Overview

Dataset statistics

Number of variables15
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory135.4 B

Variable types

Categorical2
Numeric13

Dataset

Description경남도내 시군별 착공별 착공 통계 현황입니다.
Author경상남도
URLhttps://www.data.go.kr/data/15071409/fileData.do

Alerts

is highly overall correlated with 1월 and 11 other fieldsHigh correlation
1월 is highly overall correlated with and 11 other fieldsHigh correlation
2월 is highly overall correlated with and 11 other fieldsHigh correlation
3월 is highly overall correlated with and 11 other fieldsHigh correlation
4월 is highly overall correlated with and 11 other fieldsHigh correlation
5월 is highly overall correlated with and 11 other fieldsHigh correlation
6월 is highly overall correlated with and 11 other fieldsHigh correlation
7월 is highly overall correlated with and 11 other fieldsHigh correlation
8월 is highly overall correlated with and 11 other fieldsHigh correlation
9월 is highly overall correlated with and 11 other fieldsHigh correlation
10월 is highly overall correlated with and 11 other fieldsHigh correlation
11월 is highly overall correlated with and 11 other fieldsHigh correlation
12월 is highly overall correlated with and 11 other fieldsHigh correlation
has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:37:02.249646
Analysis finished2023-12-12 15:37:24.138451
Duration21.89 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

2023-12-13T00:37:24.209842image/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 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 18
33.3%
동수 18
33.3%
연면적 18
33.3%

Length

2023-12-13T00:37:24.381980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:37:24.543948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건수 18
33.3%
동수 18
33.3%
연면적 18
33.3%


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
2023-12-13T00:37:24.713558image/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
2023-12-13T00:37:24.965806image/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%

1월
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6614.3889
Minimum-500
Maximum79510
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.9%
Memory size618.0 B
2023-12-13T00:37:25.186880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-500
5-th percentile14.6
Q136.5
median59
Q33857
95-th percentile34073.2
Maximum79510
Range80010
Interquartile range (IQR)3820.5

Descriptive statistics

Standard deviation14619.685
Coefficient of variation (CV)2.2102851
Kurtosis11.591314
Mean6614.3889
Median Absolute Deviation (MAD)41.5
Skewness3.1095961
Sum357177
Variance2.1373519 × 108
MonotonicityNot monotonic
2023-12-13T00:37:25.414798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 2
 
3.7%
35 2
 
3.7%
59 2
 
3.7%
76 2
 
3.7%
83 1
 
1.9%
4042 1
 
1.9%
27920 1
 
1.9%
29 1
 
1.9%
43 1
 
1.9%
16592 1
 
1.9%
Other values (40) 40
74.1%
ValueCountFrequency (%)
-500 1
1.9%
11 1
1.9%
12 1
1.9%
16 2
3.7%
17 1
1.9%
18 1
1.9%
19 1
1.9%
26 1
1.9%
29 1
1.9%
32 1
1.9%
ValueCountFrequency (%)
79510 1
1.9%
44206 1
1.9%
36057 1
1.9%
33005 1
1.9%
27920 1
1.9%
23721 1
1.9%
20784 1
1.9%
19777 1
1.9%
18069 1
1.9%
16592 1
1.9%

2월
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7366.1667
Minimum19
Maximum106715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:25.640178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile24
Q134.5
median72
Q34322.75
95-th percentile34667.85
Maximum106715
Range106696
Interquartile range (IQR)4288.25

Descriptive statistics

Standard deviation19154.606
Coefficient of variation (CV)2.6003492
Kurtosis16.341816
Mean7366.1667
Median Absolute Deviation (MAD)45
Skewness3.8653169
Sum397773
Variance3.6689892 × 108
MonotonicityNot monotonic
2023-12-13T00:37:25.886608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
27 3
 
5.6%
24 2
 
3.7%
34 2
 
3.7%
42 2
 
3.7%
4292 1
 
1.9%
49 1
 
1.9%
18238 1
 
1.9%
52 1
 
1.9%
5870 1
 
1.9%
4333 1
 
1.9%
Other values (39) 39
72.2%
ValueCountFrequency (%)
19 1
 
1.9%
23 1
 
1.9%
24 2
3.7%
27 3
5.6%
28 1
 
1.9%
29 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
34 2
3.7%
ValueCountFrequency (%)
106715 1
1.9%
77961 1
1.9%
41981 1
1.9%
30730 1
1.9%
28499 1
1.9%
21494 1
1.9%
18238 1
1.9%
15110 1
1.9%
11937 1
1.9%
8120 1
1.9%

3월
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18421.111
Minimum26
Maximum404589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:26.073697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile29.65
Q151.25
median106.5
Q39278
95-th percentile64714.45
Maximum404589
Range404563
Interquartile range (IQR)9226.75

Descriptive statistics

Standard deviation60840.777
Coefficient of variation (CV)3.3027745
Kurtosis32.225654
Mean18421.111
Median Absolute Deviation (MAD)69
Skewness5.3983485
Sum994740
Variance3.7016001 × 109
MonotonicityNot monotonic
2023-12-13T00:37:26.264149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 2
 
3.7%
46 2
 
3.7%
59 2
 
3.7%
88 2
 
3.7%
34 1
 
1.9%
50173 1
 
1.9%
70 1
 
1.9%
11427 1
 
1.9%
52 1
 
1.9%
77 1
 
1.9%
Other values (40) 40
74.1%
ValueCountFrequency (%)
26 1
1.9%
28 1
1.9%
29 1
1.9%
30 1
1.9%
33 1
1.9%
34 1
1.9%
39 1
1.9%
40 1
1.9%
46 2
3.7%
47 2
3.7%
ValueCountFrequency (%)
404589 1
1.9%
180118 1
1.9%
91720 1
1.9%
50173 1
1.9%
44898 1
1.9%
41833 1
1.9%
35562 1
1.9%
28385 1
1.9%
23750 1
1.9%
17533 1
1.9%

4월
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16719.87
Minimum32
Maximum259410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:26.449219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile52.9
Q174.25
median118.5
Q310774.5
95-th percentile90199.15
Maximum259410
Range259378
Interquartile range (IQR)10700.25

Descriptive statistics

Standard deviation42364.942
Coefficient of variation (CV)2.5338081
Kurtosis20.547288
Mean16719.87
Median Absolute Deviation (MAD)60.5
Skewness4.1247572
Sum902873
Variance1.7947884 × 109
MonotonicityNot monotonic
2023-12-13T00:37:26.627331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 3
 
5.6%
195 1
 
1.9%
35403 1
 
1.9%
75 1
 
1.9%
97 1
 
1.9%
13998 1
 
1.9%
72 1
 
1.9%
96 1
 
1.9%
9876 1
 
1.9%
70 1
 
1.9%
Other values (42) 42
77.8%
ValueCountFrequency (%)
32 1
 
1.9%
35 1
 
1.9%
49 1
 
1.9%
55 1
 
1.9%
61 3
5.6%
65 1
 
1.9%
66 1
 
1.9%
70 1
 
1.9%
71 1
 
1.9%
72 1
 
1.9%
ValueCountFrequency (%)
259410 1
1.9%
105955 1
1.9%
98473 1
1.9%
85744 1
1.9%
68215 1
1.9%
67386 1
1.9%
40249 1
1.9%
37009 1
1.9%
35403 1
1.9%
17336 1
1.9%

5월
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12608.611
Minimum37
Maximum192123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:26.804573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile48.3
Q170.25
median124
Q39807.75
95-th percentile76498.55
Maximum192123
Range192086
Interquartile range (IQR)9737.5

Descriptive statistics

Standard deviation32043.275
Coefficient of variation (CV)2.5413802
Kurtosis19.371397
Mean12608.611
Median Absolute Deviation (MAD)69.5
Skewness4.0920465
Sum680865
Variance1.0267714 × 109
MonotonicityNot monotonic
2023-12-13T00:37:26.965460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
47 2
 
3.7%
70 2
 
3.7%
124 2
 
3.7%
52 2
 
3.7%
88 2
 
3.7%
117 2
 
3.7%
55 1
 
1.9%
30283 1
 
1.9%
60 1
 
1.9%
93 1
 
1.9%
Other values (38) 38
70.4%
ValueCountFrequency (%)
37 1
1.9%
47 2
3.7%
49 1
1.9%
52 2
3.7%
54 1
1.9%
55 1
1.9%
57 1
1.9%
60 1
1.9%
61 1
1.9%
69 1
1.9%
ValueCountFrequency (%)
192123 1
1.9%
91584 1
1.9%
90235 1
1.9%
69102 1
1.9%
31961 1
1.9%
30283 1
1.9%
26126 1
1.9%
22184 1
1.9%
18513 1
1.9%
17189 1
1.9%

6월
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11499.111
Minimum29
Maximum178869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:27.196165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile40.65
Q160
median99.5
Q39817.75
95-th percentile41089.15
Maximum178869
Range178840
Interquartile range (IQR)9757.75

Descriptive statistics

Standard deviation29817.962
Coefficient of variation (CV)2.5930667
Kurtosis20.865687
Mean11499.111
Median Absolute Deviation (MAD)51.5
Skewness4.3030995
Sum620952
Variance8.8911085 × 108
MonotonicityNot monotonic
2023-12-13T00:37:27.395788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 2
 
3.7%
109 2
 
3.7%
50 2
 
3.7%
79 2
 
3.7%
63 1
 
1.9%
25018 1
 
1.9%
18154 1
 
1.9%
38 1
 
1.9%
66 1
 
1.9%
14420 1
 
1.9%
Other values (40) 40
74.1%
ValueCountFrequency (%)
29 1
1.9%
38 1
1.9%
40 1
1.9%
41 1
1.9%
43 1
1.9%
45 1
1.9%
46 1
1.9%
50 2
3.7%
53 2
3.7%
55 1
1.9%
ValueCountFrequency (%)
178869 1
1.9%
114065 1
1.9%
51196 1
1.9%
35647 1
1.9%
35376 1
1.9%
34014 1
1.9%
26846 1
1.9%
25018 1
1.9%
21084 1
1.9%
18154 1
1.9%

7월
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13122.13
Minimum25
Maximum237599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:27.564955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile37.65
Q163
median98
Q38119
95-th percentile66954.8
Maximum237599
Range237574
Interquartile range (IQR)8056

Descriptive statistics

Standard deviation36505.159
Coefficient of variation (CV)2.7819538
Kurtosis27.483683
Mean13122.13
Median Absolute Deviation (MAD)54
Skewness4.8049308
Sum708595
Variance1.3326266 × 109
MonotonicityNot monotonic
2023-12-13T00:37:27.754452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
98 2
 
3.7%
63 2
 
3.7%
60 2
 
3.7%
74 2
 
3.7%
49 2
 
3.7%
46 2
 
3.7%
178 1
 
1.9%
37 1
 
1.9%
66861 1
 
1.9%
59 1
 
1.9%
Other values (38) 38
70.4%
ValueCountFrequency (%)
25 1
1.9%
27 1
1.9%
37 1
1.9%
38 1
1.9%
40 1
1.9%
42 1
1.9%
46 2
3.7%
49 2
3.7%
59 1
1.9%
60 2
3.7%
ValueCountFrequency (%)
237599 1
1.9%
72978 1
1.9%
67129 1
1.9%
66861 1
1.9%
57608 1
1.9%
52825 1
1.9%
41280 1
1.9%
20679 1
1.9%
17745 1
1.9%
10894 1
1.9%

8월
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12489.981
Minimum28
Maximum173776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:28.230838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile31.65
Q144.75
median91
Q310019.25
95-th percentile49418.35
Maximum173776
Range173748
Interquartile range (IQR)9974.5

Descriptive statistics

Standard deviation34093.369
Coefficient of variation (CV)2.7296573
Kurtosis17.205219
Mean12489.981
Median Absolute Deviation (MAD)57
Skewness4.0672734
Sum674459
Variance1.1623578 × 109
MonotonicityNot monotonic
2023-12-13T00:37:28.427285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
35 3
 
5.6%
41 2
 
3.7%
80 2
 
3.7%
64 2
 
3.7%
33 2
 
3.7%
58 2
 
3.7%
145 1
 
1.9%
18771 1
 
1.9%
19473 1
 
1.9%
10588 1
 
1.9%
Other values (37) 37
68.5%
ValueCountFrequency (%)
28 1
 
1.9%
29 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 2
3.7%
35 3
5.6%
38 1
 
1.9%
41 2
3.7%
43 1
 
1.9%
44 1
 
1.9%
ValueCountFrequency (%)
173776 1
1.9%
170210 1
1.9%
58779 1
1.9%
44378 1
1.9%
43575 1
1.9%
36766 1
1.9%
23697 1
1.9%
19473 1
1.9%
18771 1
1.9%
14653 1
1.9%

9월
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11179.315
Minimum17
Maximum205620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:28.625793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile28
Q145.25
median92
Q35807.75
95-th percentile65610.35
Maximum205620
Range205603
Interquartile range (IQR)5762.5

Descriptive statistics

Standard deviation32595.645
Coefficient of variation (CV)2.9157105
Kurtosis24.724418
Mean11179.315
Median Absolute Deviation (MAD)61
Skewness4.6271219
Sum603683
Variance1.0624761 × 109
MonotonicityNot monotonic
2023-12-13T00:37:28.816058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
29 2
 
3.7%
31 2
 
3.7%
46 2
 
3.7%
28 2
 
3.7%
32 2
 
3.7%
50 2
 
3.7%
92 2
 
3.7%
11895 1
 
1.9%
38 1
 
1.9%
7010 1
 
1.9%
Other values (37) 37
68.5%
ValueCountFrequency (%)
17 1
1.9%
26 1
1.9%
28 2
3.7%
29 2
3.7%
31 2
3.7%
32 2
3.7%
37 1
1.9%
38 1
1.9%
39 1
1.9%
45 1
1.9%
ValueCountFrequency (%)
205620 1
1.9%
91830 1
1.9%
65728 1
1.9%
65547 1
1.9%
39525 1
1.9%
28485 1
1.9%
25327 1
1.9%
16200 1
1.9%
11895 1
1.9%
7810 1
1.9%

10월
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10464.87
Minimum29
Maximum111212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:29.014865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile37.6
Q156.25
median105.5
Q39715
95-th percentile59610.45
Maximum111212
Range111183
Interquartile range (IQR)9658.75

Descriptive statistics

Standard deviation22130.898
Coefficient of variation (CV)2.1147799
Kurtosis8.5631053
Mean10464.87
Median Absolute Deviation (MAD)65.5
Skewness2.810475
Sum565103
Variance4.8977663 × 108
MonotonicityNot monotonic
2023-12-13T00:37:29.199342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 2
 
3.7%
50 2
 
3.7%
46 2
 
3.7%
57 2
 
3.7%
172 1
 
1.9%
51 1
 
1.9%
99 1
 
1.9%
20728 1
 
1.9%
84 1
 
1.9%
11542 1
 
1.9%
Other values (40) 40
74.1%
ValueCountFrequency (%)
29 1
1.9%
30 1
1.9%
35 1
1.9%
39 1
1.9%
41 1
1.9%
43 1
1.9%
44 1
1.9%
46 2
3.7%
47 1
1.9%
50 2
3.7%
ValueCountFrequency (%)
111212 1
1.9%
72321 1
1.9%
61474 1
1.9%
58607 1
1.9%
49164 1
1.9%
38702 1
1.9%
28961 1
1.9%
25328 1
1.9%
20728 1
1.9%
17835 1
1.9%

11월
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11802.407
Minimum31
Maximum134907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:29.381023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile36.95
Q161.25
median97
Q36640
95-th percentile76593.45
Maximum134907
Range134876
Interquartile range (IQR)6578.75

Descriptive statistics

Standard deviation28548.295
Coefficient of variation (CV)2.4188535
Kurtosis9.546751
Mean11802.407
Median Absolute Deviation (MAD)56.5
Skewness3.1151873
Sum637330
Variance8.1500515 × 108
MonotonicityNot monotonic
2023-12-13T00:37:29.567267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 3
 
5.6%
167 1
 
1.9%
58 1
 
1.9%
45 1
 
1.9%
6340 1
 
1.9%
51 1
 
1.9%
62 1
 
1.9%
6740 1
 
1.9%
50 1
 
1.9%
66 1
 
1.9%
Other values (42) 42
77.8%
ValueCountFrequency (%)
31 1
1.9%
34 1
1.9%
35 1
1.9%
38 1
1.9%
43 1
1.9%
44 1
1.9%
45 1
1.9%
47 1
1.9%
48 1
1.9%
50 1
1.9%
ValueCountFrequency (%)
134907 1
1.9%
112652 1
1.9%
98243 1
1.9%
64936 1
1.9%
49895 1
1.9%
32410 1
1.9%
27373 1
1.9%
26447 1
1.9%
19003 1
1.9%
15298 1
1.9%

12월
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12384.111
Minimum16
Maximum161227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:37:29.724472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22.3
Q147.5
median95
Q38756.25
95-th percentile68038.5
Maximum161227
Range161211
Interquartile range (IQR)8708.75

Descriptive statistics

Standard deviation29893.662
Coefficient of variation (CV)2.4138722
Kurtosis12.637028
Mean12384.111
Median Absolute Deviation (MAD)60.5
Skewness3.3748067
Sum668742
Variance8.9363103 × 108
MonotonicityNot monotonic
2023-12-13T00:37:29.886497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 2
 
3.7%
153 1
 
1.9%
14231 1
 
1.9%
36 1
 
1.9%
47 1
 
1.9%
19688 1
 
1.9%
56 1
 
1.9%
86 1
 
1.9%
9918 1
 
1.9%
16 1
 
1.9%
Other values (43) 43
79.6%
ValueCountFrequency (%)
16 1
1.9%
20 1
1.9%
21 1
1.9%
23 1
1.9%
26 1
1.9%
34 1
1.9%
36 1
1.9%
38 1
1.9%
39 1
1.9%
40 1
1.9%
ValueCountFrequency (%)
161227 1
1.9%
100249 1
1.9%
91055 1
1.9%
55645 1
1.9%
53620 1
1.9%
43575 1
1.9%
41989 1
1.9%
19688 1
1.9%
19452 1
1.9%
16378 1
1.9%

Interactions

2023-12-13T00:37:21.653598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:02.902759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.450060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.767781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.915527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:08.852349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:10.466829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:12.205058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:13.898050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.701169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.980487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:18.387110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:20.032682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:22.083430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:03.040173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.569025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.869766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:07.029610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:08.992355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:10.607594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:12.350567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:14.012264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.810479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:17.079332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:18.517188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:20.170786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:22.212709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:03.187912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.678520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.952834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:07.434051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:09.131137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:10.759161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:12.483013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:14.127621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.913264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:17.188561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:18.643836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:20.284374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:22.312736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:03.315461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.781245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.029134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:07.541069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:09.271051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:10.876053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:12.609570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:14.243635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.993376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:17.273113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:18.750097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:20.416505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:22.445436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:03.459319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.901993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.108598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:07.652120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:09.401658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:11.035706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:12.742448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:14.450159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.080755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:17.405834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:18.876143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:20.564264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:22.576507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:03.597094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.993682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.191517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:07.751411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:09.508275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:11.158951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:12.866301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:14.921725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.173401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:17.510739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:18.968304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:20.697690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:22.727645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:03.744629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.097080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.288433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:07.853384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:09.630452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:11.302692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:12.989806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.018465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.271741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:17.642261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:19.098045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:20.833220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:22.877329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:03.883078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.186125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.378828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:07.955227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:09.766375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:11.457739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:13.124722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.105072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.379545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:17.786033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:19.218339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:20.958587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:22.996342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:03.968602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.269944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.452738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:08.118770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:09.871362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:11.591441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:13.260709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.198204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.462873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:17.883745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:19.327433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:21.065572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:23.138792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.068878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.365998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.552745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:08.265574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:09.987512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:11.730955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:13.395592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.298740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.578233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:17.998747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:19.533383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:21.187770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:23.264861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.148987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.453774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.629208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:08.405183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:10.081466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:11.847552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:13.510594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.388391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.677140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:18.086523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:19.648886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:21.302117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:23.403409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.231304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.555221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.707000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:08.547591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:10.181732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:11.968212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:13.632366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.475594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.769971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:18.189388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:19.761472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:21.406351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:23.557149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:04.327631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:05.666243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:06.805228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:08.691848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:10.300009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:12.075989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:13.771486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:15.599972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:16.864890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:18.281114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:19.895697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:37:21.545642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:37:30.000172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구분1월2월3월4월5월6월7월8월9월10월11월12월
시군1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
구분0.0001.0000.7420.5800.6540.2970.6540.3980.4740.3520.4380.3520.5960.4660.474
0.0000.7421.0000.8780.9790.9350.9670.9240.9550.9570.8190.8920.9650.9790.931
1월0.0000.5800.8781.0000.8910.8460.8200.8340.9010.8640.9220.8460.9600.9050.973
2월0.0000.6540.9790.8911.0000.8550.9520.9040.8870.9040.7800.9230.9330.9410.884
3월0.0000.2970.9350.8460.8551.0000.7720.9850.9930.9950.9270.9531.0000.9290.957
4월0.0000.6540.9670.8200.9520.7721.0000.8540.8250.8150.9400.8240.8890.9130.883
5월0.0000.3980.9240.8340.9040.9850.8541.0000.9920.9930.9310.9900.9310.9740.915
6월0.0000.4740.9550.9010.8870.9930.8250.9921.0000.9980.9530.9760.9540.9760.965
7월0.0000.3520.9570.8640.9040.9950.8150.9930.9981.0000.9460.9770.9780.9630.926
8월0.0000.4380.8190.9220.7800.9270.9400.9310.9530.9461.0000.9180.8960.8800.892
9월0.0000.3520.8920.8460.9230.9530.8240.9900.9760.9770.9181.0000.9130.9700.908
10월0.0000.5960.9650.9600.9331.0000.8890.9310.9540.9780.8960.9131.0000.9810.933
11월0.0000.4660.9790.9050.9410.9290.9130.9740.9760.9630.8800.9700.9811.0000.921
12월0.0000.4740.9310.9730.8840.9570.8830.9150.9650.9260.8920.9080.9330.9211.000
2023-12-13T00:37:30.128556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시군
구분1.0000.000
시군0.0001.000
2023-12-13T00:37:30.239237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월시군구분
1.0000.8770.9750.9540.9720.9750.9850.9760.9720.9710.9750.9600.9620.0000.410
1월0.8771.0000.8610.8050.8300.8340.8600.8600.8470.8540.8510.8580.8660.0000.423
2월0.9750.8611.0000.9180.9530.9700.9680.9430.9470.9450.9560.9520.9260.0000.331
3월0.9540.8050.9181.0000.9240.9600.9440.9230.9320.9400.9610.9150.8880.0000.227
4월0.9720.8300.9530.9241.0000.9430.9700.9390.9430.9250.9530.9400.9250.0000.331
5월0.9750.8340.9700.9600.9431.0000.9730.9400.9470.9610.9610.9330.9190.0000.320
6월0.9850.8600.9680.9440.9700.9731.0000.9480.9610.9480.9620.9500.9270.0000.397
7월0.9760.8600.9430.9230.9390.9400.9481.0000.9600.9470.9580.9330.9590.0000.276
8월0.9720.8470.9470.9320.9430.9470.9610.9601.0000.9480.9560.9450.9420.0000.360
9월0.9710.8540.9450.9400.9250.9610.9480.9470.9481.0000.9460.9280.9560.0000.276
10월0.9750.8510.9560.9610.9530.9610.9620.9580.9560.9461.0000.9340.9220.0000.438
11월0.9600.8580.9520.9150.9400.9330.9500.9330.9450.9280.9341.0000.9170.0000.315
12월0.9620.8660.9260.8880.9250.9190.9270.9590.9420.9560.9220.9171.0000.0000.344
시군0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
구분0.4100.4230.3310.2270.3310.3200.3970.2760.3600.2760.4380.3150.3440.0001.000

Missing values

2023-12-13T00:37:23.782335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:37:24.060774image/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

시군구분1월2월3월4월5월6월7월8월9월10월11월12월
0창원시건수185983115177195191146178145137172167153
1창원시동수2346101151246222227203226178185210216181
2창원시연면적18123377951010671540458985744691021788692375991737762056207232198243100249
3진주시건수102154589911011788817489988073
4진주시동수1307737711516714310998971171239296
5진주시연면적693038-50030730418332594103196134014671295877939525491642737353620
6통영시건수524353129495441634328466540
7통영시동수708443939806169936432567160
8통영시연면적33871933005812096381059559944268462067943575372672092644743575
9사천시건수598394251667750423347466144
시군구분1월2월3월4월5월6월7월8월9월10월11월12월
44산청군연면적134811597715110141631046418513842586598313736917835706512918
45함양군건수471182928615243464139353445
46함양군동수664364240748059685850434470
47함양군연면적88636100383740685680901268166288214531678103842525010171
48거창군건수545112784718846403532473826
49거창군동수77017301208610781636046685438
50거창군연면적21972318365260448981733617189144771774510801456525328464355645
51합천군건수473352330614755493531304334
52합천군동수87758374610469648244465073204
53합천군연면적1826222078446121753312299653075271034411194658615006649365271