Overview

Dataset statistics

Number of variables13
Number of observations191
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.8 KiB
Average record size in memory116.7 B

Variable types

Numeric12
Categorical1

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

기준일 has constant value ""Constant
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
전 is highly overall correlated with 번호 and 8 other fieldsHigh correlation
답 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
공장(용지) is highly overall correlated with 번호 and 9 other fieldsHigh correlation
공원 is highly overall correlated with 번호 and 9 other fieldsHigh correlation
체육(용지) 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

Reproduction

Analysis started2024-01-09 20:42:32.336073
Analysis finished2024-01-09 20:42:45.699241
Duration13.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct191
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96
Minimum1
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:45.771873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.5
Q148.5
median96
Q3143.5
95-th percentile181.5
Maximum191
Range190
Interquartile range (IQR)95

Descriptive statistics

Standard deviation55.2811
Coefficient of variation (CV)0.57584479
Kurtosis-1.2
Mean96
Median Absolute Deviation (MAD)48
Skewness0
Sum18336
Variance3056
MonotonicityStrictly decreasing
2024-01-10T05:42:45.910096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191 1
 
0.5%
190 1
 
0.5%
69 1
 
0.5%
68 1
 
0.5%
67 1
 
0.5%
66 1
 
0.5%
65 1
 
0.5%
64 1
 
0.5%
63 1
 
0.5%
62 1
 
0.5%
Other values (181) 181
94.8%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
191 1
0.5%
190 1
0.5%
189 1
0.5%
188 1
0.5%
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.4607
Minimum2005
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:46.027701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12008.5
median2012
Q32016
95-th percentile2020
Maximum2020
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.6017477
Coefficient of variation (CV)0.0022866273
Kurtosis-1.2060861
Mean2012.4607
Median Absolute Deviation (MAD)4
Skewness0.0028439323
Sum384380
Variance21.176082
MonotonicityDecreasing
2024-01-10T05:42:46.166805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2012 12
 
6.3%
2005 12
 
6.3%
2018 12
 
6.3%
2017 12
 
6.3%
2016 12
 
6.3%
2015 12
 
6.3%
2014 12
 
6.3%
2013 12
 
6.3%
2019 12
 
6.3%
2011 12
 
6.3%
Other values (6) 71
37.2%
ValueCountFrequency (%)
2005 12
6.3%
2006 12
6.3%
2007 12
6.3%
2008 12
6.3%
2009 12
6.3%
2010 12
6.3%
2011 12
6.3%
2012 12
6.3%
2013 12
6.3%
2014 12
6.3%
ValueCountFrequency (%)
2020 11
5.8%
2019 12
6.3%
2018 12
6.3%
2017 12
6.3%
2016 12
6.3%
2015 12
6.3%
2014 12
6.3%
2013 12
6.3%
2012 12
6.3%
2011 12
6.3%

월
Real number (ℝ)

Distinct12
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4712042
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:46.280559image/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 deviation3.4470366
Coefficient of variation (CV)0.53267313
Kurtosis-1.2128181
Mean6.4712042
Median Absolute Deviation (MAD)3
Skewness0.0037214541
Sum1236
Variance11.882061
MonotonicityNot monotonic
2024-01-10T05:42:46.379280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 16
8.4%
10 16
8.4%
9 16
8.4%
8 16
8.4%
7 16
8.4%
6 16
8.4%
5 16
8.4%
4 16
8.4%
3 16
8.4%
2 16
8.4%
Other values (2) 31
16.2%
ValueCountFrequency (%)
1 16
8.4%
2 16
8.4%
3 16
8.4%
4 16
8.4%
5 16
8.4%
6 16
8.4%
7 16
8.4%
8 16
8.4%
9 16
8.4%
10 16
8.4%
ValueCountFrequency (%)
12 15
7.9%
11 16
8.4%
10 16
8.4%
9 16
8.4%
8 16
8.4%
7 16
8.4%
6 16
8.4%
5 16
8.4%
4 16
8.4%
3 16
8.4%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean740.85566
Minimum739.393
Maximum742.049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:46.498272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum739.393
5-th percentile740.3745
Q1740.5635
median740.788
Q3741.213
95-th percentile742.023
Maximum742.049
Range2.656
Interquartile range (IQR)0.6495

Descriptive statistics

Standard deviation0.46673593
Coefficient of variation (CV)0.00062999576
Kurtosis1.3943086
Mean740.85566
Median Absolute Deviation (MAD)0.235
Skewness0.54269492
Sum141503.43
Variance0.21784242
MonotonicityNot monotonic
2024-01-10T05:42:46.645983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
741.202 4
 
2.1%
740.79 4
 
2.1%
740.398 4
 
2.1%
740.661 3
 
1.6%
740.595 3
 
1.6%
740.564 3
 
1.6%
742.023 3
 
1.6%
741.313 3
 
1.6%
740.389 3
 
1.6%
740.663 3
 
1.6%
Other values (130) 158
82.7%
ValueCountFrequency (%)
739.393 1
0.5%
739.423 1
0.5%
739.439 1
0.5%
740.325 1
0.5%
740.327 2
1.0%
740.355 1
0.5%
740.365 1
0.5%
740.368 1
0.5%
740.374 1
0.5%
740.375 1
0.5%
ValueCountFrequency (%)
742.049 1
 
0.5%
742.041 1
 
0.5%
742.038 1
 
0.5%
742.037 1
 
0.5%
742.036 1
 
0.5%
742.032 1
 
0.5%
742.031 1
 
0.5%
742.026 1
 
0.5%
742.023 3
1.6%
741.975 1
 
0.5%

전
Real number (ℝ)

HIGH CORRELATION 

Distinct179
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.527126
Minimum77.689
Maximum80.148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:46.773073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77.689
5-th percentile77.753
Q177.8535
median78.433
Q378.998
95-th percentile79.8935
Maximum80.148
Range2.459
Interquartile range (IQR)1.1445

Descriptive statistics

Standard deviation0.69868641
Coefficient of variation (CV)0.0088973893
Kurtosis-0.63183748
Mean78.527126
Median Absolute Deviation (MAD)0.581
Skewness0.64967668
Sum14998.681
Variance0.4881627
MonotonicityNot monotonic
2024-01-10T05:42:46.904305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.811 3
 
1.6%
78.37 3
 
1.6%
78.783 2
 
1.0%
77.825 2
 
1.0%
77.816 2
 
1.0%
77.778 2
 
1.0%
77.756 2
 
1.0%
79.147 2
 
1.0%
77.786 2
 
1.0%
79.157 2
 
1.0%
Other values (169) 169
88.5%
ValueCountFrequency (%)
77.689 1
0.5%
77.691 1
0.5%
77.708 1
0.5%
77.716 1
0.5%
77.72 1
0.5%
77.723 1
0.5%
77.738 1
0.5%
77.745 1
0.5%
77.747 1
0.5%
77.752 1
0.5%
ValueCountFrequency (%)
80.148 1
0.5%
80.146 1
0.5%
80.129 1
0.5%
80.105 1
0.5%
80.096 1
0.5%
80.055 1
0.5%
80.027 1
0.5%
79.924 1
0.5%
79.917 1
0.5%
79.903 1
0.5%

답
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.44297
Minimum192.319
Maximum198.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:47.049737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum192.319
5-th percentile192.6335
Q1195.38
median197.288
Q3198.037
95-th percentile198.3865
Maximum198.62
Range6.301
Interquartile range (IQR)2.657

Descriptive statistics

Standard deviation1.9776046
Coefficient of variation (CV)0.010067067
Kurtosis-0.79349178
Mean196.44297
Median Absolute Deviation (MAD)1.051
Skewness-0.82186775
Sum37520.607
Variance3.91092
MonotonicityNot monotonic
2024-01-10T05:42:47.195831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192.381 3
 
1.6%
198.321 3
 
1.6%
197.203 2
 
1.0%
197.052 2
 
1.0%
197.758 2
 
1.0%
197.136 2
 
1.0%
197.676 2
 
1.0%
198.261 1
 
0.5%
197.148 1
 
0.5%
197.583 1
 
0.5%
Other values (172) 172
90.1%
ValueCountFrequency (%)
192.319 1
 
0.5%
192.343 1
 
0.5%
192.381 3
1.6%
192.401 1
 
0.5%
192.472 1
 
0.5%
192.504 1
 
0.5%
192.613 1
 
0.5%
192.626 1
 
0.5%
192.641 1
 
0.5%
192.754 1
 
0.5%
ValueCountFrequency (%)
198.62 1
0.5%
198.617 1
0.5%
198.609 1
0.5%
198.496 1
0.5%
198.425 1
0.5%
198.409 1
0.5%
198.402 1
0.5%
198.4 1
0.5%
198.397 1
0.5%
198.387 1
0.5%

임야
Real number (ℝ)

HIGH CORRELATION 

Distinct187
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.09315
Minimum287.175
Maximum313.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:47.321650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum287.175
5-th percentile287.7895
Q1291.2115
median297.004
Q3310.107
95-th percentile312.375
Maximum313.51
Range26.335
Interquartile range (IQR)18.8955

Descriptive statistics

Standard deviation9.42277
Coefficient of variation (CV)0.031399484
Kurtosis-1.7280966
Mean300.09315
Median Absolute Deviation (MAD)8.783
Skewness0.081140519
Sum57317.792
Variance88.788595
MonotonicityNot monotonic
2024-01-10T05:42:47.443788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
287.313 3
 
1.6%
308.979 2
 
1.0%
309.13 2
 
1.0%
309.062 1
 
0.5%
308.823 1
 
0.5%
308.875 1
 
0.5%
308.879 1
 
0.5%
308.893 1
 
0.5%
308.949 1
 
0.5%
308.927 1
 
0.5%
Other values (177) 177
92.7%
ValueCountFrequency (%)
287.175 1
 
0.5%
287.242 1
 
0.5%
287.313 3
1.6%
287.463 1
 
0.5%
287.555 1
 
0.5%
287.651 1
 
0.5%
287.744 1
 
0.5%
287.772 1
 
0.5%
287.807 1
 
0.5%
288.031 1
 
0.5%
ValueCountFrequency (%)
313.51 1
0.5%
313.497 1
0.5%
313.464 1
0.5%
312.683 1
0.5%
312.665 1
0.5%
312.578 1
0.5%
312.551 1
0.5%
312.504 1
0.5%
312.452 1
0.5%
312.4 1
0.5%

대지
Real number (ℝ)

HIGH CORRELATION 

Distinct187
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.754408
Minimum14.189
Maximum22.211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:47.566244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.189
5-th percentile14.412
Q115.687
median17.278
Q319.3825
95-th percentile21.9395
Maximum22.211
Range8.022
Interquartile range (IQR)3.6955

Descriptive statistics

Standard deviation2.4375308
Coefficient of variation (CV)0.13729158
Kurtosis-1.1057528
Mean17.754408
Median Absolute Deviation (MAD)1.757
Skewness0.38016356
Sum3391.092
Variance5.9415564
MonotonicityNot monotonic
2024-01-10T05:42:47.702654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.096 3
 
1.6%
16.142 2
 
1.0%
16.062 2
 
1.0%
16.116 1
 
0.5%
16.292 1
 
0.5%
16.268 1
 
0.5%
16.243 1
 
0.5%
16.221 1
 
0.5%
16.198 1
 
0.5%
16.176 1
 
0.5%
Other values (177) 177
92.7%
ValueCountFrequency (%)
14.189 1
0.5%
14.203 1
0.5%
14.216 1
0.5%
14.224 1
0.5%
14.256 1
0.5%
14.286 1
0.5%
14.325 1
0.5%
14.341 1
0.5%
14.372 1
0.5%
14.401 1
0.5%
ValueCountFrequency (%)
22.211 1
 
0.5%
22.177 1
 
0.5%
22.096 3
1.6%
22.031 1
 
0.5%
21.998 1
 
0.5%
21.977 1
 
0.5%
21.97 1
 
0.5%
21.95 1
 
0.5%
21.929 1
 
0.5%
21.899 1
 
0.5%

공장(용지)
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.242832
Minimum9.271
Maximum18.993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:47.839389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.271
5-th percentile10.95
Q111.48
median13.847
Q317.1175
95-th percentile18.9755
Maximum18.993
Range9.722
Interquartile range (IQR)5.6375

Descriptive statistics

Standard deviation3.0282245
Coefficient of variation (CV)0.21261392
Kurtosis-1.6838272
Mean14.242832
Median Absolute Deviation (MAD)2.868
Skewness0.2093214
Sum2720.381
Variance9.1701435
MonotonicityNot monotonic
2024-01-10T05:42:47.968254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.261 5
 
2.6%
17.754 4
 
2.1%
11.653 4
 
2.1%
17.093 4
 
2.1%
18.989 4
 
2.1%
15.482 3
 
1.6%
11.378 3
 
1.6%
11.64 3
 
1.6%
10.953 3
 
1.6%
11.272 3
 
1.6%
Other values (127) 155
81.2%
ValueCountFrequency (%)
9.271 1
 
0.5%
9.272 2
1.0%
10.845 1
 
0.5%
10.85 1
 
0.5%
10.854 1
 
0.5%
10.871 1
 
0.5%
10.914 1
 
0.5%
10.942 1
 
0.5%
10.947 1
 
0.5%
10.953 3
1.6%
ValueCountFrequency (%)
18.993 2
1.0%
18.989 4
2.1%
18.984 1
 
0.5%
18.982 2
1.0%
18.979 1
 
0.5%
18.972 2
1.0%
18.376 2
1.0%
18.363 1
 
0.5%
17.902 1
 
0.5%
17.898 1
 
0.5%

공원
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0447906
Minimum0.057
Maximum2.142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:48.099183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.057
5-th percentile0.395
Q10.418
median1.034
Q31.669
95-th percentile2.1275
Maximum2.142
Range2.085
Interquartile range (IQR)1.251

Descriptive statistics

Standard deviation0.65786541
Coefficient of variation (CV)0.62966246
Kurtosis-1.6834052
Mean1.0447906
Median Absolute Deviation (MAD)0.623
Skewness0.23485206
Sum199.555
Variance0.43278689
MonotonicityNot monotonic
2024-01-10T05:42:48.224988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.418 22
 
11.5%
1.778 18
 
9.4%
1.034 12
 
6.3%
1.669 10
 
5.2%
0.46 10
 
5.2%
0.395 9
 
4.7%
1.816 9
 
4.7%
1.659 9
 
4.7%
2.142 7
 
3.7%
0.459 7
 
3.7%
Other values (29) 78
40.8%
ValueCountFrequency (%)
0.057 3
 
1.6%
0.395 9
4.7%
0.402 6
 
3.1%
0.404 5
 
2.6%
0.408 2
 
1.0%
0.41 1
 
0.5%
0.411 5
 
2.6%
0.418 22
11.5%
0.459 7
 
3.7%
0.46 10
5.2%
ValueCountFrequency (%)
2.142 7
 
3.7%
2.134 2
 
1.0%
2.129 1
 
0.5%
2.126 1
 
0.5%
1.936 1
 
0.5%
1.872 3
 
1.6%
1.816 9
4.7%
1.778 18
9.4%
1.693 2
 
1.0%
1.67 2
 
1.0%

체육(용지)
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73879058
Minimum0.156
Maximum1.177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:48.345325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.156
5-th percentile0.156
Q10.228
median0.982
Q31.069
95-th percentile1.1685
Maximum1.177
Range1.021
Interquartile range (IQR)0.841

Descriptive statistics

Standard deviation0.42099606
Coefficient of variation (CV)0.56984492
Kurtosis-1.6979804
Mean0.73879058
Median Absolute Deviation (MAD)0.135
Skewness-0.48725854
Sum141.109
Variance0.17723768
MonotonicityDecreasing
2024-01-10T05:42:48.734152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.196 20
 
10.5%
0.982 19
 
9.9%
0.156 15
 
7.9%
0.968 14
 
7.3%
0.229 12
 
6.3%
1.066 12
 
6.3%
1.069 10
 
5.2%
1.074 9
 
4.7%
1.165 7
 
3.7%
0.999 6
 
3.1%
Other values (25) 67
35.1%
ValueCountFrequency (%)
0.156 15
7.9%
0.16 4
 
2.1%
0.186 3
 
1.6%
0.196 20
10.5%
0.197 1
 
0.5%
0.198 1
 
0.5%
0.199 1
 
0.5%
0.205 1
 
0.5%
0.227 1
 
0.5%
0.228 2
 
1.0%
ValueCountFrequency (%)
1.177 6
3.1%
1.171 4
2.1%
1.166 1
 
0.5%
1.165 7
3.7%
1.154 5
2.6%
1.153 1
 
0.5%
1.117 3
1.6%
1.112 4
2.1%
1.079 2
 
1.0%
1.077 2
 
1.0%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct183
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.0116
Minimum125.442
Maximum139.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:48.874264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.442
5-th percentile125.8485
Q1127.002
median133.167
Q3136.8095
95-th percentile138.979
Maximum139.68
Range14.238
Interquartile range (IQR)9.8075

Descriptive statistics

Standard deviation4.8589494
Coefficient of variation (CV)0.036806989
Kurtosis-1.6147893
Mean132.0116
Median Absolute Deviation (MAD)5.074
Skewness0.045948452
Sum25214.215
Variance23.60939
MonotonicityNot monotonic
2024-01-10T05:42:49.009782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139.555 4
 
2.1%
127.077 2
 
1.0%
128.042 2
 
1.0%
128.314 2
 
1.0%
134.682 2
 
1.0%
134.621 2
 
1.0%
131.73 1
 
0.5%
131.712 1
 
0.5%
128.065 1
 
0.5%
128.093 1
 
0.5%
Other values (173) 173
90.6%
ValueCountFrequency (%)
125.442 1
0.5%
125.493 1
0.5%
125.496 1
0.5%
125.743 1
0.5%
125.744 1
0.5%
125.756 1
0.5%
125.781 1
0.5%
125.819 1
0.5%
125.829 1
0.5%
125.842 1
0.5%
ValueCountFrequency (%)
139.68 1
 
0.5%
139.555 4
2.1%
139.444 1
 
0.5%
139.301 1
 
0.5%
139.184 1
 
0.5%
138.993 1
 
0.5%
138.983 1
 
0.5%
138.975 1
 
0.5%
138.82 1
 
0.5%
138.688 1
 
0.5%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2021-10-27
191 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10-27
2nd row2021-10-27
3rd row2021-10-27
4th row2021-10-27
5th row2021-10-27

Common Values

ValueCountFrequency (%)
2021-10-27 191
100.0%

Length

2024-01-10T05:42:49.129862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:42:49.216542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-10-27 191
100.0%

Interactions

2024-01-10T05:42:44.170985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:32.663261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:33.863563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.850034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.861521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.870152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.785144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.130215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.154429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.270365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.190012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.140099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:44.276671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:32.744093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:33.950266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.931857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.958024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.952875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.863720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.219096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.245597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.354648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.290811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.220201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:44.374776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:32.820447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.038743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.026572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.042082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.033822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.975143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.312073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.367117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.448112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.399892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.303138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:44.719464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:32.899424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.125091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.101501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.127216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.105911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:38.062104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.407672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.474447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.522579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.484146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.373962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:44.794277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:32.977286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.215061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.176888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.213048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.178367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:38.138169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.495335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.583580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.594901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.558226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.456808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:44.875111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:33.302782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.297763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.252403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.310238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.257474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:38.223636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.577086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.670527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.659789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.625553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.533759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:44.967652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:33.379000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.373710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.336063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.394941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.335071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:38.313915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.688103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.753134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.736439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.697687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.626354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:45.052086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:33.459320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.450367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.421803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.472068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.406819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:38.390568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.763623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.826968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.806012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.765761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.711997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:45.145315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:33.546729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.535590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.520617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.567710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.487061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:38.477671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.850785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.913600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.892683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.843961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.811248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:45.221305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:33.626801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.613126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.597092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.641180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.553684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:38.553276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.923692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.993626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.968462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.916859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.898588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:45.298720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:33.700375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.686871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.677309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.713220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.626027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:38.927259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.997016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.074474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.036695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.984228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.982710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:45.375432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:33.780977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:34.767177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:35.764343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:36.788407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:37.702554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:39.032637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:40.076521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:41.175112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:42.111041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:43.060130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:44.073230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:42:49.296503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호년도월합계전답임야대지공장(용지)공원체육(용지)기타
번호1.0000.9850.0000.9030.9580.8680.9670.9550.9020.8730.9330.928
년도0.9851.0000.0000.9260.9360.8510.9680.9500.8950.8480.9560.906
월0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
합계0.9030.9260.0001.0000.8190.8760.8650.8990.9290.9270.8330.901
전0.9580.9360.0000.8191.0000.8180.9470.9070.8720.8660.8270.870
답0.8680.8510.0000.8760.8181.0000.8540.9620.8530.9500.8310.945
임야0.9670.9680.0000.8650.9470.8541.0000.8830.9020.8650.9980.920
대지0.9550.9500.0000.8990.9070.9620.8831.0000.8990.9500.8810.974
공장(용지)0.9020.8950.0000.9290.8720.8530.9020.8991.0000.9470.9270.889
공원0.8730.8480.0000.9270.8660.9500.8650.9500.9471.0000.8450.947
체육(용지)0.9330.9560.0000.8330.8270.8310.9980.8810.9270.8451.0000.869
기타0.9280.9060.0000.9010.8700.9450.9200.9740.8890.9470.8691.000
2024-01-10T05:42:49.432371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호년도월합계전답임야대지공장(용지)공원체육(용지)기타
번호1.0000.9980.0490.9810.518-0.863-1.0001.0001.0000.9980.9980.991
년도0.9981.000-0.0140.9770.518-0.860-0.9980.9980.9980.9970.9960.989
월0.049-0.0141.0000.0790.015-0.053-0.0480.0480.0480.0440.0430.042
합계0.9810.9770.0791.0000.501-0.835-0.9810.9810.9810.9830.9790.965
전0.5180.5180.0150.5011.000-0.222-0.5180.5180.5170.5140.5170.502
답-0.863-0.860-0.053-0.835-0.2221.0000.863-0.862-0.862-0.860-0.858-0.906
임야-1.000-0.998-0.048-0.981-0.5180.8631.000-1.000-1.000-0.998-0.998-0.991
대지1.0000.9980.0480.9810.518-0.862-1.0001.0001.0000.9980.9980.990
공장(용지)1.0000.9980.0480.9810.517-0.862-1.0001.0001.0000.9980.9980.990
공원0.9980.9970.0440.9830.514-0.860-0.9980.9980.9981.0000.9960.988
체육(용지)0.9980.9960.0430.9790.517-0.858-0.9980.9980.9980.9961.0000.988
기타0.9910.9890.0420.9650.502-0.906-0.9910.9900.9900.9880.9881.000

Missing values

2024-01-10T05:42:45.497493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:42:45.642576image/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

번호년도월합계전답임야대지공장(용지)공원체육(용지)기타기준일
0191202011742.04978.352192.319287.17522.21118.9932.1421.177139.682021-10-27
1190202010742.02378.37192.381287.31322.09618.9892.1421.177139.5552021-10-27
218920209741.97578.346192.343287.24222.17718.9932.1421.177139.5552021-10-27
318820208742.02378.37192.381287.31322.09618.9892.1421.177139.5552021-10-27
418720207742.02378.37192.381287.31322.09618.9892.1421.177139.5552021-10-27
518620206742.03178.384192.401287.46322.03118.9892.1421.177139.4442021-10-27
618520205742.03278.409192.472287.55521.99818.9842.1421.171139.3012021-10-27
718420204742.03678.433192.504287.65121.97718.9822.1341.171139.1842021-10-27
818320203742.03878.431192.613287.74421.9718.9822.1341.171138.9932021-10-27
918220202742.03778.427192.626287.77221.9518.9792.1291.171138.9832021-10-27
번호년도월합계전답임야대지공장(용지)공원체육(용지)기타기준일
18110200510740.37578.001198.214312.414.40110.9470.3950.156125.8612021-10-27
182920059740.40678.018198.229312.45214.37210.9420.3950.156125.8422021-10-27
183820058740.41278.03198.243312.50414.34110.9140.3950.156125.8292021-10-27
184720057740.40378.054198.27312.55114.32510.8710.3950.156125.7812021-10-27
185620056740.39678.081198.29312.57814.28610.8540.3950.156125.7562021-10-27
186520055740.39878.026198.306312.66514.25610.850.3950.156125.7442021-10-27
187420054740.40378.044198.321312.68314.21610.8450.3950.156125.7432021-10-27
188320053739.42378.138198.62313.46414.2249.2710.0570.156125.4932021-10-27
189220052739.43978.149198.609313.49714.2039.2720.0570.156125.4962021-10-27
190120051739.39378.15198.617313.5114.1899.2720.0570.156125.4422021-10-27