Overview

Dataset statistics

Number of variables16
Number of observations229
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.9 KiB
Average record size in memory142.6 B

Variable types

Categorical1
Text1
Numeric14

Dataset

Description2017년 기준 개발제한구역 내 시군구별 토지활용현황(지목별 면적, 건축물 별)* 개발제한구역 관리계획 수립년도마다 갱신되는 자료임
Author국토교통부
URLhttps://www.data.go.kr/data/15062357/fileData.do

Alerts

대지 is highly overall correlated with 임야 and 11 other fieldsHigh correlation
임야 is highly overall correlated with 대지 and 11 other fieldsHigh correlation
is highly overall correlated with 대지 and 11 other fieldsHigh correlation
is highly overall correlated with 대지 and 11 other fieldsHigh correlation
is highly overall correlated with 대지 and 11 other fieldsHigh correlation
is highly overall correlated with 대지 and 11 other fieldsHigh correlation
기타1 is highly overall correlated with 대지 and 11 other fieldsHigh correlation
단독주택 is highly overall correlated with 대지 and 11 other fieldsHigh correlation
공동주택 is highly overall correlated with 근린생활시설 and 1 other fieldsHigh correlation
근린생활시설 is highly overall correlated with 대지 and 12 other fieldsHigh correlation
종교시설 is highly overall correlated with 대지 and 12 other fieldsHigh correlation
창고 is highly overall correlated with 대지 and 11 other fieldsHigh correlation
동물관련시설 is highly overall correlated with 대지 and 11 other fieldsHigh correlation
기타2 is highly overall correlated with 대지 and 11 other fieldsHigh correlation
대지 has 148 (64.6%) zerosZeros
임야 has 139 (60.7%) zerosZeros
has 143 (62.4%) zerosZeros
has 147 (64.2%) zerosZeros
has 156 (68.1%) zerosZeros
has 176 (76.9%) zerosZeros
기타1 has 139 (60.7%) zerosZeros
단독주택 has 142 (62.0%) zerosZeros
공동주택 has 203 (88.6%) zerosZeros
근린생활시설 has 144 (62.9%) zerosZeros
종교시설 has 143 (62.4%) zerosZeros
창고 has 142 (62.0%) zerosZeros
동물관련시설 has 149 (65.1%) zerosZeros
기타2 has 139 (60.7%) zerosZeros

Reproduction

Analysis started2024-04-21 09:27:43.256088
Analysis finished2024-04-21 09:28:16.847701
Duration33.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
31 
서울특별시
25 
경상북도
23 
전라남도
22 
강원도
18 
Other values (12)
110 

Length

Max length7
Median length5
Mean length4.1484716
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 31
13.5%
서울특별시 25
10.9%
경상북도 23
10.0%
전라남도 22
9.6%
강원도 18
7.9%
경상남도 18
7.9%
부산광역시 16
7.0%
충청남도 15
6.6%
전라북도 14
 
6.1%
충청북도 11
 
4.8%
Other values (7) 36
15.7%

Length

2024-04-21T18:28:16.978896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 31
13.5%
서울특별시 25
10.9%
경상북도 23
10.0%
전라남도 22
9.6%
강원도 18
7.9%
경상남도 18
7.9%
부산광역시 16
7.0%
충청남도 15
6.6%
전라북도 14
 
6.1%
충청북도 11
 
4.8%
Other values (7) 36
15.7%
Distinct207
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-04-21T18:28:18.036772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9344978
Min length2

Characters and Unicode

Total characters672
Distinct characters133
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)87.3%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동구 6
 
2.6%
중구 6
 
2.6%
서구 5
 
2.2%
남구 4
 
1.7%
북구 4
 
1.7%
고성군 2
 
0.9%
강서구 2
 
0.9%
남원시 1
 
0.4%
완주군 1
 
0.4%
화순군 1
 
0.4%
Other values (197) 197
86.0%
2024-04-21T18:28:19.326541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
12.6%
79
 
11.8%
74
 
11.0%
22
 
3.3%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (123) 310
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 672
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
85
 
12.6%
79
 
11.8%
74
 
11.0%
22
 
3.3%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (123) 310
46.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 672
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
85
 
12.6%
79
 
11.8%
74
 
11.0%
22
 
3.3%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (123) 310
46.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 672
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
85
 
12.6%
79
 
11.8%
74
 
11.0%
22
 
3.3%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (123) 310
46.1%

대지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44541485
Minimum0
Maximum9.7
Zeros148
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:19.551290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3
95-th percentile2.1
Maximum9.7
Range9.7
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation1.1952532
Coefficient of variation (CV)2.6834606
Kurtosis28.641557
Mean0.44541485
Median Absolute Deviation (MAD)0
Skewness4.8523446
Sum102
Variance1.4286302
MonotonicityNot monotonic
2024-04-21T18:28:19.775784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 148
64.6%
0.2 10
 
4.4%
0.3 8
 
3.5%
0.1 8
 
3.5%
0.5 7
 
3.1%
0.4 5
 
2.2%
1.1 5
 
2.2%
0.6 5
 
2.2%
0.9 5
 
2.2%
1.6 3
 
1.3%
Other values (18) 25
 
10.9%
ValueCountFrequency (%)
0.0 148
64.6%
0.1 8
 
3.5%
0.2 10
 
4.4%
0.3 8
 
3.5%
0.4 5
 
2.2%
0.5 7
 
3.1%
0.6 5
 
2.2%
0.7 1
 
0.4%
0.9 5
 
2.2%
1.0 2
 
0.9%
ValueCountFrequency (%)
9.7 1
0.4%
8.1 1
0.4%
7.7 1
0.4%
4.6 1
0.4%
3.8 1
0.4%
3.6 1
0.4%
3.4 1
0.4%
3.2 2
0.9%
3.1 1
0.4%
2.3 1
0.4%

임야
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.263755
Minimum0
Maximum566.5
Zeros139
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:20.007732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325.3
95-th percentile172.78
Maximum566.5
Range566.5
Interquartile range (IQR)25.3

Descriptive statistics

Standard deviation75.92087
Coefficient of variation (CV)2.3531318
Kurtosis18.381108
Mean32.263755
Median Absolute Deviation (MAD)0
Skewness3.8501832
Sum7388.4
Variance5763.9785
MonotonicityNot monotonic
2024-04-21T18:28:20.245896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 139
60.7%
41.3 2
 
0.9%
23.9 1
 
0.4%
93.8 1
 
0.4%
62.5 1
 
0.4%
31.0 1
 
0.4%
76.4 1
 
0.4%
257.9 1
 
0.4%
21.1 1
 
0.4%
44.3 1
 
0.4%
Other values (80) 80
34.9%
ValueCountFrequency (%)
0.0 139
60.7%
0.1 1
 
0.4%
1.0 1
 
0.4%
1.8 1
 
0.4%
2.5 1
 
0.4%
2.9 1
 
0.4%
3.1 1
 
0.4%
3.4 1
 
0.4%
3.5 1
 
0.4%
4.4 1
 
0.4%
ValueCountFrequency (%)
566.5 1
0.4%
474.8 1
0.4%
387.9 1
0.4%
328.4 1
0.4%
282.1 1
0.4%
266.9 1
0.4%
257.9 1
0.4%
215.5 1
0.4%
186.2 1
0.4%
182.1 1
0.4%


Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2283843
Minimum0
Maximum35.8
Zeros143
Zeros (%)62.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:20.491651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile19.54
Maximum35.8
Range35.8
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation7.0072035
Coefficient of variation (CV)2.1704986
Kurtosis7.9765411
Mean3.2283843
Median Absolute Deviation (MAD)0
Skewness2.8251735
Sum739.3
Variance49.100901
MonotonicityNot monotonic
2024-04-21T18:28:20.823352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 143
62.4%
0.8 5
 
2.2%
0.1 4
 
1.7%
0.5 3
 
1.3%
1.1 2
 
0.9%
11.7 2
 
0.9%
7.9 2
 
0.9%
11.2 2
 
0.9%
5.4 2
 
0.9%
9.4 2
 
0.9%
Other values (56) 62
27.1%
ValueCountFrequency (%)
0.0 143
62.4%
0.1 4
 
1.7%
0.2 1
 
0.4%
0.3 1
 
0.4%
0.4 1
 
0.4%
0.5 3
 
1.3%
0.8 5
 
2.2%
1.0 1
 
0.4%
1.1 2
 
0.9%
1.3 1
 
0.4%
ValueCountFrequency (%)
35.8 1
0.4%
33.6 1
0.4%
33.4 1
0.4%
31.5 1
0.4%
29.9 1
0.4%
29.1 1
0.4%
27.9 1
0.4%
25.9 1
0.4%
25.3 1
0.4%
24.1 1
0.4%


Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2305677
Minimum0
Maximum180.7
Zeros147
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:21.238954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.6
95-th percentile44.76
Maximum180.7
Range180.7
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation21.084018
Coefficient of variation (CV)2.9159561
Kurtosis28.553348
Mean7.2305677
Median Absolute Deviation (MAD)0
Skewness4.8441981
Sum1655.8
Variance444.53582
MonotonicityNot monotonic
2024-04-21T18:28:21.684235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 147
64.2%
0.5 3
 
1.3%
2.0 2
 
0.9%
9.5 2
 
0.9%
12.1 2
 
0.9%
6.6 2
 
0.9%
4.4 2
 
0.9%
0.1 2
 
0.9%
1.8 2
 
0.9%
38.7 1
 
0.4%
Other values (64) 64
27.9%
ValueCountFrequency (%)
0.0 147
64.2%
0.1 2
 
0.9%
0.2 1
 
0.4%
0.3 1
 
0.4%
0.4 1
 
0.4%
0.5 3
 
1.3%
0.6 1
 
0.4%
0.8 1
 
0.4%
1.0 1
 
0.4%
1.1 1
 
0.4%
ValueCountFrequency (%)
180.7 1
0.4%
130.3 1
0.4%
104.5 1
0.4%
94.1 1
0.4%
85.8 1
0.4%
85.2 1
0.4%
59.0 1
0.4%
58.8 1
0.4%
50.2 1
0.4%
46.0 1
0.4%


Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76113537
Minimum0
Maximum28.2
Zeros156
Zeros (%)68.1%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:22.074874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile3.92
Maximum28.2
Range28.2
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation2.791736
Coefficient of variation (CV)3.6678574
Kurtosis49.397452
Mean0.76113537
Median Absolute Deviation (MAD)0
Skewness6.3655522
Sum174.3
Variance7.7937899
MonotonicityNot monotonic
2024-04-21T18:28:22.452030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.0 156
68.1%
0.1 13
 
5.7%
0.5 8
 
3.5%
0.2 6
 
2.6%
0.4 6
 
2.6%
1.4 3
 
1.3%
1.0 3
 
1.3%
0.7 3
 
1.3%
1.8 2
 
0.9%
2.7 2
 
0.9%
Other values (24) 27
 
11.8%
ValueCountFrequency (%)
0.0 156
68.1%
0.1 13
 
5.7%
0.2 6
 
2.6%
0.3 2
 
0.9%
0.4 6
 
2.6%
0.5 8
 
3.5%
0.6 2
 
0.9%
0.7 3
 
1.3%
0.8 1
 
0.4%
1.0 3
 
1.3%
ValueCountFrequency (%)
28.2 1
0.4%
16.9 1
0.4%
13.7 1
0.4%
13.6 1
0.4%
10.8 1
0.4%
8.4 1
0.4%
6.8 1
0.4%
6.3 1
0.4%
5.9 1
0.4%
5.3 1
0.4%


Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36375546
Minimum0
Maximum13.4
Zeros176
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:22.820085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.56
Maximum13.4
Range13.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5629891
Coefficient of variation (CV)4.2968128
Kurtosis46.65464
Mean0.36375546
Median Absolute Deviation (MAD)0
Skewness6.5377909
Sum83.3
Variance2.442935
MonotonicityNot monotonic
2024-04-21T18:28:23.223292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 176
76.9%
0.1 10
 
4.4%
0.2 9
 
3.9%
0.4 4
 
1.7%
0.3 4
 
1.7%
0.6 3
 
1.3%
0.8 2
 
0.9%
1.0 2
 
0.9%
0.7 2
 
0.9%
0.5 2
 
0.9%
Other values (15) 15
 
6.6%
ValueCountFrequency (%)
0.0 176
76.9%
0.1 10
 
4.4%
0.2 9
 
3.9%
0.3 4
 
1.7%
0.4 4
 
1.7%
0.5 2
 
0.9%
0.6 3
 
1.3%
0.7 2
 
0.9%
0.8 2
 
0.9%
0.9 1
 
0.4%
ValueCountFrequency (%)
13.4 1
0.4%
13.0 1
0.4%
10.0 1
0.4%
7.0 1
0.4%
5.3 1
0.4%
3.9 1
0.4%
3.4 1
0.4%
3.2 1
0.4%
2.0 1
0.4%
1.9 1
0.4%

기타1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7624454
Minimum0
Maximum118.9
Zeros139
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:23.623474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.6
95-th percentile42.28
Maximum118.9
Range118.9
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation17.06881
Coefficient of variation (CV)2.5240589
Kurtosis16.426603
Mean6.7624454
Median Absolute Deviation (MAD)0
Skewness3.7713544
Sum1548.6
Variance291.34429
MonotonicityNot monotonic
2024-04-21T18:28:24.065795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 139
60.7%
0.2 4
 
1.7%
0.5 3
 
1.3%
2.4 3
 
1.3%
19.1 2
 
0.9%
46.0 2
 
0.9%
23.0 2
 
0.9%
8.8 2
 
0.9%
30.5 2
 
0.9%
0.3 2
 
0.9%
Other values (61) 68
29.7%
ValueCountFrequency (%)
0.0 139
60.7%
0.1 2
 
0.9%
0.2 4
 
1.7%
0.3 2
 
0.9%
0.4 1
 
0.4%
0.5 3
 
1.3%
0.6 2
 
0.9%
0.9 1
 
0.4%
1.0 1
 
0.4%
1.1 1
 
0.4%
ValueCountFrequency (%)
118.9 1
0.4%
99.7 1
0.4%
97.0 1
0.4%
72.9 1
0.4%
67.0 1
0.4%
65.9 1
0.4%
55.1 1
0.4%
49.1 1
0.4%
46.0 2
0.9%
44.8 1
0.4%

단독주택
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.8865
Minimum0
Maximum51363
Zeros142
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:24.483934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3727
95-th percentile9112
Maximum51363
Range51363
Interquartile range (IQR)727

Descriptive statistics

Standard deviation5106.9614
Coefficient of variation (CV)3.1742211
Kurtosis44.51616
Mean1608.8865
Median Absolute Deviation (MAD)0
Skewness5.8382128
Sum368435
Variance26081055
MonotonicityNot monotonic
2024-04-21T18:28:24.924136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 142
62.0%
390 2
 
0.9%
1159 1
 
0.4%
1888 1
 
0.4%
1312 1
 
0.4%
4198 1
 
0.4%
157 1
 
0.4%
19073 1
 
0.4%
8821 1
 
0.4%
3885 1
 
0.4%
Other values (77) 77
33.6%
ValueCountFrequency (%)
0 142
62.0%
9 1
 
0.4%
10 1
 
0.4%
19 1
 
0.4%
22 1
 
0.4%
24 1
 
0.4%
29 1
 
0.4%
63 1
 
0.4%
66 1
 
0.4%
124 1
 
0.4%
ValueCountFrequency (%)
51363 1
0.4%
26958 1
0.4%
22950 1
0.4%
20611 1
0.4%
20538 1
0.4%
19073 1
0.4%
14241 1
0.4%
13391 1
0.4%
11596 1
0.4%
11536 1
0.4%

공동주택
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0349345
Minimum0
Maximum71
Zeros203
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:25.287304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum71
Range71
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.1219132
Coefficient of variation (CV)4.4826569
Kurtosis36.403353
Mean2.0349345
Median Absolute Deviation (MAD)0
Skewness5.8329899
Sum466
Variance83.209301
MonotonicityNot monotonic
2024-04-21T18:28:25.658362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 203
88.6%
1 4
 
1.7%
9 3
 
1.3%
2 3
 
1.3%
4 2
 
0.9%
32 1
 
0.4%
8 1
 
0.4%
30 1
 
0.4%
70 1
 
0.4%
16 1
 
0.4%
Other values (9) 9
 
3.9%
ValueCountFrequency (%)
0 203
88.6%
1 4
 
1.7%
2 3
 
1.3%
4 2
 
0.9%
6 1
 
0.4%
7 1
 
0.4%
8 1
 
0.4%
9 3
 
1.3%
10 1
 
0.4%
15 1
 
0.4%
ValueCountFrequency (%)
71 1
0.4%
70 1
0.4%
57 1
0.4%
50 1
0.4%
32 1
0.4%
31 1
0.4%
30 1
0.4%
18 1
0.4%
16 1
0.4%
15 1
0.4%

근린생활시설
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.32751
Minimum0
Maximum8608
Zeros144
Zeros (%)62.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:26.062442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q366
95-th percentile819.6
Maximum8608
Range8608
Interquartile range (IQR)66

Descriptive statistics

Standard deviation637.20714
Coefficient of variation (CV)4.2107819
Kurtosis137.49031
Mean151.32751
Median Absolute Deviation (MAD)0
Skewness10.705996
Sum34654
Variance406032.94
MonotonicityNot monotonic
2024-04-21T18:28:26.684808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 144
62.9%
6 2
 
0.9%
30 2
 
0.9%
80 2
 
0.9%
54 2
 
0.9%
22 2
 
0.9%
32 2
 
0.9%
88 2
 
0.9%
78 1
 
0.4%
339 1
 
0.4%
Other values (69) 69
30.1%
ValueCountFrequency (%)
0 144
62.9%
2 1
 
0.4%
4 1
 
0.4%
6 2
 
0.9%
7 1
 
0.4%
9 1
 
0.4%
11 1
 
0.4%
15 1
 
0.4%
19 1
 
0.4%
22 2
 
0.9%
ValueCountFrequency (%)
8608 1
0.4%
1966 1
0.4%
1630 1
0.4%
1614 1
0.4%
1525 1
0.4%
1461 1
0.4%
1184 1
0.4%
1183 1
0.4%
946 1
0.4%
928 1
0.4%

종교시설
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.231441
Minimum0
Maximum693
Zeros143
Zeros (%)62.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:27.078146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile171.6
Maximum693
Range693
Interquartile range (IQR)20

Descriptive statistics

Standard deviation83.304102
Coefficient of variation (CV)2.5067857
Kurtosis26.881275
Mean33.231441
Median Absolute Deviation (MAD)0
Skewness4.5224598
Sum7610
Variance6939.5734
MonotonicityNot monotonic
2024-04-21T18:28:27.516805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143
62.4%
2 5
 
2.2%
11 4
 
1.7%
20 3
 
1.3%
9 3
 
1.3%
12 3
 
1.3%
4 3
 
1.3%
106 2
 
0.9%
14 2
 
0.9%
33 2
 
0.9%
Other values (53) 59
25.8%
ValueCountFrequency (%)
0 143
62.4%
1 1
 
0.4%
2 5
 
2.2%
4 3
 
1.3%
8 1
 
0.4%
9 3
 
1.3%
11 4
 
1.7%
12 3
 
1.3%
14 2
 
0.9%
15 2
 
0.9%
ValueCountFrequency (%)
693 1
0.4%
591 1
0.4%
368 1
0.4%
331 1
0.4%
307 1
0.4%
217 1
0.4%
207 2
0.9%
206 1
0.4%
201 1
0.4%
184 1
0.4%

창고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.46725
Minimum0
Maximum12335
Zeros142
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:27.939196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q351
95-th percentile1337.2
Maximum12335
Range12335
Interquartile range (IQR)51

Descriptive statistics

Standard deviation1256.5754
Coefficient of variation (CV)4.0473685
Kurtosis61.088843
Mean310.46725
Median Absolute Deviation (MAD)0
Skewness7.2862553
Sum71097
Variance1578981.6
MonotonicityNot monotonic
2024-04-21T18:28:28.382683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 142
62.0%
10 3
 
1.3%
4 2
 
0.9%
34 2
 
0.9%
897 2
 
0.9%
41 2
 
0.9%
32 2
 
0.9%
2 2
 
0.9%
2276 1
 
0.4%
236 1
 
0.4%
Other values (70) 70
30.6%
ValueCountFrequency (%)
0 142
62.0%
1 1
 
0.4%
2 2
 
0.9%
3 1
 
0.4%
4 2
 
0.9%
7 1
 
0.4%
8 1
 
0.4%
10 3
 
1.3%
16 1
 
0.4%
18 1
 
0.4%
ValueCountFrequency (%)
12335 1
0.4%
11094 1
0.4%
4978 1
0.4%
4524 1
0.4%
3852 1
0.4%
3018 1
0.4%
2750 1
0.4%
2343 1
0.4%
2276 1
0.4%
1676 1
0.4%

동물관련시설
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.38865
Minimum0
Maximum13185
Zeros149
Zeros (%)65.1%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:28.791182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q375
95-th percentile3254.2
Maximum13185
Range13185
Interquartile range (IQR)75

Descriptive statistics

Standard deviation1537.0787
Coefficient of variation (CV)3.3605528
Kurtosis36.1403
Mean457.38865
Median Absolute Deviation (MAD)0
Skewness5.4875222
Sum104742
Variance2362610.9
MonotonicityNot monotonic
2024-04-21T18:28:29.242604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 149
65.1%
1 2
 
0.9%
7 2
 
0.9%
45 2
 
0.9%
3 1
 
0.4%
547 1
 
0.4%
473 1
 
0.4%
5219 1
 
0.4%
828 1
 
0.4%
195 1
 
0.4%
Other values (68) 68
29.7%
ValueCountFrequency (%)
0 149
65.1%
1 2
 
0.9%
2 1
 
0.4%
3 1
 
0.4%
4 1
 
0.4%
5 1
 
0.4%
6 1
 
0.4%
7 2
 
0.9%
8 1
 
0.4%
9 1
 
0.4%
ValueCountFrequency (%)
13185 1
0.4%
11967 1
0.4%
7379 1
0.4%
5436 1
0.4%
5272 1
0.4%
5219 1
0.4%
4208 1
0.4%
3745 1
0.4%
3741 1
0.4%
3697 1
0.4%

기타2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.50218
Minimum0
Maximum7474
Zeros139
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-21T18:28:29.646863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3203
95-th percentile2416.4
Maximum7474
Range7474
Interquartile range (IQR)203

Descriptive statistics

Standard deviation1070.9101
Coefficient of variation (CV)2.8144651
Kurtosis20.460397
Mean380.50218
Median Absolute Deviation (MAD)0
Skewness4.2884881
Sum87135
Variance1146848.5
MonotonicityNot monotonic
2024-04-21T18:28:30.091725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139
60.7%
14 3
 
1.3%
108 2
 
0.9%
138 2
 
0.9%
280 2
 
0.9%
45 1
 
0.4%
166 1
 
0.4%
515 1
 
0.4%
2440 1
 
0.4%
1746 1
 
0.4%
Other values (76) 76
33.2%
ValueCountFrequency (%)
0 139
60.7%
4 1
 
0.4%
10 1
 
0.4%
14 3
 
1.3%
17 1
 
0.4%
18 1
 
0.4%
20 1
 
0.4%
22 1
 
0.4%
45 1
 
0.4%
56 1
 
0.4%
ValueCountFrequency (%)
7474 1
0.4%
6964 1
0.4%
5938 1
0.4%
5441 1
0.4%
4430 1
0.4%
4320 1
0.4%
3594 1
0.4%
3589 1
0.4%
2741 1
0.4%
2467 1
0.4%

Interactions

2024-04-21T18:28:13.912860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:44.382709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:47.358586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:49.412389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:51.549633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:53.921045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:55.965976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:58.102058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:00.357957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:02.994274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:05.152053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:07.177709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:09.671160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:11.787949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:14.059566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:44.621361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:47.500588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:49.558006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:51.705947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:54.059788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:56.110448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:58.246457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:00.600938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:03.139900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:05.289687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:07.327492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:09.813747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:11.934671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:14.213940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:44.862992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:47.637943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:49.702879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:51.864621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:54.198224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:56.259336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:58.388463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:00.844445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:03.289972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:05.426043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:07.473387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:09.962018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:12.078515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:14.372392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:45.115224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:47.791679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:49.861272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:52.023608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:54.351291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:56.415950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:58.546044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:01.098169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:03.446928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:05.578603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:07.636666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:10.120363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:12.239050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:14.532925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:45.370832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:47.943021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:50.021649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:52.180024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:54.502944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:56.573412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:58.700562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:01.307534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:03.606811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:05.727052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:07.795579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:10.285890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:12.398957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:14.679508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:45.608663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:48.085620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:50.164965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:52.326698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:54.640086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:56.717449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:58.845836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:01.653759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:03.755677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:05.866677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:07.945443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:10.430574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:12.543720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:14.840725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:45.862201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:48.237598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:50.325058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:52.484398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:54.792503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:56.875500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:59.000892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:01.808641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:03.914351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:06.015896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:08.108445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:10.585853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:12.701508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:14.997143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:46.107343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:48.383796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:50.477052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:52.638261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:54.936889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:57.029029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:59.151667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:01.956064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:04.069832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:06.162189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:08.261488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:10.739908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:12.853389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:15.149073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:46.477689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:48.522860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:50.625834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:52.784963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:55.078993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:57.174718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:59.300817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:02.096780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:04.217226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:06.299134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:08.459106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:10.883696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:13.001348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:15.311501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:46.628718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:48.677262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:50.782986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:52.945732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:55.230447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:57.335991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:59.460984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:02.250581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:04.377284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:06.453415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:08.656625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:11.042399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:13.158978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:15.451870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:46.764680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:48.811416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:50.924602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:53.086305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:55.365566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:57.474838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:59.596356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:02.387920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:04.518217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:06.582739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:08.830047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:11.178453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:13.297855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:15.614513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:46.918344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:48.964566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:51.083256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:53.247462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:55.517806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:57.636208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:59.754482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:02.541696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:04.680283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:06.735415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:09.200302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:11.336548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:13.457685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:15.767007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:47.062145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:49.112205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:51.238560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:53.399734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:55.665762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:57.788418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:59.906770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:02.689847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:04.832954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:06.882155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:09.356454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:11.482160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:13.608260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:15.922251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:47.209255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:49.261297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:51.391044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:53.763335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:55.811388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:27:57.943938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:00.099780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:02.838405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:04.989810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:07.028124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:09.509535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:11.633499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:28:13.756236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T18:28:30.388208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도대지임야기타1단독주택공동주택근린생활시설종교시설창고동물관련시설기타2
시도1.0000.3800.5760.4310.3030.2640.3950.4250.4120.2570.0230.3870.0000.1490.280
대지0.3801.0000.8100.7440.9550.7500.7750.7870.8880.6860.9620.9360.9300.8800.925
임야0.5760.8101.0000.9110.8100.7540.8270.9270.7750.7020.6400.8740.7600.7760.779
0.4310.7440.9111.0000.7760.7110.6600.9100.7820.4530.7460.6900.7550.6870.720
0.3030.9550.8100.7761.0000.7600.6820.8040.8770.5320.9450.8860.8660.7960.917
0.2640.7500.7540.7110.7601.0000.7850.7270.8730.7600.7930.6060.8850.8290.719
0.3950.7750.8270.6600.6820.7851.0000.6670.8290.8170.4790.8200.9200.8910.706
기타10.4250.7870.9270.9100.8040.7270.6671.0000.7770.5600.8780.7210.7840.7890.742
단독주택0.4120.8880.7750.7820.8770.8730.8290.7771.0000.7540.7950.7430.9520.9030.801
공동주택0.2570.6860.7020.4530.5320.7600.8170.5600.7541.0000.4040.6740.8230.8050.592
근린생활시설0.0230.9620.6400.7460.9450.7930.4790.8780.7950.4041.0000.7410.8450.8070.921
종교시설0.3870.9360.8740.6900.8860.6060.8200.7210.7430.6740.7411.0000.8320.8320.878
창고0.0000.9300.7600.7550.8660.8850.9200.7840.9520.8230.8450.8321.0000.9750.785
동물관련시설0.1490.8800.7760.6870.7960.8290.8910.7890.9030.8050.8070.8320.9751.0000.794
기타20.2800.9250.7790.7200.9170.7190.7060.7420.8010.5920.9210.8780.7850.7941.000
2024-04-21T18:28:30.754440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지임야기타1단독주택공동주택근린생활시설종교시설창고동물관련시설기타2시도
대지1.0000.9420.9460.9370.9230.7790.9430.9550.4960.9590.9220.9400.9460.9330.165
임야0.9421.0000.9690.9410.8750.7880.9700.9580.4480.9440.9540.9550.9320.9680.266
0.9460.9691.0000.9700.8950.8110.9680.9460.3970.9420.9080.9610.9470.9510.183
0.9370.9410.9701.0000.8960.8130.9620.9210.3630.9140.8610.9470.9430.9260.128
0.9230.8750.8950.8961.0000.6800.8950.8740.4930.8980.8420.8800.8810.8810.118
0.7790.7880.8110.8130.6801.0000.7910.7620.2430.7370.7230.7890.8110.7440.185
기타10.9430.9700.9680.9620.8950.7911.0000.9560.4180.9480.9230.9630.9400.9600.180
단독주택0.9550.9580.9460.9210.8740.7620.9561.0000.5000.9710.9580.9660.9500.9670.194
공동주택0.4960.4480.3970.3630.4930.2430.4180.5001.0000.5240.5010.4300.3820.4710.115
근린생활시설0.9590.9440.9420.9140.8980.7370.9480.9710.5241.0000.9450.9580.9480.9500.000
종교시설0.9220.9540.9080.8610.8420.7230.9230.9580.5010.9451.0000.9230.9110.9520.168
창고0.9400.9550.9610.9470.8800.7890.9630.9660.4300.9580.9231.0000.9680.9660.000
동물관련시설0.9460.9320.9470.9430.8810.8110.9400.9500.3820.9480.9110.9681.0000.9380.064
기타20.9330.9680.9510.9260.8810.7440.9600.9670.4710.9500.9520.9660.9381.0000.117
시도0.1650.2660.1830.1280.1180.1850.1800.1940.1150.0000.1680.0000.0640.1171.000

Missing values

2024-04-21T18:28:16.367182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T18:28:16.717872image/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

시도시군구대지임야기타1단독주택공동주택근린생활시설종교시설창고동물관련시설기타2
0서울특별시종로구0.314.71.10.00.10.00.31159327812210345
1서울특별시중구0.00.00.00.00.00.00.00000000
2서울특별시용산구0.00.00.00.00.00.00.00000000
3서울특별시성동구0.00.00.00.00.00.00.00000000
4서울특별시광진구0.02.90.80.00.00.00.12750691018
5서울특별시동대문구0.00.00.00.00.00.00.00000000
6서울특별시중랑구0.25.30.80.50.00.13.598403084666108
7서울특별시성북구0.19.90.00.00.00.00.566942720700143
8서울특별시강북구0.422.10.10.00.10.00.67271823213870103
9서울특별시도봉구0.318.00.40.50.10.01.19641514142100183
시도시군구대지임야기타1단독주택공동주택근린생활시설종교시설창고동물관련시설기타2
219경상남도창녕군0.00.00.00.00.00.00.00000000
220경상남도고성군0.00.00.00.00.00.00.00000000
221경상남도남해군0.00.00.00.00.00.00.00000000
222경상남도하동군0.00.00.00.00.00.00.00000000
223경상남도산청군0.00.00.00.00.00.00.00000000
224경상남도함양군0.00.00.00.00.00.00.00000000
225경상남도거창군0.00.00.00.00.00.00.00000000
226경상남도합천군0.00.00.00.00.00.00.00000000
227제주특별자치도제주시0.00.00.00.00.00.00.00000000
228제주특별자치도서귀포시0.00.00.00.00.00.00.00000000