Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory143.0 B

Variable types

Numeric7
Categorical6
Text2
Boolean1

Dataset

Description부산광역시영도구_옥외광고물신우편코드_20211231
Author부산광역시 영도구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15072285

Alerts

시도 has constant value ""Constant
시도_영문명 has constant value ""Constant
시군구 has constant value ""Constant
시군구_영문명 has constant value ""Constant
새우편번호 is highly overall correlated with 법정동명 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 지번_본번 and 2 other fieldsHigh correlation
지번_본번 is highly overall correlated with 법정동코드High correlation
법정동명 is highly overall correlated with 새우편번호 and 2 other fieldsHigh correlation
행정동명 is highly overall correlated with 새우편번호 and 2 other fieldsHigh correlation
산여부 is highly imbalanced (93.4%)Imbalance
건물번호_부번 has 7079 (70.8%) zerosZeros
지번_부번 has 784 (7.8%) zerosZeros

Reproduction

Analysis started2023-12-10 17:35:57.041985
Analysis finished2023-12-10 17:36:14.365323
Duration17.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

새우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49057.201
Minimum49000
Maximum49127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:36:14.583285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49000
5-th percentile49010
Q149029
median49057
Q349079
95-th percentile49118
Maximum49127
Range127
Interquartile range (IQR)50

Descriptive statistics

Standard deviation32.190896
Coefficient of variation (CV)0.00065619104
Kurtosis-0.75280332
Mean49057.201
Median Absolute Deviation (MAD)25
Skewness0.2816592
Sum4.9057201 × 108
Variance1036.2538
MonotonicityNot monotonic
2023-12-11T02:36:14.986425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49079 393
 
3.9%
49024 245
 
2.5%
49027 229
 
2.3%
49076 212
 
2.1%
49061 190
 
1.9%
49017 187
 
1.9%
49031 185
 
1.8%
49014 185
 
1.8%
49102 170
 
1.7%
49074 165
 
1.7%
Other values (116) 7839
78.4%
ValueCountFrequency (%)
49000 58
0.6%
49001 1
 
< 0.1%
49002 1
 
< 0.1%
49003 72
0.7%
49004 24
 
0.2%
49005 116
1.2%
49006 31
 
0.3%
49007 55
0.5%
49008 70
0.7%
49009 59
0.6%
ValueCountFrequency (%)
49127 53
 
0.5%
49126 160
1.6%
49125 107
1.1%
49124 87
0.9%
49123 45
 
0.4%
49122 17
 
0.2%
49121 2
 
< 0.1%
49120 2
 
< 0.1%
49119 3
 
< 0.1%
49118 81
0.8%

시도
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

2023-12-11T02:36:15.297977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:36:15.510504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 10000
100.0%

시도_영문명
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Busan 10000
100.0%

Length

2023-12-11T02:36:15.720124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:36:15.929143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
busan 10000
100.0%

시군구
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영도구 10000
100.0%

Length

2023-12-11T02:36:16.159540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:36:16.368126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영도구 10000
100.0%

시군구_영문명
Categorical

CONSTANT 

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

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYeongdo-gu
2nd rowYeongdo-gu
3rd rowYeongdo-gu
4th rowYeongdo-gu
5th rowYeongdo-gu

Common Values

ValueCountFrequency (%)
Yeongdo-gu 10000
100.0%

Length

2023-12-11T02:36:16.565366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:36:16.772412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
yeongdo-gu 10000
100.0%

도로명
Text

Distinct429
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T02:36:17.135362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.1847
Min length3

Characters and Unicode

Total characters51847
Distinct characters158
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

Unique9 ?
Unique (%)0.1%

Sample

1st row태종로165번길
2nd row영도새싹길
3rd row선덤산길
4th row청학로21번길
5th row봉래언덕길
ValueCountFrequency (%)
태종로 408
 
4.1%
절영로 263
 
2.6%
하나길 215
 
2.1%
중복길 136
 
1.4%
해양로 113
 
1.1%
청학로 104
 
1.0%
청학동로 95
 
0.9%
청학북로 89
 
0.9%
아리랑길 86
 
0.9%
웃서발로 84
 
0.8%
Other values (419) 8407
84.1%
2023-12-11T02:36:17.868322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 7975
 
15.4%
로 5746
 
11.1%
번 3854
 
7.4%
남 1547
 
3.0%
3 1439
 
2.8%
1 1438
 
2.8%
청 1397
 
2.7%
학 1383
 
2.7%
영 1201
 
2.3%
2 1181
 
2.3%
Other values (148) 24686
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42976
82.9%
Decimal Number 8871
 
17.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 7975
18.6%
로 5746
 
13.4%
번 3854
 
9.0%
남 1547
 
3.6%
청 1397
 
3.3%
학 1383
 
3.2%
영 1201
 
2.8%
종 988
 
2.3%
태 988
 
2.3%
항 955
 
2.2%
Other values (138) 16942
39.4%
Decimal Number
ValueCountFrequency (%)
3 1439
16.2%
1 1438
16.2%
2 1181
13.3%
4 889
10.0%
9 818
9.2%
7 763
8.6%
6 715
8.1%
5 673
7.6%
0 519
 
5.9%
8 436
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42976
82.9%
Common 8871
 
17.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 7975
18.6%
로 5746
 
13.4%
번 3854
 
9.0%
남 1547
 
3.6%
청 1397
 
3.3%
학 1383
 
3.2%
영 1201
 
2.8%
종 988
 
2.3%
태 988
 
2.3%
항 955
 
2.2%
Other values (138) 16942
39.4%
Common
ValueCountFrequency (%)
3 1439
16.2%
1 1438
16.2%
2 1181
13.3%
4 889
10.0%
9 818
9.2%
7 763
8.6%
6 715
8.1%
5 673
7.6%
0 519
 
5.9%
8 436
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42976
82.9%
ASCII 8871
 
17.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 7975
18.6%
로 5746
 
13.4%
번 3854
 
9.0%
남 1547
 
3.6%
청 1397
 
3.3%
학 1383
 
3.2%
영 1201
 
2.8%
종 988
 
2.3%
태 988
 
2.3%
항 955
 
2.2%
Other values (138) 16942
39.4%
ASCII
ValueCountFrequency (%)
3 1439
16.2%
1 1438
16.2%
2 1181
13.3%
4 889
10.0%
9 818
9.2%
7 763
8.6%
6 715
8.1%
5 673
7.6%
0 519
 
5.9%
8 436
 
4.9%

도로명_영문명
Text

Distinct429
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T02:36:18.325060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23
Mean length16.534
Min length7

Characters and Unicode

Total characters165340
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st rowTaejong-ro 165beon-gil
2nd rowYeongdosaessak-gil
3rd rowSeondeomsan-gil
4th rowCheonghak-ro 21beon-gil
5th rowBongnaeeondeok-gil
ValueCountFrequency (%)
taejong-ro 988
 
6.7%
jeoryeong-ro 686
 
4.7%
cheonghak-ro 352
 
2.4%
2-gil 335
 
2.3%
cheonghakseo-ro 284
 
1.9%
namhang-ro 279
 
1.9%
3-gil 264
 
1.8%
cheonghakbuk-ro 261
 
1.8%
daepyeong-ro 254
 
1.7%
utseobal-ro 251
 
1.7%
Other values (279) 10785
73.2%
2023-12-11T02:36:19.100856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 18687
11.3%
g 16390
 
9.9%
n 15116
 
9.1%
- 13721
 
8.3%
e 13175
 
8.0%
a 11762
 
7.1%
i 9505
 
5.7%
l 8805
 
5.3%
r 7522
 
4.5%
b 4980
 
3.0%
Other values (41) 45677
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 128009
77.4%
Dash Punctuation 13721
 
8.3%
Uppercase Letter 10000
 
6.0%
Decimal Number 8871
 
5.4%
Space Separator 4739
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 18687
14.6%
g 16390
12.8%
n 15116
11.8%
e 13175
10.3%
a 11762
9.2%
i 9505
7.4%
l 8805
6.9%
r 7522
5.9%
b 4980
 
3.9%
h 4425
 
3.5%
Other values (11) 17642
13.8%
Uppercase Letter
ValueCountFrequency (%)
C 1542
15.4%
J 1295
13.0%
N 1109
11.1%
T 988
9.9%
H 971
9.7%
D 968
9.7%
B 620
6.2%
S 605
 
6.0%
Y 382
 
3.8%
U 288
 
2.9%
Other values (8) 1232
12.3%
Decimal Number
ValueCountFrequency (%)
3 1439
16.2%
1 1438
16.2%
2 1181
13.3%
4 889
10.0%
9 818
9.2%
7 763
8.6%
6 715
8.1%
5 673
7.6%
0 519
 
5.9%
8 436
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 13721
100.0%
Space Separator
ValueCountFrequency (%)
4739
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 138009
83.5%
Common 27331
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 18687
13.5%
g 16390
11.9%
n 15116
11.0%
e 13175
9.5%
a 11762
8.5%
i 9505
 
6.9%
l 8805
 
6.4%
r 7522
 
5.5%
b 4980
 
3.6%
h 4425
 
3.2%
Other values (29) 27642
20.0%
Common
ValueCountFrequency (%)
- 13721
50.2%
4739
 
17.3%
3 1439
 
5.3%
1 1438
 
5.3%
2 1181
 
4.3%
4 889
 
3.3%
9 818
 
3.0%
7 763
 
2.8%
6 715
 
2.6%
5 673
 
2.5%
Other values (2) 955
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 18687
11.3%
g 16390
 
9.9%
n 15116
 
9.1%
- 13721
 
8.3%
e 13175
 
8.0%
a 11762
 
7.1%
i 9505
 
5.7%
l 8805
 
5.3%
r 7522
 
4.5%
b 4980
 
3.0%
Other values (41) 45677
27.6%

건물번호_본번
Real number (ℝ)

Distinct653
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.5867
Minimum1
Maximum948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:36:19.372496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q117
median38
Q387
95-th percentile378
Maximum948
Range947
Interquartile range (IQR)70

Descriptive statistics

Standard deviation136.03212
Coefficient of variation (CV)1.571051
Kurtosis11.603257
Mean86.5867
Median Absolute Deviation (MAD)26
Skewness3.2204177
Sum865867
Variance18504.738
MonotonicityNot monotonic
2023-12-11T02:36:19.674795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 192
 
1.9%
8 183
 
1.8%
7 179
 
1.8%
11 179
 
1.8%
14 178
 
1.8%
16 176
 
1.8%
10 168
 
1.7%
6 166
 
1.7%
13 165
 
1.7%
9 163
 
1.6%
Other values (643) 8251
82.5%
ValueCountFrequency (%)
1 73
 
0.7%
2 114
1.1%
3 121
1.2%
4 117
1.2%
5 159
1.6%
6 166
1.7%
7 179
1.8%
8 183
1.8%
9 163
1.6%
10 168
1.7%
ValueCountFrequency (%)
948 1
< 0.1%
946 1
< 0.1%
942 1
< 0.1%
938 1
< 0.1%
936 1
< 0.1%
934 1
< 0.1%
932 1
< 0.1%
930 1
< 0.1%
928 1
< 0.1%
922 1
< 0.1%

건물번호_부번
Real number (ℝ)

ZEROS 

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2204
Minimum0
Maximum70
Zeros7079
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:36:19.964945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile12.05
Maximum70
Range70
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.6623697
Coefficient of variation (CV)2.5501575
Kurtosis34.650039
Mean2.2204
Median Absolute Deviation (MAD)0
Skewness4.8269504
Sum22204
Variance32.06243
MonotonicityNot monotonic
2023-12-11T02:36:20.805183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7079
70.8%
1 565
 
5.7%
3 268
 
2.7%
4 252
 
2.5%
6 233
 
2.3%
5 219
 
2.2%
2 193
 
1.9%
7 164
 
1.6%
8 160
 
1.6%
10 113
 
1.1%
Other values (51) 754
 
7.5%
ValueCountFrequency (%)
0 7079
70.8%
1 565
 
5.7%
2 193
 
1.9%
3 268
 
2.7%
4 252
 
2.5%
5 219
 
2.2%
6 233
 
2.3%
7 164
 
1.6%
8 160
 
1.6%
9 106
 
1.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 4
< 0.1%
65 1
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
61 1
 
< 0.1%
60 1
 
< 0.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6200115 × 109
Minimum2.6200101 × 109
Maximum2.6200121 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:36:21.128510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6200101 × 109
5-th percentile2.6200104 × 109
Q12.6200111 × 109
median2.6200118 × 109
Q32.620012 × 109
95-th percentile2.6200121 × 109
Maximum2.6200121 × 109
Range2000
Interquartile range (IQR)900

Descriptive statistics

Standard deviation566.69818
Coefficient of variation (CV)2.1629606 × 10-7
Kurtosis-0.52137964
Mean2.6200115 × 109
Median Absolute Deviation (MAD)300
Skewness-0.8249074
Sum2.6200115 × 1013
Variance321146.82
MonotonicityNot monotonic
2023-12-11T02:36:21.403375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2620012000 2603
26.0%
2620012100 1693
16.9%
2620011300 668
 
6.7%
2620011100 635
 
6.3%
2620011400 597
 
6.0%
2620011900 546
 
5.5%
2620011800 495
 
5.0%
2620011200 389
 
3.9%
2620010700 327
 
3.3%
2620010600 245
 
2.5%
Other values (11) 1802
18.0%
ValueCountFrequency (%)
2620010100 103
 
1.0%
2620010200 110
 
1.1%
2620010300 144
1.4%
2620010400 189
1.9%
2620010500 242
2.4%
2620010600 245
2.5%
2620010700 327
3.3%
2620010800 139
1.4%
2620010900 211
2.1%
2620011000 178
1.8%
ValueCountFrequency (%)
2620012100 1693
16.9%
2620012000 2603
26.0%
2620011900 546
 
5.5%
2620011800 495
 
5.0%
2620011700 209
 
2.1%
2620011600 159
 
1.6%
2620011500 118
 
1.2%
2620011400 597
 
6.0%
2620011300 668
 
6.7%
2620011200 389
 
3.9%

법정동명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
청학동
2603 
동삼동
1693 
신선동2가
668 
영선동4가
635 
신선동3가
597 
Other values (16)
3804 

Length

Max length5
Median length5
Mean length4.1408
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row봉래동4가
2nd row신선동1가
3rd row동삼동
4th row청학동
5th row봉래동5가

Common Values

ValueCountFrequency (%)
청학동 2603
26.0%
동삼동 1693
16.9%
신선동2가 668
 
6.7%
영선동4가 635
 
6.3%
신선동3가 597
 
6.0%
봉래동5가 546
 
5.5%
봉래동4가 495
 
5.0%
신선동1가 389
 
3.9%
남항동3가 327
 
3.3%
남항동2가 245
 
2.5%
Other values (11) 1802
18.0%

Length

2023-12-11T02:36:21.739722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청학동 2603
26.0%
동삼동 1693
16.9%
신선동2가 668
 
6.7%
영선동4가 635
 
6.3%
신선동3가 597
 
6.0%
봉래동5가 546
 
5.5%
봉래동4가 495
 
5.0%
신선동1가 389
 
3.9%
남항동3가 327
 
3.3%
남항동2가 245
 
2.5%
Other values (11) 1802
18.0%

행정동명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
신선동
1618 
청학제2동
1468 
남항동
1314 
청학제1동
1024 
봉래제2동
1002 
Other values (7)
3574 

Length

Max length5
Median length5
Mean length4.3768
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row봉래제2동
2nd row신선동
3rd row<NA>
4th row청학제2동
5th row봉래제2동

Common Values

ValueCountFrequency (%)
신선동 1618
16.2%
청학제2동 1468
14.7%
남항동 1314
13.1%
청학제1동 1024
10.2%
봉래제2동 1002
10.0%
동삼제1동 950
9.5%
영선제2동 785
7.8%
봉래제1동 464
 
4.6%
동삼제2동 391
 
3.9%
<NA> 368
 
3.7%
Other values (2) 616
 
6.2%

Length

2023-12-11T02:36:22.075201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신선동 1618
16.2%
청학제2동 1468
14.7%
남항동 1314
13.1%
청학제1동 1024
10.2%
봉래제2동 1002
10.0%
동삼제1동 950
9.5%
영선제2동 785
7.8%
봉래제1동 464
 
4.6%
동삼제2동 391
 
3.9%
na 368
 
3.7%
Other values (2) 616
 
6.2%

산여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
9922 
False
 
78
ValueCountFrequency (%)
True 9922
99.2%
False 78
 
0.8%
2023-12-11T02:36:22.294261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

지번_본번
Real number (ℝ)

HIGH CORRELATION 

Distinct798
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.7186
Minimum1
Maximum1492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:36:22.522867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q194
median153
Q3268
95-th percentile722.05
Maximum1492
Range1491
Interquartile range (IQR)174

Descriptive statistics

Standard deviation215.5215
Coefficient of variation (CV)0.97204969
Kurtosis5.9748192
Mean221.7186
Median Absolute Deviation (MAD)77
Skewness2.2587964
Sum2217186
Variance46449.515
MonotonicityNot monotonic
2023-12-11T02:36:22.851507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
391 136
 
1.4%
131 127
 
1.3%
468 121
 
1.2%
101 115
 
1.1%
227 107
 
1.1%
323 94
 
0.9%
1 92
 
0.9%
112 92
 
0.9%
141 90
 
0.9%
99 84
 
0.8%
Other values (788) 8942
89.4%
ValueCountFrequency (%)
1 92
0.9%
2 34
 
0.3%
3 12
 
0.1%
4 28
 
0.3%
5 38
0.4%
6 38
0.4%
7 14
 
0.1%
8 9
 
0.1%
9 14
 
0.1%
10 11
 
0.1%
ValueCountFrequency (%)
1492 1
< 0.1%
1475 1
< 0.1%
1470 1
< 0.1%
1464 2
< 0.1%
1458 1
< 0.1%
1424 2
< 0.1%
1357 1
< 0.1%
1341 1
< 0.1%
1336 1
< 0.1%
1334 1
< 0.1%

지번_순번
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.701
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:36:23.110207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5591799
Coefficient of variation (CV)0.91662548
Kurtosis7.8926959
Mean1.701
Median Absolute Deviation (MAD)0
Skewness2.8485925
Sum17010
Variance2.4310421
MonotonicityNot monotonic
2023-12-11T02:36:23.352661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 7153
71.5%
2 1487
 
14.9%
3 426
 
4.3%
4 292
 
2.9%
8 184
 
1.8%
7 150
 
1.5%
6 147
 
1.5%
5 104
 
1.0%
9 57
 
0.6%
ValueCountFrequency (%)
1 7153
71.5%
2 1487
 
14.9%
3 426
 
4.3%
4 292
 
2.9%
5 104
 
1.0%
6 147
 
1.5%
7 150
 
1.5%
8 184
 
1.8%
9 57
 
0.6%
ValueCountFrequency (%)
9 57
 
0.6%
8 184
 
1.8%
7 150
 
1.5%
6 147
 
1.5%
5 104
 
1.0%
4 292
 
2.9%
3 426
 
4.3%
2 1487
 
14.9%
1 7153
71.5%

지번_부번
Real number (ℝ)

ZEROS 

Distinct439
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.5427
Minimum0
Maximum880
Zeros784
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:36:23.673167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median11
Q337
95-th percentile181
Maximum880
Range880
Interquartile range (IQR)34

Descriptive statistics

Standard deviation80.147688
Coefficient of variation (CV)2.0268643
Kurtosis26.457265
Mean39.5427
Median Absolute Deviation (MAD)10
Skewness4.4610278
Sum395427
Variance6423.6519
MonotonicityNot monotonic
2023-12-11T02:36:24.052314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 859
 
8.6%
0 784
 
7.8%
2 721
 
7.2%
3 503
 
5.0%
4 411
 
4.1%
5 385
 
3.9%
6 317
 
3.2%
7 302
 
3.0%
8 239
 
2.4%
9 229
 
2.3%
Other values (429) 5250
52.5%
ValueCountFrequency (%)
0 784
7.8%
1 859
8.6%
2 721
7.2%
3 503
5.0%
4 411
4.1%
5 385
3.9%
6 317
 
3.2%
7 302
 
3.0%
8 239
 
2.4%
9 229
 
2.3%
ValueCountFrequency (%)
880 1
< 0.1%
783 1
< 0.1%
778 1
< 0.1%
772 1
< 0.1%
763 1
< 0.1%
760 1
< 0.1%
759 1
< 0.1%
754 1
< 0.1%
751 1
< 0.1%
744 1
< 0.1%

Interactions

2023-12-11T02:36:11.863844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:01.064337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:02.640024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:04.181921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:06.063447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:08.471107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:10.129904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:12.127120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:01.259603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:02.831319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:04.420140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:06.324744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:08.728815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:10.382966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:12.347784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:01.450348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:03.012545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:04.662869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:07.153756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:08.953357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:10.618756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:12.586259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:01.702427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:03.227609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:04.923770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:07.457850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:09.182814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:10.884654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:12.802357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:01.922955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:03.488169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:05.216085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:07.754425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:09.432168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:11.135045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:13.015773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:02.140482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:03.723501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:05.491782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:08.017643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:09.673511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:11.415051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:13.246007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:02.435230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:03.978083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:05.811252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:08.260800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:09.925819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:36:11.659568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:36:24.280649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
새우편번호건물번호_본번건물번호_부번법정동코드법정동명행정동명산여부지번_본번지번_순번지번_부번
새우편번호1.0000.5190.2850.9050.9190.8990.1750.8070.4560.369
건물번호_본번0.5191.0000.2600.4670.4660.4520.0430.5160.7000.134
건물번호_부번0.2850.2601.0000.2690.2450.2250.1010.3270.1530.000
법정동코드0.9050.4670.2691.0001.0000.9470.1060.6100.4700.291
법정동명0.9190.4660.2451.0001.0000.9720.1130.6290.6260.277
행정동명0.8990.4520.2250.9470.9721.0000.1440.7070.5060.303
산여부0.1750.0430.1010.1060.1130.1441.0000.1110.0340.107
지번_본번0.8070.5160.3270.6100.6290.7070.1111.0000.4500.305
지번_순번0.4560.7000.1530.4700.6260.5060.0340.4501.0000.139
지번_부번0.3690.1340.0000.2910.2770.3030.1070.3050.1391.000
2023-12-11T02:36:24.579364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명산여부행정동명
법정동명1.0000.0990.834
산여부0.0991.0000.138
행정동명0.8340.1381.000
2023-12-11T02:36:24.826741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
새우편번호건물번호_본번건물번호_부번법정동코드지번_본번지번_순번지번_부번법정동명행정동명산여부
새우편번호1.0000.103-0.1420.1460.2450.010-0.0430.6610.6720.134
건물번호_본번0.1031.000-0.166-0.093-0.0630.433-0.0460.1910.2110.033
건물번호_부번-0.142-0.1661.0000.1070.088-0.0040.0350.0860.0920.062
법정동코드0.146-0.0930.1071.0000.507-0.2120.2960.9990.7960.082
지번_본번0.245-0.0630.0880.5071.000-0.1820.0860.2880.3970.085
지번_순번0.0100.433-0.004-0.212-0.1821.000-0.1790.2980.2570.034
지번_부번-0.043-0.0460.0350.2960.086-0.1791.0000.1050.1330.082
법정동명0.6610.1910.0860.9990.2880.2980.1051.0000.8340.099
행정동명0.6720.2110.0920.7960.3970.2570.1330.8341.0000.138
산여부0.1340.0330.0620.0820.0850.0340.0820.0990.1381.000

Missing values

2023-12-11T02:36:13.534035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:36:14.117020image/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

새우편번호시도시도_영문명시군구시군구_영문명도로명도로명_영문명건물번호_본번건물번호_부번법정동코드법정동명행정동명산여부지번_본번지번_순번지번_부번
257449006부산광역시Busan영도구Yeongdo-gu태종로165번길Taejong-ro 165beon-gil102620011800봉래동4가봉래제2동Y4518
1382149061부산광역시Busan영도구Yeongdo-gu영도새싹길Yeongdosaessak-gil13402620011200신선동1가신선동Y97212
533749092부산광역시Busan영도구Yeongdo-gu선덤산길Seondeomsan-gil3302620012100동삼동<NA>Y221174
1059549019부산광역시Busan영도구Yeongdo-gu청학로21번길Cheonghak-ro 21beon-gil3402620012000청학동청학제2동Y45314
993749027부산광역시Busan영도구Yeongdo-gu봉래언덕길Bongnaeeondeok-gil10502620011900봉래동5가봉래제2동Y62222
1634149079부산광역시Busan영도구Yeongdo-gu남항새싹길Namhangsaessak-gil9402620011100영선동4가영선제2동Y145219
1293549032부산광역시Busan영도구Yeongdo-gu해돋이2길Haedoji 2-gil22602620012000청학동청학제1동Y4682243
734149061부산광역시Busan영도구Yeongdo-gu참우물길Chamumul-gil7902620011200신선동1가신선동Y177121
1041549066부산광역시Busan영도구Yeongdo-gu중복2길Jungbok 2-gil4302620011300신선동2가신선동Y132153
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