Overview

Dataset statistics

Number of variables12
Number of observations4519
Missing cells5060
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory472.3 KiB
Average record size in memory107.0 B

Variable types

Numeric10
Categorical2

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 전국 지가변동률 조사 통계를 조회 할 수 있는 서비스로 충남에 대한 해당기간, 해당지역의 이용상황별 지가지수 정보를 제공
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2533

Alerts

지역명 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
지역구분 레벨 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
번호 is highly overall correlated with 지역코드 and 2 other fieldsHigh correlation
지역코드 is highly overall correlated with 번호 and 3 other fieldsHigh correlation
조사일자 is highly overall correlated with 주거용_대 and 6 other fieldsHigh correlation
주거용_대 is highly overall correlated with 조사일자 and 6 other fieldsHigh correlation
상업용_대 is highly overall correlated with 조사일자 and 6 other fieldsHigh correlation
공장용지 is highly overall correlated with 조사일자 and 6 other fieldsHigh correlation
is highly overall correlated with 조사일자 and 6 other fieldsHigh correlation
is highly overall correlated with 조사일자 and 6 other fieldsHigh correlation
임야 is highly overall correlated with 조사일자 and 6 other fieldsHigh correlation
기타 is highly overall correlated with 지역코드 and 7 other fieldsHigh correlation
지역구분 레벨 is highly imbalanced (55.0%)Imbalance
공장용지 has 1004 (22.2%) missing valuesMissing
기타 has 4056 (89.8%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:20:31.482071
Analysis finished2024-01-09 22:20:40.776602
Duration9.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct4519
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2260
Minimum1
Maximum4519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:40.835798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile226.9
Q11130.5
median2260
Q33389.5
95-th percentile4293.1
Maximum4519
Range4518
Interquartile range (IQR)2259

Descriptive statistics

Standard deviation1304.6673
Coefficient of variation (CV)0.5772864
Kurtosis-1.2
Mean2260
Median Absolute Deviation (MAD)1130
Skewness0
Sum10212940
Variance1702156.7
MonotonicityStrictly increasing
2024-01-10T07:20:40.941005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3011 1
 
< 0.1%
3017 1
 
< 0.1%
3016 1
 
< 0.1%
3015 1
 
< 0.1%
3014 1
 
< 0.1%
3013 1
 
< 0.1%
3012 1
 
< 0.1%
3010 1
 
< 0.1%
3019 1
 
< 0.1%
Other values (4509) 4509
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4519 1
< 0.1%
4518 1
< 0.1%
4517 1
< 0.1%
4516 1
< 0.1%
4515 1
< 0.1%
4514 1
< 0.1%
4513 1
< 0.1%
4512 1
< 0.1%
4511 1
< 0.1%
4510 1
< 0.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44433.156
Minimum44000
Maximum44825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:41.034673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44000
5-th percentile44000
Q144150
median44250
Q344770
95-th percentile44825
Maximum44825
Range825
Interquartile range (IQR)620

Descriptive statistics

Standard deviation310.73132
Coefficient of variation (CV)0.0069932309
Kurtosis-1.8022321
Mean44433.156
Median Absolute Deviation (MAD)120
Skewness0.17940113
Sum2.0079343 × 108
Variance96553.953
MonotonicityIncreasing
2024-01-10T07:20:41.119273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
44000 282
 
6.2%
44790 282
 
6.2%
44810 282
 
6.2%
44150 282
 
6.2%
44130 282
 
6.2%
44800 282
 
6.2%
44710 282
 
6.2%
44760 282
 
6.2%
44770 282
 
6.2%
44825 275
 
6.1%
Other values (8) 1706
37.8%
ValueCountFrequency (%)
44000 282
6.2%
44130 282
6.2%
44131 169
3.7%
44133 169
3.7%
44150 282
6.2%
44180 251
5.6%
44200 251
5.6%
44210 275
6.1%
44230 247
5.5%
44250 216
4.8%
ValueCountFrequency (%)
44825 275
6.1%
44810 282
6.2%
44800 282
6.2%
44790 282
6.2%
44770 282
6.2%
44760 282
6.2%
44710 282
6.2%
44270 128
2.8%
44250 216
4.8%
44230 247
5.5%

지역명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size35.4 KiB
충남
 
282
예산군
 
282
홍성군
 
282
공주시
 
282
청양군
 
282
Other values (13)
3109 

Length

Max length3
Median length3
Mean length2.9375968
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충남 282
 
6.2%
예산군 282
 
6.2%
홍성군 282
 
6.2%
공주시 282
 
6.2%
청양군 282
 
6.2%
서천군 282
 
6.2%
부여군 282
 
6.2%
금산군 282
 
6.2%
천안시 282
 
6.2%
서산시 275
 
6.1%
Other values (8) 1706
37.8%

Length

2024-01-10T07:20:41.207438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충남 282
 
6.2%
홍성군 282
 
6.2%
공주시 282
 
6.2%
청양군 282
 
6.2%
서천군 282
 
6.2%
부여군 282
 
6.2%
금산군 282
 
6.2%
천안시 282
 
6.2%
예산군 282
 
6.2%
태안군 275
 
6.1%
Other values (8) 1706
37.8%

조사일자
Real number (ℝ)

HIGH CORRELATION 

Distinct282
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201013.1
Minimum198701
Maximum202206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:41.301735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum198701
5-th percentile199202
Q1200604
median201201
Q3201704
95-th percentile202106
Maximum202206
Range3505
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation855.5698
Coefficient of variation (CV)0.0042562888
Kurtosis0.041560833
Mean201013.1
Median Absolute Deviation (MAD)510
Skewness-0.83219355
Sum9.0837819 × 108
Variance731999.68
MonotonicityNot monotonic
2024-01-10T07:20:41.406209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201609 18
 
0.4%
201805 18
 
0.4%
201710 18
 
0.4%
202012 18
 
0.4%
201603 18
 
0.4%
201601 18
 
0.4%
201605 18
 
0.4%
201702 18
 
0.4%
201804 18
 
0.4%
202104 18
 
0.4%
Other values (272) 4339
96.0%
ValueCountFrequency (%)
198701 9
0.2%
198702 9
0.2%
198703 9
0.2%
198704 9
0.2%
198801 9
0.2%
198802 9
0.2%
198803 9
0.2%
198804 11
0.2%
198901 11
0.2%
198902 11
0.2%
ValueCountFrequency (%)
202206 18
0.4%
202205 18
0.4%
202204 18
0.4%
202203 18
0.4%
202202 18
0.4%
202201 18
0.4%
202112 18
0.4%
202111 18
0.4%
202110 18
0.4%
202109 18
0.4%

주거용_대
Real number (ℝ)

HIGH CORRELATION 

Distinct4405
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.628635
Minimum25.55172
Maximum107.935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:41.510721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.55172
5-th percentile58.988547
Q182.286432
median87.696718
Q393.893093
95-th percentile101.4041
Maximum107.935
Range82.38328
Interquartile range (IQR)11.606661

Descriptive statistics

Standard deviation13.087828
Coefficient of variation (CV)0.15284405
Kurtosis2.4967168
Mean85.628635
Median Absolute Deviation (MAD)5.7778055
Skewness-1.3973619
Sum386955.8
Variance171.29123
MonotonicityNot monotonic
2024-01-10T07:20:41.609452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 18
 
0.4%
88.729338377639 7
 
0.2%
88.7399868565978 6
 
0.1%
70.5129889048413 5
 
0.1%
71.6727358411914 4
 
0.1%
61.4783816667067 4
 
0.1%
88.7479715386983 4
 
0.1%
87.6756745601989 4
 
0.1%
71.665569284263 4
 
0.1%
61.6994197092532 3
 
0.1%
Other values (4395) 4460
98.7%
ValueCountFrequency (%)
25.5517204796568 1
< 0.1%
25.8200135446932 1
< 0.1%
26.5533019293625 1
< 0.1%
27.007873215176 1
< 0.1%
27.4210936753682 1
< 0.1%
27.7906857992708 1
< 0.1%
28.5124532036479 1
< 0.1%
28.9717899457398 1
< 0.1%
30.0901010376454 1
< 0.1%
30.1348117909355 1
< 0.1%
ValueCountFrequency (%)
107.935 1
< 0.1%
107.753 1
< 0.1%
107.569 1
< 0.1%
107.426 1
< 0.1%
107.165 1
< 0.1%
106.908 1
< 0.1%
106.902 1
< 0.1%
106.738 1
< 0.1%
106.629 1
< 0.1%
106.563 1
< 0.1%

상업용_대
Real number (ℝ)

HIGH CORRELATION 

Distinct4269
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.121295
Minimum30.507885
Maximum120.77786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:41.717076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.507885
5-th percentile69.319608
Q184.934523
median89.859141
Q396.151073
95-th percentile101.7854
Maximum120.77786
Range90.269974
Interquartile range (IQR)11.216551

Descriptive statistics

Standard deviation10.353271
Coefficient of variation (CV)0.11617056
Kurtosis3.9774543
Mean89.121295
Median Absolute Deviation (MAD)5.6683501
Skewness-1.357212
Sum402739.13
Variance107.19021
MonotonicityNot monotonic
2024-01-10T07:20:41.818910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 18
 
0.4%
88.0295669508332 11
 
0.2%
92.1096634139275 8
 
0.2%
90.5075162984971 7
 
0.2%
96.8719350158986 6
 
0.1%
98.1098749292864 5
 
0.1%
84.174539246213 5
 
0.1%
92.5744092008872 4
 
0.1%
96.8641858810281 4
 
0.1%
68.2207372070785 4
 
0.1%
Other values (4259) 4447
98.4%
ValueCountFrequency (%)
30.5078851412393 1
< 0.1%
30.8465226663071 1
< 0.1%
31.2321041996359 1
< 0.1%
32.3720760029226 1
< 0.1%
33.2493592626018 1
< 0.1%
33.9076965760014 1
< 0.1%
34.4570012605326 1
< 0.1%
35.6182022030125 1
< 0.1%
37.9881614885308 1
< 0.1%
38.5655815431564 1
< 0.1%
ValueCountFrequency (%)
120.77785952633 3
0.1%
119.558364211374 2
< 0.1%
119.248318583059 1
 
< 0.1%
116.990403789913 1
 
< 0.1%
116.864656877677 1
 
< 0.1%
115.744748701127 1
 
< 0.1%
115.594280527815 1
 
< 0.1%
115.571391613706 1
 
< 0.1%
115.513364531446 1
 
< 0.1%
115.455935678028 1
 
< 0.1%

공장용지
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2463
Distinct (%)70.1%
Missing1004
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean89.806483
Minimum28.0859
Maximum107.783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:41.921886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28.0859
5-th percentile66.554691
Q186.037832
median91.428091
Q397.233241
95-th percentile101.3294
Maximum107.783
Range79.6971
Interquartile range (IQR)11.195409

Descriptive statistics

Standard deviation10.66181
Coefficient of variation (CV)0.11871982
Kurtosis5.5515644
Mean89.806483
Median Absolute Deviation (MAD)5.6304443
Skewness-1.9195168
Sum315669.79
Variance113.67418
MonotonicityNot monotonic
2024-01-10T07:20:42.032035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.4218454579665 36
 
0.8%
93.0376827050456 33
 
0.7%
100.0 20
 
0.4%
92.7243051827891 18
 
0.4%
93.8374176799474 17
 
0.4%
93.7187151645901 16
 
0.4%
94.1327082921503 15
 
0.3%
86.4232045596325 14
 
0.3%
94.3908670499831 13
 
0.3%
91.9773869221241 11
 
0.2%
Other values (2453) 3322
73.5%
(Missing) 1004
 
22.2%
ValueCountFrequency (%)
28.085899505175 1
< 0.1%
28.3190124710679 1
< 0.1%
28.71831054691 1
< 0.1%
29.7247694137953 1
< 0.1%
29.8440683203489 1
< 0.1%
30.2746776479505 2
< 0.1%
31.31239648171 1
< 0.1%
31.6521754809323 1
< 0.1%
33.0290451143528 1
< 0.1%
33.1629591137791 1
< 0.1%
ValueCountFrequency (%)
107.783 1
< 0.1%
107.213 1
< 0.1%
106.996 1
< 0.1%
106.852 1
< 0.1%
106.424 1
< 0.1%
106.385 1
< 0.1%
106.118 1
< 0.1%
106.056 1
< 0.1%
105.737 1
< 0.1%
105.495 1
< 0.1%


Real number (ℝ)

HIGH CORRELATION 

Distinct4394
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.440014
Minimum13.278547
Maximum111.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:42.145068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.278547
5-th percentile49.173486
Q179.352957
median87.514513
Q393.658399
95-th percentile102.4087
Maximum111.3
Range98.021453
Interquartile range (IQR)14.305442

Descriptive statistics

Standard deviation16.151677
Coefficient of variation (CV)0.19357231
Kurtosis1.6729806
Mean83.440014
Median Absolute Deviation (MAD)6.8677112
Skewness-1.3505858
Sum377065.42
Variance260.87665
MonotonicityNot monotonic
2024-01-10T07:20:42.246983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 18
 
0.4%
88.9615648793763 9
 
0.2%
70.3709067742651 7
 
0.2%
88.9074040710936 5
 
0.1%
88.9606967338642 5
 
0.1%
90.7970858817651 4
 
0.1%
92.7334099929523 4
 
0.1%
87.429114606091 4
 
0.1%
91.047749416743 4
 
0.1%
88.4254847992742 4
 
0.1%
Other values (4384) 4455
98.6%
ValueCountFrequency (%)
13.2785474906512 1
< 0.1%
13.5109220717376 1
< 0.1%
14.983612577557 1
< 0.1%
16.4100524949404 1
< 0.1%
17.1320948047178 1
< 0.1%
17.9492957269028 1
< 0.1%
19.0208686817989 1
< 0.1%
19.528725875603 1
< 0.1%
20.3040162928644 1
< 0.1%
22.1086563262888 1
< 0.1%
ValueCountFrequency (%)
111.3 1
< 0.1%
110.935 1
< 0.1%
110.416 1
< 0.1%
109.987 1
< 0.1%
109.591 1
< 0.1%
109.503 1
< 0.1%
109.256 1
< 0.1%
108.96 1
< 0.1%
108.854 1
< 0.1%
108.563 1
< 0.1%


Real number (ℝ)

HIGH CORRELATION 

Distinct4420
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.273724
Minimum11.917005
Maximum108.872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:42.581919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.917005
5-th percentile51.784814
Q182.555531
median89.195441
Q394.086047
95-th percentile102.3657
Maximum108.872
Range96.954995
Interquartile range (IQR)11.530516

Descriptive statistics

Standard deviation14.953667
Coefficient of variation (CV)0.17536078
Kurtosis2.5624797
Mean85.273724
Median Absolute Deviation (MAD)5.6113006
Skewness-1.552294
Sum385351.96
Variance223.61216
MonotonicityNot monotonic
2024-01-10T07:20:42.683488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 18
 
0.4%
84.7646470197542 8
 
0.2%
84.5884235914271 4
 
0.1%
55.2802252455588 4
 
0.1%
90.8716150869714 4
 
0.1%
87.8808952539598 3
 
0.1%
71.0704918046183 3
 
0.1%
103.789 3
 
0.1%
74.2736916823436 3
 
0.1%
78.9757061839626 3
 
0.1%
Other values (4410) 4466
98.8%
ValueCountFrequency (%)
11.9170050726111 1
< 0.1%
12.3424421537033 1
< 0.1%
14.301187723496 1
< 0.1%
17.1957481187316 1
< 0.1%
18.7708786464074 1
< 0.1%
19.4090885203853 1
< 0.1%
19.7517282901437 1
< 0.1%
19.8267848576462 1
< 0.1%
20.266939481486 1
< 0.1%
20.3232565896954 1
< 0.1%
ValueCountFrequency (%)
108.872 1
< 0.1%
108.797 1
< 0.1%
108.569 1
< 0.1%
108.48 1
< 0.1%
108.436 1
< 0.1%
108.229 1
< 0.1%
108.181 1
< 0.1%
108.103 1
< 0.1%
107.847 1
< 0.1%
107.818 1
< 0.1%

임야
Real number (ℝ)

HIGH CORRELATION 

Distinct4184
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.687384
Minimum17.755861
Maximum116.82878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:42.792306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.755861
5-th percentile60.563312
Q187.271902
median91.935719
Q395.582575
95-th percentile101.3563
Maximum116.82878
Range99.072921
Interquartile range (IQR)8.3106737

Descriptive statistics

Standard deviation12.509408
Coefficient of variation (CV)0.14105059
Kurtosis4.8927048
Mean88.687384
Median Absolute Deviation (MAD)4.0442477
Skewness-2.0124946
Sum400778.29
Variance156.48528
MonotonicityNot monotonic
2024-01-10T07:20:42.902429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 18
 
0.4%
92.8317557296961 14
 
0.3%
91.0177511721656 13
 
0.3%
91.5857855558041 11
 
0.2%
71.4043721252797 8
 
0.2%
91.7543034012268 7
 
0.2%
93.8541167208269 7
 
0.2%
94.3968294618029 7
 
0.2%
91.1560785388208 7
 
0.2%
80.9431259565624 6
 
0.1%
Other values (4174) 4421
97.8%
ValueCountFrequency (%)
17.7558608614859 1
< 0.1%
18.3826427498963 1
< 0.1%
18.967210789343 1
< 0.1%
20.670466318226 1
< 0.1%
22.3468411366342 1
< 0.1%
23.2360452443685 1
< 0.1%
24.1859861621792 1
< 0.1%
24.2166063536808 1
< 0.1%
24.2936156082216 1
< 0.1%
24.5316930411822 1
< 0.1%
ValueCountFrequency (%)
116.828781895557 2
< 0.1%
116.363328581232 1
< 0.1%
110.790563249769 1
< 0.1%
106.69 1
< 0.1%
106.431 1
< 0.1%
106.386 1
< 0.1%
106.115 1
< 0.1%
106.052 1
< 0.1%
105.968 1
< 0.1%
105.939 1
< 0.1%

기타
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct170
Distinct (%)36.7%
Missing4056
Missing (%)89.8%
Infinite0
Infinite (%)0.0%
Mean88.695342
Minimum29.352252
Maximum101.132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.8 KiB
2024-01-10T07:20:43.006334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.352252
5-th percentile67.326591
Q186.639215
median92.562671
Q397.217651
95-th percentile100.99242
Maximum101.132
Range71.779748
Interquartile range (IQR)10.578436

Descriptive statistics

Standard deviation12.977905
Coefficient of variation (CV)0.14632003
Kurtosis5.0394128
Mean88.695342
Median Absolute Deviation (MAD)5.923456
Skewness-2.0684578
Sum41065.943
Variance168.42602
MonotonicityNot monotonic
2024-01-10T07:20:43.115984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.639215160945 32
 
0.7%
94.4353020632188 12
 
0.3%
90.0600578579993 12
 
0.3%
95.091651050634 12
 
0.3%
88.2533337678193 12
 
0.3%
93.2472152729556 10
 
0.2%
97.2176510316094 10
 
0.2%
94.4039114594728 10
 
0.2%
92.5626711937496 9
 
0.2%
93.3413870841934 8
 
0.2%
Other values (160) 336
 
7.4%
(Missing) 4056
89.8%
ValueCountFrequency (%)
29.352252219936 1
< 0.1%
29.9422324895567 1
< 0.1%
31.7896682341624 1
< 0.1%
35.0290354272235 1
< 0.1%
36.4827403974533 1
< 0.1%
38.077036152822 1
< 0.1%
38.5834607336545 1
< 0.1%
39.0811873771187 1
< 0.1%
39.9956871617433 1
< 0.1%
42.8193826753623 1
< 0.1%
ValueCountFrequency (%)
101.132 6
0.1%
101.086 6
0.1%
101.064 6
0.1%
100.997021764032 6
0.1%
100.951 2
 
< 0.1%
100.724 2
 
< 0.1%
100.611 6
0.1%
100.498 8
0.2%
100.275830469645 6
0.1%
100.272 8
0.2%

지역구분 레벨
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.4 KiB
1
3899 
2
 
338
0
 
282

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 3899
86.3%
2 338
 
7.5%
0 282
 
6.2%

Length

2024-01-10T07:20:43.225774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:20:43.293823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3899
86.3%
2 338
 
7.5%
0 282
 
6.2%

Interactions

2024-01-10T07:20:39.772548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:32.509233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:33.549711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.403517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.154207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.836921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.552397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.417145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.099511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.053944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.839033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:32.576345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:33.617723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.492103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.217042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.908538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.625719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.480318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.193804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.121374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.907664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:32.645337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:33.689825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.561045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.282880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.982226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.701496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.546606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.289127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.187445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.978930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:32.715962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:33.786284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.638068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.352488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.054204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.793125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.617810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.370048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.261995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:40.054196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:32.792545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:33.871620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.704899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.414490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.132964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.914736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.682600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.432374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.326102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:40.123223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:32.880841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:33.960029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.779794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.476753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.201701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.035159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.746476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.496125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.424286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:40.198791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:32.982917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.064572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.865476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.556904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.283808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.132494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.821353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.569261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.499860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:40.273574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:33.070214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.136002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.938106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.621933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.349126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.206983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.886487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.635120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.564761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:40.346808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:33.156248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.217928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.006599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.683421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.413378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.276160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.949691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.698146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.633024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:40.417518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:33.244179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:34.309847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.076960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:35.754766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:36.481507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:37.342125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.016896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:38.984086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:20:39.699319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:20:43.349076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드지역명조사일자주거용_대상업용_대공장용지임야기타지역구분 레벨
번호1.0000.9800.9760.2280.4150.5240.5310.4320.4700.4080.4690.877
지역코드0.9801.0001.0000.2490.3370.4160.5000.3820.4560.3370.0540.465
지역명0.9761.0001.0000.2280.4540.5440.5690.4840.5070.4450.4741.000
조사일자0.2280.2490.2281.0000.9150.8490.9170.8960.8980.8730.9480.224
주거용_대0.4150.3370.4540.9151.0000.9170.9790.9510.9420.9320.9790.215
상업용_대0.5240.4160.5440.8490.9171.0000.8440.8960.8810.8930.9080.190
공장용지0.5310.5000.5690.9170.9790.8441.0000.9440.9510.8500.9730.342
0.4320.3820.4840.8960.9510.8960.9441.0000.9690.9220.9020.245
0.4700.4560.5070.8980.9420.8810.9510.9691.0000.9060.9080.355
임야0.4080.3370.4450.8730.9320.8930.8500.9220.9061.0000.8940.238
기타0.4690.0540.4740.9480.9790.9080.9730.9020.9080.8941.0000.538
지역구분 레벨0.8770.4651.0000.2240.2150.1900.3420.2450.3550.2380.5381.000
2024-01-10T07:20:43.448120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명지역구분 레벨
지역명1.0000.998
지역구분 레벨0.9981.000
2024-01-10T07:20:43.519563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드조사일자주거용_대상업용_대공장용지임야기타지역명지역구분 레벨
번호1.0000.998-0.0650.0370.1580.2360.0050.0100.0640.4910.8800.812
지역코드0.9981.000-0.0670.0350.1580.2350.0030.0080.0620.5470.9990.812
조사일자-0.065-0.0671.0000.9360.7140.8960.9410.9510.8780.9850.0900.137
주거용_대0.0370.0350.9361.0000.8450.9160.9560.9460.9080.9800.1920.131
상업용_대0.1580.1580.7140.8451.0000.8930.8180.7580.7670.9790.2420.115
공장용지0.2360.2350.8960.9160.8931.0000.9320.8940.9010.9750.2620.218
0.0050.0030.9410.9560.8180.9321.0000.9560.9340.9840.2080.150
0.0100.0080.9510.9460.7580.8940.9561.0000.9060.9830.2200.228
임야0.0640.0620.8780.9080.7670.9010.9340.9061.0000.9830.1870.146
기타0.4910.5470.9850.9800.9790.9750.9840.9830.9831.0000.3010.379
지역명0.8800.9990.0900.1920.2420.2620.2080.2200.1870.3011.0000.998
지역구분 레벨0.8120.8120.1370.1310.1150.2180.1500.2280.1460.3790.9981.000

Missing values

2024-01-10T07:20:40.522640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:20:40.652540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-10T07:20:40.738004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

번호지역코드지역명조사일자주거용_대상업용_대공장용지임야기타지역구분 레벨
0144000충남20080986.44184287.58368287.26313885.49445888.43079291.31556786.6392150
1244000충남20060380.78835283.66969282.22165978.00686681.6210284.2187285.2417660
2344000충남20060982.45089484.71576883.37925680.10950983.41926286.34759386.240880
3444000충남19940163.51201179.80763265.97050250.38512655.49265767.9923667.3245720
4544000충남19950163.52458179.50466665.72634350.35990755.19899967.26757367.5332420
5644000충남19960464.41856679.97449167.14421551.4673956.04286466.68309469.1278050
6744000충남19940263.46755379.76772865.87814350.3347455.32617967.82917867.3582340
7844000충남19970365.11018880.59171867.72318752.17048356.68978867.15764870.2115780
8944000충남19970465.20134280.30158867.66223652.28004156.84851967.20465870.3800850
91044000충남20141089.04300287.71121690.14094588.21901290.00067591.96360391.8134730
번호지역코드지역명조사일자주거용_대상업용_대공장용지임야기타지역구분 레벨
4509451044825태안군20111087.63665185.8693<NA>84.96972691.3434190.890178<NA>1
4510451144825태안군20100186.07156984.879331<NA>82.46236789.50550289.725704<NA>1
4511451244825태안군20091285.87662984.764052<NA>82.25508589.29654889.506413<NA>1
4512451344825태안군20160491.595689.54405795.55412588.81897694.29231293.73432390.0600581
4513451444825태안군20151291.32904889.32440195.20596488.52831693.77823193.18012590.0600581
4514451544825태안군20160892.36193589.28705195.55412590.03079194.41826693.63918590.0600581
4515451644825태안군20190998.86331998.48113399.18604397.57017199.50546599.182359100.9970221
4516451744825태안군20161293.40538590.10356397.24693990.69940394.6111894.06529993.0813291
4517451844825태안군20180496.99245394.15267797.11954694.29837797.55002196.46377498.0471941
4518451944825태안군20180798.01453595.05986598.15230894.98898898.11169197.19035798.0471941