Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells10940
Missing cells (%)10.9%
Duplicate rows987
Duplicate rows (%)9.9%
Total size in memory888.7 KiB
Average record size in memory91.0 B

Variable types

Text5
Categorical1
Numeric3
DateTime1

Dataset

Description규정평가를 이행한 운수업체 정보 등
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15042034/fileData.do

Alerts

Dataset has 987 (9.9%) duplicate rowsDuplicates
번지 has 5652 (56.5%) missing valuesMissing
도로명 has 1322 (13.2%) missing valuesMissing
건물본번 has 1322 (13.2%) missing valuesMissing
건물부번 has 1322 (13.2%) missing valuesMissing
최종삽입날짜 has 1322 (13.2%) missing valuesMissing
건물부번 has 7201 (72.0%) zerosZeros

Reproduction

Analysis started2023-12-12 13:23:59.424824
Analysis finished2023-12-12 13:24:01.783499
Duration2.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

우편번호
Text

Distinct1105
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:24:02.012103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)1.2%

Sample

1st row340-805
2nd row403-010
3rd row412-480
4th row701-290
5th row400-103
ValueCountFrequency (%)
500-040 311
 
3.1%
152-895 114
 
1.1%
138-122 92
 
0.9%
702-847 90
 
0.9%
200-853 87
 
0.9%
356-824 84
 
0.8%
200-140 83
 
0.8%
704-914 83
 
0.8%
431-836 83
 
0.8%
480-838 81
 
0.8%
Other values (1095) 8892
88.9%
2023-12-12T22:24:02.487266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13368
19.1%
- 10000
14.3%
1 7296
10.4%
8 6746
9.6%
3 6226
8.9%
4 5816
8.3%
2 5189
 
7.4%
5 4594
 
6.6%
6 4438
 
6.3%
7 3492
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
85.7%
Dash Punctuation 10000
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13368
22.3%
1 7296
12.2%
8 6746
11.2%
3 6226
10.4%
4 5816
9.7%
2 5189
 
8.6%
5 4594
 
7.7%
6 4438
 
7.4%
7 3492
 
5.8%
9 2835
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13368
19.1%
- 10000
14.3%
1 7296
10.4%
8 6746
9.6%
3 6226
8.9%
4 5816
8.3%
2 5189
 
7.4%
5 4594
 
6.6%
6 4438
 
6.3%
7 3492
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13368
19.1%
- 10000
14.3%
1 7296
10.4%
8 6746
9.6%
3 6226
8.9%
4 5816
8.3%
2 5189
 
7.4%
5 4594
 
6.6%
6 4438
 
6.3%
7 3492
 
5.0%

시도
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서울
1823 
경기
1695 
대구
923 
광주
611 
강원
589 
Other values (12)
4359 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충남
2nd row인천
3rd row경기
4th row대구
5th row인천

Common Values

ValueCountFrequency (%)
서울 1823
18.2%
경기 1695
17.0%
대구 923
9.2%
광주 611
 
6.1%
강원 589
 
5.9%
부산 567
 
5.7%
충남 480
 
4.8%
충북 467
 
4.7%
대전 448
 
4.5%
인천 441
 
4.4%
Other values (7) 1956
19.6%

Length

2023-12-12T22:24:02.631957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 1823
18.2%
경기 1695
17.0%
대구 923
9.2%
광주 611
 
6.1%
강원 589
 
5.9%
부산 567
 
5.7%
충남 480
 
4.8%
충북 467
 
4.7%
대전 448
 
4.5%
인천 441
 
4.4%
Other values (7) 1956
19.6%

시구
Text

Distinct193
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:24:02.943466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.4395
Min length1

Characters and Unicode

Total characters34395
Distinct characters129
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 row예산군
2nd row부평구
3rd row고양시 덕양구
4th row동구
5th row중구
ValueCountFrequency (%)
북구 719
 
6.3%
동구 392
 
3.4%
남구 349
 
3.1%
중구 254
 
2.2%
달서구 245
 
2.2%
도봉구 239
 
2.1%
송파구 234
 
2.1%
서구 229
 
2.0%
춘천시 228
 
2.0%
제주시 221
 
1.9%
Other values (192) 8274
72.7%
2023-12-12T22:24:03.435471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 6562
19.1%
시 4442
 
12.9%
1422
 
4.1%
동 1143
 
3.3%
주 1040
 
3.0%
산 927
 
2.7%
천 900
 
2.6%
서 881
 
2.6%
북 852
 
2.5%
군 826
 
2.4%
Other values (119) 15400
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32973
95.9%
Space Separator 1422
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 6562
19.9%
시 4442
 
13.5%
동 1143
 
3.5%
주 1040
 
3.2%
산 927
 
2.8%
천 900
 
2.7%
서 881
 
2.7%
북 852
 
2.6%
군 826
 
2.5%
성 770
 
2.3%
Other values (118) 14630
44.4%
Space Separator
ValueCountFrequency (%)
1422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32973
95.9%
Common 1422
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 6562
19.9%
시 4442
 
13.5%
동 1143
 
3.5%
주 1040
 
3.2%
산 927
 
2.8%
천 900
 
2.7%
서 881
 
2.7%
북 852
 
2.6%
군 826
 
2.5%
성 770
 
2.3%
Other values (118) 14630
44.4%
Common
ValueCountFrequency (%)
1422
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32973
95.9%
ASCII 1422
 
4.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 6562
19.9%
시 4442
 
13.5%
동 1143
 
3.5%
주 1040
 
3.2%
산 927
 
2.8%
천 900
 
2.7%
서 881
 
2.7%
북 852
 
2.6%
군 826
 
2.5%
성 770
 
2.3%
Other values (118) 14630
44.4%
ASCII
ValueCountFrequency (%)
1422
100.0%

읍면동리
Text

Distinct901
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:24:03.779681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length4
Mean length4.8315
Min length3

Characters and Unicode

Total characters48315
Distinct characters299
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)0.8%

Sample

1st row예산읍 수철리
2nd row부평동
3rd row대자동
4th row각산동
5th row신흥동3가
ValueCountFrequency (%)
중흥동 311
 
2.7%
오류2동 114
 
1.0%
호계2동 111
 
1.0%
읍내동 108
 
0.9%
문산읍 94
 
0.8%
마천2동 92
 
0.8%
동면 87
 
0.7%
지내리 87
 
0.7%
선화동 87
 
0.7%
용현동 85
 
0.7%
Other values (1038) 10438
89.9%
2023-12-12T22:24:04.374358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10000
20.7%
동 8743
 
18.1%
리 1474
 
3.1%
읍 993
 
2.1%
2 986
 
2.0%
1 899
 
1.9%
산 813
 
1.7%
면 755
 
1.6%
천 604
 
1.3%
중 520
 
1.1%
Other values (289) 22528
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35658
73.8%
Space Separator 10000
 
20.7%
Decimal Number 2636
 
5.5%
Open Punctuation 8
 
< 0.1%
Close Punctuation 8
 
< 0.1%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8743
24.5%
리 1474
 
4.1%
읍 993
 
2.8%
산 813
 
2.3%
면 755
 
2.1%
천 604
 
1.7%
중 520
 
1.5%
가 483
 
1.4%
흥 460
 
1.3%
신 459
 
1.3%
Other values (274) 20354
57.1%
Decimal Number
ValueCountFrequency (%)
2 986
37.4%
1 899
34.1%
3 341
 
12.9%
4 139
 
5.3%
6 116
 
4.4%
5 75
 
2.8%
7 65
 
2.5%
0 7
 
0.3%
8 5
 
0.2%
9 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35656
73.8%
Common 12657
 
26.2%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8743
24.5%
리 1474
 
4.1%
읍 993
 
2.8%
산 813
 
2.3%
면 755
 
2.1%
천 604
 
1.7%
중 520
 
1.5%
가 483
 
1.4%
흥 460
 
1.3%
신 459
 
1.3%
Other values (272) 20352
57.1%
Common
ValueCountFrequency (%)
10000
79.0%
2 986
 
7.8%
1 899
 
7.1%
3 341
 
2.7%
4 139
 
1.1%
6 116
 
0.9%
5 75
 
0.6%
7 65
 
0.5%
( 8
 
0.1%
) 8
 
0.1%
Other values (5) 20
 
0.2%
Han
ValueCountFrequency (%)
華 1
50.0%
山 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35656
73.8%
ASCII 12657
 
26.2%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10000
79.0%
2 986
 
7.8%
1 899
 
7.1%
3 341
 
2.7%
4 139
 
1.1%
6 116
 
0.9%
5 75
 
0.6%
7 65
 
0.5%
( 8
 
0.1%
) 8
 
0.1%
Other values (5) 20
 
0.2%
Hangul
ValueCountFrequency (%)
동 8743
24.5%
리 1474
 
4.1%
읍 993
 
2.8%
산 813
 
2.3%
면 755
 
2.1%
천 604
 
1.7%
중 520
 
1.5%
가 483
 
1.4%
흥 460
 
1.3%
신 459
 
1.3%
Other values (272) 20352
57.1%
CJK
ValueCountFrequency (%)
華 1
50.0%
山 1
50.0%

번지
Text

MISSING 

Distinct453
Distinct (%)10.4%
Missing5652
Missing (%)56.5%
Memory size156.2 KiB
2023-12-12T22:24:04.775005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length7
Mean length6.8353266
Min length2

Characters and Unicode

Total characters29720
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)1.2%

Sample

1st row395~600
2nd row146~600
3rd row1~200
4th row783~820
5th row447~453
ValueCountFrequency (%)
90~129 114
 
2.6%
1234~1275 90
 
2.1%
891~916 83
 
1.9%
352~699 83
 
1.9%
206~386 81
 
1.9%
974~982 71
 
1.6%
576~1100 49
 
1.1%
1~300 48
 
1.1%
227-203~227-228 44
 
1.0%
146~600 41
 
0.9%
Other values (443) 3644
83.8%
2023-12-12T22:24:05.238496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4617
15.5%
~ 4040
13.6%
0 4024
13.5%
2 2858
9.6%
9 2339
7.9%
6 2073
7.0%
4 1962
6.6%
3 1940
6.5%
5 1831
 
6.2%
7 1711
 
5.8%
Other values (4) 2325
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25043
84.3%
Math Symbol 4040
 
13.6%
Space Separator 221
 
0.7%
Other Letter 221
 
0.7%
Dash Punctuation 195
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4617
18.4%
0 4024
16.1%
2 2858
11.4%
9 2339
9.3%
6 2073
8.3%
4 1962
7.8%
3 1940
7.7%
5 1831
 
7.3%
7 1711
 
6.8%
8 1688
 
6.7%
Math Symbol
ValueCountFrequency (%)
~ 4040
100.0%
Space Separator
ValueCountFrequency (%)
221
100.0%
Other Letter
ValueCountFrequency (%)
산 221
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29499
99.3%
Hangul 221
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4617
15.7%
~ 4040
13.7%
0 4024
13.6%
2 2858
9.7%
9 2339
7.9%
6 2073
7.0%
4 1962
6.7%
3 1940
6.6%
5 1831
 
6.2%
7 1711
 
5.8%
Other values (3) 2104
7.1%
Hangul
ValueCountFrequency (%)
산 221
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29499
99.3%
Hangul 221
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4617
15.7%
~ 4040
13.7%
0 4024
13.6%
2 2858
9.7%
9 2339
7.9%
6 2073
7.0%
4 1962
6.7%
3 1940
6.6%
5 1831
 
6.2%
7 1711
 
5.8%
Other values (3) 2104
7.1%
Hangul
ValueCountFrequency (%)
산 221
100.0%

순번
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6389
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:24:05.374236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q311
95-th percentile41
Maximum121
Range120
Interquartile range (IQR)10

Descriptive statistics

Standard deviation14.895721
Coefficient of variation (CV)1.7242613
Kurtosis11.160843
Mean8.6389
Median Absolute Deviation (MAD)0
Skewness2.9330999
Sum86389
Variance221.8825
MonotonicityNot monotonic
2023-12-12T22:24:05.501828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 6641
66.4%
11 1334
 
13.3%
21 663
 
6.6%
31 582
 
5.8%
17 186
 
1.9%
41 169
 
1.7%
51 138
 
1.4%
61 86
 
0.9%
101 44
 
0.4%
3 42
 
0.4%
Other values (11) 115
 
1.1%
ValueCountFrequency (%)
1 6641
66.4%
2 11
 
0.1%
3 42
 
0.4%
4 14
 
0.1%
5 7
 
0.1%
7 3
 
< 0.1%
11 1334
 
13.3%
17 186
 
1.9%
21 663
 
6.6%
27 1
 
< 0.1%
ValueCountFrequency (%)
121 3
 
< 0.1%
111 3
 
< 0.1%
101 44
 
0.4%
91 12
 
0.1%
81 15
 
0.1%
71 32
 
0.3%
61 86
0.9%
52 14
 
0.1%
51 138
1.4%
41 169
1.7%

도로명
Text

MISSING 

Distinct681
Distinct (%)7.8%
Missing1322
Missing (%)13.2%
Memory size156.2 KiB
2023-12-12T22:24:05.764799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.8304909
Min length2

Characters and Unicode

Total characters33241
Distinct characters301
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)0.7%

Sample

1st row수철길
2nd row경원대로
3rd row호국로
4th row매여로
5th row서해대로
ValueCountFrequency (%)
시민로 115
 
1.3%
연동로 114
 
1.3%
경수대로 113
 
1.3%
영서로 112
 
1.3%
2순환로 103
 
1.2%
호국로 96
 
1.1%
칠곡중앙대로 94
 
1.1%
도봉로 89
 
1.0%
소양강로 87
 
1.0%
내포로 84
 
1.0%
Other values (671) 7671
88.4%
2023-12-12T22:24:06.199396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 8420
25.3%
대 1962
 
5.9%
길 991
 
3.0%
동 792
 
2.4%
서 703
 
2.1%
산 542
 
1.6%
천 533
 
1.6%
1 505
 
1.5%
2 404
 
1.2%
경 391
 
1.2%
Other values (291) 17998
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31229
93.9%
Decimal Number 1981
 
6.0%
Other Punctuation 31
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 8420
27.0%
대 1962
 
6.3%
길 991
 
3.2%
동 792
 
2.5%
서 703
 
2.3%
산 542
 
1.7%
천 533
 
1.7%
경 391
 
1.3%
남 387
 
1.2%
안 344
 
1.1%
Other values (279) 16164
51.8%
Decimal Number
ValueCountFrequency (%)
1 505
25.5%
2 404
20.4%
0 308
15.5%
3 173
 
8.7%
6 145
 
7.3%
4 123
 
6.2%
5 103
 
5.2%
9 100
 
5.0%
8 62
 
3.1%
7 58
 
2.9%
Other Punctuation
ValueCountFrequency (%)
· 22
71.0%
. 9
29.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31229
93.9%
Common 2012
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 8420
27.0%
대 1962
 
6.3%
길 991
 
3.2%
동 792
 
2.5%
서 703
 
2.3%
산 542
 
1.7%
천 533
 
1.7%
경 391
 
1.3%
남 387
 
1.2%
안 344
 
1.1%
Other values (279) 16164
51.8%
Common
ValueCountFrequency (%)
1 505
25.1%
2 404
20.1%
0 308
15.3%
3 173
 
8.6%
6 145
 
7.2%
4 123
 
6.1%
5 103
 
5.1%
9 100
 
5.0%
8 62
 
3.1%
7 58
 
2.9%
Other values (2) 31
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31229
93.9%
ASCII 1990
 
6.0%
None 22
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 8420
27.0%
대 1962
 
6.3%
길 991
 
3.2%
동 792
 
2.5%
서 703
 
2.3%
산 542
 
1.7%
천 533
 
1.7%
경 391
 
1.3%
남 387
 
1.2%
안 344
 
1.1%
Other values (279) 16164
51.8%
ASCII
ValueCountFrequency (%)
1 505
25.4%
2 404
20.3%
0 308
15.5%
3 173
 
8.7%
6 145
 
7.3%
4 123
 
6.2%
5 103
 
5.2%
9 100
 
5.0%
8 62
 
3.1%
7 58
 
2.9%
None
ValueCountFrequency (%)
· 22
100.0%

건물본번
Real number (ℝ)

MISSING 

Distinct735
Distinct (%)8.5%
Missing1322
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean837.77138
Minimum1
Maximum8183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:24:06.374011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile49
Q1245
median500
Q31037
95-th percentile2625.4
Maximum8183
Range8182
Interquartile range (IQR)792

Descriptive statistics

Standard deviation1015.1332
Coefficient of variation (CV)1.2117067
Kurtosis13.771756
Mean837.77138
Median Absolute Deviation (MAD)336
Skewness3.1373347
Sum7270180
Variance1030495.4
MonotonicityNot monotonic
2023-12-12T22:24:06.834311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
324 114
 
1.1%
49 101
 
1.0%
2522 99
 
1.0%
245 97
 
1.0%
734 95
 
0.9%
746 94
 
0.9%
312 92
 
0.9%
813 83
 
0.8%
473 83
 
0.8%
3503 83
 
0.8%
Other values (725) 7737
77.4%
(Missing) 1322
 
13.2%
ValueCountFrequency (%)
1 17
0.2%
2 6
 
0.1%
3 14
0.1%
5 10
 
0.1%
6 5
 
0.1%
7 11
 
0.1%
14 2
 
< 0.1%
18 29
0.3%
19 22
0.2%
20 33
0.3%
ValueCountFrequency (%)
8183 7
0.1%
7907 3
 
< 0.1%
7790 2
 
< 0.1%
7741 6
0.1%
7692 4
 
< 0.1%
7623 3
 
< 0.1%
7486 6
0.1%
7330 5
0.1%
7040 10
0.1%
6566 2
 
< 0.1%

건물부번
Real number (ℝ)

MISSING  ZEROS 

Distinct52
Distinct (%)0.6%
Missing1322
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean3.1824153
Minimum0
Maximum175
Zeros7201
Zeros (%)72.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:24:06.982894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18
Maximum175
Range175
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.591578
Coefficient of variation (CV)3.6423837
Kurtosis48.089937
Mean3.1824153
Median Absolute Deviation (MAD)0
Skewness5.904305
Sum27617
Variance134.36467
MonotonicityNot monotonic
2023-12-12T22:24:07.158791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7201
72.0%
1 135
 
1.4%
5 133
 
1.3%
3 129
 
1.3%
10 114
 
1.1%
18 101
 
1.0%
6 76
 
0.8%
11 68
 
0.7%
4 67
 
0.7%
13 62
 
0.6%
Other values (42) 592
 
5.9%
(Missing) 1322
 
13.2%
ValueCountFrequency (%)
0 7201
72.0%
1 135
 
1.4%
2 42
 
0.4%
3 129
 
1.3%
4 67
 
0.7%
5 133
 
1.3%
6 76
 
0.8%
7 20
 
0.2%
8 4
 
< 0.1%
9 21
 
0.2%
ValueCountFrequency (%)
175 5
 
0.1%
106 4
 
< 0.1%
97 4
 
< 0.1%
80 4
 
< 0.1%
76 23
0.2%
74 7
 
0.1%
73 38
0.4%
70 2
 
< 0.1%
69 15
 
0.1%
61 25
0.2%

최종삽입날짜
Date

MISSING 

Distinct177
Distinct (%)2.0%
Missing1322
Missing (%)13.2%
Memory size156.2 KiB
Minimum2014-06-30 20:41:00
Maximum2014-06-30 23:52:00
2023-12-12T22:24:07.346932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:07.638083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T22:24:00.935630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:00.278350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:00.605555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:01.065950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:00.379199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:00.722994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:01.165948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:00.472538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:00.811785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:24:07.751689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도순번건물본번건물부번
시도1.0000.4500.5680.540
순번0.4501.0000.4280.322
건물본번0.5680.4281.0000.422
건물부번0.5400.3220.4221.000
2023-12-12T22:24:07.881111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번건물본번건물부번시도
순번1.000-0.105-0.0030.193
건물본번-0.1051.0000.1110.261
건물부번-0.0030.1111.0000.261
시도0.1930.2610.2611.000

Missing values

2023-12-12T22:24:01.341404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:24:01.530026image/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.
2023-12-12T22:24:01.702081image/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

우편번호시도시구읍면동리번지순번도로명건물본번건물부번최종삽입날짜
12405340-805충남예산군예산읍 수철리<NA>21수철길62002014-06-30 21:42
4907403-010인천부평구부평동<NA>1경원대로133702014-06-30 22:42
3077412-480경기고양시 덕양구대자동<NA>1호국로165302014-06-30 21:10
13969701-290대구동구각산동<NA>1매여로10602014-06-30 22:16
5252400-103인천중구신흥동3가<NA>1서해대로44602014-06-30 22:33
20945690-042제주제주시용담이동<NA>11용담로7길2802014-06-30 23:19
11454300-822대전동구용운동395~6001계족로140번길19502014-06-30 23:07
14798791-814경북포항시 북구우현동146~6001새천년대로90902014-06-30 21:05
4462471-829경기구리시인창동1~2001왕숙천로50702014-06-30 21:12
536133-816서울성동구마장동783~8201마장로30462014-06-30 20:45
우편번호시도시구읍면동리번지순번도로명건물본번건물부번최종삽입날짜
3522410-821경기고양시 일산동구식사동1000~999931고양대로1108142014-06-30 21:11
19836537-805전남완도군완도읍 석장리<NA>41<NA><NA><NA><NA>
13550701-845대구동구효목2동487~53111화랑로11302014-06-30 22:14
7107431-848경기안양시 동안구호계1동산3911<NA><NA><NA><NA>
18918503-820광주남구송하동200~46112순환로150802014-06-30 22:58
718122-872서울은평구수색동414~41621은평터널로6602014-06-30 20:59
9695360-185충북청주시 상당구지북동<NA>12순환로203202014-06-30 22:55
5345401-040인천동구송현동<NA>1인중로61402014-06-30 22:34
5261404-251인천서구가좌1동<NA>1<NA><NA><NA><NA>
13154702-847대구북구읍내동1234~127531칠곡중앙대로74602014-06-30 22:24

Duplicate rows

Most frequently occurring

우편번호시도시구읍면동리번지순번도로명건물본번건물부번최종삽입날짜# duplicates
603500-040광주북구중흥동<NA>1<NA><NA><NA><NA>311
146152-895서울구로구오류2동90~1291연동로32402014-06-30 21:14114
98138-122서울송파구마천2동<NA>1<NA><NA><NA><NA>92
899702-847대구북구읍내동1234~127531칠곡중앙대로74602014-06-30 22:2490
182200-853강원춘천시동면 지내리<NA>21소양강로73402014-06-30 21:4987
336356-824충남서산시해미면 휴암리<NA>31내포로252202014-06-30 21:1884
178200-140강원춘천시사농동<NA>1영서로3503182014-06-30 21:4883
486431-836경기안양시 동안구호계2동891~9161경수대로81302014-06-30 21:0183
917704-914대구달서구본리동352~6991대명천로24502014-06-30 22:2983
586480-838경기의정부시용현동206~3861시민로47302014-06-30 20:5981