Overview

Dataset statistics

Number of variables12
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)4.0%
Total size in memory20.5 KiB
Average record size in memory105.7 B

Variable types

Categorical4
Numeric7
Text1

Alerts

2021 has constant value ""Constant
Dataset has 8 (4.0%) duplicate rowsDuplicates
공동주택 is highly overall correlated with 02000High correlation
02000 is highly overall correlated with 공동주택High correlation
1.1 is highly overall correlated with 0 and 4 other fieldsHigh correlation
0 is highly overall correlated with 1.1 and 4 other fieldsHigh correlation
0.1 is highly overall correlated with 0.2 and 2 other fieldsHigh correlation
0.2 is highly overall correlated with 1.1 and 5 other fieldsHigh correlation
0.3 is highly overall correlated with 1.1 and 5 other fieldsHigh correlation
1.2 is highly overall correlated with 1.1 and 5 other fieldsHigh correlation
13 is highly overall correlated with 1.1 and 4 other fieldsHigh correlation
1.1 has 63 (31.7%) zerosZeros
0 has 47 (23.6%) zerosZeros
0.1 has 159 (79.9%) zerosZeros
0.2 has 140 (70.4%) zerosZeros
0.3 has 131 (65.8%) zerosZeros
1.2 has 113 (56.8%) zerosZeros
13 has 68 (34.2%) zerosZeros

Reproduction

Analysis started2023-12-10 06:31:24.924953
Analysis finished2023-12-10 06:31:34.581469
Duration9.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

1
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2
140 
3
26 
1
18 
5
 
9
6
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 140
70.4%
3 26
 
13.1%
1 18
 
9.0%
5 9
 
4.5%
6 6
 
3.0%

Length

2023-12-10T15:31:34.683856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:31:34.883022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 140
70.4%
3 26
 
13.1%
1 18
 
9.0%
5 9
 
4.5%
6 6
 
3.0%

2021
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2021
199 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 199
100.0%

Length

2023-12-10T15:31:35.140095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:31:35.351873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 199
100.0%

1.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.160804
Minimum0
Maximum524
Zeros63
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:35.577224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile104.5
Maximum524
Range524
Interquartile range (IQR)9

Descriptive statistics

Standard deviation59.963156
Coefficient of variation (CV)2.9742443
Kurtosis37.943352
Mean20.160804
Median Absolute Deviation (MAD)2
Skewness5.5844969
Sum4012
Variance3595.5801
MonotonicityNot monotonic
2023-12-10T15:31:35.942423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 63
31.7%
1 34
17.1%
2 20
 
10.1%
3 18
 
9.0%
6 5
 
2.5%
13 3
 
1.5%
9 3
 
1.5%
28 3
 
1.5%
16 2
 
1.0%
43 2
 
1.0%
Other values (39) 46
23.1%
ValueCountFrequency (%)
0 63
31.7%
1 34
17.1%
2 20
 
10.1%
3 18
 
9.0%
4 2
 
1.0%
5 2
 
1.0%
6 5
 
2.5%
7 2
 
1.0%
8 1
 
0.5%
9 3
 
1.5%
ValueCountFrequency (%)
524 1
0.5%
441 1
0.5%
230 1
0.5%
227 1
0.5%
190 1
0.5%
174 1
0.5%
151 1
0.5%
134 1
0.5%
118 2
1.0%
103 1
0.5%

0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.638191
Minimum0
Maximum745
Zeros47
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:36.189922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q39
95-th percentile122.9
Maximum745
Range745
Interquartile range (IQR)8

Descriptive statistics

Standard deviation81.637223
Coefficient of variation (CV)3.1842037
Kurtosis43.298897
Mean25.638191
Median Absolute Deviation (MAD)2
Skewness6.0668377
Sum5102
Variance6664.6361
MonotonicityNot monotonic
2023-12-10T15:31:36.418939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 47
23.6%
1 45
22.6%
2 17
 
8.5%
3 15
 
7.5%
7 8
 
4.0%
4 8
 
4.0%
9 6
 
3.0%
11 4
 
2.0%
5 3
 
1.5%
122 2
 
1.0%
Other values (37) 44
22.1%
ValueCountFrequency (%)
0 47
23.6%
1 45
22.6%
2 17
 
8.5%
3 15
 
7.5%
4 8
 
4.0%
5 3
 
1.5%
6 2
 
1.0%
7 8
 
4.0%
8 1
 
0.5%
9 6
 
3.0%
ValueCountFrequency (%)
745 1
0.5%
564 1
0.5%
462 1
0.5%
286 1
0.5%
207 1
0.5%
172 1
0.5%
162 1
0.5%
153 1
0.5%
136 1
0.5%
131 1
0.5%

14971
Text

Distinct102
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:31:36.922887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.4120603
Min length4

Characters and Unicode

Total characters1674
Distinct characters12
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

Unique69 ?
Unique (%)34.7%

Sample

1st row11500510
2nd row11305534
3rd row11680640
4th row11680660
5th row11500620
ValueCountFrequency (%)
11500611 11
 
5.5%
11305645 8
 
4.0%
11500540 6
 
3.0%
11680510 6
 
3.0%
11500630 6
 
3.0%
11305615 6
 
3.0%
11500591 6
 
3.0%
11305660 5
 
2.5%
11305534 5
 
2.5%
11305545 5
 
2.5%
Other values (92) 135
67.8%
2023-12-10T15:31:37.649357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 441
26.3%
0 432
25.8%
5 245
14.6%
6 155
 
9.3%
3 111
 
6.6%
4 71
 
4.2%
8 70
 
4.2%
2 58
 
3.5%
9 37
 
2.2%
7 36
 
2.2%
Other values (2) 18
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1656
98.9%
Other Letter 18
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 441
26.6%
0 432
26.1%
5 245
14.8%
6 155
 
9.4%
3 111
 
6.7%
4 71
 
4.3%
8 70
 
4.2%
2 58
 
3.5%
9 37
 
2.2%
7 36
 
2.2%
Other Letter
ValueCountFrequency (%)
다 9
50.0%
사 9
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1656
98.9%
Hangul 18
 
1.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 441
26.6%
0 432
26.1%
5 245
14.8%
6 155
 
9.4%
3 111
 
6.7%
4 71
 
4.3%
8 70
 
4.2%
2 58
 
3.5%
9 37
 
2.2%
7 36
 
2.2%
Hangul
ValueCountFrequency (%)
다 9
50.0%
사 9
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1656
98.9%
Hangul 18
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 441
26.6%
0 432
26.1%
5 245
14.8%
6 155
 
9.4%
3 111
 
6.7%
4 71
 
4.3%
8 70
 
4.2%
2 58
 
3.5%
9 37
 
2.2%
7 36
 
2.2%
Hangul
ValueCountFrequency (%)
다 9
50.0%
사 9
50.0%

02000
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
04000
24 
03000
22 
02000
18 
10000
18 
01000
15 
Other values (18)
102 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique5 ?
Unique (%)2.5%

Sample

1st row19000
2nd row03000
3rd row20000
4th row14000
5th row06000

Common Values

ValueCountFrequency (%)
04000 24
12.1%
03000 22
11.1%
02000 18
 
9.0%
10000 18
 
9.0%
01000 15
 
7.5%
06000 13
 
6.5%
14000 12
 
6.0%
15000 11
 
5.5%
11000 9
 
4.5%
20000 8
 
4.0%
Other values (13) 49
24.6%

Length

2023-12-10T15:31:37.968814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
04000 24
12.1%
03000 22
11.1%
02000 18
 
9.0%
10000 18
 
9.0%
01000 15
 
7.5%
06000 13
 
6.5%
14000 12
 
6.0%
15000 11
 
5.5%
11000 9
 
4.5%
20000 8
 
4.0%
Other values (13) 49
24.6%

공동주택
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
제2종근린생활시설
24 
제1종근린생활시설
22 
공동주택
18 
교육연구시설
18 
단독주택
15 
Other values (17)
102 

Length

Max length10
Median length9
Mean length5.7487437
Min length2

Unique

Unique4 ?
Unique (%)2.0%

Sample

1st row위험물저장및처리시설
2nd row제1종근린생활시설
3rd row자동차관련시설
4th row업무시설
5th row종교시설

Common Values

ValueCountFrequency (%)
제2종근린생활시설 24
12.1%
제1종근린생활시설 22
11.1%
공동주택 18
 
9.0%
교육연구시설 18
 
9.0%
단독주택 15
 
7.5%
종교시설 13
 
6.5%
업무시설 12
 
6.0%
숙박시설 11
 
5.5%
노유자시설 9
 
4.5%
자동차관련시설 8
 
4.0%
Other values (12) 49
24.6%

Length

2023-12-10T15:31:38.202408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제2종근린생활시설 24
12.1%
제1종근린생활시설 22
11.1%
공동주택 18
 
9.0%
교육연구시설 18
 
9.0%
단독주택 15
 
7.5%
종교시설 13
 
6.5%
업무시설 12
 
6.0%
숙박시설 11
 
5.5%
노유자시설 9
 
4.5%
자동차관련시설 8
 
4.0%
Other values (12) 49
24.6%

0.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91457286
Minimum0
Maximum31
Zeros159
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:38.482388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.0497125
Coefficient of variation (CV)3.3345757
Kurtosis50.552996
Mean0.91457286
Median Absolute Deviation (MAD)0
Skewness6.1533517
Sum182
Variance9.3007462
MonotonicityNot monotonic
2023-12-10T15:31:38.715881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 159
79.9%
1 15
 
7.5%
3 6
 
3.0%
2 3
 
1.5%
4 3
 
1.5%
6 3
 
1.5%
7 2
 
1.0%
9 2
 
1.0%
12 1
 
0.5%
5 1
 
0.5%
Other values (4) 4
 
2.0%
ValueCountFrequency (%)
0 159
79.9%
1 15
 
7.5%
2 3
 
1.5%
3 6
 
3.0%
4 3
 
1.5%
5 1
 
0.5%
6 3
 
1.5%
7 2
 
1.0%
8 1
 
0.5%
9 2
 
1.0%
ValueCountFrequency (%)
31 1
 
0.5%
14 1
 
0.5%
12 1
 
0.5%
11 1
 
0.5%
9 2
1.0%
8 1
 
0.5%
7 2
1.0%
6 3
1.5%
5 1
 
0.5%
4 3
1.5%

0.2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0854271
Minimum0
Maximum40
Zeros140
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:38.914400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile13.1
Maximum40
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.8272903
Coefficient of variation (CV)2.794291
Kurtosis18.444865
Mean2.0854271
Median Absolute Deviation (MAD)0
Skewness4.0796822
Sum415
Variance33.957312
MonotonicityNot monotonic
2023-12-10T15:31:39.157286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 140
70.4%
1 15
 
7.5%
2 13
 
6.5%
3 4
 
2.0%
5 4
 
2.0%
4 3
 
1.5%
6 2
 
1.0%
7 2
 
1.0%
17 2
 
1.0%
11 2
 
1.0%
Other values (10) 12
 
6.0%
ValueCountFrequency (%)
0 140
70.4%
1 15
 
7.5%
2 13
 
6.5%
3 4
 
2.0%
4 3
 
1.5%
5 4
 
2.0%
6 2
 
1.0%
7 2
 
1.0%
8 1
 
0.5%
10 2
 
1.0%
ValueCountFrequency (%)
40 1
0.5%
34 1
0.5%
32 1
0.5%
25 1
0.5%
23 2
1.0%
17 2
1.0%
16 1
0.5%
14 1
0.5%
13 1
0.5%
11 2
1.0%

0.3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5577889
Minimum0
Maximum70
Zeros131
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:39.373212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile16
Maximum70
Range70
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.4623603
Coefficient of variation (CV)3.308467
Kurtosis31.314019
Mean2.5577889
Median Absolute Deviation (MAD)0
Skewness5.2157267
Sum509
Variance71.611543
MonotonicityNot monotonic
2023-12-10T15:31:39.696337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 131
65.8%
1 29
 
14.6%
2 14
 
7.0%
3 4
 
2.0%
7 4
 
2.0%
16 2
 
1.0%
8 2
 
1.0%
12 1
 
0.5%
23 1
 
0.5%
15 1
 
0.5%
Other values (10) 10
 
5.0%
ValueCountFrequency (%)
0 131
65.8%
1 29
 
14.6%
2 14
 
7.0%
3 4
 
2.0%
4 1
 
0.5%
7 4
 
2.0%
8 2
 
1.0%
10 1
 
0.5%
12 1
 
0.5%
15 1
 
0.5%
ValueCountFrequency (%)
70 1
0.5%
52 1
0.5%
50 1
0.5%
32 1
0.5%
31 1
0.5%
24 1
0.5%
23 1
0.5%
22 1
0.5%
19 1
0.5%
16 2
1.0%

1.2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9949749
Minimum0
Maximum99
Zeros113
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:39.903238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile25.3
Maximum99
Range99
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.1779
Coefficient of variation (CV)3.038634
Kurtosis20.555573
Mean4.9949749
Median Absolute Deviation (MAD)0
Skewness4.4447121
Sum994
Variance230.36866
MonotonicityNot monotonic
2023-12-10T15:31:40.116514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 113
56.8%
1 29
 
14.6%
2 11
 
5.5%
4 9
 
4.5%
3 5
 
2.5%
5 4
 
2.0%
7 4
 
2.0%
11 3
 
1.5%
14 2
 
1.0%
10 2
 
1.0%
Other values (16) 17
 
8.5%
ValueCountFrequency (%)
0 113
56.8%
1 29
 
14.6%
2 11
 
5.5%
3 5
 
2.5%
4 9
 
4.5%
5 4
 
2.0%
6 1
 
0.5%
7 4
 
2.0%
10 2
 
1.0%
11 3
 
1.5%
ValueCountFrequency (%)
99 1
0.5%
93 1
0.5%
82 1
0.5%
74 1
0.5%
72 1
0.5%
70 1
0.5%
48 1
0.5%
40 1
0.5%
37 1
0.5%
28 1
0.5%

13
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.105528
Minimum0
Maximum341
Zeros68
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:40.354091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile87.1
Maximum341
Range341
Interquartile range (IQR)6

Descriptive statistics

Standard deviation46.1512
Coefficient of variation (CV)2.8655503
Kurtosis22.186468
Mean16.105528
Median Absolute Deviation (MAD)1
Skewness4.4766136
Sum3205
Variance2129.9333
MonotonicityNot monotonic
2023-12-10T15:31:40.574003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 68
34.2%
1 33
16.6%
2 15
 
7.5%
3 15
 
7.5%
4 8
 
4.0%
5 6
 
3.0%
6 6
 
3.0%
18 5
 
2.5%
7 4
 
2.0%
16 4
 
2.0%
Other values (26) 35
17.6%
ValueCountFrequency (%)
0 68
34.2%
1 33
16.6%
2 15
 
7.5%
3 15
 
7.5%
4 8
 
4.0%
5 6
 
3.0%
6 6
 
3.0%
7 4
 
2.0%
10 1
 
0.5%
12 2
 
1.0%
ValueCountFrequency (%)
341 1
0.5%
261 1
0.5%
260 1
0.5%
223 1
0.5%
163 1
0.5%
156 1
0.5%
155 2
1.0%
122 1
0.5%
115 1
0.5%
84 1
0.5%

Interactions

2023-12-10T15:31:32.594322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:25.789383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:26.984112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:28.062447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:29.091252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:30.220708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:31.565426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:32.744240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:25.932390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:27.112858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:28.187060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:29.237192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:30.381614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:31.705460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:32.903401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:26.071358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:27.268942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:28.327028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:29.401817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:30.619387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:31.838517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:33.057405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:26.300270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:27.430087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:28.475590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:29.573024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:30.847307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:31.953943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:33.198454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:26.499889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:27.600555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:28.634446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:29.730731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:31.120679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:32.169704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:33.345208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:26.688318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:27.766222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:28.800304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:29.902879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:31.261588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:32.331515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:33.532796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:26.839781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:27.900863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:28.940901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:30.051474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:31.376291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:32.443331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:31:40.744485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
11.1002000공동주택0.10.20.31.213
11.0000.0430.2190.5630.4740.3010.1360.2730.3090.000
1.10.0431.0000.9530.0000.0000.7000.7390.8830.8340.778
00.2190.9531.0000.0000.0000.8390.8060.9240.8310.639
020000.5630.0000.0001.0001.0000.0000.0000.0000.0000.000
공동주택0.4740.0000.0001.0001.0000.0000.0000.0000.0000.000
0.10.3010.7000.8390.0000.0001.0000.9110.8260.8660.758
0.20.1360.7390.8060.0000.0000.9111.0000.8450.8090.813
0.30.2730.8830.9240.0000.0000.8260.8451.0000.8500.826
1.20.3090.8340.8310.0000.0000.8660.8090.8501.0000.948
130.0000.7780.6390.0000.0000.7580.8130.8260.9481.000
2023-12-10T15:31:40.945209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1공동주택02000
11.0000.2450.302
공동주택0.2451.0000.997
020000.3020.9971.000
2023-12-10T15:31:41.085221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1.100.10.20.31.213102000공동주택
1.11.0000.6550.4600.5940.5350.6420.6850.0240.0000.000
00.6551.0000.4350.6360.5750.6100.6430.1400.0000.000
0.10.4600.4351.0000.6750.5960.5060.4800.2080.0000.000
0.20.5940.6360.6751.0000.7090.6700.6390.1670.0000.000
0.30.5350.5750.5960.7091.0000.6280.5340.1700.0000.000
1.20.6420.6100.5060.6700.6281.0000.7250.1930.0000.000
130.6850.6430.4800.6390.5340.7251.0000.0000.0000.000
10.0240.1400.2080.1670.1700.1930.0001.0000.3020.245
020000.0000.0000.0000.0000.0000.0000.0000.3021.0000.997
공동주택0.0000.0000.0000.0000.0000.0000.0000.2450.9971.000

Missing values

2023-12-10T15:31:34.164298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:31:34.472407image/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

120211.101497102000공동주택0.10.20.31.213
022021111150051019000위험물저장및처리시설00000
122021281131130553403000제1종근린생활시설22016
222021311168064020000자동차관련시설00015
322021001168066014000업무시설00001
422021231150062006000종교시설00115
55202101다사5980438009000의료시설00001
622021031130555517000공장00000
722021111130566014000업무시설00002
822021371130564511000노유자시설00002
922021111150059119000위험물저장및처리시설00000
120211.101497102000공동주택0.10.20.31.213
18922021001130557510000교육연구시설00023
19022021041168056511000노유자시설00003
191120210121931903000제1종근린생활시설00000
1922202150701150057003000제1종근린생활시설11106
19322021301168074011000노유자시설00001
1942202116271168061003000제1종근린생활시설001215
195120217222083101000단독주택00000
19622021101150054019000위험물저장및처리시설00000
197120213722053701000단독주택00000
19822021101150055010000교육연구시설00003

Duplicate rows

Most frequently occurring

120211.101497102000공동주택0.10.20.31.213# duplicates
022021011130553415000숙박시설000002
122021101168051020000자동차관련시설000132
2220212111130554506000종교시설000442
3220216161150061110000교육연구시설6534162
4220211391150059015000숙박시설000032
522021231221130564503000제1종근린생활시설1125182
62202128481130561504000제2종근린생활시설03211122
72202143441150063004000제2종근린생활시설02710442