Overview

Dataset statistics

Number of variables7
Number of observations629
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.4 KiB
Average record size in memory59.2 B

Variable types

Categorical3
Text1
Numeric3

Dataset

Description인천광역시 서구 최근 1년간 공동주택 실거래가 합계에 대한 데이터로 행정구역, 단지명, 합계동호수(건), 합계면적(제곱미터), 금액(백만원), 산출기간, 기준일자 항목을 제공합니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15102411&srcSe=7661IVAWM27C61E190

Alerts

산출기간 has constant value ""Constant
기준일자 has constant value ""Constant
합계동호수(건) is highly overall correlated with 합계면적(제곱미터) and 1 other fieldsHigh correlation
합계면적(제곱미터) is highly overall correlated with 합계동호수(건) and 1 other fieldsHigh correlation
합계금액(백만원) is highly overall correlated with 합계동호수(건) and 1 other fieldsHigh correlation
합계면적(제곱미터) is highly skewed (γ1 = 25.05051415)Skewed
합계금액(백만원) has 150 (23.8%) zerosZeros

Reproduction

Analysis started2024-01-28 08:37:36.423239
Analysis finished2024-01-28 08:37:37.586615
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역
Categorical

Distinct19
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
가정동
77 
당하동
73 
마전동
62 
백석동
60 
석남동
60 
Other values (14)
297 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
가정동 77
12.2%
당하동 73
11.6%
마전동 62
9.9%
백석동 60
9.5%
석남동 60
9.5%
불로동 59
9.4%
원당동 56
8.9%
가좌동 47
7.5%
청라동 43
6.8%
검암동 21
 
3.3%
Other values (9) 71
11.3%

Length

2024-01-28T17:37:37.650150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가정동 77
12.2%
당하동 73
11.6%
마전동 62
9.9%
백석동 60
9.5%
석남동 60
9.5%
불로동 59
9.4%
원당동 56
8.9%
가좌동 47
7.5%
청라동 43
6.8%
검암동 21
 
3.3%
Other values (9) 71
11.3%

단지명
Text

Distinct580
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2024-01-28T17:37:37.866857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length10.062003
Min length2

Characters and Unicode

Total characters6329
Distinct characters302
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique549 ?
Unique (%)87.3%

Sample

1st rowBL,170-3번지일대검암역로열파크씨티푸르지오2BL
2nd row검단로얄푸르지오시티
3rd row검암로얄파크씨티푸르지오1BL
4th row검암로얄파크푸르지오
5th row검암로열파크씨티푸르지오1BL
ValueCountFrequency (%)
검단신도시 22
 
2.4%
루원시티 15
 
1.7%
기타 13
 
1.4%
2차 10
 
1.1%
검단 10
 
1.1%
외 9
 
1.0%
대성베르힐 9
 
1.0%
검암역 8
 
0.9%
푸르지오 8
 
0.9%
제일풍경채 7
 
0.8%
Other values (615) 794
87.7%
2024-01-28T17:37:38.202684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
276
 
4.4%
트 256
 
4.0%
파 252
 
4.0%
아 225
 
3.6%
검 193
 
3.0%
단 182
 
2.9%
지 177
 
2.8%
시 128
 
2.0%
스 121
 
1.9%
2 119
 
1.9%
Other values (292) 4400
69.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5269
83.3%
Decimal Number 348
 
5.5%
Uppercase Letter 287
 
4.5%
Space Separator 276
 
4.4%
Lowercase Letter 58
 
0.9%
Dash Punctuation 24
 
0.4%
Open Punctuation 22
 
0.3%
Close Punctuation 22
 
0.3%
Other Punctuation 19
 
0.3%
Modifier Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 256
 
4.9%
파 252
 
4.8%
아 225
 
4.3%
검 193
 
3.7%
단 182
 
3.5%
지 177
 
3.4%
시 128
 
2.4%
스 121
 
2.3%
르 116
 
2.2%
원 104
 
2.0%
Other values (244) 3515
66.7%
Uppercase Letter
ValueCountFrequency (%)
L 73
25.4%
B 67
23.3%
A 36
12.5%
K 24
 
8.4%
S 20
 
7.0%
I 12
 
4.2%
E 12
 
4.2%
W 9
 
3.1%
V 9
 
3.1%
H 7
 
2.4%
Other values (9) 18
 
6.3%
Decimal Number
ValueCountFrequency (%)
2 119
34.2%
1 106
30.5%
3 35
 
10.1%
4 26
 
7.5%
5 15
 
4.3%
0 13
 
3.7%
6 11
 
3.2%
8 9
 
2.6%
9 7
 
2.0%
7 7
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
e 21
36.2%
s 10
17.2%
d 7
 
12.1%
r 7
 
12.1%
a 7
 
12.1%
k 4
 
6.9%
n 1
 
1.7%
p 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 12
63.2%
. 3
 
15.8%
' 3
 
15.8%
/ 1
 
5.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%
Space Separator
ValueCountFrequency (%)
276
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5269
83.3%
Common 713
 
11.3%
Latin 347
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 256
 
4.9%
파 252
 
4.8%
아 225
 
4.3%
검 193
 
3.7%
단 182
 
3.5%
지 177
 
3.4%
시 128
 
2.4%
스 121
 
2.3%
르 116
 
2.2%
원 104
 
2.0%
Other values (244) 3515
66.7%
Latin
ValueCountFrequency (%)
L 73
21.0%
B 67
19.3%
A 36
10.4%
K 24
 
6.9%
e 21
 
6.1%
S 20
 
5.8%
I 12
 
3.5%
E 12
 
3.5%
s 10
 
2.9%
W 9
 
2.6%
Other values (19) 63
18.2%
Common
ValueCountFrequency (%)
276
38.7%
2 119
16.7%
1 106
 
14.9%
3 35
 
4.9%
4 26
 
3.6%
- 24
 
3.4%
( 22
 
3.1%
) 22
 
3.1%
5 15
 
2.1%
0 13
 
1.8%
Other values (9) 55
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5267
83.2%
ASCII 1058
 
16.7%
Compat Jamo 2
 
< 0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
276
26.1%
2 119
11.2%
1 106
 
10.0%
L 73
 
6.9%
B 67
 
6.3%
A 36
 
3.4%
3 35
 
3.3%
4 26
 
2.5%
K 24
 
2.3%
- 24
 
2.3%
Other values (36) 272
25.7%
Hangul
ValueCountFrequency (%)
트 256
 
4.9%
파 252
 
4.8%
아 225
 
4.3%
검 193
 
3.7%
단 182
 
3.5%
지 177
 
3.4%
시 128
 
2.4%
스 121
 
2.3%
르 116
 
2.2%
원 104
 
2.0%
Other values (243) 3513
66.7%
Compat Jamo
ValueCountFrequency (%)
ㅣ 2
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%

합계동호수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.006359
Minimum1
Maximum923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-01-28T17:37:38.320942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q38
95-th percentile46
Maximum923
Range922
Interquartile range (IQR)7

Descriptive statistics

Standard deviation49.734294
Coefficient of variation (CV)3.8238444
Kurtosis188.74385
Mean13.006359
Median Absolute Deviation (MAD)1
Skewness11.865112
Sum8181
Variance2473.5
MonotonicityNot monotonic
2024-01-28T17:37:38.424554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 274
43.6%
2 62
 
9.9%
3 41
 
6.5%
5 29
 
4.6%
6 23
 
3.7%
7 22
 
3.5%
4 18
 
2.9%
12 12
 
1.9%
8 11
 
1.7%
13 11
 
1.7%
Other values (57) 126
20.0%
ValueCountFrequency (%)
1 274
43.6%
2 62
 
9.9%
3 41
 
6.5%
4 18
 
2.9%
5 29
 
4.6%
6 23
 
3.7%
7 22
 
3.5%
8 11
 
1.7%
9 7
 
1.1%
10 8
 
1.3%
ValueCountFrequency (%)
923 1
0.2%
364 1
0.2%
307 1
0.2%
294 1
0.2%
289 1
0.2%
257 1
0.2%
231 1
0.2%
193 1
0.2%
179 1
0.2%
175 1
0.2%

합계면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct517
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7781.6033
Minimum23.65
Maximum4222243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-01-28T17:37:38.557499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.65
5-th percentile38.068
Q172.36
median144.85
Q3562.33
95-th percentile3389.642
Maximum4222243
Range4222219.3
Interquartile range (IQR)489.97

Descriptive statistics

Standard deviation168375.27
Coefficient of variation (CV)21.637606
Kurtosis628.00814
Mean7781.6033
Median Absolute Deviation (MAD)102.38
Skewness25.050514
Sum4894628.5
Variance2.835023 × 1010
MonotonicityNot monotonic
2024-01-28T17:37:38.675187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.49 17
 
2.7%
84.99 10
 
1.6%
37.0 9
 
1.4%
84.92 7
 
1.1%
84.74 7
 
1.1%
84.98 6
 
1.0%
84.97 5
 
0.8%
42.47 4
 
0.6%
84.8 4
 
0.6%
84.69 4
 
0.6%
Other values (507) 556
88.4%
ValueCountFrequency (%)
23.65 2
0.3%
24.36 1
 
0.2%
24.7 1
 
0.2%
24.8 1
 
0.2%
24.95 1
 
0.2%
29.91 1
 
0.2%
29.93 4
0.6%
29.97 1
 
0.2%
29.98 1
 
0.2%
30.0 1
 
0.2%
ValueCountFrequency (%)
4222242.96 1
0.2%
79090.16 1
0.2%
64222.27 1
0.2%
23137.02 1
0.2%
23011.37 1
0.2%
22157.69 1
0.2%
21663.08 1
0.2%
19640.49 1
0.2%
19124.19 1
0.2%
17225.88 1
0.2%

합계금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct402
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4770.9332
Minimum0
Maximum471526
Zeros150
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-01-28T17:37:38.779014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1110
median453
Q31859
95-th percentile18840
Maximum471526
Range471526
Interquartile range (IQR)1749

Descriptive statistics

Standard deviation22925.728
Coefficient of variation (CV)4.8052921
Kurtosis280.2364
Mean4770.9332
Median Absolute Deviation (MAD)453
Skewness14.842518
Sum3000917
Variance5.25589 × 108
MonotonicityNot monotonic
2024-01-28T17:37:38.891011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 150
 
23.8%
460 4
 
0.6%
300 4
 
0.6%
180 4
 
0.6%
150 4
 
0.6%
439 3
 
0.5%
240 3
 
0.5%
280 3
 
0.5%
165 3
 
0.5%
185 3
 
0.5%
Other values (392) 448
71.2%
ValueCountFrequency (%)
0 150
23.8%
64 1
 
0.2%
70 1
 
0.2%
74 1
 
0.2%
80 3
 
0.5%
100 1
 
0.2%
110 3
 
0.5%
115 1
 
0.2%
120 2
 
0.3%
125 2
 
0.3%
ValueCountFrequency (%)
471526 1
0.2%
148504 1
0.2%
138360 1
0.2%
119994 1
0.2%
115332 1
0.2%
91106 1
0.2%
84547 1
0.2%
63386 1
0.2%
61651 1
0.2%
50466 1
0.2%

산출기간
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
최근1년
629 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row최근1년
2nd row최근1년
3rd row최근1년
4th row최근1년
5th row최근1년

Common Values

ValueCountFrequency (%)
최근1년 629
100.0%

Length

2024-01-28T17:37:38.998015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T17:37:39.067013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
최근1년 629
100.0%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2023-07-07
629 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-07
2nd row2023-07-07
3rd row2023-07-07
4th row2023-07-07
5th row2023-07-07

Common Values

ValueCountFrequency (%)
2023-07-07 629
100.0%

Length

2024-01-28T17:37:39.142049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T17:37:39.215056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-07 629
100.0%

Interactions

2024-01-28T17:37:37.175740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:36.677516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:36.931875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:37.251478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:36.742230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:37.023350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:37.349332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:36.821826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:37.104041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T17:37:39.263894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역합계동호수(건)합계면적(제곱미터)합계금액(백만원)
행정구역1.0000.0000.0000.000
합계동호수(건)0.0001.0000.0000.967
합계면적(제곱미터)0.0000.0001.0000.000
합계금액(백만원)0.0000.9670.0001.000
2024-01-28T17:37:39.342230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계동호수(건)합계면적(제곱미터)합계금액(백만원)행정구역
합계동호수(건)1.0000.9400.7960.000
합계면적(제곱미터)0.9401.0000.8710.000
합계금액(백만원)0.7960.8711.0000.000
행정구역0.0000.0000.0001.000

Missing values

2024-01-28T17:37:37.446222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T17:37:37.543872image/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

행정구역단지명합계동호수(건)합계면적(제곱미터)합계금액(백만원)산출기간기준일자
0백석동BL,170-3번지일대검암역로열파크씨티푸르지오2BL174.01488최근1년2023-07-07
1백석동검단로얄푸르지오시티137.00최근1년2023-07-07
2백석동검암로얄파크씨티푸르지오1BL129.930최근1년2023-07-07
3백석동검암로얄파크푸르지오150.980최근1년2023-07-07
4백석동검암로열파크씨티푸르지오1BL174.010최근1년2023-07-07
5백석동검암역 로열파크씨티 푸르지오159.860최근1년2023-07-07
6백석동검암역 로열파크씨티 푸르지오 1BL2186.871295최근1년2023-07-07
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