Overview

Dataset statistics

Number of variables14
Number of observations654
Missing cells180
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.6 KiB
Average record size in memory126.2 B

Variable types

Numeric14

Dataset

Description부산광역시 대기질진단평가시스템 금속물질 데이터(2020.4월까지 측정)로 측정날짜, 대기질지점코드, 납, 카드뮴, 크롬,구리 ,망간,철,니켈,비소,베릴륨,알루미늄,칼슘,마그네슘 등의 정보를 제공 합니다.
URLhttps://www.data.go.kr/data/15120962/fileData.do

Alerts

is highly overall correlated with 카드뮴 and 1 other fieldsHigh correlation
카드뮴 is highly overall correlated with High correlation
크롬 is highly overall correlated with 구리 and 3 other fieldsHigh correlation
구리 is highly overall correlated with and 4 other fieldsHigh correlation
망간 is highly overall correlated with 크롬 and 5 other fieldsHigh correlation
is highly overall correlated with 크롬 and 6 other fieldsHigh correlation
니켈 is highly overall correlated with 크롬 and 3 other fieldsHigh correlation
알루미늄 is highly overall correlated with 망간 and 3 other fieldsHigh correlation
칼슘 is highly overall correlated with 망간 and 3 other fieldsHigh correlation
마그네슘 is highly overall correlated with and 2 other fieldsHigh correlation
has 15 (2.3%) missing valuesMissing
카드뮴 has 15 (2.3%) missing valuesMissing
크롬 has 15 (2.3%) missing valuesMissing
구리 has 15 (2.3%) missing valuesMissing
망간 has 15 (2.3%) missing valuesMissing
has 15 (2.3%) missing valuesMissing
니켈 has 15 (2.3%) missing valuesMissing
비소 has 15 (2.3%) missing valuesMissing
베릴륨 has 15 (2.3%) missing valuesMissing
알루미늄 has 15 (2.3%) missing valuesMissing
칼슘 has 15 (2.3%) missing valuesMissing
마그네슘 has 15 (2.3%) missing valuesMissing
카드뮴 has 111 (17.0%) zerosZeros
크롬 has 24 (3.7%) zerosZeros
망간 has 42 (6.4%) zerosZeros
니켈 has 50 (7.6%) zerosZeros
비소 has 145 (22.2%) zerosZeros
베릴륨 has 614 (93.9%) zerosZeros

Reproduction

Analysis started2023-12-12 04:04:31.171870
Analysis finished2023-12-12 04:05:02.277918
Duration31.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정날짜
Real number (ℝ)

Distinct169
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20187175
Minimum20171115
Maximum20200410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:02.388125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20171115
5-th percentile20180109
Q120180616
median20190212
Q320191008
95-th percentile20200312
Maximum20200410
Range29295
Interquartile range (IQR)10392

Descriptive statistics

Standard deviation7677.8259
Coefficient of variation (CV)0.00038033187
Kurtosis-0.72516481
Mean20187175
Median Absolute Deviation (MAD)9299
Skewness0.18489655
Sum1.3202412 × 1010
Variance58949011
MonotonicityIncreasing
2023-12-12T13:05:02.565626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20171115 5
 
0.8%
20200406 5
 
0.8%
20200312 5
 
0.8%
20200311 5
 
0.8%
20200309 5
 
0.8%
20200214 5
 
0.8%
20200213 5
 
0.8%
20200212 5
 
0.8%
20200211 5
 
0.8%
20200210 5
 
0.8%
Other values (159) 604
92.4%
ValueCountFrequency (%)
20171115 5
0.8%
20171116 5
0.8%
20171117 5
0.8%
20171118 5
0.8%
20171119 5
0.8%
20180108 5
0.8%
20180109 5
0.8%
20180110 5
0.8%
20180111 4
0.6%
20180112 4
0.6%
ValueCountFrequency (%)
20200410 5
0.8%
20200409 5
0.8%
20200408 5
0.8%
20200407 5
0.8%
20200406 5
0.8%
20200313 5
0.8%
20200312 5
0.8%
20200311 5
0.8%
20200310 5
0.8%
20200309 5
0.8%

대기질지점코드
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221208.9
Minimum221152
Maximum221271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:02.765678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum221152
5-th percentile221152
Q1221181
median221182
Q3221251
95-th percentile221271
Maximum221271
Range119
Interquartile range (IQR)70

Descriptive statistics

Standard deviation41.272985
Coefficient of variation (CV)0.00018657923
Kurtosis-1.2350986
Mean221208.9
Median Absolute Deviation (MAD)30
Skewness0.33803062
Sum1.4467062 × 108
Variance1703.4593
MonotonicityNot monotonic
2023-12-12T13:05:02.928238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
221181 145
22.2%
221271 145
22.2%
221221 140
21.4%
221182 104
15.9%
221152 90
13.8%
221251 30
 
4.6%
ValueCountFrequency (%)
221152 90
13.8%
221181 145
22.2%
221182 104
15.9%
221221 140
21.4%
221251 30
 
4.6%
221271 145
22.2%
ValueCountFrequency (%)
221271 145
22.2%
221251 30
 
4.6%
221221 140
21.4%
221182 104
15.9%
221181 145
22.2%
221152 90
13.8%


Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct302
Distinct (%)47.3%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.019996714
Minimum0.001
Maximum0.1779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:03.083874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.0037
Q10.009
median0.0151
Q30.0241
95-th percentile0.05
Maximum0.1779
Range0.1769
Interquartile range (IQR)0.0151

Descriptive statistics

Standard deviation0.020385107
Coefficient of variation (CV)1.0194229
Kurtosis20.620242
Mean0.019996714
Median Absolute Deviation (MAD)0.0072
Skewness3.9068194
Sum12.7779
Variance0.00041555261
MonotonicityNot monotonic
2023-12-12T13:05:03.310797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0155 7
 
1.1%
0.009 7
 
1.1%
0.0147 6
 
0.9%
0.006 6
 
0.9%
0.0079 6
 
0.9%
0.0125 6
 
0.9%
0.0035 6
 
0.9%
0.007 6
 
0.9%
0.0055 6
 
0.9%
0.0106 5
 
0.8%
Other values (292) 578
88.4%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.001 1
0.2%
0.0011 1
0.2%
0.0012 1
0.2%
0.0015 1
0.2%
0.0018 1
0.2%
0.0019 1
0.2%
0.0021 1
0.2%
0.0022 1
0.2%
0.0024 1
0.2%
0.0025 2
0.3%
ValueCountFrequency (%)
0.1779 1
0.2%
0.1685 1
0.2%
0.1558 1
0.2%
0.1405 1
0.2%
0.1387 1
0.2%
0.1328 1
0.2%
0.1205 1
0.2%
0.1126 1
0.2%
0.1112 1
0.2%
0.111 1
0.2%

카드뮴
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct40
Distinct (%)6.3%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.00067010955
Minimum0
Maximum0.0057
Zeros111
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:03.866515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0001
median0.0004
Q30.0009
95-th percentile0.00231
Maximum0.0057
Range0.0057
Interquartile range (IQR)0.0008

Descriptive statistics

Standard deviation0.00083433293
Coefficient of variation (CV)1.2450695
Kurtosis7.757764
Mean0.00067010955
Median Absolute Deviation (MAD)0.0003
Skewness2.3845543
Sum0.4282
Variance6.9611143 × 10-7
MonotonicityNot monotonic
2023-12-12T13:05:04.042104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 111
17.0%
0.0001 77
11.8%
0.0002 72
11.0%
0.0003 43
 
6.6%
0.0004 42
 
6.4%
0.0005 38
 
5.8%
0.0006 31
 
4.7%
0.0007 30
 
4.6%
0.0008 24
 
3.7%
0.0011 21
 
3.2%
Other values (30) 150
22.9%
ValueCountFrequency (%)
0.0 111
17.0%
0.0001 77
11.8%
0.0002 72
11.0%
0.0003 43
 
6.6%
0.0004 42
 
6.4%
0.0005 38
 
5.8%
0.0006 31
 
4.7%
0.0007 30
 
4.6%
0.0008 24
 
3.7%
0.0009 16
 
2.4%
ValueCountFrequency (%)
0.0057 1
 
0.2%
0.0056 1
 
0.2%
0.0052 2
0.3%
0.0043 1
 
0.2%
0.0038 2
0.3%
0.0037 1
 
0.2%
0.0035 1
 
0.2%
0.0033 2
0.3%
0.0031 3
0.5%
0.003 3
0.5%

크롬
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct154
Distinct (%)24.1%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.0048423318
Minimum0
Maximum0.0593
Zeros24
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:04.235782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0003
Q10.0011
median0.0021
Q30.0044
95-th percentile0.01902
Maximum0.0593
Range0.0593
Interquartile range (IQR)0.0033

Descriptive statistics

Standard deviation0.0072522191
Coefficient of variation (CV)1.4976709
Kurtosis14.005794
Mean0.0048423318
Median Absolute Deviation (MAD)0.0012
Skewness3.2180022
Sum3.09425
Variance5.2594682 × 10-5
MonotonicityNot monotonic
2023-12-12T13:05:04.437470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0016 28
 
4.3%
0.0013 27
 
4.1%
0.001 26
 
4.0%
0.0 24
 
3.7%
0.0011 23
 
3.5%
0.0007 21
 
3.2%
0.0008 19
 
2.9%
0.0009 16
 
2.4%
0.0012 14
 
2.1%
0.002 14
 
2.1%
Other values (144) 427
65.3%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0 24
3.7%
0.0001 1
 
0.2%
0.0002 5
 
0.8%
0.0003 6
 
0.9%
0.0004 13
2.0%
0.0005 13
2.0%
0.0006 11
1.7%
0.0007 21
3.2%
0.0008 19
2.9%
0.0009 16
2.4%
ValueCountFrequency (%)
0.0593 1
0.2%
0.0545 1
0.2%
0.054 1
0.2%
0.0369 2
0.3%
0.0343 1
0.2%
0.0332 1
0.2%
0.0309 1
0.2%
0.0307 1
0.2%
0.0297 1
0.2%
0.0296 1
0.2%

구리
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)43.0%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.025177465
Minimum0.0001
Maximum0.7294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:04.617557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.0041
Q10.0082
median0.0125
Q30.02005
95-th percentile0.05083
Maximum0.7294
Range0.7293
Interquartile range (IQR)0.01185

Descriptive statistics

Standard deviation0.061842729
Coefficient of variation (CV)2.4562731
Kurtosis60.527505
Mean0.025177465
Median Absolute Deviation (MAD)0.0052
Skewness7.1902645
Sum16.0884
Variance0.0038245231
MonotonicityNot monotonic
2023-12-12T13:05:04.808284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0089 8
 
1.2%
0.0118 8
 
1.2%
0.0099 8
 
1.2%
0.0041 7
 
1.1%
0.0164 7
 
1.1%
0.0111 7
 
1.1%
0.0169 7
 
1.1%
0.0098 7
 
1.1%
0.0082 6
 
0.9%
0.012 6
 
0.9%
Other values (265) 568
86.9%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0001 1
 
0.2%
0.0012 1
 
0.2%
0.0015 1
 
0.2%
0.0017 1
 
0.2%
0.0024 1
 
0.2%
0.0025 1
 
0.2%
0.0029 2
0.3%
0.003 1
 
0.2%
0.0031 4
0.6%
0.0033 1
 
0.2%
ValueCountFrequency (%)
0.7294 1
0.2%
0.618 1
0.2%
0.5677 1
0.2%
0.5248 1
0.2%
0.4081 1
0.2%
0.3559 1
0.2%
0.3222 1
0.2%
0.3012 1
0.2%
0.2872 1
0.2%
0.2713 1
0.2%

망간
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct365
Distinct (%)57.1%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.029285133
Minimum0
Maximum0.2909
Zeros42
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:04.980289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00925
median0.0192
Q30.035
95-th percentile0.0988
Maximum0.2909
Range0.2909
Interquartile range (IQR)0.02575

Descriptive statistics

Standard deviation0.035235495
Coefficient of variation (CV)1.2031871
Kurtosis13.912142
Mean0.029285133
Median Absolute Deviation (MAD)0.0119
Skewness3.1319727
Sum18.7132
Variance0.0012415401
MonotonicityNot monotonic
2023-12-12T13:05:05.137724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 42
 
6.4%
0.0156 7
 
1.1%
0.0187 6
 
0.9%
0.0139 5
 
0.8%
0.0112 5
 
0.8%
0.0069 5
 
0.8%
0.011 5
 
0.8%
0.0211 5
 
0.8%
0.0213 4
 
0.6%
0.0117 4
 
0.6%
Other values (355) 551
84.3%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0 42
6.4%
0.0001 4
 
0.6%
0.0002 1
 
0.2%
0.0004 4
 
0.6%
0.0005 2
 
0.3%
0.0007 3
 
0.5%
0.0009 1
 
0.2%
0.0012 2
 
0.3%
0.0014 1
 
0.2%
0.0017 1
 
0.2%
ValueCountFrequency (%)
0.2909 1
0.2%
0.2745 1
0.2%
0.2405 1
0.2%
0.228 1
0.2%
0.1924 1
0.2%
0.1866 1
0.2%
0.1823 1
0.2%
0.162 1
0.2%
0.153 1
0.2%
0.1524 1
0.2%


Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct590
Distinct (%)92.3%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.58335156
Minimum0.0004
Maximum3.7294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:05.310810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0004
5-th percentile0.06757
Q10.2213
median0.4211
Q30.74125
95-th percentile1.7354
Maximum3.7294
Range3.729
Interquartile range (IQR)0.51995

Descriptive statistics

Standard deviation0.54145505
Coefficient of variation (CV)0.92817964
Kurtosis5.993468
Mean0.58335156
Median Absolute Deviation (MAD)0.2302
Skewness2.1436404
Sum372.76165
Variance0.29317357
MonotonicityNot monotonic
2023-12-12T13:05:05.518944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1369 3
 
0.5%
0.3104 3
 
0.5%
0.1909 3
 
0.5%
0.829 2
 
0.3%
1.7354 2
 
0.3%
0.8479 2
 
0.3%
0.7954 2
 
0.3%
0.7691 2
 
0.3%
0.7481 2
 
0.3%
1.7802 2
 
0.3%
Other values (580) 616
94.2%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0004 1
0.2%
0.0006 1
0.2%
0.0008 1
0.2%
0.001 2
0.3%
0.0011 2
0.3%
0.0014 1
0.2%
0.0016 2
0.3%
0.0017 1
0.2%
0.0018 1
0.2%
0.0021 2
0.3%
ValueCountFrequency (%)
3.7294 1
0.2%
3.7263 1
0.2%
3.1735 1
0.2%
2.7796 1
0.2%
2.7095 1
0.2%
2.602 1
0.2%
2.528 1
0.2%
2.4961 1
0.2%
2.3688 1
0.2%
2.3528 1
0.2%

니켈
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct148
Distinct (%)23.2%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.0046408451
Minimum0
Maximum0.0688
Zeros50
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:05.711987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0009
median0.0023
Q30.0051
95-th percentile0.01802
Maximum0.0688
Range0.0688
Interquartile range (IQR)0.0042

Descriptive statistics

Standard deviation0.0068931755
Coefficient of variation (CV)1.4853276
Kurtosis18.85149
Mean0.0046408451
Median Absolute Deviation (MAD)0.0018
Skewness3.5785236
Sum2.9655
Variance4.7515868 × 10-5
MonotonicityNot monotonic
2023-12-12T13:05:05.884345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 50
 
7.6%
0.0006 22
 
3.4%
0.0012 22
 
3.4%
0.0009 19
 
2.9%
0.0014 17
 
2.6%
0.0007 17
 
2.6%
0.0013 15
 
2.3%
0.0011 13
 
2.0%
0.0015 13
 
2.0%
0.001 13
 
2.0%
Other values (138) 438
67.0%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0 50
7.6%
0.0001 11
 
1.7%
0.0002 10
 
1.5%
0.0003 11
 
1.7%
0.0004 12
 
1.8%
0.0005 9
 
1.4%
0.0006 22
3.4%
0.0007 17
 
2.6%
0.0008 12
 
1.8%
0.0009 19
 
2.9%
ValueCountFrequency (%)
0.0688 1
0.2%
0.0483 1
0.2%
0.042 1
0.2%
0.0367 1
0.2%
0.0358 1
0.2%
0.0352 1
0.2%
0.033 1
0.2%
0.0325 1
0.2%
0.032 1
0.2%
0.0308 1
0.2%

비소
Real number (ℝ)

MISSING  ZEROS 

Distinct167
Distinct (%)26.1%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.021909937
Minimum0
Maximum1.6438
Zeros145
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:06.118941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0003
median0.0034
Q30.00755
95-th percentile0.02102
Maximum1.6438
Range1.6438
Interquartile range (IQR)0.00725

Descriptive statistics

Standard deviation0.12952629
Coefficient of variation (CV)5.9117602
Kurtosis103.43932
Mean0.021909937
Median Absolute Deviation (MAD)0.0034
Skewness9.7860376
Sum14.00045
Variance0.016777061
MonotonicityNot monotonic
2023-12-12T13:05:06.338801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 145
 
22.2%
0.0018 12
 
1.8%
0.003 10
 
1.5%
0.0031 10
 
1.5%
0.0038 9
 
1.4%
0.0046 8
 
1.2%
0.001 8
 
1.2%
0.0059 8
 
1.2%
0.0002 7
 
1.1%
0.0033 7
 
1.1%
Other values (157) 415
63.5%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0 145
22.2%
0.0001 7
 
1.1%
0.0002 7
 
1.1%
0.0003 5
 
0.8%
0.0004 6
 
0.9%
0.0005 5
 
0.8%
0.0006 5
 
0.8%
0.0007 3
 
0.5%
0.0008 5
 
0.8%
0.0009 5
 
0.8%
ValueCountFrequency (%)
1.6438 1
0.2%
1.5274 1
0.2%
1.3664 1
0.2%
1.1855 1
0.2%
1.1504 1
0.2%
0.5396 1
0.2%
0.4423 1
0.2%
0.3754 1
0.2%
0.3627 1
0.2%
0.3057 1
0.2%

베릴륨
Real number (ℝ)

MISSING  ZEROS 

Distinct23
Distinct (%)3.6%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.0027211268
Minimum0
Maximum0.1323
Zeros614
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:06.543837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.1323
Range0.1323
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.01599666
Coefficient of variation (CV)5.8786899
Kurtosis38.476873
Mean0.0027211268
Median Absolute Deviation (MAD)0
Skewness6.1807215
Sum1.7388
Variance0.00025589314
MonotonicityNot monotonic
2023-12-12T13:05:06.687931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 614
93.9%
0.0001 4
 
0.6%
0.1205 1
 
0.2%
0.0362 1
 
0.2%
0.0007 1
 
0.2%
0.1048 1
 
0.2%
0.0805 1
 
0.2%
0.0436 1
 
0.2%
0.0829 1
 
0.2%
0.0517 1
 
0.2%
Other values (13) 13
 
2.0%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0 614
93.9%
0.0001 4
 
0.6%
0.0007 1
 
0.2%
0.0362 1
 
0.2%
0.0373 1
 
0.2%
0.0436 1
 
0.2%
0.0517 1
 
0.2%
0.0646 1
 
0.2%
0.0652 1
 
0.2%
0.0775 1
 
0.2%
ValueCountFrequency (%)
0.1323 1
0.2%
0.1316 1
0.2%
0.1205 1
0.2%
0.117 1
0.2%
0.1139 1
0.2%
0.1074 1
0.2%
0.1048 1
0.2%
0.0963 1
0.2%
0.0958 1
0.2%
0.0915 1
0.2%

알루미늄
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct556
Distinct (%)87.0%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.17825266
Minimum0
Maximum0.7017
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:06.854409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02345
Q10.07055
median0.1289
Q30.2713
95-th percentile0.47084
Maximum0.7017
Range0.7017
Interquartile range (IQR)0.20075

Descriptive statistics

Standard deviation0.14151477
Coefficient of variation (CV)0.79389988
Kurtosis0.71333966
Mean0.17825266
Median Absolute Deviation (MAD)0.0747
Skewness1.1298369
Sum113.90345
Variance0.020026429
MonotonicityNot monotonic
2023-12-12T13:05:07.015130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0347 3
 
0.5%
0.2936 3
 
0.5%
0.0893 2
 
0.3%
0.025 2
 
0.3%
0.0529 2
 
0.3%
0.1131 2
 
0.3%
0.104 2
 
0.3%
0.0938 2
 
0.3%
0.1086 2
 
0.3%
0.037 2
 
0.3%
Other values (546) 617
94.3%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0 2
0.3%
0.0053 1
0.2%
0.0063 1
0.2%
0.0064 1
0.2%
0.0066 2
0.3%
0.0068 1
0.2%
0.0073 1
0.2%
0.0089 1
0.2%
0.01 1
0.2%
0.0107 1
0.2%
ValueCountFrequency (%)
0.7017 1
0.2%
0.6852 1
0.2%
0.64 1
0.2%
0.6271 1
0.2%
0.6109 1
0.2%
0.6004 1
0.2%
0.5996 1
0.2%
0.5859 1
0.2%
0.5707 1
0.2%
0.568 1
0.2%

칼슘
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct586
Distinct (%)91.7%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.42244577
Minimum0
Maximum1.7233
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:07.178417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07106
Q10.2084
median0.3378
Q30.57595
95-th percentile0.99239
Maximum1.7233
Range1.7233
Interquartile range (IQR)0.36755

Descriptive statistics

Standard deviation0.30022984
Coefficient of variation (CV)0.71069439
Kurtosis2.0957255
Mean0.42244577
Median Absolute Deviation (MAD)0.1634
Skewness1.2901507
Sum269.94285
Variance0.090137957
MonotonicityNot monotonic
2023-12-12T13:05:07.387137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3546 3
 
0.5%
0.221 3
 
0.5%
0.6779 3
 
0.5%
0.2282 2
 
0.3%
0.2635 2
 
0.3%
0.1338 2
 
0.3%
0.8257 2
 
0.3%
0.883 2
 
0.3%
0.8582 2
 
0.3%
0.6799 2
 
0.3%
Other values (576) 616
94.2%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0 1
0.2%
0.0002 2
0.3%
0.0004 1
0.2%
0.0007 1
0.2%
0.0008 2
0.3%
0.0009 2
0.3%
0.0011 1
0.2%
0.0012 1
0.2%
0.0013 1
0.2%
0.0018 1
0.2%
ValueCountFrequency (%)
1.7233 1
0.2%
1.6237 1
0.2%
1.6174 1
0.2%
1.5802 1
0.2%
1.5543 1
0.2%
1.5437 1
0.2%
1.4715 1
0.2%
1.4709 1
0.2%
1.4017 1
0.2%
1.2805 1
0.2%

마그네슘
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct553
Distinct (%)86.5%
Missing15
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.17230352
Minimum0.0031
Maximum0.5542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-12-12T13:05:07.552265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0031
5-th percentile0.0422
Q10.10405
median0.1588
Q30.2208
95-th percentile0.36643
Maximum0.5542
Range0.5511
Interquartile range (IQR)0.11675

Descriptive statistics

Standard deviation0.096696637
Coefficient of variation (CV)0.56119943
Kurtosis1.2819478
Mean0.17230352
Median Absolute Deviation (MAD)0.058
Skewness0.97777142
Sum110.10195
Variance0.0093502397
MonotonicityNot monotonic
2023-12-12T13:05:07.688540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0934 3
 
0.5%
0.2232 3
 
0.5%
0.1456 3
 
0.5%
0.2281 3
 
0.5%
0.2047 3
 
0.5%
0.1699 3
 
0.5%
0.1075 2
 
0.3%
0.1612 2
 
0.3%
0.2162 2
 
0.3%
0.2096 2
 
0.3%
Other values (543) 613
93.7%
(Missing) 15
 
2.3%
ValueCountFrequency (%)
0.0031 1
0.2%
0.0039 1
0.2%
0.0045 1
0.2%
0.0059 1
0.2%
0.0063 1
0.2%
0.0065 1
0.2%
0.0079 1
0.2%
0.0096 1
0.2%
0.01 1
0.2%
0.0105 1
0.2%
ValueCountFrequency (%)
0.5542 1
0.2%
0.5165 1
0.2%
0.5019 1
0.2%
0.4931 1
0.2%
0.4889 1
0.2%
0.4882 1
0.2%
0.48 1
0.2%
0.4682 1
0.2%
0.4589 1
0.2%
0.4584 1
0.2%

Interactions

2023-12-12T13:04:59.971429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:32.164764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:34.324281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:36.426129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:38.375855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:40.782634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:43.051792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:45.100701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.246786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:49.765944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:51.758939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:53.681078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:55.586610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:57.964492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:00.143533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:32.323134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:34.498732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:36.586558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:38.509924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:40.939480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:43.256134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:45.250719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.420559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:49.905698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:51.887046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:53.793420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:55.720480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:58.102369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:00.272125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:32.489148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:34.634857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:36.755840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:38.975952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:41.095072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:43.424968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:45.390454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.567659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:50.035856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:52.045900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:53.895198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:55.849130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:58.238991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:00.433378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:32.643546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:34.789271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:36.913857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:39.142975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:41.259094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:43.591961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:45.540557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.712099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:50.179205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:52.181774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:54.018358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:55.971960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:58.380558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:00.552234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:32.803165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:34.934199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:37.051346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:39.268722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:41.416653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:43.750077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:45.674383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:48.225787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:50.331073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:52.324769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:54.132322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:56.110634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:58.509425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:00.686024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:32.957296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:35.081260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:37.193542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:39.407389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:41.577804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:43.907137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:45.873209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:48.388998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:50.449789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:52.474423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:54.258068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:56.249047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:58.649666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:00.819292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:33.116827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:35.240719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:37.356184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:39.557989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:41.762405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:44.054833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.019666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:48.554380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:50.591305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:52.636977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:54.400905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:56.742622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:58.807890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:00.926067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:33.272720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:35.399107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:37.470337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:39.730197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:41.912064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:44.204869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.190958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:48.719985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:50.759063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:52.774685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:54.538215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:56.889397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:58.959512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:01.032627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:33.429311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:35.547332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:37.599857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:39.852025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:42.072070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:44.335968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.343084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:48.880026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:50.924226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:52.933437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:54.664701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:57.035442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:59.111808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:01.146091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:33.588254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:35.708381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:37.708396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:39.992484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:42.215881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:44.449883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.517175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:49.023705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:51.078871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:53.080676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:54.783177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:57.197856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:59.269624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:01.269631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:33.716073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:35.851865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:37.846745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:40.125881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:42.383152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:44.561435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.664682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:49.175682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:51.210579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:53.214232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:54.918460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:57.350084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:59.411696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:01.371246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:33.868625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:35.993612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:37.963782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:40.299185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:42.542086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:44.690490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.793916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:49.341975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:51.344424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:53.329083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:55.048537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:57.508480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:59.540194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:01.481698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:34.008485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:36.147127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:38.108763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:40.461583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:42.710850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:44.834757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.931405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:49.499313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:51.489300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:53.444716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:55.252708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:57.661093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:59.701675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:05:01.579366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:34.157855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:36.293020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:38.252406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:40.636253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:42.872843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:44.970033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.101990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:49.640918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:51.632344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:53.575595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:55.420491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:57.811691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:59.850741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:05:07.795911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정날짜대기질지점코드카드뮴크롬구리망간니켈비소베릴륨알루미늄칼슘마그네슘
측정날짜1.0000.4080.1550.0000.0600.0000.1640.1030.0000.1210.0860.3340.2810.262
대기질지점코드0.4081.0000.0000.0000.5920.0910.3510.3900.3240.0000.0000.0000.1890.031
0.1550.0001.0000.5520.2430.8340.3450.4060.3050.8130.8570.0830.2520.476
카드뮴0.0000.0000.5521.0000.0000.0000.1440.1380.0000.0000.0000.0850.0000.197
크롬0.0600.5920.2430.0001.0000.4340.8460.8110.8100.0000.0000.3810.3400.416
구리0.0000.0910.8340.0000.4341.0000.4580.4860.6470.7850.9280.0000.1880.383
망간0.1640.3510.3450.1440.8460.4581.0000.9550.8450.0000.0000.6420.5810.652
0.1030.3900.4060.1380.8110.4860.9551.0000.8470.0000.0000.7250.7210.688
니켈0.0000.3240.3050.0000.8100.6470.8450.8471.0000.5750.4560.3840.3770.400
비소0.1210.0000.8130.0000.0000.7850.0000.0000.5751.0000.8550.0000.1550.358
베릴륨0.0860.0000.8570.0000.0000.9280.0000.0000.4560.8551.0000.0000.1550.367
알루미늄0.3340.0000.0830.0850.3810.0000.6420.7250.3840.0000.0001.0000.8850.827
칼슘0.2810.1890.2520.0000.3400.1880.5810.7210.3770.1550.1550.8851.0000.835
마그네슘0.2620.0310.4760.1970.4160.3830.6520.6880.4000.3580.3670.8270.8351.000
2023-12-12T13:05:07.944828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정날짜대기질지점코드카드뮴크롬구리망간니켈비소베릴륨알루미늄칼슘마그네슘
측정날짜1.0000.1870.0190.1570.089-0.0420.1300.081-0.165-0.101-0.2140.1930.1510.069
대기질지점코드0.1871.000-0.135-0.079-0.383-0.308-0.285-0.301-0.365-0.045-0.062-0.034-0.054-0.049
0.019-0.1351.0000.6740.2450.6620.2160.2720.3240.4770.2980.2010.2340.139
카드뮴0.157-0.0790.6741.0000.2110.3130.1820.1910.0900.363-0.1280.1440.2200.223
크롬0.089-0.3830.2450.2111.0000.6000.7600.8500.669-0.026-0.2210.4550.4780.401
구리-0.042-0.3080.6620.3130.6001.0000.5800.6160.7710.3040.3230.3340.3250.165
망간0.130-0.2850.2160.1820.7600.5801.0000.8540.6160.187-0.2080.6330.6140.481
0.081-0.3010.2720.1910.8500.6160.8541.0000.6430.007-0.2290.7620.7450.587
니켈-0.165-0.3650.3240.0900.6690.7710.6160.6431.0000.1240.2230.2880.2510.194
비소-0.101-0.0450.4770.363-0.0260.3040.1870.0070.1241.0000.2470.0380.0940.041
베릴륨-0.214-0.0620.298-0.128-0.2210.323-0.208-0.2290.2230.2471.000-0.200-0.261-0.265
알루미늄0.193-0.0340.2010.1440.4550.3340.6330.7620.2880.038-0.2001.0000.8870.698
칼슘0.151-0.0540.2340.2200.4780.3250.6140.7450.2510.094-0.2610.8871.0000.756
마그네슘0.069-0.0490.1390.2230.4010.1650.4810.5870.1940.041-0.2650.6980.7561.000

Missing values

2023-12-12T13:05:01.715631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:05:01.928577image/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.
2023-12-12T13:05:02.134690image/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

측정날짜대기질지점코드카드뮴크롬구리망간니켈비소베릴륨알루미늄칼슘마그네슘
0201711152211520.01110.00020.00950.0990.0711.23890.00910.0020.00.10560.28510.0838
1201711152211810.00480.00.00190.01830.01430.34180.0040.00.00.04850.18930.0526
2201711152211820.00410.00.00110.01190.01020.2260.0040.00170.00.04030.21370.0556
3201711152212210.00550.00.00210.01880.0160.36220.00460.00010.00.04730.21250.0585
4201711152212710.00460.00.00210.01130.01310.25940.00270.00010.00.03720.12540.0424
5201711162211520.01540.00030.01380.02770.07721.65760.00910.00270.00.09120.38740.1111
6201711162211810.0180.00070.00330.02170.02010.47360.0050.0040.00.06440.24610.0705
7201711162211820.01820.00040.00240.01690.01850.41110.00520.00310.00.05870.28580.0685
8201711162212210.01810.00070.00360.01880.02490.48960.00510.00380.00.06940.28880.078
9201711162212710.01340.00050.00190.01580.01490.33040.00380.00290.00.05150.16470.054
측정날짜대기질지점코드카드뮴크롬구리망간니켈비소베릴륨알루미늄칼슘마그네슘
644202004092211810.02780.00060.01170.01930.07631.2660.00470.00380.00.30550.59030.2961
645202004092211820.02230.00110.00110.00510.00340.25290.00.00350.00.16770.29650.1451
646202004092212210.01670.00040.00120.01070.01710.40530.00.0070.00.26580.58790.2238
647202004092212510.0170.00080.00160.00730.00420.30590.00.00310.00.18390.40960.1598
648202004092212710.01650.00030.00090.00730.01340.31690.00.00790.00.20150.41660.1988
649202004102211810.02170.00190.00560.01640.01410.42950.00.00610.00.09210.25240.1564
650202004102211820.00920.00080.00090.00470.00420.10470.00.00520.00.05190.13040.1359
651202004102212210.0150.00130.0010.00990.00.1730.00.00280.00.08930.290.16
652202004102212510.00760.00040.00130.00530.00460.13120.00.00510.00.05290.12620.1292
653202004102212710.01180.00080.00020.00710.00.08540.00.00020.00.04560.11510.1265