Overview

Dataset statistics

Number of variables10
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory95.1 B

Variable types

Text1
Numeric9

Dataset

Description대구광역시 용도지역 현황(2014년12월말)
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15005378&dataSetDetailId=150053782aa8e6f1824cd_201909201756&provdMethod=FILE

Alerts

00.대구(㎡) is highly overall correlated with 02.동구(㎡) and 3 other fieldsHigh correlation
01.중구(㎡) is highly overall correlated with 02.동구(㎡) and 5 other fieldsHigh correlation
02.동구(㎡) is highly overall correlated with 00.대구(㎡) and 7 other fieldsHigh correlation
03.서구(㎡) is highly overall correlated with 01.중구(㎡) and 5 other fieldsHigh correlation
04.남구(㎡) is highly overall correlated with 01.중구(㎡) and 5 other fieldsHigh correlation
05.북구(㎡) is highly overall correlated with 00.대구(㎡) and 6 other fieldsHigh correlation
06.수성구(㎡) is highly overall correlated with 01.중구(㎡) and 5 other fieldsHigh correlation
07.달서구(㎡) is highly overall correlated with 00.대구(㎡) and 6 other fieldsHigh correlation
08.달성군(㎡) is highly overall correlated with 00.대구(㎡) and 1 other fieldsHigh correlation
상세구분 has unique valuesUnique
00.대구(㎡) has 3 (14.3%) zerosZeros
01.중구(㎡) has 14 (66.7%) zerosZeros
02.동구(㎡) has 6 (28.6%) zerosZeros
03.서구(㎡) has 11 (52.4%) zerosZeros
04.남구(㎡) has 14 (66.7%) zerosZeros
05.북구(㎡) has 7 (33.3%) zerosZeros
06.수성구(㎡) has 11 (52.4%) zerosZeros
07.달서구(㎡) has 8 (38.1%) zerosZeros
08.달성군(㎡) has 4 (19.0%) zerosZeros

Reproduction

Analysis started2024-04-21 14:30:48.086334
Analysis finished2024-04-21 14:31:02.781737
Duration14.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상세구분
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size296.0 B
2024-04-21T23:31:03.293032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.4285714
Min length4

Characters and Unicode

Total characters177
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row 제1종전용주거지역
2nd row 제2종전용주거지역
3rd row 제1종일반주거지역
4th row 제2종일반주거지역
5th row 제3종일반주거지역
ValueCountFrequency (%)
제1종전용주거지역 1
 
4.8%
일반공업지역 1
 
4.8%
농림지역 1
 
4.8%
계획관리지역 1
 
4.8%
생산관리지역 1
 
4.8%
보전관리지역 1
 
4.8%
자연녹지지역 1
 
4.8%
생산녹지지역 1
 
4.8%
보전녹지지역 1
 
4.8%
준공업지역 1
 
4.8%
Other values (11) 11
52.4%
2024-04-21T23:31:04.146536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38
21.5%
지 24
13.6%
역 21
 
11.9%
업 7
 
4.0%
전 6
 
3.4%
주 6
 
3.4%
거 6
 
3.4%
제 5
 
2.8%
종 5
 
2.8%
일 5
 
2.8%
Other values (28) 54
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 134
75.7%
Space Separator 38
 
21.5%
Decimal Number 5
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 24
17.9%
역 21
15.7%
업 7
 
5.2%
전 6
 
4.5%
주 6
 
4.5%
거 6
 
4.5%
제 5
 
3.7%
종 5
 
3.7%
일 5
 
3.7%
반 5
 
3.7%
Other values (24) 44
32.8%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
3 1
20.0%
Space Separator
ValueCountFrequency (%)
38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 134
75.7%
Common 43
 
24.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 24
17.9%
역 21
15.7%
업 7
 
5.2%
전 6
 
4.5%
주 6
 
4.5%
거 6
 
4.5%
제 5
 
3.7%
종 5
 
3.7%
일 5
 
3.7%
반 5
 
3.7%
Other values (24) 44
32.8%
Common
ValueCountFrequency (%)
38
88.4%
2 2
 
4.7%
1 2
 
4.7%
3 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 134
75.7%
ASCII 43
 
24.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38
88.4%
2 2
 
4.7%
1 2
 
4.7%
3 1
 
2.3%
Hangul
ValueCountFrequency (%)
지 24
17.9%
역 21
15.7%
업 7
 
5.2%
전 6
 
4.5%
주 6
 
4.5%
거 6
 
4.5%
제 5
 
3.7%
종 5
 
3.7%
일 5
 
3.7%
반 5
 
3.7%
Other values (24) 44
32.8%

00.대구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42073339
Minimum0
Maximum5.6549775 × 108
Zeros3
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:31:04.346279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1796024
median6933022
Q333838459
95-th percentile48295620
Maximum5.6549775 × 108
Range5.6549775 × 108
Interquartile range (IQR)33042435

Descriptive statistics

Standard deviation1.2111887 × 108
Coefficient of variation (CV)2.878756
Kurtosis20.059299
Mean42073339
Median Absolute Deviation (MAD)6933022
Skewness4.4371237
Sum8.8354011 × 108
Variance1.4669782 × 1016
MonotonicityNot monotonic
2024-04-21T23:31:04.542369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3
 
14.3%
211803 1
 
4.8%
48295620 1
 
4.8%
37072072 1
 
4.8%
232241 1
 
4.8%
565497752 1
 
4.8%
15303700 1
 
4.8%
37981378 1
 
4.8%
5861173 1
 
4.8%
33838459 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
0 3
14.3%
211803 1
 
4.8%
232241 1
 
4.8%
796024 1
 
4.8%
1484081 1
 
4.8%
3679249 1
 
4.8%
5861173 1
 
4.8%
6552937 1
 
4.8%
6933022 1
 
4.8%
14858436 1
 
4.8%
ValueCountFrequency (%)
565497752 1
4.8%
48295620 1
4.8%
46266572 1
4.8%
37981378 1
4.8%
37072072 1
4.8%
33838459 1
4.8%
33090134 1
4.8%
25585457 1
4.8%
15303700 1
4.8%
14858436 1
4.8%

01.중구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean335965.14
Minimum0
Maximum2773759
Zeros14
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:31:04.729255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3347367
95-th percentile1559647
Maximum2773759
Range2773759
Interquartile range (IQR)347367

Descriptive statistics

Standard deviation710939.17
Coefficient of variation (CV)2.1161099
Kurtosis6.7128428
Mean335965.14
Median Absolute Deviation (MAD)0
Skewness2.57085
Sum7055268
Variance5.0543451 × 1011
MonotonicityNot monotonic
2024-04-21T23:31:04.907024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14
66.7%
66328 1
 
4.8%
1313859 1
 
4.8%
1559647 1
 
4.8%
437306 1
 
4.8%
2773759 1
 
4.8%
347367 1
 
4.8%
557002 1
 
4.8%
ValueCountFrequency (%)
0 14
66.7%
66328 1
 
4.8%
347367 1
 
4.8%
437306 1
 
4.8%
557002 1
 
4.8%
1313859 1
 
4.8%
1559647 1
 
4.8%
2773759 1
 
4.8%
ValueCountFrequency (%)
2773759 1
 
4.8%
1559647 1
 
4.8%
1313859 1
 
4.8%
557002 1
 
4.8%
437306 1
 
4.8%
347367 1
 
4.8%
66328 1
 
4.8%
0 14
66.7%

02.동구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8674941.9
Minimum0
Maximum1.0801834 × 108
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:31:05.112478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median612841
Q35183069
95-th percentile35295620
Maximum1.0801834 × 108
Range1.0801834 × 108
Interquartile range (IQR)5183069

Descriptive statistics

Standard deviation24097119
Coefficient of variation (CV)2.7777845
Kurtosis16.116616
Mean8674941.9
Median Absolute Deviation (MAD)612841
Skewness3.9144891
Sum1.8217378 × 108
Variance5.8067116 × 1014
MonotonicityNot monotonic
2024-04-21T23:31:05.318832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 6
28.6%
641344 1
 
4.8%
159412 1
 
4.8%
35295620 1
 
4.8%
108018343 1
 
4.8%
398141 1
 
4.8%
13024138 1
 
4.8%
240410 1
 
4.8%
571176 1
 
4.8%
612841 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
0 6
28.6%
159412 1
 
4.8%
240410 1
 
4.8%
398141 1
 
4.8%
571176 1
 
4.8%
612841 1
 
4.8%
641344 1
 
4.8%
1008529 1
 
4.8%
1929483 1
 
4.8%
4025679 1
 
4.8%
ValueCountFrequency (%)
108018343 1
4.8%
35295620 1
4.8%
13024138 1
4.8%
5602742 1
4.8%
5462852 1
4.8%
5183069 1
4.8%
4025679 1
4.8%
1929483 1
4.8%
1008529 1
4.8%
641344 1
4.8%

03.서구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean825040.43
Minimum0
Maximum5403556
Zeros11
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:31:05.709291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3609112
95-th percentile4337064
Maximum5403556
Range5403556
Interquartile range (IQR)609112

Descriptive statistics

Standard deviation1589946.5
Coefficient of variation (CV)1.9271135
Kurtosis3.4582276
Mean825040.43
Median Absolute Deviation (MAD)0
Skewness2.124778
Sum17325849
Variance2.5279299 × 1012
MonotonicityNot monotonic
2024-04-21T23:31:05.906129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
52.4%
177480 1
 
4.8%
5403556 1
 
4.8%
1542806 1
 
4.8%
494164 1
 
4.8%
2369 1
 
4.8%
683414 1
 
4.8%
609112 1
 
4.8%
3626045 1
 
4.8%
449839 1
 
4.8%
ValueCountFrequency (%)
0 11
52.4%
2369 1
 
4.8%
177480 1
 
4.8%
449839 1
 
4.8%
494164 1
 
4.8%
609112 1
 
4.8%
683414 1
 
4.8%
1542806 1
 
4.8%
3626045 1
 
4.8%
4337064 1
 
4.8%
ValueCountFrequency (%)
5403556 1
4.8%
4337064 1
4.8%
3626045 1
4.8%
1542806 1
4.8%
683414 1
4.8%
609112 1
4.8%
494164 1
4.8%
449839 1
4.8%
177480 1
4.8%
2369 1
4.8%

04.남구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean830264
Minimum0
Maximum8285713
Zeros14
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:31:06.099144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3398405
95-th percentile4889507
Maximum8285713
Range8285713
Interquartile range (IQR)398405

Descriptive statistics

Standard deviation2050548.8
Coefficient of variation (CV)2.4697552
Kurtosis9.3667323
Mean830264
Median Absolute Deviation (MAD)0
Skewness3.0410589
Sum17435544
Variance4.2047504 × 1012
MonotonicityNot monotonic
2024-04-21T23:31:06.281257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14
66.7%
2031444 1
 
4.8%
4889507 1
 
4.8%
1142114 1
 
4.8%
40900 1
 
4.8%
398405 1
 
4.8%
647461 1
 
4.8%
8285713 1
 
4.8%
ValueCountFrequency (%)
0 14
66.7%
40900 1
 
4.8%
398405 1
 
4.8%
647461 1
 
4.8%
1142114 1
 
4.8%
2031444 1
 
4.8%
4889507 1
 
4.8%
8285713 1
 
4.8%
ValueCountFrequency (%)
8285713 1
 
4.8%
4889507 1
 
4.8%
2031444 1
 
4.8%
1142114 1
 
4.8%
647461 1
 
4.8%
398405 1
 
4.8%
40900 1
 
4.8%
0 14
66.7%

05.북구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4479804.3
Minimum0
Maximum67208952
Zeros7
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:31:06.470384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median301698
Q31438603
95-th percentile7829624
Maximum67208952
Range67208952
Interquartile range (IQR)1438603

Descriptive statistics

Standard deviation14534047
Coefficient of variation (CV)3.2443486
Kurtosis19.936578
Mean4479804.3
Median Absolute Deviation (MAD)301698
Skewness4.4233976
Sum94075890
Variance2.1123852 × 1014
MonotonicityNot monotonic
2024-04-21T23:31:06.678540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 7
33.3%
132016 1
 
4.8%
3267509 1
 
4.8%
7829624 1
 
4.8%
6558895 1
 
4.8%
1438603 1
 
4.8%
812359 1
 
4.8%
1401751 1
 
4.8%
301698 1
 
4.8%
1164411 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
0 7
33.3%
49123 1
 
4.8%
103798 1
 
4.8%
132016 1
 
4.8%
301698 1
 
4.8%
812359 1
 
4.8%
1164411 1
 
4.8%
1290841 1
 
4.8%
1401751 1
 
4.8%
1438603 1
 
4.8%
ValueCountFrequency (%)
67208952 1
4.8%
7829624 1
4.8%
6558895 1
4.8%
3267509 1
4.8%
2516310 1
4.8%
1438603 1
4.8%
1401751 1
4.8%
1290841 1
4.8%
1164411 1
4.8%
812359 1
4.8%

06.수성구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3641267.1
Minimum0
Maximum55981811
Zeros11
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:31:06.874565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3739795
95-th percentile6753941
Maximum55981811
Range55981811
Interquartile range (IQR)739795

Descriptive statistics

Standard deviation12167561
Coefficient of variation (CV)3.3415733
Kurtosis19.6267
Mean3641267.1
Median Absolute Deviation (MAD)0
Skewness4.3779713
Sum76466610
Variance1.4804954 × 1014
MonotonicityNot monotonic
2024-04-21T23:31:07.060887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
52.4%
6753941 1
 
4.8%
5366770 1
 
4.8%
5408153 1
 
4.8%
739795 1
 
4.8%
449093 1
 
4.8%
779827 1
 
4.8%
587799 1
 
4.8%
114269 1
 
4.8%
285152 1
 
4.8%
ValueCountFrequency (%)
0 11
52.4%
114269 1
 
4.8%
285152 1
 
4.8%
449093 1
 
4.8%
587799 1
 
4.8%
739795 1
 
4.8%
779827 1
 
4.8%
5366770 1
 
4.8%
5408153 1
 
4.8%
6753941 1
 
4.8%
ValueCountFrequency (%)
55981811 1
4.8%
6753941 1
4.8%
5408153 1
4.8%
5366770 1
4.8%
779827 1
4.8%
739795 1
4.8%
587799 1
4.8%
449093 1
4.8%
285152 1
4.8%
114269 1
4.8%

07.달서구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2968607.3
Minimum0
Maximum27508733
Zeros8
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:31:07.249829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median109866
Q31472042
95-th percentile9899240
Maximum27508733
Range27508733
Interquartile range (IQR)1472042

Descriptive statistics

Standard deviation6368006.4
Coefficient of variation (CV)2.1451158
Kurtosis11.560207
Mean2968607.3
Median Absolute Deviation (MAD)109866
Skewness3.2091055
Sum62340753
Variance4.0551505 × 1013
MonotonicityNot monotonic
2024-04-21T23:31:07.440123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 8
38.1%
2648 1
 
4.8%
4354149 1
 
4.8%
7272120 1
 
4.8%
7971163 1
 
4.8%
1278334 1
 
4.8%
965959 1
 
4.8%
1013333 1
 
4.8%
462841 1
 
4.8%
9899240 1
 
4.8%
Other values (4) 4
19.0%
ValueCountFrequency (%)
0 8
38.1%
2648 1
 
4.8%
30325 1
 
4.8%
109866 1
 
4.8%
462841 1
 
4.8%
965959 1
 
4.8%
1013333 1
 
4.8%
1278334 1
 
4.8%
1472042 1
 
4.8%
4354149 1
 
4.8%
ValueCountFrequency (%)
27508733 1
4.8%
9899240 1
4.8%
7971163 1
4.8%
7272120 1
4.8%
4354149 1
4.8%
1472042 1
4.8%
1278334 1
4.8%
1013333 1
4.8%
965959 1
4.8%
462841 1
4.8%

08.달성군(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20317448
Minimum0
Maximum2.9360013 × 108
Zeros4
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:31:07.637740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145989
median2408041
Q313000000
95-th percentile37072072
Maximum2.9360013 × 108
Range2.9360013 × 108
Interquartile range (IQR)12954011

Descriptive statistics

Standard deviation63381792
Coefficient of variation (CV)3.1195744
Kurtosis19.843824
Mean20317448
Median Absolute Deviation (MAD)2408041
Skewness4.4087394
Sum4.2666642 × 108
Variance4.0172515 × 1015
MonotonicityNot monotonic
2024-04-21T23:31:07.847641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 4
19.0%
20016 1
 
4.8%
17225688 1
 
4.8%
13000000 1
 
4.8%
37072072 1
 
4.8%
232241 1
 
4.8%
293600134 1
 
4.8%
14406743 1
 
4.8%
24877792 1
 
4.8%
2408041 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
0 4
19.0%
20016 1
 
4.8%
45989 1
 
4.8%
110130 1
 
4.8%
211803 1
 
4.8%
232241 1
 
4.8%
1267678 1
 
4.8%
2408041 1
 
4.8%
3304614 1
 
4.8%
4908927 1
 
4.8%
ValueCountFrequency (%)
293600134 1
4.8%
37072072 1
4.8%
24877792 1
4.8%
17225688 1
4.8%
14406743 1
4.8%
13000000 1
4.8%
8728284 1
4.8%
5246265 1
4.8%
4908927 1
4.8%
3304614 1
4.8%

Interactions

2024-04-21T23:31:01.039649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:48.471649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:49.743835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:51.473640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:53.374552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:55.556917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:57.113667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:58.454242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:59.732310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:01.186773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:48.610918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:49.883260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:51.617089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:53.615635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:55.791991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:57.259537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:58.595302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:59.873269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:01.332525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:48.752151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:50.062939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:51.758456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:53.859368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:55.931604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:57.406379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:58.734967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:00.019029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:01.481663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:48.893836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:50.310152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:51.900043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:54.101200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:56.068666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:57.556545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:58.877716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:00.163984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:01.631531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:49.030433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:50.555908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:52.142827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:54.340725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:56.207209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:57.701380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:59.015620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:00.305401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:01.772062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:49.163828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:50.789891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:52.380476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:54.571041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:56.334247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:57.842279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:59.150552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:00.440705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:01.930531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:49.314409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:51.044845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:52.637727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:54.820991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:56.485306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:57.999778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:59.300251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:00.605660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:02.077970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:49.454415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:51.184972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:52.881570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:55.064553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:56.620509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:58.148490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:59.440237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:00.746645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:02.222562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:49.595005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:51.328059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:53.122997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:55.305587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:56.966786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:58.299572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:30:59.581234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:31:00.890220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T23:31:08.007838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상세구분00.대구(㎡)01.중구(㎡)02.동구(㎡)03.서구(㎡)04.남구(㎡)05.북구(㎡)06.수성구(㎡)07.달서구(㎡)08.달성군(㎡)
상세구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
00.대구(㎡)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
01.중구(㎡)1.0001.0001.0000.5370.9660.8881.0000.8850.7250.885
02.동구(㎡)1.0001.0000.5371.0000.5370.5010.6280.6280.5050.628
03.서구(㎡)1.0001.0000.9660.5371.0000.8881.0000.8850.8880.885
04.남구(㎡)1.0001.0000.8880.5010.8881.0001.0001.0000.9860.655
05.북구(㎡)1.0001.0001.0000.6281.0001.0001.0000.9270.8010.927
06.수성구(㎡)1.0001.0000.8850.6280.8851.0000.9271.0001.0000.927
07.달서구(㎡)1.0001.0000.7250.5050.8880.9860.8011.0001.0000.657
08.달성군(㎡)1.0001.0000.8850.6280.8850.6550.9270.9270.6571.000
2024-04-21T23:31:08.237714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
00.대구(㎡)01.중구(㎡)02.동구(㎡)03.서구(㎡)04.남구(㎡)05.북구(㎡)06.수성구(㎡)07.달서구(㎡)08.달성군(㎡)
00.대구(㎡)1.0000.3930.7770.4860.4370.5260.4240.5760.877
01.중구(㎡)0.3931.0000.5940.6790.7330.6650.7940.6430.090
02.동구(㎡)0.7770.5941.0000.5530.6010.6450.6060.6490.516
03.서구(㎡)0.4860.6790.5531.0000.7880.8820.7090.9100.341
04.남구(㎡)0.4370.7330.6010.7881.0000.7620.8970.7010.302
05.북구(㎡)0.5260.6650.6450.8820.7621.0000.8000.9290.369
06.수성구(㎡)0.4240.7940.6060.7090.8970.8001.0000.6940.234
07.달서구(㎡)0.5760.6430.6490.9100.7010.9290.6941.0000.459
08.달성군(㎡)0.8770.0900.5160.3410.3020.3690.2340.4591.000

Missing values

2024-04-21T23:31:02.431147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T23:31:02.684041image/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

상세구분00.대구(㎡)01.중구(㎡)02.동구(㎡)03.서구(㎡)04.남구(㎡)05.북구(㎡)06.수성구(㎡)07.달서구(㎡)08.달성군(㎡)
0제1종전용주거지역7960240641344001320160264820016
1제2종전용주거지역2118030000000211803
2제1종일반주거지역2558545766328402567917748020314443267509675394143541494908927
3제2종일반주거지역4626657213138595462852540355648895077829624536677072721208728284
4제3종일반주거지역3309013415596475602742154280611421146558895540815379711633304614
5준주거지역14858436437306518306949416440900143860373979512783345246265
6중심상업지역693302227737591929483236908123594490939659590
7일반상업지역655293701008529683414398405140175177982710133331267678
8근린상업지역3679249347367612841609112647461301698587799462841110130
9유통상업지역14840810159412001164411114269045989
상세구분00.대구(㎡)01.중구(㎡)02.동구(㎡)03.서구(㎡)04.남구(㎡)05.북구(㎡)06.수성구(㎡)07.달서구(㎡)08.달성군(㎡)
11일반공업지역3383845905711763626045025163100989924017225688
12준공업지역5861173024041044983901290841014720422408041
13보전녹지지역37981378013024138004912303032524877792
14생산녹지지역1530370003981410010379828515210986614406743
15자연녹지지역56549775255700210801834343370648285713672089525598181127508733293600134
16보전관리지역000000000
17생산관리지역2322410000000232241
18계획관리지역000000000
19농림지역37072072000000037072072
20자연환경보전지역482956200352956200000013000000