Overview

Dataset statistics

Number of variables18
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory164.9 B

Variable types

Categorical1
Text1
Numeric16

Dataset

Description서울특별시 중랑구의 2021년도 용도지역 현황을 제공합니다. 대분류,중분류,행정동별로 용도지역 현황입니다. 참고해주시기 바랍니다.
Author서울특별시 중랑구
URLhttps://www.data.go.kr/data/15107467/fileData.do

Alerts

망우본동 is highly overall correlated with 망우제3동 and 14 other fieldsHigh correlation
망우제3동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
면목본동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
면목제2동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
면목3,8동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
면목제4동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
면목제5동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
묵제1동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
묵제2동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
상봉제1동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
상봉제2동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
신내1동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
신내2동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
중화제1동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
중화제2동 is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
합계(제곱미터) is highly overall correlated with 망우본동 and 14 other fieldsHigh correlation
망우본동 has 20 (74.1%) zerosZeros
망우제3동 has 20 (74.1%) zerosZeros
면목본동 has 21 (77.8%) zerosZeros
면목제2동 has 16 (59.3%) zerosZeros
면목3,8동 has 17 (63.0%) zerosZeros
면목제4동 has 16 (59.3%) zerosZeros
면목제5동 has 16 (59.3%) zerosZeros
묵제1동 has 19 (70.4%) zerosZeros
묵제2동 has 19 (70.4%) zerosZeros
상봉제1동 has 16 (59.3%) zerosZeros
상봉제2동 has 20 (74.1%) zerosZeros
신내1동 has 16 (59.3%) zerosZeros
신내2동 has 20 (74.1%) zerosZeros
중화제1동 has 17 (63.0%) zerosZeros
중화제2동 has 17 (63.0%) zerosZeros
합계(제곱미터) has 16 (59.3%) zerosZeros

Reproduction

Analysis started2023-12-12 17:27:57.297009
Analysis finished2023-12-12 17:28:25.988925
Duration28.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

Distinct4
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size348.0 B
주거지역
11 
상업지역
공업지역
녹지지역

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주거지역 11
40.7%
상업지역 6
22.2%
공업지역 5
18.5%
녹지지역 5
18.5%

Length

2023-12-13T02:28:26.071063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:28:26.195576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거지역 11
40.7%
상업지역 6
22.2%
공업지역 5
18.5%
녹지지역 5
18.5%
Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:28:26.437192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length6.6666667
Min length2

Characters and Unicode

Total characters180
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)85.2%

Sample

1st row소계
2nd row제1종전용주거지역
3rd row제2종전용주거지역
4th row전용주거지역 미분류
5th row제1종일반주거지역
ValueCountFrequency (%)
소계 4
 
12.1%
기타 4
 
12.1%
미분류 2
 
6.1%
일반상업지역 1
 
3.0%
자연녹지지역 1
 
3.0%
생산녹지지역 1
 
3.0%
보전녹지지역 1
 
3.0%
공업지역 1
 
3.0%
준공업지역 1
 
3.0%
일반공업지역 1
 
3.0%
Other values (16) 16
48.5%
2023-12-13T02:28:26.855706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27
 
15.0%
23
 
12.8%
10
 
5.6%
10
 
5.6%
9
 
5.0%
7
 
3.9%
7
 
3.9%
6
 
3.3%
6
 
3.3%
6
 
3.3%
Other values (33) 69
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 165
91.7%
Decimal Number 7
 
3.9%
Space Separator 6
 
3.3%
Close Punctuation 1
 
0.6%
Open Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
27
16.4%
23
13.9%
10
 
6.1%
10
 
6.1%
9
 
5.5%
7
 
4.2%
7
 
4.2%
6
 
3.6%
6
 
3.6%
5
 
3.0%
Other values (26) 55
33.3%
Decimal Number
ValueCountFrequency (%)
2 3
42.9%
1 2
28.6%
7 1
 
14.3%
3 1
 
14.3%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 165
91.7%
Common 15
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
27
16.4%
23
13.9%
10
 
6.1%
10
 
6.1%
9
 
5.5%
7
 
4.2%
7
 
4.2%
6
 
3.6%
6
 
3.6%
5
 
3.0%
Other values (26) 55
33.3%
Common
ValueCountFrequency (%)
6
40.0%
2 3
20.0%
1 2
 
13.3%
) 1
 
6.7%
7 1
 
6.7%
( 1
 
6.7%
3 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 165
91.7%
ASCII 15
 
8.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
27
16.4%
23
13.9%
10
 
6.1%
10
 
6.1%
9
 
5.5%
7
 
4.2%
7
 
4.2%
6
 
3.6%
6
 
3.6%
5
 
3.0%
Other values (26) 55
33.3%
ASCII
ValueCountFrequency (%)
6
40.0%
2 3
20.0%
1 2
 
13.3%
) 1
 
6.7%
7 1
 
6.7%
( 1
 
6.7%
3 1
 
6.7%

망우본동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64481.259
Minimum0
Maximum483471
Zeros20
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:27.002748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36932
95-th percentile387026
Maximum483471
Range483471
Interquartile range (IQR)6932

Descriptive statistics

Standard deviation142002.45
Coefficient of variation (CV)2.2022282
Kurtosis3.2465251
Mean64481.259
Median Absolute Deviation (MAD)0
Skewness2.137945
Sum1740994
Variance2.0164696 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:27.165745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 20
74.1%
387026 2
 
7.4%
483471 1
 
3.7%
13864 1
 
3.7%
63730 1
 
3.7%
111836 1
 
3.7%
294041 1
 
3.7%
ValueCountFrequency (%)
0 20
74.1%
13864 1
 
3.7%
63730 1
 
3.7%
111836 1
 
3.7%
294041 1
 
3.7%
387026 2
 
7.4%
483471 1
 
3.7%
ValueCountFrequency (%)
483471 1
 
3.7%
387026 2
 
7.4%
294041 1
 
3.7%
111836 1
 
3.7%
63730 1
 
3.7%
13864 1
 
3.7%
0 20
74.1%

망우제3동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52115.704
Minimum0
Maximum408624
Zeros20
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:27.340817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3171.5
95-th percentile294938
Maximum408624
Range408624
Interquartile range (IQR)171.5

Descriptive statistics

Standard deviation113747.5
Coefficient of variation (CV)2.1825956
Kurtosis3.592369
Mean52115.704
Median Absolute Deviation (MAD)0
Skewness2.1497241
Sum1407124
Variance1.2938495 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:27.512926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 20
74.1%
294938 2
 
7.4%
408624 1
 
3.7%
56938 1
 
3.7%
343 1
 
3.7%
219379 1
 
3.7%
131964 1
 
3.7%
ValueCountFrequency (%)
0 20
74.1%
343 1
 
3.7%
56938 1
 
3.7%
131964 1
 
3.7%
219379 1
 
3.7%
294938 2
 
7.4%
408624 1
 
3.7%
ValueCountFrequency (%)
408624 1
 
3.7%
294938 2
 
7.4%
219379 1
 
3.7%
131964 1
 
3.7%
56938 1
 
3.7%
343 1
 
3.7%
0 20
74.1%

면목본동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62517.407
Minimum0
Maximum843985
Zeros21
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:27.680846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile405017.5
Maximum843985
Range843985
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186765.85
Coefficient of variation (CV)2.9874215
Kurtosis13.046522
Mean62517.407
Median Absolute Deviation (MAD)0
Skewness3.5773044
Sum1687970
Variance3.4881482 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:27.839175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 21
77.8%
843985 1
 
3.7%
11513 1
 
3.7%
43733 1
 
3.7%
167140 1
 
3.7%
506965 1
 
3.7%
114634 1
 
3.7%
ValueCountFrequency (%)
0 21
77.8%
11513 1
 
3.7%
43733 1
 
3.7%
114634 1
 
3.7%
167140 1
 
3.7%
506965 1
 
3.7%
843985 1
 
3.7%
ValueCountFrequency (%)
843985 1
 
3.7%
506965 1
 
3.7%
167140 1
 
3.7%
114634 1
 
3.7%
43733 1
 
3.7%
11513 1
 
3.7%
0 21
77.8%

면목제2동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89044.148
Minimum0
Maximum673543
Zeros16
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:28.004355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q335043.5
95-th percentile618876.4
Maximum673543
Range673543
Interquartile range (IQR)35043.5

Descriptive statistics

Standard deviation198746.75
Coefficient of variation (CV)2.2320024
Kurtosis4.7527806
Mean89044.148
Median Absolute Deviation (MAD)0
Skewness2.4234354
Sum2404192
Variance3.950027 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:28.191227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16
59.3%
673543 2
 
7.4%
491321 1
 
3.7%
24483 1
 
3.7%
32855 1
 
3.7%
106695 1
 
3.7%
272377 1
 
3.7%
54911 1
 
3.7%
37232 1
 
3.7%
15217 1
 
3.7%
ValueCountFrequency (%)
0 16
59.3%
15217 1
 
3.7%
22015 1
 
3.7%
24483 1
 
3.7%
32855 1
 
3.7%
37232 1
 
3.7%
54911 1
 
3.7%
106695 1
 
3.7%
272377 1
 
3.7%
491321 1
 
3.7%
ValueCountFrequency (%)
673543 2
7.4%
491321 1
3.7%
272377 1
3.7%
106695 1
3.7%
54911 1
3.7%
37232 1
3.7%
32855 1
3.7%
24483 1
3.7%
22015 1
3.7%
15217 1
3.7%

면목3,8동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120720.93
Minimum0
Maximum892047
Zeros17
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:28.466020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q353989.5
95-th percentile841323.3
Maximum892047
Range892047
Interquartile range (IQR)53989.5

Descriptive statistics

Standard deviation270562.53
Coefficient of variation (CV)2.2412231
Kurtosis4.2908872
Mean120720.93
Median Absolute Deviation (MAD)0
Skewness2.3520768
Sum3259465
Variance7.3204083 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:28.609523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 17
63.0%
14717 2
 
7.4%
892047 2
 
7.4%
722968 1
 
3.7%
92280 1
 
3.7%
138168 1
 
3.7%
74869 1
 
3.7%
384542 1
 
3.7%
33110 1
 
3.7%
ValueCountFrequency (%)
0 17
63.0%
14717 2
 
7.4%
33110 1
 
3.7%
74869 1
 
3.7%
92280 1
 
3.7%
138168 1
 
3.7%
384542 1
 
3.7%
722968 1
 
3.7%
892047 2
 
7.4%
ValueCountFrequency (%)
892047 2
 
7.4%
722968 1
 
3.7%
384542 1
 
3.7%
138168 1
 
3.7%
92280 1
 
3.7%
74869 1
 
3.7%
33110 1
 
3.7%
14717 2
 
7.4%
0 17
63.0%

면목제4동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67853.63
Minimum0
Maximum717334
Zeros16
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:28.747025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q361393
95-th percentile238755.6
Maximum717334
Range717334
Interquartile range (IQR)61393

Descriptive statistics

Standard deviation149895.34
Coefficient of variation (CV)2.2090984
Kurtosis13.91686
Mean67853.63
Median Absolute Deviation (MAD)0
Skewness3.4610328
Sum1832048
Variance2.2468614 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:28.904751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16
59.3%
169747 2
 
7.4%
717334 1
 
3.7%
119180 1
 
3.7%
67020 1
 
3.7%
235005 1
 
3.7%
240363 1
 
3.7%
55766 1
 
3.7%
28943 1
 
3.7%
3626 1
 
3.7%
ValueCountFrequency (%)
0 16
59.3%
3626 1
 
3.7%
25317 1
 
3.7%
28943 1
 
3.7%
55766 1
 
3.7%
67020 1
 
3.7%
119180 1
 
3.7%
169747 2
 
7.4%
235005 1
 
3.7%
240363 1
 
3.7%
ValueCountFrequency (%)
717334 1
3.7%
240363 1
3.7%
235005 1
3.7%
169747 2
7.4%
119180 1
3.7%
67020 1
3.7%
55766 1
3.7%
28943 1
3.7%
25317 1
3.7%
3626 1
3.7%

면목제5동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48877.593
Minimum0
Maximum503624
Zeros16
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:29.087291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q338637.5
95-th percentile218867.3
Maximum503624
Range503624
Interquartile range (IQR)38637.5

Descriptive statistics

Standard deviation109786.49
Coefficient of variation (CV)2.2461518
Kurtosis11.503743
Mean48877.593
Median Absolute Deviation (MAD)0
Skewness3.195069
Sum1319695
Variance1.2053074 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:29.245190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16
59.3%
5613 2
 
7.4%
503624 1
 
3.7%
4462 1
 
3.7%
46575 1
 
3.7%
129825 1
 
3.7%
74641 1
 
3.7%
248120 1
 
3.7%
150611 1
 
3.7%
119911 1
 
3.7%
ValueCountFrequency (%)
0 16
59.3%
4462 1
 
3.7%
5613 2
 
7.4%
30700 1
 
3.7%
46575 1
 
3.7%
74641 1
 
3.7%
119911 1
 
3.7%
129825 1
 
3.7%
150611 1
 
3.7%
248120 1
 
3.7%
ValueCountFrequency (%)
503624 1
3.7%
248120 1
3.7%
150611 1
3.7%
129825 1
3.7%
119911 1
3.7%
74641 1
3.7%
46575 1
3.7%
30700 1
3.7%
5613 2
7.4%
4462 1
3.7%

묵제1동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47826.593
Minimum0
Maximum503755
Zeros19
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:29.397317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324105
95-th percentile240604
Maximum503755
Range503755
Interquartile range (IQR)24105

Descriptive statistics

Standard deviation112435.56
Coefficient of variation (CV)2.3509005
Kurtosis10.728773
Mean47826.593
Median Absolute Deviation (MAD)0
Skewness3.1401376
Sum1291318
Variance1.2641755 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:29.548493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 19
70.4%
141904 2
 
7.4%
503755 1
 
3.7%
31998 1
 
3.7%
76872 1
 
3.7%
95769 1
 
3.7%
282904 1
 
3.7%
16212 1
 
3.7%
ValueCountFrequency (%)
0 19
70.4%
16212 1
 
3.7%
31998 1
 
3.7%
76872 1
 
3.7%
95769 1
 
3.7%
141904 2
 
7.4%
282904 1
 
3.7%
503755 1
 
3.7%
ValueCountFrequency (%)
503755 1
 
3.7%
282904 1
 
3.7%
141904 2
 
7.4%
95769 1
 
3.7%
76872 1
 
3.7%
31998 1
 
3.7%
16212 1
 
3.7%
0 19
70.4%

묵제2동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75465.63
Minimum0
Maximum787384
Zeros19
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:29.691432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q366353
95-th percentile269855.9
Maximum787384
Range787384
Interquartile range (IQR)66353

Descriptive statistics

Standard deviation168404.34
Coefficient of variation (CV)2.231537
Kurtosis12.362464
Mean75465.63
Median Absolute Deviation (MAD)0
Skewness3.2515067
Sum2037572
Variance2.8360023 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:29.856984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 19
70.4%
231402 2
 
7.4%
787384 1
 
3.7%
65741 1
 
3.7%
243587 1
 
3.7%
129977 1
 
3.7%
281114 1
 
3.7%
66965 1
 
3.7%
ValueCountFrequency (%)
0 19
70.4%
65741 1
 
3.7%
66965 1
 
3.7%
129977 1
 
3.7%
231402 2
 
7.4%
243587 1
 
3.7%
281114 1
 
3.7%
787384 1
 
3.7%
ValueCountFrequency (%)
787384 1
 
3.7%
281114 1
 
3.7%
243587 1
 
3.7%
231402 2
 
7.4%
129977 1
 
3.7%
66965 1
 
3.7%
65741 1
 
3.7%
0 19
70.4%

상봉제1동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89410.852
Minimum0
Maximum878626
Zeros16
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:29.988228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q392015
95-th percentile315998.9
Maximum878626
Range878626
Interquartile range (IQR)92015

Descriptive statistics

Standard deviation187949.3
Coefficient of variation (CV)2.102086
Kurtosis11.980592
Mean89410.852
Median Absolute Deviation (MAD)0
Skewness3.1928418
Sum2414093
Variance3.5324941 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:30.103391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16
59.3%
281008 2
 
7.4%
878626 1
 
3.7%
132479 1
 
3.7%
134991 1
 
3.7%
228609 1
 
3.7%
330995 1
 
3.7%
51551 1
 
3.7%
47413 1
 
3.7%
43167 1
 
3.7%
ValueCountFrequency (%)
0 16
59.3%
4246 1
 
3.7%
43167 1
 
3.7%
47413 1
 
3.7%
51551 1
 
3.7%
132479 1
 
3.7%
134991 1
 
3.7%
228609 1
 
3.7%
281008 2
 
7.4%
330995 1
 
3.7%
ValueCountFrequency (%)
878626 1
3.7%
330995 1
3.7%
281008 2
7.4%
228609 1
3.7%
134991 1
3.7%
132479 1
3.7%
51551 1
3.7%
47413 1
3.7%
43167 1
3.7%
4246 1
3.7%

상봉제2동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48721.667
Minimum0
Maximum523925
Zeros20
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:30.244846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316850
95-th percentile191920.5
Maximum523925
Range523925
Interquartile range (IQR)16850

Descriptive statistics

Standard deviation112830.21
Coefficient of variation (CV)2.3158117
Kurtosis12.1675
Mean48721.667
Median Absolute Deviation (MAD)0
Skewness3.2366324
Sum1315485
Variance1.2730655 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:30.365599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 20
74.1%
133817 2
 
7.4%
523925 1
 
3.7%
33700 1
 
3.7%
172156 1
 
3.7%
117679 1
 
3.7%
200391 1
 
3.7%
ValueCountFrequency (%)
0 20
74.1%
33700 1
 
3.7%
117679 1
 
3.7%
133817 2
 
7.4%
172156 1
 
3.7%
200391 1
 
3.7%
523925 1
 
3.7%
ValueCountFrequency (%)
523925 1
 
3.7%
200391 1
 
3.7%
172156 1
 
3.7%
133817 2
 
7.4%
117679 1
 
3.7%
33700 1
 
3.7%
0 20
74.1%

신내1동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225154.26
Minimum0
Maximum1766972
Zeros16
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:30.469824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3116665
95-th percentile1588824.8
Maximum1766972
Range1766972
Interquartile range (IQR)116665

Descriptive statistics

Standard deviation505988.41
Coefficient of variation (CV)2.2472966
Kurtosis5.608189
Mean225154.26
Median Absolute Deviation (MAD)0
Skewness2.5616491
Sum6079165
Variance2.5602427 × 1011
MonotonicityNot monotonic
2023-12-13T02:28:30.895030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16
59.3%
1766972 2
 
7.4%
1173148 1
 
3.7%
197083 1
 
3.7%
133868 1
 
3.7%
399090 1
 
3.7%
411892 1
 
3.7%
31215 1
 
3.7%
99462 1
 
3.7%
89862 1
 
3.7%
ValueCountFrequency (%)
0 16
59.3%
9601 1
 
3.7%
31215 1
 
3.7%
89862 1
 
3.7%
99462 1
 
3.7%
133868 1
 
3.7%
197083 1
 
3.7%
399090 1
 
3.7%
411892 1
 
3.7%
1173148 1
 
3.7%
ValueCountFrequency (%)
1766972 2
7.4%
1173148 1
3.7%
411892 1
3.7%
399090 1
3.7%
197083 1
3.7%
133868 1
3.7%
99462 1
3.7%
89862 1
3.7%
31215 1
3.7%
9601 1
3.7%

신내2동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68480.963
Minimum0
Maximum479344
Zeros20
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:31.019196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q321656
95-th percentile445149
Maximum479344
Range479344
Interquartile range (IQR)21656

Descriptive statistics

Standard deviation150928.76
Coefficient of variation (CV)2.2039521
Kurtosis3.2723303
Mean68480.963
Median Absolute Deviation (MAD)0
Skewness2.1672686
Sum1848986
Variance2.2779491 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:31.121814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 20
74.1%
445149 2
 
7.4%
479344 1
 
3.7%
43312 1
 
3.7%
78792 1
 
3.7%
76378 1
 
3.7%
280862 1
 
3.7%
ValueCountFrequency (%)
0 20
74.1%
43312 1
 
3.7%
76378 1
 
3.7%
78792 1
 
3.7%
280862 1
 
3.7%
445149 2
 
7.4%
479344 1
 
3.7%
ValueCountFrequency (%)
479344 1
 
3.7%
445149 2
 
7.4%
280862 1
 
3.7%
78792 1
 
3.7%
76378 1
 
3.7%
43312 1
 
3.7%
0 20
74.1%

중화제1동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194973.22
Minimum0
Maximum1378229
Zeros17
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:31.228774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3135618
95-th percentile1340117.9
Maximum1378229
Range1378229
Interquartile range (IQR)135618

Descriptive statistics

Standard deviation429311.35
Coefficient of variation (CV)2.2018991
Kurtosis4.0587238
Mean194973.22
Median Absolute Deviation (MAD)0
Skewness2.3139523
Sum5264277
Variance1.8430824 × 1011
MonotonicityNot monotonic
2023-12-13T02:28:31.349940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 17
63.0%
2718 2
 
7.4%
1378229 2
 
7.4%
1251192 1
 
3.7%
177006 1
 
3.7%
223629 1
 
3.7%
579320 1
 
3.7%
127147 1
 
3.7%
144089 1
 
3.7%
ValueCountFrequency (%)
0 17
63.0%
2718 2
 
7.4%
127147 1
 
3.7%
144089 1
 
3.7%
177006 1
 
3.7%
223629 1
 
3.7%
579320 1
 
3.7%
1251192 1
 
3.7%
1378229 2
 
7.4%
ValueCountFrequency (%)
1378229 2
 
7.4%
1251192 1
 
3.7%
579320 1
 
3.7%
223629 1
 
3.7%
177006 1
 
3.7%
144089 1
 
3.7%
127147 1
 
3.7%
2718 2
 
7.4%
0 17
63.0%

중화제2동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69191.778
Minimum0
Maximum479073
Zeros17
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:31.464268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q329220.5
95-th percentile449028
Maximum479073
Range479073
Interquartile range (IQR)29220.5

Descriptive statistics

Standard deviation149102.88
Coefficient of variation (CV)2.154922
Kurtosis3.5774839
Mean69191.778
Median Absolute Deviation (MAD)0
Skewness2.210205
Sum1868178
Variance2.2231669 × 1010
MonotonicityNot monotonic
2023-12-13T02:28:31.584622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 17
63.0%
5988 2
 
7.4%
449028 2
 
7.4%
479073 1
 
3.7%
11217 1
 
3.7%
119217 1
 
3.7%
229628 1
 
3.7%
71787 1
 
3.7%
47224 1
 
3.7%
ValueCountFrequency (%)
0 17
63.0%
5988 2
 
7.4%
11217 1
 
3.7%
47224 1
 
3.7%
71787 1
 
3.7%
119217 1
 
3.7%
229628 1
 
3.7%
449028 2
 
7.4%
479073 1
 
3.7%
ValueCountFrequency (%)
479073 1
 
3.7%
449028 2
 
7.4%
229628 1
 
3.7%
119217 1
 
3.7%
71787 1
 
3.7%
47224 1
 
3.7%
11217 1
 
3.7%
5988 2
 
7.4%
0 17
63.0%

합계(제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1381817.4
Minimum0
Maximum10914994
Zeros16
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:28:31.690027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3955041
95-th percentile7352456
Maximum10914994
Range10914994
Interquartile range (IQR)955041

Descriptive statistics

Standard deviation2829401.5
Coefficient of variation (CV)2.0475944
Kurtosis4.8892318
Mean1381817.4
Median Absolute Deviation (MAD)0
Skewness2.3346383
Sum37309069
Variance8.005513 × 1012
MonotonicityNot monotonic
2023-12-13T02:28:31.800088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16
59.3%
7352456 2
 
7.4%
10914994 1
 
3.7%
1044768 1
 
3.7%
1606443 1
 
3.7%
3244348 1
 
3.7%
4154120 1
 
3.7%
865314 1
 
3.7%
387085 1
 
3.7%
280489 1
 
3.7%
ValueCountFrequency (%)
0 16
59.3%
106596 1
 
3.7%
280489 1
 
3.7%
387085 1
 
3.7%
865314 1
 
3.7%
1044768 1
 
3.7%
1606443 1
 
3.7%
3244348 1
 
3.7%
4154120 1
 
3.7%
7352456 2
 
7.4%
ValueCountFrequency (%)
10914994 1
3.7%
7352456 2
7.4%
4154120 1
3.7%
3244348 1
3.7%
1606443 1
3.7%
1044768 1
3.7%
865314 1
3.7%
387085 1
3.7%
280489 1
3.7%
106596 1
3.7%

Interactions

2023-12-13T02:28:23.934298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:57.794368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:59.080586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:00.848788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:02.853971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:04.564362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:06.012360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:07.588654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:09.531430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:11.466684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:13.198494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:14.489622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:16.496993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:19.190353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:20.786716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:22.307610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:24.036629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:57.856596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:59.175311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:00.984436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:02.955819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:04.640237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:06.126191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:07.678150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:09.616214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:11.558770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:13.277415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:14.566332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:16.620717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:19.310262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:20.865227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:22.382978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:24.143824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:57.925390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:59.295946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:01.133654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:03.058907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:04.744388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:06.239956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:07.780164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:09.750137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:11.677529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:13.365492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:14.657754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:16.757046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T02:28:04.458321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:05.891833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:07.483423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:09.441249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:11.351896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:13.102532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:14.413698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:16.381086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:19.007300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:20.700780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:22.214254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:28:23.850716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:28:31.899442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류망우본동망우제3동면목본동면목제2동면목3,8동면목제4동면목제5동묵제1동묵제2동상봉제1동상봉제2동신내1동신내2동중화제1동중화제2동합계(제곱미터)
대분류1.0000.0000.1700.1700.0000.2080.2840.2840.0000.2840.2840.1080.3610.2840.3610.5160.0000.170
중분류0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6680.6600.8390.000
망우본동0.1700.0001.0000.9930.9711.0000.9400.8950.8061.0001.0000.9401.0000.8951.0000.9461.0001.000
망우제3동0.1700.0000.9931.0000.9711.0000.9401.0000.8060.9400.9400.9400.9711.0000.9710.9460.8950.993
면목본동0.0000.0000.9710.9711.0000.8940.8180.7620.9160.8520.8080.7740.9460.7620.9370.8330.7730.971
면목제2동0.2080.0001.0001.0000.8941.0000.9880.9880.9590.9970.9970.9880.8930.9880.8930.8170.9881.000
면목3,8동0.2840.0000.9400.9400.8180.9881.0000.9870.9380.9930.9790.9880.8760.9870.8510.7400.9680.940
면목제4동0.2840.0000.8951.0000.7620.9880.9871.0000.8950.9790.9790.9820.8271.0000.7610.7720.9350.895
면목제5동0.0000.0000.8060.8060.9160.9590.9380.8951.0000.9460.9130.8910.7170.8950.6880.5080.8900.806
묵제1동0.2840.0001.0000.9400.8520.9970.9930.9790.9461.0000.9940.9790.9080.9791.0000.7400.9881.000
묵제2동0.2840.0001.0000.9400.8080.9970.9790.9790.9130.9941.0000.9891.0000.9790.8580.8360.9881.000
상봉제1동0.1080.0000.9400.9400.7740.9880.9880.9820.8910.9790.9891.0000.8450.9820.7840.8790.9730.940
상봉제2동0.3610.0001.0000.9710.9460.8930.8760.8270.7170.9081.0000.8451.0000.8270.9550.8960.8351.000
신내1동0.2840.0000.8951.0000.7620.9880.9871.0000.8950.9790.9790.9820.8271.0000.7610.7720.9350.895
신내2동0.3610.6681.0000.9710.9370.8930.8510.7610.6881.0000.8580.7840.9550.7611.0000.9501.0001.000
중화제1동0.5160.6600.9460.9460.8330.8170.7400.7720.5080.7400.8360.8790.8960.7720.9501.0000.8670.946
중화제2동0.0000.8391.0000.8950.7730.9880.9680.9350.8900.9880.9880.9730.8350.9351.0000.8671.0001.000
합계(제곱미터)0.1700.0001.0000.9930.9711.0000.9400.8950.8061.0001.0000.9401.0000.8951.0000.9461.0001.000
2023-12-13T02:28:32.065829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
망우본동망우제3동면목본동면목제2동면목3,8동면목제4동면목제5동묵제1동묵제2동상봉제1동상봉제2동신내1동신내2동중화제1동중화제2동합계(제곱미터)대분류
망우본동1.0000.9980.6660.8240.8840.8580.6180.9520.9330.8630.9890.8620.9990.8690.8770.8660.056
망우제3동0.9981.0000.6630.8220.8800.8580.6160.9480.9250.8600.9800.8620.9950.8720.8760.8640.056
면목본동0.6660.6631.0000.6440.6760.7460.6980.7950.8200.7210.7220.6370.6630.6710.7100.6930.000
면목제2동0.8240.8220.6441.0000.9610.9820.9180.8780.8670.9870.8050.9880.8210.9490.9550.9910.147
면목3,8동0.8840.8800.6760.9611.0000.9550.8200.9220.9160.9570.8750.9530.8860.9160.9170.9610.216
면목제4동0.8580.8580.7460.9820.9551.0000.9210.9040.8940.9960.8540.9850.8540.9400.9520.9920.216
면목제5동0.6180.6160.6980.9180.8200.9211.0000.7190.7290.9180.6260.8940.6160.8510.8720.9130.000
묵제1동0.9520.9480.7950.8780.9220.9040.7191.0000.9930.9070.9490.8820.9510.9090.9220.9040.216
묵제2동0.9330.9250.8200.8670.9160.8940.7290.9931.0000.9010.9440.8680.9360.9010.9180.8970.216
상봉제1동0.8630.8600.7210.9870.9570.9960.9180.9070.9011.0000.8600.9910.8620.9530.9650.9980.032
상봉제2동0.9890.9800.7220.8050.8750.8540.6260.9490.9440.8601.0000.8460.9920.8500.8650.8560.130
신내1동0.8620.8620.6370.9880.9530.9850.8940.8820.8680.9910.8461.0000.8600.9610.9610.9950.216
신내2동0.9990.9950.6630.8210.8860.8540.6160.9510.9360.8620.9920.8601.0000.8680.8760.8650.130
중화제1동0.8690.8720.6710.9490.9160.9400.8510.9090.9010.9530.8500.9610.8681.0000.9940.9630.211
중화제2동0.8770.8760.7100.9550.9170.9520.8720.9220.9180.9650.8650.9610.8760.9941.0000.9700.000
합계(제곱미터)0.8660.8640.6930.9910.9610.9920.9130.9040.8970.9980.8560.9950.8650.9630.9701.0000.056
대분류0.0560.0560.0000.1470.2160.2160.0000.2160.2160.0320.1300.2160.1300.2110.0000.0561.000

Missing values

2023-12-13T02:28:25.643733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:28:25.882221image/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

대분류중분류망우본동망우제3동면목본동면목제2동면목3,8동면목제4동면목제5동묵제1동묵제2동상봉제1동상봉제2동신내1동신내2동중화제1동중화제2동합계(제곱미터)
0주거지역소계4834714086248439854913217229687173345036245037557873848786265239251173148479344125119247907310914994
1주거지역제1종전용주거지역0000000000000000
2주거지역제2종전용주거지역0000000000000000
3주거지역전용주거지역 미분류0000000000000000
4주거지역제1종일반주거지역1386456938115132448392280119180446231998657411324793370019708343312177006112171044768
5주거지역제2종일반주거지역637303434373332855138168670204657576872243587134991172156133868787922236291192171606443
6주거지역제3종일반주거지역1118362193791671401066957486923500512982595769129977228609117679399090763785793202296283244348
7주거지역제2종일반주거지역(7층이하)29404113196450696527237738454224036374641282904281114330995200391411892280862127147717874154120
8주거지역일반주거지역 미분류0000000000000000
9주거지역준주거지역00114634549113311055766248120162126696551551031215014408947224865314
대분류중분류망우본동망우제3동면목본동면목제2동면목3,8동면목제4동면목제5동묵제1동묵제2동상봉제1동상봉제2동신내1동신내2동중화제1동중화제2동합계(제곱미터)
17공업지역소계0000000000000000
18공업지역전용공업지역0000000000000000
19공업지역일반공업지역0000000000000000
20공업지역준공업지역0000000000000000
21공업지역공업지역 기타0000000000000000
22녹지지역소계38702629493806735438920471697475613141904231402281008133817176697244514913782294490287352456
23녹지지역보전녹지지역0000000000000000
24녹지지역생산녹지지역0000000000000000
25녹지지역자연녹지지역38702629493806735438920471697475613141904231402281008133817176697244514913782294490287352456
26녹지지역녹지지역 기타0000000000000000