Overview

Dataset statistics

Number of variables9
Number of observations422
Missing cells102
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.2 KiB
Average record size in memory73.3 B

Variable types

Numeric1
Categorical4
Text4

Dataset

Description안양시 동안구 내 쓰레기 종량제봉투 판매소(업체명, 주소 등)
Author안양시시설관리공단
URLhttps://www.data.go.kr/data/15056042/fileData.do

Alerts

관리지역 has constant value ""Constant
번호 is highly overall correlated with 행정동 and 1 other fieldsHigh correlation
분류 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
법정동 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
분류 is highly imbalanced (93.9%)Imbalance
지정번호 has 97 (23.0%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:15:13.472324
Analysis finished2023-12-12 06:15:14.501116
Duration1.03 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct422
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.5
Minimum1
Maximum422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T15:15:14.607298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.05
Q1106.25
median211.5
Q3316.75
95-th percentile400.95
Maximum422
Range421
Interquartile range (IQR)210.5

Descriptive statistics

Standard deviation121.96516
Coefficient of variation (CV)0.57666742
Kurtosis-1.2
Mean211.5
Median Absolute Deviation (MAD)105.5
Skewness0
Sum89253
Variance14875.5
MonotonicityStrictly increasing
2023-12-12T15:15:14.786370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
266 1
 
0.2%
290 1
 
0.2%
289 1
 
0.2%
288 1
 
0.2%
287 1
 
0.2%
286 1
 
0.2%
285 1
 
0.2%
284 1
 
0.2%
283 1
 
0.2%
Other values (412) 412
97.6%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
422 1
0.2%
421 1
0.2%
420 1
0.2%
419 1
0.2%
418 1
0.2%
417 1
0.2%
416 1
0.2%
415 1
0.2%
414 1
0.2%
413 1
0.2%

분류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
판매처
419 
구입처
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
판매처 419
99.3%
구입처 3
 
0.7%

Length

2023-12-12T15:15:14.942275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:15:15.069133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
판매처 419
99.3%
구입처 3
 
0.7%

거래처명
Text

Distinct415
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T15:15:15.340428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.7511848
Min length3

Characters and Unicode

Total characters3271
Distinct characters315
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique408 ?
Unique (%)96.7%

Sample

1st row그랜드마트
2nd row농협안심축산물
3rd row샘마을슈퍼1
4th row싱싱그린마트
5th row씨유안양덕현점
ValueCountFrequency (%)
세븐일레븐 5
 
1.1%
지에스25 4
 
0.9%
이마트24 4
 
0.9%
씨유 3
 
0.7%
하나로할인마트 2
 
0.4%
v1점 2
 
0.4%
미니스톱 2
 
0.4%
그랜드마트 2
 
0.4%
한마음슈퍼 2
 
0.4%
싱싱마트 2
 
0.4%
Other values (422) 425
93.8%
2023-12-12T15:15:15.800116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 224
 
6.8%
스 153
 
4.7%
양 105
 
3.2%
2 102
 
3.1%
마 100
 
3.1%
트 99
 
3.0%
에 99
 
3.0%
지 98
 
3.0%
5 88
 
2.7%
븐 82
 
2.5%
Other values (305) 2121
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2981
91.1%
Decimal Number 220
 
6.7%
Space Separator 32
 
1.0%
Uppercase Letter 13
 
0.4%
Close Punctuation 11
 
0.3%
Open Punctuation 10
 
0.3%
Lowercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 224
 
7.5%
스 153
 
5.1%
양 105
 
3.5%
마 100
 
3.4%
트 99
 
3.3%
에 99
 
3.3%
지 98
 
3.3%
븐 82
 
2.8%
안 77
 
2.6%
촌 77
 
2.6%
Other values (281) 1867
62.6%
Uppercase Letter
ValueCountFrequency (%)
S 2
15.4%
C 2
15.4%
M 2
15.4%
K 1
7.7%
I 1
7.7%
T 1
7.7%
Y 1
7.7%
A 1
7.7%
D 1
7.7%
G 1
7.7%
Decimal Number
ValueCountFrequency (%)
2 102
46.4%
5 88
40.0%
1 11
 
5.0%
4 8
 
3.6%
9 6
 
2.7%
3 2
 
0.9%
0 2
 
0.9%
6 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
v 2
50.0%
s 1
25.0%
k 1
25.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2981
91.1%
Common 273
 
8.3%
Latin 17
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 224
 
7.5%
스 153
 
5.1%
양 105
 
3.5%
마 100
 
3.4%
트 99
 
3.3%
에 99
 
3.3%
지 98
 
3.3%
븐 82
 
2.8%
안 77
 
2.6%
촌 77
 
2.6%
Other values (281) 1867
62.6%
Latin
ValueCountFrequency (%)
S 2
11.8%
v 2
11.8%
C 2
11.8%
M 2
11.8%
s 1
 
5.9%
K 1
 
5.9%
I 1
 
5.9%
T 1
 
5.9%
k 1
 
5.9%
Y 1
 
5.9%
Other values (3) 3
17.6%
Common
ValueCountFrequency (%)
2 102
37.4%
5 88
32.2%
32
 
11.7%
) 11
 
4.0%
1 11
 
4.0%
( 10
 
3.7%
4 8
 
2.9%
9 6
 
2.2%
3 2
 
0.7%
0 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2981
91.1%
ASCII 290
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 224
 
7.5%
스 153
 
5.1%
양 105
 
3.5%
마 100
 
3.4%
트 99
 
3.3%
에 99
 
3.3%
지 98
 
3.3%
븐 82
 
2.8%
안 77
 
2.6%
촌 77
 
2.6%
Other values (281) 1867
62.6%
ASCII
ValueCountFrequency (%)
2 102
35.2%
5 88
30.3%
32
 
11.0%
) 11
 
3.8%
1 11
 
3.8%
( 10
 
3.4%
4 8
 
2.8%
9 6
 
2.1%
S 2
 
0.7%
v 2
 
0.7%
Other values (14) 18
 
6.2%

행정동
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
관양2동
72 
관양1동
53 
부림동
38 
비산3동
36 
호계2동
31 
Other values (14)
192 

Length

Max length4
Median length4
Mean length3.6255924
Min length3

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
관양2동 72
17.1%
관양1동 53
12.6%
부림동 38
9.0%
비산3동 36
8.5%
호계2동 31
7.3%
호계3동 27
 
6.4%
호계1동 27
 
6.4%
평촌동 25
 
5.9%
범계동 21
 
5.0%
귀인동 19
 
4.5%
Other values (9) 73
17.3%

Length

2023-12-12T15:15:15.935253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
관양2동 72
17.1%
관양1동 53
12.6%
부림동 38
9.0%
비산3동 36
8.5%
호계2동 31
7.3%
호계3동 27
 
6.4%
호계1동 27
 
6.4%
평촌동 25
 
5.9%
범계동 21
 
5.0%
귀인동 19
 
4.5%
Other values (9) 73
17.3%

법정동
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
관양동
159 
호계동
128 
비산동
78 
평촌동
47 
귀인동
 
8
Other values (2)
 
2

Length

Max length4
Median length3
Mean length3.0023697
Min length3

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
관양동 159
37.7%
호계동 128
30.3%
비산동 78
18.5%
평촌동 47
 
11.1%
귀인동 8
 
1.9%
안양동 1
 
0.2%
<NA> 1
 
0.2%

Length

2023-12-12T15:15:16.085418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:15:16.272073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관양동 159
37.7%
호계동 128
30.3%
비산동 78
18.5%
평촌동 47
 
11.1%
귀인동 8
 
1.9%
안양동 1
 
0.2%
na 1
 
0.2%

관리지역
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
동안구
422 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동안구 422
100.0%

Length

2023-12-12T15:15:16.458967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:15:16.586323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동안구 422
100.0%

대표
Text

Distinct393
Distinct (%)93.6%
Missing2
Missing (%)0.5%
Memory size3.4 KiB
2023-12-12T15:15:16.985672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0619048
Min length2

Characters and Unicode

Total characters1286
Distinct characters165
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

Unique371 ?
Unique (%)88.3%

Sample

1st row박상훈
2nd row윤태용
3rd row유창석
4th row노동익
5th row이혜란
ValueCountFrequency (%)
강희태 5
 
1.2%
임일순 4
 
0.9%
이갑수 3
 
0.7%
김선경 2
 
0.5%
1명 2
 
0.5%
최 2
 
0.5%
최중학 2
 
0.5%
강신남 2
 
0.5%
김종열 2
 
0.5%
황영순 2
 
0.5%
Other values (385) 399
93.9%
2023-12-12T15:15:17.495931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 81
 
6.3%
이 73
 
5.7%
정 46
 
3.6%
영 42
 
3.3%
박 35
 
2.7%
희 34
 
2.6%
숙 29
 
2.3%
경 28
 
2.2%
순 28
 
2.2%
현 24
 
1.9%
Other values (155) 866
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1275
99.1%
Decimal Number 5
 
0.4%
Space Separator 5
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 81
 
6.4%
이 73
 
5.7%
정 46
 
3.6%
영 42
 
3.3%
박 35
 
2.7%
희 34
 
2.7%
숙 29
 
2.3%
경 28
 
2.2%
순 28
 
2.2%
현 24
 
1.9%
Other values (152) 855
67.1%
Decimal Number
ValueCountFrequency (%)
1 5
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1275
99.1%
Common 11
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 81
 
6.4%
이 73
 
5.7%
정 46
 
3.6%
영 42
 
3.3%
박 35
 
2.7%
희 34
 
2.7%
숙 29
 
2.3%
경 28
 
2.2%
순 28
 
2.2%
현 24
 
1.9%
Other values (152) 855
67.1%
Common
ValueCountFrequency (%)
1 5
45.5%
5
45.5%
, 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1275
99.1%
ASCII 11
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 81
 
6.4%
이 73
 
5.7%
정 46
 
3.6%
영 42
 
3.3%
박 35
 
2.7%
희 34
 
2.7%
숙 29
 
2.3%
경 28
 
2.2%
순 28
 
2.2%
현 24
 
1.9%
Other values (152) 855
67.1%
ASCII
ValueCountFrequency (%)
1 5
45.5%
5
45.5%
, 1
 
9.1%

지정번호
Text

MISSING 

Distinct288
Distinct (%)88.6%
Missing97
Missing (%)23.0%
Memory size3.4 KiB
2023-12-12T15:15:17.817298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length8.52
Min length6

Characters and Unicode

Total characters2769
Distinct characters36
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique260 ?
Unique (%)80.0%

Sample

1st row갈산2014-01
2nd row갈산2019-01
3rd row갈산2015-01
4th row2019-02
5th row갈산2017-02
ValueCountFrequency (%)
관일 9
 
2.6%
2016-01 4
 
1.2%
2019-05 4
 
1.2%
2019-2 4
 
1.2%
부림동 4
 
1.2%
2018-02 3
 
0.9%
2016-02 3
 
0.9%
2019-02 3
 
0.9%
2019-06 3
 
0.9%
2019-03 2
 
0.6%
Other values (274) 306
88.7%
2023-12-12T15:15:18.221280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 551
19.9%
2 453
16.4%
1 438
15.8%
- 377
13.6%
8 88
 
3.2%
3 85
 
3.1%
9 78
 
2.8%
4 75
 
2.7%
5 73
 
2.6%
7 64
 
2.3%
Other values (26) 487
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1964
70.9%
Dash Punctuation 377
 
13.6%
Other Letter 369
 
13.3%
Other Punctuation 34
 
1.2%
Space Separator 20
 
0.7%
Lowercase Letter 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 61
16.5%
일 56
15.2%
관 43
11.7%
제 30
8.1%
부 27
7.3%
계 26
7.0%
동 26
7.0%
림 26
7.0%
산 18
 
4.9%
비 18
 
4.9%
Other values (8) 38
10.3%
Decimal Number
ValueCountFrequency (%)
0 551
28.1%
2 453
23.1%
1 438
22.3%
8 88
 
4.5%
3 85
 
4.3%
9 78
 
4.0%
4 75
 
3.8%
5 73
 
3.7%
7 64
 
3.3%
6 59
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
q 1
20.0%
l 1
20.0%
t 1
20.0%
k 1
20.0%
s 1
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 377
100.0%
Other Punctuation
ValueCountFrequency (%)
. 34
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2395
86.5%
Hangul 369
 
13.3%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 61
16.5%
일 56
15.2%
관 43
11.7%
제 30
8.1%
부 27
7.3%
계 26
7.0%
동 26
7.0%
림 26
7.0%
산 18
 
4.9%
비 18
 
4.9%
Other values (8) 38
10.3%
Common
ValueCountFrequency (%)
0 551
23.0%
2 453
18.9%
1 438
18.3%
- 377
15.7%
8 88
 
3.7%
3 85
 
3.5%
9 78
 
3.3%
4 75
 
3.1%
5 73
 
3.0%
7 64
 
2.7%
Other values (3) 113
 
4.7%
Latin
ValueCountFrequency (%)
q 1
20.0%
l 1
20.0%
t 1
20.0%
k 1
20.0%
s 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2400
86.7%
Hangul 369
 
13.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 551
23.0%
2 453
18.9%
1 438
18.2%
- 377
15.7%
8 88
 
3.7%
3 85
 
3.5%
9 78
 
3.2%
4 75
 
3.1%
5 73
 
3.0%
7 64
 
2.7%
Other values (8) 118
 
4.9%
Hangul
ValueCountFrequency (%)
호 61
16.5%
일 56
15.2%
관 43
11.7%
제 30
8.1%
부 27
7.3%
계 26
7.0%
동 26
7.0%
림 26
7.0%
산 18
 
4.9%
비 18
 
4.9%
Other values (8) 38
10.3%

도로명주소
Text

Distinct397
Distinct (%)94.7%
Missing3
Missing (%)0.7%
Memory size3.4 KiB
2023-12-12T15:15:18.570314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length20.52506
Min length13

Characters and Unicode

Total characters8600
Distinct characters76
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique380 ?
Unique (%)90.7%

Sample

1st row경기 안양시 동안구 평촌대로40번길 100
2nd row경기도 안양시 동안구 흥안대로223번길 19
3rd row경기 안양시 동안구 흥안대로223번길 19
4th row경기 안양시 동안구 갈산로72번길 5
5th row경기도 안양시 동안구 갈산로 43
ValueCountFrequency (%)
동안구 411
19.7%
안양시 411
19.7%
경기 271
 
13.0%
경기도 146
 
7.0%
시민대로 29
 
1.4%
관악대로 22
 
1.1%
경수대로 20
 
1.0%
흥안대로 19
 
0.9%
동안로 18
 
0.9%
평촌대로 17
 
0.8%
Other values (369) 724
34.7%
2023-12-12T15:15:19.086426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1669
19.4%
안 905
 
10.5%
경 462
 
5.4%
시 460
 
5.3%
동 449
 
5.2%
양 420
 
4.9%
기 418
 
4.9%
구 414
 
4.8%
로 413
 
4.8%
1 280
 
3.3%
Other values (66) 2710
31.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5361
62.3%
Space Separator 1669
 
19.4%
Decimal Number 1540
 
17.9%
Dash Punctuation 29
 
0.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 905
16.9%
경 462
8.6%
시 460
8.6%
동 449
8.4%
양 420
7.8%
기 418
7.8%
구 414
7.7%
로 413
7.7%
대 229
 
4.3%
길 181
 
3.4%
Other values (53) 1010
18.8%
Decimal Number
ValueCountFrequency (%)
1 280
18.2%
2 233
15.1%
3 215
14.0%
4 157
10.2%
5 127
8.2%
6 116
7.5%
8 111
 
7.2%
0 105
 
6.8%
7 102
 
6.6%
9 94
 
6.1%
Space Separator
ValueCountFrequency (%)
1669
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5361
62.3%
Common 3238
37.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 905
16.9%
경 462
8.6%
시 460
8.6%
동 449
8.4%
양 420
7.8%
기 418
7.8%
구 414
7.7%
로 413
7.7%
대 229
 
4.3%
길 181
 
3.4%
Other values (53) 1010
18.8%
Common
ValueCountFrequency (%)
1669
51.5%
1 280
 
8.6%
2 233
 
7.2%
3 215
 
6.6%
4 157
 
4.8%
5 127
 
3.9%
6 116
 
3.6%
8 111
 
3.4%
0 105
 
3.2%
7 102
 
3.2%
Other values (2) 123
 
3.8%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5361
62.3%
ASCII 3239
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1669
51.5%
1 280
 
8.6%
2 233
 
7.2%
3 215
 
6.6%
4 157
 
4.8%
5 127
 
3.9%
6 116
 
3.6%
8 111
 
3.4%
0 105
 
3.2%
7 102
 
3.1%
Other values (3) 124
 
3.8%
Hangul
ValueCountFrequency (%)
안 905
16.9%
경 462
8.6%
시 460
8.6%
동 449
8.4%
양 420
7.8%
기 418
7.8%
구 414
7.7%
로 413
7.7%
대 229
 
4.3%
길 181
 
3.4%
Other values (53) 1010
18.8%

Interactions

2023-12-12T15:15:14.050222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:15:19.186147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호분류행정동법정동
번호1.0000.1410.9670.839
분류0.1411.0000.6320.031
행정동0.9670.6321.0000.977
법정동0.8390.0310.9771.000
2023-12-12T15:15:19.287771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동분류법정동
행정동1.0000.5560.779
분류0.5561.0000.021
법정동0.7790.0211.000
2023-12-12T15:15:19.369188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호분류행정동법정동
번호1.0000.1070.8180.644
분류0.1071.0000.5560.021
행정동0.8180.5561.0000.779
법정동0.6440.0210.7791.000

Missing values

2023-12-12T15:15:14.174042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:15:14.303214image/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.
2023-12-12T15:15:14.420856image/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

번호분류거래처명행정동법정동관리지역대표지정번호도로명주소
01판매처그랜드마트갈산동호계동동안구박상훈갈산2014-01경기 안양시 동안구 평촌대로40번길 100
12판매처농협안심축산물갈산동호계동동안구윤태용갈산2019-01경기도 안양시 동안구 흥안대로223번길 19
23판매처샘마을슈퍼1갈산동호계동동안구유창석<NA>경기 안양시 동안구 흥안대로223번길 19
34판매처싱싱그린마트갈산동호계동동안구노동익갈산2015-01경기 안양시 동안구 갈산로72번길 5
45판매처씨유안양덕현점갈산동호계동동안구이혜란2019-02경기도 안양시 동안구 갈산로 43
56판매처씨유호계덕현점갈산동호계동동안구김규완갈산2017-02경기 안양시동안구 경수대로596번길 61-2
67판매처은혜청과마트갈산동호계동동안구함철호<NA>경기 안양시 동안구 흥안대로223번길 19
78판매처지에스25뉴타운점갈산동호계동동안구이학심<NA>경기 안양시 동안구 갈산로76번길 10
89판매처지에스25으뜸갈산동호계동동안구구본옥<NA>경기 안양시 동안구 갈산로 16
910판매처(주)새빛나관양1동관양동동안구정의선관일2018-6경기도 안양시 동안구 관악대로359번길 10-18
번호분류거래처명행정동법정동관리지역대표지정번호도로명주소
412413판매처이마트 의왕점호계3동호계동동안구이유현 이갑수2019-2경기도 의왕시 경수대로 262
413414판매처이마트24의왕대명호계3동호계동동안구안형준2018-3경기도 의왕시 포도원로 40
414415판매처정화슈퍼호계3동호계동동안구강용희<NA>경기 안양시 동안구 경수대로507번길 13
415416판매처지에스25 호계팰리스점호계3동호계동동안구안진수2018-5경기도 안양시 동안구 흥안대로94번길 68
416417판매처지에스25안양대로점호계3동호계동동안구김영희2015-4경기 안양시 동안구 경수대로 434
417418판매처지에스25안양대영점호계3동호계동동안구피해성2015-2경기 안양시 동안구 엘에스로 45
418419판매처지에스25안양호계점호계3동호계동동안구길승환2019-3경기도 안양시 동안구 경수대로 521-1
419420판매처지에스25호계대현점호계3동호계동동안구김윤주2016-7경기 안양시 동안구 경수대로 430
420421판매처하모니마트홈타운점SMK호계3동호계동동안구강진한2007-7경기 안양시 동안구 경수대로 458 지하
421422판매처홈플러스익호계점호계3동호계동동안구임일순2016-2경기 안양시 동안구 경수대로 428