Overview

Dataset statistics

Number of variables13
Number of observations107
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.2 KiB
Average record size in memory117.2 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 7 other fieldsHigh correlation
년도 is highly overall correlated with 번호 and 7 other fieldsHigh correlation
합계 is highly overall correlated with 번호 and 7 other fieldsHigh correlation
임야 is highly overall correlated with 번호 and 7 other fieldsHigh correlation
대지 is highly overall correlated with 번호 and 7 other fieldsHigh correlation
공장용지 is highly overall correlated with 번호 and 7 other fieldsHigh correlation
공원 is highly overall correlated with 번호 and 7 other fieldsHigh correlation
체육용지 is highly overall correlated with 번호 and 7 other fieldsHigh correlation
기타 is highly overall correlated with 번호 and 7 other fieldsHigh correlation
번호 has unique valuesUnique

Reproduction

Analysis started2024-01-09 20:43:00.450544
Analysis finished2024-01-09 20:43:12.590406
Duration12.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54
Minimum1
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:12.658107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.3
Q127.5
median54
Q380.5
95-th percentile101.7
Maximum107
Range106
Interquartile range (IQR)53

Descriptive statistics

Standard deviation31.032241
Coefficient of variation (CV)0.57467114
Kurtosis-1.2
Mean54
Median Absolute Deviation (MAD)27
Skewness0
Sum5778
Variance963
MonotonicityStrictly decreasing
2024-01-10T05:43:12.781268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107 1
 
0.9%
39 1
 
0.9%
28 1
 
0.9%
29 1
 
0.9%
30 1
 
0.9%
31 1
 
0.9%
32 1
 
0.9%
33 1
 
0.9%
34 1
 
0.9%
35 1
 
0.9%
Other values (97) 97
90.7%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%
98 1
0.9%

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.9626
Minimum2005
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:12.880972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12007
median2009
Q32011
95-th percentile2013
Maximum2013
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5768389
Coefficient of variation (CV)0.0012826714
Kurtosis-1.2218777
Mean2008.9626
Median Absolute Deviation (MAD)2
Skewness0.008395338
Sum214959
Variance6.6400987
MonotonicityDecreasing
2024-01-10T05:43:12.980628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2012 12
11.2%
2011 12
11.2%
2010 12
11.2%
2009 12
11.2%
2008 12
11.2%
2007 12
11.2%
2006 12
11.2%
2005 12
11.2%
2013 11
10.3%
ValueCountFrequency (%)
2005 12
11.2%
2006 12
11.2%
2007 12
11.2%
2008 12
11.2%
2009 12
11.2%
2010 12
11.2%
2011 12
11.2%
2012 12
11.2%
2013 11
10.3%
ValueCountFrequency (%)
2013 11
10.3%
2012 12
11.2%
2011 12
11.2%
2010 12
11.2%
2009 12
11.2%
2008 12
11.2%
2007 12
11.2%
2006 12
11.2%
2005 12
11.2%


Real number (ℝ)

Distinct12
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4485981
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:13.100185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.5
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4428861
Coefficient of variation (CV)0.53389684
Kurtosis-1.2096097
Mean6.4485981
Median Absolute Deviation (MAD)3
Skewness0.006453175
Sum690
Variance11.853465
MonotonicityNot monotonic
2024-01-10T05:43:13.210936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 9
8.4%
10 9
8.4%
9 9
8.4%
8 9
8.4%
7 9
8.4%
6 9
8.4%
5 9
8.4%
4 9
8.4%
3 9
8.4%
2 9
8.4%
Other values (2) 17
15.9%
ValueCountFrequency (%)
1 9
8.4%
2 9
8.4%
3 9
8.4%
4 9
8.4%
5 9
8.4%
6 9
8.4%
7 9
8.4%
8 9
8.4%
9 9
8.4%
10 9
8.4%
ValueCountFrequency (%)
12 8
7.5%
11 9
8.4%
10 9
8.4%
9 9
8.4%
8 9
8.4%
7 9
8.4%
6 9
8.4%
5 9
8.4%
4 9
8.4%
3 9
8.4%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean740.56011
Minimum739.908
Maximum740.821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:13.349725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum739.908
5-th percentile740.3361
Q1740.4085
median740.567
Q3740.6595
95-th percentile740.8007
Maximum740.821
Range0.913
Interquartile range (IQR)0.251

Descriptive statistics

Standard deviation0.17290974
Coefficient of variation (CV)0.00023348509
Kurtosis3.086082
Mean740.56011
Median Absolute Deviation (MAD)0.094
Skewness-1.1886125
Sum79239.932
Variance0.02989778
MonotonicityNot monotonic
2024-01-10T05:43:13.503558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
740.661 4
 
3.7%
740.562 4
 
3.7%
740.79 3
 
2.8%
740.663 3
 
2.8%
740.542 3
 
2.8%
740.818 3
 
2.8%
740.552 3
 
2.8%
740.556 2
 
1.9%
740.626 2
 
1.9%
740.657 2
 
1.9%
Other values (67) 78
72.9%
ValueCountFrequency (%)
739.908 1
0.9%
739.938 1
0.9%
739.954 1
0.9%
740.326 1
0.9%
740.327 1
0.9%
740.328 1
0.9%
740.355 1
0.9%
740.364 1
0.9%
740.368 1
0.9%
740.373 1
0.9%
ValueCountFrequency (%)
740.821 1
 
0.9%
740.818 3
2.8%
740.817 1
 
0.9%
740.804 1
 
0.9%
740.793 1
 
0.9%
740.792 2
1.9%
740.791 2
1.9%
740.79 3
2.8%
740.789 1
 
0.9%
740.788 1
 
0.9%


Real number (ℝ)

Distinct101
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.341
Minimum77.689
Maximum80.148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:13.660979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77.689
5-th percentile77.7275
Q177.7835
median77.887
Q378.64
95-th percentile80.0466
Maximum80.148
Range2.459
Interquartile range (IQR)0.8565

Descriptive statistics

Standard deviation0.84190692
Coefficient of variation (CV)0.010746696
Kurtosis-0.33488939
Mean78.341
Median Absolute Deviation (MAD)0.131
Skewness1.203104
Sum8382.487
Variance0.70880726
MonotonicityNot monotonic
2024-01-10T05:43:13.784547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.816 2
 
1.9%
77.76 2
 
1.9%
77.786 2
 
1.9%
77.811 2
 
1.9%
77.813 2
 
1.9%
77.756 2
 
1.9%
79.576 1
 
0.9%
77.72 1
 
0.9%
77.771 1
 
0.9%
77.775 1
 
0.9%
Other values (91) 91
85.0%
ValueCountFrequency (%)
77.689 1
0.9%
77.691 1
0.9%
77.708 1
0.9%
77.716 1
0.9%
77.72 1
0.9%
77.723 1
0.9%
77.738 1
0.9%
77.745 1
0.9%
77.747 1
0.9%
77.752 1
0.9%
ValueCountFrequency (%)
80.148 1
0.9%
80.146 1
0.9%
80.129 1
0.9%
80.105 1
0.9%
80.096 1
0.9%
80.055 1
0.9%
80.027 1
0.9%
79.924 1
0.9%
79.917 1
0.9%
79.902 1
0.9%


Real number (ℝ)

Distinct103
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.92
Minimum197.051
Maximum198.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:13.902311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum197.051
5-th percentile197.2066
Q1197.646
median197.963
Q3198.298
95-th percentile198.4069
Maximum198.62
Range1.569
Interquartile range (IQR)0.652

Descriptive statistics

Standard deviation0.41810687
Coefficient of variation (CV)0.0021125044
Kurtosis-0.96556488
Mean197.92
Median Absolute Deviation (MAD)0.327
Skewness-0.3801362
Sum21177.44
Variance0.17481336
MonotonicityNot monotonic
2024-01-10T05:43:14.253525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198.321 3
 
2.8%
197.446 2
 
1.9%
197.676 2
 
1.9%
197.459 1
 
0.9%
197.811 1
 
0.9%
198.029 1
 
0.9%
198.021 1
 
0.9%
197.963 1
 
0.9%
197.931 1
 
0.9%
197.894 1
 
0.9%
Other values (93) 93
86.9%
ValueCountFrequency (%)
197.051 1
0.9%
197.052 1
0.9%
197.136 1
0.9%
197.148 1
0.9%
197.188 1
0.9%
197.203 1
0.9%
197.215 1
0.9%
197.223 1
0.9%
197.235 1
0.9%
197.238 1
0.9%
ValueCountFrequency (%)
198.62 1
0.9%
198.617 1
0.9%
198.609 1
0.9%
198.496 1
0.9%
198.425 1
0.9%
198.409 1
0.9%
198.402 1
0.9%
198.4 1
0.9%
198.397 1
0.9%
198.387 1
0.9%

임야
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.55086
Minimum296.361
Maximum313.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:14.373924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum296.361
5-th percentile296.6559
Q1306.3515
median309.39
Q3311.2595
95-th percentile312.5699
Maximum313.51
Range17.149
Interquartile range (IQR)4.908

Descriptive statistics

Standard deviation5.3278905
Coefficient of variation (CV)0.017323608
Kurtosis-0.27683436
Mean307.55086
Median Absolute Deviation (MAD)2.014
Skewness-1.0979761
Sum32907.942
Variance28.386417
MonotonicityNot monotonic
2024-01-10T05:43:14.491525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
309.563 2
 
1.9%
311.326 1
 
0.9%
311.223 1
 
0.9%
311.197 1
 
0.9%
311.145 1
 
0.9%
311.087 1
 
0.9%
311.05 1
 
0.9%
310.99 1
 
0.9%
310.96 1
 
0.9%
310.826 1
 
0.9%
Other values (96) 96
89.7%
ValueCountFrequency (%)
296.361 1
0.9%
296.387 1
0.9%
296.421 1
0.9%
296.492 1
0.9%
296.521 1
0.9%
296.61 1
0.9%
296.763 1
0.9%
296.794 1
0.9%
296.926 1
0.9%
296.951 1
0.9%
ValueCountFrequency (%)
313.51 1
0.9%
313.497 1
0.9%
313.464 1
0.9%
312.683 1
0.9%
312.665 1
0.9%
312.578 1
0.9%
312.551 1
0.9%
312.504 1
0.9%
312.452 1
0.9%
312.4 1
0.9%

대지
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.886037
Minimum14.189
Maximum17.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:14.608994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.189
5-th percentile14.2977
Q115.1565
median15.872
Q316.7615
95-th percentile17.3623
Maximum17.56
Range3.371
Interquartile range (IQR)1.605

Descriptive statistics

Standard deviation1.006438
Coefficient of variation (CV)0.063353621
Kurtosis-1.1256635
Mean15.886037
Median Absolute Deviation (MAD)0.805
Skewness-0.054519925
Sum1699.806
Variance1.0129174
MonotonicityNot monotonic
2024-01-10T05:43:14.734651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.692 2
 
1.9%
15.084 1
 
0.9%
15.184 1
 
0.9%
15.21 1
 
0.9%
15.224 1
 
0.9%
15.323 1
 
0.9%
15.35 1
 
0.9%
15.398 1
 
0.9%
15.411 1
 
0.9%
15.428 1
 
0.9%
Other values (96) 96
89.7%
ValueCountFrequency (%)
14.189 1
0.9%
14.203 1
0.9%
14.216 1
0.9%
14.224 1
0.9%
14.256 1
0.9%
14.286 1
0.9%
14.325 1
0.9%
14.341 1
0.9%
14.372 1
0.9%
14.401 1
0.9%
ValueCountFrequency (%)
17.56 1
0.9%
17.529 1
0.9%
17.497 1
0.9%
17.385 1
0.9%
17.372 1
0.9%
17.365 1
0.9%
17.356 1
0.9%
17.347 1
0.9%
17.329 1
0.9%
17.308 1
0.9%

공장용지
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.684533
Minimum9.271
Maximum13.931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:14.855120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.271
5-th percentile10.8591
Q111.261
median11.557
Q311.8165
95-th percentile13.8915
Maximum13.931
Range4.66
Interquartile range (IQR)0.5555

Descriptive statistics

Standard deviation0.92047395
Coefficient of variation (CV)0.07877713
Kurtosis2.3325827
Mean11.684533
Median Absolute Deviation (MAD)0.296
Skewness0.9608411
Sum1250.245
Variance0.84727229
MonotonicityNot monotonic
2024-01-10T05:43:14.977366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.261 5
 
4.7%
11.653 4
 
3.7%
11.64 3
 
2.8%
11.378 3
 
2.8%
11.272 3
 
2.8%
10.953 3
 
2.8%
11.183 2
 
1.9%
11.935 2
 
1.9%
11.526 2
 
1.9%
11.48 2
 
1.9%
Other values (66) 78
72.9%
ValueCountFrequency (%)
9.271 1
 
0.9%
9.272 2
1.9%
10.845 1
 
0.9%
10.85 1
 
0.9%
10.854 1
 
0.9%
10.871 1
 
0.9%
10.914 1
 
0.9%
10.942 1
 
0.9%
10.947 1
 
0.9%
10.953 3
2.8%
ValueCountFrequency (%)
13.931 1
0.9%
13.927 1
0.9%
13.923 1
0.9%
13.915 1
0.9%
13.901 1
0.9%
13.899 1
0.9%
13.874 1
0.9%
13.868 1
0.9%
13.849 2
1.9%
13.847 2
1.9%

공원
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50171963
Minimum0
Maximum1.034
Zeros1
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:15.102705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.395
Q10.411
median0.459
Q30.469
95-th percentile1.034
Maximum1.034
Range1.034
Interquartile range (IQR)0.058

Descriptive statistics

Standard deviation0.1978971
Coefficient of variation (CV)0.39443762
Kurtosis3.6139188
Mean0.50171963
Median Absolute Deviation (MAD)0.041
Skewness2.034242
Sum53.684
Variance0.03916326
MonotonicityNot monotonic
2024-01-10T05:43:15.216133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.418 22
20.6%
1.034 10
9.3%
0.46 10
9.3%
0.395 9
 
8.4%
0.459 7
 
6.5%
0.402 6
 
5.6%
0.467 5
 
4.7%
0.462 5
 
4.7%
0.411 5
 
4.7%
0.404 5
 
4.7%
Other values (11) 23
21.5%
ValueCountFrequency (%)
0.0 1
 
0.9%
0.395 9
8.4%
0.402 6
 
5.6%
0.404 5
 
4.7%
0.408 2
 
1.9%
0.41 1
 
0.9%
0.411 5
 
4.7%
0.418 22
20.6%
0.459 7
 
6.5%
0.46 10
9.3%
ValueCountFrequency (%)
1.034 10
9.3%
1.033 2
 
1.9%
0.572 3
 
2.8%
0.499 1
 
0.9%
0.488 1
 
0.9%
0.478 3
 
2.8%
0.47 4
 
3.7%
0.469 4
 
3.7%
0.467 5
4.7%
0.463 1
 
0.9%

체육용지
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45988785
Minimum0.156
Maximum0.982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:15.321810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.156
5-th percentile0.156
Q10.196
median0.229
Q30.968
95-th percentile0.982
Maximum0.982
Range0.826
Interquartile range (IQR)0.772

Descriptive statistics

Standard deviation0.37025772
Coefficient of variation (CV)0.80510438
Kurtosis-1.5344813
Mean0.45988785
Median Absolute Deviation (MAD)0.069
Skewness0.68856576
Sum49.208
Variance0.13709078
MonotonicityNot monotonic
2024-01-10T05:43:15.423127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.196 20
18.7%
0.982 15
14.0%
0.156 15
14.0%
0.968 14
13.1%
0.229 13
12.1%
0.981 4
 
3.7%
0.16 4
 
3.7%
0.98 3
 
2.8%
0.246 3
 
2.8%
0.237 3
 
2.8%
Other values (8) 13
12.1%
ValueCountFrequency (%)
0.156 15
14.0%
0.16 4
 
3.7%
0.186 3
 
2.8%
0.196 20
18.7%
0.197 1
 
0.9%
0.198 1
 
0.9%
0.199 1
 
0.9%
0.205 1
 
0.9%
0.227 1
 
0.9%
0.228 2
 
1.9%
ValueCountFrequency (%)
0.982 15
14.0%
0.981 4
 
3.7%
0.98 3
 
2.8%
0.968 14
13.1%
0.246 3
 
2.8%
0.237 3
 
2.8%
0.232 3
 
2.8%
0.229 13
12.1%
0.228 2
 
1.9%
0.227 1
 
0.9%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.21609
Minimum125.442
Maximum133.886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T05:43:15.542872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.442
5-th percentile125.7635
Q1126.2875
median127.139
Q3128.132
95-th percentile133.6751
Maximum133.886
Range8.444
Interquartile range (IQR)1.8445

Descriptive statistics

Standard deviation2.6241696
Coefficient of variation (CV)0.020466772
Kurtosis-0.16252408
Mean128.21609
Median Absolute Deviation (MAD)0.926
Skewness1.1546684
Sum13719.122
Variance6.8862659
MonotonicityNot monotonic
2024-01-10T05:43:15.663260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.909 2
 
1.9%
127.077 2
 
1.9%
126.793 1
 
0.9%
126.28 1
 
0.9%
126.295 1
 
0.9%
126.482 1
 
0.9%
126.512 1
 
0.9%
126.537 1
 
0.9%
126.608 1
 
0.9%
126.627 1
 
0.9%
Other values (95) 95
88.8%
ValueCountFrequency (%)
125.442 1
0.9%
125.493 1
0.9%
125.496 1
0.9%
125.743 1
0.9%
125.744 1
0.9%
125.756 1
0.9%
125.781 1
0.9%
125.82 1
0.9%
125.829 1
0.9%
125.842 1
0.9%
ValueCountFrequency (%)
133.886 1
0.9%
133.867 1
0.9%
133.861 1
0.9%
133.752 1
0.9%
133.744 1
0.9%
133.73 1
0.9%
133.547 1
0.9%
133.544 1
0.9%
133.228 1
0.9%
133.201 1
0.9%
Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
Minimum2005-01-31 00:00:00
Maximum2013-11-30 00:00:00
2024-01-10T05:43:15.818981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:15.946667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-01-10T05:43:11.474940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:00.729880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.724431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.718472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.718709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.776099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.644388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.736456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.699488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.597206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.474988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.603787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.547545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:00.799689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.800856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.805310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.783614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.841633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.722368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.807224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.780379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.669116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.781715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.668773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.644629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:00.887959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.887425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.905257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.862003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.919267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.807853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.888709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.867401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.745915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.860164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.748761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.713015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:00.958745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.973712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.989630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.928240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.988028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.904449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.958040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.940361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.816427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.933019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.814102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.781670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.032623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.060453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.072655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.993459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.056975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.001880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.039024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.015536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.886109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.004052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.882558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.845358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.106072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.135129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.168695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.056329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.119005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.094265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.123849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.082438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.958144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.082641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.947059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.925979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.216810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.226419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.283874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.132666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.202095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.185722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.229894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.166794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.044235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.170482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.039980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.995755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.312647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.308754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.375092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.427601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.276087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.280558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.304604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.241203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.120421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.250524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.111548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:12.064359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.402485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.389875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.445399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.499744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.363776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.372534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.388419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.318169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.199741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.326707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.185428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:12.132298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.487571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.460340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.510563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.568363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.438998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.460927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.476692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.385651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.271821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.397015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.261030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:12.204713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.568089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.543730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.583691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.639518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.509953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.572614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.552615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.457889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.341825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.465186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.337504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:12.277242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:01.654038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:02.621170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:03.651304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:04.705850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:05.578592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:06.652817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:07.628237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:08.525652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:09.404748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:10.533772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:11.408662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:43:16.038394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호년도합계임야대지공장용지공원체육용지기타
번호1.0000.9410.0000.8560.7980.9310.9030.9870.8800.9520.8310.895
년도0.9411.0000.0000.8510.7490.8540.9730.9020.8430.9640.8140.958
0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
합계0.8560.8510.0001.0000.6720.8170.8300.8700.8490.8660.6890.801
0.7980.7490.0000.6721.0000.5830.8650.8440.7930.6810.8530.770
0.9310.8540.0000.8170.5831.0000.8070.9140.8150.9220.5950.809
임야0.9030.9730.0000.8300.8650.8071.0000.9070.8780.7890.7960.983
대지0.9870.9020.0000.8700.8440.9140.9071.0000.8750.9340.8330.887
공장용지0.8800.8430.0000.8490.7930.8150.8780.8751.0000.8690.8660.813
공원0.9520.9640.0000.8660.6810.9220.7890.9340.8691.0000.4880.800
체육용지0.8310.8140.0000.6890.8530.5950.7960.8330.8660.4881.0000.686
기타0.8950.9580.0000.8010.7700.8090.9830.8870.8130.8000.6861.000
2024-01-10T05:43:16.163182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호년도합계임야대지공장용지공원체육용지기타
번호1.0000.9940.0880.9380.380-0.235-1.0000.9990.9990.8410.9890.947
년도0.9941.000-0.0230.9240.377-0.223-0.9940.9930.9930.8520.9860.942
0.088-0.0231.0000.1570.024-0.104-0.0870.0860.091-0.0710.0590.074
합계0.9380.9240.1571.0000.324-0.165-0.9380.9380.9380.7930.9270.851
0.3800.3770.0240.3241.0000.333-0.3800.3800.3800.4720.3840.325
-0.235-0.223-0.104-0.1650.3331.0000.235-0.232-0.234-0.119-0.203-0.471
임야-1.000-0.994-0.087-0.938-0.3800.2351.000-0.999-1.000-0.841-0.989-0.947
대지0.9990.9930.0860.9380.380-0.232-0.9991.0000.9990.8420.9890.946
공장용지0.9990.9930.0910.9380.380-0.234-1.0000.9991.0000.8400.9890.947
공원0.8410.852-0.0710.7930.472-0.119-0.8410.8420.8401.0000.8390.795
체육용지0.9890.9860.0590.9270.384-0.203-0.9890.9890.9890.8391.0000.933
기타0.9470.9420.0740.8510.325-0.471-0.9470.9460.9470.7950.9331.000

Missing values

2024-01-10T05:43:12.385670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:43:12.535507image/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
0107201311740.78879.576197.459296.36117.5613.9311.0340.981133.8862013.11.30
1106201310740.78679.588197.472296.38717.52913.9271.0340.982133.8672013.10.31
210520139740.7979.707197.531296.49217.38513.9151.0340.982133.7442013.09.30
310420138740.78979.599197.471296.42117.49713.9231.0340.982133.8612013.08.31
410320137740.79179.712197.542296.52117.34713.9011.0340.982133.7522013.07.31
510220136740.7979.667197.56296.6117.30813.8991.0340.982133.732013.06.30
610120135740.79279.683197.632296.76317.27813.8741.0330.982133.5472013.05.31
710020134740.7979.696197.65296.79417.22213.8681.0340.982133.5442013.04.30
89920133740.79179.702197.697296.92617.37213.8491.0340.982133.2282013.03.31
99820132740.79279.663197.747296.95117.36513.8491.0340.982133.2012013.02.28
번호년도합계임야대지공장용지공원체육용지기타기준일
9710200510740.37678.001198.214312.414.40110.9470.3950.156125.8612005.10.31
98920059740.40678.018198.229312.45214.37210.9420.3950.156125.8422005.09.30
99820058740.41178.03198.243312.50414.34110.9140.3950.156125.8292005.08.31
100720057740.40278.054198.27312.55114.32510.8710.3950.156125.7812005.07.31
101620056740.39578.081198.29312.57814.28610.8540.3950.156125.7562005.06.30
102520055740.39978.026198.306312.66514.25610.850.3950.156125.7442005.05.31
103420054740.40478.044198.321312.68314.21610.8450.3950.156125.7432005.04.30
104320053739.93878.138198.62313.46414.2249.2710.5720.156125.4932005.03.31
105220052739.95478.149198.609313.49714.2039.2720.5720.156125.4962005.02.28
106120051739.90878.15198.617313.5114.1899.2720.5720.156125.4422005.01.31