Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells1548
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.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=408&beforeMenuCd=DOM_000000201001001000&publicdatapk=15049859

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 (86.8%)Imbalance
국가기준초과여부 is highly imbalanced (90.4%)Imbalance
충남기준초과여부 is highly imbalanced (88.3%)Imbalance
1시간평균최고값 has 424 (4.2%) missing valuesMissing
1시간평균최저값 has 1124 (11.2%) missing valuesMissing
1시간평균충남기준초과합 is highly skewed (γ1 = 26.73130267)Skewed
일평균값 has 443 (4.4%) zerosZeros
1시간평균최저값 has 380 (3.8%) zerosZeros
1시간평균국가기준초과합 has 9575 (95.8%) zerosZeros
1시간평균충남기준초과합 has 9975 (99.8%) zerosZeros
1시간평균정상상태값개수 has 424 (4.2%) zerosZeros
1시간평균정상상태측정값의합 has 441 (4.4%) zerosZeros
유효가동률 has 424 (4.2%) zerosZeros

Reproduction

Analysis started2024-01-09 20:50:29.128526
Analysis finished2024-01-09 20:50:37.917513
Duration8.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일
Date

Distinct166
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-02 00:00:00
Maximum2022-06-16 00:00:00
2024-01-10T05:50:37.983377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:38.106627image/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
도고면
 
268
공주
 
268
독곶리
 
264
대산리
 
263
송산면
 
254
Other values (39)
8683 

Length

Max length5
Median length3
Mean length3.0137
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row예산군
2nd row대산리
3rd row연무읍
4th row신방동
5th row둔포면

Common Values

ValueCountFrequency (%)
도고면 268
 
2.7%
공주 268
 
2.7%
독곶리 264
 
2.6%
대산리 263
 
2.6%
송산면 254
 
2.5%
연무읍 254
 
2.5%
예산군 253
 
2.5%
모종동 252
 
2.5%
태안읍 251
 
2.5%
대천2동 250
 
2.5%
Other values (34) 7423
74.2%

Length

2024-01-10T05:50:38.227703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도고면 268
 
2.7%
공주 268
 
2.7%
독곶리 264
 
2.6%
대산리 263
 
2.6%
송산면 254
 
2.5%
연무읍 254
 
2.5%
예산군 253
 
2.5%
모종동 252
 
2.5%
태안읍 251
 
2.5%
대천2동 250
 
2.5%
Other values (34) 7423
74.2%

항목명
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
NOx
771 
SO2
771 
NO
755 
SPD
752 
PM2.5
750 
Other values (9)
6201 

Length

Max length5
Median length3
Mean length3.001
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOx
2nd rowTMP
3rd rowCO
4th rowTMP
5th rowDIR

Common Values

ValueCountFrequency (%)
NOx 771
 
7.7%
SO2 771
 
7.7%
NO 755
 
7.5%
SPD 752
 
7.5%
PM2.5 750
 
7.5%
O3 746
 
7.5%
PM10 742
 
7.4%
NO2 736
 
7.4%
TMP 732
 
7.3%
CO 731
 
7.3%
Other values (4) 2514
25.1%

Length

2024-01-10T05:50:38.335743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nox 771
 
7.7%
so2 771
 
7.7%
no 755
 
7.5%
spd 752
 
7.5%
pm2.5 750
 
7.5%
o3 746
 
7.5%
pm10 742
 
7.4%
no2 736
 
7.4%
tmp 732
 
7.3%
co 731
 
7.3%
Other values (4) 2514
25.1%

일평균값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1027
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.23773
Minimum-9.9
Maximum360
Zeros443
Zeros (%)4.4%
Negative151
Negative (%)1.5%
Memory size166.0 KiB
2024-01-10T05:50:38.448722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.9
5-th percentile0
Q10.011
median0.7
Q329
95-th percentile225
Maximum360
Range369.9
Interquartile range (IQR)28.989

Descriptive statistics

Standard deviation69.914459
Coefficient of variation (CV)2.0420296
Kurtosis7.1574389
Mean34.23773
Median Absolute Deviation (MAD)0.7
Skewness2.7540929
Sum342377.3
Variance4888.0315
MonotonicityNot monotonic
2024-01-10T05:50:38.570314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 531
 
5.3%
0.0 443
 
4.4%
0.004 297
 
3.0%
0.002 268
 
2.7%
0.3 228
 
2.3%
0.4 215
 
2.1%
0.005 150
 
1.5%
0.5 140
 
1.4%
0.007 133
 
1.3%
0.006 119
 
1.2%
Other values (1017) 7476
74.8%
ValueCountFrequency (%)
-9.9 1
 
< 0.1%
-7.7 1
 
< 0.1%
-6.5 1
 
< 0.1%
-6.0 1
 
< 0.1%
-5.9 1
 
< 0.1%
-5.8 1
 
< 0.1%
-5.6 1
 
< 0.1%
-5.5 2
< 0.1%
-5.4 3
< 0.1%
-5.3 3
< 0.1%
ValueCountFrequency (%)
360.0 37
0.4%
345.0 1
 
< 0.1%
343.0 1
 
< 0.1%
337.5 47
0.5%
331.0 1
 
< 0.1%
327.0 1
 
< 0.1%
324.0 1
 
< 0.1%
323.0 2
 
< 0.1%
322.0 2
 
< 0.1%
318.0 1
 
< 0.1%

상태값
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
SMPL
9435 
NA01
 
354
M002
 
131
D004
 
67
J009
 
5
Other values (3)
 
8

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
SMPL 9435
94.3%
NA01 354
 
3.5%
M002 131
 
1.3%
D004 67
 
0.7%
J009 5
 
0.1%
J002 3
 
< 0.1%
K009 3
 
< 0.1%
J001 2
 
< 0.1%

Length

2024-01-10T05:50:38.682315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:50:38.774795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
smpl 9435
94.3%
na01 354
 
3.5%
m002 131
 
1.3%
d004 67
 
0.7%
j009 5
 
< 0.1%
j002 3
 
< 0.1%
k009 3
 
< 0.1%
j001 2
 
< 0.1%

국가기준초과여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9876 
1
 
124

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 9876
98.8%
1 124
 
1.2%

Length

2024-01-10T05:50:38.874259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:50:38.950030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9876
98.8%
1 124
 
1.2%

충남기준초과여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9843 
1
 
157

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 9843
98.4%
1 157
 
1.6%

Length

2024-01-10T05:50:39.041635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:50:39.121518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9843
98.4%
1 157
 
1.6%

1시간평균최고값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1217
Distinct (%)12.7%
Missing424
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean64.050525
Minimum-4.6
Maximum1145
Zeros21
Zeros (%)0.2%
Negative41
Negative (%)0.4%
Memory size166.0 KiB
2024-01-10T05:50:39.217064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.6
5-th percentile0.004
Q10.037
median2.6
Q353
95-th percentile360
Maximum1145
Range1149.6
Interquartile range (IQR)52.963

Descriptive statistics

Standard deviation151.28513
Coefficient of variation (CV)2.3619656
Kurtosis14.032181
Mean64.050525
Median Absolute Deviation (MAD)2.597
Skewness3.6440935
Sum613347.83
Variance22887.192
MonotonicityNot monotonic
2024-01-10T05:50:39.345048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004 298
 
3.0%
0.003 260
 
2.6%
0.005 172
 
1.7%
0.4 155
 
1.6%
0.5 125
 
1.2%
270.0 116
 
1.2%
0.6 114
 
1.1%
0.006 104
 
1.0%
0.009 91
 
0.9%
0.7 91
 
0.9%
Other values (1207) 8050
80.5%
(Missing) 424
 
4.2%
ValueCountFrequency (%)
-4.6 1
< 0.1%
-4.4 1
< 0.1%
-4.2 1
< 0.1%
-4.0 1
< 0.1%
-3.9 1
< 0.1%
-3.8 1
< 0.1%
-3.6 2
< 0.1%
-2.7 2
< 0.1%
-2.6 1
< 0.1%
-2.3 1
< 0.1%
ValueCountFrequency (%)
1145.0 1
< 0.1%
1124.0 1
< 0.1%
1107.0 1
< 0.1%
1095.0 1
< 0.1%
1023.0 1
< 0.1%
1018.0 1
< 0.1%
962.0 1
< 0.1%
954.0 1
< 0.1%
944.0 1
< 0.1%
937.0 1
< 0.1%

1시간평균최고시간
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
07
756 
00
745 
08
717 
14
 
646
12
 
635
Other values (20)
6501 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21
2nd row14
3rd row08
4th row13
5th row17

Common Values

ValueCountFrequency (%)
07 756
 
7.6%
00 745
 
7.4%
08 717
 
7.2%
14 646
 
6.5%
12 635
 
6.3%
15 604
 
6.0%
13 524
 
5.2%
23 497
 
5.0%
16 444
 
4.4%
09 442
 
4.4%
Other values (15) 3990
39.9%

Length

2024-01-10T05:50:39.451671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07 756
 
7.9%
00 745
 
7.8%
08 717
 
7.5%
14 646
 
6.7%
12 635
 
6.6%
15 604
 
6.3%
13 524
 
5.5%
23 497
 
5.2%
16 444
 
4.6%
09 442
 
4.6%
Other values (14) 3566
37.2%

1시간평균최저값
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct718
Distinct (%)8.1%
Missing1124
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean7.3802364
Minimum-18.4
Maximum99.9
Zeros380
Zeros (%)3.8%
Negative245
Negative (%)2.5%
Memory size166.0 KiB
2024-01-10T05:50:39.555223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-18.4
5-th percentile0
Q10.004
median0.2
Q311
95-th percentile38
Maximum99.9
Range118.3
Interquartile range (IQR)10.996

Descriptive statistics

Standard deviation13.801155
Coefficient of variation (CV)1.8700154
Kurtosis5.2307208
Mean7.3802364
Median Absolute Deviation (MAD)0.198
Skewness2.1942041
Sum65506.978
Variance190.47189
MonotonicityNot monotonic
2024-01-10T05:50:39.671001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 737
 
7.4%
0.003 719
 
7.2%
0.2 415
 
4.2%
0.3 383
 
3.8%
0.0 380
 
3.8%
1.0 293
 
2.9%
0.004 238
 
2.4%
0.006 222
 
2.2%
0.005 212
 
2.1%
0.007 205
 
2.1%
Other values (708) 5072
50.7%
(Missing) 1124
 
11.2%
ValueCountFrequency (%)
-18.4 1
< 0.1%
-13.9 1
< 0.1%
-12.5 1
< 0.1%
-12.4 1
< 0.1%
-12.1 1
< 0.1%
-12.0 2
< 0.1%
-11.9 1
< 0.1%
-11.5 1
< 0.1%
-11.2 1
< 0.1%
-11.0 1
< 0.1%
ValueCountFrequency (%)
99.9 1
< 0.1%
99.8 1
< 0.1%
96.5 1
< 0.1%
89.0 1
< 0.1%
87.0 1
< 0.1%
85.6 1
< 0.1%
85.0 1
< 0.1%
83.6 1
< 0.1%
83.3 1
< 0.1%
81.0 2
< 0.1%

1시간평균최저시간
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
00
2418 
1124 
23
 
453
01
 
447
06
 
433
Other values (20)
5125 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row05
3rd row12
4th row05
5th row

Common Values

ValueCountFrequency (%)
00 2418
24.2%
1124
 
11.2%
23 453
 
4.5%
01 447
 
4.5%
06 433
 
4.3%
05 412
 
4.1%
07 408
 
4.1%
14 377
 
3.8%
02 376
 
3.8%
03 350
 
3.5%
Other values (15) 3202
32.0%

Length

2024-01-10T05:50:39.780434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00 2418
27.2%
23 453
 
5.1%
01 447
 
5.0%
06 433
 
4.9%
05 412
 
4.6%
07 408
 
4.6%
14 377
 
4.2%
02 376
 
4.2%
03 350
 
3.9%
13 321
 
3.6%
Other values (14) 2881
32.5%

1시간평균국가기준초과합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.345
Minimum0
Maximum24
Zeros9575
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:50:39.871046image/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 deviation2.1189205
Coefficient of variation (CV)6.1417985
Kurtosis62.424687
Mean0.345
Median Absolute Deviation (MAD)0
Skewness7.547524
Sum3450
Variance4.489824
MonotonicityNot monotonic
2024-01-10T05:50:39.972552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 9575
95.8%
1 60
 
0.6%
2 49
 
0.5%
4 30
 
0.3%
5 30
 
0.3%
6 28
 
0.3%
3 28
 
0.3%
11 23
 
0.2%
7 19
 
0.2%
13 16
 
0.2%
Other values (15) 142
 
1.4%
ValueCountFrequency (%)
0 9575
95.8%
1 60
 
0.6%
2 49
 
0.5%
3 28
 
0.3%
4 30
 
0.3%
5 30
 
0.3%
6 28
 
0.3%
7 19
 
0.2%
8 11
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
24 13
0.1%
23 6
0.1%
22 4
 
< 0.1%
21 5
 
0.1%
20 5
 
0.1%
19 3
 
< 0.1%
18 8
0.1%
17 14
0.1%
16 10
0.1%
15 9
0.1%

1시간평균충남기준초과합
Real number (ℝ)

SKEWED  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0089
Minimum0
Maximum8
Zeros9975
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:50:40.064147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20451155
Coefficient of variation (CV)22.978825
Kurtosis781.01566
Mean0.0089
Median Absolute Deviation (MAD)0
Skewness26.731303
Sum89
Variance0.041824972
MonotonicityNot monotonic
2024-01-10T05:50:40.161263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 9975
99.8%
4 5
 
0.1%
2 5
 
0.1%
1 5
 
0.1%
6 3
 
< 0.1%
5 3
 
< 0.1%
3 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 9975
99.8%
1 5
 
0.1%
2 5
 
0.1%
3 2
 
< 0.1%
4 5
 
0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 3
 
< 0.1%
5 3
 
< 0.1%
4 5
 
0.1%
3 2
 
< 0.1%
2 5
 
0.1%
1 5
 
0.1%
0 9975
99.8%

1시간평균정상상태값개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.6238
Minimum0
Maximum24
Zeros424
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:50:40.269643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q124
median24
Q324
95-th percentile24
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.0422828
Coefficient of variation (CV)0.22287515
Kurtosis14.778106
Mean22.6238
Median Absolute Deviation (MAD)0
Skewness-4.0226484
Sum226238
Variance25.424616
MonotonicityNot monotonic
2024-01-10T05:50:40.375450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
24 8533
85.3%
22 500
 
5.0%
0 424
 
4.2%
23 254
 
2.5%
21 93
 
0.9%
20 36
 
0.4%
13 28
 
0.3%
10 22
 
0.2%
17 14
 
0.1%
7 14
 
0.1%
Other values (13) 82
 
0.8%
ValueCountFrequency (%)
0 424
4.2%
1 1
 
< 0.1%
2 1
 
< 0.1%
5 3
 
< 0.1%
6 4
 
< 0.1%
7 14
 
0.1%
8 7
 
0.1%
9 11
 
0.1%
10 22
 
0.2%
11 10
 
0.1%
ValueCountFrequency (%)
24 8533
85.3%
23 254
 
2.5%
22 500
 
5.0%
21 93
 
0.9%
20 36
 
0.4%
19 13
 
0.1%
18 6
 
0.1%
17 14
 
0.1%
16 1
 
< 0.1%
15 3
 
< 0.1%

1시간평균정상상태측정값의합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4437
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean475.0648
Minimum-238.2
Maximum8286
Zeros441
Zeros (%)4.4%
Negative151
Negative (%)1.5%
Memory size166.0 KiB
2024-01-10T05:50:40.485123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-238.2
5-th percentile0
Q10.26475
median16.5
Q3506.55
95-th percentile1992.195
Maximum8286
Range8524.2
Interquartile range (IQR)506.28525

Descriptive statistics

Standard deviation1039.5849
Coefficient of variation (CV)2.1883012
Kurtosis17.659061
Mean475.0648
Median Absolute Deviation (MAD)16.5
Skewness3.8854596
Sum4750648
Variance1080736.8
MonotonicityNot monotonic
2024-01-10T05:50:40.617499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 441
 
4.4%
270.0 119
 
1.2%
0.072 61
 
0.6%
247.5 61
 
0.6%
180.0 48
 
0.5%
292.5 47
 
0.5%
337.5 47
 
0.5%
90.0 46
 
0.5%
0.048 44
 
0.4%
360.0 44
 
0.4%
Other values (4427) 9042
90.4%
ValueCountFrequency (%)
-238.2 1
< 0.1%
-185.9 1
< 0.1%
-154.9 1
< 0.1%
-144.9 1
< 0.1%
-141.2 1
< 0.1%
-139.7 1
< 0.1%
-134.3 1
< 0.1%
-132.8 1
< 0.1%
-131.9 1
< 0.1%
-130.7 1
< 0.1%
ValueCountFrequency (%)
8286.0 1
< 0.1%
8232.0 1
< 0.1%
7941.0 1
< 0.1%
7840.0 1
< 0.1%
7773.0 1
< 0.1%
7757.0 1
< 0.1%
7746.0 1
< 0.1%
7732.0 1
< 0.1%
7717.0 1
< 0.1%
7632.0 1
< 0.1%

생성일시
Text

Distinct172
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T05:50:40.883058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length21.2826
Min length21

Characters and Unicode

Total characters212826
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 row2022-06-07 오후 4:54:38
2nd row2022-07-14 오후 2:11:02
3rd row2022-02-16 오전 11:51:42
4th row2022-06-09 오전 10:59:29
5th row2022-04-29 오후 3:09:04
ValueCountFrequency (%)
오후 7006
23.4%
오전 2994
 
10.0%
2022-04-13 1194
 
4.0%
2022-02-15 896
 
3.0%
2022-05-17 776
 
2.6%
2022-08-23 581
 
1.9%
2022-05-27 564
 
1.9%
2022-09-02 492
 
1.6%
2022-07-14 488
 
1.6%
2022-03-07 476
 
1.6%
Other values (200) 14533
48.4%
2024-01-10T05:50:41.253726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 42758
20.1%
0 29938
14.1%
- 20000
9.4%
20000
9.4%
: 20000
9.4%
1 16897
 
7.9%
4 10895
 
5.1%
오 10000
 
4.7%
3 8695
 
4.1%
5 7325
 
3.4%
Other values (6) 26318
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132826
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 42758
32.2%
0 29938
22.5%
1 16897
 
12.7%
4 10895
 
8.2%
3 8695
 
6.5%
5 7325
 
5.5%
7 5679
 
4.3%
6 3857
 
2.9%
9 3397
 
2.6%
8 3385
 
2.5%
Other Letter
ValueCountFrequency (%)
오 10000
50.0%
후 7006
35.0%
전 2994
 
15.0%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Space Separator
ValueCountFrequency (%)
20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 192826
90.6%
Hangul 20000
 
9.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 42758
22.2%
0 29938
15.5%
- 20000
10.4%
20000
10.4%
: 20000
10.4%
1 16897
 
8.8%
4 10895
 
5.7%
3 8695
 
4.5%
5 7325
 
3.8%
7 5679
 
2.9%
Other values (3) 10639
 
5.5%
Hangul
ValueCountFrequency (%)
오 10000
50.0%
후 7006
35.0%
전 2994
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192826
90.6%
Hangul 20000
 
9.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 42758
22.2%
0 29938
15.5%
- 20000
10.4%
20000
10.4%
: 20000
10.4%
1 16897
 
8.8%
4 10895
 
5.7%
3 8695
 
4.5%
5 7325
 
3.8%
7 5679
 
2.9%
Other values (3) 10639
 
5.5%
Hangul
ValueCountFrequency (%)
오 10000
50.0%
후 7006
35.0%
전 2994
 
15.0%

유효가동률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.345502
Minimum0
Maximum100
Zeros424
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:50:41.398709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile83.33
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.403754
Coefficient of variation (CV)0.21399807
Kurtosis17.431705
Mean95.345502
Median Absolute Deviation (MAD)0
Skewness-4.3823778
Sum953455.02
Variance416.31317
MonotonicityNot monotonic
2024-01-10T05:50:41.769981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
100.0 9301
93.0%
0.0 424
 
4.2%
95.83 93
 
0.9%
91.67 47
 
0.5%
87.5 21
 
0.2%
83.33 18
 
0.2%
70.83 11
 
0.1%
79.17 8
 
0.1%
90.91 6
 
0.1%
95.65 5
 
0.1%
Other values (38) 66
 
0.7%
ValueCountFrequency (%)
0.0 424
4.2%
4.17 1
 
< 0.1%
8.33 1
 
< 0.1%
25.0 1
 
< 0.1%
27.27 1
 
< 0.1%
29.17 1
 
< 0.1%
36.36 1
 
< 0.1%
37.5 2
 
< 0.1%
38.1 2
 
< 0.1%
39.13 1
 
< 0.1%
ValueCountFrequency (%)
100.0 9301
93.0%
95.83 93
 
0.9%
95.65 5
 
0.1%
95.45 5
 
0.1%
95.24 2
 
< 0.1%
93.75 2
 
< 0.1%
93.33 2
 
< 0.1%
91.67 47
 
0.5%
91.3 2
 
< 0.1%
90.91 6
 
0.1%

Interactions

2024-01-10T05:50:36.813096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:31.358989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:32.177378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.059513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.888660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.578098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.283187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.937895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:36.910118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:31.446761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:32.299495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.167444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.977570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.671887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.367024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:36.023591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:37.001817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:31.538737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:32.404785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.276000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.063102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.756131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.452404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:36.102342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:37.083790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:31.647663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:32.514483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.382706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.148916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.849050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.531351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:36.183566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:37.170018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:31.749151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:32.617269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.494804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.230737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.933010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.609313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:36.262075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:37.265456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:31.865523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:32.733326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.606645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.323974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.023903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.700598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:36.351221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:37.348464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:31.967849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:32.847952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.713645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.408937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.113086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.784324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:36.429679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:37.425849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:32.073746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:32.949244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:33.804785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:34.495243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.199756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:35.860887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:50:36.503468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:50:41.853420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명항목명일평균값상태값국가기준초과여부충남기준초과여부1시간평균최고값1시간평균최고시간1시간평균최저값1시간평균최저시간1시간평균국가기준초과합1시간평균충남기준초과합1시간평균정상상태값개수1시간평균정상상태측정값의합유효가동률
측정소명1.0000.1620.2580.7080.0540.0780.2290.6140.1270.4280.0620.0000.6610.1850.660
항목명0.1621.0000.7410.0740.4500.4130.7410.7060.7300.7570.3850.1210.1070.7390.103
일평균값0.2580.7411.0000.0820.3390.3360.8940.4730.7600.5420.3390.0000.0960.9400.091
상태값0.7080.0740.0821.0000.0430.0370.0000.6960.0800.4830.0510.0000.7710.0680.724
국가기준초과여부0.0540.4500.3390.0431.0000.9830.1150.1220.2710.1370.9850.0000.0610.3240.045
충남기준초과여부0.0780.4130.3360.0370.9831.0000.2460.1270.3210.1520.9680.0000.0530.3300.048
1시간평균최고값0.2290.7410.8940.0000.1150.2461.0000.5080.3160.6550.1580.0000.0000.9180.000
1시간평균최고시간0.6140.7060.4730.6960.1220.1270.5081.0000.3330.6960.1560.0580.6990.4830.697
1시간평균최저값0.1270.7300.7600.0800.2710.3210.3160.3331.0000.4080.3460.0000.1250.7700.010
1시간평균최저시간0.4280.7570.5420.4830.1370.1520.6550.6960.4081.0000.1820.0880.4920.4410.486
1시간평균국가기준초과합0.0620.3850.3390.0510.9850.9680.1580.1560.3460.1821.0000.3670.0790.3460.030
1시간평균충남기준초과합0.0000.1210.0000.0000.0000.0000.0000.0580.0000.0880.3671.0000.0000.0000.000
1시간평균정상상태값개수0.6610.1070.0960.7710.0610.0530.0000.6990.1250.4920.0790.0001.0000.0700.943
1시간평균정상상태측정값의합0.1850.7390.9400.0680.3240.3300.9180.4830.7700.4410.3460.0000.0701.0000.059
유효가동률0.6600.1030.0910.7240.0450.0480.0000.6970.0100.4860.0300.0000.9430.0591.000
2024-01-10T05:50:41.989262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명국가기준초과여부항목명1시간평균최고시간충남기준초과여부1시간평균최저시간상태값
측정소명1.0000.0430.0480.1770.0620.1080.354
국가기준초과여부0.0431.0000.3530.1050.8840.1180.032
항목명0.0480.3531.0000.3040.3230.3500.033
1시간평균최고시간0.1770.1050.3041.0000.1090.1920.362
충남기준초과여부0.0620.8840.3230.1091.0000.1310.028
1시간평균최저시간0.1080.1180.3500.1920.1311.0000.214
상태값0.3540.0320.0330.3620.0280.2141.000
2024-01-10T05:50:42.101500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일평균값1시간평균최고값1시간평균최저값1시간평균국가기준초과합1시간평균충남기준초과합1시간평균정상상태값개수1시간평균정상상태측정값의합유효가동률측정소명항목명상태값국가기준초과여부충남기준초과여부1시간평균최고시간1시간평균최저시간
일평균값1.0000.9760.7700.182-0.0150.2500.9490.2420.0910.4130.0390.2600.2580.1860.223
1시간평균최고값0.9761.0000.7220.197-0.0220.0730.932-0.0540.0810.4140.0000.0880.1890.2120.295
1시간평균최저값0.7700.7221.0000.226-0.0120.0030.770-0.0820.0440.4110.0400.2080.2460.1280.162
1시간평균국가기준초과합0.1820.1970.2261.0000.233-0.0580.224-0.0380.0210.1660.0240.8960.8480.0550.064
1시간평균충남기준초과합-0.015-0.022-0.0120.2331.000-0.001-0.0150.0140.0000.0510.0000.0000.0000.0220.034
1시간평균정상상태값개수0.2500.0730.003-0.058-0.0011.0000.2470.6990.3030.0450.5200.0580.0500.3550.211
1시간평균정상상태측정값의합0.9490.9320.7700.224-0.0150.2471.0000.2360.0650.4110.0320.2480.2530.1910.171
유효가동률0.242-0.054-0.082-0.0380.0140.6990.2361.0000.3030.0430.4640.0450.0480.3520.209
측정소명0.0910.0810.0440.0210.0000.3030.0650.3031.0000.0480.3540.0430.0620.1770.108
항목명0.4130.4140.4110.1660.0510.0450.4110.0430.0481.0000.0330.3530.3230.3040.350
상태값0.0390.0000.0400.0240.0000.5200.0320.4640.3540.0331.0000.0320.0280.3620.214
국가기준초과여부0.2600.0880.2080.8960.0000.0580.2480.0450.0430.3530.0321.0000.8840.1050.118
충남기준초과여부0.2580.1890.2460.8480.0000.0500.2530.0480.0620.3230.0280.8841.0000.1090.131
1시간평균최고시간0.1860.2120.1280.0550.0220.3550.1910.3520.1770.3040.3620.1050.1091.0000.192
1시간평균최저시간0.2230.2950.1620.0640.0340.2110.1710.2090.1080.3500.2140.1180.1310.1921.000

Missing values

2024-01-10T05:50:37.546969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:50:37.737486image/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.
2024-01-10T05:50:37.866920image/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
719162022-05-07예산군NOx0.009SMPL000.012210.0061500240.2082022-06-07 오후 4:54:38100.0
903932022-06-07대산리TMP18.4SMPL0023.31415.2050024442.02022-07-14 오후 2:11:02100.0
10452022-01-03연무읍CO0.7SMPL001.1080.512002416.42022-02-16 오전 11:51:42100.0
740112022-05-11신방동TMP18.7SMPL0023.61314.2050024449.82022-06-09 오전 10:59:29100.0
610252022-04-18둔포면DIR247.5SMPL00247.517<NA>0024247.52022-04-29 오후 3:09:04100.0
75402022-01-15홍성읍O30.018SMPL000.034220.0040100240.4342022-02-15 오후 8:49:45100.0
612322022-04-18이동차1DIR247.5SMPL00247.523<NA>0024247.52022-04-29 오후 3:09:04100.0
165672022-01-31동문동NO20.015SMPL000.04080.0040000240.3542022-09-02 오후 4:17:18100.0
340552022-03-02홍성읍SOL167.0SMPL00606.0121.00000244012.02022-04-07 오후 4:01:17100.0
229092022-02-11당진시청사DIR225.0SMPL00225.021<NA>0024225.02022-03-02 오후 4:41:51100.0
측정일측정소명항목명일평균값상태값국가기준초과여부충남기준초과여부1시간평균최고값1시간평균최고시간1시간평균최저값1시간평균최저시간1시간평균국가기준초과합1시간평균충남기준초과합1시간평균정상상태값개수1시간평균정상상태측정값의합생성일시유효가동률
810622022-05-23예산군HUM67.8SMPL0095.90536.21400241627.32022-06-08 오후 3:43:07100.0
596582022-04-16성거읍SO20.003SMPL000.004080.0020200240.0692022-04-29 오후 3:08:24100.0
733892022-05-10백석동PM2.56.0SMPL0019.0231.0110021124.02022-05-27 오후 1:37:3887.5
354842022-03-05원북면CO0.3SMPL000.9000.20600247.62022-04-07 오후 4:01:19100.0
594082022-04-15탄천면TMP12.3SMPL0019.0147.0060024294.12022-05-17 오후 4:26:52100.0
850902022-05-29합덕읍O30.0NA0100<NA><NA>0000.02022-08-23 오전 10:28:460.0
78432022-01-15공주PM2.529.0SMPL0040.01117.0172024701.02022-02-15 오후 8:49:45100.0
43622022-01-09예산군O30.022SMPL000.043140.0030800240.5192022-01-10 오전 1:00:23100.0
280132022-02-20독곶리PM1031.0SMPL0048.00122.0230024735.02022-03-07 오전 11:35:34100.0
510932022-04-01동문동NO20.011SMPL000.025060.0061100240.2582022-04-11 오후 5:07:04100.0