Overview

Dataset statistics

Number of variables11
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory103.3 B

Variable types

Categorical1
Text1
Numeric9

Dataset

Description대구광역시 용도지역 현황(2017년12월말)
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15005378&dataSetDetailId=150053781c96acc90ee48_201909201758&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 8 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 2 other fieldsHigh correlation
구분 is highly overall correlated with 02.동구(㎡) 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 5 (23.8%) 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 started2024-04-17 12:07:54.755691
Analysis finished2024-04-17 12:08:00.836846
Duration6.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
주거지역
6 
상업지역
4 
공업지역
3 
녹지지역
3 
관리지역
3 
Other values (2)
2 

Length

Max length8
Median length4
Mean length4.1904762
Min length4

Unique

Unique2 ?
Unique (%)9.5%

Sample

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

Common Values

ValueCountFrequency (%)
주거지역 6
28.6%
상업지역 4
19.0%
공업지역 3
14.3%
녹지지역 3
14.3%
관리지역 3
14.3%
농림지역 1
 
4.8%
자연환경보전지역 1
 
4.8%

Length

2024-04-17T21:08:00.895155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T21:08:00.992212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거지역 6
28.6%
상업지역 4
19.0%
공업지역 3
14.3%
녹지지역 3
14.3%
관리지역 3
14.3%
농림지역 1
 
4.8%
자연환경보전지역 1
 
4.8%

상세구분
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-04-17T21:08:01.153205image/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%
2024-04-17T21:08:01.417233image/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%
Mean42076339
Minimum0
Maximum5.6403705 × 108
Zeros3
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T21:08:01.516657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1260819
median7653994
Q333585995
95-th percentile48295620
Maximum5.6403705 × 108
Range5.6403705 × 108
Interquartile range (IQR)33325176

Descriptive statistics

Standard deviation1.2077845 × 108
Coefficient of variation (CV)2.8704602
Kurtosis20.0607
Mean42076339
Median Absolute Deviation (MAD)7653994
Skewness4.4372522
Sum8.8360311 × 108
Variance1.4587435 × 1016
MonotonicityNot monotonic
2024-04-17T21:08:01.609354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3
 
14.3%
232551 1
 
4.8%
48295620 1
 
4.8%
37072072 1
 
4.8%
232241 1
 
4.8%
564037046 1
 
4.8%
15588900 1
 
4.8%
37981378 1
 
4.8%
7653994 1
 
4.8%
33354530 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
0 3
14.3%
232241 1
 
4.8%
232551 1
 
4.8%
260819 1
 
4.8%
1421127 1
 
4.8%
3680363 1
 
4.8%
6295258 1
 
4.8%
6883650 1
 
4.8%
7653994 1
 
4.8%
15179940 1
 
4.8%
ValueCountFrequency (%)
564037046 1
4.8%
48295620 1
4.8%
45513834 1
4.8%
37981378 1
4.8%
37072072 1
4.8%
33585995 1
4.8%
33354530 1
4.8%
26333794 1
4.8%
15588900 1
4.8%
15179940 1
4.8%

01.중구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336017.62
Minimum0
Maximum2773759
Zeros14
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T21:08:01.692296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3347367
95-th percentile1550257
Maximum2773759
Range2773759
Interquartile range (IQR)347367

Descriptive statistics

Standard deviation710800.21
Coefficient of variation (CV)2.1153659
Kurtosis6.7113497
Mean336017.62
Median Absolute Deviation (MAD)0
Skewness2.5698821
Sum7056370
Variance5.0523694 × 1011
MonotonicityNot monotonic
2024-04-17T21:08:01.770864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14
66.7%
66328 1
 
4.8%
1323249 1
 
4.8%
1550257 1
 
4.8%
437306 1
 
4.8%
2773759 1
 
4.8%
347367 1
 
4.8%
558104 1
 
4.8%
ValueCountFrequency (%)
0 14
66.7%
66328 1
 
4.8%
347367 1
 
4.8%
437306 1
 
4.8%
558104 1
 
4.8%
1323249 1
 
4.8%
1550257 1
 
4.8%
2773759 1
 
4.8%
ValueCountFrequency (%)
2773759 1
 
4.8%
1550257 1
 
4.8%
1323249 1
 
4.8%
558104 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 

Distinct17
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8674363.4
Minimum0
Maximum1.0794492 × 108
Zeros5
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T21:08:02.078208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120748
median571176
Q35218058
95-th percentile35295620
Maximum1.0794492 × 108
Range1.0794492 × 108
Interquartile range (IQR)5197310

Descriptive statistics

Standard deviation24083816
Coefficient of variation (CV)2.7764362
Kurtosis16.102849
Mean8674363.4
Median Absolute Deviation (MAD)571176
Skewness3.9122993
Sum1.8216163 × 108
Variance5.8003021 × 1014
MonotonicityNot monotonic
2024-04-17T21:08:02.165122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 5
23.8%
102290 1
 
4.8%
20748 1
 
4.8%
35295620 1
 
4.8%
107944925 1
 
4.8%
398141 1
 
4.8%
13024138 1
 
4.8%
407848 1
 
4.8%
571176 1
 
4.8%
204208 1
 
4.8%
Other values (7) 7
33.3%
ValueCountFrequency (%)
0 5
23.8%
20748 1
 
4.8%
102290 1
 
4.8%
204208 1
 
4.8%
398141 1
 
4.8%
407848 1
 
4.8%
571176 1
 
4.8%
612841 1
 
4.8%
998137 1
 
4.8%
1880111 1
 
4.8%
ValueCountFrequency (%)
107944925 1
4.8%
35295620 1
4.8%
13024138 1
4.8%
5532222 1
4.8%
5341471 1
4.8%
5218058 1
4.8%
4609698 1
4.8%
1880111 1
4.8%
998137 1
4.8%
612841 1
4.8%

03.서구(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean825157.29
Minimum0
Maximum5404557
Zeros11
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T21:08:02.250123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3683414
95-th percentile4337436
Maximum5404557
Range5404557
Interquartile range (IQR)683414

Descriptive statistics

Standard deviation1536127.6
Coefficient of variation (CV)1.8616179
Kurtosis3.9926747
Mean825157.29
Median Absolute Deviation (MAD)0
Skewness2.1695881
Sum17328303
Variance2.3596879 × 1012
MonotonicityNot monotonic
2024-04-17T21:08:02.347933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
52.4%
177480 1
 
4.8%
5404557 1
 
4.8%
1542806 1
 
4.8%
494164 1
 
4.8%
2369 1
 
4.8%
683414 1
 
4.8%
609112 1
 
4.8%
2950869 1
 
4.8%
1126096 1
 
4.8%
ValueCountFrequency (%)
0 11
52.4%
2369 1
 
4.8%
177480 1
 
4.8%
494164 1
 
4.8%
609112 1
 
4.8%
683414 1
 
4.8%
1126096 1
 
4.8%
1542806 1
 
4.8%
2950869 1
 
4.8%
4337436 1
 
4.8%
ValueCountFrequency (%)
5404557 1
4.8%
4337436 1
4.8%
2950869 1
4.8%
1542806 1
4.8%
1126096 1
4.8%
683414 1
4.8%
609112 1
4.8%
494164 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%
Mean829948.33
Minimum0
Maximum8279084
Zeros14
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T21:08:02.437079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3398405
95-th percentile4889507
Maximum8279084
Range8279084
Interquartile range (IQR)398405

Descriptive statistics

Standard deviation2049343.9
Coefficient of variation (CV)2.4692427
Kurtosis9.3573073
Mean829948.33
Median Absolute Deviation (MAD)0
Skewness3.0398456
Sum17428915
Variance4.1998103 × 1012
MonotonicityNot monotonic
2024-04-17T21:08:02.541213image/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%
8279084 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%
8279084 1
 
4.8%
ValueCountFrequency (%)
8279084 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%
Mean4479610.1
Minimum0
Maximum65330612
Zeros7
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T21:08:02.634943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median302812
Q32239967
95-th percentile7918888
Maximum65330612
Range65330612
Interquartile range (IQR)2239967

Descriptive statistics

Standard deviation14124124
Coefficient of variation (CV)3.1529807
Kurtosis19.770382
Mean4479610.1
Median Absolute Deviation (MAD)302812
Skewness4.398524
Sum94071813
Variance1.9949089 × 1014
MonotonicityNot monotonic
2024-04-17T21:08:02.724906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 7
33.3%
135865 1
 
4.8%
3434443 1
 
4.8%
7918888 1
 
4.8%
7085865 1
 
4.8%
1727570 1
 
4.8%
812359 1
 
4.8%
1224464 1
 
4.8%
302812 1
 
4.8%
1056661 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
0 7
33.3%
49123 1
 
4.8%
103798 1
 
4.8%
135865 1
 
4.8%
302812 1
 
4.8%
812359 1
 
4.8%
1056661 1
 
4.8%
1224464 1
 
4.8%
1727570 1
 
4.8%
2239967 1
 
4.8%
ValueCountFrequency (%)
65330612 1
4.8%
7918888 1
4.8%
7085865 1
4.8%
3434443 1
4.8%
2649386 1
4.8%
2239967 1
4.8%
1727570 1
4.8%
1224464 1
4.8%
1056661 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%
Mean3644601.1
Minimum0
Maximum56038203
Zeros11
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T21:08:02.809928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3740782
95-th percentile6751325
Maximum56038203
Range56038203
Interquartile range (IQR)740782

Descriptive statistics

Standard deviation12179770
Coefficient of variation (CV)3.3418664
Kurtosis19.627648
Mean3644601.1
Median Absolute Deviation (MAD)0
Skewness4.3781062
Sum76536623
Variance1.483468 × 1014
MonotonicityNot monotonic
2024-04-17T21:08:02.893540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
52.4%
6751325 1
 
4.8%
5330207 1
 
4.8%
5459966 1
 
4.8%
740782 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%
740782 1
 
4.8%
779827 1
 
4.8%
5330207 1
 
4.8%
5459966 1
 
4.8%
6751325 1
 
4.8%
ValueCountFrequency (%)
56038203 1
4.8%
6751325 1
4.8%
5459966 1
4.8%
5330207 1
4.8%
779827 1
4.8%
740782 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%
Mean2968727.9
Minimum0
Maximum27511266
Zeros8
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T21:08:02.981015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median109866
Q31472042
95-th percentile9899240
Maximum27511266
Range27511266
Interquartile range (IQR)1472042

Descriptive statistics

Standard deviation6368620.6
Coefficient of variation (CV)2.1452356
Kurtosis11.560266
Mean2968727.9
Median Absolute Deviation (MAD)109866
Skewness3.2091113
Sum62343286
Variance4.0559329 × 1013
MonotonicityNot monotonic
2024-04-17T21:08:03.066229image/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%
7278161 1
 
4.8%
7968151 1
 
4.8%
1275305 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%
1275305 1
 
4.8%
1472042 1
 
4.8%
4354149 1
 
4.8%
ValueCountFrequency (%)
27511266 1
4.8%
9899240 1
4.8%
7968151 1
4.8%
7278161 1
4.8%
4354149 1
4.8%
1472042 1
4.8%
1275305 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%
Mean20317913
Minimum0
Maximum2.9403742 × 108
Zeros4
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-17T21:08:03.171625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145989
median2408041
Q313000000
95-th percentile37072072
Maximum2.9403742 × 108
Range2.9403742 × 108
Interquartile range (IQR)12954011

Descriptive statistics

Standard deviation63482277
Coefficient of variation (CV)3.1244487
Kurtosis19.845011
Mean20317913
Median Absolute Deviation (MAD)2408041
Skewness4.4089352
Sum4.2667617 × 108
Variance4.0299995 × 1015
MonotonicityNot monotonic
2024-04-17T21:08:03.272171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 4
19.0%
20016 1
 
4.8%
17283859 1
 
4.8%
13000000 1
 
4.8%
37072072 1
 
4.8%
232241 1
 
4.8%
294037416 1
 
4.8%
14691943 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%
1197678 1
 
4.8%
2408041 1
 
4.8%
3304614 1
 
4.8%
4908927 1
 
4.8%
ValueCountFrequency (%)
294037416 1
4.8%
37072072 1
4.8%
24877792 1
4.8%
17283859 1
4.8%
14691943 1
4.8%
13000000 1
4.8%
8027794 1
4.8%
5245855 1
4.8%
4908927 1
4.8%
3304614 1
4.8%

Interactions

2024-04-17T21:07:59.972666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.017906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.574395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.184157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.785770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.580546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.161375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.763221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.352313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:08:00.053003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.077406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.644975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.244676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.850398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.641301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.226496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.834653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.431247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:08:00.122274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.139570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.712249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.315941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.916752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.704177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.291761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.906973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.495948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:08:00.201835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.198934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.776011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.391926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.199015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.766533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.358497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.971187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.560290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:08:00.282967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.261549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.841941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.454803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.258284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.827039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.433311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.032678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.623675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:08:00.346308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.319700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.902321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.516069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.322443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.887601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.497875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.096679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.686350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:08:00.415542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.383575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.975524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.581954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.387535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.952870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.561803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.160049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.753373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:08:00.491877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.442779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.044607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.654596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.451252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.015609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.626182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.222014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.830616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:08:00.562274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:55.504831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.106596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:56.718581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:57.514091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.091969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:58.694156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.287055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T21:07:59.897767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T21:08:03.346700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상세구분00.대구(㎡)01.중구(㎡)02.동구(㎡)03.서구(㎡)04.남구(㎡)05.북구(㎡)06.수성구(㎡)07.달서구(㎡)08.달성군(㎡)
구분1.0001.0000.1320.0000.7270.0000.0000.0000.0000.0000.773
상세구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
00.대구(㎡)0.1321.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
01.중구(㎡)0.0001.0001.0001.0000.5370.9330.8881.0000.8850.7250.885
02.동구(㎡)0.7271.0001.0000.5371.0000.5450.5010.6310.6280.5050.628
03.서구(㎡)0.0001.0001.0000.9330.5451.0000.8150.9940.8860.8520.886
04.남구(㎡)0.0001.0001.0000.8880.5010.8151.0001.0001.0000.9860.655
05.북구(㎡)0.0001.0001.0001.0000.6310.9941.0001.0000.9281.0000.928
06.수성구(㎡)0.0001.0001.0000.8850.6280.8861.0000.9281.0001.0000.927
07.달서구(㎡)0.0001.0001.0000.7250.5050.8520.9861.0001.0001.0000.657
08.달성군(㎡)0.7731.0001.0000.8850.6280.8860.6550.9280.9270.6571.000
2024-04-17T21:08:03.452564image/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(㎡)구분
00.대구(㎡)1.0000.3910.7970.4890.4400.5310.4160.5810.8810.000
01.중구(㎡)0.3911.0000.6040.6480.7330.6500.7940.6430.0900.000
02.동구(㎡)0.7970.6041.0000.5900.6130.6550.6240.6770.5530.535
03.서구(㎡)0.4890.6480.5901.0000.7450.8870.6640.9200.3430.000
04.남구(㎡)0.4400.7330.6130.7451.0000.7370.8970.7010.3020.000
05.북구(㎡)0.5310.6500.6550.8870.7371.0000.7700.9330.3660.000
06.수성구(㎡)0.4160.7940.6240.6640.8970.7701.0000.6940.2340.000
07.달서구(㎡)0.5810.6430.6770.9200.7010.9330.6941.0000.4590.000
08.달성군(㎡)0.8810.0900.5530.3430.3020.3660.2340.4591.0000.623
구분0.0000.0000.5350.0000.0000.0000.0000.0000.6231.000

Missing values

2024-04-17T21:08:00.662409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T21:08:00.787955image/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

구분상세구분00.대구(㎡)01.중구(㎡)02.동구(㎡)03.서구(㎡)04.남구(㎡)05.북구(㎡)06.수성구(㎡)07.달서구(㎡)08.달성군(㎡)
0주거지역제1종전용주거지역2608190102290001358650264820016
1주거지역제2종전용주거지역23255102074800000211803
2주거지역제1종일반주거지역2633379466328460969817748020314443434443675132543541494908927
3주거지역제2종일반주거지역4551383413232495341471540455748895077918888533020772781618027794
4주거지역제3종일반주거지역3358599515502575532222154280611421147085865545996679681513304614
5주거지역준주거지역15179940437306521805849416440900172757074078212753055245855
6상업지역중심상업지역688365027737591880111236908123594490939659590
7상업지역일반상업지역62952580998137683414398405122446477982710133331197678
8상업지역근린상업지역3680363347367612841609112647461302812587799462841110130
9상업지역유통상업지역14211270204208001056661114269045989
구분상세구분00.대구(㎡)01.중구(㎡)02.동구(㎡)03.서구(㎡)04.남구(㎡)05.북구(㎡)06.수성구(㎡)07.달서구(㎡)08.달성군(㎡)
11공업지역일반공업지역3335453005711762950869026493860989924017283859
12공업지역준공업지역76539940407848112609602239967014720422408041
13녹지지역보전녹지지역37981378013024138004912303032524877792
14녹지지역생산녹지지역1558890003981410010379828515210986614691943
15녹지지역자연녹지지역56403704655810410794492543374368279084653306125603820327511266294037416
16관리지역보전관리지역000000000
17관리지역생산관리지역2322410000000232241
18관리지역계획관리지역000000000
19농림지역농림지역37072072000000037072072
20자연환경보전지역자연환경보전지역482956200352956200000013000000