Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells1505
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory154.0 B

Variable types

DateTime1
Categorical7
Numeric8
Text1

Dataset

Description충청남도 대기오염측정망시스템에서 추출한 자료로 측정일, 측정소명, 일평균값 등을 나타낸 데이터를 개방하고자 합니다.
URLhttps://www.data.go.kr/data/15049859/fileData.do

Alerts

일평균값 is highly overall correlated with 1시간평균최고값 and 2 other fieldsHigh correlation
1시간평균최고값 is highly overall correlated with 일평균값 and 2 other fieldsHigh correlation
1시간평균최저값 is highly overall correlated with 일평균값 and 2 other fieldsHigh correlation
1시간평균국가기준초과합 is highly overall correlated with 국가기준초과여부 and 1 other fieldsHigh correlation
1시간평균정상상태값개수 is highly overall correlated with 유효가동률 and 1 other fieldsHigh correlation
1시간평균정상상태측정값의합 is highly overall correlated with 일평균값 and 2 other fieldsHigh correlation
유효가동률 is highly overall correlated with 1시간평균정상상태값개수High correlation
상태값 is highly overall correlated with 1시간평균정상상태값개수High correlation
국가기준초과여부 is highly overall correlated with 1시간평균국가기준초과합 and 1 other fieldsHigh correlation
충남기준초과여부 is highly overall correlated with 1시간평균국가기준초과합 and 1 other fieldsHigh correlation
상태값 is highly imbalanced (87.9%)Imbalance
국가기준초과여부 is highly imbalanced (90.3%)Imbalance
충남기준초과여부 is highly imbalanced (88.5%)Imbalance
1시간평균최고값 has 398 (4.0%) missing valuesMissing
1시간평균최저값 has 1107 (11.1%) missing valuesMissing
1시간평균충남기준초과합 is highly skewed (γ1 = 36.56063478)Skewed
일평균값 has 416 (4.2%) zerosZeros
1시간평균최저값 has 395 (4.0%) zerosZeros
1시간평균국가기준초과합 has 9559 (95.6%) zerosZeros
1시간평균충남기준초과합 has 9977 (99.8%) zerosZeros
1시간평균정상상태값개수 has 398 (4.0%) zerosZeros
1시간평균정상상태측정값의합 has 411 (4.1%) zerosZeros
유효가동률 has 398 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-12 03:01:19.955957
Analysis finished2023-12-12 03:01:34.119748
Duration14.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct166
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-02 00:00:00
Maximum2022-06-16 00:00:00
2023-12-12T12:01:34.213109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:34.385225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

측정소명
Categorical

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
금산읍
 
274
성동면
 
263
태안읍
 
261
예산군
 
256
서천읍
 
253
Other values (39)
8693 

Length

Max length5
Median length3
Mean length3.0251
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성황동
2nd row복운리
3rd row장항읍
4th row당진시청사
5th row주교면

Common Values

ValueCountFrequency (%)
금산읍 274
 
2.7%
성동면 263
 
2.6%
태안읍 261
 
2.6%
예산군 256
 
2.6%
서천읍 253
 
2.5%
인주면 253
 
2.5%
이원면 250
 
2.5%
홍성읍 250
 
2.5%
당진시청사 248
 
2.5%
송악면 248
 
2.5%
Other values (34) 7444
74.4%

Length

2023-12-12T12:01:34.568755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금산읍 274
 
2.7%
성동면 263
 
2.6%
태안읍 261
 
2.6%
예산군 256
 
2.6%
서천읍 253
 
2.5%
인주면 253
 
2.5%
이원면 250
 
2.5%
홍성읍 250
 
2.5%
당진시청사 248
 
2.5%
송악면 248
 
2.5%
Other values (34) 7444
74.4%

항목명
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
SO2
782 
NO2
772 
CO
753 
PM2.5
752 
HUM
750 
Other values (9)
6191 

Length

Max length5
Median length3
Mean length3.0038
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTEM
2nd rowNOx
3rd rowO3
4th rowNOx
5th rowSPD

Common Values

ValueCountFrequency (%)
SO2 782
 
7.8%
NO2 772
 
7.7%
CO 753
 
7.5%
PM2.5 752
 
7.5%
HUM 750
 
7.5%
SPD 744
 
7.4%
PM10 742
 
7.4%
O3 741
 
7.4%
NOx 739
 
7.4%
DIR 730
 
7.3%
Other values (4) 2495
24.9%

Length

2023-12-12T12:01:34.734169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
so2 782
 
7.8%
no2 772
 
7.7%
co 753
 
7.5%
pm2.5 752
 
7.5%
hum 750
 
7.5%
spd 744
 
7.4%
pm10 742
 
7.4%
o3 741
 
7.4%
nox 739
 
7.4%
dir 730
 
7.3%
Other values (4) 2495
24.9%

일평균값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1032
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.12484
Minimum-6.2
Maximum360
Zeros416
Zeros (%)4.2%
Negative155
Negative (%)1.6%
Memory size166.0 KiB
2023-12-12T12:01:34.908493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.2
5-th percentile0
Q10.011
median0.7
Q330.25
95-th percentile232
Maximum360
Range366.2
Interquartile range (IQR)30.239

Descriptive statistics

Standard deviation71.694664
Coefficient of variation (CV)2.0411385
Kurtosis7.0022519
Mean35.12484
Median Absolute Deviation (MAD)0.7
Skewness2.7334298
Sum351248.4
Variance5140.1248
MonotonicityNot monotonic
2023-12-12T12:01:35.074070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 533
 
5.3%
0.0 416
 
4.2%
0.004 334
 
3.3%
0.002 280
 
2.8%
0.3 243
 
2.4%
0.4 233
 
2.3%
0.005 141
 
1.4%
0.008 140
 
1.4%
0.5 128
 
1.3%
270.0 119
 
1.2%
Other values (1022) 7433
74.3%
ValueCountFrequency (%)
-6.2 1
 
< 0.1%
-5.8 2
< 0.1%
-5.7 1
 
< 0.1%
-5.6 3
< 0.1%
-5.5 1
 
< 0.1%
-5.3 2
< 0.1%
-5.1 1
 
< 0.1%
-5.0 1
 
< 0.1%
-4.8 3
< 0.1%
-4.7 4
< 0.1%
ValueCountFrequency (%)
360.0 51
0.5%
353.0 1
 
< 0.1%
346.0 1
 
< 0.1%
339.0 2
 
< 0.1%
338.0 1
 
< 0.1%
337.5 49
0.5%
336.0 1
 
< 0.1%
333.0 2
 
< 0.1%
329.0 1
 
< 0.1%
323.0 1
 
< 0.1%

상태값
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
SMPL
9456 
NA01
 
340
M002
 
130
D004
 
59
J002
 
5
Other values (4)
 
10

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSMPL
2nd rowNA01
3rd rowSMPL
4th rowD004
5th rowSMPL

Common Values

ValueCountFrequency (%)
SMPL 9456
94.6%
NA01 340
 
3.4%
M002 130
 
1.3%
D004 59
 
0.6%
J002 5
 
0.1%
K009 4
 
< 0.1%
J008 3
 
< 0.1%
J009 2
 
< 0.1%
J010 1
 
< 0.1%

Length

2023-12-12T12:01:35.230797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:01:35.352348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
smpl 9456
94.6%
na01 340
 
3.4%
m002 130
 
1.3%
d004 59
 
0.6%
j002 5
 
< 0.1%
k009 4
 
< 0.1%
j008 3
 
< 0.1%
j009 2
 
< 0.1%
j010 1
 
< 0.1%

국가기준초과여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9875 
1
 
125

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 9875
98.8%
1 125
 
1.2%

Length

2023-12-12T12:01:35.509020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:01:35.630879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9875
98.8%
1 125
 
1.2%

충남기준초과여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9845 
1
 
155

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 9845
98.5%
1 155
 
1.6%

Length

2023-12-12T12:01:36.083486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:01:36.175616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9845
98.5%
1 155
 
1.6%

1시간평균최고값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1238
Distinct (%)12.9%
Missing398
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean65.176033
Minimum-4.4
Maximum1117
Zeros15
Zeros (%)0.1%
Negative35
Negative (%)0.4%
Memory size166.0 KiB
2023-12-12T12:01:36.314765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.4
5-th percentile0.004
Q10.035
median2.6
Q354
95-th percentile360
Maximum1117
Range1121.4
Interquartile range (IQR)53.965

Descriptive statistics

Standard deviation153.16816
Coefficient of variation (CV)2.3500688
Kurtosis13.797395
Mean65.176033
Median Absolute Deviation (MAD)2.597
Skewness3.6160948
Sum625820.27
Variance23460.486
MonotonicityNot monotonic
2023-12-12T12:01:36.490618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004 283
 
2.8%
0.003 244
 
2.4%
0.005 215
 
2.1%
0.4 151
 
1.5%
0.5 148
 
1.5%
0.006 137
 
1.4%
0.6 123
 
1.2%
270.0 119
 
1.2%
0.007 102
 
1.0%
0.3 82
 
0.8%
Other values (1228) 7998
80.0%
(Missing) 398
 
4.0%
ValueCountFrequency (%)
-4.4 1
< 0.1%
-4.3 1
< 0.1%
-3.8 1
< 0.1%
-3.5 1
< 0.1%
-3.3 1
< 0.1%
-2.9 2
< 0.1%
-2.7 2
< 0.1%
-2.5 1
< 0.1%
-2.4 2
< 0.1%
-2.1 2
< 0.1%
ValueCountFrequency (%)
1117.0 1
< 0.1%
1105.0 1
< 0.1%
1095.0 1
< 0.1%
1034.0 1
< 0.1%
1022.0 1
< 0.1%
1017.0 1
< 0.1%
1003.0 1
< 0.1%
986.0 1
< 0.1%
984.0 1
< 0.1%
981.0 1
< 0.1%
Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
00
762 
07
727 
08
718 
15
 
645
12
 
623
Other values (20)
6525 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row
3rd row15
4th row
5th row23

Common Values

ValueCountFrequency (%)
00 762
 
7.6%
07 727
 
7.3%
08 718
 
7.2%
15 645
 
6.5%
12 623
 
6.2%
14 606
 
6.1%
13 551
 
5.5%
23 531
 
5.3%
09 419
 
4.2%
16 416
 
4.2%
Other values (15) 4002
40.0%

Length

2023-12-12T12:01:36.650818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00 762
 
7.9%
07 727
 
7.6%
08 718
 
7.5%
15 645
 
6.7%
12 623
 
6.5%
14 606
 
6.3%
13 551
 
5.7%
23 531
 
5.5%
09 419
 
4.4%
16 416
 
4.3%
Other values (14) 3604
37.5%

1시간평균최저값
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct742
Distinct (%)8.3%
Missing1107
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean7.4457606
Minimum-37.3
Maximum100
Zeros395
Zeros (%)4.0%
Negative272
Negative (%)2.7%
Memory size166.0 KiB
2023-12-12T12:01:36.809464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-37.3
5-th percentile0
Q10.004
median0.1
Q311
95-th percentile38.9
Maximum100
Range137.3
Interquartile range (IQR)10.996

Descriptive statistics

Standard deviation14.124908
Coefficient of variation (CV)1.8970404
Kurtosis5.5852484
Mean7.4457606
Median Absolute Deviation (MAD)0.1
Skewness2.2431465
Sum66215.149
Variance199.51304
MonotonicityNot monotonic
2023-12-12T12:01:36.982598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 774
 
7.7%
0.003 694
 
6.9%
0.3 405
 
4.0%
0.2 402
 
4.0%
0.0 395
 
4.0%
1.0 271
 
2.7%
0.005 232
 
2.3%
0.006 227
 
2.3%
0.004 224
 
2.2%
0.1 200
 
2.0%
Other values (732) 5069
50.7%
(Missing) 1107
 
11.1%
ValueCountFrequency (%)
-37.3 1
< 0.1%
-20.8 1
< 0.1%
-14.8 1
< 0.1%
-12.5 1
< 0.1%
-12.1 1
< 0.1%
-11.7 1
< 0.1%
-11.5 1
< 0.1%
-11.2 1
< 0.1%
-11.1 2
< 0.1%
-10.9 1
< 0.1%
ValueCountFrequency (%)
100.0 2
< 0.1%
96.0 1
< 0.1%
94.6 1
< 0.1%
90.4 1
< 0.1%
90.0 2
< 0.1%
87.0 1
< 0.1%
86.5 1
< 0.1%
86.2 1
< 0.1%
85.3 1
< 0.1%
85.0 2
< 0.1%
Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
00
2455 
1107 
06
 
461
01
 
461
23
 
442
Other values (20)
5074 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row06
2nd row
3rd row23
4th row
5th row06

Common Values

ValueCountFrequency (%)
00 2455
24.6%
1107
 
11.1%
06 461
 
4.6%
01 461
 
4.6%
23 442
 
4.4%
05 393
 
3.9%
07 392
 
3.9%
14 386
 
3.9%
12 354
 
3.5%
02 346
 
3.5%
Other values (15) 3203
32.0%

Length

2023-12-12T12:01:37.161389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00 2455
27.6%
06 461
 
5.2%
01 461
 
5.2%
23 442
 
5.0%
05 393
 
4.4%
07 392
 
4.4%
14 386
 
4.3%
12 354
 
4.0%
02 346
 
3.9%
15 333
 
3.7%
Other values (14) 2870
32.3%

1시간평균국가기준초과합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3614
Minimum0
Maximum24
Zeros9559
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:01:37.299259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2422517
Coefficient of variation (CV)6.204349
Kurtosis62.266298
Mean0.3614
Median Absolute Deviation (MAD)0
Skewness7.6046617
Sum3614
Variance5.0276928
MonotonicityNot monotonic
2023-12-12T12:01:37.445450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 9559
95.6%
1 75
 
0.8%
2 47
 
0.5%
4 44
 
0.4%
3 34
 
0.3%
10 22
 
0.2%
5 21
 
0.2%
6 19
 
0.2%
7 17
 
0.2%
24 16
 
0.2%
Other values (15) 146
 
1.5%
ValueCountFrequency (%)
0 9559
95.6%
1 75
 
0.8%
2 47
 
0.5%
3 34
 
0.3%
4 44
 
0.4%
5 21
 
0.2%
6 19
 
0.2%
7 17
 
0.2%
8 11
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
24 16
0.2%
23 9
0.1%
22 5
 
0.1%
21 9
0.1%
20 10
0.1%
19 11
0.1%
18 5
 
0.1%
17 7
0.1%
16 13
0.1%
15 7
0.1%

1시간평균충남기준초과합
Real number (ℝ)

SKEWED  ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0094
Minimum0
Maximum14
Zeros9977
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:01:37.579331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25596522
Coefficient of variation (CV)27.230342
Kurtosis1546.9042
Mean0.0094
Median Absolute Deviation (MAD)0
Skewness36.560635
Sum94
Variance0.065518192
MonotonicityNot monotonic
2023-12-12T12:01:37.725198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 9977
99.8%
2 6
 
0.1%
1 5
 
0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 9977
99.8%
1 5
 
0.1%
2 6
 
0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 1
 
< 0.1%
9 2
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
3 3
< 0.1%
2 6
0.1%
1 5
0.1%

1시간평균정상상태값개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.6783
Minimum0
Maximum24
Zeros398
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:01:37.913073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q124
median24
Q324
95-th percentile24
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.9084028
Coefficient of variation (CV)0.2164361
Kurtosis15.772072
Mean22.6783
Median Absolute Deviation (MAD)0
Skewness-4.1339459
Sum226783
Variance24.092418
MonotonicityNot monotonic
2023-12-12T12:01:38.039894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
24 8534
85.3%
22 501
 
5.0%
0 398
 
4.0%
23 264
 
2.6%
21 106
 
1.1%
20 24
 
0.2%
13 23
 
0.2%
10 19
 
0.2%
11 17
 
0.2%
19 17
 
0.2%
Other values (12) 97
 
1.0%
ValueCountFrequency (%)
0 398
4.0%
4 1
 
< 0.1%
5 4
 
< 0.1%
6 4
 
< 0.1%
7 12
 
0.1%
8 9
 
0.1%
9 10
 
0.1%
10 19
 
0.2%
11 17
 
0.2%
12 12
 
0.1%
ValueCountFrequency (%)
24 8534
85.3%
23 264
 
2.6%
22 501
 
5.0%
21 106
 
1.1%
20 24
 
0.2%
19 17
 
0.2%
18 10
 
0.1%
17 15
 
0.1%
16 5
 
0.1%
15 5
 
0.1%

1시간평균정상상태측정값의합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4412
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean481.00522
Minimum-147.9
Maximum8462
Zeros411
Zeros (%)4.1%
Negative157
Negative (%)1.6%
Memory size166.0 KiB
2023-12-12T12:01:38.192732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-147.9
5-th percentile0
Q10.26175
median17.3
Q3513
95-th percentile2040
Maximum8462
Range8609.9
Interquartile range (IQR)512.73825

Descriptive statistics

Standard deviation1052.599
Coefficient of variation (CV)2.1883317
Kurtosis17.99312
Mean481.00522
Median Absolute Deviation (MAD)17.3
Skewness3.9077911
Sum4810052.2
Variance1107964.7
MonotonicityNot monotonic
2023-12-12T12:01:38.378321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 411
 
4.1%
270.0 121
 
1.2%
247.5 61
 
0.6%
0.072 58
 
0.6%
360.0 57
 
0.6%
292.5 53
 
0.5%
337.5 49
 
0.5%
225.0 47
 
0.5%
112.5 42
 
0.4%
0.048 40
 
0.4%
Other values (4402) 9061
90.6%
ValueCountFrequency (%)
-147.9 1
< 0.1%
-139.7 1
< 0.1%
-138.3 1
< 0.1%
-136.0 1
< 0.1%
-135.0 1
< 0.1%
-134.9 1
< 0.1%
-134.0 1
< 0.1%
-132.8 1
< 0.1%
-128.0 1
< 0.1%
-127.6 1
< 0.1%
ValueCountFrequency (%)
8462.0 1
< 0.1%
8313.0 1
< 0.1%
8145.0 1
< 0.1%
8140.0 1
< 0.1%
8116.0 1
< 0.1%
8066.0 1
< 0.1%
7999.0 1
< 0.1%
7993.0 1
< 0.1%
7893.0 1
< 0.1%
7746.0 1
< 0.1%
Distinct172
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T12:01:38.880759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length21.2874
Min length21

Characters and Unicode

Total characters212874
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-06-08 오후 2:56:38
2nd row2022-05-27 오후 1:37:35
3rd row2022-05-06 오전 10:41:37
4th row2022-06-09 오후 2:41:38
5th row2022-05-27 오후 1:37:37
ValueCountFrequency (%)
오후 6943
23.1%
오전 3057
 
10.2%
2022-04-13 1238
 
4.1%
2022-02-15 866
 
2.9%
2022-05-17 770
 
2.6%
2022-08-23 607
 
2.0%
2022-05-27 556
 
1.9%
2022-09-02 501
 
1.7%
2022-03-07 490
 
1.6%
2022-07-14 459
 
1.5%
Other values (200) 14513
48.4%
2023-12-12T12:01:39.586609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 42606
20.0%
0 30215
14.2%
- 20000
9.4%
20000
9.4%
: 20000
9.4%
1 16855
 
7.9%
4 10610
 
5.0%
10000
 
4.7%
3 8840
 
4.2%
5 7233
 
3.4%
Other values (6) 26515
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132874
62.4%
Dash Punctuation 20000
 
9.4%
Space Separator 20000
 
9.4%
Other Punctuation 20000
 
9.4%
Other Letter 20000
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 42606
32.1%
0 30215
22.7%
1 16855
 
12.7%
4 10610
 
8.0%
3 8840
 
6.7%
5 7233
 
5.4%
7 5672
 
4.3%
6 3924
 
3.0%
8 3484
 
2.6%
9 3435
 
2.6%
Other Letter
ValueCountFrequency (%)
10000
50.0%
6943
34.7%
3057
 
15.3%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Space Separator
ValueCountFrequency (%)
20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 192874
90.6%
Hangul 20000
 
9.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 42606
22.1%
0 30215
15.7%
- 20000
10.4%
20000
10.4%
: 20000
10.4%
1 16855
 
8.7%
4 10610
 
5.5%
3 8840
 
4.6%
5 7233
 
3.8%
7 5672
 
2.9%
Other values (3) 10843
 
5.6%
Hangul
ValueCountFrequency (%)
10000
50.0%
6943
34.7%
3057
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192874
90.6%
Hangul 20000
 
9.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 42606
22.1%
0 30215
15.7%
- 20000
10.4%
20000
10.4%
: 20000
10.4%
1 16855
 
8.7%
4 10610
 
5.5%
3 8840
 
4.6%
5 7233
 
3.8%
7 5672
 
2.9%
Other values (3) 10843
 
5.6%
Hangul
ValueCountFrequency (%)
10000
50.0%
6943
34.7%
3057
 
15.3%

유효가동률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.584175
Minimum0
Maximum100
Zeros398
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:01:39.804849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile87.5
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.829928
Coefficient of variation (CV)0.20746037
Kurtosis18.689164
Mean95.584175
Median Absolute Deviation (MAD)0
Skewness-4.5180999
Sum955841.75
Variance393.22604
MonotonicityNot monotonic
2023-12-12T12:01:40.017705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 9318
93.2%
0.0 398
 
4.0%
95.83 97
 
1.0%
91.67 40
 
0.4%
87.5 23
 
0.2%
83.33 9
 
0.1%
95.45 9
 
0.1%
70.83 9
 
0.1%
90.91 8
 
0.1%
79.17 6
 
0.1%
Other values (44) 83
 
0.8%
ValueCountFrequency (%)
0.0 398
4.0%
22.73 1
 
< 0.1%
25.0 1
 
< 0.1%
29.17 3
 
< 0.1%
35.0 1
 
< 0.1%
38.1 1
 
< 0.1%
39.13 1
 
< 0.1%
40.91 1
 
< 0.1%
41.67 2
 
< 0.1%
42.86 1
 
< 0.1%
ValueCountFrequency (%)
100.0 9318
93.2%
95.83 97
 
1.0%
95.65 6
 
0.1%
95.45 9
 
0.1%
95.24 2
 
< 0.1%
95.0 2
 
< 0.1%
94.74 1
 
< 0.1%
93.33 1
 
< 0.1%
91.67 40
 
0.4%
91.3 2
 
< 0.1%

Interactions

2023-12-12T12:01:32.358963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:23.524401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:24.636426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:25.782197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:27.153006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:28.725574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:29.945398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:31.076687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:32.499462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:23.637877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:24.769228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:26.006752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:27.316478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:28.899605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:30.097680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:31.247527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:32.641462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:23.772653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:24.884784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:26.173804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:27.487273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:29.056504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:30.241722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:31.401883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:32.835280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:23.912804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:25.010597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:26.346312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:27.664662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:29.211914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:30.396099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:31.557535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:32.977588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:24.041722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:25.128355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:26.505276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:27.806576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:29.338738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:30.535335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:31.737728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:33.137511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:24.203556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:25.269603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:26.675617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:28.297845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:29.479266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:30.695849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:31.916915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:33.257937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:24.357348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:25.435900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:26.850679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:28.424806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:29.639940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:30.814212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:32.076990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:33.375156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:24.493006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:25.582071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:26.982350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:28.570631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:29.792667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:30.943119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:01:32.221861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:01:40.164282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명항목명일평균값상태값국가기준초과여부충남기준초과여부1시간평균최고값1시간평균최고시간1시간평균최저값1시간평균최저시간1시간평균국가기준초과합1시간평균충남기준초과합1시간평균정상상태값개수1시간평균정상상태측정값의합유효가동률
측정소명1.0000.1860.2700.6910.0890.1030.2350.6160.1320.4220.0450.0000.6530.1790.663
항목명0.1861.0000.7400.0890.4330.4020.7400.7020.7200.7630.3790.0930.1170.7270.120
일평균값0.2700.7401.0000.0760.3470.3400.9210.4630.7620.5530.3250.0000.1180.9580.077
상태값0.6910.0890.0761.0000.0340.0310.0000.6820.0590.4580.0000.0000.7870.0450.870
국가기준초과여부0.0890.4330.3470.0341.0000.9860.1710.0900.3020.1470.9900.0000.1170.3670.017
충남기준초과여부0.1030.4020.3400.0310.9861.0000.2820.0910.3310.1580.9710.0000.1080.3590.016
1시간평균최고값0.2350.7400.9210.0000.1710.2821.0000.5050.3170.6530.2130.0000.0000.9130.000
1시간평균최고시간0.6160.7020.4630.6820.0900.0910.5051.0000.3190.6940.1210.0420.7060.4700.699
1시간평균최저값0.1320.7200.7620.0590.3020.3310.3170.3191.0000.3930.3560.0000.0710.7620.129
1시간평균최저시간0.4220.7630.5530.4580.1470.1580.6530.6940.3931.0000.1720.1000.4890.4390.480
1시간평균국가기준초과합0.0450.3790.3250.0000.9900.9710.2130.1210.3560.1721.0000.3730.1210.3480.022
1시간평균충남기준초과합0.0000.0930.0000.0000.0000.0000.0000.0420.0000.1000.3731.0000.0000.0000.000
1시간평균정상상태값개수0.6530.1170.1180.7870.1170.1080.0000.7060.0710.4890.1210.0001.0000.0690.851
1시간평균정상상태측정값의합0.1790.7270.9580.0450.3670.3590.9130.4700.7620.4390.3480.0000.0691.0000.049
유효가동률0.6630.1200.0770.8700.0170.0160.0000.6990.1290.4800.0220.0000.8510.0491.000
2023-12-12T12:01:40.386156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가기준초과여부충남기준초과여부상태값1시간평균최고시간항목명1시간평균최저시간측정소명
국가기준초과여부1.0000.8930.0340.0780.3390.1270.071
충남기준초과여부0.8931.0000.0310.0790.3150.1370.082
상태값0.0340.0311.0000.3400.0370.1930.328
1시간평균최고시간0.0780.0790.3401.0000.3020.1910.178
항목명0.3390.3150.0370.3021.0000.3550.055
1시간평균최저시간0.1270.1370.1930.1910.3551.0000.106
측정소명0.0710.0820.3280.1780.0550.1061.000
2023-12-12T12:01:40.570560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일평균값1시간평균최고값1시간평균최저값1시간평균국가기준초과합1시간평균충남기준초과합1시간평균정상상태값개수1시간평균정상상태측정값의합유효가동률측정소명항목명상태값국가기준초과여부충남기준초과여부1시간평균최고시간1시간평균최저시간
일평균값1.0000.9730.7590.183-0.0150.2520.9480.2290.0960.4120.0340.2660.2610.1810.229
1시간평균최고값0.9731.0000.7060.199-0.0220.0880.929-0.0570.0830.4130.0000.1310.2160.2100.293
1시간평균최저값0.7590.7061.0000.234-0.0080.0160.758-0.0750.0460.4010.0280.2320.2540.1220.155
1시간평균국가기준초과합0.1830.1990.2341.0000.220-0.0450.228-0.0520.0120.1630.0000.9060.8470.0420.062
1시간평균충남기준초과합-0.015-0.022-0.0080.2201.0000.009-0.0150.0130.0000.0440.0000.0000.0000.0200.041
1시간평균정상상태값개수0.2520.0880.016-0.0450.0091.0000.2500.6900.2870.0470.5110.0900.0830.3350.194
1시간평균정상상태측정값의합0.9480.9290.7580.228-0.0150.2501.0000.2230.0620.4000.0200.2820.2760.1850.170
유효가동률0.229-0.057-0.075-0.0520.0130.6900.2231.0000.3050.0510.4600.0170.0160.3540.205
측정소명0.0960.0830.0460.0120.0000.2870.0620.3051.0000.0550.3280.0710.0820.1780.106
항목명0.4120.4130.4010.1630.0440.0470.4000.0510.0551.0000.0370.3390.3150.3020.355
상태값0.0340.0000.0280.0000.0000.5110.0200.4600.3280.0371.0000.0340.0310.3400.193
국가기준초과여부0.2660.1310.2320.9060.0000.0900.2820.0170.0710.3390.0341.0000.8930.0780.127
충남기준초과여부0.2610.2160.2540.8470.0000.0830.2760.0160.0820.3150.0310.8931.0000.0790.137
1시간평균최고시간0.1810.2100.1220.0420.0200.3350.1850.3540.1780.3020.3400.0780.0791.0000.191
1시간평균최저시간0.2290.2930.1550.0620.0410.1940.1700.2050.1060.3550.1930.1270.1370.1911.000

Missing values

2023-12-12T12:01:33.573342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:01:33.876308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T12:01:34.050344image/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

측정일측정소명항목명일평균값상태값국가기준초과여부충남기준초과여부1시간평균최고값1시간평균최고시간1시간평균최저값1시간평균최저시간1시간평균국가기준초과합1시간평균충남기준초과합1시간평균정상상태값개수1시간평균정상상태측정값의합생성일시유효가동률
818932022-05-24성황동TEM25.2SMPL0025.71524.9060024605.32022-06-08 오후 2:56:38100.0
702122022-05-04복운리NOx0.0NA0100<NA><NA>0000.02022-05-27 오후 1:37:350.0
673832022-04-29장항읍O30.043SMPL000.057150.0282300210.8972022-05-06 오전 10:41:37100.0
859732022-05-31당진시청사NOx0.0D00400<NA><NA>0000.02022-06-09 오후 2:41:380.0
725262022-05-08주교면SPD1.9SMPL003.7230.406002444.82022-05-27 오후 1:37:37100.0
742582022-05-11복운리SO20.0NA0100<NA><NA>0000.02022-06-09 오전 10:59:290.0
415862022-03-16송산면TEM25.0SMPL0025.91524.1000024599.22022-04-13 오후 1:22:54100.0
599962022-04-16삽교읍NO0.003SMPL000.009020.0012000240.0742022-04-29 오후 3:08:24100.0
569212022-04-11대천2동SPD1.4SMPL003.3130.303002433.82022-05-17 오전 10:41:31100.0
118612022-01-23백석동SO20.003SMPL000.005000.0022300240.0832022-06-24 오후 3:29:09100.0
측정일측정소명항목명일평균값상태값국가기준초과여부충남기준초과여부1시간평균최고값1시간평균최고시간1시간평균최저값1시간평균최저시간1시간평균국가기준초과합1시간평균충남기준초과합1시간평균정상상태값개수1시간평균정상상태측정값의합생성일시유효가동률
496102022-03-29당진시청사SO20.004SMPL000.005040.0030000240.0882022-04-13 오전 10:31:32100.0
881582022-06-04대천2동NOx0.009SMPL000.015170.0040000240.2112022-07-14 오후 2:10:09100.0
761012022-05-14성거읍DIR270.0SMPL00270.021<NA>0024270.02022-06-08 오후 1:39:04100.0
843642022-05-28도고면O30.058SMPL000.097130.0262300241.3862022-06-09 오후 2:41:10100.0
248562022-02-14주교면NOx0.018SMPL000.055080.0072100240.4322022-03-07 오후 2:02:49100.0
3192022-01-02부여읍NOx0.024SMPL000.038220.0151300240.5662022-02-16 오전 11:51:29100.0
286192022-02-21배방읍PM2.516.0SMPL0028.01011.0180024372.02022-02-24 오후 9:11:15100.0
3112022-01-02금산읍HUM68.3SMPL0084.02344.31500241639.22022-02-16 오전 11:51:29100.0
818512022-05-24서천읍SPD1.1SMPL003.0160.100002426.22022-06-08 오후 2:56:38100.0
335782022-03-01태안읍HUM68.4SMPL0087.51150.82200241641.12022-04-07 오후 4:01:17100.0