Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells7650
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory144.0 B

Variable types

Numeric6
Categorical5
Text4
Boolean1

Dataset

Description부산광역시 북구 관내에 있는 U옥외광고물통합관리시스템의 새주소관리정보로 우편번호, 시군구, 읍면동, 번지, 도로명 등의 항목을 제공하고 있습니다.
Author부산광역시 북구
URLhttps://www.data.go.kr/data/15050087/fileData.do

Alerts

우편번호1 has constant value ""Constant
시도 has constant value ""Constant
시군구 has constant value ""Constant
순번 is highly overall correlated with 우편번호2 and 2 other fieldsHigh correlation
우편번호2 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
연결이미지 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
신우편번호 is highly overall correlated with 읍면동High correlation
읍면동 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
산(확인) is highly imbalanced (94.6%)Imbalance
도로위계 is highly imbalanced (57.2%)Imbalance
건물명 has 7650 (76.5%) missing valuesMissing
순번 has unique valuesUnique
건물번호2 has 6843 (68.4%) zerosZeros

Reproduction

Analysis started2024-04-21 01:14:50.385799
Analysis finished2024-04-21 01:14:57.685039
Duration7.3 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%
Mean8661.0364
Minimum2
Maximum17361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:14:57.749121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile844.85
Q14346.75
median8628.5
Q313013.25
95-th percentile16528.05
Maximum17361
Range17359
Interquartile range (IQR)8666.5

Descriptive statistics

Standard deviation5018.6149
Coefficient of variation (CV)0.57944738
Kurtosis-1.199594
Mean8661.0364
Median Absolute Deviation (MAD)4338
Skewness0.0068522644
Sum86610364
Variance25186495
MonotonicityNot monotonic
2024-04-21T10:14:57.870076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6776 1
 
< 0.1%
290 1
 
< 0.1%
8010 1
 
< 0.1%
12683 1
 
< 0.1%
9698 1
 
< 0.1%
9916 1
 
< 0.1%
17131 1
 
< 0.1%
8707 1
 
< 0.1%
13810 1
 
< 0.1%
11752 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
ValueCountFrequency (%)
17361 1
< 0.1%
17358 1
< 0.1%
17357 1
< 0.1%
17354 1
< 0.1%
17353 1
< 0.1%
17351 1
< 0.1%
17345 1
< 0.1%
17343 1
< 0.1%
17342 1
< 0.1%
17339 1
< 0.1%

우편번호1
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
616
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row616
2nd row616
3rd row616
4th row616
5th row616

Common Values

ValueCountFrequency (%)
616 10000
100.0%

Length

2024-04-21T10:14:57.984921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:14:58.071669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
616 10000
100.0%

우편번호2
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean813.8319
Minimum90
Maximum861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:14:58.168628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile800
Q1805
median816
Q3828
95-th percentile834
Maximum861
Range771
Interquartile range (IQR)23

Descriptive statistics

Standard deviation36.66077
Coefficient of variation (CV)0.045047103
Kurtosis303.15887
Mean813.8319
Median Absolute Deviation (MAD)12
Skewness-15.729843
Sum8138319
Variance1344.012
MonotonicityNot monotonic
2024-04-21T10:14:58.297109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
830 751
 
7.5%
802 581
 
5.8%
800 529
 
5.3%
831 515
 
5.1%
801 483
 
4.8%
806 445
 
4.5%
805 414
 
4.1%
817 412
 
4.1%
807 404
 
4.0%
803 404
 
4.0%
Other values (119) 5062
50.6%
ValueCountFrequency (%)
90 14
0.1%
110 4
 
< 0.1%
120 3
 
< 0.1%
701 2
 
< 0.1%
702 2
 
< 0.1%
703 3
 
< 0.1%
706 1
 
< 0.1%
715 1
 
< 0.1%
716 2
 
< 0.1%
718 1
 
< 0.1%
ValueCountFrequency (%)
861 3
 
< 0.1%
854 65
0.7%
853 29
 
0.3%
852 89
0.9%
851 6
 
0.1%
849 9
 
0.1%
848 1
 
< 0.1%
847 1
 
< 0.1%
846 123
1.2%
845 35
 
0.4%

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

2024-04-21T10:14:58.414660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:14:58.493544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 10000
100.0%

시군구
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
북구 10000
100.0%

Length

2024-04-21T10:14:58.572062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:14:58.643776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
북구 10000
100.0%

읍면동
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
구포동
4210 
만덕동
2411 
덕천동
2075 
화명동
898 
금곡동
 
406

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구포동
2nd row덕천동
3rd row만덕동
4th row화명동
5th row덕천동

Common Values

ValueCountFrequency (%)
구포동 4210
42.1%
만덕동 2411
24.1%
덕천동 2075
20.8%
화명동 898
 
9.0%
금곡동 406
 
4.1%

Length

2024-04-21T10:14:58.728466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:14:58.821810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구포동 4210
42.1%
만덕동 2411
24.1%
덕천동 2075
20.8%
화명동 898
 
9.0%
금곡동 406
 
4.1%

번지
Text

Distinct7705
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:14:59.113901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.6219
Min length1

Characters and Unicode

Total characters56219
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6073 ?
Unique (%)60.7%

Sample

1st row424-13
2nd row411-12
3rd row927-1
4th row428-8
5th row426-11
ValueCountFrequency (%)
216-7 10
 
0.1%
1030-2 9
 
0.1%
2322 9
 
0.1%
01월 9
 
0.1%
07일 9
 
0.1%
373-1 9
 
0.1%
870 8
 
0.1%
1172 8
 
0.1%
1898 8
 
0.1%
1170-1 8
 
0.1%
Other values (7698) 9929
99.1%
2024-04-21T10:14:59.542227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9766
17.4%
- 8789
15.6%
2 6504
11.6%
3 5282
9.4%
8 4423
7.9%
4 4417
7.9%
0 3826
 
6.8%
5 3380
 
6.0%
7 3300
 
5.9%
6 3255
 
5.8%
Other values (4) 3277
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47382
84.3%
Dash Punctuation 8789
 
15.6%
Other Letter 32
 
0.1%
Space Separator 16
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9766
20.6%
2 6504
13.7%
3 5282
11.1%
8 4423
9.3%
4 4417
9.3%
0 3826
 
8.1%
5 3380
 
7.1%
7 3300
 
7.0%
6 3255
 
6.9%
9 3229
 
6.8%
Other Letter
ValueCountFrequency (%)
월 16
50.0%
일 16
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 8789
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56187
99.9%
Hangul 32
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9766
17.4%
- 8789
15.6%
2 6504
11.6%
3 5282
9.4%
8 4423
7.9%
4 4417
7.9%
0 3826
 
6.8%
5 3380
 
6.0%
7 3300
 
5.9%
6 3255
 
5.8%
Other values (2) 3245
 
5.8%
Hangul
ValueCountFrequency (%)
월 16
50.0%
일 16
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56187
99.9%
Hangul 32
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9766
17.4%
- 8789
15.6%
2 6504
11.6%
3 5282
9.4%
8 4423
7.9%
4 4417
7.9%
0 3826
 
6.8%
5 3380
 
6.0%
7 3300
 
5.9%
6 3255
 
5.8%
Other values (2) 3245
 
5.8%
Hangul
ValueCountFrequency (%)
월 16
50.0%
일 16
50.0%

도로명
Text

Distinct421
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:14:59.773567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.9322
Min length3

Characters and Unicode

Total characters69322
Distinct characters82
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

Unique4 ?
Unique (%)< 0.1%

Sample

1st row시장샛길
2nd row의성로115번길
3rd row만덕2로44번길
4th row산성로48번길
5th row의성로127번길
ValueCountFrequency (%)
금곡대로 198
 
2.0%
덕천로 196
 
2.0%
시랑로 187
 
1.9%
백양대로 184
 
1.8%
모분재로 176
 
1.8%
상학로 172
 
1.7%
만덕대로 130
 
1.3%
만덕1로42번길 123
 
1.2%
만덕1로 111
 
1.1%
의성로121번길 111
 
1.1%
Other values (411) 8412
84.1%
2024-04-21T10:15:00.115683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 9458
 
13.6%
길 7548
 
10.9%
번 7006
 
10.1%
1 4695
 
6.8%
대 2742
 
4.0%
2 2514
 
3.6%
덕 2490
 
3.6%
3 1901
 
2.7%
6 1848
 
2.7%
만 1607
 
2.3%
Other values (72) 27513
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50492
72.8%
Decimal Number 18830
 
27.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 9458
18.7%
길 7548
14.9%
번 7006
13.9%
대 2742
 
5.4%
덕 2490
 
4.9%
만 1607
 
3.2%
시 1177
 
2.3%
상 995
 
2.0%
성 955
 
1.9%
천 943
 
1.9%
Other values (62) 15571
30.8%
Decimal Number
ValueCountFrequency (%)
1 4695
24.9%
2 2514
13.4%
3 1901
10.1%
6 1848
 
9.8%
0 1562
 
8.3%
7 1452
 
7.7%
4 1444
 
7.7%
5 1436
 
7.6%
8 1267
 
6.7%
9 711
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50492
72.8%
Common 18830
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 9458
18.7%
길 7548
14.9%
번 7006
13.9%
대 2742
 
5.4%
덕 2490
 
4.9%
만 1607
 
3.2%
시 1177
 
2.3%
상 995
 
2.0%
성 955
 
1.9%
천 943
 
1.9%
Other values (62) 15571
30.8%
Common
ValueCountFrequency (%)
1 4695
24.9%
2 2514
13.4%
3 1901
10.1%
6 1848
 
9.8%
0 1562
 
8.3%
7 1452
 
7.7%
4 1444
 
7.7%
5 1436
 
7.6%
8 1267
 
6.7%
9 711
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50492
72.8%
ASCII 18830
 
27.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 9458
18.7%
길 7548
14.9%
번 7006
13.9%
대 2742
 
5.4%
덕 2490
 
4.9%
만 1607
 
3.2%
시 1177
 
2.3%
상 995
 
2.0%
성 955
 
1.9%
천 943
 
1.9%
Other values (62) 15571
30.8%
ASCII
ValueCountFrequency (%)
1 4695
24.9%
2 2514
13.4%
3 1901
10.1%
6 1848
 
9.8%
0 1562
 
8.3%
7 1452
 
7.7%
4 1444
 
7.7%
5 1436
 
7.6%
8 1267
 
6.7%
9 711
 
3.8%

건물번호
Real number (ℝ)

Distinct568
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.4038
Minimum1
Maximum1789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:15:00.237839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q115
median32
Q363
95-th percentile290
Maximum1789
Range1788
Interquartile range (IQR)48

Descriptive statistics

Standard deviation219.8839
Coefficient of variation (CV)2.5157248
Kurtosis29.014784
Mean87.4038
Median Absolute Deviation (MAD)20
Skewness5.1869652
Sum874038
Variance48348.931
MonotonicityNot monotonic
2024-04-21T10:15:00.350648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 237
 
2.4%
7 229
 
2.3%
15 221
 
2.2%
13 219
 
2.2%
11 215
 
2.1%
14 209
 
2.1%
10 207
 
2.1%
20 205
 
2.1%
5 201
 
2.0%
16 195
 
1.9%
Other values (558) 7862
78.6%
ValueCountFrequency (%)
1 48
 
0.5%
2 62
 
0.6%
3 114
1.1%
4 131
1.3%
5 201
2.0%
6 171
1.7%
7 229
2.3%
8 184
1.8%
9 237
2.4%
10 207
2.1%
ValueCountFrequency (%)
1789 1
 
< 0.1%
1787 1
 
< 0.1%
1780 1
 
< 0.1%
1778 1
 
< 0.1%
1774 1
 
< 0.1%
1770 1
 
< 0.1%
1768 4
< 0.1%
1766 1
 
< 0.1%
1758 2
< 0.1%
1757 2
< 0.1%

건물번호2
Real number (ℝ)

ZEROS 

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0877
Minimum0
Maximum138
Zeros6843
Zeros (%)68.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:15:00.465622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile12
Maximum138
Range138
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.8694168
Coefficient of variation (CV)2.8114273
Kurtosis99.089372
Mean2.0877
Median Absolute Deviation (MAD)0
Skewness7.363856
Sum20877
Variance34.450054
MonotonicityNot monotonic
2024-04-21T10:15:00.581605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6843
68.4%
1 1149
 
11.5%
2 240
 
2.4%
7 166
 
1.7%
5 160
 
1.6%
6 152
 
1.5%
8 148
 
1.5%
4 138
 
1.4%
3 136
 
1.4%
9 110
 
1.1%
Other values (51) 758
 
7.6%
ValueCountFrequency (%)
0 6843
68.4%
1 1149
 
11.5%
2 240
 
2.4%
3 136
 
1.4%
4 138
 
1.4%
5 160
 
1.6%
6 152
 
1.5%
7 166
 
1.7%
8 148
 
1.5%
9 110
 
1.1%
ValueCountFrequency (%)
138 1
 
< 0.1%
118 1
 
< 0.1%
115 1
 
< 0.1%
102 1
 
< 0.1%
83 1
 
< 0.1%
79 3
< 0.1%
78 1
 
< 0.1%
75 1
 
< 0.1%
71 2
< 0.1%
66 1
 
< 0.1%

건물명
Text

MISSING 

Distinct1528
Distinct (%)65.0%
Missing7650
Missing (%)76.5%
Memory size156.2 KiB
2024-04-21T10:15:00.770046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length5.7097872
Min length2

Characters and Unicode

Total characters13418
Distinct characters545
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

Unique1082 ?
Unique (%)46.0%

Sample

1st row대성타워맨션
2nd row대주 하이빌
3rd row시영아파트
4th row동성빌라
5th row중앙하이츠빌라
ValueCountFrequency (%)
시영아파트 18
 
0.7%
빌라 11
 
0.4%
그린코아 10
 
0.4%
주공아파트 10
 
0.4%
화명 9
 
0.4%
주민센터 9
 
0.4%
수정강변타운 9
 
0.4%
중앙하이츠빌라 9
 
0.4%
화명동대림타운 8
 
0.3%
대경빌라 8
 
0.3%
Other values (1580) 2446
96.0%
2024-04-21T10:15:01.283537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빌 666
 
5.0%
라 548
 
4.1%
아 411
 
3.1%
트 356
 
2.7%
파 322
 
2.4%
원 238
 
1.8%
동 234
 
1.7%
197
 
1.5%
이 193
 
1.4%
대 190
 
1.4%
Other values (535) 10063
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12941
96.4%
Space Separator 197
 
1.5%
Decimal Number 132
 
1.0%
Uppercase Letter 79
 
0.6%
Open Punctuation 22
 
0.2%
Close Punctuation 22
 
0.2%
Other Punctuation 16
 
0.1%
Lowercase Letter 8
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 666
 
5.1%
라 548
 
4.2%
아 411
 
3.2%
트 356
 
2.8%
파 322
 
2.5%
원 238
 
1.8%
동 234
 
1.8%
이 193
 
1.5%
대 190
 
1.5%
성 186
 
1.4%
Other values (495) 9597
74.2%
Uppercase Letter
ValueCountFrequency (%)
I 13
16.5%
G 11
13.9%
S 10
12.7%
K 10
12.7%
L 9
11.4%
V 6
7.6%
T 3
 
3.8%
B 3
 
3.8%
E 2
 
2.5%
D 2
 
2.5%
Other values (8) 10
12.7%
Decimal Number
ValueCountFrequency (%)
2 53
40.2%
3 25
18.9%
1 20
 
15.2%
4 9
 
6.8%
5 8
 
6.1%
8 6
 
4.5%
7 6
 
4.5%
6 5
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
e 3
37.5%
t 1
 
12.5%
a 1
 
12.5%
m 1
 
12.5%
d 1
 
12.5%
p 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
· 7
43.8%
. 6
37.5%
& 2
 
12.5%
, 1
 
6.2%
Space Separator
ValueCountFrequency (%)
197
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12941
96.4%
Common 390
 
2.9%
Latin 87
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 666
 
5.1%
라 548
 
4.2%
아 411
 
3.2%
트 356
 
2.8%
파 322
 
2.5%
원 238
 
1.8%
동 234
 
1.8%
이 193
 
1.5%
대 190
 
1.5%
성 186
 
1.4%
Other values (495) 9597
74.2%
Latin
ValueCountFrequency (%)
I 13
14.9%
G 11
12.6%
S 10
11.5%
K 10
11.5%
L 9
10.3%
V 6
 
6.9%
T 3
 
3.4%
e 3
 
3.4%
B 3
 
3.4%
E 2
 
2.3%
Other values (14) 17
19.5%
Common
ValueCountFrequency (%)
197
50.5%
2 53
 
13.6%
3 25
 
6.4%
( 22
 
5.6%
) 22
 
5.6%
1 20
 
5.1%
4 9
 
2.3%
5 8
 
2.1%
· 7
 
1.8%
8 6
 
1.5%
Other values (6) 21
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12941
96.4%
ASCII 470
 
3.5%
None 7
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빌 666
 
5.1%
라 548
 
4.2%
아 411
 
3.2%
트 356
 
2.8%
파 322
 
2.5%
원 238
 
1.8%
동 234
 
1.8%
이 193
 
1.5%
대 190
 
1.5%
성 186
 
1.4%
Other values (495) 9597
74.2%
ASCII
ValueCountFrequency (%)
197
41.9%
2 53
 
11.3%
3 25
 
5.3%
( 22
 
4.7%
) 22
 
4.7%
1 20
 
4.3%
I 13
 
2.8%
G 11
 
2.3%
S 10
 
2.1%
K 10
 
2.1%
Other values (29) 87
18.5%
None
ValueCountFrequency (%)
· 7
100.0%

산(확인)
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9938 
True
 
62
ValueCountFrequency (%)
False 9938
99.4%
True 62
 
0.6%
2024-04-21T10:15:01.377038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

도로위계
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4
7680 
2
1654 
1
 
651
5
 
11
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 7680
76.8%
2 1654
 
16.5%
1 651
 
6.5%
5 11
 
0.1%
3 4
 
< 0.1%

Length

2024-04-21T10:15:01.457218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:15:01.550049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 7680
76.8%
2 1654
 
16.5%
1 651
 
6.5%
5 11
 
0.1%
3 4
 
< 0.1%

지번
Text

Distinct8118
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:15:01.870571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.6111
Min length5

Characters and Unicode

Total characters96111
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6711 ?
Unique (%)67.1%

Sample

1st row구포동 424-13
2nd row덕천동 411-12
3rd row만덕동 927-1
4th row화명동 428-8
5th row덕천동 426-11
ValueCountFrequency (%)
구포동 4210
21.1%
만덕동 2411
 
12.1%
덕천동 2075
 
10.4%
화명동 898
 
4.5%
금곡동 406
 
2.0%
216-7 10
 
< 0.1%
2322 9
 
< 0.1%
1030-2 9
 
< 0.1%
산1-7 9
 
< 0.1%
373-1 9
 
< 0.1%
Other values (7725) 9954
49.8%
2024-04-21T10:15:02.323847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10000
 
10.4%
10000
 
10.4%
1 9811
 
10.2%
- 9191
 
9.6%
2 6450
 
6.7%
3 5179
 
5.4%
덕 4486
 
4.7%
8 4355
 
4.5%
4 4284
 
4.5%
구 4210
 
4.4%
Other values (13) 28145
29.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46858
48.8%
Other Letter 30062
31.3%
Space Separator 10000
 
10.4%
Dash Punctuation 9191
 
9.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10000
33.3%
덕 4486
14.9%
구 4210
14.0%
포 4210
14.0%
만 2411
 
8.0%
천 2075
 
6.9%
화 898
 
3.0%
명 898
 
3.0%
금 406
 
1.4%
곡 406
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 9811
20.9%
2 6450
13.8%
3 5179
11.1%
8 4355
9.3%
4 4284
9.1%
0 3728
 
8.0%
7 3355
 
7.2%
5 3301
 
7.0%
9 3202
 
6.8%
6 3193
 
6.8%
Space Separator
ValueCountFrequency (%)
10000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66049
68.7%
Hangul 30062
31.3%

Most frequent character per script

Common
ValueCountFrequency (%)
10000
15.1%
1 9811
14.9%
- 9191
13.9%
2 6450
9.8%
3 5179
7.8%
8 4355
6.6%
4 4284
6.5%
0 3728
 
5.6%
7 3355
 
5.1%
5 3301
 
5.0%
Other values (2) 6395
9.7%
Hangul
ValueCountFrequency (%)
동 10000
33.3%
덕 4486
14.9%
구 4210
14.0%
포 4210
14.0%
만 2411
 
8.0%
천 2075
 
6.9%
화 898
 
3.0%
명 898
 
3.0%
금 406
 
1.4%
곡 406
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66049
68.7%
Hangul 30062
31.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10000
33.3%
덕 4486
14.9%
구 4210
14.0%
포 4210
14.0%
만 2411
 
8.0%
천 2075
 
6.9%
화 898
 
3.0%
명 898
 
3.0%
금 406
 
1.4%
곡 406
 
1.4%
ASCII
ValueCountFrequency (%)
10000
15.1%
1 9811
14.9%
- 9191
13.9%
2 6450
9.8%
3 5179
7.8%
8 4355
6.6%
4 4284
6.5%
0 3728
 
5.6%
7 3355
 
5.1%
5 3301
 
5.0%
Other values (2) 6395
9.7%

연결이미지
Real number (ℝ)

HIGH CORRELATION 

Distinct733
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24903.738
Minimum4062
Maximum59036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:15:02.442102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4062
5-th percentile10056
Q117054
median22047.5
Q331051
95-th percentile45053
Maximum59036
Range54974
Interquartile range (IQR)13997

Descriptive statistics

Standard deviation11421.942
Coefficient of variation (CV)0.45864367
Kurtosis-0.65495782
Mean24903.738
Median Absolute Deviation (MAD)5994.5
Skewness0.61416614
Sum2.4903738 × 108
Variance1.3046075 × 108
MonotonicityNot monotonic
2024-04-21T10:15:02.551806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42046 76
 
0.8%
40046 70
 
0.7%
44045 67
 
0.7%
43047 66
 
0.7%
43046 63
 
0.6%
44046 62
 
0.6%
15053 62
 
0.6%
40047 60
 
0.6%
11057 58
 
0.6%
41046 58
 
0.6%
Other values (723) 9358
93.6%
ValueCountFrequency (%)
4062 16
0.2%
5061 13
0.1%
5062 14
0.1%
5063 1
 
< 0.1%
6059 4
 
< 0.1%
6060 9
0.1%
6061 15
0.1%
6062 14
0.1%
6063 13
0.1%
6064 11
0.1%
ValueCountFrequency (%)
59036 1
 
< 0.1%
58037 3
 
< 0.1%
58036 5
0.1%
53046 1
 
< 0.1%
53043 1
 
< 0.1%
53042 1
 
< 0.1%
52050 1
 
< 0.1%
52049 8
0.1%
52047 1
 
< 0.1%
52042 1
 
< 0.1%

신우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46580.809
Minimum46500
Maximum46653
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:15:02.671710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46500
5-th percentile46506
Q146554
median46579
Q346614
95-th percentile46646.05
Maximum46653
Range153
Interquartile range (IQR)60

Descriptive statistics

Standard deviation40.160496
Coefficient of variation (CV)0.00086216829
Kurtosis-0.86625265
Mean46580.809
Median Absolute Deviation (MAD)31
Skewness-0.072570617
Sum4.6580809 × 108
Variance1612.8655
MonotonicityNot monotonic
2024-04-21T10:15:02.804389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46557 867
 
8.7%
46585 251
 
2.5%
46609 158
 
1.6%
46615 155
 
1.6%
46578 154
 
1.5%
46552 153
 
1.5%
46546 149
 
1.5%
46574 142
 
1.4%
46595 139
 
1.4%
46618 138
 
1.4%
Other values (144) 7694
76.9%
ValueCountFrequency (%)
46500 12
 
0.1%
46501 103
1.0%
46502 67
0.7%
46503 67
0.7%
46504 66
0.7%
46505 77
0.8%
46506 123
1.2%
46507 17
 
0.2%
46508 16
 
0.2%
46509 16
 
0.2%
ValueCountFrequency (%)
46653 84
0.8%
46652 52
 
0.5%
46651 6
 
0.1%
46650 44
 
0.4%
46649 132
1.3%
46648 77
0.8%
46647 105
1.1%
46646 24
 
0.2%
46645 38
 
0.4%
46644 40
 
0.4%

Interactions

2024-04-21T10:14:56.786465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:53.478845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:54.116656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:54.815943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:55.544148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:56.096267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:56.879442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:53.619940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:54.228338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:54.953904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:55.647651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:56.190571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:56.972027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:53.715172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:54.346711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:55.075364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:55.748939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:56.442604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:57.071154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:53.823600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:54.456728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:55.213506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:55.836351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:56.522621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:57.160051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:53.915898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:54.579183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:55.333317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:55.922954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:56.610592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:57.252008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:54.011766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:54.704747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:55.431816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:56.004277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:14:56.696743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:15:02.894289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번우편번호2읍면동건물번호건물번호2산(확인)도로위계연결이미지신우편번호
순번1.0000.1410.9900.4200.1140.0670.4130.8850.895
우편번호20.1411.0000.1600.2900.0000.0000.0690.1210.145
읍면동0.9900.1601.0000.3030.1110.0280.3420.9100.958
건물번호0.4200.2900.3031.0000.0000.0000.5620.3250.347
건물번호20.1140.0000.1110.0001.0000.3030.7230.2150.120
산(확인)0.0670.0000.0280.0000.3031.0000.0510.5090.063
도로위계0.4130.0690.3420.5620.7230.0511.0000.3070.394
연결이미지0.8850.1210.9100.3250.2150.5090.3071.0000.831
신우편번호0.8950.1450.9580.3470.1200.0630.3940.8311.000
2024-04-21T10:15:03.000021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산(확인)도로위계읍면동
산(확인)1.0000.0620.034
도로위계0.0621.0000.133
읍면동0.0340.1331.000
2024-04-21T10:15:03.085784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번우편번호2건물번호건물번호2연결이미지신우편번호읍면동산(확인)도로위계
순번1.0000.816-0.031-0.0420.802-0.4560.8540.0510.183
우편번호20.8161.000-0.021-0.0150.688-0.3600.1310.0000.056
건물번호-0.031-0.0211.000-0.014-0.020-0.0840.1800.0000.368
건물번호2-0.042-0.015-0.0141.000-0.0260.0180.0460.2320.383
연결이미지0.8020.688-0.020-0.0261.000-0.2540.6070.3920.132
신우편번호-0.456-0.360-0.0840.018-0.2541.0000.7150.0490.174
읍면동0.8540.1310.1800.0460.6070.7151.0000.0340.133
산(확인)0.0510.0000.0000.2320.3920.0490.0341.0000.062
도로위계0.1830.0560.3680.3830.1320.1740.1330.0621.000

Missing values

2024-04-21T10:14:57.395789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:14:57.590909image/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

순번우편번호1우편번호2시도시군구읍면동번지도로명건물번호건물번호2건물명산(확인)도로위계지번연결이미지신우편번호
66446776616801부산광역시북구구포동424-13시장샛길271<NA>N4구포동 424-131505446583
1083810511616821부산광역시북구덕천동411-12의성로115번길270<NA>N4덕천동 411-122305046567
1169513435616756부산광역시북구만덕동927-1만덕2로44번길720대성타워맨션N4만덕동 927-14005046565
1522716721616834부산광역시북구화명동428-8산성로48번길482<NA>N4화명동 428-82702846530
900710960616822부산광역시북구덕천동426-11의성로127번길831<NA>N4덕천동 426-112605146573
1460413087616828부산광역시북구만덕동828-6만덕1로42번길220<NA>N4만덕동 828-64004646557
15543393616800부산광역시북구구포동1198-11모분재로23번길680<NA>N4구포동 1198-112105446596
5041908616802부산광역시북구구포동1053-4낙동북로6390대주 하이빌N2구포동 1053-41005646585
1539716476616834부산광역시북구화명동499-8산성로685<NA>N2화명동 499-82902646529
2846477616806부산광역시북구구포동923-49구남로14번가길160<NA>N4구포동 923-49905946649
순번우편번호1우편번호2시도시군구읍면동번지도로명건물번호건물번호2건물명산(확인)도로위계지번연결이미지신우편번호
95077405616810부산광역시북구금곡동35004금곡대로616번길90수곡교회N4금곡동 95-112500946505
92139680616815부산광역시북구덕천동768-6만덕대로156번길2911<NA>N4덕천동 768-63104946572
4751877616801부산광역시북구구포동612-19낙동대로1780번길690<NA>N4구포동 612-191705346582
68644453616804부산광역시북구구포동774-31백양대로1102번길260정원아트빌라N4구포동 774-311405746634
12893306616803부산광역시북구구포동676-6모분재로16번길170<NA>N4구포동 676-61805546599
2745192616802부산광역시북구구포동1018-8가람로48번길510<NA>N4구포동 1018-81305646585
944610393616822부산광역시북구덕천동429-53의성로1270<NA>N2덕천동 429-532205146574
96628039616817부산광역시북구덕천동539-7금곡대로950성훈강변아파트N1덕천동 539-71804446543
56936698616805부산광역시북구구포동866-14시랑로94번길920한샘빌라N4구포동 866-142006446643
1165813398616827부산광역시북구만덕동347만덕2로37번길34<NA>N4만덕동 3474405146610