Overview

Dataset statistics

Number of variables14
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory128.4 B

Variable types

Text1
Categorical2
Numeric11

Dataset

Description3월 광주광역시 대기중금속측정망 6곳에서 24시간씩 5일간 5회 채취하여 공정시험기준법에 따라 전처리하여 대기중 중금속 12종을 ICP-OES를 통해 농도를 분석하였음.
Author광주광역시
URLhttps://www.data.go.kr/data/3076141/fileData.do

Alerts

베릴륨(Be) has constant value ""Constant
납(Pb) is highly overall correlated with 카드뮴(Cd) and 4 other fieldsHigh correlation
카드뮴(Cd) is highly overall correlated with 납(Pb) and 8 other fieldsHigh correlation
크롬(Cr) is highly overall correlated with 납(Pb) and 5 other fieldsHigh correlation
구리(Cu) is highly overall correlated with 카드뮴(Cd) and 5 other fieldsHigh correlation
망간(Mn) is highly overall correlated with 납(Pb) and 7 other fieldsHigh correlation
철(Fe) is highly overall correlated with 납(Pb) and 8 other fieldsHigh correlation
니켈(Ni) is highly overall correlated with 납(Pb) and 6 other fieldsHigh correlation
알루미늄(Al) is highly overall correlated with 카드뮴(Cd) and 5 other fieldsHigh correlation
칼슘(Ca) is highly overall correlated with 카드뮴(Cd) and 5 other fieldsHigh correlation
마그네슘(Mg) is highly overall correlated with 카드뮴(Cd) and 4 other fieldsHigh correlation
일자 is highly overall correlated with 마그네슘(Mg)High correlation
지점 has unique valuesUnique
철(Fe) has unique valuesUnique
알루미늄(Al) has unique valuesUnique
칼슘(Ca) has unique valuesUnique
마그네슘(Mg) has unique valuesUnique
크롬(Cr) has 10 (33.3%) zerosZeros
니켈(Ni) has 12 (40.0%) zerosZeros

Reproduction

Analysis started2024-04-21 02:49:00.487498
Analysis finished2024-04-21 02:49:12.120821
Duration11.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지점
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-21T11:49:12.238989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6666667
Min length3

Characters and Unicode

Total characters110
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row건국동1
2nd row건국동2
3rd row건국동3
4th row건국동4
5th row건국동5
ValueCountFrequency (%)
건국동1 1
 
3.3%
건국동2 1
 
3.3%
평동4 1
 
3.3%
평동3 1
 
3.3%
평동2 1
 
3.3%
평동1 1
 
3.3%
노대동5 1
 
3.3%
노대동4 1
 
3.3%
노대동3 1
 
3.3%
노대동2 1
 
3.3%
Other values (20) 20
66.7%
2024-04-21T11:49:12.509845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
27.3%
1 6
 
5.5%
2 6
 
5.5%
3 6
 
5.5%
4 6
 
5.5%
5 6
 
5.5%
5
 
4.5%
5
 
4.5%
5
 
4.5%
5
 
4.5%
Other values (6) 30
27.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
72.7%
Decimal Number 30
 
27.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
30
37.5%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 6
20.0%
2 6
20.0%
3 6
20.0%
4 6
20.0%
5 6
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80
72.7%
Common 30
 
27.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
30
37.5%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
Common
ValueCountFrequency (%)
1 6
20.0%
2 6
20.0%
3 6
20.0%
4 6
20.0%
5 6
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80
72.7%
ASCII 30
 
27.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
30
37.5%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
5
 
6.2%
ASCII
ValueCountFrequency (%)
1 6
20.0%
2 6
20.0%
3 6
20.0%
4 6
20.0%
5 6
20.0%

일자
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
(2024-03-11 00:00∼2024-03-11 24:00)
(2024-03-12 00:00∼2024-03-12 24:00)
(2024-03-13 00:00∼2024-03-13 24:00)
(2024-03-14 00:00∼2024-03-14 24:00)
(2024-03-15 00:00∼2024-03-15 24:00)
Other values (4)

Length

Max length35
Median length35
Mean length35
Min length35

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row(2024-03-11 00:00∼2024-03-11 24:00)
2nd row(2024-03-12 00:00∼2024-03-12 24:00)
3rd row(2024-03-13 00:00∼2024-03-13 24:00)
4th row(2024-03-14 00:00∼2024-03-14 24:00)
5th row(2024-03-15 00:00∼2024-03-15 24:00)

Common Values

ValueCountFrequency (%)
(2024-03-11 00:00∼2024-03-11 24:00) 6
20.0%
(2024-03-12 00:00∼2024-03-12 24:00) 5
16.7%
(2024-03-13 00:00∼2024-03-13 24:00) 5
16.7%
(2024-03-14 00:00∼2024-03-14 24:00) 5
16.7%
(2024-03-15 00:00∼2024-03-15 24:00) 5
16.7%
(2024-03-20 00:00∼2024-03-20 24:00) 1
 
3.3%
(2024-03-27 00:00∼2024-03-27 24:00) 1
 
3.3%
(2024-03-28 00:00∼2024-03-28 24:00) 1
 
3.3%
(2024-03-31 00:00∼2024-03-31 24:00) 1
 
3.3%

Length

2024-04-21T11:49:12.632099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:49:12.736185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
24:00 30
33.3%
2024-03-11 6
 
6.7%
00:00∼2024-03-11 6
 
6.7%
2024-03-14 5
 
5.6%
00:00∼2024-03-15 5
 
5.6%
00:00∼2024-03-14 5
 
5.6%
2024-03-15 5
 
5.6%
00:00∼2024-03-13 5
 
5.6%
2024-03-13 5
 
5.6%
00:00∼2024-03-12 5
 
5.6%
Other values (9) 13
14.4%

납(Pb)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01212
Minimum0.0008
Maximum0.0604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:12.861737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0008
5-th percentile0.001845
Q10.004775
median0.00775
Q30.01105
95-th percentile0.05044
Maximum0.0604
Range0.0596
Interquartile range (IQR)0.006275

Descriptive statistics

Standard deviation0.01487635
Coefficient of variation (CV)1.2274216
Kurtosis5.5430203
Mean0.01212
Median Absolute Deviation (MAD)0.00325
Skewness2.4750428
Sum0.3636
Variance0.00022130579
MonotonicityNot monotonic
2024-04-21T11:49:12.968398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0027 2
 
6.7%
0.0054 2
 
6.7%
0.0081 1
 
3.3%
0.0193 1
 
3.3%
0.0192 1
 
3.3%
0.0604 1
 
3.3%
0.0562 1
 
3.3%
0.0434 1
 
3.3%
0.0115 1
 
3.3%
0.0109 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0008 1
3.3%
0.0018 1
3.3%
0.0019 1
3.3%
0.0023 1
3.3%
0.0027 2
6.7%
0.0041 1
3.3%
0.0046 1
3.3%
0.0053 1
3.3%
0.0054 2
6.7%
0.0064 1
3.3%
ValueCountFrequency (%)
0.0604 1
3.3%
0.0562 1
3.3%
0.0434 1
3.3%
0.0193 1
3.3%
0.0192 1
3.3%
0.0145 1
3.3%
0.0115 1
3.3%
0.0111 1
3.3%
0.0109 1
3.3%
0.0106 1
3.3%

카드뮴(Cd)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00039666667
Minimum0.0001
Maximum0.004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:13.127072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.0001
Q10.0002
median0.0003
Q30.0004
95-th percentile0.000555
Maximum0.004
Range0.0039
Interquartile range (IQR)0.0002

Descriptive statistics

Standard deviation0.00069455187
Coefficient of variation (CV)1.7509711
Kurtosis27.404912
Mean0.00039666667
Median Absolute Deviation (MAD)0.0001
Skewness5.1335353
Sum0.0119
Variance4.824023 × 10-7
MonotonicityNot monotonic
2024-04-21T11:49:13.269379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0001 7
23.3%
0.0002 7
23.3%
0.0003 6
20.0%
0.0004 6
20.0%
0.0005 2
 
6.7%
0.004 1
 
3.3%
0.0006 1
 
3.3%
ValueCountFrequency (%)
0.0001 7
23.3%
0.0002 7
23.3%
0.0003 6
20.0%
0.0004 6
20.0%
0.0005 2
 
6.7%
0.0006 1
 
3.3%
0.004 1
 
3.3%
ValueCountFrequency (%)
0.004 1
 
3.3%
0.0006 1
 
3.3%
0.0005 2
 
6.7%
0.0004 6
20.0%
0.0003 6
20.0%
0.0002 7
23.3%
0.0001 7
23.3%

크롬(Cr)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0012466667
Minimum0
Maximum0.006
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:13.432713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00075
Q30.002
95-th percentile0.0043
Maximum0.006
Range0.006
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0015402045
Coefficient of variation (CV)1.2354582
Kurtosis2.2312765
Mean0.0012466667
Median Absolute Deviation (MAD)0.00075
Skewness1.5917417
Sum0.0374
Variance2.3722299 × 10-6
MonotonicityNot monotonic
2024-04-21T11:49:13.546153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 10
33.3%
0.0022 2
 
6.7%
0.0043 2
 
6.7%
0.002 2
 
6.7%
0.0006 2
 
6.7%
0.0008 2
 
6.7%
0.0009 1
 
3.3%
0.001 1
 
3.3%
0.0007 1
 
3.3%
0.0021 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
0.0 10
33.3%
0.0003 1
 
3.3%
0.0005 1
 
3.3%
0.0006 2
 
6.7%
0.0007 1
 
3.3%
0.0008 2
 
6.7%
0.0009 1
 
3.3%
0.001 1
 
3.3%
0.0011 1
 
3.3%
0.0015 1
 
3.3%
ValueCountFrequency (%)
0.006 1
3.3%
0.0043 2
6.7%
0.0035 1
3.3%
0.0022 2
6.7%
0.0021 1
3.3%
0.002 2
6.7%
0.0015 1
3.3%
0.0011 1
3.3%
0.001 1
3.3%
0.0009 1
3.3%

구리(Cu)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0073933333
Minimum0.0033
Maximum0.0151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:13.669409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0033
5-th percentile0.003725
Q10.005175
median0.0062
Q30.0086
95-th percentile0.013655
Maximum0.0151
Range0.0118
Interquartile range (IQR)0.003425

Descriptive statistics

Standard deviation0.0032133271
Coefficient of variation (CV)0.43462495
Kurtosis0.078377706
Mean0.0073933333
Median Absolute Deviation (MAD)0.00185
Skewness0.9460571
Sum0.2218
Variance1.0325471 × 10-5
MonotonicityNot monotonic
2024-04-21T11:49:13.815714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0059 3
 
10.0%
0.0083 2
 
6.7%
0.0041 1
 
3.3%
0.0035 1
 
3.3%
0.0151 1
 
3.3%
0.0136 1
 
3.3%
0.0087 1
 
3.3%
0.0123 1
 
3.3%
0.0076 1
 
3.3%
0.008 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.0033 1
3.3%
0.0035 1
3.3%
0.004 1
3.3%
0.0041 1
3.3%
0.0043 1
3.3%
0.0044 1
3.3%
0.0049 1
3.3%
0.0051 1
3.3%
0.0054 1
3.3%
0.0055 1
3.3%
ValueCountFrequency (%)
0.0151 1
3.3%
0.0137 1
3.3%
0.0136 1
3.3%
0.0123 1
3.3%
0.0113 1
3.3%
0.0106 1
3.3%
0.0093 1
3.3%
0.0087 1
3.3%
0.0083 2
6.7%
0.008 1
3.3%

망간(Mn)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023253333
Minimum0.0021
Maximum0.0977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:13.944082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0021
5-th percentile0.00607
Q10.008225
median0.0121
Q30.01995
95-th percentile0.083145
Maximum0.0977
Range0.0956
Interquartile range (IQR)0.011725

Descriptive statistics

Standard deviation0.026361842
Coefficient of variation (CV)1.1336801
Kurtosis2.3779579
Mean0.023253333
Median Absolute Deviation (MAD)0.0056
Skewness1.8839701
Sum0.6976
Variance0.00069494671
MonotonicityNot monotonic
2024-04-21T11:49:14.069619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0097 2
 
6.7%
0.0064 2
 
6.7%
0.0106 2
 
6.7%
0.0066 1
 
3.3%
0.0977 1
 
3.3%
0.0885 1
 
3.3%
0.0511 1
 
3.3%
0.0738 1
 
3.3%
0.0766 1
 
3.3%
0.0201 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.0021 1
3.3%
0.0058 1
3.3%
0.0064 2
6.7%
0.0066 1
3.3%
0.0067 1
3.3%
0.0075 1
3.3%
0.0082 1
3.3%
0.0083 1
3.3%
0.0097 2
6.7%
0.0106 2
6.7%
ValueCountFrequency (%)
0.0977 1
3.3%
0.0885 1
3.3%
0.0766 1
3.3%
0.0738 1
3.3%
0.0511 1
3.3%
0.0363 1
3.3%
0.0214 1
3.3%
0.0201 1
3.3%
0.0195 1
3.3%
0.0193 1
3.3%

철(Fe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40683667
Minimum0.0962
Maximum1.1068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:14.195479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0962
5-th percentile0.156655
Q10.205775
median0.3151
Q30.53035
95-th percentile0.97416
Maximum1.1068
Range1.0106
Interquartile range (IQR)0.324575

Descriptive statistics

Standard deviation0.26214232
Coefficient of variation (CV)0.64434291
Kurtosis1.1807699
Mean0.40683667
Median Absolute Deviation (MAD)0.11415
Skewness1.3254236
Sum12.2051
Variance0.068718597
MonotonicityNot monotonic
2024-04-21T11:49:14.311705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.2029 1
 
3.3%
0.2013 1
 
3.3%
1.0413 1
 
3.3%
0.3399 1
 
3.3%
0.626 1
 
3.3%
0.6725 1
 
3.3%
0.8921 1
 
3.3%
0.4852 1
 
3.3%
0.4525 1
 
3.3%
0.257 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0962 1
3.3%
0.1522 1
3.3%
0.1621 1
3.3%
0.1814 1
3.3%
0.2006 1
3.3%
0.2013 1
3.3%
0.2029 1
3.3%
0.2034 1
3.3%
0.2129 1
3.3%
0.2521 1
3.3%
ValueCountFrequency (%)
1.1068 1
3.3%
1.0413 1
3.3%
0.8921 1
3.3%
0.6725 1
3.3%
0.672 1
3.3%
0.634 1
3.3%
0.626 1
3.3%
0.5454 1
3.3%
0.4852 1
3.3%
0.4525 1
3.3%

니켈(Ni)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00071
Minimum0
Maximum0.0027
Zeros12
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:14.443830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00035
Q30.001275
95-th percentile0.00231
Maximum0.0027
Range0.0027
Interquartile range (IQR)0.001275

Descriptive statistics

Standard deviation0.00083803958
Coefficient of variation (CV)1.1803374
Kurtosis-0.2760714
Mean0.00071
Median Absolute Deviation (MAD)0.00035
Skewness0.93581466
Sum0.0213
Variance7.0231034 × 10-7
MonotonicityNot monotonic
2024-04-21T11:49:14.615492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 12
40.0%
0.0007 3
 
10.0%
0.0012 2
 
6.7%
0.0001 2
 
6.7%
0.0008 1
 
3.3%
0.0018 1
 
3.3%
0.0016 1
 
3.3%
0.0003 1
 
3.3%
0.0004 1
 
3.3%
0.0014 1
 
3.3%
Other values (5) 5
16.7%
ValueCountFrequency (%)
0.0 12
40.0%
0.0001 2
 
6.7%
0.0003 1
 
3.3%
0.0004 1
 
3.3%
0.0007 3
 
10.0%
0.0008 1
 
3.3%
0.0012 2
 
6.7%
0.0013 1
 
3.3%
0.0014 1
 
3.3%
0.0016 1
 
3.3%
ValueCountFrequency (%)
0.0027 1
3.3%
0.0024 1
3.3%
0.0022 1
3.3%
0.0018 1
3.3%
0.0017 1
3.3%
0.0016 1
3.3%
0.0014 1
3.3%
0.0013 1
3.3%
0.0012 2
6.7%
0.0008 1
3.3%

비소(As)
Real number (ℝ)

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0027066667
Minimum0.0008
Maximum0.0069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:14.759958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0008
5-th percentile0.0011
Q10.001525
median0.002
Q30.0037
95-th percentile0.005465
Maximum0.0069
Range0.0061
Interquartile range (IQR)0.002175

Descriptive statistics

Standard deviation0.0015754437
Coefficient of variation (CV)0.5820605
Kurtosis0.32505225
Mean0.0027066667
Median Absolute Deviation (MAD)0.00065
Skewness1.0678389
Sum0.0812
Variance2.482023 × 10-6
MonotonicityNot monotonic
2024-04-21T11:49:15.027823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0037 3
 
10.0%
0.0018 3
 
10.0%
0.0019 2
 
6.7%
0.0014 2
 
6.7%
0.0026 2
 
6.7%
0.005 2
 
6.7%
0.0011 2
 
6.7%
0.0015 2
 
6.7%
0.0056 1
 
3.3%
0.004 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
0.0008 1
 
3.3%
0.0011 2
6.7%
0.0013 1
 
3.3%
0.0014 2
6.7%
0.0015 2
6.7%
0.0016 1
 
3.3%
0.0017 1
 
3.3%
0.0018 3
10.0%
0.0019 2
6.7%
0.0021 1
 
3.3%
ValueCountFrequency (%)
0.0069 1
 
3.3%
0.0056 1
 
3.3%
0.0053 1
 
3.3%
0.005 2
6.7%
0.004 1
 
3.3%
0.0037 3
10.0%
0.0034 1
 
3.3%
0.0027 1
 
3.3%
0.0026 2
6.7%
0.0023 1
 
3.3%

베릴륨(Be)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

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 30
100.0%

Length

2024-04-21T11:49:15.174293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:49:15.273446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

알루미늄(Al)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18211667
Minimum0.0204
Maximum0.8588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:15.380490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0204
5-th percentile0.05193
Q10.09525
median0.1363
Q30.23485
95-th percentile0.317225
Maximum0.8588
Range0.8384
Interquartile range (IQR)0.1396

Descriptive statistics

Standard deviation0.15286803
Coefficient of variation (CV)0.83939613
Kurtosis13.184004
Mean0.18211667
Median Absolute Deviation (MAD)0.0643
Skewness3.1230211
Sum5.4635
Variance0.023368633
MonotonicityNot monotonic
2024-04-21T11:49:15.501227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0907 1
 
3.3%
0.0772 1
 
3.3%
0.2117 1
 
3.3%
0.2058 1
 
3.3%
0.1261 1
 
3.3%
0.0578 1
 
3.3%
0.2664 1
 
3.3%
0.3188 1
 
3.3%
0.305 1
 
3.3%
0.2317 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0204 1
3.3%
0.0504 1
3.3%
0.0538 1
3.3%
0.0578 1
3.3%
0.0772 1
3.3%
0.078 1
3.3%
0.0833 1
3.3%
0.0907 1
3.3%
0.1089 1
3.3%
0.1094 1
3.3%
ValueCountFrequency (%)
0.8588 1
3.3%
0.3188 1
3.3%
0.3153 1
3.3%
0.305 1
3.3%
0.2664 1
3.3%
0.2615 1
3.3%
0.2429 1
3.3%
0.2359 1
3.3%
0.2317 1
3.3%
0.2117 1
3.3%

칼슘(Ca)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45698333
Minimum0.0434
Maximum0.9924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:15.635033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0434
5-th percentile0.096525
Q10.2569
median0.3914
Q30.645075
95-th percentile0.969075
Maximum0.9924
Range0.949
Interquartile range (IQR)0.388175

Descriptive statistics

Standard deviation0.28152603
Coefficient of variation (CV)0.61605318
Kurtosis-0.75345361
Mean0.45698333
Median Absolute Deviation (MAD)0.17975
Skewness0.54997096
Sum13.7095
Variance0.079256908
MonotonicityNot monotonic
2024-04-21T11:49:15.757697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.2463 1
 
3.3%
0.2558 1
 
3.3%
0.552 1
 
3.3%
0.5738 1
 
3.3%
0.2602 1
 
3.3%
0.0968 1
 
3.3%
0.4285 1
 
3.3%
0.6968 1
 
3.3%
0.6667 1
 
3.3%
0.2787 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0434 1
3.3%
0.0963 1
3.3%
0.0968 1
3.3%
0.1382 1
3.3%
0.1591 1
3.3%
0.2143 1
3.3%
0.2463 1
3.3%
0.2558 1
3.3%
0.2602 1
3.3%
0.2787 1
3.3%
ValueCountFrequency (%)
0.9924 1
3.3%
0.9909 1
3.3%
0.9424 1
3.3%
0.8989 1
3.3%
0.821 1
3.3%
0.7695 1
3.3%
0.6968 1
3.3%
0.6667 1
3.3%
0.5802 1
3.3%
0.5738 1
3.3%

마그네슘(Mg)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13796667
Minimum0.0231
Maximum0.5064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-21T11:49:15.893634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0231
5-th percentile0.048715
Q10.09525
median0.1346
Q30.1615
95-th percentile0.23591
Maximum0.5064
Range0.4833
Interquartile range (IQR)0.06625

Descriptive statistics

Standard deviation0.086510348
Coefficient of variation (CV)0.62703803
Kurtosis11.065541
Mean0.13796667
Median Absolute Deviation (MAD)0.03395
Skewness2.7236039
Sum4.139
Variance0.0074840402
MonotonicityNot monotonic
2024-04-21T11:49:16.037137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0731 1
 
3.3%
0.0865 1
 
3.3%
0.1334 1
 
3.3%
0.1392 1
 
3.3%
0.143 1
 
3.3%
0.0611 1
 
3.3%
0.1168 1
 
3.3%
0.1562 1
 
3.3%
0.1594 1
 
3.3%
0.1656 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0231 1
3.3%
0.0457 1
3.3%
0.0524 1
3.3%
0.0611 1
3.3%
0.0658 1
3.3%
0.0731 1
3.3%
0.0865 1
3.3%
0.0943 1
3.3%
0.0981 1
3.3%
0.1032 1
3.3%
ValueCountFrequency (%)
0.5064 1
3.3%
0.2369 1
3.3%
0.2347 1
3.3%
0.1848 1
3.3%
0.1824 1
3.3%
0.1717 1
3.3%
0.1656 1
3.3%
0.1622 1
3.3%
0.1594 1
3.3%
0.1562 1
3.3%

Interactions

2024-04-21T11:49:11.008872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:02.388619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.311353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.179056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.018932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.825138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.799534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.561798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.347655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.190732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.979095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.084739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:02.533655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.393247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.264498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.092150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.899902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.875547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.635190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.441487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.258419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.061566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.160025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:02.618719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.475254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.357228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.173460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.978850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.953911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.713821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.527272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.337520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.141156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.239377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:02.692490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.556475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.428679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.245481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.235077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.020560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.789602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.603407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.412135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.217105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.310267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:02.764176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.632019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.506088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.324546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.304468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.090103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.859472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.675251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.478910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.288844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.379227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:02.835571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.711503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.579331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.394284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.378623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.156393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.929919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.745822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.545632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.370331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.442783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:02.918257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.795312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.645386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.459030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.444384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.218724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.996528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.814117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.613060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.440484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.535236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.002188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.878127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.716182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.529697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.512756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.286985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.066406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.895656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.681269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.523384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.634670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.086821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.957111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.793637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.606709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.584641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.362931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.139556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.975394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.753591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.787386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.705324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.153021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.025828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.870420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.672672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.655107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.426605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.207308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.040847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.825726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.861458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:11.775904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:03.230189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.095394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:04.947414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:05.746944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:06.725621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:07.493468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:08.274992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.109806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:09.890559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:49:10.938387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:49:16.119141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점일자납(Pb)카드뮴(Cd)크롬(Cr)구리(Cu)망간(Mn)철(Fe)니켈(Ni)비소(As)알루미늄(Al)칼슘(Ca)마그네슘(Mg)
지점1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
일자1.0001.0000.0000.0000.0000.0000.6090.4690.0000.4490.6720.2290.843
납(Pb)1.0000.0001.0000.9050.7080.7950.7680.7320.8070.7770.1520.5550.000
카드뮴(Cd)1.0000.0000.9051.0001.0000.7750.6180.5530.7600.0000.2840.4280.000
크롬(Cr)1.0000.0000.7081.0001.0000.7630.9080.6590.7560.5880.5040.5650.000
구리(Cu)1.0000.0000.7950.7750.7631.0000.7180.8150.7410.0000.7850.6150.076
망간(Mn)1.0000.6090.7680.6180.9080.7181.0000.7120.6830.5860.6920.5220.654
철(Fe)1.0000.4690.7320.5530.6590.8150.7121.0000.8110.0000.6850.7750.698
니켈(Ni)1.0000.0000.8070.7600.7560.7410.6830.8111.0000.0000.3690.3140.000
비소(As)1.0000.4490.7770.0000.5880.0000.5860.0000.0001.0000.0000.0000.433
알루미늄(Al)1.0000.6720.1520.2840.5040.7850.6920.6850.3690.0001.0000.9030.726
칼슘(Ca)1.0000.2290.5550.4280.5650.6150.5220.7750.3140.0000.9031.0000.690
마그네슘(Mg)1.0000.8430.0000.0000.0000.0760.6540.6980.0000.4330.7260.6901.000
2024-04-21T11:49:16.468984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납(Pb)카드뮴(Cd)크롬(Cr)구리(Cu)망간(Mn)철(Fe)니켈(Ni)비소(As)알루미늄(Al)칼슘(Ca)마그네슘(Mg)일자
납(Pb)1.0000.7320.5800.3400.7040.5760.7340.4040.2710.1320.2180.000
카드뮴(Cd)0.7321.0000.6610.5930.8790.8250.794-0.1030.7330.6280.5490.000
크롬(Cr)0.5800.6611.0000.8340.6430.6650.781-0.2230.4620.3270.3680.000
구리(Cu)0.3400.5930.8341.0000.6000.7080.682-0.4680.4670.5230.3530.000
망간(Mn)0.7040.8790.6430.6001.0000.8850.681-0.0690.5600.5340.3510.349
철(Fe)0.5760.8250.6650.7080.8851.0000.635-0.2470.6890.7150.5150.222
니켈(Ni)0.7340.7940.7810.6820.6810.6351.000-0.0120.5200.3640.2800.000
비소(As)0.404-0.103-0.223-0.468-0.069-0.247-0.0121.000-0.351-0.431-0.1820.112
알루미늄(Al)0.2710.7330.4620.4670.5600.6890.520-0.3511.0000.8110.6260.421
칼슘(Ca)0.1320.6280.3270.5230.5340.7150.364-0.4310.8111.0000.7340.000
마그네슘(Mg)0.2180.5490.3680.3530.3510.5150.280-0.1820.6260.7341.0000.568
일자0.0000.0000.0000.0000.3490.2220.0000.1120.4210.0000.5681.000

Missing values

2024-04-21T11:49:11.881130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:49:12.050982image/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

지점일자납(Pb)카드뮴(Cd)크롬(Cr)구리(Cu)망간(Mn)철(Fe)니켈(Ni)비소(As)베릴륨(Be)알루미늄(Al)칼슘(Ca)마그네슘(Mg)
0건국동1(2024-03-11 00:00∼2024-03-11 24:00)0.00270.00010.00.00410.00970.20290.00.003700.09070.24630.0731
1건국동2(2024-03-12 00:00∼2024-03-12 24:00)0.00740.00010.00.00430.00970.16210.00.00500.05040.13820.0658
2건국동3(2024-03-13 00:00∼2024-03-13 24:00)0.00660.00020.00.0040.01060.20340.00.003400.0780.21430.1358
3건국동4(2024-03-14 00:00∼2024-03-14 24:00)0.00540.00030.00.00490.01950.31770.00.001900.1280.40710.1104
4건국동5(2024-03-15 00:00∼2024-03-15 24:00)0.00460.00020.00.00440.01330.2750.00.001800.12790.37710.0981
5농성동1(2024-03-11 00:00∼2024-03-11 24:00)0.00530.00030.00210.01130.01410.54540.00120.001600.24290.94240.1622
6농성동2(2024-03-12 00:00∼2024-03-12 24:00)0.01110.00020.00080.00690.01080.31250.00080.005300.10940.35010.1119
7농성동3(2024-03-13 00:00∼2024-03-13 24:00)0.00870.00030.00090.0060.01150.34580.00070.002700.14420.58020.2347
8농성동4(2024-03-14 00:00∼2024-03-14 24:00)0.00880.00040.00220.01060.01930.6340.00180.001400.23590.89890.1848
9농성동5(2024-03-15 00:00∼2024-03-15 24:00)0.00840.00040.0020.01370.02140.6720.00160.002600.26150.99090.1824
지점일자납(Pb)카드뮴(Cd)크롬(Cr)구리(Cu)망간(Mn)철(Fe)니켈(Ni)비소(As)베릴륨(Be)알루미늄(Al)칼슘(Ca)마그네슘(Mg)
20노대동1(2024-03-11 00:00∼2024-03-11 24:00)0.00410.00020.00050.00540.00820.25210.00040.002300.31530.28470.0943
21노대동2(2024-03-12 00:00∼2024-03-12 24:00)0.01450.00020.00030.00330.00670.15220.00070.006900.08330.09630.0524
22노대동3(2024-03-13 00:00∼2024-03-13 24:00)0.01060.00040.00060.00510.01060.2570.00070.003700.23170.27870.1656
23노대동4(2024-03-14 00:00∼2024-03-14 24:00)0.01090.00040.00110.0080.01920.45250.00140.001900.3050.66670.1594
24노대동5(2024-03-15 00:00∼2024-03-15 24:00)0.01150.00050.00150.00760.02010.48520.00170.002100.31880.69680.1562
25평동1(2024-03-11 00:00∼2024-03-11 24:00)0.04340.00050.00430.01230.07660.89210.00220.001400.26640.42850.1168
26평동2(2024-03-12 00:00∼2024-03-12 24:00)0.05620.00030.00220.00830.07380.67250.00120.00500.05780.09680.0611
27평동3(2024-03-13 00:00∼2024-03-13 24:00)0.06040.00040.00350.00870.05110.6260.00130.00400.12610.26020.143
28평동4(2024-03-14 00:00∼2024-03-14 24:00)0.01920.0040.0060.01360.08850.33990.00240.002600.20580.57380.1392
29평동5(2024-03-15 00:00∼2024-03-15 24:00)0.01930.00060.00430.01510.09771.04130.00270.001100.21170.5520.1334