Overview

Dataset statistics

Number of variables10
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory90.4 B

Variable types

Categorical2
Numeric8

Dataset

Description시군별 착공별 착공통계현황입니다.(신축, 증축, 용도변경 등)
Author경상남도
URLhttps://www.data.go.kr/data/15050514/fileData.do

Alerts

계 is highly overall correlated with 주거용 and 5 other fieldsHigh correlation
주거용 is highly overall correlated with 계 and 5 other fieldsHigh correlation
상업용 is highly overall correlated with 계 and 5 other fieldsHigh correlation
농수산용 is highly overall correlated with 계 and 5 other fieldsHigh correlation
공업용 is highly overall correlated with 계 and 5 other fieldsHigh correlation
문교/사회용 is highly overall correlated with 계 and 5 other fieldsHigh correlation
기타 is highly overall correlated with 계 and 5 other fieldsHigh correlation
계 has unique valuesUnique
주거용 has unique valuesUnique
공공용 has 9 (16.7%) zerosZeros

Reproduction

Analysis started2023-12-12 20:27:33.176234
Analysis finished2023-12-12 20:27:39.585313
Duration6.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

Distinct18
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size564.0 B
창원시
 
3
진주시
 
3
통영시
 
3
사천시
 
3
김해시
 
3
Other values (13)
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 창원시
2nd row 창원시
3rd row 창원시
4th row 진주시
5th row 진주시

Common Values

ValueCountFrequency (%)
창원시 3
 
5.6%
진주시 3
 
5.6%
통영시 3
 
5.6%
사천시 3
 
5.6%
김해시 3
 
5.6%
밀양시 3
 
5.6%
거제시 3
 
5.6%
양산시 3
 
5.6%
의령군 3
 
5.6%
함안군 3
 
5.6%
Other values (8) 24
44.4%

Length

2023-12-13T05:27:39.648112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원시 3
 
5.6%
진주시 3
 
5.6%
거창군 3
 
5.6%
함양군 3
 
5.6%
산청군 3
 
5.6%
하동군 3
 
5.6%
남해군 3
 
5.6%
고성군 3
 
5.6%
창녕군 3
 
5.6%
함안군 3
 
5.6%
Other values (8) 24
44.4%

구분
Categorical

Distinct3
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size564.0 B
건수
18 
동수
18 
연면적
18 

Length

Max length5
Median length4
Mean length4.3333333
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 건수
2nd row 동수
3rd row 연면적
4th row 건수
5th row 동수

Common Values

ValueCountFrequency (%)
건수 18
33.3%
동수 18
33.3%
연면적 18
33.3%

Length

2023-12-13T05:27:39.759740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:27:39.855845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건수 18
33.3%
동수 18
33.3%
연면적 18
33.3%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144672.07
Minimum316
Maximum1812337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T05:27:39.975699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum316
5-th percentile457.5
Q1643
median1158.5
Q3123267.25
95-th percentile788755
Maximum1812337
Range1812021
Interquartile range (IQR)122624.25

Descriptive statistics

Standard deviation335574.92
Coefficient of variation (CV)2.3195556
Kurtosis12.527886
Mean144672.07
Median Absolute Deviation (MAD)675.5
Skewness3.349988
Sum7812292
Variance1.1261053 × 1011
MonotonicityNot monotonic
2023-12-13T05:27:40.139971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1859 1
 
1.9%
72936 1
 
1.9%
572 1
 
1.9%
845 1
 
1.9%
177156 1
 
1.9%
562 1
 
1.9%
832 1
 
1.9%
146842 1
 
1.9%
461 1
 
1.9%
636 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
316 1
1.9%
442 1
1.9%
451 1
1.9%
461 1
1.9%
471 1
1.9%
473 1
1.9%
524 1
1.9%
545 1
1.9%
562 1
1.9%
572 1
1.9%
ValueCountFrequency (%)
1812337 1
1.9%
1163918 1
1.9%
940309 1
1.9%
707149 1
1.9%
693038 1
1.9%
356401 1
1.9%
338853 1
1.9%
338719 1
1.9%
243209 1
1.9%
219723 1
1.9%

주거용
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54180.296
Minimum199
Maximum703016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T05:27:40.310983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum199
5-th percentile249.4
Q1332.25
median586.5
Q339476.5
95-th percentile308839.6
Maximum703016
Range702817
Interquartile range (IQR)39144.25

Descriptive statistics

Standard deviation129907.24
Coefficient of variation (CV)2.3976843
Kurtosis12.684758
Mean54180.296
Median Absolute Deviation (MAD)304
Skewness3.3812763
Sum2925736
Variance1.6875892 × 1010
MonotonicityNot monotonic
2023-12-13T05:27:40.450544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1042 1
 
1.9%
33563 1
 
1.9%
331 1
 
1.9%
379 1
 
1.9%
40674 1
 
1.9%
321 1
 
1.9%
408 1
 
1.9%
51331 1
 
1.9%
326 1
 
1.9%
457 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
199 1
1.9%
237 1
1.9%
239 1
1.9%
255 1
1.9%
266 1
1.9%
279 1
1.9%
286 1
1.9%
289 1
1.9%
290 1
1.9%
301 1
1.9%
ValueCountFrequency (%)
703016 1
1.9%
440616 1
1.9%
366635 1
1.9%
277719 1
1.9%
233450 1
1.9%
224186 1
1.9%
143840 1
1.9%
82736 1
1.9%
71106 1
1.9%
56289 1
1.9%

상업용
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27719.611
Minimum34
Maximum365528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T05:27:40.578485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile61.65
Q1110.25
median292
Q316810
95-th percentile170891.1
Maximum365528
Range365494
Interquartile range (IQR)16699.75

Descriptive statistics

Standard deviation72800.404
Coefficient of variation (CV)2.626314
Kurtosis12.474832
Mean27719.611
Median Absolute Deviation (MAD)200.5
Skewness3.4956686
Sum1496859
Variance5.2998988 × 109
MonotonicityNot monotonic
2023-12-13T05:27:40.705140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 2
 
3.7%
181 2
 
3.7%
491 1
 
1.9%
16942 1
 
1.9%
104 1
 
1.9%
144 1
 
1.9%
23460 1
 
1.9%
92 1
 
1.9%
17204 1
 
1.9%
77 1
 
1.9%
Other values (42) 42
77.8%
ValueCountFrequency (%)
34 1
1.9%
45 1
1.9%
61 1
1.9%
62 1
1.9%
77 1
1.9%
81 1
1.9%
91 1
1.9%
92 1
1.9%
95 1
1.9%
96 1
1.9%
ValueCountFrequency (%)
365528 1
1.9%
313584 1
1.9%
184246 1
1.9%
163700 1
1.9%
136983 1
1.9%
60632 1
1.9%
46075 1
1.9%
39697 1
1.9%
26840 1
1.9%
23590 1
1.9%

농수산용
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4814.0741
Minimum6
Maximum56757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T05:27:41.145657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8.65
Q119
median43.5
Q34715.5
95-th percentile22112.4
Maximum56757
Range56751
Interquartile range (IQR)4696.5

Descriptive statistics

Standard deviation10244.414
Coefficient of variation (CV)2.1280135
Kurtosis12.623181
Mean4814.0741
Median Absolute Deviation (MAD)31
Skewness3.229417
Sum259960
Variance1.0494803 × 108
MonotonicityNot monotonic
2023-12-13T05:27:41.294571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
13 3
 
5.6%
36 2
 
3.7%
19 2
 
3.7%
9 2
 
3.7%
27 2
 
3.7%
18 1
 
1.9%
4339 1
 
1.9%
15 1
 
1.9%
67 1
 
1.9%
16572 1
 
1.9%
Other values (38) 38
70.4%
ValueCountFrequency (%)
6 1
 
1.9%
7 1
 
1.9%
8 1
 
1.9%
9 2
3.7%
12 1
 
1.9%
13 3
5.6%
14 1
 
1.9%
15 1
 
1.9%
16 1
 
1.9%
18 1
 
1.9%
ValueCountFrequency (%)
56757 1
1.9%
30625 1
1.9%
30084 1
1.9%
17820 1
1.9%
16769 1
1.9%
16572 1
1.9%
14349 1
1.9%
13660 1
1.9%
11427 1
1.9%
10775 1
1.9%

공업용
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33532.685
Minimum3
Maximum513782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T05:27:41.451761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.95
Q124.25
median127.5
Q311721.5
95-th percentile177496.1
Maximum513782
Range513779
Interquartile range (IQR)11697.25

Descriptive statistics

Standard deviation87118.578
Coefficient of variation (CV)2.5980198
Kurtosis18.199925
Mean33532.685
Median Absolute Deviation (MAD)120
Skewness3.947561
Sum1810765
Variance7.5896467 × 109
MonotonicityNot monotonic
2023-12-13T05:27:41.596921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3 3
 
5.6%
15 2
 
3.7%
8 2
 
3.7%
13 2
 
3.7%
181 1
 
1.9%
2160 1
 
1.9%
366 1
 
1.9%
216721 1
 
1.9%
50 1
 
1.9%
127 1
 
1.9%
Other values (39) 39
72.2%
ValueCountFrequency (%)
3 3
5.6%
6 1
 
1.9%
7 1
 
1.9%
8 2
3.7%
13 2
3.7%
15 2
3.7%
16 1
 
1.9%
17 1
 
1.9%
24 1
 
1.9%
25 1
 
1.9%
ValueCountFrequency (%)
513782 1
1.9%
278343 1
1.9%
216721 1
1.9%
156375 1
1.9%
117685 1
1.9%
101297 1
1.9%
91524 1
1.9%
75117 1
1.9%
71973 1
1.9%
52220 1
1.9%

공공용
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449.66667
Minimum-2
Maximum8096
Zeros9
Zeros (%)16.7%
Negative1
Negative (%)1.9%
Memory size618.0 B
2023-12-13T05:27:41.740250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q11
median3
Q3101.25
95-th percentile2046.95
Maximum8096
Range8098
Interquartile range (IQR)100.25

Descriptive statistics

Standard deviation1487.2924
Coefficient of variation (CV)3.3075442
Kurtosis19.434402
Mean449.66667
Median Absolute Deviation (MAD)3
Skewness4.3702912
Sum24282
Variance2212038.6
MonotonicityNot monotonic
2023-12-13T05:27:41.875107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 9
16.7%
2 7
13.0%
3 6
 
11.1%
1 6
 
11.1%
5 3
 
5.6%
4 2
 
3.7%
230 1
 
1.9%
216 1
 
1.9%
130 1
 
1.9%
-2 1
 
1.9%
Other values (17) 17
31.5%
ValueCountFrequency (%)
-2 1
 
1.9%
0 9
16.7%
1 6
11.1%
2 7
13.0%
3 6
11.1%
4 2
 
3.7%
5 3
 
5.6%
6 1
 
1.9%
7 1
 
1.9%
8 1
 
1.9%
ValueCountFrequency (%)
8096 1
1.9%
6945 1
1.9%
2864 1
1.9%
1607 1
1.9%
1229 1
1.9%
1112 1
1.9%
547 1
1.9%
510 1
1.9%
346 1
1.9%
230 1
1.9%

문교/사회용
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10858.056
Minimum4
Maximum275437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T05:27:42.022936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.65
Q122.25
median39.5
Q34319.25
95-th percentile43150.05
Maximum275437
Range275433
Interquartile range (IQR)4297

Descriptive statistics

Standard deviation39270.438
Coefficient of variation (CV)3.61671
Kurtosis40.530132
Mean10858.056
Median Absolute Deviation (MAD)27.5
Skewness6.08937
Sum586335
Variance1.5421673 × 109
MonotonicityNot monotonic
2023-12-13T05:27:42.143920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
12 2
 
3.7%
10 2
 
3.7%
20 2
 
3.7%
50 2
 
3.7%
22 2
 
3.7%
30 2
 
3.7%
26 2
 
3.7%
33 2
 
3.7%
2961 1
 
1.9%
25 1
 
1.9%
Other values (36) 36
66.7%
ValueCountFrequency (%)
4 1
1.9%
8 1
1.9%
9 1
1.9%
10 2
3.7%
12 2
3.7%
15 1
1.9%
16 1
1.9%
17 1
1.9%
20 2
3.7%
22 2
3.7%
ValueCountFrequency (%)
275437 1
1.9%
70219 1
1.9%
59870 1
1.9%
34147 1
1.9%
26661 1
1.9%
25949 1
1.9%
23760 1
1.9%
13398 1
1.9%
13362 1
1.9%
8869 1
1.9%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13117.685
Minimum18
Maximum180207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T05:27:42.313404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile34.3
Q170
median100.5
Q310740.5
95-th percentile70932.5
Maximum180207
Range180189
Interquartile range (IQR)10670.5

Descriptive statistics

Standard deviation32880.795
Coefficient of variation (CV)2.5066004
Kurtosis14.881
Mean13117.685
Median Absolute Deviation (MAD)52.5
Skewness3.7026094
Sum708355
Variance1.0811467 × 109
MonotonicityNot monotonic
2023-12-13T05:27:42.474318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 2
 
3.7%
51 2
 
3.7%
100 2
 
3.7%
110 2
 
3.7%
73 1
 
1.9%
92 1
 
1.9%
9901 1
 
1.9%
14950 1
 
1.9%
77 1
 
1.9%
99 1
 
1.9%
Other values (40) 40
74.1%
ValueCountFrequency (%)
18 1
1.9%
23 1
1.9%
33 1
1.9%
35 1
1.9%
39 1
1.9%
40 1
1.9%
45 1
1.9%
51 2
3.7%
60 2
3.7%
61 1
1.9%
ValueCountFrequency (%)
180207 1
1.9%
128982 1
1.9%
88125 1
1.9%
61675 1
1.9%
41493 1
1.9%
36529 1
1.9%
28293 1
1.9%
24831 1
1.9%
19339 1
1.9%
14950 1
1.9%

Interactions

2023-12-13T05:27:38.646621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:33.584009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.404705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.144258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.897661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.758119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.347961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.980886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.749439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:33.724025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.490621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.236911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.992096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.835823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.427893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.065096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.862106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:33.841499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.582922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.336340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.073645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.907542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.510174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.144434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.944687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:33.952959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.682370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.424238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.378633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.989375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.590885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.223180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:39.026082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.039361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.774992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.511861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.441708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.064467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.665077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.297896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:39.107920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.120287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.854934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.598033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.508195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.128733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.737137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.370216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:39.207581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.213570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.962348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.695235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.594643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.204599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.817610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.453258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:39.283314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:34.308248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.048102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:35.785256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:36.670902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.275133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:37.899621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:27:38.542027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:27:42.590814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구분계주거용상업용농수산용공업용공공용문교/사회용기타
시군1.0000.0000.0000.0000.0000.0000.0000.0000.0600.000
구분0.0001.0000.7420.4190.3670.7820.4740.2970.1700.425
계0.0000.7421.0000.9110.9650.7550.9420.3770.9490.897
주거용0.0000.4190.9111.0000.9840.5470.9700.0000.9420.987
상업용0.0000.3670.9650.9841.0000.4180.9900.5431.0000.988
농수산용0.0000.7820.7550.5470.4181.0000.6420.7270.5590.541
공업용0.0000.4740.9420.9700.9900.6421.0000.5640.8860.972
공공용0.0000.2970.3770.0000.5430.7270.5641.0000.3640.372
문교/사회용0.0600.1700.9490.9421.0000.5590.8860.3641.0000.880
기타0.0000.4250.8970.9870.9880.5410.9720.3720.8801.000
2023-12-13T05:27:42.741882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구분
시군1.0000.000
구분0.0001.000
2023-12-13T05:27:42.839363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계주거용상업용농수산용공업용공공용문교/사회용기타시군구분
계1.0000.9660.9700.7020.9150.4430.9400.8050.0000.410
주거용0.9661.0000.9360.6530.8340.3990.8890.7870.0000.300
상업용0.9700.9361.0000.6280.9020.4750.9050.7610.0000.251
농수산용0.7020.6530.6281.0000.7360.4820.7610.8000.0000.446
공업용0.9150.8340.9020.7361.0000.4400.8430.7180.0000.344
공공용0.4430.3990.4750.4820.4401.0000.4930.4160.0000.227
문교/사회용0.9400.8890.9050.7610.8430.4931.0000.8410.0000.156
기타0.8050.7870.7610.8000.7180.4160.8411.0000.0000.300
시군0.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
구분0.4100.3000.2510.4460.3440.2270.1560.3000.0001.000

Missing values

2023-12-13T05:27:39.398596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:27:39.530181image/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

시군구분계주거용상업용농수산용공업용공공용문교/사회용기타
0창원시건수185910424911818135173
1창원시동수2346115658438353778130
2창원시연면적181233770301636552881992783431607275437180207
3진주시건수1021513267208543993
4진주시동수130757232036180450145
5진주시연면적6930382777191369831142711768512295987088125
6통영시건수5243011437752239
7통영시동수708382181148113082
8통영시연면적33871923345046075235117671851339841493
9사천시건수598237181135292284
시군구분계주거용상업용농수산용공업용공공용문교/사회용기타
44산청군연면적13481149146235901434942813462376019339
45함양군건수47129062131511674
46함양군동수664368912847128101
47함양군연면적886363018711221541323412-2737111034
48거창군건수545289812713124110
49거창군동수7703671208525130142
50거창군연면적219723827362239630625421811301336228293
51합천군건수4732666152352660
52합천군동수87733810911516519995
53합천군연면적18262235884164145675752220216886912262