Overview

Dataset statistics

Number of variables14
Number of observations46
Missing cells88
Missing cells (%)13.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory122.9 B

Variable types

Text3
Numeric8
Categorical2
DateTime1

Dataset

Description서울특별시 금천구 아파트 단지 정보 데이터로 아파트명, 지번주소, 조경면적, 세대수 등의 항목으로 구성되어 있습니다.
Author서울특별시 금천구
URLhttps://www.data.go.kr/data/15102426/fileData.do

Alerts

주용도 has constant value ""Constant
대지면적(제곱미터) is highly overall correlated with 건축면적(제곱미터) and 5 other fieldsHigh correlation
건축면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 4 other fieldsHigh correlation
건폐율 is highly overall correlated with 대지면적(제곱미터) and 4 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 4 other fieldsHigh correlation
용적율 is highly overall correlated with 건폐율High correlation
세대수 is highly overall correlated with 대지면적(제곱미터) and 5 other fieldsHigh correlation
주 건축물 수 is highly overall correlated with 대지면적(제곱미터) and 5 other fieldsHigh correlation
부속 건축물 수 is highly overall correlated with 대지면적(제곱미터) and 5 other fieldsHigh correlation
대지면적(제곱미터) has 12 (26.1%) missing valuesMissing
건폐율 has 12 (26.1%) missing valuesMissing
용적율 has 12 (26.1%) missing valuesMissing
조경면적(제곱미터) has 40 (87.0%) missing valuesMissing
부속 건축물 수 has 12 (26.1%) missing valuesMissing
위치(주소) has unique valuesUnique
건축면적(제곱미터) has unique valuesUnique
연면적(제곱미터) has unique valuesUnique
부속 건축물 수 has 1 (2.2%) zerosZeros

Reproduction

Analysis started2023-12-12 19:33:45.893461
Analysis finished2023-12-12 19:33:54.459012
Duration8.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct41
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T04:33:54.663070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length7.3913043
Min length5

Characters and Unicode

Total characters340
Distinct characters100
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)82.6%

Sample

1st row시흥아파트
2nd row무지개아파트
3rd row럭키남서울아파트
4th row남서울건영아파트
5th row현대아파트
ValueCountFrequency (%)
금천 4
 
6.2%
롯데캐슬 4
 
6.2%
삼익아파트 3
 
4.6%
골드파크 3
 
4.6%
벽산아파트 3
 
4.6%
2차 2
 
3.1%
주공아파트 2
 
3.1%
지웰 1
 
1.5%
e편한세상 1
 
1.5%
탑스빌아파트 1
 
1.5%
Other values (41) 41
63.1%
2023-12-13T04:33:55.074045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 38
 
11.2%
트 37
 
10.9%
아 35
 
10.3%
19
 
5.6%
산 12
 
3.5%
차 6
 
1.8%
천 5
 
1.5%
드 5
 
1.5%
금 5
 
1.5%
도 4
 
1.2%
Other values (90) 174
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 312
91.8%
Space Separator 19
 
5.6%
Decimal Number 7
 
2.1%
Lowercase Letter 1
 
0.3%
Letter Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 38
 
12.2%
트 37
 
11.9%
아 35
 
11.2%
산 12
 
3.8%
차 6
 
1.9%
천 5
 
1.6%
드 5
 
1.6%
금 5
 
1.6%
도 4
 
1.3%
독 4
 
1.3%
Other values (84) 161
51.6%
Decimal Number
ValueCountFrequency (%)
2 4
57.1%
3 2
28.6%
1 1
 
14.3%
Space Separator
ValueCountFrequency (%)
19
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 312
91.8%
Common 26
 
7.6%
Latin 2
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 38
 
12.2%
트 37
 
11.9%
아 35
 
11.2%
산 12
 
3.8%
차 6
 
1.9%
천 5
 
1.6%
드 5
 
1.6%
금 5
 
1.6%
도 4
 
1.3%
독 4
 
1.3%
Other values (84) 161
51.6%
Common
ValueCountFrequency (%)
19
73.1%
2 4
 
15.4%
3 2
 
7.7%
1 1
 
3.8%
Latin
ValueCountFrequency (%)
e 1
50.0%
Ⅱ 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 312
91.8%
ASCII 27
 
7.9%
Number Forms 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 38
 
12.2%
트 37
 
11.9%
아 35
 
11.2%
산 12
 
3.8%
차 6
 
1.9%
천 5
 
1.6%
드 5
 
1.6%
금 5
 
1.6%
도 4
 
1.3%
독 4
 
1.3%
Other values (84) 161
51.6%
ASCII
ValueCountFrequency (%)
19
70.4%
2 4
 
14.8%
3 2
 
7.4%
e 1
 
3.7%
1 1
 
3.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

위치(주소)
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T04:33:55.327698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length19.73913
Min length18

Characters and Unicode

Total characters908
Distinct characters25
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

Unique46 ?
Unique (%)100.0%

Sample

1st row서울특별시 금천구 시흥동 817-29
2nd row서울특별시 금천구 시흥동 109-1
3rd row서울특별시 금천구 시흥동 1002-1
4th row서울특별시 금천구 시흥동 992-2
5th row서울특별시 금천구 시흥동 220-2
ValueCountFrequency (%)
서울특별시 46
25.0%
금천구 46
25.0%
시흥동 22
12.0%
독산동 20
10.9%
가산동 4
 
2.2%
1140-0 1
 
0.5%
1007-13 1
 
0.5%
1010-0 1
 
0.5%
711-2 1
 
0.5%
955-0 1
 
0.5%
Other values (41) 41
22.3%
2023-12-13T04:33:56.184490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138
15.2%
시 68
 
7.5%
1 61
 
6.7%
0 56
 
6.2%
구 46
 
5.1%
- 46
 
5.1%
울 46
 
5.1%
동 46
 
5.1%
서 46
 
5.1%
천 46
 
5.1%
Other values (15) 309
34.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 507
55.8%
Decimal Number 217
23.9%
Space Separator 138
 
15.2%
Dash Punctuation 46
 
5.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 68
13.4%
구 46
9.1%
울 46
9.1%
동 46
9.1%
서 46
9.1%
천 46
9.1%
금 46
9.1%
별 46
9.1%
특 46
9.1%
산 25
 
4.9%
Other values (3) 46
9.1%
Decimal Number
ValueCountFrequency (%)
1 61
28.1%
0 56
25.8%
2 19
 
8.8%
3 17
 
7.8%
4 14
 
6.5%
8 12
 
5.5%
9 12
 
5.5%
6 11
 
5.1%
5 8
 
3.7%
7 7
 
3.2%
Space Separator
ValueCountFrequency (%)
138
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 507
55.8%
Common 401
44.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 68
13.4%
구 46
9.1%
울 46
9.1%
동 46
9.1%
서 46
9.1%
천 46
9.1%
금 46
9.1%
별 46
9.1%
특 46
9.1%
산 25
 
4.9%
Other values (3) 46
9.1%
Common
ValueCountFrequency (%)
138
34.4%
1 61
15.2%
0 56
14.0%
- 46
 
11.5%
2 19
 
4.7%
3 17
 
4.2%
4 14
 
3.5%
8 12
 
3.0%
9 12
 
3.0%
6 11
 
2.7%
Other values (2) 15
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 507
55.8%
ASCII 401
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
34.4%
1 61
15.2%
0 56
14.0%
- 46
 
11.5%
2 19
 
4.7%
3 17
 
4.2%
4 14
 
3.5%
8 12
 
3.0%
9 12
 
3.0%
6 11
 
2.7%
Other values (2) 15
 
3.7%
Hangul
ValueCountFrequency (%)
시 68
13.4%
구 46
9.1%
울 46
9.1%
동 46
9.1%
서 46
9.1%
천 46
9.1%
금 46
9.1%
별 46
9.1%
특 46
9.1%
산 25
 
4.9%
Other values (3) 46
9.1%

대지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)100.0%
Missing12
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean25140.969
Minimum2111.4
Maximum107560.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T04:33:56.384731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2111.4
5-th percentile3822.0075
Q16350.48
median10380.3
Q331912.25
95-th percentile88158.12
Maximum107560.8
Range105449.4
Interquartile range (IQR)25561.77

Descriptive statistics

Standard deviation27543.502
Coefficient of variation (CV)1.0955625
Kurtosis2.1113185
Mean25140.969
Median Absolute Deviation (MAD)6611.325
Skewness1.6469973
Sum854792.95
Variance7.5864451 × 108
MonotonicityNot monotonic
2023-12-13T04:33:56.553781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8815.3 1
 
2.2%
22047.6 1
 
2.2%
24466.0 1
 
2.2%
7368.62 1
 
2.2%
8819.5 1
 
2.2%
4842.3 1
 
2.2%
8080.7 1
 
2.2%
86318.8 1
 
2.2%
8158.7 1
 
2.2%
64312.0 1
 
2.2%
Other values (24) 24
52.2%
(Missing) 12
26.1%
ValueCountFrequency (%)
2111.4 1
2.2%
3592.2 1
2.2%
3945.75 1
2.2%
4102.6 1
2.2%
4842.3 1
2.2%
5018.8 1
2.2%
5018.9 1
2.2%
5148.78 1
2.2%
6011.1 1
2.2%
7368.62 1
2.2%
ValueCountFrequency (%)
107560.8 1
2.2%
91574.0 1
2.2%
86318.8 1
2.2%
64312.0 1
2.2%
51940.5 1
2.2%
50942.1 1
2.2%
47399.2 1
2.2%
35358.0 1
2.2%
32229.8 1
2.2%
30959.6 1
2.2%

건축면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4459.9152
Minimum656.7
Maximum20363.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T04:33:56.764568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum656.7
5-th percentile801.0325
Q11410.365
median2786.6
Q35657.465
95-th percentile15450.475
Maximum20363.91
Range19707.21
Interquartile range (IQR)4247.1

Descriptive statistics

Standard deviation4620.7235
Coefficient of variation (CV)1.0360564
Kurtosis3.7454282
Mean4459.9152
Median Absolute Deviation (MAD)1610.3385
Skewness1.9928805
Sum205156.1
Variance21351086
MonotonicityNot monotonic
2023-12-13T04:33:56.949099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
772.64 1
 
2.2%
1907.61 1
 
2.2%
6457.35 1
 
2.2%
2876.59 1
 
2.2%
1335.88 1
 
2.2%
5234.96 1
 
2.2%
1915.67 1
 
2.2%
20363.91 1
 
2.2%
3815.12 1
 
2.2%
3930.642 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
656.7 1
2.2%
671.93 1
2.2%
772.64 1
2.2%
886.21 1
2.2%
973.34 1
2.2%
1055.76 1
2.2%
1221.673 1
2.2%
1251.95 1
2.2%
1286.132 1
2.2%
1335.88 1
2.2%
ValueCountFrequency (%)
20363.91 1
2.2%
17676.9745 1
2.2%
16339.62 1
2.2%
12783.04 1
2.2%
11759.8243 1
2.2%
8882.73 1
2.2%
8320.06 1
2.2%
6457.35 1
2.2%
6419.3729 1
2.2%
6081.33 1
2.2%

건폐율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)100.0%
Missing12
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean26.748671
Minimum11.71
Maximum59.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T04:33:57.124484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.71
5-th percentile14.5165
Q118.31
median23.205
Q329.3
95-th percentile59.14218
Maximum59.95
Range48.24
Interquartile range (IQR)10.99

Descriptive statistics

Standard deviation13.357242
Coefficient of variation (CV)0.49936098
Kurtosis1.8709819
Mean26.748671
Median Absolute Deviation (MAD)5.595
Skewness1.6249685
Sum909.4548
Variance178.41592
MonotonicityNot monotonic
2023-12-13T04:33:57.283678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
36.6 1
 
2.2%
17.3 1
 
2.2%
16.07 1
 
2.2%
23.82 1
 
2.2%
21.63 1
 
2.2%
26.56 1
 
2.2%
15.12 1
 
2.2%
14.8 1
 
2.2%
23.48 1
 
2.2%
18.29 1
 
2.2%
Other values (24) 24
52.2%
(Missing) 12
26.1%
ValueCountFrequency (%)
11.71 1
2.2%
13.99 1
2.2%
14.8 1
2.2%
15.12 1
2.2%
16.07 1
2.2%
16.33 1
2.2%
17.3 1
2.2%
17.84 1
2.2%
18.29 1
2.2%
18.37 1
2.2%
ValueCountFrequency (%)
59.95 1
2.2%
59.2948 1
2.2%
59.06 1
2.2%
57.55 1
2.2%
36.6 1
2.2%
35.24 1
2.2%
33.67 1
2.2%
29.66 1
2.2%
29.39 1
2.2%
29.03 1
2.2%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74300.197
Minimum3705.57
Maximum363995.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T04:33:57.475961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3705.57
5-th percentile6589.6825
Q118095.542
median33543.44
Q380256.679
95-th percentile269829.06
Maximum363995.29
Range360289.72
Interquartile range (IQR)62161.136

Descriptive statistics

Standard deviation88115.887
Coefficient of variation (CV)1.1859442
Kurtosis2.7127242
Mean74300.197
Median Absolute Deviation (MAD)22152.995
Skewness1.8489005
Sum3417809.1
Variance7.7644096 × 109
MonotonicityNot monotonic
2023-12-13T04:33:57.653113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
3705.57 1
 
2.2%
28838.95 1
 
2.2%
113864.9 1
 
2.2%
45775.01 1
 
2.2%
19454.82 1
 
2.2%
92068.67 1
 
2.2%
32445.52 1
 
2.2%
363995.29 1
 
2.2%
79449.24 1
 
2.2%
79864.724 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
3705.57 1
2.2%
4911.3 1
2.2%
5364.14 1
2.2%
10266.31 1
2.2%
10773.87 1
2.2%
12007.02 1
2.2%
12125.19 1
2.2%
12814.26 1
2.2%
13856.703 1
2.2%
16680.08 1
2.2%
ValueCountFrequency (%)
363995.29 1
2.2%
302427.174 1
2.2%
270973.945 1
2.2%
266394.41 1
2.2%
246607.5249 1
2.2%
194383.76 1
2.2%
171424.4884 1
2.2%
130809.41 1
2.2%
113864.9 1
2.2%
99704.7971 1
2.2%

용적율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)100.0%
Missing12
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean300.05432
Minimum120
Maximum817.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T04:33:57.814619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile147.675
Q1249.365
median275.34
Q3314.25
95-th percentile493.3855
Maximum817.09
Range697.09
Interquartile range (IQR)64.885

Descriptive statistics

Standard deviation122.14301
Coefficient of variation (CV)0.40706967
Kurtosis9.3012376
Mean300.05432
Median Absolute Deviation (MAD)26.13
Skewness2.4402003
Sum10201.847
Variance14918.915
MonotonicityNot monotonic
2023-12-13T04:33:58.013935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
249.93 1
 
2.2%
265.94 1
 
2.2%
253.43 1
 
2.2%
249.1 1
 
2.2%
249.33 1
 
2.2%
226.15 1
 
2.2%
255.35 1
 
2.2%
249.23 1
 
2.2%
293.38 1
 
2.2%
297.46 1
 
2.2%
Other values (24) 24
52.2%
(Missing) 12
26.1%
ValueCountFrequency (%)
120.0 1
2.2%
123.69 1
2.2%
160.59 1
2.2%
193.35 1
2.2%
226.15 1
2.2%
249.1 1
2.2%
249.19 1
2.2%
249.23 1
2.2%
249.33 1
2.2%
249.47 1
2.2%
ValueCountFrequency (%)
817.09 1
2.2%
499.97 1
2.2%
489.84 1
2.2%
387.96 1
2.2%
386.1168 1
2.2%
363.25 1
2.2%
362.62 1
2.2%
331.88 1
2.2%
314.65 1
2.2%
313.05 1
2.2%

조경면적(제곱미터)
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing40
Missing (%)87.0%
Memory size500.0 B
2023-12-13T04:33:58.233266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.5
Min length5

Characters and Unicode

Total characters39
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st row26043.41
2nd row1932.76
3rd row613.33
4th row6787.1
5th row1903.83
ValueCountFrequency (%)
26043.41 1
16.7%
1932.76 1
16.7%
613.33 1
16.7%
6787.1 1
16.7%
1903.83 1
16.7%
9,856 1
16.7%
2023-12-13T04:33:58.634732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 7
17.9%
6 5
12.8%
. 5
12.8%
1 5
12.8%
9 3
7.7%
7 3
7.7%
8 3
7.7%
2 2
 
5.1%
0 2
 
5.1%
4 2
 
5.1%
Other values (2) 2
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33
84.6%
Other Punctuation 6
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 7
21.2%
6 5
15.2%
1 5
15.2%
9 3
9.1%
7 3
9.1%
8 3
9.1%
2 2
 
6.1%
0 2
 
6.1%
4 2
 
6.1%
5 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 5
83.3%
, 1
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 7
17.9%
6 5
12.8%
. 5
12.8%
1 5
12.8%
9 3
7.7%
7 3
7.7%
8 3
7.7%
2 2
 
5.1%
0 2
 
5.1%
4 2
 
5.1%
Other values (2) 2
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 7
17.9%
6 5
12.8%
. 5
12.8%
1 5
12.8%
9 3
7.7%
7 3
7.7%
8 3
7.7%
2 2
 
5.1%
0 2
 
5.1%
4 2
 
5.1%
Other values (2) 2
 
5.1%

주용도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
공동주택
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공동주택 46
100.0%

Length

2023-12-13T04:33:58.820789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:33:58.948819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동주택 46
100.0%

기타용도
Categorical

Distinct22
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Memory size500.0 B
아파트
14 
공동주택(아파트)
11 
공동주택
2 
공동주택(아파트),생활편익시설,유치원,의료시설,제1,2종근린생활시설
 
1
아파트,판매시설,근린생활시설
 
1
Other values (17)
17 

Length

Max length41
Median length37
Mean length11.73913
Min length2

Unique

Unique19 ?
Unique (%)41.3%

Sample

1st row아파트
2nd row아파트
3rd row아파트
4th row아파트
5th row공동주택(아파트)

Common Values

ValueCountFrequency (%)
아파트 14
30.4%
공동주택(아파트) 11
23.9%
공동주택 2
 
4.3%
공동주택(아파트),생활편익시설,유치원,의료시설,제1,2종근린생활시설 1
 
2.2%
아파트,판매시설,근린생활시설 1
 
2.2%
아파트, 복합상가 1
 
2.2%
아파트, (제1,2종)근린생활시설, 슈퍼, 유치원, 판매시설, 교육연구시설 1
 
2.2%
주택 1
 
2.2%
공동주택(아파트), 상가(생활편익시설),제2종근린생활시설 1
 
2.2%
아파트, 교육연구및복지시설(보육시설) 1
 
2.2%
Other values (12) 12
26.1%

Length

2023-12-13T04:33:59.108210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아파트 18
30.5%
공동주택(아파트 14
23.7%
공동주택 2
 
3.4%
제1,2종근린생활시설 2
 
3.4%
교육연구시설 2
 
3.4%
생활편익시설 1
 
1.7%
아파트,판매시설 1
 
1.7%
아파트(도시형생활주택),업무시설 1
 
1.7%
공동주택,업무시설,판매시설,업무시설,교육연구시설,제2종근린생활시설 1
 
1.7%
공동주택(아파트),(제1,2종)근린생활시설,유치원 1
 
1.7%
Other values (16) 16
27.1%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean593.71739
Minimum45
Maximum2810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T04:33:59.286103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile83.75
Q1158.75
median253
Q3905.25
95-th percentile1758.75
Maximum2810
Range2765
Interquartile range (IQR)746.5

Descriptive statistics

Standard deviation638.48145
Coefficient of variation (CV)1.0753962
Kurtosis2.7955326
Mean593.71739
Median Absolute Deviation (MAD)154
Skewness1.7072374
Sum27311
Variance407658.56
MonotonicityNot monotonic
2023-12-13T04:33:59.447311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
154 2
 
4.3%
79 1
 
2.2%
206 1
 
2.2%
341 1
 
2.2%
148 1
 
2.2%
700 1
 
2.2%
229 1
 
2.2%
2810 1
 
2.2%
1288 1
 
2.2%
566 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
45 1
2.2%
49 1
2.2%
79 1
2.2%
98 1
2.2%
100 1
2.2%
112 1
2.2%
123 1
2.2%
124 1
2.2%
140 1
2.2%
148 1
2.2%
ValueCountFrequency (%)
2810 1
2.2%
2336 1
2.2%
1764 1
2.2%
1743 1
2.2%
1495 1
2.2%
1297 1
2.2%
1288 1
2.2%
1236 1
2.2%
1000 1
2.2%
996 1
2.2%

주 건축물 수
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6956522
Minimum2
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T04:33:59.587181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q38
95-th percentile20.25
Maximum28
Range26
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.2923925
Coefficient of variation (CV)0.9397729
Kurtosis2.3839024
Mean6.6956522
Median Absolute Deviation (MAD)2
Skewness1.686888
Sum308
Variance39.594203
MonotonicityNot monotonic
2023-12-13T04:33:59.730631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 14
30.4%
4 8
17.4%
3 5
 
10.9%
8 4
 
8.7%
15 3
 
6.5%
5 3
 
6.5%
10 2
 
4.3%
6 1
 
2.2%
18 1
 
2.2%
22 1
 
2.2%
Other values (4) 4
 
8.7%
ValueCountFrequency (%)
2 14
30.4%
3 5
 
10.9%
4 8
17.4%
5 3
 
6.5%
6 1
 
2.2%
8 4
 
8.7%
10 2
 
4.3%
12 1
 
2.2%
14 1
 
2.2%
15 3
 
6.5%
ValueCountFrequency (%)
28 1
 
2.2%
22 1
 
2.2%
21 1
 
2.2%
18 1
 
2.2%
15 3
6.5%
14 1
 
2.2%
12 1
 
2.2%
10 2
4.3%
8 4
8.7%
6 1
 
2.2%

부속 건축물 수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)47.1%
Missing12
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean7.4411765
Minimum0
Maximum36
Zeros1
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T04:33:59.873398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3.5
Q39
95-th percentile26.5
Maximum36
Range36
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.0124196
Coefficient of variation (CV)1.2111552
Kurtosis3.4246375
Mean7.4411765
Median Absolute Deviation (MAD)2.5
Skewness1.9419866
Sum253
Variance81.223708
MonotonicityNot monotonic
2023-12-13T04:34:00.021764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 7
15.2%
1 6
13.0%
3 3
 
6.5%
4 3
 
6.5%
8 2
 
4.3%
9 2
 
4.3%
11 2
 
4.3%
18 1
 
2.2%
33 1
 
2.2%
7 1
 
2.2%
Other values (6) 6
13.0%
(Missing) 12
26.1%
ValueCountFrequency (%)
0 1
 
2.2%
1 6
13.0%
2 7
15.2%
3 3
6.5%
4 3
6.5%
5 1
 
2.2%
7 1
 
2.2%
8 2
 
4.3%
9 2
 
4.3%
11 2
 
4.3%
ValueCountFrequency (%)
36 1
2.2%
33 1
2.2%
23 1
2.2%
21 1
2.2%
18 1
2.2%
13 1
2.2%
11 2
4.3%
9 2
4.3%
8 2
4.3%
7 1
2.2%

사용승인일
Date

Distinct44
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size500.0 B
Minimum1975-10-18 00:00:00
Maximum2021-04-29 00:00:00
2023-12-13T04:34:00.190166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:34:00.368227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

Interactions

2023-12-13T04:33:53.085449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:46.451287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:47.315810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:48.654061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:49.601746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:50.554790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.468014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.259785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:53.194106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:46.542472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:47.434365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:48.752607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:49.712452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:50.661129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.563171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.364452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:53.294789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:46.647279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:47.575739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:48.870223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:49.873481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:50.770930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.661874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.474394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:53.397343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:46.748409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:47.694341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:48.986973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:49.984313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:50.909223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.768055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.578950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:53.498681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:46.858031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:47.831101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:49.105164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:50.092425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.034413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.870531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.702144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:53.609115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:46.981740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:47.962279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:49.237079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:50.210423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.150393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.988807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.807573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:53.705106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:47.075078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:48.416992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:49.355395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:50.315149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.254944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.071412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.898933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:53.821145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:47.184649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:48.525434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:49.477635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:50.424180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:51.349802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.159188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:33:52.985704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:34:00.489580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아파트명위치(주소)대지면적(제곱미터)건축면적(제곱미터)건폐율연면적(제곱미터)용적율조경면적(제곱미터)기타용도세대수주 건축물 수부속 건축물 수사용승인일
아파트명1.0001.0000.0000.0000.8650.0000.9651.0000.0000.0000.0000.7200.996
위치(주소)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대지면적(제곱미터)0.0001.0001.0000.9150.0000.8860.0001.0000.5260.9650.9600.8460.000
건축면적(제곱미터)0.0001.0000.9151.0000.0000.9880.0001.0000.8080.9270.9290.8050.000
건폐율0.8651.0000.0000.0001.0000.1160.4581.0000.0000.0000.4150.0001.000
연면적(제곱미터)0.0001.0000.8860.9880.1161.0000.4371.0000.8790.9160.8890.8380.000
용적율0.9651.0000.0000.0000.4580.4371.0001.0000.0000.0000.0000.0001.000
조경면적(제곱미터)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기타용도0.0001.0000.5260.8080.0000.8790.0001.0001.0000.8040.7510.5430.000
세대수0.0001.0000.9650.9270.0000.9160.0001.0000.8041.0000.9650.7920.000
주 건축물 수0.0001.0000.9600.9290.4150.8890.0001.0000.7510.9651.0000.7990.000
부속 건축물 수0.7201.0000.8460.8050.0000.8380.0001.0000.5430.7920.7991.0000.000
사용승인일0.9961.0000.0000.0001.0000.0001.0001.0000.0000.0000.0000.0001.000
2023-12-13T04:34:00.652720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지면적(제곱미터)건축면적(제곱미터)건폐율연면적(제곱미터)용적율세대수주 건축물 수부속 건축물 수기타용도
대지면적(제곱미터)1.0000.925-0.6870.887-0.2370.9050.9090.7640.000
건축면적(제곱미터)0.9251.000-0.3910.9510.0010.9350.8870.6210.377
건폐율-0.687-0.3911.000-0.4350.580-0.503-0.580-0.6050.000
연면적(제곱미터)0.8870.951-0.4351.0000.1370.9400.8830.6230.469
용적율-0.2370.0010.5800.1371.0000.008-0.122-0.1890.000
세대수0.9050.935-0.5030.9400.0081.0000.8820.6930.369
주 건축물 수0.9090.887-0.5800.883-0.1220.8821.0000.6390.386
부속 건축물 수0.7640.621-0.6050.623-0.1890.6930.6391.0000.202
기타용도0.0000.3770.0000.4690.0000.3690.3860.2021.000

Missing values

2023-12-13T04:33:53.990958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:33:54.222132image/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.
2023-12-13T04:33:54.376842image/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

아파트명위치(주소)대지면적(제곱미터)건축면적(제곱미터)건폐율연면적(제곱미터)용적율조경면적(제곱미터)주용도기타용도세대수주 건축물 수부속 건축물 수사용승인일
0시흥아파트서울특별시 금천구 시흥동 817-29<NA>772.64<NA>3705.57<NA><NA>공동주택아파트792<NA>1975-10-18
1무지개아파트서울특별시 금천구 시흥동 109-1<NA>4592.41<NA>48428.13<NA><NA>공동주택아파트640621980-12-24
2럭키남서울아파트서울특별시 금천구 시흥동 1002-151940.56081.3311.7175305.77123.69<NA>공동주택아파트98615<NA>1981-12-20
3남서울건영아파트서울특별시 금천구 시흥동 992-2<NA>4442.35<NA>59050.05<NA><NA>공동주택아파트2604<NA>1982-12-27
4현대아파트서울특별시 금천구 시흥동 220-29835.01806.6418.3712814.26120.0<NA>공동주택공동주택(아파트)140421984-04-25
5성지아파트서울특별시 금천구 시흥동 산173-8<NA>1908.84<NA>22596.65<NA><NA>공동주택아파트2333<NA>1986-08-14
6건영아파트서울특별시 금천구 시흥동 384-12<NA>3270.12<NA>48057.43<NA><NA>공동주택아파트,판매시설,근린생활시설6194<NA>1989-10-19
7주공아파트서울특별시 금천구 독산동 1088-135358.04947.9313.9961577.08160.59<NA>공동주택아파트1297871990-11-14
8주공아파트서울특별시 금천구 독산동 1088-0<NA>3723.25<NA>54409.5<NA><NA>공동주택아파트, 복합상가840511991-08-20
9한신아파트서울특별시 금천구 독산동 1093-447399.28882.7318.74130809.41249.19<NA>공동주택아파트, (제1,2종)근린생활시설, 슈퍼, 유치원, 판매시설, 교육연구시설100015<NA>1991-08-20
아파트명위치(주소)대지면적(제곱미터)건축면적(제곱미터)건폐율연면적(제곱미터)용적율조경면적(제곱미터)주용도기타용도세대수주 건축물 수부속 건축물 수사용승인일
36백운한비치Ⅱ서울특별시 금천구 시흥동 1018-04842.31286.13226.5613856.703226.15<NA>공동주택공동주택(아파트)112212008-01-15
37금천 이랜드 해가든 아파트서울특별시 금천구 독산동 1143-08080.71221.67315.1228564.92255.35<NA>공동주택공동주택192512008-04-21
38한양수자인아파트서울특별시 금천구 독산동 1144-08815.33226.5436.631321.366249.93<NA>공동주택공동주택(아파트)246552010-04-28
39남서울힐스테이트서울특별시 금천구 시흥동 1026-086318.812783.0414.8302427.174249.23<NA>공동주택아파트176421132014-08-14
40금천 롯데캐슬 골드파크 1차서울특별시 금천구 독산동 1147-064312.011759.824318.29270973.945297.4626043.41공동주택공동주택(아파트)174314232016-12-09
41금천 롯데캐슬 골드파크 2차서울특별시 금천구 독산동 1150-010355.05958.9157.5579162.45489.841932.76공동주택공동주택,업무시설,판매시설,업무시설,교육연구시설,제2종근린생활시설292802017-09-28
42가산 지웰 에스테이트 2차서울특별시 금천구 가산동 143-62111.41251.9559.294812007.02386.1168613.33공동주택아파트(도시형생활주택),업무시설, 제1,2종근린생활시설2382<NA>2018-08-29
43금천 롯데캐슬 골드파크 3차서울특별시 금천구 독산동 1155-029932.117676.974559.06246607.5249499.976787.1공동주택아파트,판매시설1236822018-10-30
44e편한세상 독산 더타워서울특별시 금천구 독산동 1007-137841.44700.9659.9599704.7971817.091903.83공동주택공동주택(아파트)432412019-11-28
45독산역 롯데캐슬서울특별시 금천구 독산동 1160-030959.66419.372920.73171424.4884363.259,856공동주택아파트,업무시설,근린생활시설92710212021-04-29