Overview

Dataset statistics

Number of variables20
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.6 KiB
Average record size in memory178.8 B

Variable types

DateTime1
Categorical2
Numeric17

Alerts

조회자료구분 has constant value ""Constant
서울 is highly overall correlated with 부산 and 16 other fieldsHigh correlation
부산 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
대구 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
인천 is highly overall correlated with 서울 and 16 other fieldsHigh correlation
광주 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
대전 is highly overall correlated with 서울 and 16 other fieldsHigh correlation
울산 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
경기 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
강원 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
충북 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
충남 is highly overall correlated with 서울 and 16 other fieldsHigh correlation
전북 is highly overall correlated with 서울 and 16 other fieldsHigh correlation
전남 is highly overall correlated with 서울 and 16 other fieldsHigh correlation
경북 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
경남 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
제주 is highly overall correlated with 서울 and 16 other fieldsHigh correlation
세종 is highly overall correlated with 서울 and 16 other fieldsHigh correlation
조회항목 is highly overall correlated with 서울 and 7 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 13:43:46.432985
Analysis finished2023-12-10 13:44:37.366671
Duration50.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct12
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size708.0 B
Minimum2021-04-19 00:00:00
Maximum2021-04-30 00:00:00
2023-12-10T22:44:37.473431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:37.654284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

조회항목
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size708.0 B
SO2
12 
PM10
12 
NO2
12 
PM2.5
12 
CO
12 

Length

Max length5
Median length4
Mean length3.1666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSO2
2nd rowPM10
3rd rowNO2
4th rowPM2.5
5th rowCO

Common Values

ValueCountFrequency (%)
SO2 12
16.7%
PM10 12
16.7%
NO2 12
16.7%
PM2.5 12
16.7%
CO 12
16.7%
O3 12
16.7%

Length

2023-12-10T22:44:37.848956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:38.047863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
so2 12
16.7%
pm10 12
16.7%
no2 12
16.7%
pm2.5 12
16.7%
co 12
16.7%
o3 12
16.7%

조회자료구분
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size708.0 B
일평균
72 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일평균 72
100.0%

Length

2023-12-10T22:44:38.270815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:38.447075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일평균 72
100.0%

서울
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.624125
Minimum0.002
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:38.615844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.003
Q10.02325
median0.1815
Q320.25
95-th percentile63.25
Maximum75
Range74.998
Interquartile range (IQR)20.22675

Descriptive statistics

Standard deviation19.962161
Coefficient of variation (CV)1.7173044
Kurtosis2.51069
Mean11.624125
Median Absolute Deviation (MAD)0.1785
Skewness1.8205656
Sum836.937
Variance398.48788
MonotonicityNot monotonic
2023-12-10T22:44:38.835352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.003 7
 
9.7%
0.5 4
 
5.6%
0.4 4
 
5.6%
0.3 3
 
4.2%
0.043 3
 
4.2%
34.0 2
 
2.8%
0.021 2
 
2.8%
0.005 2
 
2.8%
37.0 2
 
2.8%
24.0 2
 
2.8%
Other values (38) 41
56.9%
ValueCountFrequency (%)
0.002 1
 
1.4%
0.003 7
9.7%
0.004 2
 
2.8%
0.005 2
 
2.8%
0.014 1
 
1.4%
0.015 1
 
1.4%
0.016 1
 
1.4%
0.018 1
 
1.4%
0.021 2
 
2.8%
0.024 2
 
2.8%
ValueCountFrequency (%)
75.0 1
1.4%
72.0 1
1.4%
67.0 1
1.4%
66.0 1
1.4%
61.0 1
1.4%
48.0 1
1.4%
41.0 1
1.4%
37.0 2
2.8%
34.0 2
2.8%
30.0 1
1.4%

부산
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.113833
Minimum0.002
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:39.094543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.003
Q10.017
median0.1305
Q315
95-th percentile54.35
Maximum69
Range68.998
Interquartile range (IQR)14.983

Descriptive statistics

Standard deviation17.7368
Coefficient of variation (CV)1.7537168
Kurtosis3.0458071
Mean10.113833
Median Absolute Deviation (MAD)0.128
Skewness1.9250539
Sum728.196
Variance314.59406
MonotonicityNot monotonic
2023-12-10T22:44:39.321380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.3 8
 
11.1%
0.003 7
 
9.7%
12.0 4
 
5.6%
0.002 3
 
4.2%
0.042 3
 
4.2%
0.2 3
 
4.2%
0.046 2
 
2.8%
0.004 2
 
2.8%
25.0 2
 
2.8%
0.017 2
 
2.8%
Other values (32) 36
50.0%
ValueCountFrequency (%)
0.002 3
4.2%
0.003 7
9.7%
0.004 2
 
2.8%
0.009 1
 
1.4%
0.01 2
 
2.8%
0.011 2
 
2.8%
0.017 2
 
2.8%
0.018 1
 
1.4%
0.02 1
 
1.4%
0.021 2
 
2.8%
ValueCountFrequency (%)
69.0 1
1.4%
65.0 1
1.4%
64.0 1
1.4%
56.0 1
1.4%
53.0 1
1.4%
41.0 1
1.4%
34.0 1
1.4%
32.0 1
1.4%
31.0 1
1.4%
28.0 1
1.4%

대구
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.567569
Minimum0.002
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:39.847579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.002
Q10.014
median0.183
Q316.75
95-th percentile62.95
Maximum82
Range81.998
Interquartile range (IQR)16.736

Descriptive statistics

Standard deviation20.793172
Coefficient of variation (CV)1.7975403
Kurtosis3.3383519
Mean11.567569
Median Absolute Deviation (MAD)0.1805
Skewness2.0061985
Sum832.865
Variance432.35602
MonotonicityNot monotonic
2023-12-10T22:44:40.451914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.002 9
 
12.5%
0.3 9
 
12.5%
0.016 3
 
4.2%
0.003 2
 
2.8%
0.008 2
 
2.8%
0.054 2
 
2.8%
0.5 2
 
2.8%
0.048 2
 
2.8%
0.014 2
 
2.8%
56.0 1
 
1.4%
Other values (38) 38
52.8%
ValueCountFrequency (%)
0.002 9
12.5%
0.003 2
 
2.8%
0.004 1
 
1.4%
0.006 1
 
1.4%
0.007 1
 
1.4%
0.008 2
 
2.8%
0.01 1
 
1.4%
0.014 2
 
2.8%
0.016 3
 
4.2%
0.019 1
 
1.4%
ValueCountFrequency (%)
82.0 1
1.4%
78.0 1
1.4%
71.0 1
1.4%
69.0 1
1.4%
58.0 1
1.4%
56.0 1
1.4%
43.0 1
1.4%
35.0 1
1.4%
34.0 1
1.4%
32.0 1
1.4%

인천
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.218944
Minimum0.003
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:40.780034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.003
Q10.02175
median0.235
Q318.25
95-th percentile55.05
Maximum79
Range78.997
Interquartile range (IQR)18.22825

Descriptive statistics

Standard deviation19.50506
Coefficient of variation (CV)1.7385825
Kurtosis3.2739269
Mean11.218944
Median Absolute Deviation (MAD)0.232
Skewness1.9461948
Sum807.764
Variance380.44737
MonotonicityNot monotonic
2023-12-10T22:44:41.025081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.003 7
 
9.7%
0.4 5
 
6.9%
0.04 4
 
5.6%
0.6 4
 
5.6%
0.004 3
 
4.2%
0.5 3
 
4.2%
0.015 2
 
2.8%
0.014 2
 
2.8%
25.0 2
 
2.8%
13.0 2
 
2.8%
Other values (36) 38
52.8%
ValueCountFrequency (%)
0.003 7
9.7%
0.004 3
4.2%
0.005 1
 
1.4%
0.006 1
 
1.4%
0.014 2
 
2.8%
0.015 2
 
2.8%
0.02 1
 
1.4%
0.021 1
 
1.4%
0.022 1
 
1.4%
0.023 1
 
1.4%
ValueCountFrequency (%)
79.0 1
1.4%
72.0 2
2.8%
60.0 1
1.4%
51.0 1
1.4%
43.0 1
1.4%
42.0 1
1.4%
37.0 1
1.4%
36.0 1
1.4%
30.0 1
1.4%
28.0 2
2.8%

광주
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.741944
Minimum0.003
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:41.339555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.003
Q10.01175
median0.2795
Q320
95-th percentile53.8
Maximum93
Range92.997
Interquartile range (IQR)19.98825

Descriptive statistics

Standard deviation20.412258
Coefficient of variation (CV)1.7384053
Kurtosis4.5752213
Mean11.741944
Median Absolute Deviation (MAD)0.276
Skewness2.0961069
Sum845.42
Variance416.66028
MonotonicityNot monotonic
2023-12-10T22:44:41.617391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.003 8
 
11.1%
0.5 8
 
11.1%
0.004 3
 
4.2%
0.6 3
 
4.2%
0.01 2
 
2.8%
12.0 2
 
2.8%
0.048 2
 
2.8%
56.0 2
 
2.8%
29.0 2
 
2.8%
0.052 2
 
2.8%
Other values (37) 38
52.8%
ValueCountFrequency (%)
0.003 8
11.1%
0.004 3
 
4.2%
0.006 1
 
1.4%
0.007 1
 
1.4%
0.008 1
 
1.4%
0.009 1
 
1.4%
0.01 2
 
2.8%
0.011 1
 
1.4%
0.012 1
 
1.4%
0.013 1
 
1.4%
ValueCountFrequency (%)
93.0 1
1.4%
85.0 1
1.4%
56.0 2
2.8%
52.0 1
1.4%
46.0 1
1.4%
43.0 1
1.4%
40.0 1
1.4%
33.0 1
1.4%
31.0 1
1.4%
29.0 2
2.8%

대전
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.980347
Minimum0.002
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:41.856379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.003
Q10.01475
median0.178
Q316.25
95-th percentile56.45
Maximum87
Range86.998
Interquartile range (IQR)16.23525

Descriptive statistics

Standard deviation19.766985
Coefficient of variation (CV)1.8002149
Kurtosis4.2222001
Mean10.980347
Median Absolute Deviation (MAD)0.176
Skewness2.1145945
Sum790.585
Variance390.7337
MonotonicityNot monotonic
2023-12-10T22:44:42.080040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.4 8
 
11.1%
0.003 6
 
8.3%
0.004 3
 
4.2%
0.002 3
 
4.2%
57.0 2
 
2.8%
0.011 2
 
2.8%
9.0 2
 
2.8%
0.049 2
 
2.8%
0.3 2
 
2.8%
24.0 2
 
2.8%
Other values (37) 40
55.6%
ValueCountFrequency (%)
0.002 3
4.2%
0.003 6
8.3%
0.004 3
4.2%
0.008 1
 
1.4%
0.009 1
 
1.4%
0.011 2
 
2.8%
0.012 1
 
1.4%
0.014 1
 
1.4%
0.015 1
 
1.4%
0.017 1
 
1.4%
ValueCountFrequency (%)
87.0 1
1.4%
79.0 1
1.4%
57.0 2
2.8%
56.0 1
1.4%
51.0 1
1.4%
43.0 1
1.4%
38.0 1
1.4%
32.0 1
1.4%
31.0 1
1.4%
26.0 1
1.4%

울산
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.717403
Minimum0.003
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:42.366169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.004
Q10.022
median0.182
Q315.5
95-th percentile56.9
Maximum74
Range73.997
Interquartile range (IQR)15.478

Descriptive statistics

Standard deviation18.759387
Coefficient of variation (CV)1.7503669
Kurtosis3.0325943
Mean10.717403
Median Absolute Deviation (MAD)0.178
Skewness1.9163531
Sum771.653
Variance351.9146
MonotonicityNot monotonic
2023-12-10T22:44:42.704803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.004 5
 
6.9%
0.4 5
 
6.9%
0.3 4
 
5.6%
0.022 3
 
4.2%
11.0 2
 
2.8%
26.0 2
 
2.8%
0.003 2
 
2.8%
0.043 2
 
2.8%
0.005 2
 
2.8%
0.012 2
 
2.8%
Other values (39) 43
59.7%
ValueCountFrequency (%)
0.003 2
 
2.8%
0.004 5
6.9%
0.005 2
 
2.8%
0.006 2
 
2.8%
0.007 1
 
1.4%
0.01 1
 
1.4%
0.012 2
 
2.8%
0.013 1
 
1.4%
0.015 1
 
1.4%
0.022 3
4.2%
ValueCountFrequency (%)
74.0 1
1.4%
73.0 1
1.4%
60.0 1
1.4%
58.0 1
1.4%
56.0 1
1.4%
42.0 1
1.4%
40.0 1
1.4%
39.0 1
1.4%
31.0 1
1.4%
30.0 1
1.4%

경기
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.346236
Minimum0.003
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:42.989057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.003
Q10.019
median0.184
Q318.5
95-th percentile68
Maximum78
Range77.997
Interquartile range (IQR)18.481

Descriptive statistics

Standard deviation21.397754
Coefficient of variation (CV)1.7331398
Kurtosis2.5962356
Mean12.346236
Median Absolute Deviation (MAD)0.181
Skewness1.8484279
Sum888.929
Variance457.86386
MonotonicityNot monotonic
2023-12-10T22:44:43.314875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.003 7
 
9.7%
0.5 5
 
6.9%
0.4 5
 
6.9%
0.004 4
 
5.6%
0.019 3
 
4.2%
23.0 2
 
2.8%
35.0 2
 
2.8%
0.3 2
 
2.8%
17.0 2
 
2.8%
0.038 2
 
2.8%
Other values (35) 38
52.8%
ValueCountFrequency (%)
0.003 7
9.7%
0.004 4
5.6%
0.005 1
 
1.4%
0.012 1
 
1.4%
0.013 1
 
1.4%
0.014 2
 
2.8%
0.018 1
 
1.4%
0.019 3
4.2%
0.022 1
 
1.4%
0.026 1
 
1.4%
ValueCountFrequency (%)
78.0 1
1.4%
77.0 1
1.4%
76.0 1
1.4%
68.0 2
2.8%
53.0 1
1.4%
45.0 1
1.4%
38.0 1
1.4%
37.0 1
1.4%
35.0 2
2.8%
32.0 1
1.4%

강원
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.659625
Minimum0.002
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:43.550028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.002
Q10.00875
median0.1805
Q316
95-th percentile52.25
Maximum63
Range62.998
Interquartile range (IQR)15.99125

Descriptive statistics

Standard deviation16.88645
Coefficient of variation (CV)1.7481476
Kurtosis2.8055025
Mean9.659625
Median Absolute Deviation (MAD)0.1785
Skewness1.9002555
Sum695.493
Variance285.15219
MonotonicityNot monotonic
2023-12-10T22:44:43.773762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.002 7
 
9.7%
0.4 6
 
8.3%
0.003 5
 
6.9%
0.3 3
 
4.2%
0.008 3
 
4.2%
0.5 3
 
4.2%
21.0 2
 
2.8%
0.041 2
 
2.8%
16.0 2
 
2.8%
8.0 2
 
2.8%
Other values (32) 37
51.4%
ValueCountFrequency (%)
0.002 7
9.7%
0.003 5
6.9%
0.005 1
 
1.4%
0.007 2
 
2.8%
0.008 3
4.2%
0.009 1
 
1.4%
0.01 2
 
2.8%
0.011 1
 
1.4%
0.013 1
 
1.4%
0.014 1
 
1.4%
ValueCountFrequency (%)
63.0 1
1.4%
61.0 1
1.4%
59.0 1
1.4%
55.0 1
1.4%
50.0 1
1.4%
49.0 1
1.4%
32.0 1
1.4%
30.0 1
1.4%
29.0 2
2.8%
25.0 1
1.4%

충북
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.192903
Minimum0.002
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:44.009119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.002
Q10.01275
median0.1815
Q318.25
95-th percentile64.35
Maximum74
Range73.998
Interquartile range (IQR)18.23725

Descriptive statistics

Standard deviation19.523206
Coefficient of variation (CV)1.7442487
Kurtosis2.7266531
Mean11.192903
Median Absolute Deviation (MAD)0.1795
Skewness1.8776474
Sum805.889
Variance381.15558
MonotonicityNot monotonic
2023-12-10T22:44:44.263823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.002 8
 
11.1%
0.4 6
 
8.3%
0.003 4
 
5.6%
35.0 3
 
4.2%
0.01 3
 
4.2%
0.5 3
 
4.2%
24.0 2
 
2.8%
0.047 2
 
2.8%
11.0 2
 
2.8%
66.0 2
 
2.8%
Other values (34) 37
51.4%
ValueCountFrequency (%)
0.002 8
11.1%
0.003 4
5.6%
0.009 1
 
1.4%
0.01 3
 
4.2%
0.011 1
 
1.4%
0.012 1
 
1.4%
0.013 1
 
1.4%
0.014 2
 
2.8%
0.016 1
 
1.4%
0.018 1
 
1.4%
ValueCountFrequency (%)
74.0 1
 
1.4%
68.0 1
 
1.4%
66.0 2
2.8%
63.0 1
 
1.4%
48.0 1
 
1.4%
36.0 1
 
1.4%
35.0 3
4.2%
31.0 1
 
1.4%
29.0 1
 
1.4%
27.0 1
 
1.4%

충남
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.411972
Minimum0.003
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:44.601216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.004
Q10.012
median0.1855
Q320.5
95-th percentile60.5
Maximum84
Range83.997
Interquartile range (IQR)20.488

Descriptive statistics

Standard deviation20.802184
Coefficient of variation (CV)1.6759773
Kurtosis2.3959474
Mean12.411972
Median Absolute Deviation (MAD)0.1815
Skewness1.7472788
Sum893.662
Variance432.73086
MonotonicityNot monotonic
2023-12-10T22:44:44.927721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.004 8
 
11.1%
0.4 8
 
11.1%
0.012 3
 
4.2%
0.005 3
 
4.2%
0.011 3
 
4.2%
66.0 2
 
2.8%
0.5 2
 
2.8%
17.0 2
 
2.8%
37.0 2
 
2.8%
0.3 2
 
2.8%
Other values (34) 37
51.4%
ValueCountFrequency (%)
0.003 1
 
1.4%
0.004 8
11.1%
0.005 3
 
4.2%
0.009 1
 
1.4%
0.011 3
 
4.2%
0.012 3
 
4.2%
0.013 1
 
1.4%
0.014 1
 
1.4%
0.017 2
 
2.8%
0.019 1
 
1.4%
ValueCountFrequency (%)
84.0 1
1.4%
75.0 1
1.4%
66.0 2
2.8%
56.0 1
1.4%
48.0 1
1.4%
42.0 1
1.4%
40.0 1
1.4%
38.0 1
1.4%
37.0 2
2.8%
33.0 2
2.8%

전북
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.844208
Minimum0.003
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:45.155857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.003
Q10.01
median0.18
Q319.5
95-th percentile56
Maximum89
Range88.997
Interquartile range (IQR)19.49

Descriptive statistics

Standard deviation20.69798
Coefficient of variation (CV)1.7475191
Kurtosis4.0462422
Mean11.844208
Median Absolute Deviation (MAD)0.177
Skewness2.0336851
Sum852.783
Variance428.4064
MonotonicityNot monotonic
2023-12-10T22:44:45.392637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.003 11
 
15.3%
0.3 8
 
11.1%
0.01 5
 
6.9%
0.4 4
 
5.6%
32.0 3
 
4.2%
28.0 2
 
2.8%
0.06 2
 
2.8%
56.0 2
 
2.8%
0.049 2
 
2.8%
0.014 2
 
2.8%
Other values (29) 31
43.1%
ValueCountFrequency (%)
0.003 11
15.3%
0.004 1
 
1.4%
0.007 1
 
1.4%
0.008 1
 
1.4%
0.01 5
6.9%
0.012 2
 
2.8%
0.013 1
 
1.4%
0.014 2
 
2.8%
0.041 1
 
1.4%
0.046 1
 
1.4%
ValueCountFrequency (%)
89.0 1
 
1.4%
87.0 1
 
1.4%
62.0 1
 
1.4%
56.0 2
2.8%
46.0 1
 
1.4%
43.0 1
 
1.4%
39.0 1
 
1.4%
32.0 3
4.2%
31.0 1
 
1.4%
29.0 1
 
1.4%

전남
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.550625
Minimum0.003
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:45.605138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.003
Q10.00875
median0.1785
Q314.5
95-th percentile45.45
Maximum73
Range72.997
Interquartile range (IQR)14.49125

Descriptive statistics

Standard deviation16.359793
Coefficient of variation (CV)1.7129552
Kurtosis3.0780133
Mean9.550625
Median Absolute Deviation (MAD)0.1755
Skewness1.8706175
Sum687.645
Variance267.64283
MonotonicityNot monotonic
2023-12-10T22:44:45.820357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.003 8
 
11.1%
0.4 7
 
9.7%
0.008 4
 
5.6%
0.004 4
 
5.6%
14.0 3
 
4.2%
0.5 3
 
4.2%
51.0 2
 
2.8%
22.0 2
 
2.8%
0.052 2
 
2.8%
23.0 2
 
2.8%
Other values (32) 35
48.6%
ValueCountFrequency (%)
0.003 8
11.1%
0.004 4
5.6%
0.006 1
 
1.4%
0.007 1
 
1.4%
0.008 4
5.6%
0.009 2
 
2.8%
0.01 1
 
1.4%
0.014 1
 
1.4%
0.015 1
 
1.4%
0.017 1
 
1.4%
ValueCountFrequency (%)
73.0 1
1.4%
51.0 2
2.8%
46.0 1
1.4%
45.0 1
1.4%
43.0 1
1.4%
42.0 1
1.4%
34.0 1
1.4%
30.0 1
1.4%
28.0 1
1.4%
24.0 1
1.4%

경북
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.139694
Minimum0.003
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:46.101729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.003
Q10.00975
median0.183
Q316.5
95-th percentile61
Maximum79
Range78.997
Interquartile range (IQR)16.49025

Descriptive statistics

Standard deviation19.681249
Coefficient of variation (CV)1.7667674
Kurtosis2.9894528
Mean11.139694
Median Absolute Deviation (MAD)0.18
Skewness1.9332251
Sum802.058
Variance387.35156
MonotonicityNot monotonic
2023-12-10T22:44:46.309901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.003 8
 
11.1%
0.3 7
 
9.7%
0.004 4
 
5.6%
0.012 3
 
4.2%
13.0 3
 
4.2%
0.4 3
 
4.2%
0.057 2
 
2.8%
0.5 2
 
2.8%
21.0 2
 
2.8%
0.009 2
 
2.8%
Other values (33) 36
50.0%
ValueCountFrequency (%)
0.003 8
11.1%
0.004 4
5.6%
0.006 1
 
1.4%
0.007 1
 
1.4%
0.008 2
 
2.8%
0.009 2
 
2.8%
0.01 1
 
1.4%
0.012 3
 
4.2%
0.014 1
 
1.4%
0.015 1
 
1.4%
ValueCountFrequency (%)
79.0 1
1.4%
68.0 1
1.4%
67.0 1
1.4%
61.0 2
2.8%
54.0 1
1.4%
44.0 1
1.4%
33.0 1
1.4%
32.0 1
1.4%
30.0 1
1.4%
29.0 2
2.8%

경남
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6610139
Minimum0.003
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:46.529912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.003
Q10.01375
median0.18
Q314.75
95-th percentile48.8
Maximum67
Range66.997
Interquartile range (IQR)14.73625

Descriptive statistics

Standard deviation16.477838
Coefficient of variation (CV)1.7056013
Kurtosis2.6155772
Mean9.6610139
Median Absolute Deviation (MAD)0.177
Skewness1.8154778
Sum695.593
Variance271.51913
MonotonicityNot monotonic
2023-12-10T22:44:46.763930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.003 9
 
12.5%
0.4 8
 
11.1%
0.004 3
 
4.2%
0.5 2
 
2.8%
13.0 2
 
2.8%
0.015 2
 
2.8%
0.01 2
 
2.8%
0.3 2
 
2.8%
24.0 2
 
2.8%
28.0 2
 
2.8%
Other values (36) 38
52.8%
ValueCountFrequency (%)
0.003 9
12.5%
0.004 3
 
4.2%
0.008 1
 
1.4%
0.009 1
 
1.4%
0.01 2
 
2.8%
0.011 1
 
1.4%
0.013 1
 
1.4%
0.014 1
 
1.4%
0.015 2
 
2.8%
0.018 1
 
1.4%
ValueCountFrequency (%)
67.0 1
1.4%
57.0 1
1.4%
56.0 1
1.4%
51.0 1
1.4%
47.0 1
1.4%
38.0 1
1.4%
35.0 1
1.4%
34.0 1
1.4%
29.0 1
1.4%
28.0 2
2.8%

제주
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3205833
Minimum0.001
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:46.999594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.00155
Q10.008
median0.1325
Q316.25
95-th percentile44.9
Maximum59
Range58.999
Interquartile range (IQR)16.242

Descriptive statistics

Standard deviation15.671402
Coefficient of variation (CV)1.6813757
Kurtosis1.9077683
Mean9.3205833
Median Absolute Deviation (MAD)0.1315
Skewness1.6868049
Sum671.082
Variance245.59285
MonotonicityNot monotonic
2023-12-10T22:44:47.209099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.3 9
 
12.5%
0.002 7
 
9.7%
0.001 4
 
5.6%
0.007 3
 
4.2%
0.008 3
 
4.2%
0.2 3
 
4.2%
0.009 3
 
4.2%
0.065 2
 
2.8%
0.048 2
 
2.8%
0.063 2
 
2.8%
Other values (28) 34
47.2%
ValueCountFrequency (%)
0.001 4
5.6%
0.002 7
9.7%
0.003 1
 
1.4%
0.006 1
 
1.4%
0.007 3
4.2%
0.008 3
4.2%
0.009 3
4.2%
0.012 2
 
2.8%
0.039 1
 
1.4%
0.044 1
 
1.4%
ValueCountFrequency (%)
59.0 1
1.4%
57.0 1
1.4%
47.0 1
1.4%
46.0 1
1.4%
44.0 1
1.4%
41.0 2
2.8%
35.0 1
1.4%
31.0 1
1.4%
26.0 2
2.8%
23.0 2
2.8%

세종
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.917194
Minimum0.003
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-10T22:44:47.443999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.003
Q10.014
median0.181
Q317
95-th percentile56.45
Maximum77
Range76.997
Interquartile range (IQR)16.986

Descriptive statistics

Standard deviation18.983687
Coefficient of variation (CV)1.7388796
Kurtosis2.9328709
Mean10.917194
Median Absolute Deviation (MAD)0.178
Skewness1.8905648
Sum786.038
Variance360.38036
MonotonicityNot monotonic
2023-12-10T22:44:48.091676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.004 6
 
8.3%
0.4 6
 
8.3%
0.5 5
 
6.9%
0.003 5
 
6.9%
28.0 2
 
2.8%
17.0 2
 
2.8%
0.016 2
 
2.8%
0.05 2
 
2.8%
0.014 2
 
2.8%
27.0 2
 
2.8%
Other values (37) 38
52.8%
ValueCountFrequency (%)
0.003 5
6.9%
0.004 6
8.3%
0.005 1
 
1.4%
0.009 1
 
1.4%
0.01 1
 
1.4%
0.011 1
 
1.4%
0.012 1
 
1.4%
0.013 1
 
1.4%
0.014 2
 
2.8%
0.016 2
 
2.8%
ValueCountFrequency (%)
77.0 1
1.4%
72.0 1
1.4%
59.0 1
1.4%
57.0 1
1.4%
56.0 1
1.4%
46.0 1
1.4%
41.0 1
1.4%
39.0 1
1.4%
33.0 1
1.4%
32.0 1
1.4%

Interactions

2023-12-10T22:44:33.922467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:47.802722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:50.348207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:53.218673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:55.604530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:58.604265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:01.562864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:04.963032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:08.465020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:10.814243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:13.993519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:16.840976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:19.080111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:21.974948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:25.230233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:28.544245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:31.079872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:34.176254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:47.952672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:50.465045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:53.384236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:55.820183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:58.753160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:01.822709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:05.206517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:08.635698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:10.961157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:14.131361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:16.962435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:19.256887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:22.139560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:25.391322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:28.705048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:31.221791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:34.315218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-10T22:44:27.702459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:30.546176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:33.286452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:36.263504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:49.891118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:52.741098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:55.199604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:58.028427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:01.013432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:04.306682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:07.897002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:10.397272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:13.371236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:16.424719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:18.637380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:21.546598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:24.568891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:27.838059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:30.678931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:33.457837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:36.419503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:50.058975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:52.893403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:55.342232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:58.201820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:01.201689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:04.487719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:08.061465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:10.539137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:13.580889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:16.583545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:18.785071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:21.689756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:24.812826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:28.088413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:30.797684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:33.618504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:36.584900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:50.195158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:53.035233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:55.464153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:58.388706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:01.374726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:04.754316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:08.268845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:10.667695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:13.780538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:16.712882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:18.918572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:21.831901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:25.004542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:28.365713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:30.923335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:33.767845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:44:48.383919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시조회항목서울부산대구인천광주대전울산경기강원충북충남전북전남경북경남제주세종
측정일시1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
조회항목0.0001.0000.7720.7320.6720.7760.7030.7400.6900.7600.7580.6890.7590.7320.7380.7040.7530.7930.747
서울0.0000.7721.0000.9660.9370.9770.9090.9160.9430.9930.9800.9000.9230.8870.8860.9270.9550.9460.904
부산0.0000.7320.9661.0000.9480.9720.9190.8890.9810.9690.9620.9150.9300.8870.8960.9580.9870.9610.871
대구0.0000.6720.9370.9481.0000.9240.9590.9510.9400.9250.8790.9310.9860.9470.9240.9930.9300.8680.908
인천0.0000.7760.9770.9720.9241.0000.9250.8840.9730.9880.9570.8870.9490.8740.8800.9300.9620.9430.891
광주0.0000.7030.9090.9190.9590.9251.0000.9760.8980.8910.8570.9580.9570.9880.9620.9190.9260.8550.972
대전0.0000.7400.9160.8890.9510.8840.9761.0000.8560.8930.8720.9540.9340.9900.9680.9380.9030.8790.996
울산0.0000.6900.9430.9810.9400.9730.8980.8561.0000.9680.9490.9040.9180.8550.8750.9380.9750.9330.849
경기0.0000.7600.9930.9690.9250.9880.8910.8930.9681.0000.9880.9400.9290.8710.8970.9340.9590.9290.901
강원0.0000.7580.9800.9620.8790.9570.8570.8720.9490.9881.0000.9130.8860.8500.8770.9060.9550.9340.892
충북0.0000.6890.9000.9150.9310.8870.9580.9540.9040.9400.9131.0000.9340.9580.9570.9520.9170.8190.960
충남0.0000.7590.9230.9300.9860.9490.9570.9340.9180.9290.8860.9341.0000.9300.9300.9790.9160.8880.932
전북0.0000.7320.8870.8870.9470.8740.9880.9900.8550.8710.8500.9580.9301.0000.9750.9430.9100.9150.977
전남0.0000.7380.8860.8960.9240.8800.9620.9680.8750.8970.8770.9570.9300.9751.0000.8990.9160.8990.954
경북0.0000.7040.9270.9580.9930.9300.9190.9380.9380.9340.9060.9520.9790.9430.8991.0000.9510.8670.913
경남0.0000.7530.9550.9870.9300.9620.9260.9030.9750.9590.9550.9170.9160.9100.9160.9511.0000.9410.883
제주0.0000.7930.9460.9610.8680.9430.8550.8790.9330.9290.9340.8190.8880.9150.8990.8670.9411.0000.843
세종0.0000.7470.9040.8710.9080.8910.9720.9960.8490.9010.8920.9600.9320.9770.9540.9130.8830.8431.000
2023-12-10T22:44:48.704594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울부산대구인천광주대전울산경기강원충북충남전북전남경북경남제주세종조회항목
서울1.0000.9900.9920.9950.9870.9900.9910.9970.9930.9910.9910.9870.9880.9910.9890.9780.9900.503
부산0.9901.0000.9950.9880.9910.9920.9950.9910.9890.9920.9920.9920.9940.9930.9970.9880.9910.459
대구0.9920.9951.0000.9930.9930.9950.9960.9950.9920.9960.9930.9940.9960.9980.9980.9860.9940.420
인천0.9950.9880.9931.0000.9880.9910.9910.9970.9930.9920.9940.9900.9900.9940.9900.9800.9910.508
광주0.9870.9910.9930.9881.0000.9890.9910.9890.9860.9880.9880.9940.9950.9910.9930.9870.9880.480
대전0.9900.9920.9950.9910.9891.0000.9930.9940.9920.9960.9950.9950.9920.9950.9940.9830.9970.523
울산0.9910.9950.9960.9910.9910.9931.0000.9930.9930.9950.9910.9910.9930.9960.9950.9840.9910.417
경기0.9970.9910.9950.9970.9890.9940.9931.0000.9960.9950.9940.9900.9910.9950.9920.9830.9930.489
강원0.9930.9890.9920.9930.9860.9920.9930.9961.0000.9940.9900.9880.9870.9940.9890.9790.9920.487
충북0.9910.9920.9960.9920.9880.9960.9950.9950.9941.0000.9930.9930.9920.9970.9940.9820.9970.465
충남0.9910.9920.9930.9940.9880.9950.9910.9940.9900.9931.0000.9940.9920.9930.9930.9840.9950.515
전북0.9870.9920.9940.9900.9940.9950.9910.9900.9880.9930.9941.0000.9940.9940.9940.9880.9940.513
전남0.9880.9940.9960.9900.9950.9920.9930.9910.9870.9920.9920.9941.0000.9950.9970.9890.9920.520
경북0.9910.9930.9980.9940.9910.9950.9960.9950.9940.9970.9930.9940.9951.0000.9960.9860.9950.452
경남0.9890.9970.9980.9900.9930.9940.9950.9920.9890.9940.9930.9940.9970.9961.0000.9900.9930.481
제주0.9780.9880.9860.9800.9870.9830.9840.9830.9790.9820.9840.9880.9890.9860.9901.0000.9820.530
세종0.9900.9910.9940.9910.9880.9970.9910.9930.9920.9970.9950.9940.9920.9950.9930.9821.0000.530
조회항목0.5030.4590.4200.5080.4800.5230.4170.4890.4870.4650.5150.5130.5200.4520.4810.5300.5301.000

Missing values

2023-12-10T22:44:36.836431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:44:37.235404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

측정일시조회항목조회자료구분서울부산대구인천광주대전울산경기강원충북충남전북전남경북경남제주세종
02021-04-19SO2일평균0.0030.0020.0020.0040.0030.0040.0040.0040.0030.0030.0040.0030.0030.0040.0030.0020.004
12021-04-19PM10일평균48.041.043.042.040.043.039.053.050.048.048.043.034.044.034.031.046.0
22021-04-19NO2일평균0.0240.0180.0140.020.0170.0150.0220.0190.010.0130.0120.0120.0080.0090.0140.0090.013
32021-04-19PM2.5일평균22.015.016.019.020.016.015.023.019.022.022.019.014.016.014.012.021.0
42021-04-19CO일평균0.50.30.30.50.50.40.40.50.50.40.40.30.30.40.40.30.4
52021-04-19O3일평균0.0420.0460.0480.0480.0440.0470.0470.0470.0540.0520.0540.0480.050.0570.0490.0570.054
62021-04-20SO2일평균0.0040.0030.0030.0040.0030.0040.0060.0040.0030.0030.0050.0030.0040.0040.0040.0020.005
72021-04-20CO일평균0.50.30.40.50.60.50.50.50.50.60.40.40.50.40.40.30.5
82021-04-20O3일평균0.0450.0610.0660.0430.0540.0530.0640.050.0610.0630.0580.0550.0560.0660.060.0630.06
92021-04-20NO2일평균0.0350.0210.0160.0280.020.0240.0230.0280.0130.0180.0170.0140.0140.0120.0180.0120.021
측정일시조회항목조회자료구분서울부산대구인천광주대전울산경기강원충북충남전북전남경북경남제주세종
622021-04-29NO2일평균0.0270.020.0160.0260.010.0170.0220.0220.0090.0140.0140.010.0090.0120.0150.0070.016
632021-04-29PM10일평균66.065.082.079.093.079.074.076.049.066.084.089.073.068.067.057.072.0
642021-04-29PM2.5일평균21.025.026.028.029.022.026.024.017.024.030.028.023.023.024.023.020.0
652021-04-29SO2일평균0.0030.0030.0020.0030.0030.0030.0040.0030.0020.0020.0040.0030.0030.0030.0030.0020.003
662021-04-30SO2일평균0.0020.0020.0020.0030.0030.0020.0040.0030.0020.0020.0030.0030.0030.0030.0030.0020.003
672021-04-30NO2일평균0.0180.0110.0070.0140.0070.0080.0120.0140.0070.0090.0090.0070.0060.0070.0080.0080.009
682021-04-30O3일평균0.0430.060.0580.050.0590.0560.0580.0480.0430.0530.0570.060.0550.0570.060.0650.057
692021-04-30PM10일평균34.064.056.026.043.051.056.035.024.035.040.046.045.054.056.059.041.0
702021-04-30CO일평균0.40.30.30.40.50.40.40.40.40.40.40.30.40.30.40.30.4
712021-04-30PM2.5일평균17.023.019.013.020.016.021.016.013.016.017.019.017.018.020.022.013.0