Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells9250
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory976.6 KiB
Average record size in memory100.0 B

Variable types

Categorical3
Text4
Numeric4

Dataset

Description부산광역시_도로명주소정보_20230131
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15028854

Alerts

시도명 has constant value ""Constant
X좌표 is highly overall correlated with 시군구명High correlation
Y좌표 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with X좌표 and 1 other fieldsHigh correlation
건물용도분류 is highly imbalanced (68.9%)Imbalance
건물명 has 9234 (92.3%) missing valuesMissing
도로명주소(건물부번) has 5601 (56.0%) zerosZeros

Reproduction

Analysis started2023-12-10 16:33:36.547701
Analysis finished2023-12-10 16:33:40.422148
Duration3.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 10000
100.0%

Length

2023-12-11T01:33:40.479450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:33:40.564948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 10000
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산진구
3426 
서구
1993 
영도구
1882 
동구
1816 
중구
883 

Length

Max length4
Median length3
Mean length2.8734
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영도구
2nd row부산진구
3rd row영도구
4th row동구
5th row동구

Common Values

ValueCountFrequency (%)
부산진구 3426
34.3%
서구 1993
19.9%
영도구 1882
18.8%
동구 1816
18.2%
중구 883
 
8.8%

Length

2023-12-11T01:33:40.667998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:33:40.776648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산진구 3426
34.3%
서구 1993
19.9%
영도구 1882
18.8%
동구 1816
18.2%
중구 883
 
8.8%

읍면동명
Text

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:33:41.039541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.7248
Min length3

Characters and Unicode

Total characters37248
Distinct characters59
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남항동3가
2nd row가야동
3rd row청학동
4th row초량동
5th row초량동
ValueCountFrequency (%)
초량동 527
 
5.3%
청학동 523
 
5.2%
수정동 503
 
5.0%
범천동 490
 
4.9%
전포동 487
 
4.9%
범일동 467
 
4.7%
가야동 399
 
4.0%
당감동 383
 
3.8%
남부민동 347
 
3.5%
좌천동 319
 
3.2%
Other values (90) 5555
55.5%
2023-12-11T01:33:41.450128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10806
29.0%
가 3470
 
9.3%
부 1191
 
3.2%
신 1124
 
3.0%
범 1069
 
2.9%
2 1060
 
2.8%
대 995
 
2.7%
초 919
 
2.5%
전 898
 
2.4%
천 809
 
2.2%
Other values (49) 14907
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34177
91.8%
Decimal Number 3071
 
8.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10806
31.6%
가 3470
 
10.2%
부 1191
 
3.5%
신 1124
 
3.3%
범 1069
 
3.1%
대 995
 
2.9%
초 919
 
2.7%
전 898
 
2.6%
천 809
 
2.4%
남 795
 
2.3%
Other values (43) 12101
35.4%
Decimal Number
ValueCountFrequency (%)
2 1060
34.5%
1 767
25.0%
3 711
23.2%
4 312
 
10.2%
5 202
 
6.6%
6 19
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34177
91.8%
Common 3071
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10806
31.6%
가 3470
 
10.2%
부 1191
 
3.5%
신 1124
 
3.3%
범 1069
 
3.1%
대 995
 
2.9%
초 919
 
2.7%
전 898
 
2.6%
천 809
 
2.4%
남 795
 
2.3%
Other values (43) 12101
35.4%
Common
ValueCountFrequency (%)
2 1060
34.5%
1 767
25.0%
3 711
23.2%
4 312
 
10.2%
5 202
 
6.6%
6 19
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34177
91.8%
ASCII 3071
 
8.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10806
31.6%
가 3470
 
10.2%
부 1191
 
3.5%
신 1124
 
3.3%
범 1069
 
3.1%
대 995
 
2.9%
초 919
 
2.7%
전 898
 
2.6%
천 809
 
2.4%
남 795
 
2.3%
Other values (43) 12101
35.4%
ASCII
ValueCountFrequency (%)
2 1060
34.5%
1 767
25.0%
3 711
23.2%
4 312
 
10.2%
5 202
 
6.6%
6 19
 
0.6%

도로명
Text

Distinct2199
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:33:41.674761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.4273
Min length2

Characters and Unicode

Total characters64273
Distinct characters223
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique495 ?
Unique (%)5.0%

Sample

1st row남항남로
2nd row가야대로521번길
3rd row까치길
4th row홍곡로
5th row초량중로
ValueCountFrequency (%)
망양로 124
 
1.2%
중앙대로 104
 
1.0%
엄광로 84
 
0.8%
해돋이로 83
 
0.8%
태종로 64
 
0.6%
충무대로 50
 
0.5%
절영로 47
 
0.5%
까치고개로 46
 
0.5%
구덕로 46
 
0.5%
보동길 44
 
0.4%
Other values (2189) 9308
93.1%
2023-12-11T01:33:42.000088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 8779
 
13.7%
길 7687
 
12.0%
번 6517
 
10.1%
1 2919
 
4.5%
2 2031
 
3.2%
대 1813
 
2.8%
3 1798
 
2.8%
5 1508
 
2.3%
4 1483
 
2.3%
7 1380
 
2.1%
Other values (213) 28358
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48599
75.6%
Decimal Number 15674
 
24.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 8779
18.1%
길 7687
 
15.8%
번 6517
 
13.4%
대 1813
 
3.7%
양 1009
 
2.1%
동 794
 
1.6%
남 751
 
1.5%
가 737
 
1.5%
중 645
 
1.3%
성 594
 
1.2%
Other values (203) 19273
39.7%
Decimal Number
ValueCountFrequency (%)
1 2919
18.6%
2 2031
13.0%
3 1798
11.5%
5 1508
9.6%
4 1483
9.5%
7 1380
8.8%
6 1331
8.5%
9 1200
7.7%
8 1073
 
6.8%
0 951
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48599
75.6%
Common 15674
 
24.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 8779
18.1%
길 7687
 
15.8%
번 6517
 
13.4%
대 1813
 
3.7%
양 1009
 
2.1%
동 794
 
1.6%
남 751
 
1.5%
가 737
 
1.5%
중 645
 
1.3%
성 594
 
1.2%
Other values (203) 19273
39.7%
Common
ValueCountFrequency (%)
1 2919
18.6%
2 2031
13.0%
3 1798
11.5%
5 1508
9.6%
4 1483
9.5%
7 1380
8.8%
6 1331
8.5%
9 1200
7.7%
8 1073
 
6.8%
0 951
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48599
75.6%
ASCII 15674
 
24.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 8779
18.1%
길 7687
 
15.8%
번 6517
 
13.4%
대 1813
 
3.7%
양 1009
 
2.1%
동 794
 
1.6%
남 751
 
1.5%
가 737
 
1.5%
중 645
 
1.3%
성 594
 
1.2%
Other values (203) 19273
39.7%
ASCII
ValueCountFrequency (%)
1 2919
18.6%
2 2031
13.0%
3 1798
11.5%
5 1508
9.6%
4 1483
9.5%
7 1380
8.8%
6 1331
8.5%
9 1200
7.7%
8 1073
 
6.8%
0 951
 
6.1%

도로명주소(건물본번)
Real number (ℝ)

Distinct567
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.15
Minimum1
Maximum975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:33:42.115014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q112
median26
Q358.25
95-th percentile268.05
Maximum975
Range974
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation119.71474
Coefficient of variation (CV)1.8375248
Kurtosis20.217596
Mean65.15
Median Absolute Deviation (MAD)17
Skewness4.1544382
Sum651500
Variance14331.62
MonotonicityNot monotonic
2023-12-11T01:33:42.220898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 265
 
2.6%
6 262
 
2.6%
5 262
 
2.6%
9 251
 
2.5%
7 247
 
2.5%
11 242
 
2.4%
10 234
 
2.3%
14 223
 
2.2%
3 223
 
2.2%
12 220
 
2.2%
Other values (557) 7571
75.7%
ValueCountFrequency (%)
1 91
 
0.9%
2 90
 
0.9%
3 223
2.2%
4 199
2.0%
5 262
2.6%
6 262
2.6%
7 247
2.5%
8 265
2.6%
9 251
2.5%
10 234
2.3%
ValueCountFrequency (%)
975 1
< 0.1%
971 1
< 0.1%
962 1
< 0.1%
954 1
< 0.1%
953 1
< 0.1%
949 1
< 0.1%
948 1
< 0.1%
945 1
< 0.1%
943 1
< 0.1%
936 1
< 0.1%

도로명주소(건물부번)
Real number (ℝ)

ZEROS 

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8913
Minimum0
Maximum121
Zeros5601
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:33:42.326506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile13
Maximum121
Range121
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.9331614
Coefficient of variation (CV)2.052074
Kurtosis44.412167
Mean2.8913
Median Absolute Deviation (MAD)0
Skewness4.8378261
Sum28913
Variance35.202405
MonotonicityNot monotonic
2023-12-11T01:33:42.451556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5601
56.0%
1 1065
 
10.7%
3 381
 
3.8%
5 371
 
3.7%
2 359
 
3.6%
6 320
 
3.2%
4 318
 
3.2%
7 260
 
2.6%
8 232
 
2.3%
10 161
 
1.6%
Other values (53) 932
 
9.3%
ValueCountFrequency (%)
0 5601
56.0%
1 1065
 
10.7%
2 359
 
3.6%
3 381
 
3.8%
4 318
 
3.2%
5 371
 
3.7%
6 320
 
3.2%
7 260
 
2.6%
8 232
 
2.3%
9 147
 
1.5%
ValueCountFrequency (%)
121 1
< 0.1%
91 1
< 0.1%
88 1
< 0.1%
72 1
< 0.1%
71 1
< 0.1%
66 1
< 0.1%
62 1
< 0.1%
60 1
< 0.1%
59 1
< 0.1%
58 1
< 0.1%

건물명
Text

MISSING 

Distinct742
Distinct (%)96.9%
Missing9234
Missing (%)92.3%
Memory size156.2 KiB
2023-12-11T01:33:42.692803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length5.6671018
Min length2

Characters and Unicode

Total characters4341
Distinct characters394
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique722 ?
Unique (%)94.3%

Sample

1st row부산지방국토관리청
2nd row대청주택
3rd row경선빌라
4th row목연정빌라
5th row정원주택(다동)
ValueCountFrequency (%)
한일연립 4
 
0.5%
오피스텔 4
 
0.5%
서면 4
 
0.5%
국일아파트 3
 
0.4%
경로당 3
 
0.4%
더 3
 
0.4%
동심빌라 3
 
0.4%
건아아파트 2
 
0.2%
대신 2
 
0.2%
더샵 2
 
0.2%
Other values (799) 819
96.5%
2023-12-11T01:33:43.151354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빌 312
 
7.2%
라 201
 
4.6%
동 107
 
2.5%
아 97
 
2.2%
83
 
1.9%
트 82
 
1.9%
스 77
 
1.8%
이 70
 
1.6%
파 69
 
1.6%
주 68
 
1.6%
Other values (384) 3175
73.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4085
94.1%
Decimal Number 98
 
2.3%
Space Separator 83
 
1.9%
Uppercase Letter 35
 
0.8%
Open Punctuation 13
 
0.3%
Close Punctuation 13
 
0.3%
Lowercase Letter 6
 
0.1%
Other Punctuation 6
 
0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 312
 
7.6%
라 201
 
4.9%
동 107
 
2.6%
아 97
 
2.4%
트 82
 
2.0%
스 77
 
1.9%
이 70
 
1.7%
파 69
 
1.7%
주 68
 
1.7%
대 67
 
1.6%
Other values (347) 2935
71.8%
Uppercase Letter
ValueCountFrequency (%)
B 4
11.4%
G 4
11.4%
O 4
11.4%
C 4
11.4%
E 4
11.4%
N 2
 
5.7%
R 2
 
5.7%
W 2
 
5.7%
S 2
 
5.7%
A 1
 
2.9%
Other values (6) 6
17.1%
Decimal Number
ValueCountFrequency (%)
2 27
27.6%
1 23
23.5%
3 19
19.4%
8 6
 
6.1%
7 6
 
6.1%
5 6
 
6.1%
4 5
 
5.1%
9 3
 
3.1%
6 2
 
2.0%
0 1
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
o 3
50.0%
c 2
33.3%
n 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
, 3
50.0%
/ 2
33.3%
& 1
 
16.7%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅲ 1
50.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4085
94.1%
Common 213
 
4.9%
Latin 43
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 312
 
7.6%
라 201
 
4.9%
동 107
 
2.6%
아 97
 
2.4%
트 82
 
2.0%
스 77
 
1.9%
이 70
 
1.7%
파 69
 
1.7%
주 68
 
1.7%
대 67
 
1.6%
Other values (347) 2935
71.8%
Latin
ValueCountFrequency (%)
B 4
 
9.3%
G 4
 
9.3%
O 4
 
9.3%
C 4
 
9.3%
E 4
 
9.3%
o 3
 
7.0%
N 2
 
4.7%
R 2
 
4.7%
W 2
 
4.7%
S 2
 
4.7%
Other values (11) 12
27.9%
Common
ValueCountFrequency (%)
83
39.0%
2 27
 
12.7%
1 23
 
10.8%
3 19
 
8.9%
( 13
 
6.1%
) 13
 
6.1%
8 6
 
2.8%
7 6
 
2.8%
5 6
 
2.8%
4 5
 
2.3%
Other values (6) 12
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4085
94.1%
ASCII 254
 
5.9%
Number Forms 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빌 312
 
7.6%
라 201
 
4.9%
동 107
 
2.6%
아 97
 
2.4%
트 82
 
2.0%
스 77
 
1.9%
이 70
 
1.7%
파 69
 
1.7%
주 68
 
1.7%
대 67
 
1.6%
Other values (347) 2935
71.8%
ASCII
ValueCountFrequency (%)
83
32.7%
2 27
 
10.6%
1 23
 
9.1%
3 19
 
7.5%
( 13
 
5.1%
) 13
 
5.1%
8 6
 
2.4%
7 6
 
2.4%
5 6
 
2.4%
4 5
 
2.0%
Other values (25) 53
20.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅲ 1
50.0%

건물용도분류
Categorical

IMBALANCE 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
주택
6484 
근린생활시설
2454 
유통시설
 
324
업무시설
 
165
숙박시설
 
95
Other values (30)
 
478

Length

Max length17
Median length2
Mean length3.3272
Min length2

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row주택
2nd row근린생활시설
3rd row주택
4th row주택
5th row근린생활시설

Common Values

ValueCountFrequency (%)
주택 6484
64.8%
근린생활시설 2454
 
24.5%
유통시설 324
 
3.2%
업무시설 165
 
1.7%
숙박시설 95
 
0.9%
공장/창고시설 93
 
0.9%
교육및복지시설 90
 
0.9%
의료시설 67
 
0.7%
자동차관련시설 53
 
0.5%
종교시설 52
 
0.5%
Other values (25) 123
 
1.2%

Length

2023-12-11T01:33:43.286575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주택 6484
64.8%
근린생활시설 2454
 
24.5%
유통시설 324
 
3.2%
업무시설 165
 
1.7%
숙박시설 95
 
0.9%
공장/창고시설 93
 
0.9%
교육및복지시설 90
 
0.9%
의료시설 67
 
0.7%
자동차관련시설 53
 
0.5%
종교시설 52
 
0.5%
Other values (25) 123
 
1.2%

관할행정동
Text

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:33:43.528179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.5451
Min length3

Characters and Unicode

Total characters45451
Distinct characters55
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남항동
2nd row가야제2동
3rd row청학제1동
4th row초량제3동
5th row초량제2동
ValueCountFrequency (%)
범천제2동 348
 
3.5%
신선동 342
 
3.4%
범일제1동 328
 
3.3%
청학제2동 317
 
3.2%
남항동 288
 
2.9%
아미동 278
 
2.8%
전포제1동 267
 
2.7%
가야제1동 264
 
2.6%
부전제1동 256
 
2.6%
초읍동 250
 
2.5%
Other values (55) 7062
70.6%
2023-12-11T01:33:43.889518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10806
23.8%
제 7172
15.8%
1 3125
 
6.9%
2 2708
 
6.0%
부 1305
 
2.9%
신 1101
 
2.4%
범 1049
 
2.3%
초 918
 
2.0%
남 917
 
2.0%
전 898
 
2.0%
Other values (45) 15452
34.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38279
84.2%
Decimal Number 7172
 
15.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10806
28.2%
제 7172
18.7%
부 1305
 
3.4%
신 1101
 
2.9%
범 1049
 
2.7%
초 918
 
2.4%
남 917
 
2.4%
전 898
 
2.3%
대 862
 
2.3%
정 794
 
2.1%
Other values (39) 12457
32.5%
Decimal Number
ValueCountFrequency (%)
1 3125
43.6%
2 2708
37.8%
3 600
 
8.4%
4 299
 
4.2%
5 245
 
3.4%
6 195
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38279
84.2%
Common 7172
 
15.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10806
28.2%
제 7172
18.7%
부 1305
 
3.4%
신 1101
 
2.9%
범 1049
 
2.7%
초 918
 
2.4%
남 917
 
2.4%
전 898
 
2.3%
대 862
 
2.3%
정 794
 
2.1%
Other values (39) 12457
32.5%
Common
ValueCountFrequency (%)
1 3125
43.6%
2 2708
37.8%
3 600
 
8.4%
4 299
 
4.2%
5 245
 
3.4%
6 195
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38279
84.2%
ASCII 7172
 
15.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10806
28.2%
제 7172
18.7%
부 1305
 
3.4%
신 1101
 
2.9%
범 1049
 
2.7%
초 918
 
2.4%
남 917
 
2.4%
전 898
 
2.3%
대 862
 
2.3%
정 794
 
2.1%
Other values (39) 12457
32.5%
ASCII
ValueCountFrequency (%)
1 3125
43.6%
2 2708
37.8%
3 600
 
8.4%
4 299
 
4.2%
5 245
 
3.4%
6 195
 
2.7%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct9992
Distinct (%)100.0%
Missing8
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1140452.4
Minimum1136959.9
Maximum1144373.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:33:44.027054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1136959.9
5-th percentile1138062.3
Q11139066.7
median1140480.7
Q31141601.7
95-th percentile1142977.5
Maximum1144373.2
Range7413.3151
Interquartile range (IQR)2534.974

Descriptive statistics

Standard deviation1545.2499
Coefficient of variation (CV)0.0013549447
Kurtosis-0.8666331
Mean1140452.4
Median Absolute Deviation (MAD)1248.7261
Skewness0.061785697
Sum1.13954 × 1010
Variance2387797.1
MonotonicityNot monotonic
2023-12-11T01:33:44.170493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1142868.829295 1
 
< 0.1%
1139954.256272 1
 
< 0.1%
1138718.825169 1
 
< 0.1%
1143117.56066 1
 
< 0.1%
1138741.477584 1
 
< 0.1%
1142269.536237 1
 
< 0.1%
1138850.658402 1
 
< 0.1%
1141382.270751 1
 
< 0.1%
1139747.291043 1
 
< 0.1%
1141458.281836 1
 
< 0.1%
Other values (9982) 9982
99.8%
(Missing) 8
 
0.1%
ValueCountFrequency (%)
1136959.876224 1
< 0.1%
1136978.334906 1
< 0.1%
1136982.732253 1
< 0.1%
1136993.14608 1
< 0.1%
1137063.674344 1
< 0.1%
1137096.924592 1
< 0.1%
1137101.136232 1
< 0.1%
1137105.990758 1
< 0.1%
1137123.709354 1
< 0.1%
1137131.507444 1
< 0.1%
ValueCountFrequency (%)
1144373.191278 1
< 0.1%
1144308.400553 1
< 0.1%
1144294.807768 1
< 0.1%
1144272.245439 1
< 0.1%
1144271.019374 1
< 0.1%
1144256.233884 1
< 0.1%
1144253.825579 1
< 0.1%
1144251.30579 1
< 0.1%
1144251.201866 1
< 0.1%
1144249.414854 1
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct9992
Distinct (%)100.0%
Missing8
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1682100.4
Minimum1675130.4
Maximum1689149.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:33:44.301993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1675130.4
5-th percentile1677393.8
Q11678948.4
median1681645.7
Q31685173
95-th percentile1687463.9
Maximum1689149.1
Range14018.713
Interquartile range (IQR)6224.6147

Descriptive statistics

Standard deviation3399.3881
Coefficient of variation (CV)0.0020209187
Kurtosis-1.2726707
Mean1682100.4
Median Absolute Deviation (MAD)3068.0562
Skewness0.13596405
Sum1.6807547 × 1010
Variance11555840
MonotonicityNot monotonic
2023-12-11T01:33:44.424768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1677314.96972 1
 
< 0.1%
1679053.380266 1
 
< 0.1%
1678509.774231 1
 
< 0.1%
1685149.664091 1
 
< 0.1%
1678190.598928 1
 
< 0.1%
1686044.688535 1
 
< 0.1%
1684909.879283 1
 
< 0.1%
1678724.488194 1
 
< 0.1%
1678612.300479 1
 
< 0.1%
1687841.538955 1
 
< 0.1%
Other values (9982) 9982
99.8%
(Missing) 8
 
0.1%
ValueCountFrequency (%)
1675130.38226 1
< 0.1%
1675139.224797 1
< 0.1%
1675178.556567 1
< 0.1%
1675220.517948 1
< 0.1%
1675270.94819 1
< 0.1%
1675300.568557 1
< 0.1%
1675309.630838 1
< 0.1%
1675311.950225 1
< 0.1%
1675325.736325 1
< 0.1%
1675335.898673 1
< 0.1%
ValueCountFrequency (%)
1689149.095421 1
< 0.1%
1689074.247793 1
< 0.1%
1689070.718499 1
< 0.1%
1689013.015685 1
< 0.1%
1689003.160587 1
< 0.1%
1689002.592217 1
< 0.1%
1688997.004881 1
< 0.1%
1688993.617243 1
< 0.1%
1688987.375888 1
< 0.1%
1688977.969537 1
< 0.1%

Interactions

2023-12-11T01:33:39.310612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.058800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.495309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.890827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:39.419235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.142032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.591970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.987593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:39.515486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.241987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.701885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:39.076310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:39.622593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.392894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:38.801505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:33:39.183998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:33:44.513234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명읍면동명도로명주소(건물본번)도로명주소(건물부번)건물용도분류관할행정동X좌표Y좌표
시군구명1.0001.0000.2220.1280.4181.0000.8640.961
읍면동명1.0001.0000.4690.3840.5640.9980.9530.987
도로명주소(건물본번)0.2220.4691.0000.0000.2370.5170.3100.269
도로명주소(건물부번)0.1280.3840.0001.0000.5740.2230.1520.051
건물용도분류0.4180.5640.2370.5741.0000.5110.1930.331
관할행정동1.0000.9980.5170.2230.5111.0000.9670.987
X좌표0.8640.9530.3100.1520.1930.9671.0000.774
Y좌표0.9610.9870.2690.0510.3310.9870.7741.000
2023-12-11T01:33:44.626206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명건물용도분류
시군구명1.0000.210
건물용도분류0.2101.000
2023-12-11T01:33:44.703107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명주소(건물본번)도로명주소(건물부번)X좌표Y좌표시군구명건물용도분류
도로명주소(건물본번)1.000-0.0360.049-0.0040.0940.085
도로명주소(건물부번)-0.0361.000-0.1490.0310.0780.263
X좌표0.049-0.1491.0000.2900.5340.069
Y좌표-0.0040.0310.2901.0000.7280.123
시군구명0.0940.0780.5340.7281.0000.210
건물용도분류0.0850.2630.0690.1230.2101.000

Missing values

2023-12-11T01:33:39.799499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:33:40.238700image/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.
2023-12-11T01:33:40.364852image/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

시도명시군구명읍면동명도로명도로명주소(건물본번)도로명주소(건물부번)건물명건물용도분류관할행정동X좌표Y좌표
46783부산광역시영도구남항동3가남항남로415<NA>주택남항동1140248.6393551677677.06175
90080부산광역시부산진구가야동가야대로521번길210<NA>근린생활시설가야제2동1139265.9046441685425.43809
56467부산광역시영도구청학동까치길10<NA>주택청학제1동1141848.5784321678723.197568
28663부산광역시동구초량동홍곡로308<NA>주택초량제3동1140307.2122571681898.582536
27804부산광역시동구초량동초량중로670부산지방국토관리청근린생활시설초량제2동1140208.4753491681380.528948
61545부산광역시영도구동삼동동삼로68번길420<NA>주택동삼제1동1143353.2842641677181.739351
2976부산광역시중구대청동1가대청로99번안길70대청주택주택대청동1139567.5228111679809.726746
47848부산광역시영도구영선동2가영일길730<NA>주택영선제1동1140649.8128731677975.460796
39105부산광역시동구좌천동성남이로7번길221<NA>주택범일제5동1141747.1381571682922.893745
82615부산광역시부산진구부암동백양순환로1360<NA>주택부암제3동1140310.6557051687011.823086
시도명시군구명읍면동명도로명도로명주소(건물본번)도로명주소(건물부번)건물명건물용도분류관할행정동X좌표Y좌표
71477부산광역시부산진구부전동부전로20번길140<NA>숙박시설부전제2동1141814.9982741685262.328171
46903부산광역시영도구남항동3가남항남로11번길195<NA>주택남항동1140065.4076221677821.736847
52162부산광역시영도구신선동3가영마루길850<NA>주택신선동1140909.8903131677332.26279
52678부산광역시영도구봉래동1가대교로458<NA>주택봉래제1동1140376.8212011678748.298747
85032부산광역시부산진구당감동당감로310<NA>유통시설당감제4동1140189.6203681686546.18403
85610부산광역시부산진구당감동가야대로681번길230<NA>주택당감제2동1140796.0039141685797.305094
78758부산광역시부산진구범전동시민공원로76번길4512<NA>주택부전제1동1142015.0545541686742.323887
72226부산광역시부산진구부전동신천대로50번길610<NA>근린생활시설부전제2동1142077.253081685369.062606
42853부산광역시동구범일동범일로65번길340<NA>주택범일제2동1141893.1636051683425.984849
55109부산광역시영도구청학동태종로3900유안펠리체주택청학제2동1142700.3955571678583.8202