Overview

Dataset statistics

Number of variables14
Number of observations30
Missing cells20
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory128.3 B

Variable types

Text1
Categorical3
Numeric10

Dataset

Description12월 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

일자 is highly overall correlated with 카드뮴(Cd) and 1 other fieldsHigh correlation
베릴륨(Be) is highly overall correlated with 납(Pb) and 11 other fieldsHigh correlation
카드뮴(Cd) is highly overall correlated with 납(Pb) and 11 other fieldsHigh correlation
납(Pb) is highly overall correlated with 크롬(Cr) and 8 other fieldsHigh correlation
크롬(Cr) is highly overall correlated with 납(Pb) and 8 other fieldsHigh correlation
구리(Cu) is highly overall correlated with 납(Pb) and 8 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 9 other fieldsHigh correlation
니켈(Ni) is highly overall correlated with 납(Pb) and 8 other fieldsHigh correlation
비소(As) is highly overall correlated with 납(Pb) and 2 other fieldsHigh correlation
알루미늄(Al) is highly overall correlated with 납(Pb) and 9 other fieldsHigh correlation
칼슘(Ca) is highly overall correlated with 크롬(Cr) and 6 other fieldsHigh correlation
마그네슘(Mg) is highly overall correlated with 철(Fe) and 5 other fieldsHigh correlation
카드뮴(Cd) is highly imbalanced (64.7%)Imbalance
베릴륨(Be) is highly imbalanced (64.7%)Imbalance
납(Pb) has 2 (6.7%) missing valuesMissing
크롬(Cr) has 2 (6.7%) missing valuesMissing
구리(Cu) has 2 (6.7%) missing valuesMissing
망간(Mn) has 2 (6.7%) missing valuesMissing
철(Fe) has 2 (6.7%) missing valuesMissing
니켈(Ni) has 2 (6.7%) missing valuesMissing
비소(As) has 2 (6.7%) missing valuesMissing
알루미늄(Al) has 2 (6.7%) missing valuesMissing
칼슘(Ca) has 2 (6.7%) missing valuesMissing
마그네슘(Mg) has 2 (6.7%) missing valuesMissing
지점 has unique valuesUnique
크롬(Cr) has 5 (16.7%) zerosZeros
니켈(Ni) has 12 (40.0%) zerosZeros

Reproduction

Analysis started2024-03-14 21:28:05.316283
Analysis finished2024-03-14 21:28:31.925353
Duration26.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지점
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size368.0 B
2024-03-15T06:28:32.564226image/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%
2024-03-15T06:28:33.775683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 30
27.3%
1 6
 
5.5%
2 6
 
5.5%
3 6
 
5.5%
4 6
 
5.5%
5 6
 
5.5%
건 5
 
4.5%
국 5
 
4.5%
농 5
 
4.5%
성 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 (%)
동 30
37.5%
건 5
 
6.2%
국 5
 
6.2%
농 5
 
6.2%
성 5
 
6.2%
두 5
 
6.2%
암 5
 
6.2%
서 5
 
6.2%
노 5
 
6.2%
대 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 (%)
동 30
37.5%
건 5
 
6.2%
국 5
 
6.2%
농 5
 
6.2%
성 5
 
6.2%
두 5
 
6.2%
암 5
 
6.2%
서 5
 
6.2%
노 5
 
6.2%
대 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 (%)
동 30
37.5%
건 5
 
6.2%
국 5
 
6.2%
농 5
 
6.2%
성 5
 
6.2%
두 5
 
6.2%
암 5
 
6.2%
서 5
 
6.2%
노 5
 
6.2%
대 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 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size368.0 B
(2024-01-08 00:00∼2024-01-08 24:00)
6 
(2024-01-09 00:00∼2024-01-09 24:00)
6 
(2024-01-10 00:00∼2024-01-10 24:00)
6 
(2024-01-11 00:00∼2024-01-11 24:00)
6 
(2024-01-12 00:00∼2024-01-12 24:00)
5 

Length

Max length35
Median length35
Mean length35
Min length35

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row(2024-01-08 00:00∼2024-01-08 24:00)
2nd row(2024-01-09 00:00∼2024-01-09 24:00)
3rd row(2024-01-10 00:00∼2024-01-10 24:00)
4th row(2024-01-11 00:00∼2024-01-11 24:00)
5th row(2024-01-12 00:00∼2024-01-12 24:00)

Common Values

ValueCountFrequency (%)
(2024-01-08 00:00∼2024-01-08 24:00) 6
20.0%
(2024-01-09 00:00∼2024-01-09 24:00) 6
20.0%
(2024-01-10 00:00∼2024-01-10 24:00) 6
20.0%
(2024-01-11 00:00∼2024-01-11 24:00) 6
20.0%
(2024-01-12 00:00∼2024-01-12 24:00) 5
16.7%
(2024-01-16 00:00∼2024-01-16 24:00) 1
 
3.3%

Length

2024-03-15T06:28:34.243623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:28:34.521004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
24:00 30
33.3%
2024-01-08 6
 
6.7%
00:00∼2024-01-08 6
 
6.7%
2024-01-09 6
 
6.7%
00:00∼2024-01-09 6
 
6.7%
2024-01-10 6
 
6.7%
00:00∼2024-01-10 6
 
6.7%
2024-01-11 6
 
6.7%
00:00∼2024-01-11 6
 
6.7%
2024-01-12 5
 
5.6%
Other values (3) 7
 
7.8%

납(Pb)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)92.9%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.018985714
Minimum0.0029
Maximum0.0648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:34.742729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0029
5-th percentile0.003585
Q10.005575
median0.0167
Q30.0267
95-th percentile0.054405
Maximum0.0648
Range0.0619
Interquartile range (IQR)0.021125

Descriptive statistics

Standard deviation0.016664082
Coefficient of variation (CV)0.8777169
Kurtosis1.9204905
Mean0.018985714
Median Absolute Deviation (MAD)0.01055
Skewness1.4102071
Sum0.5316
Variance0.00027769164
MonotonicityNot monotonic
2024-03-15T06:28:34.982039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0267 2
 
6.7%
0.0049 2
 
6.7%
0.015 1
 
3.3%
0.0272 1
 
3.3%
0.0648 1
 
3.3%
0.0627 1
 
3.3%
0.039 1
 
3.3%
0.0184 1
 
3.3%
0.0308 1
 
3.3%
0.0069 1
 
3.3%
Other values (16) 16
53.3%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.0029 1
3.3%
0.0032 1
3.3%
0.0043 1
3.3%
0.0044 1
3.3%
0.0046 1
3.3%
0.0049 2
6.7%
0.0058 1
3.3%
0.0061 1
3.3%
0.0063 1
3.3%
0.0069 1
3.3%
ValueCountFrequency (%)
0.0648 1
3.3%
0.0627 1
3.3%
0.039 1
3.3%
0.0364 1
3.3%
0.0308 1
3.3%
0.0272 1
3.3%
0.0267 2
6.7%
0.0265 1
3.3%
0.0214 1
3.3%
0.0213 1
3.3%

카드뮴(Cd)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size368.0 B
0
28 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.2
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 28
93.3%
<NA> 2
 
6.7%

Length

2024-03-15T06:28:35.496227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:28:35.875681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
93.3%
na 2
 
6.7%

크롬(Cr)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)57.1%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.0017857143
Minimum0
Maximum0.0124
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:36.276420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.000375
median0.0006
Q30.001525
95-th percentile0.00747
Maximum0.0124
Range0.0124
Interquartile range (IQR)0.00115

Descriptive statistics

Standard deviation0.0028984944
Coefficient of variation (CV)1.6231569
Kurtosis6.4071925
Mean0.0017857143
Median Absolute Deviation (MAD)0.00055
Skewness2.4899094
Sum0.05
Variance8.4012698 × 10-6
MonotonicityNot monotonic
2024-03-15T06:28:36.736679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 5
16.7%
0.0004 5
16.7%
0.0006 3
10.0%
0.0008 3
10.0%
0.0013 1
 
3.3%
0.0001 1
 
3.3%
0.0015 1
 
3.3%
0.0016 1
 
3.3%
0.0003 1
 
3.3%
0.0031 1
 
3.3%
Other values (6) 6
20.0%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.0 5
16.7%
0.0001 1
 
3.3%
0.0003 1
 
3.3%
0.0004 5
16.7%
0.0006 3
10.0%
0.0007 1
 
3.3%
0.0008 3
10.0%
0.0013 1
 
3.3%
0.0015 1
 
3.3%
0.0016 1
 
3.3%
ValueCountFrequency (%)
0.0124 1
 
3.3%
0.0081 1
 
3.3%
0.0063 1
 
3.3%
0.005 1
 
3.3%
0.0034 1
 
3.3%
0.0031 1
 
3.3%
0.0016 1
 
3.3%
0.0015 1
 
3.3%
0.0013 1
 
3.3%
0.0008 3
10.0%

구리(Cu)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)89.3%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.0070107143
Minimum0.0021
Maximum0.0262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:37.197295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0021
5-th percentile0.00257
Q10.003875
median0.00515
Q30.0078
95-th percentile0.017115
Maximum0.0262
Range0.0241
Interquartile range (IQR)0.003925

Descriptive statistics

Standard deviation0.0053200765
Coefficient of variation (CV)0.75884943
Kurtosis5.7838026
Mean0.0070107143
Median Absolute Deviation (MAD)0.00205
Skewness2.2384776
Sum0.1963
Variance2.8303214 × 10-5
MonotonicityNot monotonic
2024-03-15T06:28:37.720414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0027 2
 
6.7%
0.0047 2
 
6.7%
0.0077 2
 
6.7%
0.0038 1
 
3.3%
0.0032 1
 
3.3%
0.0151 1
 
3.3%
0.0262 1
 
3.3%
0.0073 1
 
3.3%
0.0182 1
 
3.3%
0.0101 1
 
3.3%
Other values (15) 15
50.0%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.0021 1
3.3%
0.0025 1
3.3%
0.0027 2
6.7%
0.0032 1
3.3%
0.0037 1
3.3%
0.0038 1
3.3%
0.0039 1
3.3%
0.004 1
3.3%
0.0041 1
3.3%
0.0043 1
3.3%
ValueCountFrequency (%)
0.0262 1
3.3%
0.0182 1
3.3%
0.0151 1
3.3%
0.0105 1
3.3%
0.0101 1
3.3%
0.0099 1
3.3%
0.0081 1
3.3%
0.0077 2
6.7%
0.0073 1
3.3%
0.0068 1
3.3%

망간(Mn)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)89.3%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.030992857
Minimum0.0032
Maximum0.2262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:38.173693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0032
5-th percentile0.00399
Q10.009
median0.0139
Q30.0225
95-th percentile0.11756
Maximum0.2262
Range0.223
Interquartile range (IQR)0.0135

Descriptive statistics

Standard deviation0.048247855
Coefficient of variation (CV)1.5567411
Kurtosis10.01784
Mean0.030992857
Median Absolute Deviation (MAD)0.00665
Skewness3.0298305
Sum0.8678
Variance0.0023278555
MonotonicityNot monotonic
2024-03-15T06:28:38.584685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0064 2
 
6.7%
0.0099 2
 
6.7%
0.0191 2
 
6.7%
0.0193 1
 
3.3%
0.0049 1
 
3.3%
0.0991 1
 
3.3%
0.2262 1
 
3.3%
0.0546 1
 
3.3%
0.1275 1
 
3.3%
0.0698 1
 
3.3%
Other values (15) 15
50.0%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.0032 1
3.3%
0.0035 1
3.3%
0.0049 1
3.3%
0.006 1
3.3%
0.0064 2
6.7%
0.0081 1
3.3%
0.0093 1
3.3%
0.0097 1
3.3%
0.0099 2
6.7%
0.0119 1
3.3%
ValueCountFrequency (%)
0.2262 1
3.3%
0.1275 1
3.3%
0.0991 1
3.3%
0.0698 1
3.3%
0.0546 1
3.3%
0.0258 1
3.3%
0.0255 1
3.3%
0.0215 1
3.3%
0.0193 1
3.3%
0.0191 2
6.7%

철(Fe)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.44601786
Minimum0.1366
Maximum1.7518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:38.993447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1366
5-th percentile0.15515
Q10.222625
median0.31
Q30.475375
95-th percentile1.25527
Maximum1.7518
Range1.6152
Interquartile range (IQR)0.25275

Descriptive statistics

Standard deviation0.38202801
Coefficient of variation (CV)0.85653075
Kurtosis4.8460578
Mean0.44601786
Median Absolute Deviation (MAD)0.10285
Skewness2.2078856
Sum12.4885
Variance0.1459454
MonotonicityNot monotonic
2024-03-15T06:28:39.455668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.1626 1
 
3.3%
1.1852 1
 
3.3%
1.7518 1
 
3.3%
0.625 1
 
3.3%
1.293 1
 
3.3%
0.7454 1
 
3.3%
0.2574 1
 
3.3%
0.4513 1
 
3.3%
0.1558 1
 
3.3%
0.4592 1
 
3.3%
Other values (18) 18
60.0%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.1366 1
3.3%
0.1548 1
3.3%
0.1558 1
3.3%
0.1626 1
3.3%
0.1971 1
3.3%
0.2172 1
3.3%
0.22 1
3.3%
0.2235 1
3.3%
0.2335 1
3.3%
0.2436 1
3.3%
ValueCountFrequency (%)
1.7518 1
3.3%
1.293 1
3.3%
1.1852 1
3.3%
0.7454 1
3.3%
0.6448 1
3.3%
0.625 1
3.3%
0.5239 1
3.3%
0.4592 1
3.3%
0.4513 1
3.3%
0.3647 1
3.3%

니켈(Ni)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)42.9%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.00048928571
Minimum0
Maximum0.0033
Zeros12
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:39.858109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00015
Q30.000425
95-th percentile0.002195
Maximum0.0033
Range0.0033
Interquartile range (IQR)0.000425

Descriptive statistics

Standard deviation0.0008328173
Coefficient of variation (CV)1.7021084
Kurtosis4.3880235
Mean0.00048928571
Median Absolute Deviation (MAD)0.00015
Skewness2.1783583
Sum0.0137
Variance6.9358466 × 10-7
MonotonicityNot monotonic
2024-03-15T06:28:40.208367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 12
40.0%
0.0002 3
 
10.0%
0.0003 3
 
10.0%
0.0001 2
 
6.7%
0.0005 1
 
3.3%
0.0007 1
 
3.3%
0.0013 1
 
3.3%
0.0004 1
 
3.3%
0.0015 1
 
3.3%
0.0023 1
 
3.3%
Other values (2) 2
 
6.7%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.0 12
40.0%
0.0001 2
 
6.7%
0.0002 3
 
10.0%
0.0003 3
 
10.0%
0.0004 1
 
3.3%
0.0005 1
 
3.3%
0.0007 1
 
3.3%
0.0013 1
 
3.3%
0.0015 1
 
3.3%
0.002 1
 
3.3%
ValueCountFrequency (%)
0.0033 1
 
3.3%
0.0023 1
 
3.3%
0.002 1
 
3.3%
0.0015 1
 
3.3%
0.0013 1
 
3.3%
0.0007 1
 
3.3%
0.0005 1
 
3.3%
0.0004 1
 
3.3%
0.0003 3
10.0%
0.0002 3
10.0%

비소(As)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)92.9%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.0069071429
Minimum0.0014
Maximum0.0191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:40.499442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0014
5-th percentile0.001605
Q10.0025
median0.00395
Q30.01025
95-th percentile0.01636
Maximum0.0191
Range0.0177
Interquartile range (IQR)0.00775

Descriptive statistics

Standard deviation0.0053020561
Coefficient of variation (CV)0.76761929
Kurtosis-0.55117521
Mean0.0069071429
Median Absolute Deviation (MAD)0.0023
Skewness0.79451777
Sum0.1934
Variance2.8111799 × 10-5
MonotonicityNot monotonic
2024-03-15T06:28:40.919756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0025 3
 
10.0%
0.0091 1
 
3.3%
0.0039 1
 
3.3%
0.0172 1
 
3.3%
0.0191 1
 
3.3%
0.004 1
 
3.3%
0.002 1
 
3.3%
0.0138 1
 
3.3%
0.0035 1
 
3.3%
0.0037 1
 
3.3%
Other values (16) 16
53.3%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.0014 1
 
3.3%
0.0015 1
 
3.3%
0.0018 1
 
3.3%
0.002 1
 
3.3%
0.0021 1
 
3.3%
0.0022 1
 
3.3%
0.0025 3
10.0%
0.0033 1
 
3.3%
0.0034 1
 
3.3%
0.0035 1
 
3.3%
ValueCountFrequency (%)
0.0191 1
3.3%
0.0172 1
3.3%
0.0148 1
3.3%
0.0138 1
3.3%
0.0134 1
3.3%
0.0114 1
3.3%
0.0107 1
3.3%
0.0101 1
3.3%
0.0098 1
3.3%
0.0096 1
3.3%

베릴륨(Be)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size368.0 B
0
28 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.2
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 28
93.3%
<NA> 2
 
6.7%

Length

2024-03-15T06:28:41.289807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:28:41.524009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
93.3%
na 2
 
6.7%

알루미늄(Al)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.13525714
Minimum0.0451
Maximum0.242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:41.824013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0451
5-th percentile0.05574
Q10.096775
median0.1298
Q30.16625
95-th percentile0.23354
Maximum0.242
Range0.1969
Interquartile range (IQR)0.069475

Descriptive statistics

Standard deviation0.053735142
Coefficient of variation (CV)0.39728136
Kurtosis-0.44756519
Mean0.13525714
Median Absolute Deviation (MAD)0.0365
Skewness0.32471669
Sum3.7872
Variance0.0028874655
MonotonicityNot monotonic
2024-03-15T06:28:42.461340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0638 1
 
3.3%
0.2075 1
 
3.3%
0.1662 1
 
3.3%
0.1645 1
 
3.3%
0.1796 1
 
3.3%
0.1551 1
 
3.3%
0.1307 1
 
3.3%
0.2418 1
 
3.3%
0.1239 1
 
3.3%
0.1841 1
 
3.3%
Other values (18) 18
60.0%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.0451 1
3.3%
0.0514 1
3.3%
0.0638 1
3.3%
0.0677 1
3.3%
0.0856 1
3.3%
0.0906 1
3.3%
0.0925 1
3.3%
0.0982 1
3.3%
0.1006 1
3.3%
0.1074 1
3.3%
ValueCountFrequency (%)
0.242 1
3.3%
0.2418 1
3.3%
0.2182 1
3.3%
0.2075 1
3.3%
0.1841 1
3.3%
0.1796 1
3.3%
0.1664 1
3.3%
0.1662 1
3.3%
0.1645 1
3.3%
0.1551 1
3.3%

칼슘(Ca)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.359675
Minimum0.1341
Maximum0.898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:42.850549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1341
5-th percentile0.166565
Q10.2223
median0.30805
Q30.474525
95-th percentile0.610005
Maximum0.898
Range0.7639
Interquartile range (IQR)0.252225

Descriptive statistics

Standard deviation0.17580822
Coefficient of variation (CV)0.48879745
Kurtosis1.6457182
Mean0.359675
Median Absolute Deviation (MAD)0.10225
Skewness1.1786524
Sum10.0709
Variance0.030908531
MonotonicityNot monotonic
2024-03-15T06:28:43.259041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.2298 1
 
3.3%
0.4418 1
 
3.3%
0.251 1
 
3.3%
0.2156 1
 
3.3%
0.4729 1
 
3.3%
0.2938 1
 
3.3%
0.2239 1
 
3.3%
0.5515 1
 
3.3%
0.196 1
 
3.3%
0.6037 1
 
3.3%
Other values (18) 18
60.0%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.1341 1
3.3%
0.161 1
3.3%
0.1769 1
3.3%
0.196 1
3.3%
0.2156 1
3.3%
0.2164 1
3.3%
0.2175 1
3.3%
0.2239 1
3.3%
0.2298 1
3.3%
0.2481 1
3.3%
ValueCountFrequency (%)
0.898 1
3.3%
0.6134 1
3.3%
0.6037 1
3.3%
0.5529 1
3.3%
0.5515 1
3.3%
0.4971 1
3.3%
0.4794 1
3.3%
0.4729 1
3.3%
0.4418 1
3.3%
0.4233 1
3.3%

마그네슘(Mg)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.10439643
Minimum0.0616
Maximum0.2193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T06:28:43.723109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0616
5-th percentile0.0657
Q10.080425
median0.0956
Q30.117125
95-th percentile0.16937
Maximum0.2193
Range0.1577
Interquartile range (IQR)0.0367

Descriptive statistics

Standard deviation0.03523367
Coefficient of variation (CV)0.3374988
Kurtosis3.2793325
Mean0.10439643
Median Absolute Deviation (MAD)0.0187
Skewness1.6188731
Sum2.9231
Variance0.0012414115
MonotonicityNot monotonic
2024-03-15T06:28:44.126237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0954 1
 
3.3%
0.131 1
 
3.3%
0.0929 1
 
3.3%
0.0778 1
 
3.3%
0.1083 1
 
3.3%
0.0958 1
 
3.3%
0.0734 1
 
3.3%
0.1079 1
 
3.3%
0.076 1
 
3.3%
0.177 1
 
3.3%
Other values (18) 18
60.0%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
0.0616 1
3.3%
0.0622 1
3.3%
0.0722 1
3.3%
0.0734 1
3.3%
0.0757 1
3.3%
0.076 1
3.3%
0.0778 1
3.3%
0.0813 1
3.3%
0.082 1
3.3%
0.0895 1
3.3%
ValueCountFrequency (%)
0.2193 1
3.3%
0.177 1
3.3%
0.1552 1
3.3%
0.1312 1
3.3%
0.131 1
3.3%
0.1298 1
3.3%
0.1199 1
3.3%
0.1162 1
3.3%
0.1134 1
3.3%
0.1083 1
3.3%

Interactions

2024-03-15T06:28:27.570538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:06.187726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:08.670266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:10.716703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:12.952230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:15.344436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:17.518201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:19.710486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:21.514693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:24.883263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:28.039230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:06.444401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:08.922062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:10.963069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:13.134197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:15.509964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:17.774165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:19.923864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:21.859903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:25.177933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:28.304383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:06.698366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:09.133746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:11.216101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:13.403890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:15.678883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:18.029831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:20.114113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:22.184192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:25.455335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:28.555374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:06.940233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:09.326764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:11.367579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:13.579534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:15.908617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:18.275211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:20.271965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:22.479982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:25.694400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:28.801522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:07.181036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:09.527437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:11.506352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:13.854644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:16.058020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:18.473050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:20.417091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:22.790342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:25.973047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:29.082159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:07.459558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:09.694064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:11.669845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:14.124338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:16.229090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:18.686155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:20.585343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:23.136708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:26.214830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:29.349255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:07.680287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:09.850997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:11.909157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:14.377784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:16.748763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:18.891398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:20.749206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:23.468650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:26.480569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:29.608959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:07.947846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:10.005055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:12.156307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:14.625998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:16.909995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:19.046592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:20.901780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:23.788394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:26.743600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:29.886609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:08.225113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:10.216217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:12.419275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:14.884543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:17.084895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:19.234161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:21.137498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:24.213979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:27.022159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:30.158185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:08.405638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:10.435787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:12.689095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:15.104654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:17.256393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:19.465471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:21.345239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:24.544743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:28:27.293657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T06:28:44.500081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점일자납(Pb)크롬(Cr)구리(Cu)망간(Mn)철(Fe)니켈(Ni)비소(As)알루미늄(Al)칼슘(Ca)마그네슘(Mg)
지점1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
일자1.0001.0000.5550.0000.0000.0000.4040.0000.6990.0000.5160.432
납(Pb)1.0000.5551.0000.8070.8410.6230.8330.8540.8180.4650.4230.503
크롬(Cr)1.0000.0000.8071.0000.9770.9220.9750.9560.7250.1430.0000.321
구리(Cu)1.0000.0000.8410.9771.0000.9020.9740.9480.6840.6350.3020.279
망간(Mn)1.0000.0000.6230.9220.9021.0000.9230.8680.6140.2180.0000.000
철(Fe)1.0000.4040.8330.9750.9740.9231.0000.9610.6800.5170.5740.659
니켈(Ni)1.0000.0000.8540.9560.9480.8680.9611.0000.7090.5610.7400.743
비소(As)1.0000.6990.8180.7250.6840.6140.6800.7091.0000.0000.0000.000
알루미늄(Al)1.0000.0000.4650.1430.6350.2180.5170.5610.0001.0000.5610.717
칼슘(Ca)1.0000.5160.4230.0000.3020.0000.5740.7400.0000.5611.0000.946
마그네슘(Mg)1.0000.4320.5030.3210.2790.0000.6590.7430.0000.7170.9461.000
2024-03-15T06:28:44.840171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자베릴륨(Be)카드뮴(Cd)
일자1.0001.0001.000
베릴륨(Be)1.0001.0001.000
카드뮴(Cd)1.0001.0001.000
2024-03-15T06:28:45.063433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납(Pb)크롬(Cr)구리(Cu)망간(Mn)철(Fe)니켈(Ni)비소(As)알루미늄(Al)칼슘(Ca)마그네슘(Mg)일자카드뮴(Cd)베릴륨(Be)
납(Pb)1.0000.5840.5990.6920.6540.7270.8140.6290.2170.1310.3511.0001.000
크롬(Cr)0.5841.0000.8740.6110.8880.7700.2920.8550.5550.4900.0001.0001.000
구리(Cu)0.5990.8741.0000.7280.9430.7710.3540.8300.6700.4870.0001.0001.000
망간(Mn)0.6920.6110.7281.0000.8310.8320.4670.6680.2520.2370.0001.0001.000
철(Fe)0.6540.8880.9430.8311.0000.8710.3520.8730.6400.5550.2301.0001.000
니켈(Ni)0.7270.7700.7710.8320.8711.0000.4640.8100.4250.5060.0001.0001.000
비소(As)0.8140.2920.3540.4670.3520.4641.0000.3880.036-0.1320.4521.0001.000
알루미늄(Al)0.6290.8550.8300.6680.8730.8100.3881.0000.7320.6530.0001.0001.000
칼슘(Ca)0.2170.5550.6700.2520.6400.4250.0360.7321.0000.8300.2861.0001.000
마그네슘(Mg)0.1310.4900.4870.2370.5550.506-0.1320.6530.8301.0000.2231.0001.000
일자0.3510.0000.0000.0000.2300.0000.4520.0000.2860.2231.0001.0001.000
카드뮴(Cd)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
베릴륨(Be)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-03-15T06:28:30.567703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T06:28:31.153143image/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.
2024-03-15T06:28:31.602746image/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

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(2024-01-08 00:00∼2024-01-08 24:00)0.003200.00.00270.00640.13660.00.002100.04510.13410.0757
1건국동2(2024-01-09 00:00∼2024-01-09 24:00)0.004600.00.00370.01490.19710.00.004900.05140.1610.0616
2건국동3(2024-01-10 00:00∼2024-01-10 24:00)0.020400.00.0040.02150.22350.00.010100.08560.17690.0622
3건국동4(2024-01-11 00:00∼2024-01-11 24:00)0.021300.00030.00430.02550.30820.00020.009600.10740.21640.082
4건국동5(2024-01-12 00:00∼2024-01-12 24:00)0.006300.00010.00380.01930.31180.00030.002200.12890.38980.1298
5농성동1(2024-01-08 00:00∼2024-01-08 24:00)0.006100.00040.00320.00490.220.00.001500.09250.29470.1162
6농성동2(2024-01-09 00:00∼2024-01-09 24:00)0.005800.00130.00810.00990.36470.00010.003300.12870.49710.1134
7농성동3(2024-01-10 00:00∼2024-01-10 24:00)0.026700.00060.00470.01190.29460.00010.013400.13570.35130.0965
8농성동4(2024-01-11 00:00∼2024-01-11 24:00)0.036400.00150.00990.01910.52390.00050.014800.21820.61340.1552
9농성동5(2024-01-12 00:00∼2024-01-12 24:00)0.010200.00160.01050.02580.64480.00070.003400.2420.8980.2193
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(2024-01-08 00:00∼2024-01-08 24:00)0.004900.00040.00210.0060.15580.00.002500.12390.1960.076
21노대동2(2024-01-09 00:00∼2024-01-09 24:00)0.006900.00310.01010.0160.45130.00020.003500.24180.55150.1079
22노대동3(2024-01-10 00:00∼2024-01-10 24:00)0.030800.00070.00470.01290.25740.00030.013800.13070.22390.0734
23노대동4(2024-01-11 00:00∼2024-01-11 24:00)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
24노대동5(2024-01-12 00:00∼2024-01-12 24:00)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25평동1(2024-01-08 00:00∼2024-01-08 24:00)0.018400.00340.00770.06980.74540.00030.00200.15510.29380.0958
26평동2(2024-01-09 00:00∼2024-01-09 24:00)0.03900.0050.01820.12751.2930.00150.00400.17960.47290.1083
27평동3(2024-01-10 00:00∼2024-01-10 24:00)0.062700.00810.00730.05460.6250.00230.019100.16450.21560.0778
28평동4(2024-01-11 00:00∼2024-01-11 24:00)0.064800.01240.02620.22621.75180.00330.017200.16620.2510.0929
29평동5(2024-01-12 00:00∼2024-01-12 24:00)0.027200.00630.01510.09911.18520.0020.003900.20750.44180.131