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_20211231
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15005378&dataSetDetailId=150053781961bfc6589de&provdMethod=FILE

Alerts

대구(m2) is highly overall correlated with 동구(m2) and 3 other fieldsHigh correlation
중구(m2) is highly overall correlated with 동구(m2) and 5 other fieldsHigh correlation
동구(m2) is highly overall correlated with 대구(m2) and 8 other fieldsHigh correlation
서구(m2) is highly overall correlated with 중구(m2) and 5 other fieldsHigh correlation
남구(m2) is highly overall correlated with 중구(m2) and 5 other fieldsHigh correlation
북구(m2) is highly overall correlated with 대구(m2) and 6 other fieldsHigh correlation
수성구(m2) is highly overall correlated with 중구(m2) and 5 other fieldsHigh correlation
달서구(m2) is highly overall correlated with 대구(m2) and 6 other fieldsHigh correlation
달성군(m2) is highly overall correlated with 대구(m2) and 2 other fieldsHigh correlation
구분 is highly overall correlated with 동구(m2) and 1 other fieldsHigh correlation
상세구분 has unique valuesUnique
대구(m2) has 4 (19.0%) zerosZeros
중구(m2) has 14 (66.7%) zerosZeros
동구(m2) has 5 (23.8%) zerosZeros
서구(m2) has 11 (52.4%) zerosZeros
남구(m2) has 14 (66.7%) zerosZeros
북구(m2) has 6 (28.6%) zerosZeros
수성구(m2) has 11 (52.4%) zerosZeros
달서구(m2) has 8 (38.1%) zerosZeros
달성군(m2) has 5 (23.8%) zerosZeros

Reproduction

Analysis started2024-04-19 05:44:15.297188
Analysis finished2024-04-19 05:44:25.450955
Duration10.15 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

2024-04-19T14:44:25.536077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:44:25.667978image/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
2024-04-19T14:44:25.875204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.6190476
Min length4

Characters and Unicode

Total characters139
Distinct characters37
Distinct categories2 ?
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 row제1종전용주거지역
2nd row제2종전용주거지역
3rd row제1종일반주거지역
4th row제2종일반주거지역
5th row제3종일반주거지역
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%
2024-04-19T14:44:26.200657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 24
17.3%
역 21
15.1%
업 7
 
5.0%
전 6
 
4.3%
주 6
 
4.3%
거 6
 
4.3%
제 5
 
3.6%
반 5
 
3.6%
일 5
 
3.6%
종 5
 
3.6%
Other values (27) 49
35.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 134
96.4%
Decimal Number 5
 
3.6%

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%
반 5
 
3.7%
일 5
 
3.7%
종 5
 
3.7%
Other values (24) 44
32.8%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
2 2
40.0%
3 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 134
96.4%
Common 5
 
3.6%

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%
반 5
 
3.7%
일 5
 
3.7%
종 5
 
3.7%
Other values (24) 44
32.8%
Common
ValueCountFrequency (%)
1 2
40.0%
2 2
40.0%
3 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 134
96.4%
ASCII 5
 
3.6%

Most frequent character per block

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%
반 5
 
3.7%
일 5
 
3.7%
종 5
 
3.7%
Other values (24) 44
32.8%
ASCII
ValueCountFrequency (%)
1 2
40.0%
2 2
40.0%
3 1
20.0%

대구(m2)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42080868
Minimum0
Maximum5.642178 × 108
Zeros4
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:44:26.322654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1289865
median7639287
Q333573534
95-th percentile48295620
Maximum5.642178 × 108
Range5.642178 × 108
Interquartile range (IQR)33283669

Descriptive statistics

Standard deviation1.2081321 × 108
Coefficient of variation (CV)2.8709772
Kurtosis20.065378
Mean42080868
Median Absolute Deviation (MAD)7639287
Skewness4.4379666
Sum8.8369822 × 108
Variance1.4595832 × 1016
MonotonicityNot monotonic
2024-04-19T14:44:26.445598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 4
19.0%
289865 1
 
4.8%
48295620 1
 
4.8%
35892842 1
 
4.8%
564217797 1
 
4.8%
16140761 1
 
4.8%
37945258 1
 
4.8%
7639287 1
 
4.8%
33573534 1
 
4.8%
1438051 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
0 4
19.0%
276532 1
 
4.8%
289865 1
 
4.8%
1438051 1
 
4.8%
3680363 1
 
4.8%
6454312 1
 
4.8%
6883650 1
 
4.8%
7639287 1
 
4.8%
15326937 1
 
4.8%
16140761 1
 
4.8%
ValueCountFrequency (%)
564217797 1
4.8%
48295620 1
4.8%
45817598 1
4.8%
37945258 1
4.8%
35892842 1
4.8%
33573534 1
4.8%
33552608 1
4.8%
26273204 1
4.8%
16140761 1
4.8%
15326937 1
4.8%

중구(m2)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336011.48
Minimum0
Maximum2773759
Zeros14
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:44:26.587914image/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 deviation710798.2
Coefficient of variation (CV)2.1153986
Kurtosis6.7115727
Mean336011.48
Median Absolute Deviation (MAD)0
Skewness2.5699297
Sum7056241
Variance5.0523408 × 1011
MonotonicityNot monotonic
2024-04-19T14:44:26.708375image/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%
557975 1
 
4.8%
ValueCountFrequency (%)
0 14
66.7%
66328 1
 
4.8%
347367 1
 
4.8%
437306 1
 
4.8%
557975 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%
557975 1
 
4.8%
437306 1
 
4.8%
347367 1
 
4.8%
66328 1
 
4.8%
0 14
66.7%

동구(m2)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8673822.9
Minimum0
Maximum1.079421 × 108
Zeros5
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:44:26.866292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120748
median571176
Q35222607
95-th percentile35295620
Maximum1.079421 × 108
Range1.079421 × 108
Interquartile range (IQR)5201859

Descriptive statistics

Standard deviation24082786
Coefficient of variation (CV)2.7764904
Kurtosis16.104413
Mean8673822.9
Median Absolute Deviation (MAD)571176
Skewness3.9125432
Sum1.8215028 × 108
Variance5.7998058 × 1014
MonotonicityNot monotonic
2024-04-19T14:44:26.995901image/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%
107942102 1
 
4.8%
398141 1
 
4.8%
12994348 1
 
4.8%
407848 1
 
4.8%
571176 1
 
4.8%
203964 1
 
4.8%
Other values (7) 7
33.3%
ValueCountFrequency (%)
0 5
23.8%
20748 1
 
4.8%
102290 1
 
4.8%
203964 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 (%)
107942102 1
4.8%
35295620 1
4.8%
12994348 1
4.8%
5531515 1
4.8%
5336087 1
4.8%
5222607 1
4.8%
4632745 1
4.8%
1880111 1
4.8%
998137 1
4.8%
612841 1
4.8%

서구(m2)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean824733.71
Minimum0
Maximum5404557
Zeros11
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:44:27.110305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3683414
95-th percentile4315272
Maximum5404557
Range5404557
Interquartile range (IQR)683414

Descriptive statistics

Standard deviation1533804.6
Coefficient of variation (CV)1.8597573
Kurtosis3.9962057
Mean824733.71
Median Absolute Deviation (MAD)0
Skewness2.1686096
Sum17319408
Variance2.3525564 × 1012
MonotonicityNot monotonic
2024-04-19T14:44:27.244858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
52.4%
177890 1
 
4.8%
5404557 1
 
4.8%
1542806 1
 
4.8%
499185 1
 
4.8%
2369 1
 
4.8%
683414 1
 
4.8%
609112 1
 
4.8%
2954063 1
 
4.8%
1130740 1
 
4.8%
ValueCountFrequency (%)
0 11
52.4%
2369 1
 
4.8%
177890 1
 
4.8%
499185 1
 
4.8%
609112 1
 
4.8%
683414 1
 
4.8%
1130740 1
 
4.8%
1542806 1
 
4.8%
2954063 1
 
4.8%
4315272 1
 
4.8%
ValueCountFrequency (%)
5404557 1
4.8%
4315272 1
4.8%
2954063 1
4.8%
1542806 1
4.8%
1130740 1
4.8%
683414 1
4.8%
609112 1
4.8%
499185 1
4.8%
177890 1
4.8%
2369 1
4.8%

남구(m2)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean830069.19
Minimum0
Maximum8280222
Zeros14
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:44:27.385751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3398405
95-th percentile4889507
Maximum8280222
Range8280222
Interquartile range (IQR)398405

Descriptive statistics

Standard deviation2049591.8
Coefficient of variation (CV)2.4691818
Kurtosis9.3574484
Mean830069.19
Median Absolute Deviation (MAD)0
Skewness3.0398024
Sum17431453
Variance4.2008264 × 1012
MonotonicityNot monotonic
2024-04-19T14:44:27.501334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14
66.7%
2032844 1
 
4.8%
4889507 1
 
4.8%
1142114 1
 
4.8%
40900 1
 
4.8%
398405 1
 
4.8%
647461 1
 
4.8%
8280222 1
 
4.8%
ValueCountFrequency (%)
0 14
66.7%
40900 1
 
4.8%
398405 1
 
4.8%
647461 1
 
4.8%
1142114 1
 
4.8%
2032844 1
 
4.8%
4889507 1
 
4.8%
8280222 1
 
4.8%
ValueCountFrequency (%)
8280222 1
 
4.8%
4889507 1
 
4.8%
2032844 1
 
4.8%
1142114 1
 
4.8%
647461 1
 
4.8%
398405 1
 
4.8%
40900 1
 
4.8%
0 14
66.7%

북구(m2)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4475600.1
Minimum0
Maximum65171801
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:44:27.612017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median302812
Q32164934
95-th percentile7985515
Maximum65171801
Range65171801
Interquartile range (IQR)2164934

Descriptive statistics

Standard deviation14088477
Coefficient of variation (CV)3.1478409
Kurtosis19.768539
Mean4475600.1
Median Absolute Deviation (MAD)302812
Skewness4.3982511
Sum93987602
Variance1.9848518 × 1014
MonotonicityNot monotonic
2024-04-19T14:44:27.767322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 6
28.6%
164911 1
 
4.8%
1073829 1
 
4.8%
65171801 1
 
4.8%
65948 1
 
4.8%
49123 1
 
4.8%
2164934 1
 
4.8%
2847541 1
 
4.8%
302812 1
 
4.8%
43981 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
0 6
28.6%
43981 1
 
4.8%
49123 1
 
4.8%
65948 1
 
4.8%
164911 1
 
4.8%
302812 1
 
4.8%
812359 1
 
4.8%
1073829 1
 
4.8%
1324338 1
 
4.8%
1633118 1
 
4.8%
ValueCountFrequency (%)
65171801 1
4.8%
7985515 1
4.8%
6980795 1
4.8%
3366597 1
4.8%
2847541 1
4.8%
2164934 1
4.8%
1633118 1
4.8%
1324338 1
4.8%
1073829 1
4.8%
812359 1
4.8%

수성구(m2)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3644548.8
Minimum0
Maximum55489330
Zeros11
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:44:27.901339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3841307
95-th percentile6791138
Maximum55489330
Range55489330
Interquartile range (IQR)841307

Descriptive statistics

Standard deviation12060664
Coefficient of variation (CV)3.3092339
Kurtosis19.562974
Mean3644548.8
Median Absolute Deviation (MAD)0
Skewness4.3685272
Sum76535524
Variance1.4545963 × 1014
MonotonicityNot monotonic
2024-04-19T14:44:28.022423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
52.4%
6791138 1
 
4.8%
5551104 1
 
4.8%
5463191 1
 
4.8%
967861 1
 
4.8%
449093 1
 
4.8%
841307 1
 
4.8%
587799 1
 
4.8%
114269 1
 
4.8%
280432 1
 
4.8%
ValueCountFrequency (%)
0 11
52.4%
114269 1
 
4.8%
280432 1
 
4.8%
449093 1
 
4.8%
587799 1
 
4.8%
841307 1
 
4.8%
967861 1
 
4.8%
5463191 1
 
4.8%
5551104 1
 
4.8%
6791138 1
 
4.8%
ValueCountFrequency (%)
55489330 1
4.8%
6791138 1
4.8%
5551104 1
4.8%
5463191 1
4.8%
967861 1
4.8%
841307 1
4.8%
587799 1
4.8%
449093 1
4.8%
280432 1
4.8%
114269 1
4.8%

달서구(m2)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2969171.7
Minimum0
Maximum27567026
Zeros8
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:44:28.143448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30746
Q31479878
95-th percentile9922144
Maximum27567026
Range27567026
Interquartile range (IQR)1479878

Descriptive statistics

Standard deviation6382549.9
Coefficient of variation (CV)2.1496062
Kurtosis11.564298
Mean2969171.7
Median Absolute Deviation (MAD)30746
Skewness3.2096442
Sum62352606
Variance4.0736944 × 1013
MonotonicityNot monotonic
2024-04-19T14:44:28.261684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 8
38.1%
2648 1
 
4.8%
4362595 1
 
4.8%
7278161 1
 
4.8%
7967975 1
 
4.8%
1275305 1
 
4.8%
965959 1
 
4.8%
1013333 1
 
4.8%
462841 1
 
4.8%
9922144 1
 
4.8%
Other values (4) 4
19.0%
ValueCountFrequency (%)
0 8
38.1%
2648 1
 
4.8%
23995 1
 
4.8%
30746 1
 
4.8%
462841 1
 
4.8%
965959 1
 
4.8%
1013333 1
 
4.8%
1275305 1
 
4.8%
1479878 1
 
4.8%
4362595 1
 
4.8%
ValueCountFrequency (%)
27567026 1
4.8%
9922144 1
4.8%
7967975 1
4.8%
7278161 1
4.8%
4362595 1
4.8%
1479878 1
4.8%
1275305 1
4.8%
1013333 1
4.8%
965959 1
4.8%
462841 1
4.8%

달성군(m2)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20326910
Minimum0
Maximum2.9489407 × 108
Zeros5
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-19T14:44:28.729271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120016
median2455887
Q313000000
95-th percentile35892842
Maximum2.9489407 × 108
Range2.9489407 × 108
Interquartile range (IQR)12979984

Descriptive statistics

Standard deviation63651879
Coefficient of variation (CV)3.1314095
Kurtosis19.884498
Mean20326910
Median Absolute Deviation (MAD)2455887
Skewness4.4144753
Sum4.268651 × 108
Variance4.0515617 × 1015
MonotonicityNot monotonic
2024-04-19T14:44:28.855928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 5
23.8%
20016 1
 
4.8%
17278610 1
 
4.8%
13000000 1
 
4.8%
35892842 1
 
4.8%
294894069 1
 
4.8%
15365494 1
 
4.8%
24877792 1
 
4.8%
2455887 1
 
4.8%
45989 1
 
4.8%
Other values (7) 7
33.3%
ValueCountFrequency (%)
0 5
23.8%
20016 1
 
4.8%
45989 1
 
4.8%
110130 1
 
4.8%
211803 1
 
4.8%
1195378 1
 
4.8%
2455887 1
 
4.8%
3373955 1
 
4.8%
4843067 1
 
4.8%
5250655 1
 
4.8%
ValueCountFrequency (%)
294894069 1
4.8%
35892842 1
4.8%
24877792 1
4.8%
17278610 1
4.8%
15365494 1
4.8%
13000000 1
4.8%
8049418 1
4.8%
5250655 1
4.8%
4843067 1
4.8%
3373955 1
4.8%

Interactions

2024-04-19T14:44:24.293284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:15.714643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:16.721573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.628733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:18.727299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:19.856304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:21.563198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.578384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.500580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.385180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:15.846476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:16.815546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.718377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:18.863064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:19.956249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:21.674072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.666882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.590182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.478315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:15.956413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:16.912264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.834958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:18.986382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:20.055631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:21.786577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.759273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.683569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.560168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:16.048396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.004552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.934312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:19.100940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:20.222058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:21.906872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.850882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.776599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.647541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:16.140166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.090990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:18.030573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:19.201072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:20.445990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.024110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.961411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.859898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.729055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:16.230551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.196883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:18.134400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:19.304435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:20.568240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.130373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.054474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.935065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.817711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:16.348253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.309952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:18.265938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:19.466143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:20.677016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.245396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.181123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.027658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.906722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:16.498310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.419011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:18.412960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:19.619968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:21.192912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.357970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.275956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.115789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:25.009505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:16.605750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:17.521030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:18.563538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:19.740004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:21.426922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:22.462742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:23.376995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:44:24.200015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T14:44:28.954406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상세구분대구(m2)중구(m2)동구(m2)서구(m2)남구(m2)북구(m2)수성구(m2)달서구(m2)달성군(m2)
구분1.0001.0000.1320.0000.7270.0000.0000.0000.0000.0000.773
상세구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대구(m2)0.1321.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중구(m2)0.0001.0001.0001.0000.5370.9330.8881.0000.9760.7250.885
동구(m2)0.7271.0001.0000.5371.0000.5450.5010.6310.6310.5050.628
서구(m2)0.0001.0001.0000.9330.5451.0000.8150.9940.9770.8520.886
남구(m2)0.0001.0001.0000.8880.5010.8151.0001.0001.0000.9860.655
북구(m2)0.0001.0001.0001.0000.6310.9941.0001.0000.9571.0000.928
수성구(m2)0.0001.0001.0000.9760.6310.9771.0000.9571.0000.8670.928
달서구(m2)0.0001.0001.0000.7250.5050.8520.9861.0000.8671.0000.657
달성군(m2)0.7731.0001.0000.8850.6280.8860.6550.9280.9280.6571.000
2024-04-19T14:44:29.103971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대구(m2)중구(m2)동구(m2)서구(m2)남구(m2)북구(m2)수성구(m2)달서구(m2)달성군(m2)구분
대구(m2)1.0000.3730.7900.4910.4250.5150.4080.5840.9060.000
중구(m2)0.3731.0000.6040.6480.7330.6460.8080.6430.1140.000
동구(m2)0.7900.6041.0000.5900.6130.6510.6270.6770.5890.535
서구(m2)0.4910.6480.5901.0000.7450.8810.6650.9200.3610.000
남구(m2)0.4250.7330.6130.7451.0000.7320.8980.7010.3180.000
북구(m2)0.5150.6460.6510.8810.7321.0000.7680.9260.4030.000
수성구(m2)0.4080.8080.6270.6650.8980.7681.0000.6940.2710.000
달서구(m2)0.5840.6430.6770.9200.7010.9260.6941.0000.4830.000
달성군(m2)0.9060.1140.5890.3610.3180.4030.2710.4831.0000.623
구분0.0000.0000.5350.0000.0000.0000.0000.0000.6231.000

Missing values

2024-04-19T14:44:25.174136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:44:25.388806image/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

구분상세구분대구(m2)중구(m2)동구(m2)서구(m2)남구(m2)북구(m2)수성구(m2)달서구(m2)달성군(m2)
0주거지역제1종전용주거지역2898650102290001649110264820016
1주거지역제2종전용주거지역276532020748004398100211803
2주거지역제1종일반주거지역2627320466328463274517789020328443366597679113843625954843067
3주거지역제2종일반주거지역4581759813232495336087540455748895077985515555110472781618049418
4주거지역제3종일반주거지역3355260815502575531515154280611421146980795546319179679753373955
5주거지역준주거지역15326937437306522260749918540900163311896786112753055250655
6상업지역중심상업지역688365027737591880111236908123594490939659590
7상업지역일반상업지역64543120998137683414398405132433884130710133331195378
8상업지역근린상업지역3680363347367612841609112647461302812587799462841110130
9상업지역유통상업지역14380510203964001073829114269045989
구분상세구분대구(m2)중구(m2)동구(m2)서구(m2)남구(m2)북구(m2)수성구(m2)달서구(m2)달성군(m2)
11공업지역일반공업지역3357353405711762954063028475410992214417278610
12공업지역준공업지역76392870407848113074002164934014798782455887
13녹지지역보전녹지지역37945258012994348004912302399524877792
14녹지지역생산녹지지역16140761039814100659482804323074615365494
15녹지지역자연녹지지역56421779755797510794210243152728280222651718015548933027567026294894069
16관리지역보전관리지역000000000
17관리지역생산관리지역000000000
18관리지역계획관리지역000000000
19농림지역농림지역35892842000000035892842
20자연환경보전지역자연환경보전지역482956200352956200000013000000