Overview

Dataset statistics

Number of variables10
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory85.4 B

Variable types

Categorical7
Numeric3

Dataset

Description2014년 9월 기준 건축허가 통계 자료입니다. 시 군 별로 허가통계의 구분(주거용, 농업용, 농수산용 등)을 정리
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15071387

Alerts

계 is highly overall correlated with 주거용 and 5 other fieldsHigh correlation
주거용 is highly overall correlated with 계 and 5 other fieldsHigh correlation
상업용 is highly overall correlated with 계 and 5 other fieldsHigh correlation
농수산용 is highly overall correlated with 기타 High correlation
공업용 is highly overall correlated with 계 and 4 other fieldsHigh correlation
공공용 is highly overall correlated with 계 and 4 other fieldsHigh correlation
문교/사회용 is highly overall correlated with 계 and 5 other fieldsHigh correlation
기타 is highly overall correlated with 계 and 4 other fieldsHigh correlation
공공용 is highly imbalanced (57.4%)Imbalance

Reproduction

Analysis started2023-06-11 08:58:29.704150
Analysis finished2023-06-11 08:58:33.249205
Duration3.55 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

지역
Categorical

Distinct18
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size560.0 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

2023-06-11T17:58:33.328115image/svg+xmlMatplotlib v3.7.1, 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 size560.0 B
건수
18 
동수
18 
연면적
18 

Length

Max length5
Median length4
Mean length4.3333333
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 건수
2nd row 동수
3rd row 연면적
4th row 건수
5th row 동수

Common Values

ValueCountFrequency (%)
건수 18
33.3%
동수 18
33.3%
연면적 18
33.3%

Length

2023-06-11T17:58:33.513271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-11T17:58:33.697877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
건수 18
33.3%
동수 18
33.3%
연면적 18
33.3%

계
Real number (ℝ)

Distinct49
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14245.537
Minimum33
Maximum347303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2023-06-11T17:58:33.887017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile35
Q156.25
median96
Q37229.5
95-th percentile59546.35
Maximum347303
Range347270
Interquartile range (IQR)7173.25

Descriptive statistics

Standard deviation49228.166
Coefficient of variation (CV)3.4556904
Kurtosis41.19345
Mean14245.537
Median Absolute Deviation (MAD)56
Skewness6.1310725
Sum769259
Variance2.4234123 × 109
MonotonicityNot monotonic
2023-06-11T17:58:34.087898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
44 3
 
5.6%
35 2
 
3.7%
57 2
 
3.7%
80 2
 
3.7%
142 1
 
1.9%
347303 1
 
1.9%
87 1
 
1.9%
99 1
 
1.9%
30850 1
 
1.9%
33 1
 
1.9%
Other values (39) 39
72.2%
ValueCountFrequency (%)
33 1
 
1.9%
34 1
 
1.9%
35 2
3.7%
37 1
 
1.9%
39 1
 
1.9%
44 3
5.6%
46 1
 
1.9%
50 1
 
1.9%
52 1
 
1.9%
53 1
 
1.9%
ValueCountFrequency (%)
347303 1
1.9%
77198 1
1.9%
60899 1
1.9%
58818 1
1.9%
49504 1
1.9%
30850 1
1.9%
28281 1
1.9%
20506 1
1.9%
16094 1
1.9%
15084 1
1.9%

주거용
Real number (ℝ)

Distinct45
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7437.2407
Minimum20
Maximum244057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2023-06-11T17:58:34.280270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q130.25
median45.5
Q32891.25
95-th percentile22890.05
Maximum244057
Range244037
Interquartile range (IQR)2861

Descriptive statistics

Standard deviation33516.76
Coefficient of variation (CV)4.5066122
Kurtosis49.32486
Mean7437.2407
Median Absolute Deviation (MAD)23.5
Skewness6.90229
Sum401611
Variance1.1233732 × 109
MonotonicityNot monotonic
2023-06-11T17:58:34.505007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
22 5
 
9.3%
32 3
 
5.6%
34 2
 
3.7%
35 2
 
3.7%
25 2
 
3.7%
33 1
 
1.9%
44 1
 
1.9%
47 1
 
1.9%
9638 1
 
1.9%
24 1
 
1.9%
Other values (35) 35
64.8%
ValueCountFrequency (%)
20 1
 
1.9%
21 1
 
1.9%
22 5
9.3%
24 1
 
1.9%
25 2
 
3.7%
26 1
 
1.9%
27 1
 
1.9%
28 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
ValueCountFrequency (%)
244057 1
1.9%
33443 1
1.9%
29756 1
1.9%
19193 1
1.9%
16270 1
1.9%
9638 1
1.9%
7727 1
1.9%
7706 1
1.9%
4847 1
1.9%
4280 1
1.9%

상업용
Real number (ℝ)

Distinct39
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3144.3519
Minimum5
Maximum59657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2023-06-11T17:58:34.685973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q111
median25
Q31600.25
95-th percentile14965
Maximum59657
Range59652
Interquartile range (IQR)1589.25

Descriptive statistics

Standard deviation9711.0042
Coefficient of variation (CV)3.0883962
Kurtosis23.764243
Mean3144.3519
Median Absolute Deviation (MAD)18.5
Skewness4.6589332
Sum169795
Variance94303603
MonotonicityNot monotonic
2023-06-11T17:58:34.849633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
11 4
 
7.4%
7 3
 
5.6%
8 3
 
5.6%
6 3
 
5.6%
10 3
 
5.6%
25 2
 
3.7%
14 2
 
3.7%
15 2
 
3.7%
20 2
 
3.7%
50 1
 
1.9%
Other values (29) 29
53.7%
ValueCountFrequency (%)
5 1
 
1.9%
6 3
5.6%
7 3
5.6%
8 3
5.6%
10 3
5.6%
11 4
7.4%
12 1
 
1.9%
13 1
 
1.9%
14 2
3.7%
15 2
3.7%
ValueCountFrequency (%)
59657 1
1.9%
35203 1
1.9%
19385 1
1.9%
12585 1
1.9%
9937 1
1.9%
5673 1
1.9%
3852 1
1.9%
3014 1
1.9%
3002 1
1.9%
2892 1
1.9%

농수산용
Categorical

Distinct24
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size560.0 B
21 
1
5 
2
3 
4
3 
5
 
2
Other values (19)
20 

Length

Max length4
Median length3
Mean length2
Min length1

Unique

Unique18 ?
Unique (%)33.3%

Sample

1st row
2nd row
3rd row
4th row1
5th row1

Common Values

ValueCountFrequency (%)
21
38.9%
1 5
 
9.3%
2 3
 
5.6%
4 3
 
5.6%
5 2
 
3.7%
9 2
 
3.7%
7248 1
 
1.9%
261 1
 
1.9%
90 1
 
1.9%
1268 1
 
1.9%
Other values (14) 14
25.9%

Length

2023-06-11T17:58:35.059847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 5
 
15.2%
4 3
 
9.1%
2 3
 
9.1%
5 2
 
6.1%
9 2
 
6.1%
22 1
 
3.0%
12 1
 
3.0%
6 1
 
3.0%
330 1
 
3.0%
3 1
 
3.0%
Other values (13) 13
39.4%

공업용
Categorical

Distinct28
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Memory size560.0 B
15 
1
8 
6
3 
2
 
2
21
 
2
Other values (23)
24 

Length

Max length5
Median length4
Mean length2.1296296
Min length1

Unique

Unique22 ?
Unique (%)40.7%

Sample

1st row26
2nd row35
3rd row34816
4th row6
5th row9

Common Values

ValueCountFrequency (%)
15
27.8%
1 8
14.8%
6 3
 
5.6%
2 2
 
3.7%
21 2
 
3.7%
4 2
 
3.7%
1646 1
 
1.9%
34816 1
 
1.9%
9 1
 
1.9%
9175 1
 
1.9%
Other values (18) 18
33.3%

Length

2023-06-11T17:58:35.256825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 8
20.5%
6 3
 
7.7%
2 2
 
5.1%
21 2
 
5.1%
4 2
 
5.1%
32878 1
 
2.6%
26 1
 
2.6%
5336 1
 
2.6%
560 1
 
2.6%
390 1
 
2.6%
Other values (17) 17
43.6%

공공용
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size560.0 B
42 
1
7 
2
 
1
2998
 
1
636
 
1
Other values (2)
 
2

Length

Max length4
Median length2
Mean length1.9259259
Min length1

Unique

Unique5 ?
Unique (%)9.3%

Sample

1st row1
2nd row2
3rd row2998
4th row1
5th row1

Common Values

ValueCountFrequency (%)
42
77.8%
1 7
 
13.0%
2 1
 
1.9%
2998 1
 
1.9%
636 1
 
1.9%
394 1
 
1.9%
65 1
 
1.9%

Length

2023-06-11T17:58:35.701757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-11T17:58:35.931157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 7
58.3%
2 1
 
8.3%
2998 1
 
8.3%
636 1
 
8.3%
394 1
 
8.3%
65 1
 
8.3%

문교/사회용
Categorical

Distinct23
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Memory size560.0 B
2
16 
9 
3
4 
6
3 
1
3 
Other values (18)
19 

Length

Max length4
Median length1
Mean length1.7777778
Min length1

Unique

Unique17 ?
Unique (%)31.5%

Sample

1st row6
2nd row6
3rd row5040
4th row4
5th row5

Common Values

ValueCountFrequency (%)
2 16
29.6%
9
16.7%
3 4
 
7.4%
6 3
 
5.6%
1 3
 
5.6%
5 2
 
3.7%
127 1
 
1.9%
4 1
 
1.9%
610 1
 
1.9%
48 1
 
1.9%
Other values (13) 13
24.1%

Length

2023-06-11T17:58:36.145697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 16
35.6%
3 4
 
8.9%
6 3
 
6.7%
1 3
 
6.7%
5 2
 
4.4%
1089 1
 
2.2%
216 1
 
2.2%
466 1
 
2.2%
773 1
 
2.2%
423 1
 
2.2%
Other values (12) 12
26.7%

기타
Categorical

Distinct32
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Memory size560.0 B
6
6 
4
4 
 
3
8
 
3
9
 
3
Other values (27)
35 

Length

Max length4
Median length3.5
Mean length1.9259259
Min length1

Unique

Unique21 ?
Unique (%)38.9%

Sample

1st row3
2nd row4
3rd row735
4th row6
5th row8

Common Values

ValueCountFrequency (%)
6 6
 
11.1%
4 4
 
7.4%
3
 
5.6%
8 3
 
5.6%
9 3
 
5.6%
7 3
 
5.6%
5 3
 
5.6%
3 2
 
3.7%
2 2
 
3.7%
12 2
 
3.7%
Other values (22) 23
42.6%

Length

2023-06-11T17:58:36.358291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6 6
 
11.8%
4 4
 
7.8%
8 3
 
5.9%
9 3
 
5.9%
7 3
 
5.9%
5 3
 
5.9%
3 2
 
3.9%
2 2
 
3.9%
12 2
 
3.9%
10 2
 
3.9%
Other values (21) 21
41.2%

Interactions

2023-06-11T17:58:32.141200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T17:58:30.983146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T17:58:31.641280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T17:58:32.326240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T17:58:31.342228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T17:58:31.811326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T17:58:32.477385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T17:58:31.478101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-11T17:58:31.956346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-11T17:58:36.529719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
계주거용상업용
계1.0000.9820.920
주거용0.9821.0000.860
상업용0.9200.8601.000
2023-06-11T17:58:36.789214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
계주거용상업용
계1.0000.9630.975
주거용0.9631.0000.929
상업용0.9750.9291.000
2023-06-11T17:58:36.979882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
계주거용상업용
계1.0000.8540.873
주거용0.8541.0000.788
상업용0.8730.7881.000
2023-06-11T17:58:37.189901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
지역구분계주거용상업용농수산용공업용공공용문교/사회용기타
지역1.0000.0000.0000.0000.2090.6960.7380.5890.6770.839
구분0.0001.0000.2330.4070.2400.4160.6240.1960.6200.503
계0.0000.2331.0000.8340.9590.5001.0000.8621.0001.000
주거용0.0000.4070.8341.0000.9260.0001.0000.7391.0001.000
상업용0.2090.2400.9590.9261.0000.0001.0000.9411.0000.961
농수산용0.6960.4160.5000.0000.0001.0000.8660.0000.8090.957
공업용0.7380.6241.0001.0001.0000.8661.0001.0000.9490.897
공공용0.5890.1960.8620.7390.9410.0001.0001.0000.9500.760
문교/사회용0.6770.6201.0001.0001.0000.8090.9490.9501.0000.977
기타0.8390.5031.0001.0000.9610.9570.8970.7600.9771.000
2023-06-11T17:58:37.485810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
농수산용문교/사회용공공용지역기타공업용구분
농수산용1.0000.3220.0000.2190.5060.3550.136
문교/사회용0.3221.0000.6500.2200.6140.5420.306
공공용0.0000.6501.0000.2610.2850.7190.120
지역0.2190.2200.2611.0000.2860.2260.000
기타0.5060.6140.2850.2861.0000.3720.165
공업용0.3550.5420.7190.2260.3721.0000.273
구분0.1360.3060.1200.0000.1650.2731.000
2023-06-11T17:58:37.716941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
계주거용상업용지역구분농수산용공업용공공용문교/사회용기타
계1.0000.9630.9750.0000.2170.1780.7210.7690.7870.663
주거용0.9631.0000.9290.0000.1480.0000.7140.6380.7800.657
상업용0.9750.9291.0000.0000.0890.0000.7360.8710.8040.555
지역0.0000.0000.0001.0000.0000.2190.2260.2610.2200.286
구분0.2170.1480.0890.0001.0000.1360.2730.1200.3060.165
농수산용0.1780.0000.0000.2190.1361.0000.3550.0000.3220.506
공업용0.7210.7140.7360.2260.2730.3551.0000.7190.5420.372
공공용0.7690.6380.8710.2610.1200.0000.7191.0000.6500.285
문교/사회용0.7870.7800.8040.2200.3060.3220.5420.6501.0000.614
기타0.6630.6570.5550.2860.1650.5060.3720.2850.6141.000

Missing values

2023-06-11T17:58:32.792662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-11T17:58:33.157485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

지역구분계주거용상업용농수산용공업용공공용문교/사회용기타
0창원시건수2021016526163
1창원시동수2511317335264
2창원시연면적347303244057596573481629985040735
3진주시건수87442516146
4진주시동수99472819158
5진주시연면적30850963899372619175636610593
6통영시건수33227112
7통영시동수37248122
8통영시연면적4584268213029045951
9사천시건수613115726
지역구분계주거용상업용농수산용공업용공공용문교/사회용기타
44산청군연면적62323739942773778
45함양군건수3520744
46함양군동수52261196
47함양군연면적10798194228924787466711
48거창군건수442283128
49거창군동수5022115129
50거창군연면적1223377273014330560216386
51합천군건수392272116
52합천군동수63301062213
53합천군연면적75622250166712961148091426