Overview

Dataset statistics

Number of variables4
Number of observations92
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory35.4 B

Variable types

Categorical2
Numeric2

Dataset

Description지역별(서울,경인,부산 등) 공무원임대아파트 임대보증금 현황을 제공하여 해당지역 주택공급 및 전세금 측정 등에 활용
URLhttps://www.data.go.kr/data/15052964/fileData.do

Alerts

전용면적(제곱미터) is highly overall correlated with 금액(천원)High correlation
금액(천원) is highly overall correlated with 전용면적(제곱미터)High correlation
지역 is highly overall correlated with 단지명High correlation
단지명 is highly overall correlated with 지역High correlation

Reproduction

Analysis started2023-12-12 12:05:59.273547
Analysis finished2023-12-12 12:06:00.066239
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size868.0 B
경기
12 
부산
12 
세종
10 
충남
8 
강원
6 
Other values (10)
44 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row서울
3rd row인천
4th row인천
5th row경기

Common Values

ValueCountFrequency (%)
경기 12
13.0%
부산 12
13.0%
세종 10
10.9%
충남 8
8.7%
강원 6
 
6.5%
경남 6
 
6.5%
대구 6
 
6.5%
전남 6
 
6.5%
울산 5
 
5.4%
전북 5
 
5.4%
Other values (5) 16
17.4%

Length

2023-12-12T21:06:00.138500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 12
13.0%
부산 12
13.0%
세종 10
10.9%
충남 8
8.7%
강원 6
 
6.5%
경남 6
 
6.5%
대구 6
 
6.5%
전남 6
 
6.5%
울산 5
 
5.4%
전북 5
 
5.4%
Other values (5) 16
17.4%

단지명
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Memory size868.0 B
세종
10 
충남내포
 
5
서귀포강정
 
5
화성동탄
 
4
부산범천
 
4
Other values (40)
64 

Length

Max length5
Median length4
Mean length3.8695652
Min length2

Unique

Unique22 ?
Unique (%)23.9%

Sample

1st row서울상계
2nd row서울상계
3rd row인천가좌
4th row인천가좌
5th row부천상동

Common Values

ValueCountFrequency (%)
세종 10
 
10.9%
충남내포 5
 
5.4%
서귀포강정 5
 
5.4%
화성동탄 4
 
4.3%
부산범천 4
 
4.3%
춘천후평 3
 
3.3%
울산야음 3
 
3.3%
대구복현 3
 
3.3%
경북안동 3
 
3.3%
여수둔덕 3
 
3.3%
Other values (35) 49
53.3%

Length

2023-12-12T21:06:00.289290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
세종 10
 
10.9%
서귀포강정 5
 
5.4%
충남내포 5
 
5.4%
화성동탄 4
 
4.3%
부산범천 4
 
4.3%
춘천후평 3
 
3.3%
울산야음 3
 
3.3%
대구복현 3
 
3.3%
경북안동 3
 
3.3%
여수둔덕 3
 
3.3%
Other values (35) 49
53.3%

전용면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.369565
Minimum21
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-12T21:06:00.455785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile30
Q149
median59
Q372.5
95-th percentile84
Maximum85
Range64
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation16.964551
Coefficient of variation (CV)0.28574491
Kurtosis-0.58289488
Mean59.369565
Median Absolute Deviation (MAD)10.5
Skewness-0.15075405
Sum5462
Variance287.79599
MonotonicityNot monotonic
2023-12-12T21:06:00.619460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
59 30
32.6%
84 17
18.5%
49 4
 
4.3%
30 4
 
4.3%
54 3
 
3.3%
41 2
 
2.2%
42 2
 
2.2%
74 2
 
2.2%
77 2
 
2.2%
64 2
 
2.2%
Other values (22) 24
26.1%
ValueCountFrequency (%)
21 1
 
1.1%
24 1
 
1.1%
27 1
 
1.1%
29 1
 
1.1%
30 4
4.3%
34 1
 
1.1%
35 1
 
1.1%
36 1
 
1.1%
39 2
2.2%
41 2
2.2%
ValueCountFrequency (%)
85 1
 
1.1%
84 17
18.5%
77 2
 
2.2%
76 1
 
1.1%
74 2
 
2.2%
72 1
 
1.1%
70 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
64 2
 
2.2%

금액(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136415.87
Minimum26220
Maximum425580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-12T21:06:01.069776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26220
5-th percentile36333
Q189275
median135885
Q3180325
95-th percentile243716
Maximum425580
Range399360
Interquartile range (IQR)91050

Descriptive statistics

Standard deviation70133.763
Coefficient of variation (CV)0.51411733
Kurtosis2.0939347
Mean136415.87
Median Absolute Deviation (MAD)45790
Skewness0.88016043
Sum12550260
Variance4.9187447 × 109
MonotonicityNot monotonic
2023-12-12T21:06:01.231067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140120 2
 
2.2%
123640 1
 
1.1%
34840 1
 
1.1%
149990 1
 
1.1%
111200 1
 
1.1%
92800 1
 
1.1%
36760 1
 
1.1%
30450 1
 
1.1%
26220 1
 
1.1%
46310 1
 
1.1%
Other values (81) 81
88.0%
ValueCountFrequency (%)
26220 1
1.1%
30450 1
1.1%
34840 1
1.1%
35630 1
1.1%
36190 1
1.1%
36450 1
1.1%
36760 1
1.1%
36870 1
1.1%
40210 1
1.1%
41640 1
1.1%
ValueCountFrequency (%)
425580 1
1.1%
319330 1
1.1%
259480 1
1.1%
247950 1
1.1%
245520 1
1.1%
242240 1
1.1%
241380 1
1.1%
229710 1
1.1%
227190 1
1.1%
218930 1
1.1%

Interactions

2023-12-12T21:05:59.654122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:05:59.439437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:05:59.757259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:05:59.543276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:06:01.351390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역단지명전용면적(제곱미터)금액(천원)
지역1.0001.0000.4980.493
단지명1.0001.0000.0000.908
전용면적(제곱미터)0.4980.0001.0000.458
금액(천원)0.4930.9080.4581.000
2023-12-12T21:06:01.501584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단지명지역
단지명1.0000.781
지역0.7811.000
2023-12-12T21:06:01.606109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전용면적(제곱미터)금액(천원)지역단지명
전용면적(제곱미터)1.0000.6910.1990.000
금액(천원)0.6911.0000.2330.482
지역0.1990.2331.0000.781
단지명0.0000.4820.7811.000

Missing values

2023-12-12T21:05:59.913876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:06:00.018787image/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

지역단지명전용면적(제곱미터)금액(천원)
0서울서울상계41123640
1서울서울상계49152800
2인천인천가좌59164550
3인천인천가좌72183730
4경기부천상동59229710
5경기안양석수59241380
6경기파주교하59144100
7경기화성동탄84196790
8경기성남판교84425580
9경기수원광교59242240
지역단지명전용면적(제곱미터)금액(천원)
82세종세종3686350
83세종세종46110330
84세종세종59124920
85세종세종74140120
86세종세종84146000
87제주서귀포강정2453870
88제주서귀포강정3576830
89제주서귀포강정3991020
90제주서귀포강정59130770
91제주서귀포강정84194400