Overview

Dataset statistics

Number of variables15
Number of observations72
Missing cells144
Missing cells (%)13.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory134.8 B

Variable types

Categorical2
DateTime1
Numeric12

Dataset

Description대기중금속 측정 지역, 월, 년도, 중금속 항목 등 대기중금속 조회 정보를 제공하는 경상북도 대기중금속 측정자료 현황입니다.
Author경상북도
URLhttps://www.data.go.kr/data/15064064/fileData.do

Alerts

납(Pb) is highly overall correlated with 카드뮴(Cd) and 9 other fieldsHigh correlation
카드뮴(Cd) is highly overall correlated with 납(Pb) and 4 other fieldsHigh correlation
크롬(Cr) is highly overall correlated with 납(Pb) and 7 other fieldsHigh correlation
구리(Cu) is highly overall correlated with 납(Pb) and 8 other fieldsHigh correlation
망가니즈(Mn) is highly overall correlated with 납(Pb) and 7 other fieldsHigh correlation
철(Fe) is highly overall correlated with 납(Pb) and 7 other fieldsHigh correlation
비소(As) is highly overall correlated with 납(Pb) and 1 other fieldsHigh correlation
칼슘(Ca) is highly overall correlated with 납(Pb) and 7 other fieldsHigh correlation
알루미늄(Al) is highly overall correlated with 납(Pb) and 8 other fieldsHigh correlation
마그네슘(Mg) is highly overall correlated with 납(Pb) and 7 other fieldsHigh correlation
아연(Zn) is highly overall correlated with 납(Pb) and 8 other fieldsHigh correlation
베릴륨(Be) is highly imbalanced (52.6%)Imbalance
납(Pb) has 12 (16.7%) missing valuesMissing
카드뮴(Cd) has 12 (16.7%) missing valuesMissing
크롬(Cr) has 12 (16.7%) missing valuesMissing
구리(Cu) has 12 (16.7%) missing valuesMissing
망가니즈(Mn) has 12 (16.7%) missing valuesMissing
철(Fe) has 12 (16.7%) missing valuesMissing
니켈(Ni) has 12 (16.7%) missing valuesMissing
비소(As) has 12 (16.7%) missing valuesMissing
칼슘(Ca) has 12 (16.7%) missing valuesMissing
알루미늄(Al) has 12 (16.7%) missing valuesMissing
마그네슘(Mg) has 12 (16.7%) missing valuesMissing
아연(Zn) has 12 (16.7%) missing valuesMissing
카드뮴(Cd) has 4 (5.6%) zerosZeros
크롬(Cr) has 1 (1.4%) zerosZeros
알루미늄(Al) has 1 (1.4%) zerosZeros

Reproduction

Analysis started2024-04-21 13:07:10.352013
Analysis finished2024-04-21 13:07:44.142617
Duration33.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

Distinct6
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size704.0 B
구미시 4공단
12 
포항시 장흥동
12 
포항시 3공단
12 
포항시 대송면
12 
포항시 장량동
12 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구미시 4공단
2nd row구미시 4공단
3rd row구미시 4공단
4th row구미시 4공단
5th row구미시 4공단

Common Values

ValueCountFrequency (%)
구미시 4공단 12
16.7%
포항시 장흥동 12
16.7%
포항시 3공단 12
16.7%
포항시 대송면 12
16.7%
포항시 장량동 12
16.7%
봉화군 석포면 12
16.7%

Length

2024-04-21T22:07:44.256913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:07:44.452381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포항시 48
33.3%
구미시 12
 
8.3%
4공단 12
 
8.3%
장흥동 12
 
8.3%
3공단 12
 
8.3%
대송면 12
 
8.3%
장량동 12
 
8.3%
봉화군 12
 
8.3%
석포면 12
 
8.3%

연월
Date

Distinct12
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size704.0 B
Minimum2023-01-20 00:00:00
Maximum2023-12-20 00:00:00
2024-04-21T22:07:44.639324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:44.812220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

납(Pb)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)90.0%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.028643333
Minimum0.0008
Maximum0.1581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:45.044129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0008
5-th percentile0.003165
Q10.01185
median0.01955
Q30.036625
95-th percentile0.086215
Maximum0.1581
Range0.1573
Interquartile range (IQR)0.024775

Descriptive statistics

Standard deviation0.028062131
Coefficient of variation (CV)0.97970897
Kurtosis8.0131627
Mean0.028643333
Median Absolute Deviation (MAD)0.0115
Skewness2.5186221
Sum1.7186
Variance0.00078748318
MonotonicityNot monotonic
2024-04-21T22:07:45.301789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0373 2
 
2.8%
0.0166 2
 
2.8%
0.0191 2
 
2.8%
0.0335 2
 
2.8%
0.0331 2
 
2.8%
0.0099 2
 
2.8%
0.0018 1
 
1.4%
0.0185 1
 
1.4%
0.0189 1
 
1.4%
0.014 1
 
1.4%
Other values (44) 44
61.1%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.0008 1
1.4%
0.0018 1
1.4%
0.0025 1
1.4%
0.0032 1
1.4%
0.0052 1
1.4%
0.0058 1
1.4%
0.0073 1
1.4%
0.0077 1
1.4%
0.008 1
1.4%
0.0081 1
1.4%
ValueCountFrequency (%)
0.1581 1
1.4%
0.1132 1
1.4%
0.0903 1
1.4%
0.086 1
1.4%
0.0786 1
1.4%
0.0561 1
1.4%
0.0547 1
1.4%
0.0495 1
1.4%
0.0415 1
1.4%
0.04 1
1.4%

카드뮴(Cd)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)41.7%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.0011216667
Minimum0
Maximum0.0076
Zeros4
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:45.537536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0002
median0.0006
Q30.0011
95-th percentile0.00444
Maximum0.0076
Range0.0076
Interquartile range (IQR)0.0009

Descriptive statistics

Standard deviation0.0015244995
Coefficient of variation (CV)1.3591378
Kurtosis6.5402189
Mean0.0011216667
Median Absolute Deviation (MAD)0.0004
Skewness2.5039798
Sum0.0673
Variance2.3240989 × 10-6
MonotonicityNot monotonic
2024-04-21T22:07:45.753759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0002 9
12.5%
0.0001 5
 
6.9%
0.0009 4
 
5.6%
0.0005 4
 
5.6%
0.0011 4
 
5.6%
0.0 4
 
5.6%
0.0003 3
 
4.2%
0.0006 3
 
4.2%
0.0004 3
 
4.2%
0.0008 3
 
4.2%
Other values (15) 18
25.0%
(Missing) 12
16.7%
ValueCountFrequency (%)
0.0 4
5.6%
0.0001 5
6.9%
0.0002 9
12.5%
0.0003 3
 
4.2%
0.0004 3
 
4.2%
0.0005 4
5.6%
0.0006 3
 
4.2%
0.0007 2
 
2.8%
0.0008 3
 
4.2%
0.0009 4
5.6%
ValueCountFrequency (%)
0.0076 1
1.4%
0.0058 1
1.4%
0.0052 1
1.4%
0.0044 1
1.4%
0.0043 1
1.4%
0.0032 1
1.4%
0.0031 1
1.4%
0.0021 1
1.4%
0.002 1
1.4%
0.0019 1
1.4%

크롬(Cr)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct45
Distinct (%)75.0%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.0044316667
Minimum0
Maximum0.0191
Zeros1
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:45.995512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000495
Q10.0016
median0.0038
Q30.0064
95-th percentile0.010105
Maximum0.0191
Range0.0191
Interquartile range (IQR)0.0048

Descriptive statistics

Standard deviation0.0037052181
Coefficient of variation (CV)0.8360778
Kurtosis3.2699027
Mean0.0044316667
Median Absolute Deviation (MAD)0.00235
Skewness1.4599
Sum0.2659
Variance1.3728641 × 10-5
MonotonicityNot monotonic
2024-04-21T22:07:46.254445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0016 5
 
6.9%
0.0027 4
 
5.6%
0.0007 3
 
4.2%
0.001 2
 
2.8%
0.0022 2
 
2.8%
0.0059 2
 
2.8%
0.0006 2
 
2.8%
0.004 2
 
2.8%
0.007 2
 
2.8%
0.0019 1
 
1.4%
Other values (35) 35
48.6%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.0 1
 
1.4%
0.0001 1
 
1.4%
0.0004 1
 
1.4%
0.0005 1
 
1.4%
0.0006 2
 
2.8%
0.0007 3
4.2%
0.0008 1
 
1.4%
0.0009 1
 
1.4%
0.001 2
 
2.8%
0.0016 5
6.9%
ValueCountFrequency (%)
0.0191 1
1.4%
0.0141 1
1.4%
0.0121 1
1.4%
0.01 1
1.4%
0.0091 1
1.4%
0.009 1
1.4%
0.0087 1
1.4%
0.0078 1
1.4%
0.0074 1
1.4%
0.0073 1
1.4%

구리(Cu)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)88.3%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.014795
Minimum0.0011
Maximum0.0958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:46.517032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0011
5-th percentile0.00267
Q10.00545
median0.00905
Q30.015175
95-th percentile0.047645
Maximum0.0958
Range0.0947
Interquartile range (IQR)0.009725

Descriptive statistics

Standard deviation0.017612865
Coefficient of variation (CV)1.1904607
Kurtosis9.3822196
Mean0.014795
Median Absolute Deviation (MAD)0.00415
Skewness2.8973378
Sum0.8877
Variance0.00031021303
MonotonicityNot monotonic
2024-04-21T22:07:46.776217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0066 2
 
2.8%
0.0061 2
 
2.8%
0.0098 2
 
2.8%
0.0027 2
 
2.8%
0.0053 2
 
2.8%
0.0175 2
 
2.8%
0.0067 2
 
2.8%
0.005 1
 
1.4%
0.0088 1
 
1.4%
0.0122 1
 
1.4%
Other values (43) 43
59.7%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.0011 1
1.4%
0.002 1
1.4%
0.0021 1
1.4%
0.0027 2
2.8%
0.0035 1
1.4%
0.0037 1
1.4%
0.004 1
1.4%
0.0041 1
1.4%
0.0042 1
1.4%
0.0045 1
1.4%
ValueCountFrequency (%)
0.0958 1
1.4%
0.0782 1
1.4%
0.0523 1
1.4%
0.0474 1
1.4%
0.0458 1
1.4%
0.0437 1
1.4%
0.0275 1
1.4%
0.0259 1
1.4%
0.0251 1
1.4%
0.0196 1
1.4%

망가니즈(Mn)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)96.7%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.069826667
Minimum0.0017
Maximum0.2881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:47.026303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0017
5-th percentile0.0071
Q10.014975
median0.04615
Q30.097025
95-th percentile0.19951
Maximum0.2881
Range0.2864
Interquartile range (IQR)0.08205

Descriptive statistics

Standard deviation0.069751253
Coefficient of variation (CV)0.99891998
Kurtosis0.85036778
Mean0.069826667
Median Absolute Deviation (MAD)0.0359
Skewness1.26451
Sum4.1896
Variance0.0048652372
MonotonicityNot monotonic
2024-04-21T22:07:47.273539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0071 2
 
2.8%
0.0137 2
 
2.8%
0.0191 1
 
1.4%
0.0992 1
 
1.4%
0.0536 1
 
1.4%
0.0466 1
 
1.4%
0.016 1
 
1.4%
0.0623 1
 
1.4%
0.0239 1
 
1.4%
0.035 1
 
1.4%
Other values (48) 48
66.7%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.0017 1
1.4%
0.0039 1
1.4%
0.0071 2
2.8%
0.0075 1
1.4%
0.0082 1
1.4%
0.0087 1
1.4%
0.0092 1
1.4%
0.01 1
1.4%
0.0101 1
1.4%
0.0104 1
1.4%
ValueCountFrequency (%)
0.2881 1
1.4%
0.2318 1
1.4%
0.2282 1
1.4%
0.198 1
1.4%
0.1949 1
1.4%
0.1898 1
1.4%
0.1854 1
1.4%
0.176 1
1.4%
0.1679 1
1.4%
0.1422 1
1.4%

철(Fe)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)100.0%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.86780167
Minimum0.028
Maximum4.2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:47.733553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.028
5-th percentile0.075635
Q10.2176
median0.6687
Q31.05165
95-th percentile2.854265
Maximum4.2024
Range4.1744
Interquartile range (IQR)0.83405

Descriptive statistics

Standard deviation0.8627135
Coefficient of variation (CV)0.99413671
Kurtosis3.4861983
Mean0.86780167
Median Absolute Deviation (MAD)0.4545
Skewness1.749616
Sum52.0681
Variance0.74427457
MonotonicityNot monotonic
2024-04-21T22:07:47.979395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6478 1
 
1.4%
2.2779 1
 
1.4%
0.6299 1
 
1.4%
0.6252 1
 
1.4%
0.2074 1
 
1.4%
0.8265 1
 
1.4%
0.3769 1
 
1.4%
0.4044 1
 
1.4%
0.495 1
 
1.4%
0.1637 1
 
1.4%
Other values (50) 50
69.4%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.028 1
1.4%
0.0557 1
1.4%
0.0611 1
1.4%
0.0764 1
1.4%
0.0918 1
1.4%
0.0945 1
1.4%
0.1318 1
1.4%
0.1334 1
1.4%
0.1341 1
1.4%
0.1357 1
1.4%
ValueCountFrequency (%)
4.2024 1
1.4%
3.0521 1
1.4%
2.9885 1
1.4%
2.8472 1
1.4%
2.2779 1
1.4%
2.0221 1
1.4%
1.795 1
1.4%
1.7311 1
1.4%
1.67 1
1.4%
1.6576 1
1.4%

니켈(Ni)
Real number (ℝ)

MISSING 

Distinct45
Distinct (%)75.0%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.013203333
Minimum0.0003
Maximum0.1408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:48.210681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0003
5-th percentile0.000895
Q10.002975
median0.0048
Q30.008125
95-th percentile0.06208
Maximum0.1408
Range0.1405
Interquartile range (IQR)0.00515

Descriptive statistics

Standard deviation0.026982442
Coefficient of variation (CV)2.0436084
Kurtosis13.70968
Mean0.013203333
Median Absolute Deviation (MAD)0.0026
Skewness3.6688345
Sum0.7922
Variance0.00072805219
MonotonicityNot monotonic
2024-04-21T22:07:48.459616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0022 3
 
4.2%
0.0071 3
 
4.2%
0.005 3
 
4.2%
0.0015 2
 
2.8%
0.0068 2
 
2.8%
0.0031 2
 
2.8%
0.0037 2
 
2.8%
0.0075 2
 
2.8%
0.0116 2
 
2.8%
0.0045 2
 
2.8%
Other values (35) 37
51.4%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.0003 1
 
1.4%
0.0007 1
 
1.4%
0.0008 1
 
1.4%
0.0009 1
 
1.4%
0.0011 1
 
1.4%
0.0012 2
2.8%
0.0014 1
 
1.4%
0.0015 2
2.8%
0.0022 3
4.2%
0.0023 1
 
1.4%
ValueCountFrequency (%)
0.1408 1
1.4%
0.1289 1
1.4%
0.0826 1
1.4%
0.061 1
1.4%
0.056 1
1.4%
0.0291 1
1.4%
0.017 1
1.4%
0.0161 1
1.4%
0.0138 1
1.4%
0.0136 1
1.4%

비소(As)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)90.0%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.013795
Minimum0.0015
Maximum0.0797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:48.725183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.00269
Q10.005175
median0.01
Q30.0163
95-th percentile0.04592
Maximum0.0797
Range0.0782
Interquartile range (IQR)0.011125

Descriptive statistics

Standard deviation0.01510542
Coefficient of variation (CV)1.0949924
Kurtosis9.4448943
Mean0.013795
Median Absolute Deviation (MAD)0.0049
Skewness2.9340574
Sum0.8277
Variance0.0002281737
MonotonicityNot monotonic
2024-04-21T22:07:48.990479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0057 3
 
4.2%
0.0186 2
 
2.8%
0.0051 2
 
2.8%
0.01 2
 
2.8%
0.0112 2
 
2.8%
0.005 1
 
1.4%
0.014 1
 
1.4%
0.0041 1
 
1.4%
0.003 1
 
1.4%
0.0054 1
 
1.4%
Other values (44) 44
61.1%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.0015 1
1.4%
0.0019 1
1.4%
0.0025 1
1.4%
0.0027 1
1.4%
0.0028 1
1.4%
0.003 1
1.4%
0.0031 1
1.4%
0.0039 1
1.4%
0.0041 1
1.4%
0.0044 1
1.4%
ValueCountFrequency (%)
0.0797 1
1.4%
0.0723 1
1.4%
0.0558 1
1.4%
0.0454 1
1.4%
0.0285 1
1.4%
0.0234 1
1.4%
0.022 1
1.4%
0.0215 1
1.4%
0.021 1
1.4%
0.0195 1
1.4%

칼슘(Ca)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)100.0%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.68148
Minimum0.041
Maximum2.8601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:49.260688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.041
5-th percentile0.065365
Q10.249625
median0.48365
Q30.9056
95-th percentile1.98546
Maximum2.8601
Range2.8191
Interquartile range (IQR)0.655975

Descriptive statistics

Standard deviation0.64057325
Coefficient of variation (CV)0.93997366
Kurtosis1.8775286
Mean0.68148
Median Absolute Deviation (MAD)0.2986
Skewness1.5029892
Sum40.8888
Variance0.41033409
MonotonicityNot monotonic
2024-04-21T22:07:49.513130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.57 1
 
1.4%
1.8071 1
 
1.4%
0.3926 1
 
1.4%
0.3734 1
 
1.4%
0.1306 1
 
1.4%
0.5151 1
 
1.4%
0.2054 1
 
1.4%
0.2569 1
 
1.4%
0.4922 1
 
1.4%
0.3058 1
 
1.4%
Other values (50) 50
69.4%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.041 1
1.4%
0.0431 1
1.4%
0.059 1
1.4%
0.0657 1
1.4%
0.0731 1
1.4%
0.0987 1
1.4%
0.1013 1
1.4%
0.1044 1
1.4%
0.1177 1
1.4%
0.1198 1
1.4%
ValueCountFrequency (%)
2.8601 1
1.4%
2.2679 1
1.4%
2.2507 1
1.4%
1.9715 1
1.4%
1.8812 1
1.4%
1.8071 1
1.4%
1.6613 1
1.4%
1.5361 1
1.4%
1.3923 1
1.4%
1.3033 1
1.4%

알루미늄(Al)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct60
Distinct (%)100.0%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.34971833
Minimum0
Maximum2.0671
Zeros1
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:49.772352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.036325
Q10.0915
median0.16425
Q30.33515
95-th percentile1.712485
Maximum2.0671
Range2.0671
Interquartile range (IQR)0.24365

Descriptive statistics

Standard deviation0.4947607
Coefficient of variation (CV)1.4147405
Kurtosis4.8139732
Mean0.34971833
Median Absolute Deviation (MAD)0.0993
Skewness2.3589736
Sum20.9831
Variance0.24478815
MonotonicityNot monotonic
2024-04-21T22:07:50.011703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1787 1
 
1.4%
1.7388 1
 
1.4%
0.1415 1
 
1.4%
0.1518 1
 
1.4%
0.0386 1
 
1.4%
0.1908 1
 
1.4%
0.0492 1
 
1.4%
0.0747 1
 
1.4%
0.1958 1
 
1.4%
0.1062 1
 
1.4%
Other values (50) 50
69.4%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.0 1
1.4%
0.0327 1
1.4%
0.0349 1
1.4%
0.0364 1
1.4%
0.0375 1
1.4%
0.0386 1
1.4%
0.0407 1
1.4%
0.0408 1
1.4%
0.0478 1
1.4%
0.0492 1
1.4%
ValueCountFrequency (%)
2.0671 1
1.4%
1.9961 1
1.4%
1.7388 1
1.4%
1.7111 1
1.4%
1.3969 1
1.4%
1.2875 1
1.4%
0.8455 1
1.4%
0.7694 1
1.4%
0.6535 1
1.4%
0.5398 1
1.4%

마그네슘(Mg)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct59
Distinct (%)98.3%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.26195833
Minimum0.0245
Maximum1.2063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:50.254171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0245
5-th percentile0.03095
Q10.094675
median0.17205
Q30.29035
95-th percentile0.832345
Maximum1.2063
Range1.1818
Interquartile range (IQR)0.195675

Descriptive statistics

Standard deviation0.26654419
Coefficient of variation (CV)1.0175061
Kurtosis2.8447901
Mean0.26195833
Median Absolute Deviation (MAD)0.08975
Skewness1.8293467
Sum15.7175
Variance0.071045805
MonotonicityNot monotonic
2024-04-21T22:07:50.520019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3375 2
 
2.8%
0.0742 1
 
1.4%
0.8826 1
 
1.4%
0.1707 1
 
1.4%
0.1224 1
 
1.4%
0.0998 1
 
1.4%
0.228 1
 
1.4%
0.0622 1
 
1.4%
0.0895 1
 
1.4%
0.1949 1
 
1.4%
Other values (49) 49
68.1%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.0245 1
1.4%
0.0253 1
1.4%
0.0262 1
1.4%
0.0312 1
1.4%
0.0456 1
1.4%
0.0464 1
1.4%
0.0471 1
1.4%
0.055 1
1.4%
0.0622 1
1.4%
0.0726 1
1.4%
ValueCountFrequency (%)
1.2063 1
1.4%
1.0164 1
1.4%
0.8826 1
1.4%
0.8297 1
1.4%
0.7444 1
1.4%
0.7422 1
1.4%
0.7234 1
1.4%
0.7213 1
1.4%
0.5791 1
1.4%
0.4149 1
1.4%

베릴륨(Be)
Categorical

IMBALANCE 

Distinct3
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size704.0 B
0.0
59 
<NA>
12 
0.0001
 
1

Length

Max length6
Median length3
Mean length3.2083333
Min length3

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 59
81.9%
<NA> 12
 
16.7%
0.0001 1
 
1.4%

Length

2024-04-21T22:07:50.770386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:07:50.958129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59
81.9%
na 12
 
16.7%
0.0001 1
 
1.4%

아연(Zn)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)100.0%
Missing12
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.34047167
Minimum0.0211
Maximum1.5286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-04-21T22:07:51.163742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0211
5-th percentile0.040175
Q10.080975
median0.22715
Q30.4945
95-th percentile0.9816
Maximum1.5286
Range1.5075
Interquartile range (IQR)0.413525

Descriptive statistics

Standard deviation0.32846538
Coefficient of variation (CV)0.96473632
Kurtosis2.4945942
Mean0.34047167
Median Absolute Deviation (MAD)0.16775
Skewness1.5191051
Sum20.4283
Variance0.10788951
MonotonicityNot monotonic
2024-04-21T22:07:51.424129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2144 1
 
1.4%
0.1288 1
 
1.4%
0.2158 1
 
1.4%
0.3683 1
 
1.4%
0.0735 1
 
1.4%
0.2503 1
 
1.4%
0.1168 1
 
1.4%
0.1348 1
 
1.4%
0.108 1
 
1.4%
0.0446 1
 
1.4%
Other values (50) 50
69.4%
(Missing) 12
 
16.7%
ValueCountFrequency (%)
0.0211 1
1.4%
0.0297 1
1.4%
0.0302 1
1.4%
0.0407 1
1.4%
0.0419 1
1.4%
0.0446 1
1.4%
0.0567 1
1.4%
0.0572 1
1.4%
0.0607 1
1.4%
0.0655 1
1.4%
ValueCountFrequency (%)
1.5286 1
1.4%
1.2354 1
1.4%
1.1431 1
1.4%
0.9731 1
1.4%
0.8318 1
1.4%
0.7773 1
1.4%
0.7241 1
1.4%
0.7033 1
1.4%
0.6365 1
1.4%
0.6295 1
1.4%

Interactions

2024-04-21T22:07:40.955321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:11.302857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:13.263317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:16.374889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:19.535592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:22.746016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:25.814751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:28.761948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:31.862983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:34.790039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:37.036151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:39.108262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:41.124540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:11.468069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:13.536115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:16.650134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:19.793363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:23.013313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:26.069563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:29.031747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:32.135988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:34.953475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:37.295773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:39.274623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:41.494865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:11.634029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:13.799686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:16.918194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:20.255025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:23.273225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:26.322843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:29.297968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:32.402730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:35.110806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:37.547620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:39.433999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:41.664404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:11.805506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:14.069598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:17.189473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:20.515908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:23.541960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:26.579260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:29.567402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:32.887615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:35.285216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:37.772167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:39.602481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:41.817884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:11.956351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:14.321749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:17.442587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:20.756880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:23.786885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:26.817373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:29.816480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:33.137411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:35.430017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:37.914491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:39.748164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:41.976632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:12.116528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:14.578355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:17.705384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:21.005111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:24.039421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:27.059771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:30.071452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:33.397628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:35.584650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:38.056458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:39.898928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:42.118884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:12.258858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:14.820449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:17.949681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:21.236016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:24.275528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:27.287386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:30.314667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:33.640789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:35.739573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:38.183240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:40.037655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:42.284295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:12.422990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:15.082301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:18.218977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:21.493064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:24.536642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:27.535994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:30.572910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:33.910149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:35.898466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:38.334523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:40.194196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:42.468364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:12.591619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:15.354025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:18.494891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:21.752758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:24.804454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:27.793904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:30.841131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:34.153150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:36.066203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:38.491352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:40.357097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:42.632692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:12.750568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:15.611940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:18.754169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:22.002469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:25.055891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:28.035371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:31.095372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:34.311036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:36.247396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:38.637566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:40.506265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:42.779757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:12.894143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:15.852309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:19.002343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:22.236917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:25.296818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:28.266479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:31.339416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:34.457958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:36.509069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:38.807286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:40.647117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:42.986693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:13.055020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:16.108548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:19.261778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:22.486257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:25.548329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:28.507801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:31.593661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:34.616066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:36.772546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:38.953152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:07:40.793570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T22:07:51.624022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역연월납(Pb)카드뮴(Cd)크롬(Cr)구리(Cu)망가니즈(Mn)철(Fe)니켈(Ni)비소(As)칼슘(Ca)알루미늄(Al)마그네슘(Mg)베릴륨(Be)아연(Zn)
지역1.0000.0000.5640.4470.2750.6560.6440.5780.2910.3990.3340.3160.3360.0550.724
연월0.0001.0000.0000.3700.3400.0000.4520.2590.0000.4840.4000.6580.7260.0710.000
납(Pb)0.5640.0001.0000.9630.5780.7050.7400.5470.0000.6960.5770.6280.6560.0000.866
카드뮴(Cd)0.4470.3700.9631.0000.4430.3850.5750.3800.0000.8070.5570.7770.5220.0000.797
크롬(Cr)0.2750.3400.5780.4431.0000.7730.8320.9230.5300.0000.9110.6540.8610.0000.638
구리(Cu)0.6560.0000.7050.3850.7731.0000.8570.8310.0000.3390.8000.7540.8400.0000.877
망가니즈(Mn)0.6440.4520.7400.5750.8320.8571.0000.8340.0000.5590.7620.7000.9340.0000.912
철(Fe)0.5780.2590.5470.3800.9230.8310.8341.0000.0000.0000.9530.8330.9370.0000.693
니켈(Ni)0.2910.0000.0000.0000.5300.0000.0000.0001.0000.0000.2960.0000.2680.0000.000
비소(As)0.3990.4840.6960.8070.0000.3390.5590.0000.0001.0000.0000.2930.0000.0000.487
칼슘(Ca)0.3340.4000.5770.5570.9110.8000.7620.9530.2960.0001.0000.8830.8850.0000.732
알루미늄(Al)0.3160.6580.6280.7770.6540.7540.7000.8330.0000.2930.8831.0000.8980.0000.733
마그네슘(Mg)0.3360.7260.6560.5220.8610.8400.9340.9370.2680.0000.8850.8981.0000.0000.853
베릴륨(Be)0.0550.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.071
아연(Zn)0.7240.0000.8660.7970.6380.8770.9120.6930.0000.4870.7320.7330.8530.0711.000
2024-04-21T22:07:51.874142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역베릴륨(Be)
지역1.0000.000
베릴륨(Be)0.0001.000
2024-04-21T22:07:52.034523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납(Pb)카드뮴(Cd)크롬(Cr)구리(Cu)망가니즈(Mn)철(Fe)니켈(Ni)비소(As)칼슘(Ca)알루미늄(Al)마그네슘(Mg)아연(Zn)지역베릴륨(Be)
납(Pb)1.0000.8930.5420.7460.5610.585-0.0090.5800.6050.6120.5020.8190.3480.000
카드뮴(Cd)0.8931.0000.4250.5440.3840.461-0.1240.6200.4850.5320.3940.6760.2590.000
크롬(Cr)0.5420.4251.0000.6640.7170.7520.384-0.0480.7050.6710.6830.5410.1270.000
구리(Cu)0.7460.5440.6641.0000.8630.8130.3130.2710.7600.7170.6780.8390.4600.000
망가니즈(Mn)0.5610.3840.7170.8631.0000.9280.4580.1350.8500.7620.8140.6910.3900.000
철(Fe)0.5850.4610.7520.8130.9281.0000.3670.1800.9420.8950.9140.6450.3180.000
니켈(Ni)-0.009-0.1240.3840.3130.4580.3671.000-0.2280.3590.3110.3070.0430.1710.000
비소(As)0.5800.620-0.0480.2710.1350.180-0.2281.0000.2090.2380.1370.4040.2450.000
칼슘(Ca)0.6050.4850.7050.7600.8500.9420.3590.2091.0000.9650.9570.6140.1610.000
알루미늄(Al)0.6120.5320.6710.7170.7620.8950.3110.2380.9651.0000.9250.5490.1720.000
마그네슘(Mg)0.5020.3940.6830.6780.8140.9140.3070.1370.9570.9251.0000.5310.1690.000
아연(Zn)0.8190.6760.5410.8390.6910.6450.0430.4040.6140.5490.5311.0000.4690.000
지역0.3480.2590.1270.4600.3900.3180.1710.2450.1610.1720.1690.4691.0000.000
베릴륨(Be)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-21T22:07:43.228989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T22:07:43.568041image/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.
2024-04-21T22:07:43.877920image/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

지역연월납(Pb)카드뮴(Cd)크롬(Cr)구리(Cu)망가니즈(Mn)철(Fe)니켈(Ni)비소(As)칼슘(Ca)알루미늄(Al)마그네슘(Mg)베릴륨(Be)아연(Zn)
0구미시 4공단2023-01-200.02440.00050.00910.01020.02920.4660.0610.00270.44230.20110.1470.00.0788
1구미시 4공단2023-02-200.01530.00050.00590.00690.02560.27740.08260.00970.33320.11360.08460.00.0567
2구미시 4공단2023-03-200.01440.00050.00450.00930.04570.7940.14080.00311.0480.45160.33750.00.119
3구미시 4공단2023-04-200.00990.00030.00780.00660.08181.670.0560.00491.66131.28750.74440.00.111
4구미시 4공단2023-05-200.01210.00020.00530.00610.0310.24980.12890.00710.22780.09710.09830.00.0809
5구미시 4공단2023-06-200.00730.00010.00220.00490.01550.2210.02910.00150.26450.10410.080.00.0851
6구미시 4공단2023-07-200.00180.00.00220.00270.00870.09180.00760.00190.18740.06150.09640.00.1259
7구미시 4공단2023-08-200.00320.00.00050.00420.00710.07640.01010.00280.04310.00.04710.00.0572
8구미시 4공단2023-09-200.00080.00.00.00450.01370.09450.0170.00250.06570.03490.03120.00.0407
9구미시 4공단2023-10-200.00810.00020.00070.00410.01010.13410.01380.00830.11980.05570.04640.00.0302
지역연월납(Pb)카드뮴(Cd)크롬(Cr)구리(Cu)망가니즈(Mn)철(Fe)니켈(Ni)비소(As)칼슘(Ca)알루미늄(Al)마그네슘(Mg)베릴륨(Be)아연(Zn)
62봉화군 석포면2023-03-200.09030.00580.00570.01840.03680.97260.0040.07971.30330.65350.41170.01.1431
63봉화군 석포면2023-04-200.03730.00190.00410.00830.06162.02210.00370.01661.88121.71110.82970.00.4082
64봉화군 석포면2023-05-200.05610.00310.00360.00870.01210.28880.00120.0220.3390.1560.1160.00.393
65봉화군 석포면2023-06-200.04950.00440.00160.01210.01820.27710.00080.0210.38630.17990.11160.00.6365
66봉화군 석포면2023-07-200.03310.00320.00040.00550.00710.06110.00070.05580.0410.03270.02530.00.3698
67봉화군 석포면2023-08-200.01540.00110.00060.00110.00170.0280.00030.0120.07310.03750.02620.00.1327
68봉화군 석포면2023-09-200.00930.00040.00070.00270.00390.05570.00140.07230.0590.04080.02450.00.0713
69봉화군 석포면2023-10-200.0080.00020.00070.0040.010.13180.01360.02340.11770.05470.04560.00.0297
70봉화군 석포면2023-11-20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
71봉화군 석포면2023-12-20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>