Overview

Dataset statistics

Number of variables15
Number of observations398
Missing cells548
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.2 KiB
Average record size in memory134.3 B

Variable types

Text1
Numeric14

Dataset

Description상업용 부동산의 층별효용비율을 조사한 데이터
Author한국부동산원
URLhttps://www.data.go.kr/data/15002275/fileData.do

Alerts

2019(1)_지하1층 is highly overall correlated with 2019(1)_1층 and 12 other fieldsHigh correlation
2019(1)_1층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(1)_2층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(1)_3층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(1)_4층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(1)_5층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(1)_6-10층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(2)_지하1층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(2)_1층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(2)_2층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(2)_3층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(2)_4층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(2)_5층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(2)_6-10층 is highly overall correlated with 2019(1)_지하1층 and 12 other fieldsHigh correlation
2019(1)_지하1층 has 90 (22.6%) missing valuesMissing
2019(1)_2층 has 4 (1.0%) missing valuesMissing
2019(1)_3층 has 14 (3.5%) missing valuesMissing
2019(1)_4층 has 32 (8.0%) missing valuesMissing
2019(1)_5층 has 58 (14.6%) missing valuesMissing
2019(1)_6-10층 has 74 (18.6%) missing valuesMissing
2019(2)_지하1층 has 90 (22.6%) missing valuesMissing
2019(2)_2층 has 4 (1.0%) missing valuesMissing
2019(2)_3층 has 14 (3.5%) missing valuesMissing
2019(2)_4층 has 32 (8.0%) missing valuesMissing
2019(2)_5층 has 58 (14.6%) missing valuesMissing
2019(2)_6-10층 has 74 (18.6%) missing valuesMissing
시도 광역상권 하위상권 구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:32:12.316830
Analysis finished2023-12-12 06:32:34.639463
Duration22.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct398
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2023-12-12T15:32:34.906749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length11.394472
Min length6

Characters and Unicode

Total characters4535
Distinct characters192
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique398 ?
Unique (%)100.0%

Sample

1st row서울 임대료
2nd row서울 효용비율
3rd row서울 도심 임대료
4th row서울 도심 효용비율
5th row서울 도심 동대문 임대료
ValueCountFrequency (%)
임대료 199
 
16.2%
효용비율 199
 
16.2%
경기 78
 
6.4%
서울 76
 
6.2%
경남 36
 
2.9%
기타 34
 
2.8%
충남 24
 
2.0%
부산 24
 
2.0%
강남 20
 
1.6%
충북 18
 
1.5%
Other values (191) 520
42.3%
2023-12-12T15:32:35.398536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
830
18.3%
255
 
5.6%
205
 
4.5%
199
 
4.4%
199
 
4.4%
199
 
4.4%
199
 
4.4%
199
 
4.4%
134
 
3.0%
112
 
2.5%
Other values (182) 2004
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3699
81.6%
Space Separator 830
 
18.3%
Decimal Number 4
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
255
 
6.9%
205
 
5.5%
199
 
5.4%
199
 
5.4%
199
 
5.4%
199
 
5.4%
199
 
5.4%
134
 
3.6%
112
 
3.0%
108
 
2.9%
Other values (178) 1890
51.1%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 2
50.0%
Space Separator
ValueCountFrequency (%)
830
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3699
81.6%
Common 836
 
18.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
255
 
6.9%
205
 
5.5%
199
 
5.4%
199
 
5.4%
199
 
5.4%
199
 
5.4%
199
 
5.4%
134
 
3.6%
112
 
3.0%
108
 
2.9%
Other values (178) 1890
51.1%
Common
ValueCountFrequency (%)
830
99.3%
2 2
 
0.2%
/ 2
 
0.2%
1 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3699
81.6%
ASCII 836
 
18.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
830
99.3%
2 2
 
0.2%
/ 2
 
0.2%
1 2
 
0.2%
Hangul
ValueCountFrequency (%)
255
 
6.9%
205
 
5.5%
199
 
5.4%
199
 
5.4%
199
 
5.4%
199
 
5.4%
199
 
5.4%
134
 
3.6%
112
 
3.0%
108
 
2.9%
Other values (178) 1890
51.1%

2019(1)_지하1층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct232
Distinct (%)75.3%
Missing90
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean16.988084
Minimum1.7
Maximum172.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:35.582845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile3.235
Q15.7
median15
Q322.51
95-th percentile44.1265
Maximum172.3
Range170.6
Interquartile range (IQR)16.81

Descriptive statistics

Standard deviation15.663363
Coefficient of variation (CV)0.92202054
Kurtosis32.048377
Mean16.988084
Median Absolute Deviation (MAD)8.55
Skewness3.9798657
Sum5232.33
Variance245.34094
MonotonicityNot monotonic
2023-12-12T15:32:35.748968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.7 6
 
1.5%
6.6 5
 
1.3%
4.2 5
 
1.3%
4.1 5
 
1.3%
4.3 5
 
1.3%
6.9 5
 
1.3%
5.8 4
 
1.0%
3.7 4
 
1.0%
2.9 4
 
1.0%
3.4 4
 
1.0%
Other values (222) 261
65.6%
(Missing) 90
 
22.6%
ValueCountFrequency (%)
1.7 2
0.5%
2.3 2
0.5%
2.5 2
0.5%
2.6 3
0.8%
2.9 4
1.0%
3.0 1
 
0.3%
3.2 2
0.5%
3.3 1
 
0.3%
3.4 4
1.0%
3.5 2
0.5%
ValueCountFrequency (%)
172.3 1
0.3%
83.92 1
0.3%
76.13 1
0.3%
58.72 1
0.3%
58.58 1
0.3%
52.7 1
0.3%
50.54 1
0.3%
49.9 1
0.3%
48.97 1
0.3%
45.97 1
0.3%

2019(1)_1층
Real number (ℝ)

HIGH CORRELATION 

Distinct166
Distinct (%)41.9%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean65.394192
Minimum4.7
Maximum205.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:35.911438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile13.075
Q125.225
median100
Q3100
95-th percentile100
Maximum205.3
Range200.6
Interquartile range (IQR)74.775

Descriptive statistics

Standard deviation37.795686
Coefficient of variation (CV)0.57796702
Kurtosis-1.3706257
Mean65.394192
Median Absolute Deviation (MAD)2.6
Skewness-0.12663762
Sum25896.1
Variance1428.5139
MonotonicityNot monotonic
2023-12-12T15:32:36.427684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 198
49.7%
52.6 3
 
0.8%
23.6 3
 
0.8%
10.6 3
 
0.8%
21.0 3
 
0.8%
20.3 2
 
0.5%
33.2 2
 
0.5%
16.7 2
 
0.5%
13.0 2
 
0.5%
14.7 2
 
0.5%
Other values (156) 176
44.2%
ValueCountFrequency (%)
4.7 1
0.3%
6.3 2
0.5%
8.8 1
0.3%
8.9 1
0.3%
9.2 1
0.3%
9.4 1
0.3%
9.6 1
0.3%
9.8 1
0.3%
10.1 1
0.3%
10.2 1
0.3%
ValueCountFrequency (%)
205.3 1
 
0.3%
105.2 1
 
0.3%
100.0 198
49.7%
87.8 1
 
0.3%
85.0 1
 
0.3%
79.0 1
 
0.3%
76.0 1
 
0.3%
75.0 1
 
0.3%
74.7 1
 
0.3%
74.1 1
 
0.3%

2019(1)_2층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct278
Distinct (%)70.6%
Missing4
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean27.301015
Minimum1.2
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:36.577127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile5.365
Q110.025
median26.9
Q340.25
95-th percentile58.81
Maximum156
Range154.8
Interquartile range (IQR)30.225

Descriptive statistics

Standard deviation18.763076
Coefficient of variation (CV)0.68726661
Kurtosis4.4111852
Mean27.301015
Median Absolute Deviation (MAD)15.65
Skewness1.1362033
Sum10756.6
Variance352.05303
MonotonicityNot monotonic
2023-12-12T15:32:36.765951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.7 5
 
1.3%
8.5 4
 
1.0%
38.0 4
 
1.0%
8.1 4
 
1.0%
8.6 4
 
1.0%
35.4 4
 
1.0%
27.1 4
 
1.0%
38.6 4
 
1.0%
12.7 3
 
0.8%
7.8 3
 
0.8%
Other values (268) 355
89.2%
(Missing) 4
 
1.0%
ValueCountFrequency (%)
1.2 1
0.3%
1.5 1
0.3%
3.3 1
0.3%
3.6 1
0.3%
3.8 1
0.3%
4.0 1
0.3%
4.2 1
0.3%
4.3 1
0.3%
4.4 2
0.5%
4.5 1
0.3%
ValueCountFrequency (%)
156.0 1
0.3%
80.8 1
0.3%
76.0 1
0.3%
70.8 1
0.3%
67.1 1
0.3%
66.7 1
0.3%
65.9 1
0.3%
65.1 1
0.3%
64.9 1
0.3%
64.5 1
0.3%

2019(1)_3층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct244
Distinct (%)63.5%
Missing14
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean22.413281
Minimum2.8
Maximum124.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:36.957076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile4.5
Q17.975
median20.9
Q332
95-th percentile49.57
Maximum124.3
Range121.5
Interquartile range (IQR)24.025

Descriptive statistics

Standard deviation16.431939
Coefficient of variation (CV)0.73313402
Kurtosis5.7619619
Mean22.413281
Median Absolute Deviation (MAD)12.35
Skewness1.5528064
Sum8606.7
Variance270.00862
MonotonicityNot monotonic
2023-12-12T15:32:37.111670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 8
 
2.0%
6.8 6
 
1.5%
8.5 6
 
1.5%
5.9 6
 
1.5%
4.4 5
 
1.3%
8.8 5
 
1.3%
28.9 5
 
1.3%
6.4 4
 
1.0%
7.7 4
 
1.0%
18.3 4
 
1.0%
Other values (234) 331
83.2%
(Missing) 14
 
3.5%
ValueCountFrequency (%)
2.8 1
 
0.3%
2.9 1
 
0.3%
3.0 1
 
0.3%
3.2 1
 
0.3%
3.4 1
 
0.3%
3.6 1
 
0.3%
3.7 1
 
0.3%
4.1 3
0.8%
4.2 1
 
0.3%
4.3 3
0.8%
ValueCountFrequency (%)
124.3 1
0.3%
119.1 1
0.3%
65.2 1
0.3%
62.3 1
0.3%
61.8 1
0.3%
61.3 1
0.3%
60.6 1
0.3%
60.1 1
0.3%
59.0 1
0.3%
57.7 2
0.5%

2019(1)_4층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct238
Distinct (%)65.0%
Missing32
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean20.18388
Minimum1.8
Maximum112.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:37.329731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile4.525
Q17.325
median18.3
Q328.675
95-th percentile47.35
Maximum112.9
Range111.1
Interquartile range (IQR)21.35

Descriptive statistics

Standard deviation14.922016
Coefficient of variation (CV)0.73930365
Kurtosis4.0115903
Mean20.18388
Median Absolute Deviation (MAD)10.8
Skewness1.4237397
Sum7387.3
Variance222.66656
MonotonicityNot monotonic
2023-12-12T15:32:37.518679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1 6
 
1.5%
5.2 5
 
1.3%
6.6 5
 
1.3%
6.7 5
 
1.3%
5.4 5
 
1.3%
24.8 4
 
1.0%
6.8 4
 
1.0%
6.9 4
 
1.0%
5.7 4
 
1.0%
7.5 4
 
1.0%
Other values (228) 320
80.4%
(Missing) 32
 
8.0%
ValueCountFrequency (%)
1.8 1
0.3%
2.1 1
0.3%
2.2 1
0.3%
2.5 1
0.3%
3.0 1
0.3%
3.1 2
0.5%
3.5 1
0.3%
3.6 2
0.5%
3.8 1
0.3%
3.9 1
0.3%
ValueCountFrequency (%)
112.9 1
0.3%
72.2 1
0.3%
69.5 1
0.3%
64.8 1
0.3%
64.7 1
0.3%
62.8 1
0.3%
61.6 1
0.3%
58.8 1
0.3%
55.3 1
0.3%
53.2 1
0.3%

2019(1)_5층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct216
Distinct (%)63.5%
Missing58
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean19.516471
Minimum3.1
Maximum107.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:37.726010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile4.4
Q16.875
median17.45
Q326.9
95-th percentile43.66
Maximum107.5
Range104.4
Interquartile range (IQR)20.025

Descriptive statistics

Standard deviation14.408817
Coefficient of variation (CV)0.7382901
Kurtosis3.8192712
Mean19.516471
Median Absolute Deviation (MAD)10.1
Skewness1.4158131
Sum6635.6
Variance207.61401
MonotonicityNot monotonic
2023-12-12T15:32:37.902870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1 7
 
1.8%
6.5 6
 
1.5%
6.0 5
 
1.3%
5.3 4
 
1.0%
7.6 4
 
1.0%
17.9 4
 
1.0%
6.6 4
 
1.0%
30.7 4
 
1.0%
5.1 4
 
1.0%
5.4 4
 
1.0%
Other values (206) 294
73.9%
(Missing) 58
 
14.6%
ValueCountFrequency (%)
3.1 1
0.3%
3.3 2
0.5%
3.4 1
0.3%
3.5 1
0.3%
3.6 2
0.5%
3.7 1
0.3%
3.8 1
0.3%
3.9 1
0.3%
4.0 2
0.5%
4.1 1
0.3%
ValueCountFrequency (%)
107.5 1
0.3%
65.0 1
0.3%
62.9 1
0.3%
61.6 1
0.3%
60.6 1
0.3%
57.8 1
0.3%
55.3 1
0.3%
54.1 1
0.3%
54.0 1
0.3%
53.6 1
0.3%

2019(1)_6-10층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct209
Distinct (%)64.5%
Missing74
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean18.64537
Minimum2.5
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:38.137230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile4.2
Q16.9
median15.4
Q326.425
95-th percentile44.7
Maximum92
Range89.5
Interquartile range (IQR)19.525

Descriptive statistics

Standard deviation14.518248
Coefficient of variation (CV)0.77865161
Kurtosis3.1247062
Mean18.64537
Median Absolute Deviation (MAD)9.05
Skewness1.5048897
Sum6041.1
Variance210.77951
MonotonicityNot monotonic
2023-12-12T15:32:38.298764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.9 6
 
1.5%
6.6 5
 
1.3%
5.2 5
 
1.3%
9.2 5
 
1.3%
5.5 4
 
1.0%
7.2 4
 
1.0%
5.6 4
 
1.0%
7.5 4
 
1.0%
5.0 4
 
1.0%
5.8 4
 
1.0%
Other values (199) 279
70.1%
(Missing) 74
 
18.6%
ValueCountFrequency (%)
2.5 1
0.3%
2.7 1
0.3%
2.9 1
0.3%
3.0 2
0.5%
3.3 1
0.3%
3.4 1
0.3%
3.5 1
0.3%
3.7 2
0.5%
3.8 2
0.5%
3.9 2
0.5%
ValueCountFrequency (%)
92.0 1
0.3%
77.6 1
0.3%
71.4 1
0.3%
68.2 1
0.3%
64.9 1
0.3%
60.2 1
0.3%
59.1 1
0.3%
58.4 1
0.3%
57.5 1
0.3%
56.7 1
0.3%

2019(2)_지하1층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct227
Distinct (%)73.7%
Missing90
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean17.042825
Minimum1.7
Maximum172.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:38.483999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile3.27
Q15.775
median15.05
Q322.5075
95-th percentile43.9975
Maximum172.3
Range170.6
Interquartile range (IQR)16.7325

Descriptive statistics

Standard deviation15.67247
Coefficient of variation (CV)0.91959345
Kurtosis31.914831
Mean17.042825
Median Absolute Deviation (MAD)8.6
Skewness3.9643943
Sum5249.19
Variance245.62631
MonotonicityNot monotonic
2023-12-12T15:32:38.694360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 5
 
1.3%
3.4 5
 
1.3%
4.2 5
 
1.3%
4.7 5
 
1.3%
6.6 5
 
1.3%
3.7 5
 
1.3%
4.1 4
 
1.0%
2.9 4
 
1.0%
6.9 4
 
1.0%
5.8 4
 
1.0%
Other values (217) 262
65.8%
(Missing) 90
 
22.6%
ValueCountFrequency (%)
1.7 2
 
0.5%
2.3 2
 
0.5%
2.5 3
0.8%
2.6 2
 
0.5%
2.9 4
1.0%
3.0 1
 
0.3%
3.2 2
 
0.5%
3.4 5
1.3%
3.5 2
 
0.5%
3.6 4
1.0%
ValueCountFrequency (%)
172.3 1
0.3%
83.92 1
0.3%
76.22 1
0.3%
58.72 1
0.3%
58.63 1
0.3%
52.85 1
0.3%
50.45 1
0.3%
49.9 1
0.3%
48.94 1
0.3%
46.74 1
0.3%

2019(2)_1층
Real number (ℝ)

HIGH CORRELATION 

Distinct163
Distinct (%)41.2%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean65.375
Minimum4.7
Maximum205.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:38.900175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile13
Q125.15
median100
Q3100
95-th percentile100
Maximum205.3
Range200.6
Interquartile range (IQR)74.85

Descriptive statistics

Standard deviation37.823783
Coefficient of variation (CV)0.57856648
Kurtosis-1.370627
Mean65.375
Median Absolute Deviation (MAD)2.55
Skewness-0.12763955
Sum25888.5
Variance1430.6386
MonotonicityNot monotonic
2023-12-12T15:32:39.098551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 198
49.7%
18.5 3
 
0.8%
21.0 3
 
0.8%
25.2 3
 
0.8%
10.6 3
 
0.8%
16.6 3
 
0.8%
21.8 2
 
0.5%
24.9 2
 
0.5%
30.0 2
 
0.5%
11.9 2
 
0.5%
Other values (153) 175
44.0%
ValueCountFrequency (%)
4.7 1
0.3%
6.2 1
0.3%
6.3 1
0.3%
7.6 1
0.3%
8.7 1
0.3%
8.8 1
0.3%
9.2 1
0.3%
9.4 1
0.3%
9.6 1
0.3%
10.1 1
0.3%
ValueCountFrequency (%)
205.3 1
 
0.3%
105.1 1
 
0.3%
100.0 198
49.7%
87.8 1
 
0.3%
85.0 1
 
0.3%
79.0 1
 
0.3%
77.5 1
 
0.3%
75.1 1
 
0.3%
74.7 1
 
0.3%
74.1 1
 
0.3%

2019(2)_2층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct265
Distinct (%)67.3%
Missing4
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean27.296193
Minimum1.2
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:39.304958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile5.365
Q110
median26.9
Q340
95-th percentile58.28
Maximum156
Range154.8
Interquartile range (IQR)30

Descriptive statistics

Standard deviation18.863254
Coefficient of variation (CV)0.69105806
Kurtosis4.4006967
Mean27.296193
Median Absolute Deviation (MAD)15.65
Skewness1.157746
Sum10754.7
Variance355.82235
MonotonicityNot monotonic
2023-12-12T15:32:39.503420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.7 6
 
1.5%
6.3 5
 
1.3%
34.9 4
 
1.0%
38.0 4
 
1.0%
38.4 4
 
1.0%
41.2 4
 
1.0%
8.2 4
 
1.0%
9.7 3
 
0.8%
26.9 3
 
0.8%
28.5 3
 
0.8%
Other values (255) 354
88.9%
(Missing) 4
 
1.0%
ValueCountFrequency (%)
1.2 1
0.3%
1.5 1
0.3%
3.2 1
0.3%
3.3 1
0.3%
3.6 1
0.3%
4.0 2
0.5%
4.3 1
0.3%
4.4 2
0.5%
4.5 2
0.5%
4.6 1
0.3%
ValueCountFrequency (%)
156.0 1
0.3%
82.7 1
0.3%
80.8 1
0.3%
76.0 1
0.3%
70.8 1
0.3%
66.8 1
0.3%
66.7 1
0.3%
66.2 1
0.3%
65.4 1
0.3%
65.2 1
0.3%

2019(2)_3층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct250
Distinct (%)65.1%
Missing14
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean22.420312
Minimum2.8
Maximum124.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:39.728346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile4.5
Q17.85
median20.95
Q331.925
95-th percentile49.535
Maximum124.3
Range121.5
Interquartile range (IQR)24.075

Descriptive statistics

Standard deviation16.546607
Coefficient of variation (CV)0.73801855
Kurtosis5.7907017
Mean22.420312
Median Absolute Deviation (MAD)12.35
Skewness1.5816789
Sum8609.4
Variance273.79019
MonotonicityNot monotonic
2023-12-12T15:32:39.940703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 7
 
1.8%
8.8 6
 
1.5%
5.7 6
 
1.5%
6.8 5
 
1.3%
4.3 5
 
1.3%
5.9 5
 
1.3%
8.5 5
 
1.3%
6.1 5
 
1.3%
22.1 4
 
1.0%
28.9 4
 
1.0%
Other values (240) 332
83.4%
(Missing) 14
 
3.5%
ValueCountFrequency (%)
2.8 1
 
0.3%
2.9 1
 
0.3%
3.0 1
 
0.3%
3.2 1
 
0.3%
3.4 1
 
0.3%
3.6 2
 
0.5%
4.1 3
0.8%
4.3 5
1.3%
4.4 4
1.0%
4.5 2
 
0.5%
ValueCountFrequency (%)
124.3 1
0.3%
119.2 1
0.3%
78.5 1
0.3%
65.2 1
0.3%
62.4 1
0.3%
61.8 1
0.3%
60.6 1
0.3%
60.1 1
0.3%
59.0 1
0.3%
57.7 1
0.3%

2019(2)_4층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct236
Distinct (%)64.5%
Missing32
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean20.260656
Minimum1.8
Maximum113.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:40.143093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile4.6
Q17.325
median18.3
Q328.75
95-th percentile47.2
Maximum113.1
Range111.3
Interquartile range (IQR)21.425

Descriptive statistics

Standard deviation15.100107
Coefficient of variation (CV)0.74529213
Kurtosis4.0814208
Mean20.260656
Median Absolute Deviation (MAD)10.85
Skewness1.4603783
Sum7415.4
Variance228.01324
MonotonicityNot monotonic
2023-12-12T15:32:40.328794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1 6
 
1.5%
6.7 6
 
1.5%
5.2 5
 
1.3%
6.6 5
 
1.3%
26.5 4
 
1.0%
8.8 4
 
1.0%
22.5 4
 
1.0%
6.9 4
 
1.0%
6.0 4
 
1.0%
7.5 4
 
1.0%
Other values (226) 320
80.4%
(Missing) 32
 
8.0%
ValueCountFrequency (%)
1.8 1
0.3%
2.1 1
0.3%
2.2 1
0.3%
2.5 1
0.3%
3.0 1
0.3%
3.1 2
0.5%
3.5 1
0.3%
3.6 2
0.5%
3.8 1
0.3%
3.9 1
0.3%
ValueCountFrequency (%)
113.1 1
0.3%
74.8 1
0.3%
72.2 1
0.3%
69.5 1
0.3%
64.8 2
0.5%
62.6 1
0.3%
61.6 1
0.3%
55.3 1
0.3%
53.3 1
0.3%
53.2 2
0.5%

2019(2)_5층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct223
Distinct (%)65.6%
Missing58
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean19.540882
Minimum3.1
Maximum107.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:40.502694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile4.4
Q16.975
median17.45
Q327
95-th percentile43.945
Maximum107.6
Range104.5
Interquartile range (IQR)20.025

Descriptive statistics

Standard deviation14.515846
Coefficient of variation (CV)0.74284499
Kurtosis3.815618
Mean19.540882
Median Absolute Deviation (MAD)10.05
Skewness1.4324529
Sum6643.9
Variance210.7098
MonotonicityNot monotonic
2023-12-12T15:32:40.657662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1 7
 
1.8%
6.0 5
 
1.3%
6.5 5
 
1.3%
5.3 5
 
1.3%
23.5 4
 
1.0%
7.6 4
 
1.0%
22.2 4
 
1.0%
6.6 4
 
1.0%
5.4 3
 
0.8%
17.9 3
 
0.8%
Other values (213) 296
74.4%
(Missing) 58
 
14.6%
ValueCountFrequency (%)
3.1 1
0.3%
3.3 2
0.5%
3.4 1
0.3%
3.5 1
0.3%
3.6 2
0.5%
3.7 1
0.3%
3.8 1
0.3%
3.9 2
0.5%
4.0 1
0.3%
4.1 1
0.3%
ValueCountFrequency (%)
107.6 1
0.3%
65.0 1
0.3%
64.0 1
0.3%
62.9 1
0.3%
61.6 1
0.3%
60.6 1
0.3%
57.8 1
0.3%
55.4 1
0.3%
54.1 1
0.3%
53.8 1
0.3%

2019(2)_6-10층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct207
Distinct (%)63.9%
Missing74
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean18.708025
Minimum2.5
Maximum92.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T15:32:40.816645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile4.2
Q16.9
median15.35
Q326.325
95-th percentile44.7
Maximum92.1
Range89.6
Interquartile range (IQR)19.425

Descriptive statistics

Standard deviation14.859768
Coefficient of variation (CV)0.79429912
Kurtosis4.0320988
Mean18.708025
Median Absolute Deviation (MAD)8.9
Skewness1.6659755
Sum6061.4
Variance220.81269
MonotonicityNot monotonic
2023-12-12T15:32:41.016992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 6
 
1.5%
5.5 6
 
1.5%
7.4 6
 
1.5%
9.2 5
 
1.3%
5.9 5
 
1.3%
5.2 5
 
1.3%
27.7 4
 
1.0%
8.2 4
 
1.0%
5.0 4
 
1.0%
5.8 4
 
1.0%
Other values (197) 275
69.1%
(Missing) 74
 
18.6%
ValueCountFrequency (%)
2.5 1
0.3%
2.7 1
0.3%
2.8 1
0.3%
3.0 2
0.5%
3.3 1
0.3%
3.4 2
0.5%
3.7 2
0.5%
3.8 2
0.5%
3.9 2
0.5%
4.0 2
0.5%
ValueCountFrequency (%)
92.1 1
0.3%
89.6 1
0.3%
77.6 1
0.3%
71.3 1
0.3%
68.7 1
0.3%
64.9 1
0.3%
60.0 1
0.3%
59.1 1
0.3%
58.1 1
0.3%
57.5 1
0.3%

Interactions

2023-12-12T15:32:32.861552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:12.943476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:14.706369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:16.344897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.153018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.400014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:20.949528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.344494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:23.751477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:25.448256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.924603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.557754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.813244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.568681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.938523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:13.044519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:14.857153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:16.470152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.237707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.512950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.067159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.436766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:24.188783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:25.537122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:27.076995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.664799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:30.185546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.674383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.017777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:13.167338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:15.007556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:16.594795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.347146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.615596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.179136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.540217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:24.311699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:25.625872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:27.233046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.793730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:30.306100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.757477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.115676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:13.281178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:15.142376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:16.722227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.443039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.733134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.297229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.649255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:24.413757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:25.743132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:27.343113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.898273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:30.426396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.856842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.192260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:13.389188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:15.252429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:16.842714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.545004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.828050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.392651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.739280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:24.497868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:25.862192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:27.460340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.978293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:30.525889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.954973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.277336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:13.522573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:15.344237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:16.949998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.645433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.997807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.482495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.858792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:24.588313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.001061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:27.594476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.052342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:30.642890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.050607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.346521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:13.635767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:15.452783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:17.356685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.748133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:20.115518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.563769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.963598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:24.681801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.081041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:27.717090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.128205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:30.737332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.147189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.417702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:13.759471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:15.553208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:17.439065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.822467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:20.227972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.655391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:23.045075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:24.760856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.159041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:27.823066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.198732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:30.877810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.240219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.510518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:13.909964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:15.695683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:17.544641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.910791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:20.324951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.772127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:23.157676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:24.847428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.269160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:27.931245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.288247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:30.998126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.351756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.587414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:14.050769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:15.819570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:17.653079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.004662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:20.413482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.875087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:23.252957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:24.953703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.384142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.047381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.393669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.095754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.451082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.669431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:14.214612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:15.928153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:17.796037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.091820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:20.541815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:21.988435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:23.370604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:25.066724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.513052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.172560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.497133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.192081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.552296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.769077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:14.364974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:16.052055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:17.907495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.171867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:20.668482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.098423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:23.463631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:25.152796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.626993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.269768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.590717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.276033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.633549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.852656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:14.489722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:16.153779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:17.989533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.249096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:20.769092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.180061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:23.560614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:25.242985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.715867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.365436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.663153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.380851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.711785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:33.926205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:14.590984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:16.242169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:18.076411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:19.317755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:20.860243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:22.262526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:23.643821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:25.348938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:26.818543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:28.461174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:29.740053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:31.473857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:32.789465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:32:41.167497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2019(1)_지하1층2019(1)_1층2019(1)_2층2019(1)_3층2019(1)_4층2019(1)_5층2019(1)_6-10층2019(2)_지하1층2019(2)_1층2019(2)_2층2019(2)_3층2019(2)_4층2019(2)_5층2019(2)_6-10층
2019(1)_지하1층1.0000.7270.9020.8760.7710.7410.9031.0000.7260.7410.7040.7740.7390.813
2019(1)_1층0.7271.0000.9270.8770.6440.5840.6170.7271.0000.9280.7230.6430.5860.617
2019(1)_2층0.9020.9271.0000.9460.7710.7450.9180.9020.9260.9980.8310.7660.7370.914
2019(1)_3층0.8760.8770.9461.0000.8860.9660.8560.8760.8760.9430.9890.8830.9660.814
2019(1)_4층0.7710.6440.7710.8861.0000.8840.8550.7710.6410.7760.9610.9990.8860.821
2019(1)_5층0.7410.5840.7450.9660.8841.0000.8930.7410.5800.7340.8860.8871.0000.861
2019(1)_6-10층0.9030.6170.9180.8560.8550.8931.0000.9030.6140.7900.8470.8540.8881.000
2019(2)_지하1층1.0000.7270.9020.8760.7710.7410.9031.0000.7260.7410.7040.7740.7390.813
2019(2)_1층0.7261.0000.9260.8760.6410.5800.6140.7261.0000.9270.7210.6410.5820.614
2019(2)_2층0.7410.9280.9980.9430.7760.7340.7900.7410.9271.0000.8590.7910.7330.832
2019(2)_3층0.7040.7230.8310.9890.9610.8860.8470.7040.7210.8591.0000.9650.8820.836
2019(2)_4층0.7740.6430.7660.8830.9990.8870.8540.7740.6410.7910.9651.0000.8800.819
2019(2)_5층0.7390.5860.7370.9660.8861.0000.8880.7390.5820.7330.8820.8801.0000.857
2019(2)_6-10층0.8130.6170.9140.8140.8210.8611.0000.8130.6140.8320.8360.8190.8571.000
2023-12-12T15:32:41.706112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2019(1)_지하1층2019(1)_1층2019(1)_2층2019(1)_3층2019(1)_4층2019(1)_5층2019(1)_6-10층2019(2)_지하1층2019(2)_1층2019(2)_2층2019(2)_3층2019(2)_4층2019(2)_5층2019(2)_6-10층
2019(1)_지하1층1.0000.8500.8540.8500.8430.8140.7900.9990.8510.8560.8520.8440.8110.790
2019(1)_1층0.8501.0000.9050.8920.8760.8540.8310.8521.0000.9060.8930.8760.8540.830
2019(1)_2층0.8540.9051.0000.9610.9330.9120.8850.8560.9060.9990.9600.9320.9110.884
2019(1)_3층0.8500.8920.9611.0000.9650.9440.9070.8530.8930.9600.9990.9630.9430.906
2019(1)_4층0.8430.8760.9330.9651.0000.9610.9260.8460.8760.9330.9630.9990.9600.926
2019(1)_5층0.8140.8540.9120.9440.9611.0000.9570.8180.8550.9140.9450.9621.0000.957
2019(1)_6-10층0.7900.8310.8850.9070.9260.9571.0000.7930.8310.8860.9060.9260.9561.000
2019(2)_지하1층0.9990.8520.8560.8530.8460.8180.7931.0000.8520.8580.8550.8480.8160.792
2019(2)_1층0.8511.0000.9060.8930.8760.8550.8310.8521.0000.9060.8930.8760.8540.831
2019(2)_2층0.8560.9060.9990.9600.9330.9140.8860.8580.9061.0000.9600.9340.9140.886
2019(2)_3층0.8520.8930.9600.9990.9630.9450.9060.8550.8930.9601.0000.9630.9440.906
2019(2)_4층0.8440.8760.9320.9630.9990.9620.9260.8480.8760.9340.9631.0000.9610.926
2019(2)_5층0.8110.8540.9110.9430.9601.0000.9560.8160.8540.9140.9440.9611.0000.957
2019(2)_6-10층0.7900.8300.8840.9060.9260.9571.0000.7920.8310.8860.9060.9260.9571.000

Missing values

2023-12-12T15:32:34.042599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:32:34.251202image/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.
2023-12-12T15:32:34.462039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도 광역상권 하위상권 구분2019(1)_지하1층2019(1)_1층2019(1)_2층2019(1)_3층2019(1)_4층2019(1)_5층2019(1)_6-10층2019(2)_지하1층2019(2)_1층2019(2)_2층2019(2)_3층2019(2)_4층2019(2)_5층2019(2)_6-10층
0서울 임대료15.051.722.418.415.314.714.315.051.722.318.415.314.714.3
1서울 효용비율29.09100.043.435.729.728.527.729.09100.043.235.629.728.327.7
2서울 도심 임대료28.987.854.339.318.311.16.928.987.854.339.318.310.36.9
3서울 도심 효용비율32.94100.061.944.720.812.67.832.96100.061.944.720.811.77.9
4서울 도심 동대문 임대료45.2105.266.752.353.213.213.245.2105.166.752.353.211.413.4
5서울 도심 동대문 효용비율42.95100.063.349.750.612.612.542.99100.063.449.850.610.812.7
6서울 도심 남대문 임대료172.3205.3156.0124.3<NA><NA><NA>172.3205.3156.0124.3<NA><NA><NA>
7서울 도심 남대문 효용비율83.92100.076.060.6<NA><NA><NA>83.92100.076.060.6<NA><NA><NA>
8서울 도심 을지로 임대료6.652.618.911.87.114.714.86.652.618.911.87.114.714.8
9서울 도심 을지로 효용비율12.47100.035.922.413.427.928.112.47100.035.922.413.427.928.1
시도 광역상권 하위상권 구분2019(1)_지하1층2019(1)_1층2019(1)_2층2019(1)_3층2019(1)_4층2019(1)_5층2019(1)_6-10층2019(2)_지하1층2019(2)_1층2019(2)_2층2019(2)_3층2019(2)_4층2019(2)_5층2019(2)_6-10층
388경남 창원의창구청 임대료1.78.83.63.23.0<NA><NA>1.78.73.63.23.0<NA><NA>
389경남 창원의창구청 효용비율19.49100.041.336.634.5<NA><NA>19.51100.041.236.634.5<NA><NA>
390경남 활천동 임대료<NA>10.65.1<NA><NA><NA><NA><NA>10.65.1<NA><NA><NA><NA>
391경남 활천동 효용비율<NA>100.048.4<NA><NA><NA><NA><NA>100.048.5<NA><NA><NA><NA>
392제주 임대료2.610.66.66.05.46.47.62.510.66.66.05.66.47.5
393제주 효용비율24.18100.062.156.951.160.671.423.98100.062.956.453.360.671.3
394제주 노형오거리 임대료2.912.68.26.55.96.67.32.912.68.26.55.96.67.3
395제주 노형오거리 효용비율23.16100.065.151.547.252.258.423.07100.065.251.447.252.258.1
396제주 제주 임대료3.811.07.16.25.26.18.53.810.97.26.15.86.18.5
397제주 제주 효용비율35.09100.064.356.347.455.377.634.86100.066.255.553.255.477.6