Overview

Dataset statistics

Number of variables12
Number of observations156
Missing cells57
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.4 KiB
Average record size in memory100.8 B

Variable types

Numeric4
Text5
DateTime1
Categorical2

Dataset

Description경기도 군포시 주택 현황 데이터로 번호, 아파트명, 동수, 세대수, 도로명주소, 지번주소, 우편번호, 전화번호, 팩스번호, 준공일자, 관리, 데이터기준일자 항목을 제공합니다.
Author경기도 군포시
URLhttps://www.data.go.kr/data/15016310/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
번호 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
동수 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
관리 is highly overall correlated with 번호High correlation
전화번호 has 28 (17.9%) missing valuesMissing
팩스번호 has 29 (18.6%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:43:17.524851
Analysis finished2023-12-12 07:43:19.855337
Duration2.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.5
Minimum1
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:43:19.965913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.75
Q139.75
median78.5
Q3117.25
95-th percentile148.25
Maximum156
Range155
Interquartile range (IQR)77.5

Descriptive statistics

Standard deviation45.177428
Coefficient of variation (CV)0.57550864
Kurtosis-1.2
Mean78.5
Median Absolute Deviation (MAD)39
Skewness0
Sum12246
Variance2041
MonotonicityStrictly increasing
2023-12-12T16:43:20.113976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
109 1
 
0.6%
102 1
 
0.6%
103 1
 
0.6%
104 1
 
0.6%
105 1
 
0.6%
106 1
 
0.6%
107 1
 
0.6%
108 1
 
0.6%
110 1
 
0.6%
Other values (146) 146
93.6%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
156 1
0.6%
155 1
0.6%
154 1
0.6%
153 1
0.6%
152 1
0.6%
151 1
0.6%
150 1
0.6%
149 1
0.6%
148 1
0.6%
147 1
0.6%

아파트명
Text

Distinct140
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T16:43:20.441833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length4.4358974
Min length2

Characters and Unicode

Total characters692
Distinct characters163
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)85.9%

Sample

1st row무궁화
2nd row화성
3rd row한성목화
4th row충무1
5th row충무2
ValueCountFrequency (%)
한솔솔파크 7
 
4.5%
대원칸타빌 6
 
3.8%
누리에뜰 3
 
1.9%
장미 2
 
1.3%
은성 2
 
1.3%
무궁화 2
 
1.3%
덕산2차 1
 
0.6%
당동대흥 1
 
0.6%
덕산1차 1
 
0.6%
동문 1
 
0.6%
Other values (130) 130
83.3%
2023-12-12T16:43:20.873112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
성 23
 
3.3%
2 20
 
2.9%
동 19
 
2.7%
대 18
 
2.6%
지 17
 
2.5%
1 17
 
2.5%
한 16
 
2.3%
차 16
 
2.3%
솔 15
 
2.2%
파 14
 
2.0%
Other values (153) 517
74.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 627
90.6%
Decimal Number 59
 
8.5%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%
Dash Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 23
 
3.7%
동 19
 
3.0%
대 18
 
2.9%
지 17
 
2.7%
한 16
 
2.6%
차 16
 
2.6%
솔 15
 
2.4%
파 14
 
2.2%
크 13
 
2.1%
이 13
 
2.1%
Other values (142) 463
73.8%
Decimal Number
ValueCountFrequency (%)
2 20
33.9%
1 17
28.8%
3 7
 
11.9%
5 5
 
8.5%
7 3
 
5.1%
4 3
 
5.1%
6 3
 
5.1%
0 1
 
1.7%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 627
90.6%
Common 65
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 23
 
3.7%
동 19
 
3.0%
대 18
 
2.9%
지 17
 
2.7%
한 16
 
2.6%
차 16
 
2.6%
솔 15
 
2.4%
파 14
 
2.2%
크 13
 
2.1%
이 13
 
2.1%
Other values (142) 463
73.8%
Common
ValueCountFrequency (%)
2 20
30.8%
1 17
26.2%
3 7
 
10.8%
5 5
 
7.7%
7 3
 
4.6%
4 3
 
4.6%
6 3
 
4.6%
( 2
 
3.1%
) 2
 
3.1%
- 2
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 627
90.6%
ASCII 65
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
성 23
 
3.7%
동 19
 
3.0%
대 18
 
2.9%
지 17
 
2.7%
한 16
 
2.6%
차 16
 
2.6%
솔 15
 
2.4%
파 14
 
2.2%
크 13
 
2.1%
이 13
 
2.1%
Other values (142) 463
73.8%
ASCII
ValueCountFrequency (%)
2 20
30.8%
1 17
26.2%
3 7
 
10.8%
5 5
 
7.7%
7 3
 
4.6%
4 3
 
4.6%
6 3
 
4.6%
( 2
 
3.1%
) 2
 
3.1%
- 2
 
3.1%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9551282
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:43:20.998166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q39
95-th percentile17.25
Maximum29
Range28
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.9485351
Coefficient of variation (CV)0.99889288
Kurtosis1.7238592
Mean5.9551282
Median Absolute Deviation (MAD)2
Skewness1.394186
Sum929
Variance35.38507
MonotonicityNot monotonic
2023-12-12T16:43:21.127258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 49
31.4%
2 24
15.4%
8 8
 
5.1%
11 7
 
4.5%
9 7
 
4.5%
5 7
 
4.5%
4 6
 
3.8%
12 6
 
3.8%
10 6
 
3.8%
3 6
 
3.8%
Other values (14) 30
19.2%
ValueCountFrequency (%)
1 49
31.4%
2 24
15.4%
3 6
 
3.8%
4 6
 
3.8%
5 7
 
4.5%
6 5
 
3.2%
7 6
 
3.8%
8 8
 
5.1%
9 7
 
4.5%
10 6
 
3.8%
ValueCountFrequency (%)
29 1
 
0.6%
26 1
 
0.6%
23 1
 
0.6%
22 1
 
0.6%
21 2
 
1.3%
20 1
 
0.6%
18 1
 
0.6%
17 1
 
0.6%
16 3
1.9%
15 5
3.2%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467.60256
Minimum24
Maximum2644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:43:21.291148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile35
Q155
median319.5
Q3732
95-th percentile1417.75
Maximum2644
Range2620
Interquartile range (IQR)677

Descriptive statistics

Standard deviation517.19938
Coefficient of variation (CV)1.1060662
Kurtosis2.9069239
Mean467.60256
Median Absolute Deviation (MAD)271.5
Skewness1.596903
Sum72946
Variance267495.2
MonotonicityNot monotonic
2023-12-12T16:43:21.503412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 5
 
3.2%
48 5
 
3.2%
45 5
 
3.2%
40 5
 
3.2%
90 4
 
2.6%
60 4
 
2.6%
50 3
 
1.9%
35 3
 
1.9%
72 3
 
1.9%
30 3
 
1.9%
Other values (111) 116
74.4%
ValueCountFrequency (%)
24 1
 
0.6%
30 3
1.9%
31 1
 
0.6%
33 1
 
0.6%
35 3
1.9%
36 5
3.2%
37 1
 
0.6%
40 5
3.2%
42 1
 
0.6%
45 5
3.2%
ValueCountFrequency (%)
2644 1
0.6%
2489 1
0.6%
2042 1
0.6%
1827 1
0.6%
1778 1
0.6%
1639 1
0.6%
1601 1
0.6%
1471 1
0.6%
1400 1
0.6%
1342 1
0.6%

도로명주소
Text

Distinct150
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T16:43:21.774787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length37
Mean length31.717949
Min length17

Characters and Unicode

Total characters4948
Distinct characters176
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)95.5%

Sample

1st row경기도 군포시 산본로 296 (금정동, 무궁화주공아파트)
2nd row경기도 군포시 번영로550번길 6 (금정동, 화성무궁화아파트)
3rd row경기도 군포시 번영로550번길 5 (금정동, 한성목화아파트)
4th row경기도 군포시 산본로 299 (금정동, 충무1차아파트)
5th row경기도 군포시 산본로 299 (금정동, 충무2차아파트)
ValueCountFrequency (%)
군포시 157
 
17.1%
경기도 156
 
17.0%
산본동 42
 
4.6%
당동 37
 
4.0%
금정동 30
 
3.3%
당정동 23
 
2.5%
6 12
 
1.3%
고산로185번길 10
 
1.1%
부곡동 8
 
0.9%
대야미동 7
 
0.8%
Other values (306) 437
47.6%
2023-12-12T16:43:22.248381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
772
 
15.6%
군 168
 
3.4%
포 168
 
3.4%
동 168
 
3.4%
시 162
 
3.3%
도 161
 
3.3%
기 159
 
3.2%
경 158
 
3.2%
로 155
 
3.1%
( 151
 
3.1%
Other values (166) 2726
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3055
61.7%
Space Separator 772
 
15.6%
Decimal Number 652
 
13.2%
Open Punctuation 151
 
3.1%
Close Punctuation 151
 
3.1%
Other Punctuation 149
 
3.0%
Dash Punctuation 18
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 168
 
5.5%
포 168
 
5.5%
동 168
 
5.5%
시 162
 
5.3%
도 161
 
5.3%
기 159
 
5.2%
경 158
 
5.2%
로 155
 
5.1%
파 136
 
4.5%
아 134
 
4.4%
Other values (151) 1486
48.6%
Decimal Number
ValueCountFrequency (%)
1 140
21.5%
2 108
16.6%
3 69
10.6%
5 68
10.4%
6 67
10.3%
9 50
 
7.7%
4 47
 
7.2%
0 35
 
5.4%
7 35
 
5.4%
8 33
 
5.1%
Space Separator
ValueCountFrequency (%)
772
100.0%
Open Punctuation
ValueCountFrequency (%)
( 151
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Other Punctuation
ValueCountFrequency (%)
, 149
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3055
61.7%
Common 1893
38.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 168
 
5.5%
포 168
 
5.5%
동 168
 
5.5%
시 162
 
5.3%
도 161
 
5.3%
기 159
 
5.2%
경 158
 
5.2%
로 155
 
5.1%
파 136
 
4.5%
아 134
 
4.4%
Other values (151) 1486
48.6%
Common
ValueCountFrequency (%)
772
40.8%
( 151
 
8.0%
) 151
 
8.0%
, 149
 
7.9%
1 140
 
7.4%
2 108
 
5.7%
3 69
 
3.6%
5 68
 
3.6%
6 67
 
3.5%
9 50
 
2.6%
Other values (5) 168
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3055
61.7%
ASCII 1893
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
772
40.8%
( 151
 
8.0%
) 151
 
8.0%
, 149
 
7.9%
1 140
 
7.4%
2 108
 
5.7%
3 69
 
3.6%
5 68
 
3.6%
6 67
 
3.5%
9 50
 
2.6%
Other values (5) 168
 
8.9%
Hangul
ValueCountFrequency (%)
군 168
 
5.5%
포 168
 
5.5%
동 168
 
5.5%
시 162
 
5.3%
도 161
 
5.3%
기 159
 
5.2%
경 158
 
5.2%
로 155
 
5.1%
파 136
 
4.5%
아 134
 
4.4%
Other values (151) 1486
48.6%

지번주소
Text

Distinct151
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T16:43:22.623559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length16.608974
Min length14

Characters and Unicode

Total characters2591
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)93.6%

Sample

1st row경기도 군포시 금정동 849
2nd row경기도 군포시 금정동 849-1
3rd row경기도 군포시 금정동 850
4th row경기도 군포시 금정동 872
5th row경기도 군포시 금정동 873-2
ValueCountFrequency (%)
군포시 157
25.0%
경기도 156
24.8%
산본동 42
 
6.7%
당동 38
 
6.0%
금정동 30
 
4.8%
당정동 23
 
3.7%
부곡동 9
 
1.4%
대야미동 8
 
1.3%
도마교동 6
 
1.0%
1120 2
 
0.3%
Other values (154) 158
25.1%
2023-12-12T16:43:23.128508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
473
18.3%
1 171
 
6.6%
도 162
 
6.3%
군 157
 
6.1%
포 157
 
6.1%
시 157
 
6.1%
경 156
 
6.0%
기 156
 
6.0%
동 156
 
6.0%
- 87
 
3.4%
Other values (23) 759
29.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1386
53.5%
Decimal Number 636
24.5%
Space Separator 473
 
18.3%
Dash Punctuation 87
 
3.4%
Other Punctuation 9
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 162
11.7%
군 157
11.3%
포 157
11.3%
시 157
11.3%
경 156
11.3%
기 156
11.3%
동 156
11.3%
당 61
 
4.4%
정 53
 
3.8%
본 42
 
3.0%
Other values (10) 129
9.3%
Decimal Number
ValueCountFrequency (%)
1 171
26.9%
7 74
11.6%
0 66
 
10.4%
9 57
 
9.0%
5 55
 
8.6%
4 47
 
7.4%
6 47
 
7.4%
2 45
 
7.1%
8 43
 
6.8%
3 31
 
4.9%
Space Separator
ValueCountFrequency (%)
473
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 87
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1386
53.5%
Common 1205
46.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 162
11.7%
군 157
11.3%
포 157
11.3%
시 157
11.3%
경 156
11.3%
기 156
11.3%
동 156
11.3%
당 61
 
4.4%
정 53
 
3.8%
본 42
 
3.0%
Other values (10) 129
9.3%
Common
ValueCountFrequency (%)
473
39.3%
1 171
 
14.2%
- 87
 
7.2%
7 74
 
6.1%
0 66
 
5.5%
9 57
 
4.7%
5 55
 
4.6%
4 47
 
3.9%
6 47
 
3.9%
2 45
 
3.7%
Other values (3) 83
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1386
53.5%
ASCII 1205
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
473
39.3%
1 171
 
14.2%
- 87
 
7.2%
7 74
 
6.1%
0 66
 
5.5%
9 57
 
4.7%
5 55
 
4.6%
4 47
 
3.9%
6 47
 
3.9%
2 45
 
3.7%
Other values (3) 83
 
6.9%
Hangul
ValueCountFrequency (%)
도 162
11.7%
군 157
11.3%
포 157
11.3%
시 157
11.3%
경 156
11.3%
기 156
11.3%
동 156
11.3%
당 61
 
4.4%
정 53
 
3.8%
본 42
 
3.0%
Other values (10) 129
9.3%

우편번호
Real number (ℝ)

Distinct56
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15848.231
Minimum15801
Maximum15888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:43:23.321993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15801
5-th percentile15803.75
Q115827
median15851
Q315869
95-th percentile15884.5
Maximum15888
Range87
Interquartile range (IQR)42

Descriptive statistics

Standard deviation24.94713
Coefficient of variation (CV)0.0015741271
Kurtosis-0.98776907
Mean15848.231
Median Absolute Deviation (MAD)21
Skewness-0.25312145
Sum2472324
Variance622.35931
MonotonicityNot monotonic
2023-12-12T16:43:23.466727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15851 19
 
12.2%
15861 7
 
4.5%
15884 6
 
3.8%
15856 5
 
3.2%
15801 5
 
3.2%
15859 5
 
3.2%
15830 4
 
2.6%
15874 4
 
2.6%
15839 4
 
2.6%
15853 4
 
2.6%
Other values (46) 93
59.6%
ValueCountFrequency (%)
15801 5
3.2%
15802 2
 
1.3%
15803 1
 
0.6%
15804 2
 
1.3%
15806 3
1.9%
15809 1
 
0.6%
15810 1
 
0.6%
15811 1
 
0.6%
15813 1
 
0.6%
15814 1
 
0.6%
ValueCountFrequency (%)
15888 3
1.9%
15887 3
1.9%
15886 2
 
1.3%
15884 6
3.8%
15882 1
 
0.6%
15881 2
 
1.3%
15880 3
1.9%
15876 2
 
1.3%
15875 3
1.9%
15874 4
2.6%

전화번호
Text

MISSING 

Distinct116
Distinct (%)90.6%
Missing28
Missing (%)17.9%
Memory size1.3 KiB
2023-12-12T16:43:23.734704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.046875
Min length12

Characters and Unicode

Total characters1542
Distinct characters11
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

Unique113 ?
Unique (%)88.3%

Sample

1st row031-391-0775
2nd row031-391-4509
3rd row031-392-0513
4th row031-391-0126
5th row031-391-4307
ValueCountFrequency (%)
031-427-3816 6
 
4.7%
031-454-6245 6
 
4.7%
070-7431-0130 3
 
2.3%
031-399-6452 1
 
0.8%
031-391-1183 1
 
0.8%
031-399-1130 1
 
0.8%
031-393-3463 1
 
0.8%
031-391-0775 1
 
0.8%
031-394-8788 1
 
0.8%
031-502-0542 1
 
0.8%
Other values (106) 106
82.8%
2023-12-12T16:43:24.133979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 271
17.6%
- 256
16.6%
1 209
13.6%
0 201
13.0%
9 116
7.5%
4 115
7.5%
7 81
 
5.3%
2 78
 
5.1%
6 75
 
4.9%
5 75
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1286
83.4%
Dash Punctuation 256
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 271
21.1%
1 209
16.3%
0 201
15.6%
9 116
9.0%
4 115
8.9%
7 81
 
6.3%
2 78
 
6.1%
6 75
 
5.8%
5 75
 
5.8%
8 65
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1542
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 271
17.6%
- 256
16.6%
1 209
13.6%
0 201
13.0%
9 116
7.5%
4 115
7.5%
7 81
 
5.3%
2 78
 
5.1%
6 75
 
4.9%
5 75
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 271
17.6%
- 256
16.6%
1 209
13.6%
0 201
13.0%
9 116
7.5%
4 115
7.5%
7 81
 
5.3%
2 78
 
5.1%
6 75
 
4.9%
5 75
 
4.9%

팩스번호
Text

MISSING 

Distinct115
Distinct (%)90.6%
Missing29
Missing (%)18.6%
Memory size1.3 KiB
2023-12-12T16:43:24.409959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.007874
Min length12

Characters and Unicode

Total characters1525
Distinct characters11
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

Unique112 ?
Unique (%)88.2%

Sample

1st row031-392-1112
2nd row031-396-0911
3rd row031-396-0511
4th row031-391-0031
5th row031-347-4307
ValueCountFrequency (%)
031-454-6246 6
 
4.7%
031-427-3817 6
 
4.7%
031-451-2260 3
 
2.4%
031-396-0911 1
 
0.8%
031-502-0542 1
 
0.8%
031-419-0228 1
 
0.8%
031-399-6453 1
 
0.8%
031-397-1068 1
 
0.8%
031-407-3420 1
 
0.8%
031-393-4100 1
 
0.8%
Other values (105) 105
82.7%
2023-12-12T16:43:24.819249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 275
18.0%
- 254
16.7%
1 199
13.0%
0 196
12.9%
4 115
7.5%
9 100
 
6.6%
2 94
 
6.2%
7 81
 
5.3%
6 80
 
5.2%
5 72
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1271
83.3%
Dash Punctuation 254
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 275
21.6%
1 199
15.7%
0 196
15.4%
4 115
9.0%
9 100
 
7.9%
2 94
 
7.4%
7 81
 
6.4%
6 80
 
6.3%
5 72
 
5.7%
8 59
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1525
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 275
18.0%
- 254
16.7%
1 199
13.0%
0 196
12.9%
4 115
7.5%
9 100
 
6.6%
2 94
 
6.2%
7 81
 
5.3%
6 80
 
5.2%
5 72
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 275
18.0%
- 254
16.7%
1 199
13.0%
0 196
12.9%
4 115
7.5%
9 100
 
6.6%
2 94
 
6.2%
7 81
 
5.3%
6 80
 
5.2%
5 72
 
4.7%

준공일자
Date

Distinct132
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum1983-03-31 00:00:00
Maximum2022-03-25 00:00:00
2023-12-12T16:43:25.004214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:25.128374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
위탁
109 
자치
47 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위탁 109
69.9%
자치 47
30.1%

Length

2023-12-12T16:43:25.238345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:43:25.319628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁 109
69.9%
자치 47
30.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2022-09-27
156 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-27
2nd row2022-09-27
3rd row2022-09-27
4th row2022-09-27
5th row2022-09-27

Common Values

ValueCountFrequency (%)
2022-09-27 156
100.0%

Length

2023-12-12T16:43:25.411211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:43:25.493958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-27 156
100.0%

Interactions

2023-12-12T16:43:18.870960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:17.883155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.202996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.572409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.952947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:17.960190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.311528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.650824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:19.025754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.035718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.403986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.736696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:19.098762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.121536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.493958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:43:18.802590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:43:25.567581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호동수세대수우편번호관리
번호1.0000.6960.6080.9290.937
동수0.6961.0000.8370.5860.660
세대수0.6080.8371.0000.5320.495
우편번호0.9290.5860.5321.0000.628
관리0.9370.6600.4950.6281.000
2023-12-12T16:43:25.663571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호동수세대수우편번호관리
번호1.000-0.785-0.851-0.0810.759
동수-0.7851.0000.8990.0390.488
세대수-0.8510.8991.0000.0960.485
우편번호-0.0810.0390.0961.0000.465
관리0.7590.4880.4850.4651.000

Missing values

2023-12-12T16:43:19.507714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:43:19.669855image/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.
2023-12-12T16:43:19.782755image/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

번호아파트명동수세대수도로명주소지번주소우편번호전화번호팩스번호준공일자관리데이터기준일자
01무궁화151329경기도 군포시 산본로 296 (금정동, 무궁화주공아파트)경기도 군포시 금정동 84915836031-391-0775031-392-11121992-04-15위탁2022-09-27
12화성6402경기도 군포시 번영로550번길 6 (금정동, 화성무궁화아파트)경기도 군포시 금정동 849-115836031-391-4509031-396-09111992-12-15위탁2022-09-27
23한성목화8420경기도 군포시 번영로550번길 5 (금정동, 한성목화아파트)경기도 군포시 금정동 85015835031-392-0513031-396-05111992-12-15위탁2022-09-27
34충무1202489경기도 군포시 산본로 299 (금정동, 충무1차아파트)경기도 군포시 금정동 87215862031-391-0126031-391-00311992-04-15위탁2022-09-27
45충무24476경기도 군포시 산본로 299 (금정동, 충무2차아파트)경기도 군포시 금정동 873-215862031-391-4307031-347-43071993-06-30위탁2022-09-27
56다산11829경기도 군포시 오금로 16 (금정동, 다산아파트)경기도 군포시 금정동 871-1115863031-391-0214031-392-27161992-04-15위탁2022-09-27
67퇴계191011경기도 군포시 광정로 25-20 (금정동, 퇴계1차아파트)경기도 군포시 금정동 87515864031-391-4987031-397-92631993-06-30위탁2022-09-27
78퇴계29981경기도 군포시 광정로 25-20 (금정동, 퇴계2차아파트)경기도 군포시 금정동 87515864031-395-6962031-397-36221995-05-01위탁2022-09-27
89율곡212042경기도 군포시 오금로 43 (금정동, 율곡아파트)경기도 군포시 금정동 87615864031-344-6566031-344-65671994-05-15위탁2022-09-27
910소월11790경기도 군포시 오금로 34 (금정동, 소월아파트)경기도 군포시 금정동 871-715863031-391-4036031-391-40371992-12-15위탁2022-09-27
번호아파트명동수세대수도로명주소지번주소우편번호전화번호팩스번호준공일자관리데이터기준일자
146147영화135경기도 군포시 당산로132번길 16 (금정동, 영화아파트)경기도 군포시 금정동 769-115830<NA><NA>1987-12-17자치2022-09-27
147148은성145경기도 군포시 번영로624번길 55 (금정동, 은성아파트)경기도 군포시 금정동 772-1515830<NA><NA>1987-08-04자치2022-09-27
148149영화5차148경기도 군포시 금재로35번길 14 (금정동, 영화5차아파트)경기도 군포시 금정동 75415832<NA><NA>1986-12-06자치2022-09-27
149150상안284경기도 군포시 번영로587번길 39 (금정동, 상안아파트)경기도 군포시 금정동 71015827<NA><NA>1987-12-30자치2022-09-27
150151신안135경기도 군포시 번영로587번길 27 (금정동, 신안아파트)경기도 군포시 금정동 71115827<NA><NA>1988-07-04자치2022-09-27
151152금환136경기도 군포시 군포로775번길 69 (산본동, 금환아파트)경기도 군포시 산본동 83-2815806<NA><NA>1987-12-09자치2022-09-27
152153한양272경기도 군포시 번영로561번길 14 (금정동, 한양아파트)경기도 군포시 금정동 71315828<NA><NA>1989-10-18자치2022-09-27
153154기오5차124경기도 군포시 금재로 49 (금정동, 기오5차아파트)경기도 군포시 금정동 753-1615832<NA><NA>1985-10-29자치2022-09-27
154155기오6차136경기도 군포시 번영로609번길 30 (금정동, 기오6차아파트)경기도 군포시 금정동 73415813<NA><NA>1986-07-24자치2022-09-27
155156장미250경기도 군포시 금산로21번길 4 (금정동, 장미아파트)경기도 군포시 금정동 706-1915827<NA><NA>1989-07-25자치2022-09-27