Overview

Dataset statistics

Number of variables12
Number of observations133
Missing cells52
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.4 KiB
Average record size in memory103.0 B

Variable types

Numeric6
Text4
Categorical2

Dataset

Description대구광역시_남구 공동주택 현황에 대한 데이터로 (아파트명,소재지,동수,세대수,전화번호) 등의 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/3073010/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
구분 is highly overall correlated with 동수 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 행정동High correlation
동수 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
행정동 is highly overall correlated with 경도High correlation
전화번호 has 52 (39.1%) missing valuesMissing
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:25:29.247486
Analysis finished2023-12-12 18:25:35.132250
Duration5.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67
Minimum1
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:25:35.242717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.6
Q134
median67
Q3100
95-th percentile126.4
Maximum133
Range132
Interquartile range (IQR)66

Descriptive statistics

Standard deviation38.53786
Coefficient of variation (CV)0.57519194
Kurtosis-1.2
Mean67
Median Absolute Deviation (MAD)33
Skewness0
Sum8911
Variance1485.1667
MonotonicityStrictly increasing
2023-12-13T03:25:35.465738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
85 1
 
0.8%
99 1
 
0.8%
98 1
 
0.8%
97 1
 
0.8%
96 1
 
0.8%
95 1
 
0.8%
94 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
Other values (123) 123
92.5%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
133 1
0.8%
132 1
0.8%
131 1
0.8%
130 1
0.8%
129 1
0.8%
128 1
0.8%
127 1
0.8%
126 1
0.8%
125 1
0.8%
124 1
0.8%

아파트명
Text

Distinct132
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T03:25:35.781592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length6.6541353
Min length2

Characters and Unicode

Total characters885
Distinct characters204
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)98.5%

Sample

1st row보성상아맨션
2nd row이천주공1단지
3rd row강변타운
4th row이천주공2단지
5th row효성타운1차아파트
ValueCountFrequency (%)
스카이파크 2
 
1.4%
앞산리슈빌앤리마크 2
 
1.4%
리치빌 1
 
0.7%
대명하이츠 1
 
0.7%
강남골든하이츠 1
 
0.7%
궁전하이츠 1
 
0.7%
영대맨션 1
 
0.7%
한일빌라 1
 
0.7%
삼성공원아파트 1
 
0.7%
봉덕비젼파크 1
 
0.7%
Other values (133) 133
91.7%
2023-12-13T03:25:36.364730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55
 
6.2%
아 35
 
4.0%
파 27
 
3.1%
트 27
 
3.1%
맨 25
 
2.8%
션 25
 
2.8%
대 22
 
2.5%
스 22
 
2.5%
성 18
 
2.0%
이 18
 
2.0%
Other values (194) 611
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 766
86.6%
Space Separator 55
 
6.2%
Decimal Number 28
 
3.2%
Other Punctuation 11
 
1.2%
Open Punctuation 10
 
1.1%
Close Punctuation 10
 
1.1%
Uppercase Letter 5
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 35
 
4.6%
파 27
 
3.5%
트 27
 
3.5%
맨 25
 
3.3%
션 25
 
3.3%
대 22
 
2.9%
스 22
 
2.9%
성 18
 
2.3%
이 18
 
2.3%
동 15
 
2.0%
Other values (175) 532
69.5%
Decimal Number
ValueCountFrequency (%)
1 8
28.6%
2 7
25.0%
3 3
 
10.7%
5 3
 
10.7%
6 2
 
7.1%
0 2
 
7.1%
4 1
 
3.6%
7 1
 
3.6%
8 1
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
A 2
40.0%
B 2
40.0%
C 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 10
90.9%
. 1
 
9.1%
Open Punctuation
ValueCountFrequency (%)
( 7
70.0%
[ 3
30.0%
Close Punctuation
ValueCountFrequency (%)
) 7
70.0%
] 3
30.0%
Space Separator
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 766
86.6%
Common 114
 
12.9%
Latin 5
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 35
 
4.6%
파 27
 
3.5%
트 27
 
3.5%
맨 25
 
3.3%
션 25
 
3.3%
대 22
 
2.9%
스 22
 
2.9%
성 18
 
2.3%
이 18
 
2.3%
동 15
 
2.0%
Other values (175) 532
69.5%
Common
ValueCountFrequency (%)
55
48.2%
, 10
 
8.8%
1 8
 
7.0%
( 7
 
6.1%
) 7
 
6.1%
2 7
 
6.1%
3 3
 
2.6%
[ 3
 
2.6%
] 3
 
2.6%
5 3
 
2.6%
Other values (6) 8
 
7.0%
Latin
ValueCountFrequency (%)
A 2
40.0%
B 2
40.0%
C 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 766
86.6%
ASCII 119
 
13.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55
46.2%
, 10
 
8.4%
1 8
 
6.7%
( 7
 
5.9%
) 7
 
5.9%
2 7
 
5.9%
3 3
 
2.5%
[ 3
 
2.5%
] 3
 
2.5%
5 3
 
2.5%
Other values (9) 13
 
10.9%
Hangul
ValueCountFrequency (%)
아 35
 
4.6%
파 27
 
3.5%
트 27
 
3.5%
맨 25
 
3.3%
션 25
 
3.3%
대 22
 
2.9%
스 22
 
2.9%
성 18
 
2.3%
이 18
 
2.3%
동 15
 
2.0%
Other values (175) 532
69.5%

소재지 도로명주소
Text

Distinct132
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T03:25:36.775852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length34
Mean length28.112782
Min length16

Characters and Unicode

Total characters3739
Distinct characters196
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

Unique131 ?
Unique (%)98.5%

Sample

1st row대구광역시 남구 명덕로 236(이천동, 보성상아맨션)
2nd row대구광역시 남구 명덕로68길 77(이천동, 이천주공1단지)
3rd row대구광역시 남구 명덕로68길 19(이천동, 이천강변타운)
4th row대구광역시 남구 희망로 57(이천동, 이천주공2단지)
5th row대구광역시 남구 효성중앙길 37,38(봉덕동, 효성타운)
ValueCountFrequency (%)
대구광역시 133
 
20.6%
남구 133
 
20.6%
대명로 8
 
1.2%
현충로 6
 
0.9%
앞산순환로 4
 
0.6%
대덕로 4
 
0.6%
대명역2길 4
 
0.6%
성당로 4
 
0.6%
효성중앙길 3
 
0.5%
대경길 3
 
0.5%
Other values (293) 343
53.2%
2023-12-13T03:25:37.407857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
512
 
13.7%
대 274
 
7.3%
구 268
 
7.2%
역 139
 
3.7%
남 138
 
3.7%
광 136
 
3.6%
시 135
 
3.6%
동 132
 
3.5%
) 124
 
3.3%
( 124
 
3.3%
Other values (186) 1757
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2429
65.0%
Space Separator 512
 
13.7%
Decimal Number 430
 
11.5%
Close Punctuation 124
 
3.3%
Open Punctuation 124
 
3.3%
Other Punctuation 114
 
3.0%
Dash Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 274
 
11.3%
구 268
 
11.0%
역 139
 
5.7%
남 138
 
5.7%
광 136
 
5.6%
시 135
 
5.6%
동 132
 
5.4%
명 121
 
5.0%
로 101
 
4.2%
길 84
 
3.5%
Other values (170) 901
37.1%
Decimal Number
ValueCountFrequency (%)
1 85
19.8%
2 64
14.9%
3 54
12.6%
4 48
11.2%
7 35
8.1%
6 34
 
7.9%
5 31
 
7.2%
8 30
 
7.0%
0 27
 
6.3%
9 22
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 113
99.1%
. 1
 
0.9%
Space Separator
ValueCountFrequency (%)
512
100.0%
Close Punctuation
ValueCountFrequency (%)
) 124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2429
65.0%
Common 1310
35.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 274
 
11.3%
구 268
 
11.0%
역 139
 
5.7%
남 138
 
5.7%
광 136
 
5.6%
시 135
 
5.6%
동 132
 
5.4%
명 121
 
5.0%
로 101
 
4.2%
길 84
 
3.5%
Other values (170) 901
37.1%
Common
ValueCountFrequency (%)
512
39.1%
) 124
 
9.5%
( 124
 
9.5%
, 113
 
8.6%
1 85
 
6.5%
2 64
 
4.9%
3 54
 
4.1%
4 48
 
3.7%
7 35
 
2.7%
6 34
 
2.6%
Other values (6) 117
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2429
65.0%
ASCII 1310
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
512
39.1%
) 124
 
9.5%
( 124
 
9.5%
, 113
 
8.6%
1 85
 
6.5%
2 64
 
4.9%
3 54
 
4.1%
4 48
 
3.7%
7 35
 
2.7%
6 34
 
2.6%
Other values (6) 117
 
8.9%
Hangul
ValueCountFrequency (%)
대 274
 
11.3%
구 268
 
11.0%
역 139
 
5.7%
남 138
 
5.7%
광 136
 
5.6%
시 135
 
5.6%
동 132
 
5.4%
명 121
 
5.0%
로 101
 
4.2%
길 84
 
3.5%
Other values (170) 901
37.1%

소재지 지번주소
Text

Distinct132
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T03:25:37.799865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length24.864662
Min length11

Characters and Unicode

Total characters3307
Distinct characters179
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

Unique131 ?
Unique (%)98.5%

Sample

1st row대구광역시 남구 이천동 650 보성상아맨션
2nd row대구광역시 남구 이천동 121-70 대구이천주공1아파트
3rd row대구광역시 남구 이천동 214-456 강변타운
4th row대구광역시 남구 이천동 551-1 이천주공2단지
5th row대구광역시 남구 봉덕동 1071,1141 효성타운
ValueCountFrequency (%)
대구광역시 132
19.9%
남구 131
19.7%
대명동 83
 
12.5%
봉덕동 39
 
5.9%
이천동 11
 
1.7%
앞산 5
 
0.8%
스카이파크 2
 
0.3%
새한맨션아파트 2
 
0.3%
우방코스모스아파트 2
 
0.3%
리마크 2
 
0.3%
Other values (249) 255
38.4%
2023-12-13T03:25:38.289004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
531
 
16.1%
구 266
 
8.0%
대 232
 
7.0%
1 163
 
4.9%
동 140
 
4.2%
광 135
 
4.1%
역 134
 
4.1%
시 133
 
4.0%
남 132
 
4.0%
- 110
 
3.3%
Other values (169) 1331
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2013
60.9%
Decimal Number 647
 
19.6%
Space Separator 531
 
16.1%
Dash Punctuation 110
 
3.3%
Other Punctuation 4
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 266
13.2%
대 232
 
11.5%
동 140
 
7.0%
광 135
 
6.7%
역 134
 
6.7%
시 133
 
6.6%
남 132
 
6.6%
명 94
 
4.7%
아 53
 
2.6%
파 49
 
2.4%
Other values (153) 645
32.0%
Decimal Number
ValueCountFrequency (%)
1 163
25.2%
3 79
12.2%
2 78
12.1%
0 61
 
9.4%
6 56
 
8.7%
5 50
 
7.7%
9 46
 
7.1%
4 44
 
6.8%
7 39
 
6.0%
8 31
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 3
75.0%
. 1
 
25.0%
Space Separator
ValueCountFrequency (%)
531
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2013
60.9%
Common 1294
39.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 266
13.2%
대 232
 
11.5%
동 140
 
7.0%
광 135
 
6.7%
역 134
 
6.7%
시 133
 
6.6%
남 132
 
6.6%
명 94
 
4.7%
아 53
 
2.6%
파 49
 
2.4%
Other values (153) 645
32.0%
Common
ValueCountFrequency (%)
531
41.0%
1 163
 
12.6%
- 110
 
8.5%
3 79
 
6.1%
2 78
 
6.0%
0 61
 
4.7%
6 56
 
4.3%
5 50
 
3.9%
9 46
 
3.6%
4 44
 
3.4%
Other values (6) 76
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2013
60.9%
ASCII 1294
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
531
41.0%
1 163
 
12.6%
- 110
 
8.5%
3 79
 
6.1%
2 78
 
6.0%
0 61
 
4.7%
6 56
 
4.3%
5 50
 
3.9%
9 46
 
3.6%
4 44
 
3.4%
Other values (6) 76
 
5.9%
Hangul
ValueCountFrequency (%)
구 266
13.2%
대 232
 
11.5%
동 140
 
7.0%
광 135
 
6.7%
역 134
 
6.7%
시 133
 
6.6%
남 132
 
6.6%
명 94
 
4.7%
아 53
 
2.6%
파 49
 
2.4%
Other values (153) 645
32.0%

위도
Real number (ℝ)

Distinct131
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.843293
Minimum35.829409
Maximum35.857116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:25:38.453711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.829409
5-th percentile35.833942
Q135.838514
median35.843343
Q335.847312
95-th percentile35.854025
Maximum35.857116
Range0.02770715
Interquartile range (IQR)0.00879736

Descriptive statistics

Standard deviation0.0061739834
Coefficient of variation (CV)0.00017224934
Kurtosis-0.54098813
Mean35.843293
Median Absolute Deviation (MAD)0.00443737
Skewness0.17154921
Sum4767.158
Variance3.8118071 × 10-5
MonotonicityNot monotonic
2023-12-13T03:25:38.625762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.83728769 2
 
1.5%
35.83985968 2
 
1.5%
35.84467562 1
 
0.8%
35.84479676 1
 
0.8%
35.85418493 1
 
0.8%
35.85073936 1
 
0.8%
35.8504498 1
 
0.8%
35.84406015 1
 
0.8%
35.85093118 1
 
0.8%
35.83084664 1
 
0.8%
Other values (121) 121
91.0%
ValueCountFrequency (%)
35.82940904 1
0.8%
35.83027789 1
0.8%
35.83084664 1
0.8%
35.83154797 1
0.8%
35.83312845 1
0.8%
35.83367949 1
0.8%
35.83372144 1
0.8%
35.83408937 1
0.8%
35.83433645 1
0.8%
35.83478141 1
0.8%
ValueCountFrequency (%)
35.85711619 1
0.8%
35.85606274 1
0.8%
35.85530357 1
0.8%
35.85447691 1
0.8%
35.85434234 1
0.8%
35.85418493 1
0.8%
35.85403599 1
0.8%
35.85401847 1
0.8%
35.85401086 1
0.8%
35.85392361 1
0.8%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct131
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.5842
Minimum128.55737
Maximum128.60651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:25:38.792382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.55737
5-th percentile128.56172
Q1128.57286
median128.58357
Q3128.59616
95-th percentile128.60461
Maximum128.60651
Range0.0491437
Interquartile range (IQR)0.0232935

Descriptive statistics

Standard deviation0.014267946
Coefficient of variation (CV)0.00011096189
Kurtosis-1.2267816
Mean128.5842
Median Absolute Deviation (MAD)0.0121931
Skewness-0.14197043
Sum17101.698
Variance0.00020357429
MonotonicityNot monotonic
2023-12-13T03:25:38.955473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.5830777 2
 
1.5%
128.565474 2
 
1.5%
128.5855341 1
 
0.8%
128.5920664 1
 
0.8%
128.5742892 1
 
0.8%
128.5766888 1
 
0.8%
128.5750482 1
 
0.8%
128.5692234 1
 
0.8%
128.5927985 1
 
0.8%
128.5635061 1
 
0.8%
Other values (121) 121
91.0%
ValueCountFrequency (%)
128.5573656 1
0.8%
128.558656 1
0.8%
128.5589672 1
0.8%
128.5605194 1
0.8%
128.5606183 1
0.8%
128.5606878 1
0.8%
128.5614836 1
0.8%
128.5618805 1
0.8%
128.5619862 1
0.8%
128.5620924 1
0.8%
ValueCountFrequency (%)
128.6065093 1
0.8%
128.6058825 1
0.8%
128.6056731 1
0.8%
128.6053248 1
0.8%
128.6047701 1
0.8%
128.604723 1
0.8%
128.6046457 1
0.8%
128.6045795 1
0.8%
128.6045773 1
0.8%
128.6045412 1
0.8%

건축연도
Real number (ℝ)

Distinct43
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1997.4286
Minimum1976
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:25:39.101037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1976
5-th percentile1979
Q11982
median1998
Q32012
95-th percentile2021
Maximum2023
Range47
Interquartile range (IQR)30

Descriptive statistics

Standard deviation15.168391
Coefficient of variation (CV)0.0075939592
Kurtosis-1.4431969
Mean1997.4286
Median Absolute Deviation (MAD)16
Skewness0.141539
Sum265658
Variance230.08009
MonotonicityNot monotonic
2023-12-13T03:25:39.255445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1980 11
 
8.3%
1979 10
 
7.5%
2016 8
 
6.0%
1982 6
 
4.5%
1981 6
 
4.5%
2004 5
 
3.8%
1983 5
 
3.8%
2002 5
 
3.8%
2006 5
 
3.8%
2022 4
 
3.0%
Other values (33) 68
51.1%
ValueCountFrequency (%)
1976 2
 
1.5%
1977 2
 
1.5%
1978 2
 
1.5%
1979 10
7.5%
1980 11
8.3%
1981 6
4.5%
1982 6
4.5%
1983 5
3.8%
1984 2
 
1.5%
1986 1
 
0.8%
ValueCountFrequency (%)
2023 1
 
0.8%
2022 4
3.0%
2021 3
 
2.3%
2020 1
 
0.8%
2019 3
 
2.3%
2018 4
3.0%
2017 3
 
2.3%
2016 8
6.0%
2015 4
3.0%
2014 1
 
0.8%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6616541
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:25:39.380648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile8
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5933692
Coefficient of variation (CV)0.97434493
Kurtosis6.3052801
Mean2.6616541
Median Absolute Deviation (MAD)0
Skewness2.2636432
Sum354
Variance6.7255639
MonotonicityNot monotonic
2023-12-13T03:25:39.498483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 68
51.1%
2 20
 
15.0%
4 13
 
9.8%
3 10
 
7.5%
6 7
 
5.3%
5 5
 
3.8%
10 3
 
2.3%
8 2
 
1.5%
7 2
 
1.5%
16 1
 
0.8%
Other values (2) 2
 
1.5%
ValueCountFrequency (%)
1 68
51.1%
2 20
 
15.0%
3 10
 
7.5%
4 13
 
9.8%
5 5
 
3.8%
6 7
 
5.3%
7 2
 
1.5%
8 2
 
1.5%
9 1
 
0.8%
10 3
 
2.3%
ValueCountFrequency (%)
16 1
 
0.8%
12 1
 
0.8%
10 3
 
2.3%
9 1
 
0.8%
8 2
 
1.5%
7 2
 
1.5%
6 7
5.3%
5 5
 
3.8%
4 13
9.8%
3 10
7.5%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.3985
Minimum8
Maximum1162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:25:39.678143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12
Q120
median45
Q3198
95-th percentile551.2
Maximum1162
Range1154
Interquartile range (IQR)178

Descriptive statistics

Standard deviation219.47703
Coefficient of variation (CV)1.4307639
Kurtosis5.4160117
Mean153.3985
Median Absolute Deviation (MAD)29
Skewness2.1875088
Sum20402
Variance48170.166
MonotonicityNot monotonic
2023-12-13T03:25:39.855063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 6
 
4.5%
28 6
 
4.5%
18 5
 
3.8%
40 5
 
3.8%
12 4
 
3.0%
48 4
 
3.0%
14 4
 
3.0%
13 4
 
3.0%
24 3
 
2.3%
45 3
 
2.3%
Other values (69) 89
66.9%
ValueCountFrequency (%)
8 1
 
0.8%
10 1
 
0.8%
11 2
 
1.5%
12 4
3.0%
13 4
3.0%
14 4
3.0%
16 6
4.5%
17 3
2.3%
18 5
3.8%
19 3
2.3%
ValueCountFrequency (%)
1162 1
0.8%
1051 1
0.8%
975 1
0.8%
635 1
0.8%
622 1
0.8%
576 1
0.8%
553 1
0.8%
550 1
0.8%
528 1
0.8%
510 1
0.8%

전화번호
Text

MISSING 

Distinct80
Distinct (%)98.8%
Missing52
Missing (%)39.1%
Memory size1.2 KiB
2023-12-13T03:25:40.155987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.012346
Min length12

Characters and Unicode

Total characters973
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

Unique79 ?
Unique (%)97.5%

Sample

1st row053-473-9023
2nd row053-476-8111
3rd row053-475-5069
4th row053-474-2010
5th row053-474-8003
ValueCountFrequency (%)
053-622-5847 2
 
2.5%
053-471-2552 1
 
1.2%
053-621-9896 1
 
1.2%
053-427-9933 1
 
1.2%
053-475-4073 1
 
1.2%
053-471-6111 1
 
1.2%
053-473-7214 1
 
1.2%
053-471-2775 1
 
1.2%
053-471-5882 1
 
1.2%
053-624-9958 1
 
1.2%
Other values (70) 70
86.4%
2023-12-13T03:25:40.594387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 162
16.6%
0 135
13.9%
5 128
13.2%
3 107
11.0%
2 87
8.9%
6 78
8.0%
4 69
7.1%
7 67
6.9%
1 65
6.7%
9 43
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 811
83.4%
Dash Punctuation 162
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 135
16.6%
5 128
15.8%
3 107
13.2%
2 87
10.7%
6 78
9.6%
4 69
8.5%
7 67
8.3%
1 65
8.0%
9 43
 
5.3%
8 32
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 162
16.6%
0 135
13.9%
5 128
13.2%
3 107
11.0%
2 87
8.9%
6 78
8.0%
4 69
7.1%
7 67
6.9%
1 65
6.7%
9 43
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 162
16.6%
0 135
13.9%
5 128
13.2%
3 107
11.0%
2 87
8.9%
6 78
8.0%
4 69
7.1%
7 67
6.9%
1 65
6.7%
9 43
 
4.4%

행정동
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
봉덕3동
21 
대명1동
14 
대명10동
13 
대명4동
13 
이천동
11 
Other values (8)
61 

Length

Max length5
Median length4
Mean length4.075188
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row이천동
2nd row이천동
3rd row이천동
4th row이천동
5th row봉덕2동

Common Values

ValueCountFrequency (%)
봉덕3동 21
15.8%
대명1동 14
10.5%
대명10동 13
9.8%
대명4동 13
9.8%
이천동 11
8.3%
봉덕2동 11
8.3%
대명2동 10
7.5%
대명9동 8
 
6.0%
대명11동 8
 
6.0%
대명5동 7
 
5.3%
Other values (3) 17
12.8%

Length

2023-12-13T03:25:40.768252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
봉덕3동 21
15.8%
대명1동 14
10.5%
대명10동 13
9.8%
대명4동 13
9.8%
이천동 11
8.3%
봉덕2동 11
8.3%
대명2동 10
7.5%
대명9동 8
 
6.0%
대명11동 8
 
6.0%
대명5동 7
 
5.3%
Other values (3) 17
12.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-04-26
133 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-04-26
2nd row2023-04-26
3rd row2023-04-26
4th row2023-04-26
5th row2023-04-26

Common Values

ValueCountFrequency (%)
2023-04-26 133
100.0%

Length

2023-12-13T03:25:40.943002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:25:41.068408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-04-26 133
100.0%

Interactions

2023-12-13T03:25:34.029466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:30.046897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:30.891153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:31.620852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:32.289363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:33.304071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:34.163809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:30.202018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:31.018576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:31.724858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:32.732498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:33.419704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:34.279752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:30.364977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:31.137986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:31.834754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:32.850416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:33.547085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:34.396907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:30.484048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:31.281783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:31.933648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:32.984027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:33.663283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:34.521692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:30.608832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:31.391732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:32.057513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:33.085749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:33.781308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:34.651937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:30.746173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:31.505744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:32.167028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:33.195562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:33.898988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:25:41.156652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분위도경도건축연도동수세대수전화번호행정동
구분1.0000.4550.5680.7970.5670.5670.0000.621
위도0.4551.0000.5070.2890.2110.1120.9640.769
경도0.5680.5071.0000.0000.2300.2240.9350.837
건축연도0.7970.2890.0001.0000.3050.3580.0000.330
동수0.5670.2110.2300.3051.0000.8141.0000.568
세대수0.5670.1120.2240.3580.8141.0001.0000.396
전화번호0.0000.9640.9350.0001.0001.0001.0000.855
행정동0.6210.7690.8370.3300.5680.3960.8551.000
2023-12-13T03:25:41.573841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분위도경도건축연도동수세대수행정동
구분1.0000.096-0.3410.256-0.697-0.7860.309
위도0.0961.0000.2160.030-0.188-0.1840.449
경도-0.3410.2161.0000.1050.2860.2130.547
건축연도0.2560.0300.1051.0000.022-0.0010.145
동수-0.697-0.1880.2860.0221.0000.8060.230
세대수-0.786-0.1840.213-0.0010.8061.0000.185
행정동0.3090.4490.5470.1450.2300.1851.000

Missing values

2023-12-13T03:25:34.818608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:25:35.046501image/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

구분아파트명소재지 도로명주소소재지 지번주소위도경도건축연도동수세대수전화번호행정동데이터기준일자
01보성상아맨션대구광역시 남구 명덕로 236(이천동, 보성상아맨션)대구광역시 남구 이천동 650 보성상아맨션35.853814128.5948919875510053-473-9023이천동2023-04-26
12이천주공1단지대구광역시 남구 명덕로68길 77(이천동, 이천주공1단지)대구광역시 남구 이천동 121-70 대구이천주공1아파트35.852128128.60588319996576053-476-8111이천동2023-04-26
23강변타운대구광역시 남구 명덕로68길 19(이천동, 이천강변타운)대구광역시 남구 이천동 214-456 강변타운35.854477128.60650919943480053-475-5069이천동2023-04-26
34이천주공2단지대구광역시 남구 희망로 57(이천동, 이천주공2단지)대구광역시 남구 이천동 551-1 이천주공2단지35.847916128.60457720024320053-474-2010이천동2023-04-26
45효성타운1차아파트대구광역시 남구 효성중앙길 37,38(봉덕동, 효성타운)대구광역시 남구 봉덕동 1071,1141 효성타운35.838358128.6035971988161162053-474-8003봉덕2동2023-04-26
56미리내맨션대구광역시 남구 효성로 13(봉덕동, 미리내맨션)대구광역시 남구 봉덕동 산89-3 미리내아파트35.833721128.59892419826408053-471-1900봉덕3동2023-04-26
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