Overview

Dataset statistics

Number of variables15
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory139.1 B

Variable types

Text1
Numeric13
Categorical1

Dataset

Description서울주택도시공사의 자치구별 임대주택 현황( 영구임대 영구임대,공공임대,국민임대,장기전세,주거환경,외국인임대,행복주택,재개발임대,역세권청년,다가구,도시형생활주택,전세임대,장기안심,기타임대)입니다.(단위 : 세대수)
Author서울주택도시공사
URLhttps://www.data.go.kr/data/15063237/fileData.do

Alerts

영구임대 is highly overall correlated with 공공임대 and 2 other fieldsHigh correlation
공공임대 is highly overall correlated with 영구임대 High correlation
국민임대 is highly overall correlated with 장기전세 and 2 other fieldsHigh correlation
장기전세 is highly overall correlated with 국민임대 and 3 other fieldsHigh correlation
행복주택 is highly overall correlated with 국민임대 and 4 other fieldsHigh correlation
다가구 is highly overall correlated with 도시형생활주택 and 2 other fieldsHigh correlation
도시형생활주택 is highly overall correlated with 행복주택 and 1 other fieldsHigh correlation
전세임대 is highly overall correlated with 다가구 and 1 other fieldsHigh correlation
장기안심 is highly overall correlated with 다가구 and 1 other fieldsHigh correlation
기타임대 is highly overall correlated with 영구임대 and 3 other fieldsHigh correlation
외국인임대 is highly overall correlated with 영구임대 and 2 other fieldsHigh correlation
외국인임대 is highly imbalanced (70.5%)Imbalance
자치구 has unique valuesUnique
다가구 has unique valuesUnique
도시형생활주택 has unique valuesUnique
전세임대 has unique valuesUnique
장기안심 has unique valuesUnique
영구임대 has 20 (76.9%) zerosZeros
공공임대 has 18 (69.2%) zerosZeros
국민임대 has 12 (46.2%) zerosZeros
장기전세 has 4 (15.4%) zerosZeros
주거환경 has 18 (69.2%) zerosZeros
행복주택 has 2 (7.7%) zerosZeros
재개발임대 has 3 (11.5%) zerosZeros
역세권청년 has 8 (30.8%) zerosZeros
다가구 has 1 (3.8%) zerosZeros
도시형생활주택 has 1 (3.8%) zerosZeros
전세임대 has 1 (3.8%) zerosZeros
장기안심 has 1 (3.8%) zerosZeros
기타임대 has 8 (30.8%) zerosZeros

Reproduction

Analysis started2023-12-12 20:53:28.171873
Analysis finished2023-12-12 20:53:46.184847
Duration18.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T05:53:46.339377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6
Min length5

Characters and Unicode

Total characters156
Distinct characters41
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

Unique26 ?
Unique (%)100.0%

Sample

1st row 강남구
2nd row 강동구
3rd row 강북구
4th row 강서구
5th row 관악구
ValueCountFrequency (%)
강남구 1
 
3.8%
강동구 1
 
3.8%
중랑구 1
 
3.8%
중구 1
 
3.8%
종로구 1
 
3.8%
은평구 1
 
3.8%
용산구 1
 
3.8%
영등포구 1
 
3.8%
양천구 1
 
3.8%
송파구 1
 
3.8%
Other values (16) 16
61.5%
2023-12-13T05:53:46.683625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
75
48.1%
26
 
16.7%
4
 
2.6%
4
 
2.6%
3
 
1.9%
2
 
1.3%
2
 
1.3%
2
 
1.3%
2
 
1.3%
2
 
1.3%
Other values (31) 34
21.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81
51.9%
Space Separator 75
48.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
32.1%
4
 
4.9%
4
 
4.9%
3
 
3.7%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
Other values (30) 32
39.5%
Space Separator
ValueCountFrequency (%)
75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81
51.9%
Common 75
48.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
32.1%
4
 
4.9%
4
 
4.9%
3
 
3.7%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
Other values (30) 32
39.5%
Common
ValueCountFrequency (%)
75
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81
51.9%
ASCII 75
48.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
75
100.0%
Hangul
ValueCountFrequency (%)
26
32.1%
4
 
4.9%
4
 
4.9%
3
 
3.7%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
Other values (30) 32
39.5%

영구임대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean872
Minimum0
Maximum7441
Zeros20
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:46.796999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5714.75
Maximum7441
Range7441
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2033.8991
Coefficient of variation (CV)2.3324531
Kurtosis5.0031013
Mean872
Median Absolute Deviation (MAD)0
Skewness2.410892
Sum22672
Variance4136745.6
MonotonicityNot monotonic
2023-12-13T05:53:46.891183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 20
76.9%
4271 1
 
3.8%
7441 1
 
3.8%
6196 1
 
3.8%
1807 1
 
3.8%
146 1
 
3.8%
2811 1
 
3.8%
ValueCountFrequency (%)
0 20
76.9%
146 1
 
3.8%
1807 1
 
3.8%
2811 1
 
3.8%
4271 1
 
3.8%
6196 1
 
3.8%
7441 1
 
3.8%
ValueCountFrequency (%)
7441 1
 
3.8%
6196 1
 
3.8%
4271 1
 
3.8%
2811 1
 
3.8%
1807 1
 
3.8%
146 1
 
3.8%
0 20
76.9%

공공임대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean670.46154
Minimum0
Maximum5492
Zeros18
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:46.982101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3663.75
95-th percentile4247
Maximum5492
Range5492
Interquartile range (IQR)663.75

Descriptive statistics

Standard deviation1461.5762
Coefficient of variation (CV)2.1799553
Kurtosis5.8886463
Mean670.46154
Median Absolute Deviation (MAD)0
Skewness2.544096
Sum17432
Variance2136205
MonotonicityNot monotonic
2023-12-13T05:53:47.081973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 18
69.2%
1230 1
 
3.8%
2870 1
 
3.8%
5492 1
 
3.8%
820 1
 
3.8%
1258 1
 
3.8%
4706 1
 
3.8%
195 1
 
3.8%
861 1
 
3.8%
ValueCountFrequency (%)
0 18
69.2%
195 1
 
3.8%
820 1
 
3.8%
861 1
 
3.8%
1230 1
 
3.8%
1258 1
 
3.8%
2870 1
 
3.8%
4706 1
 
3.8%
5492 1
 
3.8%
ValueCountFrequency (%)
5492 1
 
3.8%
4706 1
 
3.8%
2870 1
 
3.8%
1258 1
 
3.8%
1230 1
 
3.8%
861 1
 
3.8%
820 1
 
3.8%
195 1
 
3.8%
0 18
69.2%

국민임대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1209.1923
Minimum0
Maximum6404
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:47.184917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median315
Q31838
95-th percentile5203.75
Maximum6404
Range6404
Interquartile range (IQR)1838

Descriptive statistics

Standard deviation1812.2706
Coefficient of variation (CV)1.4987447
Kurtosis2.3287111
Mean1209.1923
Median Absolute Deviation (MAD)315
Skewness1.7392453
Sum31439
Variance3284324.8
MonotonicityNot monotonic
2023-12-13T05:53:47.289752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 12
46.2%
1607 1
 
3.8%
6404 1
 
3.8%
4594 1
 
3.8%
3190 1
 
3.8%
693 1
 
3.8%
1915 1
 
3.8%
1957 1
 
3.8%
400 1
 
3.8%
254 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
0 12
46.2%
254 1
 
3.8%
376 1
 
3.8%
400 1
 
3.8%
693 1
 
3.8%
1116 1
 
3.8%
1440 1
 
3.8%
1607 1
 
3.8%
1915 1
 
3.8%
1957 1
 
3.8%
ValueCountFrequency (%)
6404 1
3.8%
5407 1
3.8%
4594 1
3.8%
3190 1
3.8%
2086 1
3.8%
1957 1
3.8%
1915 1
3.8%
1607 1
3.8%
1440 1
3.8%
1116 1
3.8%

장기전세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1178.2692
Minimum0
Maximum5456
Zeros4
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:47.396819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145.75
median223
Q31958.25
95-th percentile4145.75
Maximum5456
Range5456
Interquartile range (IQR)1912.5

Descriptive statistics

Standard deviation1626.2645
Coefficient of variation (CV)1.3802147
Kurtosis0.65949395
Mean1178.2692
Median Absolute Deviation (MAD)223
Skewness1.32646
Sum30635
Variance2644736.3
MonotonicityNot monotonic
2023-12-13T05:53:47.506649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4
 
15.4%
2050 1
 
3.8%
56 1
 
3.8%
252 1
 
3.8%
1683 1
 
3.8%
3749 1
 
3.8%
48 1
 
3.8%
1633 1
 
3.8%
3034 1
 
3.8%
341 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
0 4
15.4%
2 1
 
3.8%
18 1
 
3.8%
45 1
 
3.8%
48 1
 
3.8%
56 1
 
3.8%
65 1
 
3.8%
108 1
 
3.8%
192 1
 
3.8%
194 1
 
3.8%
ValueCountFrequency (%)
5456 1
3.8%
4216 1
3.8%
3935 1
3.8%
3749 1
3.8%
3034 1
3.8%
2123 1
3.8%
2050 1
3.8%
1683 1
3.8%
1633 1
3.8%
1171 1
3.8%

주거환경
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.5
Minimum0
Maximum779
Zeros18
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:47.606970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q385
95-th percentile302
Maximum779
Range779
Interquartile range (IQR)85

Descriptive statistics

Standard deviation168.81072
Coefficient of variation (CV)2.2359036
Kurtosis12.287429
Mean75.5
Median Absolute Deviation (MAD)0
Skewness3.2628852
Sum1963
Variance28497.06
MonotonicityNot monotonic
2023-12-13T05:53:47.730055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 18
69.2%
330 1
 
3.8%
105 1
 
3.8%
779 1
 
3.8%
218 1
 
3.8%
178 1
 
3.8%
25 1
 
3.8%
175 1
 
3.8%
153 1
 
3.8%
ValueCountFrequency (%)
0 18
69.2%
25 1
 
3.8%
105 1
 
3.8%
153 1
 
3.8%
175 1
 
3.8%
178 1
 
3.8%
218 1
 
3.8%
330 1
 
3.8%
779 1
 
3.8%
ValueCountFrequency (%)
779 1
 
3.8%
330 1
 
3.8%
218 1
 
3.8%
178 1
 
3.8%
175 1
 
3.8%
153 1
 
3.8%
105 1
 
3.8%
25 1
 
3.8%
0 18
69.2%

외국인임대
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
0
24 
175
 
1
2
 
1

Length

Max length3
Median length1
Mean length1.0769231
Min length1

Unique

Unique2 ?
Unique (%)7.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24
92.3%
175 1
 
3.8%
2 1
 
3.8%

Length

2023-12-13T05:53:47.864832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:53:47.989928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24
92.3%
175 1
 
3.8%
2 1
 
3.8%

행복주택
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean633.15385
Minimum0
Maximum2302
Zeros2
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:48.089283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q1162.5
median339.5
Q3838.5
95-th percentile2096.75
Maximum2302
Range2302
Interquartile range (IQR)676

Descriptive statistics

Standard deviation701.54137
Coefficient of variation (CV)1.1080109
Kurtosis0.76597158
Mean633.15385
Median Absolute Deviation (MAD)291
Skewness1.3752657
Sum16462
Variance492160.3
MonotonicityNot monotonic
2023-12-13T05:53:48.225679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2
 
7.7%
348 2
 
7.7%
888 1
 
3.8%
199 1
 
3.8%
847 1
 
3.8%
36 1
 
3.8%
205 1
 
3.8%
1952 1
 
3.8%
137 1
 
3.8%
331 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 2
7.7%
36 1
3.8%
38 1
3.8%
59 1
3.8%
137 1
3.8%
153 1
3.8%
191 1
3.8%
199 1
3.8%
205 1
3.8%
255 1
3.8%
ValueCountFrequency (%)
2302 1
3.8%
2145 1
3.8%
1952 1
3.8%
1925 1
3.8%
1142 1
3.8%
888 1
3.8%
847 1
3.8%
813 1
3.8%
763 1
3.8%
592 1
3.8%

재개발임대
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2627.6538
Minimum0
Maximum8214
Zeros3
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:48.346478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1204.5
median1943
Q34441
95-th percentile7507.75
Maximum8214
Range8214
Interquartile range (IQR)4236.5

Descriptive statistics

Standard deviation2596.2309
Coefficient of variation (CV)0.98804146
Kurtosis-0.57340337
Mean2627.6538
Median Absolute Deviation (MAD)1904.5
Skewness0.73065103
Sum68319
Variance6740415.1
MonotonicityNot monotonic
2023-12-13T05:53:48.779343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 3
 
11.5%
81 1
 
3.8%
24 1
 
3.8%
4476 1
 
3.8%
858 1
 
3.8%
2992 1
 
3.8%
1262 1
 
3.8%
2034 1
 
3.8%
3291 1
 
3.8%
612 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 3
11.5%
24 1
 
3.8%
27 1
 
3.8%
50 1
 
3.8%
81 1
 
3.8%
575 1
 
3.8%
612 1
 
3.8%
805 1
 
3.8%
858 1
 
3.8%
1262 1
 
3.8%
ValueCountFrequency (%)
8214 1
3.8%
7802 1
3.8%
6625 1
3.8%
5672 1
3.8%
5581 1
3.8%
4481 1
3.8%
4476 1
3.8%
4336 1
3.8%
3711 1
3.8%
3291 1
3.8%

역세권청년
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.038462
Minimum0
Maximum717
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:48.917576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median57
Q3113.25
95-th percentile272.5
Maximum717
Range717
Interquartile range (IQR)113.25

Descriptive statistics

Standard deviation147.19116
Coefficient of variation (CV)1.5820464
Kurtosis13.295429
Mean93.038462
Median Absolute Deviation (MAD)57
Skewness3.3363088
Sum2419
Variance21665.238
MonotonicityNot monotonic
2023-12-13T05:53:49.079664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 8
30.8%
143 1
 
3.8%
64 1
 
3.8%
30 1
 
3.8%
116 1
 
3.8%
717 1
 
3.8%
105 1
 
3.8%
88 1
 
3.8%
22 1
 
3.8%
68 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0 8
30.8%
22 1
 
3.8%
30 1
 
3.8%
31 1
 
3.8%
37 1
 
3.8%
50 1
 
3.8%
64 1
 
3.8%
68 1
 
3.8%
75 1
 
3.8%
88 1
 
3.8%
ValueCountFrequency (%)
717 1
3.8%
286 1
3.8%
232 1
3.8%
148 1
3.8%
143 1
3.8%
118 1
3.8%
116 1
3.8%
105 1
3.8%
89 1
3.8%
88 1
3.8%

다가구
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean826.65385
Minimum0
Maximum2759
Zeros1
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:49.212162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.5
Q1274.25
median589.5
Q31260.25
95-th percentile2364
Maximum2759
Range2759
Interquartile range (IQR)986

Descriptive statistics

Standard deviation768.41598
Coefficient of variation (CV)0.92954987
Kurtosis0.54245176
Mean826.65385
Median Absolute Deviation (MAD)475.5
Skewness1.1054923
Sum21493
Variance590463.12
MonotonicityNot monotonic
2023-12-13T05:53:49.337789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
91 1
 
3.8%
534 1
 
3.8%
0 1
 
3.8%
586 1
 
3.8%
27 1
 
3.8%
137 1
 
3.8%
1279 1
 
3.8%
9 1
 
3.8%
334 1
 
3.8%
820 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
0 1
3.8%
9 1
3.8%
27 1
3.8%
91 1
3.8%
137 1
3.8%
172 1
3.8%
271 1
3.8%
284 1
3.8%
306 1
3.8%
334 1
3.8%
ValueCountFrequency (%)
2759 1
3.8%
2429 1
3.8%
2169 1
3.8%
1712 1
3.8%
1413 1
3.8%
1281 1
3.8%
1279 1
3.8%
1204 1
3.8%
1096 1
3.8%
820 1
3.8%

도시형생활주택
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean390.84615
Minimum0
Maximum1053
Zeros1
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:49.450546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1220.5
median299.5
Q3457.75
95-th percentile1044.5
Maximum1053
Range1053
Interquartile range (IQR)237.25

Descriptive statistics

Standard deviation312.96501
Coefficient of variation (CV)0.80073708
Kurtosis0.4129808
Mean390.84615
Median Absolute Deviation (MAD)151
Skewness1.1027531
Sum10162
Variance97947.095
MonotonicityNot monotonic
2023-12-13T05:53:49.575688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
237 1
 
3.8%
240 1
 
3.8%
0 1
 
3.8%
274 1
 
3.8%
60 1
 
3.8%
77 1
 
3.8%
421 1
 
3.8%
20 1
 
3.8%
465 1
 
3.8%
379 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
0 1
3.8%
20 1
3.8%
60 1
3.8%
77 1
3.8%
115 1
3.8%
125 1
3.8%
215 1
3.8%
237 1
3.8%
240 1
3.8%
263 1
3.8%
ValueCountFrequency (%)
1053 1
3.8%
1047 1
3.8%
1037 1
3.8%
932 1
3.8%
616 1
3.8%
551 1
3.8%
465 1
3.8%
436 1
3.8%
421 1
3.8%
379 1
3.8%

전세임대
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean677.69231
Minimum0
Maximum1347
Zeros1
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:49.693437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile128
Q1416.25
median637
Q3962.5
95-th percentile1214
Maximum1347
Range1347
Interquartile range (IQR)546.25

Descriptive statistics

Standard deviation374.12541
Coefficient of variation (CV)0.55205792
Kurtosis-0.91303067
Mean677.69231
Median Absolute Deviation (MAD)259.5
Skewness0.066722416
Sum17620
Variance139969.82
MonotonicityNot monotonic
2023-12-13T05:53:49.820638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
230 1
 
3.8%
243 1
 
3.8%
0 1
 
3.8%
1205 1
 
3.8%
94 1
 
3.8%
237 1
 
3.8%
1347 1
 
3.8%
530 1
 
3.8%
486 1
 
3.8%
845 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
0 1
3.8%
94 1
3.8%
230 1
3.8%
237 1
3.8%
243 1
3.8%
406 1
3.8%
412 1
3.8%
429 1
3.8%
486 1
3.8%
499 1
3.8%
ValueCountFrequency (%)
1347 1
3.8%
1217 1
3.8%
1205 1
3.8%
1167 1
3.8%
1161 1
3.8%
1004 1
3.8%
975 1
3.8%
925 1
3.8%
845 1
3.8%
832 1
3.8%

장기안심
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.46154
Minimum0
Maximum368
Zeros1
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:49.957647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q1115.5
median165.5
Q3223.25
95-th percentile316.75
Maximum368
Range368
Interquartile range (IQR)107.75

Descriptive statistics

Standard deviation89.668381
Coefficient of variation (CV)0.52296498
Kurtosis-0.12032606
Mean171.46154
Median Absolute Deviation (MAD)53
Skewness0.24168901
Sum4458
Variance8040.4185
MonotonicityNot monotonic
2023-12-13T05:53:50.091734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
107 1
 
3.8%
120 1
 
3.8%
0 1
 
3.8%
251 1
 
3.8%
31 1
 
3.8%
43 1
 
3.8%
313 1
 
3.8%
114 1
 
3.8%
121 1
 
3.8%
201 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
0 1
3.8%
31 1
3.8%
43 1
3.8%
84 1
3.8%
107 1
3.8%
111 1
3.8%
114 1
3.8%
120 1
3.8%
121 1
3.8%
135 1
3.8%
ValueCountFrequency (%)
368 1
3.8%
318 1
3.8%
313 1
3.8%
266 1
3.8%
251 1
3.8%
247 1
3.8%
226 1
3.8%
215 1
3.8%
201 1
3.8%
200 1
3.8%

기타임대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.115385
Minimum0
Maximum307
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T05:53:50.249312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29.5
Q375
95-th percentile195.25
Maximum307
Range307
Interquartile range (IQR)75

Descriptive statistics

Standard deviation73.080956
Coefficient of variation (CV)1.4582539
Kurtosis6.0820293
Mean50.115385
Median Absolute Deviation (MAD)29.5
Skewness2.3573876
Sum1303
Variance5340.8262
MonotonicityNot monotonic
2023-12-13T05:53:50.361096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 8
30.8%
30 3
 
11.5%
77 1
 
3.8%
222 1
 
3.8%
29 1
 
3.8%
26 1
 
3.8%
46 1
 
3.8%
92 1
 
3.8%
31 1
 
3.8%
115 1
 
3.8%
Other values (7) 7
26.9%
ValueCountFrequency (%)
0 8
30.8%
6 1
 
3.8%
8 1
 
3.8%
10 1
 
3.8%
26 1
 
3.8%
29 1
 
3.8%
30 3
 
11.5%
31 1
 
3.8%
46 1
 
3.8%
72 1
 
3.8%
ValueCountFrequency (%)
307 1
3.8%
222 1
3.8%
115 1
3.8%
96 1
3.8%
92 1
3.8%
77 1
3.8%
76 1
3.8%
72 1
3.8%
46 1
3.8%
31 1
3.8%

Interactions

2023-12-13T05:53:44.482625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:28.559607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.034216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.270539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.480551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.671496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.960957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.617945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.987294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.395161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.446682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.502660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:42.889753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:44.617062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:28.635343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.125349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.377861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.583587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.815312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:35.059788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.723568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:38.095069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.498282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.543868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.596026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:43.072368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:44.714181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:28.704032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.205323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.471195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.681989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.902158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:35.133307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.824615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:38.186615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.588247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.619937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.668909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:43.201630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:44.814619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:28.780715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.289996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.571131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.790740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.992115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:35.232093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.927515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:38.291524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.664355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.693379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.771465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:43.349807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:44.916726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:28.853766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.365892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.652071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.881762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.081848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:35.331812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.026114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:38.407926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.739305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.760292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.841824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:43.442888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:45.034514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:28.936726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.460924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.744065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.987673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.195751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:35.437043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.133074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:38.511713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.823660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.836680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:42.196121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:43.562901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:45.142039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:29.010998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.548332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.833471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.066096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.286994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:35.904200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.221002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:38.590983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.895053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.905631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:42.265388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:43.677339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:45.237196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:29.095298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.632760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.916610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.142036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.377282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.003419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.338264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:38.706510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.965685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.986239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:42.346488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:43.778662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:45.344429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:29.200623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.750376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.024985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.248548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.471327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.118846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.450989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:38.827993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.040641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.084196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:42.437503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:43.914047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:45.431534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:29.273312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.833697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.134018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.312975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.553027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.213813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.553081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:38.925277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.128009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.160818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:42.525660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:44.019880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:45.518514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:29.698154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:30.936463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.220125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.380369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.654049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.315178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.650458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.023354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.202430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.235513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:42.602218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:44.144147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:45.621387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:29.800298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.038013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.314201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.463287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.751813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.409696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.779242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.133338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.281038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.328743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:42.694355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:44.270257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:45.711805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:29.918553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:31.169020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:32.398902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:33.557837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:34.850927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:36.505310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:37.894339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:39.270107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:40.360512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:41.416540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:42.783758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:44.384580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:53:50.459677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구영구임대공공임대국민임대장기전세주거환경외국인임대행복주택재개발임대역세권청년다가구도시형생활주택전세임대장기안심기타임대
자치구1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
영구임대1.0001.0000.9800.7600.5200.0000.9000.0000.0000.5160.0000.0000.0380.7340.907
공공임대1.0000.9801.0000.8790.6690.0000.5320.0000.0000.4420.0000.4240.2030.7620.894
국민임대1.0000.7600.8791.0000.9190.0000.5450.7570.0000.0000.7660.8500.1720.5970.896
장기전세1.0000.5200.6690.9191.0000.0000.6630.9070.0000.0000.7150.5360.1580.5220.768
주거환경1.0000.0000.0000.0000.0001.0000.1080.0000.5450.0000.8950.0000.5700.0000.000
외국인임대1.0000.9000.5320.5450.6630.1081.0000.8410.0000.4130.0000.0000.0000.0000.551
행복주택1.0000.0000.0000.7570.9070.0000.8411.0000.0000.0000.6260.6150.0000.0000.744
재개발임대1.0000.0000.0000.0000.0000.5450.0000.0001.0000.0000.1680.4230.0000.1840.000
역세권청년1.0000.5160.4420.0000.0000.0000.4130.0000.0001.0000.0000.0000.0000.0000.161
다가구1.0000.0000.0000.7660.7150.8950.0000.6260.1680.0001.0000.7370.3560.0000.415
도시형생활주택1.0000.0000.4240.8500.5360.0000.0000.6150.4230.0000.7371.0000.5670.6560.456
전세임대1.0000.0380.2030.1720.1580.5700.0000.0000.0000.0000.3560.5671.0000.7800.661
장기안심1.0000.7340.7620.5970.5220.0000.0000.0000.1840.0000.0000.6560.7801.0000.720
기타임대1.0000.9070.8940.8960.7680.0000.5510.7440.0000.1610.4150.4560.6610.7201.000
2023-12-13T05:53:50.648139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영구임대공공임대국민임대장기전세주거환경행복주택재개발임대역세권청년다가구도시형생활주택전세임대장기안심기타임대외국인임대
영구임대1.0000.6630.4710.443-0.1770.179-0.4040.393-0.210-0.312-0.0020.2260.5310.575
공공임대0.6631.0000.4620.395-0.0280.250-0.2840.289-0.055-0.0180.1600.4230.4520.224
국민임대0.4710.4621.0000.907-0.1690.681-0.3880.1450.2630.2190.1750.4090.7360.349
장기전세0.4430.3950.9071.000-0.1160.719-0.3300.0980.3640.3700.1760.4100.7670.510
주거환경-0.177-0.028-0.169-0.1161.000-0.0430.1620.2550.1330.1170.2690.219-0.1770.000
행복주택0.1790.2500.6810.719-0.0431.000-0.0030.0720.3600.5000.1470.3800.6090.737
재개발임대-0.404-0.284-0.388-0.3300.162-0.0031.000-0.270-0.0110.342-0.105-0.089-0.2870.000
역세권청년0.3930.2890.1450.0980.2550.072-0.2701.000-0.104-0.2550.1660.295-0.0540.319
다가구-0.210-0.0550.2630.3640.1330.360-0.011-0.1041.0000.6960.5900.5070.3500.000
도시형생활주택-0.312-0.0180.2190.3700.1170.5000.342-0.2550.6961.0000.2390.2890.1560.000
전세임대-0.0020.1600.1750.1760.2690.147-0.1050.1660.5900.2391.0000.8630.2560.000
장기안심0.2260.4230.4090.4100.2190.380-0.0890.2950.5070.2890.8631.0000.4360.000
기타임대0.5310.4520.7360.767-0.1770.609-0.287-0.0540.3500.1560.2560.4361.0000.236
외국인임대0.5750.2240.3490.5100.0000.7370.0000.3190.0000.0000.0000.0000.2361.000

Missing values

2023-12-13T05:53:45.851508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:53:46.095654image/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

자치구영구임대공공임대국민임대장기전세주거환경외국인임대행복주택재개발임대역세권청년다가구도시형생활주택전세임대장기안심기타임대
0강남구42711230160720500088801439123723010730
1강동구0064045456002302575502429104783221576
2강북구000180015337110120428211611740
3강서구74412870459442160019102327281251167318307
4관악구0002330025556723127127710042470
5광진구000450038271485932639752260
6구로구003190212300214580502169103765114396
7금천구00019210500185202759105360311110
8노원구619654926931940032829587544931777826672
9도봉구000077905950118171211512171978
자치구영구임대공공임대국민임대장기전세주거환경외국인임대행복주택재개발임대역세권청년다가구도시형생활주택전세임대장기안심기타임대
16성북구002543410076378020128155172120077
17송파구0125854073034250192561288141393292536831
18양천구0470611161633008133291082037984520192
19영등포구0195048175033120341053344654861210
20용산구00000013712627179205301140
21은평구00208637491530195229921161279421134731346
22종로구0000002058580137772374326
23중구00000036447602760943129
24중랑구2811861144016830084724305862741205251222
25의정부시003762520000000000