.2%
27.75 1
0.2%
27.29 1
0.2%
26.91 1
0.2%
26.75 1
0.2%
26.53 1
0.2%
26.38 1
0.2%

습도(퍼센트)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct337
Distinct (%)86.0%
Missing36
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean61.81824
Minimum0.02
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-04-30T07:52:04.055141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile27.7675
Q144.1975
median60.76
Q380.485
95-th percentile97
Maximum100
Range99.98
Interquartile range (IQR)36.2875

Descriptive statistics

Standard deviation22.064776
Coefficient of variation (CV)0.35692986
Kurtosis-0.99527734
Mean61.81824
Median Absolute Deviation (MAD)18.055
Skewness0.021286944
Sum24232.75
Variance486.85433
MonotonicityNot monotonic
2024-04-30T07:52:04.510766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97.0 15
 
3.5%
100.0 6
 
1.4%
91.79 3
 
0.7%
67.21 3
 
0.7%
40.0 3
 
0.7%
45.16 2
 
0.5%
79.89 2
 
0.5%
76.58 2
 
0.5%
56.53 2
 
0.5%
59.05 2
 
0.5%
Other values (327) 352
82.2%
(Missing) 36
 
8.4%
ValueCountFrequency (%)
0.02 1
0.2%
16.95 1
0.2%
19.79 1
0.2%
19.89 1
0.2%
20.5 1
0.2%
21.47 1
0.2%
21.72 1
0.2%
21.74 1
0.2%
22.0 1
0.2%
22.62 1
0.2%
ValueCountFrequency (%)
100.0 6
 
1.4%
99.79 1
 
0.2%
98.55 1
 
0.2%
98.37 1
 
0.2%
98.05 1
 
0.2%
98.0 1
 
0.2%
97.37 1
 
0.2%
97.0 15
3.5%
96.53 1
 
0.2%
96.42 1
 
0.2%

풍속(meter per sec)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct69
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9178575
Minimum0
Maximum757
Zeros87
Zeros (%)20.3%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-04-30T07:52:04.668039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.09
Q30.21
95-th percentile0.5165
Maximum757
Range757
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation36.58425
Coefficient of variation (CV)19.075584
Kurtosis427.97637
Mean1.9178575
Median Absolute Deviation (MAD)0.09
Skewness20.687307
Sum820.843
Variance1338.4074
MonotonicityNot monotonic
2024-04-30T07:52:04.797741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 87
20.3%
0.01 32
 
7.5%
0.02 20
 
4.7%
0.06 17
 
4.0%
0.19 15
 
3.5%
0.03 14
 
3.3%
0.09 11
 
2.6%
0.14 11
 
2.6%
0.16 11
 
2.6%
0.12 11
 
2.6%
Other values (59) 199
46.5%
ValueCountFrequency (%)
0.0 87
20.3%
0.01 32
 
7.5%
0.016 1
 
0.2%
0.02 20
 
4.7%
0.03 14
 
3.3%
0.037 1
 
0.2%
0.04 10
 
2.3%
0.05 10
 
2.3%
0.06 17
 
4.0%
0.07 10
 
2.3%
ValueCountFrequency (%)
757.0 1
0.2%
1.39 1
0.2%
1.29 1
0.2%
1.08 1
0.2%
1.06 1
0.2%
0.94 1
0.2%
0.79 2
0.5%
0.74 1
0.2%
0.7 1
0.2%
0.67 1
0.2%

음이온
Real number (ℝ)

MISSING 

Distinct392
Distinct (%)94.5%
Missing13
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean538.51342
Minimum0
Maximum3975.83
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-04-30T07:52:04.932956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.913
Q1291.45
median502.43
Q3716.78
95-th percentile1084.6
Maximum3975.83
Range3975.83
Interquartile range (IQR)425.33

Descriptive statistics

Standard deviation356.74268
Coefficient of variation (CV)0.66245829
Kurtosis19.914041
Mean538.51342
Median Absolute Deviation (MAD)213.23
Skewness2.501833
Sum223483.07
Variance127265.34
MonotonicityNot monotonic
2024-04-30T07:52:05.081224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
761.51 2
 
0.5%
246.81 2
 
0.5%
615.48 2
 
0.5%
205.52 2
 
0.5%
323.58 2
 
0.5%
88.34 2
 
0.5%
63.95 2
 
0.5%
27.19 2
 
0.5%
623.79 2
 
0.5%
92.46 2
 
0.5%
Other values (382) 395
92.3%
(Missing) 13
 
3.0%
ValueCountFrequency (%)
0.0 1
0.2%
3.34 1
0.2%
4.72 1
0.2%
6.51 1
0.2%
11.74 1
0.2%
14.22 1
0.2%
18.77 1
0.2%
27.19 2
0.5%
29.84 2
0.5%
41.01 1
0.2%
ValueCountFrequency (%)
3975.83 1
0.2%
1547.06 1
0.2%
1479.0 1
0.2%
1455.0 1
0.2%
1451.67 1
0.2%
1403.77 1
0.2%
1387.5 1
0.2%
1342.52 1
0.2%
1306.19 1
0.2%
1290.0 1
0.2%

Interactions

2024-04-30T07:52:01.383566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:57.259641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.027697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.620213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.275965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.914685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.532405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:01.472682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:57.387020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.105223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.708168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.357460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.986928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.625428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:01.575437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:57.594214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.179916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.790760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.451902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.070618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.717646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:01.687606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:57.673377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.263243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.872923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.552594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.163114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.815046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:01.785859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:57.767713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.359935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.967392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.651763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.268926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.910291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:01.879305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:57.850493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.435976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.053829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.732148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.349567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:01.170611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:02.003690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:57.943659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:58.521930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.174093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:51:59.818064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:00.440003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:52:01.265443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:52:05.195003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도(도)경도(도)임상코드온도(섭씨)습도(퍼센트)풍속(meter per sec)음이온
연번1.0000.8860.8380.7820.2270.4610.0000.457
위도(도)0.8861.0000.9820.9130.0000.1570.0060.161
경도(도)0.8380.9821.0000.9400.0610.1890.1160.180
임상코드0.7820.9130.9401.0000.2360.2810.4270.211
온도(섭씨)0.2270.0000.0610.2361.0000.7591.0000.454
습도(퍼센트)0.4610.1570.1890.2810.7591.0001.0000.247
풍속(meter per sec)0.0000.0060.1160.4271.0001.0001.000NaN
음이온0.4570.1610.1800.2110.4540.247NaN1.000
2024-04-30T07:52:05.319597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도(도)경도(도)온도(섭씨)습도(퍼센트)풍속(meter per sec)음이온임상코드
연번1.0000.1680.0310.0240.052-0.0310.3320.595
위도(도)0.1681.0000.294-0.0360.0810.0510.1230.612
경도(도)0.0310.2941.000-0.0210.091-0.199-0.0200.678
온도(섭씨)0.024-0.036-0.0211.0000.433-0.3170.3950.163
습도(퍼센트)0.0520.0810.0910.4331.000-0.5080.2970.169
풍속(meter per sec)-0.0310.051-0.199-0.317-0.5081.000-0.1040.286
음이온0.3320.123-0.0200.3950.297-0.1041.0000.173
임상코드0.5950.6120.6780.1630.1690.2860.1731.000

Missing values

2024-04-30T07:52:02.130880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:52:02.260074image/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.
2024-04-30T07:52:02.360289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번위도(도)경도(도)임상코드측정일시(24H)온도(섭씨)습도(퍼센트)풍속(meter per sec)음이온
0136.850182128.47222소나무2019-01-11 08:00:00-0.5552.920.0272.13
1236.850182128.47222소나무2019-01-11 12:00:001.8651.050.0209.27
2336.850182128.47222소나무2019-01-11 17:00:001.8253.370.0191.0
3436.850182128.47222소나무2019-02-14 08:00:00-4.1653.050.0175.01
4536.850182128.47222소나무2019-02-14 12:00:001.4847.160.0711.74
5636.850182128.47222소나무2019-02-14 17:00:001.7552.050.060.72
6736.850182128.47222소나무2019-03-28 08:00:008.1667.00.0218.77
7836.850182128.47222소나무2019-03-28 12:00:0011.6759.740.12237.7
8936.850182128.47222소나무2019-03-28 17:00:0011.5257.260.0185.87
91036.850182128.47222소나무2019-04-17 08:00:0014.4427.680.0844.94
연번위도(도)경도(도)임상코드측정일시(24H)온도(섭씨)습도(퍼센트)풍속(meter per sec)음이온
41841936.850064128.472225소나무2023-10-18 17:00:0016.5971.290.0526.6
41942036.833594128.466442소나무2023-10-18 08:00:0015.6260.890.02653.35
42042136.833594128.466442소나무2023-10-18 12:00:0018.8459.430.0717.37
42142236.833594128.466442소나무2023-10-18 17:00:0014.1767.210.0714.35
42242336.850182128.47222잣나무2023-10-17 08:00:0011.067.260.0486.0
42342436.850182128.47222잣나무2023-10-17 12:00:0014.5652.210.26789.0
42442536.850182128.47222잣나무2023-10-17 17:00:0010.2178.790.19696.0
42542636.852228128.469678기타활엽수2023-10-17 08:00:0012.267.950.01467.0
42642736.852228128.469678기타활엽수2023-10-17 12:00:0014.6559.20.03673.0
42742836.852228128.469678기타활엽수2023-10-17 17:00:0067.420.02757.0<NA>