Overview

Dataset statistics

Number of variables10
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory91.7 B

Variable types

Categorical2
Numeric8

Dataset

Description시군별 착공별 착공통계현황입니다.
Author경상남도
URLhttps://www.data.go.kr/data/15071411/fileData.do

Alerts

is highly overall correlated with 주거용 and 7 other fieldsHigh correlation
주거용 is highly overall correlated with and 7 other fieldsHigh correlation
상업용 is highly overall correlated with and 7 other fieldsHigh correlation
농수산용 is highly overall correlated with and 7 other fieldsHigh correlation
공업용 is highly overall correlated with and 7 other fieldsHigh correlation
공공용 is highly overall correlated with and 6 other fieldsHigh correlation
문교_사회용 is highly overall correlated with and 7 other fieldsHigh correlation
기타 is highly overall correlated with and 7 other fieldsHigh correlation
구분 is highly overall correlated with and 6 other fieldsHigh correlation
공업용 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:09:50.460993
Analysis finished2023-12-12 03:09:58.192415
Duration7.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

월별
Categorical

Distinct12
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
1월
2월
3월
4월
5월
Other values (7)
21 

Length

Max length3
Median length2
Mean length2.25
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1월
2nd row1월
3rd row1월
4th row2월
5th row2월

Common Values

ValueCountFrequency (%)
1월 3
8.3%
2월 3
8.3%
3월 3
8.3%
4월 3
8.3%
5월 3
8.3%
6월 3
8.3%
7월 3
8.3%
8월 3
8.3%
9월 3
8.3%
10월 3
8.3%
Other values (2) 6
16.7%

Length

2023-12-12T12:09:58.270291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1월 3
8.3%
2월 3
8.3%
3월 3
8.3%
4월 3
8.3%
5월 3
8.3%
6월 3
8.3%
7월 3
8.3%
8월 3
8.3%
9월 3
8.3%
10월 3
8.3%
Other values (2) 6
16.7%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
건수
12 
동수
12 
연면적
12 

Length

Max length3
Median length2
Mean length2.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건수
2nd row동수
3rd row연면적
4th row건수
5th row동수

Common Values

ValueCountFrequency (%)
건수 12
33.3%
동수 12
33.3%
연면적 12
33.3%

Length

2023-12-12T12:09:58.440594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:58.571522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건수 12
33.3%
동수 12
33.3%
연면적 12
33.3%


Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217008.11
Minimum749
Maximum991830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T12:09:58.720043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum749
5-th percentile1013.25
Q11229.75
median1675
Q3571749
95-th percentile754034.75
Maximum991830
Range991081
Interquartile range (IQR)570519.25

Descriptive statistics

Standard deviation324816.35
Coefficient of variation (CV)1.4967936
Kurtosis-0.51005853
Mean217008.11
Median Absolute Deviation (MAD)545
Skewness1.0452039
Sum7812292
Variance1.0550566 × 1011
MonotonicityNot monotonic
2023-12-12T12:09:59.249348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1675 2
 
5.6%
749 1
 
2.8%
705686 1
 
2.8%
1525 1
 
2.8%
671844 1
 
2.8%
1065 1
 
2.8%
1415 1
 
2.8%
601203 1
 
2.8%
1354 1
 
2.8%
1818 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
749 1
2.8%
858 1
2.8%
1065 1
2.8%
1090 1
2.8%
1104 1
2.8%
1110 1
2.8%
1172 1
2.8%
1214 1
2.8%
1217 1
2.8%
1234 1
2.8%
ValueCountFrequency (%)
991830 1
2.8%
899081 1
2.8%
705686 1
2.8%
677168 1
2.8%
671844 1
2.8%
665975 1
2.8%
634335 1
2.8%
618063 1
2.8%
601203 1
2.8%
561931 1
2.8%

주거용
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81270.444
Minimum329
Maximum490556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T12:09:59.418273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum329
5-th percentile434.75
Q1672.5
median823.5
Q3156508.5
95-th percentile368220
Maximum490556
Range490227
Interquartile range (IQR)155836

Descriptive statistics

Standard deviation136618.58
Coefficient of variation (CV)1.6810364
Kurtosis2.1865594
Mean81270.444
Median Absolute Deviation (MAD)260.5
Skewness1.7162405
Sum2925736
Variance1.8664635 × 1010
MonotonicityNot monotonic
2023-12-12T12:09:59.576444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
671 2
 
5.6%
699 2
 
5.6%
329 1
 
2.8%
242388 1
 
2.8%
162090 1
 
2.8%
568 1
 
2.8%
154648 1
 
2.8%
586 1
 
2.8%
711 1
 
2.8%
746 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
329 1
2.8%
422 1
2.8%
439 1
2.8%
536 1
2.8%
568 1
2.8%
570 1
2.8%
586 1
2.8%
671 2
5.6%
673 1
2.8%
674 1
2.8%
ValueCountFrequency (%)
490556 1
2.8%
452334 1
2.8%
340182 1
2.8%
298915 1
2.8%
242388 1
2.8%
214153 1
2.8%
202813 1
2.8%
167319 1
2.8%
162090 1
2.8%
154648 1
2.8%

상업용
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41579.417
Minimum207
Maximum169358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T12:09:59.724103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum207
5-th percentile236.5
Q1279.25
median339
Q3106027.5
95-th percentile139402
Maximum169358
Range169151
Interquartile range (IQR)105748.25

Descriptive statistics

Standard deviation60352.809
Coefficient of variation (CV)1.4515069
Kurtosis-1.0944908
Mean41579.417
Median Absolute Deviation (MAD)85
Skewness0.87082311
Sum1496859
Variance3.6424616 × 109
MonotonicityNot monotonic
2023-12-12T12:09:59.868324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
245 2
 
5.6%
211 1
 
2.8%
352 1
 
2.8%
326 1
 
2.8%
169358 1
 
2.8%
252 1
 
2.8%
321 1
 
2.8%
130921 1
 
2.8%
307 1
 
2.8%
372 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
207 1
2.8%
211 1
2.8%
245 2
5.6%
249 1
2.8%
252 1
2.8%
256 1
2.8%
268 1
2.8%
277 1
2.8%
280 1
2.8%
288 1
2.8%
ValueCountFrequency (%)
169358 1
2.8%
148369 1
2.8%
136413 1
2.8%
134718 1
2.8%
130921 1
2.8%
124421 1
2.8%
117577 1
2.8%
113131 1
2.8%
107406 1
2.8%
105568 1
2.8%

농수산용
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7221.1111
Minimum17
Maximum39926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T12:10:00.065257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile20.75
Q138
median70.5
Q319345.75
95-th percentile26751.75
Maximum39926
Range39909
Interquartile range (IQR)19307.75

Descriptive statistics

Standard deviation11448.428
Coefficient of variation (CV)1.5854108
Kurtosis0.87256131
Mean7221.1111
Median Absolute Deviation (MAD)44
Skewness1.3839597
Sum259960
Variance1.310665 × 108
MonotonicityNot monotonic
2023-12-12T12:10:00.271817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
17 2
 
5.6%
28 2
 
5.6%
39 2
 
5.6%
22 1
 
2.8%
8913 1
 
2.8%
44 1
 
2.8%
121 1
 
2.8%
39926 1
 
2.8%
35 1
 
2.8%
72 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
17 2
5.6%
22 1
2.8%
24 1
2.8%
25 1
2.8%
28 2
5.6%
30 1
2.8%
35 1
2.8%
39 2
5.6%
42 1
2.8%
43 1
2.8%
ValueCountFrequency (%)
39926 1
2.8%
34983 1
2.8%
24008 1
2.8%
22015 1
2.8%
21592 1
2.8%
20448 1
2.8%
20049 1
2.8%
19923 1
2.8%
19786 1
2.8%
19199 1
2.8%

공업용
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50299.028
Minimum89
Maximum230376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T12:10:00.477849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile91.75
Q1116
median227.5
Q3117731.25
95-th percentile187452.5
Maximum230376
Range230287
Interquartile range (IQR)117615.25

Descriptive statistics

Standard deviation75522.164
Coefficient of variation (CV)1.5014637
Kurtosis-0.41727744
Mean50299.028
Median Absolute Deviation (MAD)122.5
Skewness1.0634164
Sum1810765
Variance5.7035972 × 109
MonotonicityNot monotonic
2023-12-12T12:10:00.620563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
89 1
 
2.8%
216 1
 
2.8%
117 1
 
2.8%
228 1
 
2.8%
152877 1
 
2.8%
92 1
 
2.8%
177 1
 
2.8%
160663 1
 
2.8%
113 1
 
2.8%
277 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
89 1
2.8%
91 1
2.8%
92 1
2.8%
101 1
2.8%
103 1
2.8%
104 1
2.8%
106 1
2.8%
112 1
2.8%
113 1
2.8%
117 1
2.8%
ValueCountFrequency (%)
230376 1
2.8%
211499 1
2.8%
179437 1
2.8%
165316 1
2.8%
160663 1
2.8%
152877 1
2.8%
133811 1
2.8%
131524 1
2.8%
119712 1
2.8%
117071 1
2.8%

공공용
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean674.5
Minimum1
Maximum8345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T12:10:00.769251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5.5
Q3166.75
95-th percentile4104.5
Maximum8345
Range8344
Interquartile range (IQR)163.75

Descriptive statistics

Standard deviation1792.7859
Coefficient of variation (CV)2.657948
Kurtosis11.035423
Mean674.5
Median Absolute Deviation (MAD)4.5
Skewness3.2933844
Sum24282
Variance3214081.3
MonotonicityNot monotonic
2023-12-12T12:10:00.916342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4 5
 
13.9%
1 4
 
11.1%
3 4
 
11.1%
5 3
 
8.3%
2 2
 
5.6%
7 1
 
2.8%
148 1
 
2.8%
536 1
 
2.8%
669 1
 
2.8%
3470 1
 
2.8%
Other values (13) 13
36.1%
ValueCountFrequency (%)
1 4
11.1%
2 2
 
5.6%
3 4
11.1%
4 5
13.9%
5 3
8.3%
6 1
 
2.8%
7 1
 
2.8%
10 1
 
2.8%
12 1
 
2.8%
13 1
 
2.8%
ValueCountFrequency (%)
8345 1
2.8%
6008 1
2.8%
3470 1
2.8%
2809 1
2.8%
1524 1
2.8%
669 1
2.8%
536 1
2.8%
239 1
2.8%
223 1
2.8%
148 1
2.8%

문교_사회용
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16287.083
Minimum23
Maximum168189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T12:10:01.074592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile27.25
Q137
median56.5
Q318758.25
95-th percentile76978.25
Maximum168189
Range168166
Interquartile range (IQR)18721.25

Descriptive statistics

Standard deviation35686.009
Coefficient of variation (CV)2.191062
Kurtosis10.788417
Mean16287.083
Median Absolute Deviation (MAD)25.5
Skewness3.1726965
Sum586335
Variance1.2734912 × 109
MonotonicityNot monotonic
2023-12-12T12:10:01.238221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
29 2
 
5.6%
59 2
 
5.6%
44 2
 
5.6%
37 2
 
5.6%
41 2
 
5.6%
30188 1
 
2.8%
25 1
 
2.8%
24078 1
 
2.8%
33 1
 
2.8%
73 1
 
2.8%
Other values (21) 21
58.3%
ValueCountFrequency (%)
23 1
2.8%
25 1
2.8%
28 1
2.8%
29 2
5.6%
33 1
2.8%
35 1
2.8%
36 1
2.8%
37 2
5.6%
41 2
5.6%
42 1
2.8%
ValueCountFrequency (%)
168189 1
2.8%
123800 1
2.8%
61371 1
2.8%
41118 1
2.8%
35498 1
2.8%
30188 1
2.8%
29427 1
2.8%
28308 1
2.8%
24078 1
2.8%
16985 1
2.8%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19676.528
Minimum65
Maximum172843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T12:10:01.385114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile83
Q1110
median151.5
Q324897.5
95-th percentile86251.25
Maximum172843
Range172778
Interquartile range (IQR)24787.5

Descriptive statistics

Standard deviation38266.83
Coefficient of variation (CV)1.9447959
Kurtosis7.3051418
Mean19676.528
Median Absolute Deviation (MAD)55.5
Skewness2.5823994
Sum708355
Variance1.4643503 × 109
MonotonicityNot monotonic
2023-12-12T12:10:01.543893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
147 2
 
5.6%
65 1
 
2.8%
30770 1
 
2.8%
22940 1
 
2.8%
86 1
 
2.8%
131 1
 
2.8%
31012 1
 
2.8%
90 1
 
2.8%
123 1
 
2.8%
122 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
65 1
2.8%
74 1
2.8%
86 1
2.8%
90 1
2.8%
94 1
2.8%
95 1
2.8%
100 1
2.8%
103 1
2.8%
107 1
2.8%
111 1
2.8%
ValueCountFrequency (%)
172843 1
2.8%
118511 1
2.8%
75498 1
2.8%
67422 1
2.8%
63514 1
2.8%
50032 1
2.8%
38047 1
2.8%
31012 1
2.8%
30770 1
2.8%
22940 1
2.8%

Interactions

2023-12-12T12:09:56.987932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:50.851124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:51.742518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:52.977921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.814560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.584045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.278294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:56.037353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:57.095272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:50.945795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:51.854929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.121534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.899325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.687857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.361277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:56.163737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:57.180172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:51.063175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:51.949547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.244773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.988355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.768242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.449178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:56.265195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:57.282523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:51.189446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:52.062970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.346382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.078579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.859102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.542458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:56.411533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:57.397160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:51.320966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:52.189710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.457789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.176898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.951830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.652835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:56.558773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:57.497517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:51.419795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:52.299148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.546297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.263770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.023867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.730390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:56.659048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:57.590678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:51.514179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:52.729948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.633554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.352027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.100551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.814034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:56.762257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:57.694730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:51.637357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:52.848805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:53.725263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:54.460541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.189534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:55.919295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:56.891545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:10:01.682105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월별구분주거용상업용농수산용공업용공공용문교_사회용기타
월별1.0000.0000.0000.0000.0000.0000.0000.2370.0000.000
구분0.0001.0000.9130.9130.9130.7230.9140.3630.8430.667
0.0000.9131.0000.9850.9260.8100.9110.8900.9520.874
주거용0.0000.9130.9851.0000.9220.8770.9020.8880.9150.821
상업용0.0000.9130.9260.9221.0000.9000.9450.8370.9610.802
농수산용0.0000.7230.8100.8770.9001.0000.8480.8400.8240.876
공업용0.0000.9140.9110.9020.9450.8481.0000.8900.9320.811
공공용0.2370.3630.8900.8880.8370.8400.8901.0000.9610.794
문교_사회용0.0000.8430.9520.9150.9610.8240.9320.9611.0000.791
기타0.0000.6670.8740.8210.8020.8760.8110.7940.7911.000
2023-12-12T12:10:01.812199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월별구분
월별1.0000.000
구분0.0001.000
2023-12-12T12:10:01.909795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주거용상업용농수산용공업용공공용문교_사회용기타월별구분
1.0000.9600.9370.9130.9190.8060.8850.9790.0000.615
주거용0.9601.0000.9080.8370.8310.8370.8030.9560.0000.615
상업용0.9370.9081.0000.8940.8900.7630.8560.9340.0000.615
농수산용0.9130.8370.8941.0000.9100.7070.8660.9150.0000.564
공업용0.9190.8310.8900.9101.0000.7700.9070.9100.0000.615
공공용0.8060.8370.7630.7070.7701.0000.7950.8320.0000.140
문교_사회용0.8850.8030.8560.8660.9070.7951.0000.8850.0000.506
기타0.9790.9560.9340.9150.9100.8320.8851.0000.0000.534
월별0.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
구분0.6150.6150.6150.5640.6150.1400.5060.5340.0001.000

Missing values

2023-12-12T12:09:57.923292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:09:58.127992image/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

월별구분주거용상업용농수산용공업용공공용문교_사회용기타
01월건수749329211228942965
11월동수111042226843221449103
21월연면적3553189372610740621592972472391698518123
32월건수858439207179112974
42월동수117253624930197159100
52월연면적395743893511364137922117071482830816630
63월건수12176732804210312395
73월동수169382337088212137162
83월연면적9918304905561015933498317943711612302172843
94월건수159294431846104442134
월별구분주거용상업용농수산용공업용공공용문교_사회용기타
269월연면적601203242388130921891316066334702407830770
2710월건수135474630728113533122
2810월동수181888137272277544167
2910월연면적561931167319134718220151315246693018875498
3011월건수128169928824125335107
3111월동수171481436061255473147
3211월연면적634335202813148369191992114995361387238047
3312월건수11045702563910633694
3412월동수1663699324792273184147
3512월연면적665975340182113131197861071981483549850032