Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells8941
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory109.0 B

Variable types

Numeric5
Categorical2
Text5

Dataset

Description부산광역시 도로명주소에 대한 데이터로 시도명, 시군구명, 읍면동명, 도로명주소(건물본번), 도로명주소(건물부번), 건물명, 건물용도분류, 관할행정동, X좌표, Y좌표 항목정보를 제공합니다.
Author부산광역시
URLhttps://www.data.go.kr/data/15028854/fileData.do

Alerts

시도명 has constant value ""Constant
순번 is highly overall correlated with X좌표 and 2 other fieldsHigh correlation
X좌표 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
건물명 has 8941 (89.4%) missing valuesMissing
순번 has unique valuesUnique
부번 has 5710 (57.1%) zerosZeros

Reproduction

Analysis started2024-04-21 01:03:14.927175
Analysis finished2024-04-21 01:03:19.895981
Duration4.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50116.8
Minimum19
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:03:19.964632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile4710.15
Q125329.75
median49915.5
Q374940.75
95-th percentile95136.3
Maximum100000
Range99981
Interquartile range (IQR)49611

Descriptive statistics

Standard deviation28877.159
Coefficient of variation (CV)0.57619718
Kurtosis-1.1896112
Mean50116.8
Median Absolute Deviation (MAD)24775
Skewness-0.0043519374
Sum5.01168 × 108
Variance8.3389031 × 108
MonotonicityNot monotonic
2024-04-21T10:03:20.080309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1294 1
 
< 0.1%
745 1
 
< 0.1%
57742 1
 
< 0.1%
85555 1
 
< 0.1%
2758 1
 
< 0.1%
90378 1
 
< 0.1%
62056 1
 
< 0.1%
56000 1
 
< 0.1%
92868 1
 
< 0.1%
15571 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
19 1
< 0.1%
20 1
< 0.1%
34 1
< 0.1%
38 1
< 0.1%
40 1
< 0.1%
45 1
< 0.1%
62 1
< 0.1%
76 1
< 0.1%
79 1
< 0.1%
89 1
< 0.1%
ValueCountFrequency (%)
100000 1
< 0.1%
99999 1
< 0.1%
99986 1
< 0.1%
99977 1
< 0.1%
99971 1
< 0.1%
99968 1
< 0.1%
99927 1
< 0.1%
99925 1
< 0.1%
99923 1
< 0.1%
99920 1
< 0.1%

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

2024-04-21T10:03:20.209977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:03:20.300337image/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
2846 
영도구
2210 
서구
2084 
동구
2006 
중구
854 

Length

Max length4
Median length3
Mean length2.7902
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산진구 2846
28.5%
영도구 2210
22.1%
서구 2084
20.8%
동구 2006
20.1%
중구 854
 
8.5%

Length

2024-04-21T10:03:20.409086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:03:20.498572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산진구 2846
28.5%
영도구 2210
22.1%
서구 2084
20.8%
동구 2006
20.1%
중구 854
 
8.5%

읍면동명
Text

Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:03:20.729333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.7558
Min length3

Characters and Unicode

Total characters37558
Distinct characters56
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 row대청동4가
2nd row청학동
3rd row개금동
4th row동삼동
5th row초량동
ValueCountFrequency (%)
청학동 625
 
6.2%
수정동 577
 
5.8%
범천동 566
 
5.7%
초량동 561
 
5.6%
범일동 524
 
5.2%
가야동 518
 
5.2%
당감동 462
 
4.6%
동삼동 444
 
4.4%
개금동 401
 
4.0%
좌천동 344
 
3.4%
Other values (87) 4978
49.8%
2024-04-21T10:03:21.082479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10984
29.2%
가 3730
 
9.9%
부 1249
 
3.3%
범 1231
 
3.3%
신 1175
 
3.1%
2 1128
 
3.0%
대 1059
 
2.8%
천 910
 
2.4%
남 776
 
2.1%
수 766
 
2.0%
Other values (46) 14550
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34346
91.4%
Decimal Number 3212
 
8.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10984
32.0%
가 3730
 
10.9%
부 1249
 
3.6%
범 1231
 
3.6%
신 1175
 
3.4%
대 1059
 
3.1%
천 910
 
2.6%
남 776
 
2.3%
수 766
 
2.2%
청 742
 
2.2%
Other values (40) 11724
34.1%
Decimal Number
ValueCountFrequency (%)
2 1128
35.1%
3 759
23.6%
1 756
23.5%
4 357
 
11.1%
5 194
 
6.0%
6 18
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34346
91.4%
Common 3212
 
8.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10984
32.0%
가 3730
 
10.9%
부 1249
 
3.6%
범 1231
 
3.6%
신 1175
 
3.4%
대 1059
 
3.1%
천 910
 
2.6%
남 776
 
2.3%
수 766
 
2.2%
청 742
 
2.2%
Other values (40) 11724
34.1%
Common
ValueCountFrequency (%)
2 1128
35.1%
3 759
23.6%
1 756
23.5%
4 357
 
11.1%
5 194
 
6.0%
6 18
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34346
91.4%
ASCII 3212
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10984
32.0%
가 3730
 
10.9%
부 1249
 
3.6%
범 1231
 
3.6%
신 1175
 
3.4%
대 1059
 
3.1%
천 910
 
2.6%
남 776
 
2.3%
수 766
 
2.2%
청 742
 
2.2%
Other values (40) 11724
34.1%
ASCII
ValueCountFrequency (%)
2 1128
35.1%
3 759
23.6%
1 756
23.5%
4 357
 
11.1%
5 194
 
6.0%
6 18
 
0.6%

도로명
Text

Distinct2065
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:03:21.316171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.3402
Min length2

Characters and Unicode

Total characters63402
Distinct characters217
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

Unique406 ?
Unique (%)4.1%

Sample

1st row대청북길
2nd row해양로
3rd row엄광로44번길
4th row웃서발로39번길
5th row영초길232번길
ValueCountFrequency (%)
망양로 118
 
1.2%
해돋이로 90
 
0.9%
태종로 89
 
0.9%
엄광로 86
 
0.9%
중앙대로 73
 
0.7%
절영로 63
 
0.6%
보수대로 54
 
0.5%
가야대로 47
 
0.5%
보동길 47
 
0.5%
구덕로 46
 
0.5%
Other values (2055) 9287
92.9%
2024-04-21T10:03:21.635969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 8720
 
13.8%
길 7493
 
11.8%
번 6267
 
9.9%
1 2736
 
4.3%
2 1939
 
3.1%
3 1739
 
2.7%
대 1696
 
2.7%
5 1464
 
2.3%
7 1368
 
2.2%
4 1364
 
2.2%
Other values (207) 28616
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48390
76.3%
Decimal Number 15012
 
23.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 8720
18.0%
길 7493
 
15.5%
번 6267
 
13.0%
대 1696
 
3.5%
양 1054
 
2.2%
동 692
 
1.4%
가 692
 
1.4%
남 672
 
1.4%
중 623
 
1.3%
망 571
 
1.2%
Other values (197) 19910
41.1%
Decimal Number
ValueCountFrequency (%)
1 2736
18.2%
2 1939
12.9%
3 1739
11.6%
5 1464
9.8%
7 1368
9.1%
4 1364
9.1%
6 1233
8.2%
9 1117
7.4%
8 1031
 
6.9%
0 1021
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48390
76.3%
Common 15012
 
23.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 8720
18.0%
길 7493
 
15.5%
번 6267
 
13.0%
대 1696
 
3.5%
양 1054
 
2.2%
동 692
 
1.4%
가 692
 
1.4%
남 672
 
1.4%
중 623
 
1.3%
망 571
 
1.2%
Other values (197) 19910
41.1%
Common
ValueCountFrequency (%)
1 2736
18.2%
2 1939
12.9%
3 1739
11.6%
5 1464
9.8%
7 1368
9.1%
4 1364
9.1%
6 1233
8.2%
9 1117
7.4%
8 1031
 
6.9%
0 1021
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48390
76.3%
ASCII 15012
 
23.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 8720
18.0%
길 7493
 
15.5%
번 6267
 
13.0%
대 1696
 
3.5%
양 1054
 
2.2%
동 692
 
1.4%
가 692
 
1.4%
남 672
 
1.4%
중 623
 
1.3%
망 571
 
1.2%
Other values (197) 19910
41.1%
ASCII
ValueCountFrequency (%)
1 2736
18.2%
2 1939
12.9%
3 1739
11.6%
5 1464
9.8%
7 1368
9.1%
4 1364
9.1%
6 1233
8.2%
9 1117
7.4%
8 1031
 
6.9%
0 1021
 
6.8%

본번
Real number (ℝ)

Distinct563
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.2393
Minimum1
Maximum957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:03:21.756639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q112
median26
Q359
95-th percentile273
Maximum957
Range956
Interquartile range (IQR)47

Descriptive statistics

Standard deviation117.36775
Coefficient of variation (CV)1.7990345
Kurtosis18.462652
Mean65.2393
Median Absolute Deviation (MAD)17
Skewness3.964691
Sum652393
Variance13775.19
MonotonicityNot monotonic
2024-04-21T10:03:22.059736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 266
 
2.7%
6 260
 
2.6%
8 259
 
2.6%
7 259
 
2.6%
12 256
 
2.6%
11 249
 
2.5%
9 247
 
2.5%
5 230
 
2.3%
14 228
 
2.3%
3 224
 
2.2%
Other values (553) 7522
75.2%
ValueCountFrequency (%)
1 83
 
0.8%
2 113
1.1%
3 224
2.2%
4 209
2.1%
5 230
2.3%
6 260
2.6%
7 259
2.6%
8 259
2.6%
9 247
2.5%
10 266
2.7%
ValueCountFrequency (%)
957 1
< 0.1%
936 2
< 0.1%
925 1
< 0.1%
922 1
< 0.1%
921 1
< 0.1%
915 1
< 0.1%
909 1
< 0.1%
898 1
< 0.1%
897 1
< 0.1%
892 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct67
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9741
Minimum0
Maximum121
Zeros5710
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:03:22.168129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile14
Maximum121
Range121
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.4110794
Coefficient of variation (CV)2.1556368
Kurtosis42.254576
Mean2.9741
Median Absolute Deviation (MAD)0
Skewness4.9175203
Sum29741
Variance41.101939
MonotonicityNot monotonic
2024-04-21T10:03:22.310325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5710
57.1%
1 1091
 
10.9%
3 366
 
3.7%
5 332
 
3.3%
2 323
 
3.2%
6 304
 
3.0%
4 296
 
3.0%
7 247
 
2.5%
8 197
 
2.0%
9 145
 
1.5%
Other values (57) 989
 
9.9%
ValueCountFrequency (%)
0 5710
57.1%
1 1091
 
10.9%
2 323
 
3.2%
3 366
 
3.7%
4 296
 
3.0%
5 332
 
3.3%
6 304
 
3.0%
7 247
 
2.5%
8 197
 
2.0%
9 145
 
1.5%
ValueCountFrequency (%)
121 1
< 0.1%
91 1
< 0.1%
88 1
< 0.1%
86 1
< 0.1%
78 1
< 0.1%
76 1
< 0.1%
74 1
< 0.1%
73 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%

건물명
Text

MISSING 

Distinct818
Distinct (%)77.2%
Missing8941
Missing (%)89.4%
Memory size156.2 KiB
2024-04-21T10:03:22.523668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length6.1633617
Min length2

Characters and Unicode

Total characters6527
Distinct characters434
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique700 ?
Unique (%)66.1%

Sample

1st row신오빌라
2nd row부암초등학교
3rd row오륙빌라
4th row지성하이츠빌
5th row국민주택
ValueCountFrequency (%)
국민주택 45
 
3.8%
주공아파트3단지 9
 
0.8%
한일연립 9
 
0.8%
롯데캐슬 8
 
0.7%
화승삼성아파트 8
 
0.7%
아파트 7
 
0.6%
대신 7
 
0.6%
백양푸른채 7
 
0.6%
서면 5
 
0.4%
당감동태화현대2차아파트 5
 
0.4%
Other values (901) 1087
90.8%
2024-04-21T10:03:22.839309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빌 271
 
4.2%
아 209
 
3.2%
트 189
 
2.9%
파 183
 
2.8%
라 179
 
2.7%
동 144
 
2.2%
주 142
 
2.2%
138
 
2.1%
교 129
 
2.0%
학 115
 
1.8%
Other values (424) 4828
74.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6156
94.3%
Space Separator 138
 
2.1%
Decimal Number 95
 
1.5%
Uppercase Letter 47
 
0.7%
Lowercase Letter 33
 
0.5%
Open Punctuation 20
 
0.3%
Close Punctuation 20
 
0.3%
Other Punctuation 14
 
0.2%
Dash Punctuation 3
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 271
 
4.4%
아 209
 
3.4%
트 189
 
3.1%
파 183
 
3.0%
라 179
 
2.9%
동 144
 
2.3%
주 142
 
2.3%
교 129
 
2.1%
학 115
 
1.9%
대 113
 
1.8%
Other values (374) 4482
72.8%
Uppercase Letter
ValueCountFrequency (%)
S 6
12.8%
T 5
10.6%
A 5
10.6%
C 4
 
8.5%
D 4
 
8.5%
B 4
 
8.5%
K 3
 
6.4%
H 2
 
4.3%
I 2
 
4.3%
P 2
 
4.3%
Other values (8) 10
21.3%
Lowercase Letter
ValueCountFrequency (%)
e 6
18.2%
o 5
15.2%
i 3
9.1%
l 3
9.1%
n 3
9.1%
v 3
9.1%
a 2
 
6.1%
k 2
 
6.1%
w 1
 
3.0%
x 1
 
3.0%
Other values (4) 4
12.1%
Decimal Number
ValueCountFrequency (%)
1 28
29.5%
2 27
28.4%
3 20
21.1%
7 7
 
7.4%
4 5
 
5.3%
6 3
 
3.2%
9 2
 
2.1%
8 2
 
2.1%
5 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 11
78.6%
· 1
 
7.1%
. 1
 
7.1%
& 1
 
7.1%
Space Separator
ValueCountFrequency (%)
138
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6156
94.3%
Common 290
 
4.4%
Latin 81
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 271
 
4.4%
아 209
 
3.4%
트 189
 
3.1%
파 183
 
3.0%
라 179
 
2.9%
동 144
 
2.3%
주 142
 
2.3%
교 129
 
2.1%
학 115
 
1.9%
대 113
 
1.8%
Other values (374) 4482
72.8%
Latin
ValueCountFrequency (%)
e 6
 
7.4%
S 6
 
7.4%
T 5
 
6.2%
A 5
 
6.2%
o 5
 
6.2%
C 4
 
4.9%
D 4
 
4.9%
B 4
 
4.9%
i 3
 
3.7%
l 3
 
3.7%
Other values (23) 36
44.4%
Common
ValueCountFrequency (%)
138
47.6%
1 28
 
9.7%
2 27
 
9.3%
3 20
 
6.9%
( 20
 
6.9%
) 20
 
6.9%
, 11
 
3.8%
7 7
 
2.4%
4 5
 
1.7%
- 3
 
1.0%
Other values (7) 11
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6156
94.3%
ASCII 369
 
5.7%
None 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빌 271
 
4.4%
아 209
 
3.4%
트 189
 
3.1%
파 183
 
3.0%
라 179
 
2.9%
동 144
 
2.3%
주 142
 
2.3%
교 129
 
2.1%
학 115
 
1.9%
대 113
 
1.8%
Other values (374) 4482
72.8%
ASCII
ValueCountFrequency (%)
138
37.4%
1 28
 
7.6%
2 27
 
7.3%
3 20
 
5.4%
( 20
 
5.4%
) 20
 
5.4%
, 11
 
3.0%
7 7
 
1.9%
e 6
 
1.6%
S 6
 
1.6%
Other values (38) 86
23.3%
None
ValueCountFrequency (%)
· 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

건축물용도
Text

Distinct137
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:03:23.048375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length4
Mean length4.3748
Min length2

Characters and Unicode

Total characters43748
Distinct characters174
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.3%

Sample

1st row연립주택
2nd row일반공장
3rd row기타제1종근생
4th row단독주택
5th row단독주택
ValueCountFrequency (%)
단독주택 5816
58.1%
기타제1종근생 937
 
9.4%
일반음식점 365
 
3.6%
상점 341
 
3.4%
다세대주택 295
 
2.9%
아파트 262
 
2.6%
다가구주택 240
 
2.4%
소매점 202
 
2.0%
기타제2종근생 173
 
1.7%
연립주택 141
 
1.4%
Other values (129) 1234
 
12.3%
2024-04-21T10:03:23.397074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 6554
15.0%
택 6500
14.9%
단 5821
13.3%
독 5817
13.3%
기 1286
 
2.9%
타 1271
 
2.9%
종 1230
 
2.8%
제 1226
 
2.8%
생 1203
 
2.7%
근 1200
 
2.7%
Other values (164) 11640
26.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42475
97.1%
Decimal Number 1200
 
2.7%
Close Punctuation 33
 
0.1%
Open Punctuation 33
 
0.1%
Space Separator 6
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 6554
15.4%
택 6500
15.3%
단 5821
13.7%
독 5817
13.7%
기 1286
 
3.0%
타 1271
 
3.0%
종 1230
 
2.9%
제 1226
 
2.9%
생 1203
 
2.8%
근 1200
 
2.8%
Other values (158) 10367
24.4%
Decimal Number
ValueCountFrequency (%)
1 1025
85.4%
2 175
 
14.6%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42475
97.1%
Common 1273
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 6554
15.4%
택 6500
15.3%
단 5821
13.7%
독 5817
13.7%
기 1286
 
3.0%
타 1271
 
3.0%
종 1230
 
2.9%
제 1226
 
2.9%
생 1203
 
2.8%
근 1200
 
2.8%
Other values (158) 10367
24.4%
Common
ValueCountFrequency (%)
1 1025
80.5%
2 175
 
13.7%
) 33
 
2.6%
( 33
 
2.6%
6
 
0.5%
. 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42475
97.1%
ASCII 1273
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 6554
15.4%
택 6500
15.3%
단 5821
13.7%
독 5817
13.7%
기 1286
 
3.0%
타 1271
 
3.0%
종 1230
 
2.9%
제 1226
 
2.9%
생 1203
 
2.8%
근 1200
 
2.8%
Other values (158) 10367
24.4%
ASCII
ValueCountFrequency (%)
1 1025
80.5%
2 175
 
13.7%
) 33
 
2.6%
( 33
 
2.6%
6
 
0.5%
. 1
 
0.1%

관할행정동
Text

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:03:23.602667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.608
Min length3

Characters and Unicode

Total characters46080
Distinct characters54
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row대청동
2nd row청학제1동
3rd row개금제2동
4th row동삼제1동
5th row초량제2동
ValueCountFrequency (%)
범천제2동 433
 
4.3%
범일제1동 373
 
3.7%
청학제2동 363
 
3.6%
가야제1동 355
 
3.5%
신선동 335
 
3.4%
아미동 304
 
3.0%
부전제1동 302
 
3.0%
남항동 299
 
3.0%
청학제1동 264
 
2.6%
동삼제1동 262
 
2.6%
Other values (52) 6710
67.1%
2024-04-21T10:03:23.931202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10985
23.8%
제 7473
16.2%
1 3144
 
6.8%
2 2723
 
5.9%
부 1390
 
3.0%
범 1190
 
2.6%
신 1144
 
2.5%
대 925
 
2.0%
남 901
 
2.0%
천 808
 
1.8%
Other values (44) 15397
33.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38607
83.8%
Decimal Number 7473
 
16.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10985
28.5%
제 7473
19.4%
부 1390
 
3.6%
범 1190
 
3.1%
신 1144
 
3.0%
대 925
 
2.4%
남 901
 
2.3%
천 808
 
2.1%
수 766
 
2.0%
청 743
 
1.9%
Other values (38) 12282
31.8%
Decimal Number
ValueCountFrequency (%)
1 3144
42.1%
2 2723
36.4%
3 734
 
9.8%
4 355
 
4.8%
5 316
 
4.2%
6 201
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38607
83.8%
Common 7473
 
16.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10985
28.5%
제 7473
19.4%
부 1390
 
3.6%
범 1190
 
3.1%
신 1144
 
3.0%
대 925
 
2.4%
남 901
 
2.3%
천 808
 
2.1%
수 766
 
2.0%
청 743
 
1.9%
Other values (38) 12282
31.8%
Common
ValueCountFrequency (%)
1 3144
42.1%
2 2723
36.4%
3 734
 
9.8%
4 355
 
4.8%
5 316
 
4.2%
6 201
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38607
83.8%
ASCII 7473
 
16.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10985
28.5%
제 7473
19.4%
부 1390
 
3.6%
범 1190
 
3.1%
신 1144
 
3.0%
대 925
 
2.4%
남 901
 
2.3%
천 808
 
2.1%
수 766
 
2.0%
청 743
 
1.9%
Other values (38) 12282
31.8%
ASCII
ValueCountFrequency (%)
1 3144
42.1%
2 2723
36.4%
3 734
 
9.8%
4 355
 
4.8%
5 316
 
4.2%
6 201
 
2.7%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct9992
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1140282.9
Minimum1136729.6
Maximum1145007.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:03:24.053278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1136729.6
5-th percentile1138005.7
Q11138969.9
median1140307.1
Q31141328.7
95-th percentile1142716.7
Maximum1145007.7
Range8278.055
Interquartile range (IQR)2358.8137

Descriptive statistics

Standard deviation1479.94
Coefficient of variation (CV)0.0012978709
Kurtosis-0.67660459
Mean1140282.9
Median Absolute Deviation (MAD)1172.6915
Skewness0.15279423
Sum1.1402829 × 1010
Variance2190222.5
MonotonicityNot monotonic
2024-04-21T10:03:24.176415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1140267.142 2
 
< 0.1%
1141333.436 2
 
< 0.1%
1140621.02 2
 
< 0.1%
1138245.483 2
 
< 0.1%
1141022.535 2
 
< 0.1%
1140454.532 2
 
< 0.1%
1137879.769 2
 
< 0.1%
1142069.343 2
 
< 0.1%
1141390.291 1
 
< 0.1%
1141485.852 1
 
< 0.1%
Other values (9982) 9982
99.8%
ValueCountFrequency (%)
1136729.646 1
< 0.1%
1136959.685 1
< 0.1%
1137006.583 1
< 0.1%
1137098.703 1
< 0.1%
1137121.401 1
< 0.1%
1137131.487 1
< 0.1%
1137186.391 1
< 0.1%
1137228.015 1
< 0.1%
1137254.483 1
< 0.1%
1137255.225 1
< 0.1%
ValueCountFrequency (%)
1145007.701 1
< 0.1%
1144991.132 1
< 0.1%
1144925.091 1
< 0.1%
1144804.926 1
< 0.1%
1144351.678 1
< 0.1%
1144342.982 1
< 0.1%
1144316.777 1
< 0.1%
1144304.262 1
< 0.1%
1144290.51 1
< 0.1%
1144290.067 1
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct9992
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1681621.5
Minimum1674581.4
Maximum1688181.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:03:24.302652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1674581.4
5-th percentile1677245.1
Q11678746.8
median1681093.6
Q31684550.8
95-th percentile1686783.9
Maximum1688181.6
Range13600.185
Interquartile range (IQR)5804.0267

Descriptive statistics

Standard deviation3191.3807
Coefficient of variation (CV)0.0018977997
Kurtosis-1.2250096
Mean1681621.5
Median Absolute Deviation (MAD)2699.336
Skewness0.17185107
Sum1.6816215 × 1010
Variance10184911
MonotonicityNot monotonic
2024-04-21T10:03:24.430605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1677855.873 2
 
< 0.1%
1678194.709 2
 
< 0.1%
1679077.762 2
 
< 0.1%
1680982.409 2
 
< 0.1%
1678482.28 2
 
< 0.1%
1678545.13 2
 
< 0.1%
1682968.863 2
 
< 0.1%
1684282.823 2
 
< 0.1%
1679995.354 1
 
< 0.1%
1676227.341 1
 
< 0.1%
Other values (9982) 9982
99.8%
ValueCountFrequency (%)
1674581.397 1
< 0.1%
1674612.417 1
< 0.1%
1674700.564 1
< 0.1%
1675122.26 1
< 0.1%
1675138.792 1
< 0.1%
1675139.711 1
< 0.1%
1675149.54 1
< 0.1%
1675156.243 1
< 0.1%
1675177.309 1
< 0.1%
1675179.514 1
< 0.1%
ValueCountFrequency (%)
1688181.582 1
< 0.1%
1688116.761 1
< 0.1%
1688077.101 1
< 0.1%
1688056.617 1
< 0.1%
1687971.827 1
< 0.1%
1687963.064 1
< 0.1%
1687929.83 1
< 0.1%
1687927.028 1
< 0.1%
1687918.551 1
< 0.1%
1687915.353 1
< 0.1%

Interactions

2024-04-21T10:03:19.238647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:17.608757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.037671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.456765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.838664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:19.313509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:17.733116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.119964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.533802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.917473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:19.394995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:17.807388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.213084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.605442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.993905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:19.487046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:17.884501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.301241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.684766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:19.086661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:19.564188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:17.954668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.375288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:18.756498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:03:19.158380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:03:24.518149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군구명읍면동명본번부번관할행정동X좌표Y좌표
순번1.0000.9990.9960.2950.1060.9930.8380.918
시군구명0.9991.0001.0000.2450.1211.0000.8640.963
읍면동명0.9961.0001.0000.4540.4030.9970.9420.967
본번0.2950.2450.4541.0000.0550.5130.3830.344
부번0.1060.1210.4030.0551.0000.2150.0980.090
관할행정동0.9931.0000.9970.5130.2151.0000.9580.979
X좌표0.8380.8640.9420.3830.0980.9581.0000.769
Y좌표0.9180.9630.9670.3440.0900.9790.7691.000
2024-04-21T10:03:24.622526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번본번부번X좌표Y좌표시군구명
순번1.0000.127-0.1120.5380.5080.949
본번0.1271.000-0.0460.030-0.0480.104
부번-0.112-0.0461.000-0.1200.0200.070
X좌표0.5380.030-0.1201.0000.0690.533
Y좌표0.508-0.0480.0200.0691.0000.733
시군구명0.9490.1040.0700.5330.7331.000

Missing values

2024-04-21T10:03:19.695055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:03:19.828890image/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

순번시도명시군구명읍면동명도로명본번부번건물명건축물용도관할행정동X좌표Y좌표
12931294부산광역시중구대청동4가대청북길111신오빌라연립주택대청동1139296.7431679995.354
7141071411부산광역시영도구청학동해양로3350<NA>일반공장청학제1동1142216.9261679320.037
8004580046부산광역시부산진구개금동엄광로44번길221<NA>기타제1종근생개금제2동1138569.5761684617.802
5444854449부산광역시영도구동삼동웃서발로39번길340<NA>단독주택동삼제1동1142945.7111677307.543
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