Overview

Dataset statistics

Number of variables13
Number of observations134
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 KiB
Average record size in memory117.0 B

Variable types

Numeric12
DateTime1

Dataset

Description서산시 월별 지목별 토지통계로 번호, 년도, 월, 합계, 전, 답, 임야, 대지, 공장(용지), 공원, 체육(용지), 기타, 기준일을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=451&beforeMenuCd=DOM_000000201001001000&publicdatapk=15000655

Alerts

번호 is highly overall correlated with 년도 and 9 other fieldsHigh correlation
년도 is highly overall correlated with 번호 and 9 other fieldsHigh correlation
합계 is highly overall correlated with 번호 and 9 other fieldsHigh correlation
is highly overall correlated with 번호 and 8 other fieldsHigh correlation
is highly overall correlated with 번호 and 8 other fieldsHigh correlation
임야 is highly overall correlated with 번호 and 9 other fieldsHigh correlation
대지 is highly overall correlated with 번호 and 9 other fieldsHigh correlation
공장용지 is highly overall correlated with 번호 and 9 other fieldsHigh correlation
공원 is highly overall correlated with 번호 and 9 other fieldsHigh correlation
체육용지 is highly overall correlated with 번호 and 9 other fieldsHigh correlation
기타 is highly overall correlated with 번호 and 9 other fieldsHigh correlation
번호 has unique valuesUnique
기준일 has unique valuesUnique

Reproduction

Analysis started2024-01-09 20:43:17.480504
Analysis finished2024-01-09 20:43:30.169516
Duration12.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct134
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.5
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:30.262257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.65
Q134.25
median67.5
Q3100.75
95-th percentile127.35
Maximum134
Range133
Interquartile range (IQR)66.5

Descriptive statistics

Standard deviation38.826537
Coefficient of variation (CV)0.57520796
Kurtosis-1.2
Mean67.5
Median Absolute Deviation (MAD)33.5
Skewness0
Sum9045
Variance1507.5
MonotonicityStrictly decreasing
2024-01-10T05:43:30.466153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134 1
 
0.7%
49 1
 
0.7%
35 1
 
0.7%
36 1
 
0.7%
37 1
 
0.7%
38 1
 
0.7%
39 1
 
0.7%
40 1
 
0.7%
41 1
 
0.7%
42 1
 
0.7%
Other values (124) 124
92.5%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%
130 1
0.7%
129 1
0.7%
128 1
0.7%
127 1
0.7%
126 1
0.7%
125 1
0.7%

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.0896
Minimum2005
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:30.607871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12007
median2010
Q32013
95-th percentile2015
Maximum2016
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2338963
Coefficient of variation (CV)0.001608832
Kurtosis-1.1989941
Mean2010.0896
Median Absolute Deviation (MAD)3
Skewness0.013139852
Sum269352
Variance10.458086
MonotonicityDecreasing
2024-01-10T05:43:30.717388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2015 12
9.0%
2014 12
9.0%
2013 12
9.0%
2012 12
9.0%
2011 12
9.0%
2010 12
9.0%
2009 12
9.0%
2008 12
9.0%
2007 12
9.0%
2006 12
9.0%
Other values (2) 14
10.4%
ValueCountFrequency (%)
2005 12
9.0%
2006 12
9.0%
2007 12
9.0%
2008 12
9.0%
2009 12
9.0%
2010 12
9.0%
2011 12
9.0%
2012 12
9.0%
2013 12
9.0%
2014 12
9.0%
ValueCountFrequency (%)
2016 2
 
1.5%
2015 12
9.0%
2014 12
9.0%
2013 12
9.0%
2012 12
9.0%
2011 12
9.0%
2010 12
9.0%
2009 12
9.0%
2008 12
9.0%
2007 12
9.0%


Real number (ℝ)

Distinct12
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4253731
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:30.818348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4930153
Coefficient of variation (CV)0.5436284
Kurtosis-1.2339063
Mean6.4253731
Median Absolute Deviation (MAD)3
Skewness0.018958527
Sum861
Variance12.201156
MonotonicityNot monotonic
2024-01-10T05:43:31.197837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 12
9.0%
1 12
9.0%
12 11
8.2%
11 11
8.2%
10 11
8.2%
9 11
8.2%
8 11
8.2%
7 11
8.2%
6 11
8.2%
5 11
8.2%
Other values (2) 22
16.4%
ValueCountFrequency (%)
1 12
9.0%
2 12
9.0%
3 11
8.2%
4 11
8.2%
5 11
8.2%
6 11
8.2%
7 11
8.2%
8 11
8.2%
9 11
8.2%
10 11
8.2%
ValueCountFrequency (%)
12 11
8.2%
11 11
8.2%
10 11
8.2%
9 11
8.2%
8 11
8.2%
7 11
8.2%
6 11
8.2%
5 11
8.2%
4 11
8.2%
3 11
8.2%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean740.61884
Minimum739.908
Maximum741.194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:31.314645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum739.908
5-th percentile740.36085
Q1740.552
median740.6075
Q3740.79075
95-th percentile740.8257
Maximum741.194
Range1.286
Interquartile range (IQR)0.23875

Descriptive statistics

Standard deviation0.2014712
Coefficient of variation (CV)0.00027203089
Kurtosis2.3005499
Mean740.61884
Median Absolute Deviation (MAD)0.181
Skewness-0.45430722
Sum99242.925
Variance0.040590644
MonotonicityNot monotonic
2024-01-10T05:43:31.451962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
740.661 4
 
3.0%
740.79 4
 
3.0%
740.562 4
 
3.0%
740.542 3
 
2.2%
740.818 3
 
2.2%
740.552 3
 
2.2%
740.663 3
 
2.2%
740.825 3
 
2.2%
740.398 2
 
1.5%
740.664 2
 
1.5%
Other values (89) 103
76.9%
ValueCountFrequency (%)
739.908 1
0.7%
739.938 1
0.7%
739.954 1
0.7%
740.326 1
0.7%
740.327 1
0.7%
740.328 1
0.7%
740.355 1
0.7%
740.364 1
0.7%
740.368 1
0.7%
740.373 1
0.7%
ValueCountFrequency (%)
741.194 1
 
0.7%
741.193 1
 
0.7%
741.186 1
 
0.7%
740.844 1
 
0.7%
740.842 1
 
0.7%
740.841 1
 
0.7%
740.827 1
 
0.7%
740.825 3
2.2%
740.821 1
 
0.7%
740.818 3
2.2%


Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.511343
Minimum77.689
Maximum80.148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:31.611313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77.689
5-th percentile77.74255
Q177.811
median78.0095
Q379.178
95-th percentile79.96005
Maximum80.148
Range2.459
Interquartile range (IQR)1.367

Descriptive statistics

Standard deviation0.82851445
Coefficient of variation (CV)0.010552799
Kurtosis-1.2543205
Mean78.511343
Median Absolute Deviation (MAD)0.255
Skewness0.61285292
Sum10520.52
Variance0.6864362
MonotonicityNot monotonic
2024-01-10T05:43:31.735798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.756 2
 
1.5%
79.147 2
 
1.5%
79.157 2
 
1.5%
77.813 2
 
1.5%
77.811 2
 
1.5%
77.816 2
 
1.5%
77.786 2
 
1.5%
77.76 2
 
1.5%
77.747 1
 
0.7%
77.72 1
 
0.7%
Other values (116) 116
86.6%
ValueCountFrequency (%)
77.689 1
0.7%
77.691 1
0.7%
77.708 1
0.7%
77.716 1
0.7%
77.72 1
0.7%
77.723 1
0.7%
77.738 1
0.7%
77.745 1
0.7%
77.747 1
0.7%
77.752 1
0.7%
ValueCountFrequency (%)
80.148 1
0.7%
80.146 1
0.7%
80.129 1
0.7%
80.105 1
0.7%
80.096 1
0.7%
80.055 1
0.7%
80.027 1
0.7%
79.924 1
0.7%
79.917 1
0.7%
79.902 1
0.7%


Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.59565
Minimum195.803
Maximum198.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:31.846828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum195.803
5-th percentile196.006
Q1197.23575
median197.761
Q3198.25575
95-th percentile198.4007
Maximum198.62
Range2.817
Interquartile range (IQR)1.02

Descriptive statistics

Standard deviation0.77911464
Coefficient of variation (CV)0.0039429747
Kurtosis-0.10697799
Mean197.59565
Median Absolute Deviation (MAD)0.507
Skewness-0.96198547
Sum26477.817
Variance0.60701963
MonotonicityNot monotonic
2024-01-10T05:43:31.979968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198.321 3
 
2.2%
197.676 2
 
1.5%
197.446 2
 
1.5%
197.136 2
 
1.5%
197.758 1
 
0.7%
197.73 1
 
0.7%
197.737 1
 
0.7%
197.751 1
 
0.7%
197.764 1
 
0.7%
197.684 1
 
0.7%
Other values (119) 119
88.8%
ValueCountFrequency (%)
195.803 1
0.7%
195.82 1
0.7%
195.853 1
0.7%
195.862 1
0.7%
195.885 1
0.7%
195.953 1
0.7%
195.967 1
0.7%
196.027 1
0.7%
196.043 1
0.7%
196.059 1
0.7%
ValueCountFrequency (%)
198.62 1
0.7%
198.617 1
0.7%
198.609 1
0.7%
198.496 1
0.7%
198.425 1
0.7%
198.409 1
0.7%
198.402 1
0.7%
198.4 1
0.7%
198.397 1
0.7%
198.387 1
0.7%

임야
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.59088
Minimum291.96
Maximum313.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:32.154118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum291.96
5-th percentile292.28845
Q1296.77075
median308.886
Q3310.9265
95-th percentile312.52045
Maximum313.51
Range21.55
Interquartile range (IQR)14.15575

Descriptive statistics

Standard deviation7.5980043
Coefficient of variation (CV)0.02494495
Kurtosis-1.3863251
Mean304.59088
Median Absolute Deviation (MAD)3.2455
Skewness-0.55552665
Sum40815.178
Variance57.729669
MonotonicityNot monotonic
2024-01-10T05:43:32.312480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
309.563 2
 
1.5%
291.96 1
 
0.7%
310.456 1
 
0.7%
310.241 1
 
0.7%
310.29 1
 
0.7%
310.329 1
 
0.7%
310.378 1
 
0.7%
310.428 1
 
0.7%
310.508 1
 
0.7%
310.087 1
 
0.7%
Other values (123) 123
91.8%
ValueCountFrequency (%)
291.96 1
0.7%
291.978 1
0.7%
292.017 1
0.7%
292.06 1
0.7%
292.143 1
0.7%
292.217 1
0.7%
292.254 1
0.7%
292.307 1
0.7%
292.445 1
0.7%
292.479 1
0.7%
ValueCountFrequency (%)
313.51 1
0.7%
313.497 1
0.7%
313.464 1
0.7%
312.683 1
0.7%
312.665 1
0.7%
312.578 1
0.7%
312.551 1
0.7%
312.504 1
0.7%
312.452 1
0.7%
312.4 1
0.7%

대지
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.401328
Minimum14.189
Maximum19.023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:32.445516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.189
5-th percentile14.3354
Q115.41525
median16.232
Q317.35375
95-th percentile18.73055
Maximum19.023
Range4.834
Interquartile range (IQR)1.9385

Descriptive statistics

Standard deviation1.378274
Coefficient of variation (CV)0.084034289
Kurtosis-0.97166067
Mean16.401328
Median Absolute Deviation (MAD)1.034
Skewness0.20043327
Sum2197.778
Variance1.8996391
MonotonicityNot monotonic
2024-01-10T05:43:32.579197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.692 2
 
1.5%
19.023 1
 
0.7%
15.579 1
 
0.7%
15.658 1
 
0.7%
15.645 1
 
0.7%
15.63 1
 
0.7%
15.611 1
 
0.7%
15.594 1
 
0.7%
15.568 1
 
0.7%
15.69 1
 
0.7%
Other values (123) 123
91.8%
ValueCountFrequency (%)
14.189 1
0.7%
14.203 1
0.7%
14.216 1
0.7%
14.224 1
0.7%
14.256 1
0.7%
14.286 1
0.7%
14.325 1
0.7%
14.341 1
0.7%
14.372 1
0.7%
14.401 1
0.7%
ValueCountFrequency (%)
19.023 1
0.7%
18.996 1
0.7%
18.918 1
0.7%
18.886 1
0.7%
18.851 1
0.7%
18.791 1
0.7%
18.752 1
0.7%
18.719 1
0.7%
18.669 1
0.7%
18.629 1
0.7%

공장용지
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.691687
Minimum9.271
Maximum17.078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:32.692900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.271
5-th percentile10.89895
Q111.2985
median11.653
Q313.8725
95-th percentile17.0514
Maximum17.078
Range7.807
Interquartile range (IQR)2.574

Descriptive statistics

Standard deviation2.1902473
Coefficient of variation (CV)0.17257338
Kurtosis-0.20438455
Mean12.691687
Median Absolute Deviation (MAD)0.392
Skewness1.104509
Sum1700.686
Variance4.7971832
MonotonicityNot monotonic
2024-01-10T05:43:32.809661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.261 5
 
3.7%
11.653 4
 
3.0%
11.64 3
 
2.2%
11.378 3
 
2.2%
11.272 3
 
2.2%
15.482 3
 
2.2%
10.953 3
 
2.2%
17.054 3
 
2.2%
11.176 2
 
1.5%
9.272 2
 
1.5%
Other values (83) 103
76.9%
ValueCountFrequency (%)
9.271 1
 
0.7%
9.272 2
1.5%
10.845 1
 
0.7%
10.85 1
 
0.7%
10.854 1
 
0.7%
10.871 1
 
0.7%
10.914 1
 
0.7%
10.942 1
 
0.7%
10.947 1
 
0.7%
10.953 3
2.2%
ValueCountFrequency (%)
17.078 1
 
0.7%
17.065 1
 
0.7%
17.058 2
1.5%
17.054 3
2.2%
17.05 1
 
0.7%
17.021 2
1.5%
16.988 1
 
0.7%
16.968 1
 
0.7%
16.964 2
1.5%
16.961 1
 
0.7%

공원
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71749254
Minimum0
Maximum1.659
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:32.923123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.395
Q10.418
median0.46
Q31.034
95-th percentile1.659
Maximum1.659
Range1.659
Interquartile range (IQR)0.616

Descriptive statistics

Standard deviation0.47058924
Coefficient of variation (CV)0.65588033
Kurtosis-0.24345434
Mean0.71749254
Median Absolute Deviation (MAD)0.049
Skewness1.1810921
Sum96.144
Variance0.22145424
MonotonicityNot monotonic
2024-01-10T05:43:33.029341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.418 22
16.4%
1.034 10
 
7.5%
0.46 10
 
7.5%
0.395 9
 
6.7%
1.659 8
 
6.0%
0.459 7
 
5.2%
0.402 6
 
4.5%
1.642 5
 
3.7%
1.641 5
 
3.7%
0.404 5
 
3.7%
Other values (21) 47
35.1%
ValueCountFrequency (%)
0.0 1
 
0.7%
0.395 9
6.7%
0.402 6
 
4.5%
0.404 5
 
3.7%
0.408 2
 
1.5%
0.41 1
 
0.7%
0.411 5
 
3.7%
0.418 22
16.4%
0.459 7
 
5.2%
0.46 10
7.5%
ValueCountFrequency (%)
1.659 8
6.0%
1.658 2
 
1.5%
1.649 1
 
0.7%
1.642 5
3.7%
1.641 5
3.7%
1.387 1
 
0.7%
1.289 2
 
1.5%
1.286 1
 
0.7%
1.284 1
 
0.7%
1.273 1
 
0.7%

체육용지
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57796269
Minimum0.156
Maximum1.066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:33.134059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.156
5-th percentile0.156
Q10.196
median0.237
Q30.982
95-th percentile1.066
Maximum1.066
Range0.91
Interquartile range (IQR)0.786

Descriptive statistics

Standard deviation0.40628377
Coefficient of variation (CV)0.70295848
Kurtosis-1.9835344
Mean0.57796269
Median Absolute Deviation (MAD)0.081
Skewness0.12564162
Sum77.447
Variance0.1650665
MonotonicityNot monotonic
2024-01-10T05:43:33.244580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.196 20
14.9%
0.156 15
11.2%
0.982 15
11.2%
0.968 14
10.4%
0.229 13
9.7%
1.066 9
 
6.7%
1.063 6
 
4.5%
0.999 6
 
4.5%
0.16 4
 
3.0%
0.981 4
 
3.0%
Other values (14) 28
20.9%
ValueCountFrequency (%)
0.156 15
11.2%
0.16 4
 
3.0%
0.186 3
 
2.2%
0.196 20
14.9%
0.197 1
 
0.7%
0.198 1
 
0.7%
0.199 1
 
0.7%
0.205 1
 
0.7%
0.227 1
 
0.7%
0.228 2
 
1.5%
ValueCountFrequency (%)
1.066 9
6.7%
1.063 6
 
4.5%
1.054 1
 
0.7%
1.045 2
 
1.5%
1.043 3
 
2.2%
0.999 6
 
4.5%
0.982 15
11.2%
0.981 4
 
3.0%
0.98 3
 
2.2%
0.968 14
10.4%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.53251
Minimum125.442
Maximum135.685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:43:33.355825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.442
5-th percentile125.80635
Q1126.6665
median127.924
Q3133.54625
95-th percentile134.8701
Maximum135.685
Range10.243
Interquartile range (IQR)6.87975

Descriptive statistics

Standard deviation3.5273253
Coefficient of variation (CV)0.027231196
Kurtosis-1.4857312
Mean129.53251
Median Absolute Deviation (MAD)1.9575
Skewness0.51314352
Sum17357.357
Variance12.442024
MonotonicityNot monotonic
2024-01-10T05:43:33.477000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.077 2
 
1.5%
127.909 2
 
1.5%
126.869 1
 
0.7%
126.988 1
 
0.7%
126.967 1
 
0.7%
126.952 1
 
0.7%
126.937 1
 
0.7%
126.889 1
 
0.7%
135.685 1
 
0.7%
127.139 1
 
0.7%
Other values (122) 122
91.0%
ValueCountFrequency (%)
125.442 1
0.7%
125.493 1
0.7%
125.496 1
0.7%
125.743 1
0.7%
125.744 1
0.7%
125.756 1
0.7%
125.781 1
0.7%
125.82 1
0.7%
125.829 1
0.7%
125.842 1
0.7%
ValueCountFrequency (%)
135.685 1
0.7%
135.663 1
0.7%
135.644 1
0.7%
135.229 1
0.7%
135.115 1
0.7%
134.968 1
0.7%
134.952 1
0.7%
134.826 1
0.7%
134.731 1
0.7%
134.717 1
0.7%

기준일
Date

UNIQUE 

Distinct134
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum2005-01-31 00:00:00
Maximum2016-02-29 00:00:00
2024-01-10T05:43:33.596489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:33.726103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-01-10T05:43:28.906842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:17.770421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.745807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:19.973024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.854828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.763474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.650713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.565334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.591138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:25.897301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.924330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.845130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.987625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:17.864004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.820976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.050686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.939171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.837070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.726707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.637556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.675506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:25.983846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.001513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.939537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.065970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:17.948600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.897603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.125386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.011554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.917246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.811305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.710900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.758877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.079839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.079072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.023237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.141215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.025663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.967160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.193949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.086784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.988234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.889636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.787772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.853248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.168736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.152936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.112652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.224188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.106423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:19.045706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.275177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.162066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.068775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.970190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.874585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.946562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.278620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.233026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.211146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.293391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.181066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:19.120428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.342901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.236573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.136641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.045531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.970725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:25.039874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.356399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.303173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.300817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.374938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.262096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:19.193380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.418847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.309464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.210707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.118843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.061549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:25.139852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.435428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.383081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.392685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.443580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.332943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:19.262600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.486070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.373758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.283152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.187332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.149726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:25.222751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.506999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.453009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.469246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.521919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.424159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:19.345421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.558686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.447984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.358586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.261001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.237078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:25.311982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.589583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.525712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.560312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.612957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.513945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:19.449790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.639510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.528366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.441393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.347056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.331241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:25.415842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.677171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.608908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.673167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.708881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.595041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:19.540683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.712214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.613798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.518665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.422009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.419516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:25.735094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.763958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.681276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.762257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:29.795387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:18.667730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:19.620913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:20.784050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:21.688280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:22.582190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:23.490769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:24.499194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:25.809943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:26.842624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:27.756820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:28.836595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:43:33.819895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호년도합계임야대지공장용지공원체육용지기타
번호1.0000.9830.0000.8350.9430.8910.9190.9830.8850.8200.8960.918
년도0.9831.0000.0000.8470.9140.8910.9340.9800.8930.7920.9570.915
0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
합계0.8350.8470.0001.0000.6960.7260.7830.8210.9390.8970.7370.790
0.9430.9140.0000.6961.0000.7530.9030.9360.8380.8130.8490.843
0.8910.8910.0000.7260.7531.0000.8260.8920.7790.7190.7690.928
임야0.9190.9340.0000.7830.9030.8261.0000.9290.9170.8640.9870.889
대지0.9830.9800.0000.8210.9360.8920.9291.0000.8950.8450.8980.897
공장용지0.8850.8930.0000.9390.8380.7790.9170.8951.0000.9950.7770.766
공원0.8200.7920.0000.8970.8130.7190.8640.8450.9951.0000.6900.710
체육용지0.8960.9570.0000.7370.8490.7690.9870.8980.7770.6901.0000.842
기타0.9180.9150.0000.7900.8430.9280.8890.8970.7660.7100.8421.000
2024-01-10T05:43:33.945380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호년도합계임야대지공장용지공원체육용지기타
번호1.0000.9960.0500.9470.583-0.602-1.0001.0001.0000.9190.9940.973
년도0.9961.000-0.0370.9380.583-0.596-0.9960.9960.9960.9240.9920.970
0.050-0.0371.0000.1070.011-0.060-0.0500.0490.053-0.0480.0350.042
합계0.9470.9380.1071.0000.561-0.525-0.9470.9470.9470.8710.9410.902
0.5830.5830.0110.5611.000-0.073-0.5830.5830.5830.6250.5840.553
-0.602-0.596-0.060-0.525-0.0731.0000.602-0.601-0.602-0.544-0.587-0.725
임야-1.000-0.996-0.050-0.947-0.5830.6021.000-1.000-1.000-0.919-0.994-0.973
대지1.0000.9960.0490.9470.583-0.601-1.0001.0000.9990.9200.9940.973
공장용지1.0000.9960.0530.9470.583-0.602-1.0000.9991.0000.9190.9940.973
공원0.9190.924-0.0480.8710.625-0.544-0.9190.9200.9191.0000.9180.895
체육용지0.9940.9920.0350.9410.584-0.587-0.9940.9940.9940.9181.0000.966
기타0.9730.9700.0420.9020.553-0.725-0.9730.9730.9730.8950.9661.000

Missing values

2024-01-10T05:43:29.931032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:43:30.109785image/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

번호년도합계임야대지공장용지공원체육용지기타기준일
013420162741.19378.919195.803291.9619.02317.0781.6591.066135.6852016-02-29
113320161741.19478.947195.82291.97818.99617.0651.6591.066135.6632016-01-31
2132201512741.18678.971195.853292.01718.91817.0581.6591.066135.6442015-12-31
3131201511740.79978.979195.862292.0618.88617.0581.6591.066135.2292015-11-30
4130201510740.7979.017195.885292.14318.85117.0541.6591.066135.1152015-10-31
512920159740.79879.09195.953292.21718.79117.0541.6591.066134.9682015-09-30
612820158740.80179.097195.967292.25418.75217.0541.6591.066134.9522015-08-31
712720157740.879.147196.027292.30718.71917.051.6581.066134.8262015-07-31
812620156740.81479.181196.043292.44518.66917.0211.6581.066134.7312015-06-30
912520155740.81479.187196.059292.47918.62917.0211.6591.063134.7172015-05-31
번호년도합계임야대지공장용지공원체육용지기타기준일
12410200510740.37678.001198.214312.414.40110.9470.3950.156125.8612005-10-31
125920059740.40678.018198.229312.45214.37210.9420.3950.156125.8422005-09-30
126820058740.41178.03198.243312.50414.34110.9140.3950.156125.8292005-08-31
127720057740.40278.054198.27312.55114.32510.8710.3950.156125.7812005-07-31
128620056740.39578.081198.29312.57814.28610.8540.3950.156125.7562005-06-30
129520055740.39978.026198.306312.66514.25610.850.3950.156125.7442005-05-31
130420054740.40478.044198.321312.68314.21610.8450.3950.156125.7432005-04-30
131320053739.93878.138198.62313.46414.2249.2710.5720.156125.4932005-03-31
132220052739.95478.149198.609313.49714.2039.2720.5720.156125.4962005-02-28
133120051739.90878.15198.617313.5114.1899.2720.5720.156125.4422005-01-31