Overview

Dataset statistics

Number of variables10
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory90.1 B

Variable types

Text3
Numeric4
Categorical3

Dataset

Description대전광역시 서구 관내에서 운영 중인 자율방범대 초소 현황에 대한 정보(행정동/법정동, 위경도, 개수 등)를 제공합니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15124356/fileData.do

Alerts

행정동 코드 is highly overall correlated with 법정동 코드 and 1 other fieldsHigh correlation
법정동 코드 is highly overall correlated with 행정동 코드 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 법정동 코드High correlation
남녀공용 is highly overall correlated with 행정동 코드High correlation
초소위치 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:43:41.598993
Analysis finished2023-12-12 09:43:44.065193
Duration2.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct22
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T18:43:44.189492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4230769
Min length2

Characters and Unicode

Total characters89
Distinct characters31
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

Unique18 ?
Unique (%)69.2%

Sample

1st row복수동
2nd row도마1동
3rd row도마2동
4th row도마2동
5th row정림동
ValueCountFrequency (%)
도마2동 2
 
7.7%
괴정동 2
 
7.7%
변동 2
 
7.7%
관저1동 2
 
7.7%
가장동 1
 
3.8%
복수동 1
 
3.8%
갈마1동 1
 
3.8%
관저2동 1
 
3.8%
가수원동 1
 
3.8%
만년동 1
 
3.8%
Other values (12) 12
46.2%
2023-12-12T18:43:44.569238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
29.2%
1 6
 
6.7%
2 5
 
5.6%
마 4
 
4.5%
도 3
 
3.4%
산 3
 
3.4%
월 3
 
3.4%
평 3
 
3.4%
정 3
 
3.4%
저 3
 
3.4%
Other values (21) 30
33.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
85.4%
Decimal Number 13
 
14.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
34.2%
마 4
 
5.3%
도 3
 
3.9%
산 3
 
3.9%
월 3
 
3.9%
평 3
 
3.9%
정 3
 
3.9%
저 3
 
3.9%
관 3
 
3.9%
둔 3
 
3.9%
Other values (18) 22
28.9%
Decimal Number
ValueCountFrequency (%)
1 6
46.2%
2 5
38.5%
3 2
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76
85.4%
Common 13
 
14.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
34.2%
마 4
 
5.3%
도 3
 
3.9%
산 3
 
3.9%
월 3
 
3.9%
평 3
 
3.9%
정 3
 
3.9%
저 3
 
3.9%
관 3
 
3.9%
둔 3
 
3.9%
Other values (18) 22
28.9%
Common
ValueCountFrequency (%)
1 6
46.2%
2 5
38.5%
3 2
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76
85.4%
ASCII 13
 
14.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
34.2%
마 4
 
5.3%
도 3
 
3.9%
산 3
 
3.9%
월 3
 
3.9%
평 3
 
3.9%
정 3
 
3.9%
저 3
 
3.9%
관 3
 
3.9%
둔 3
 
3.9%
Other values (18) 22
28.9%
ASCII
ValueCountFrequency (%)
1 6
46.2%
2 5
38.5%
3 2
 
15.4%

행정동 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170576 × 109
Minimum3.017051 × 109
Maximum3.017066 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:43:44.707166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017051 × 109
5-th percentile3.0170522 × 109
Q13.0170542 × 109
median3.0170578 × 109
Q33.0170596 × 109
95-th percentile3.0170648 × 109
Maximum3.017066 × 109
Range15000
Interquartile range (IQR)5350

Descriptive statistics

Standard deviation3980.4522
Coefficient of variation (CV)1.319316 × 10-6
Kurtosis-0.34257486
Mean3.0170576 × 109
Median Absolute Deviation (MAD)2250
Skewness0.41795802
Sum7.8443498 × 1010
Variance15844000
MonotonicityNot monotonic
2023-12-12T18:43:44.833337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3017053000 2
 
7.7%
3017054000 2
 
7.7%
3017059600 2
 
7.7%
3017056000 2
 
7.7%
3017051000 1
 
3.8%
3017058100 1
 
3.8%
3017060000 1
 
3.8%
3017059700 1
 
3.8%
3017059000 1
 
3.8%
3017065000 1
 
3.8%
Other values (12) 12
46.2%
ValueCountFrequency (%)
3017051000 1
3.8%
3017052000 1
3.8%
3017053000 2
7.7%
3017053500 1
3.8%
3017054000 2
7.7%
3017055000 1
3.8%
3017055500 1
3.8%
3017056000 2
7.7%
3017057000 1
3.8%
3017057500 1
3.8%
ValueCountFrequency (%)
3017066000 1
3.8%
3017065000 1
3.8%
3017064000 1
3.8%
3017063000 1
3.8%
3017060000 1
3.8%
3017059700 1
3.8%
3017059600 2
7.7%
3017059000 1
3.8%
3017058800 1
3.8%
3017058700 1
3.8%

법정동명
Text

Distinct16
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T18:43:45.008857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9230769
Min length2

Characters and Unicode

Total characters76
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)38.5%

Sample

1st row복수동
2nd row도마동
3rd row도마동
4th row도마동
5th row정림동
ValueCountFrequency (%)
도마동 3
11.5%
둔산동 3
11.5%
월평동 3
11.5%
관저동 3
11.5%
변동 2
 
7.7%
괴정동 2
 
7.7%
복수동 1
 
3.8%
정림동 1
 
3.8%
용문동 1
 
3.8%
탄방동 1
 
3.8%
Other values (6) 6
23.1%
2023-12-12T18:43:45.357610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
34.2%
마 4
 
5.3%
도 3
 
3.9%
정 3
 
3.9%
둔 3
 
3.9%
산 3
 
3.9%
월 3
 
3.9%
평 3
 
3.9%
관 3
 
3.9%
저 3
 
3.9%
Other values (18) 22
28.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
34.2%
마 4
 
5.3%
도 3
 
3.9%
정 3
 
3.9%
둔 3
 
3.9%
산 3
 
3.9%
월 3
 
3.9%
평 3
 
3.9%
관 3
 
3.9%
저 3
 
3.9%
Other values (18) 22
28.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
34.2%
마 4
 
5.3%
도 3
 
3.9%
정 3
 
3.9%
둔 3
 
3.9%
산 3
 
3.9%
월 3
 
3.9%
평 3
 
3.9%
관 3
 
3.9%
저 3
 
3.9%
Other values (18) 22
28.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
34.2%
마 4
 
5.3%
도 3
 
3.9%
정 3
 
3.9%
둔 3
 
3.9%
산 3
 
3.9%
월 3
 
3.9%
평 3
 
3.9%
관 3
 
3.9%
저 3
 
3.9%
Other values (18) 22
28.9%

법정동 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.017011 × 109
Minimum3.0170101 × 109
Maximum3.0170128 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:43:45.495775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0170101 × 109
5-th percentile3.0170102 × 109
Q13.0170104 × 109
median3.017011 × 109
Q33.0170113 × 109
95-th percentile3.0170117 × 109
Maximum3.0170128 × 109
Range2700
Interquartile range (IQR)875

Descriptive statistics

Standard deviation625.66887
Coefficient of variation (CV)2.0738038 × 10-7
Kurtosis1.1778051
Mean3.017011 × 109
Median Absolute Deviation (MAD)500
Skewness0.73031058
Sum7.8442286 × 1010
Variance391461.54
MonotonicityNot monotonic
2023-12-12T18:43:45.634519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3017011300 3
11.5%
3017011600 3
11.5%
3017011200 3
11.5%
3017010300 3
11.5%
3017010200 2
 
7.7%
3017010800 2
 
7.7%
3017010100 1
 
3.8%
3017011700 1
 
3.8%
3017011400 1
 
3.8%
3017012800 1
 
3.8%
Other values (6) 6
23.1%
ValueCountFrequency (%)
3017010100 1
 
3.8%
3017010200 2
7.7%
3017010300 3
11.5%
3017010400 1
 
3.8%
3017010500 1
 
3.8%
3017010600 1
 
3.8%
3017010800 2
7.7%
3017010900 1
 
3.8%
3017011000 1
 
3.8%
3017011100 1
 
3.8%
ValueCountFrequency (%)
3017012800 1
 
3.8%
3017011700 1
 
3.8%
3017011600 3
11.5%
3017011400 1
 
3.8%
3017011300 3
11.5%
3017011200 3
11.5%
3017011100 1
 
3.8%
3017011000 1
 
3.8%
3017010900 1
 
3.8%
3017010800 2
7.7%

초소위치
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T18:43:45.867366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19.5
Mean length17.846154
Min length15

Characters and Unicode

Total characters464
Distinct characters54
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

Unique26 ?
Unique (%)100.0%

Sample

1st row대전광역시 서구 복수동 산2-3
2nd row대전광역시 서구 도마동 134-45
3rd row대전광역시 서구 도마동 155-28
4th row대전광역시 서구 도마동 109-2
5th row대전광역시 서구 정림동 402-6
ValueCountFrequency (%)
대전광역시 26
25.0%
서구 26
25.0%
도마동 3
 
2.9%
관저동 3
 
2.9%
월평동 2
 
1.9%
변동 2
 
1.9%
흑석동 1
 
1.0%
가장동 1
 
1.0%
40-13 1
 
1.0%
내동 1
 
1.0%
Other values (38) 38
36.5%
2023-12-12T18:43:46.281420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78
16.8%
대 27
 
5.8%
서 27
 
5.8%
광 26
 
5.6%
역 26
 
5.6%
시 26
 
5.6%
구 26
 
5.6%
전 26
 
5.6%
1 23
 
5.0%
동 21
 
4.5%
Other values (44) 158
34.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 266
57.3%
Decimal Number 102
 
22.0%
Space Separator 78
 
16.8%
Dash Punctuation 18
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 27
10.2%
서 27
10.2%
광 26
9.8%
역 26
9.8%
시 26
9.8%
구 26
9.8%
전 26
9.8%
동 21
7.9%
로 5
 
1.9%
마 4
 
1.5%
Other values (32) 52
19.5%
Decimal Number
ValueCountFrequency (%)
1 23
22.5%
3 18
17.6%
2 12
11.8%
0 9
 
8.8%
4 9
 
8.8%
8 9
 
8.8%
5 8
 
7.8%
6 7
 
6.9%
9 6
 
5.9%
7 1
 
1.0%
Space Separator
ValueCountFrequency (%)
78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 266
57.3%
Common 198
42.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 27
10.2%
서 27
10.2%
광 26
9.8%
역 26
9.8%
시 26
9.8%
구 26
9.8%
전 26
9.8%
동 21
7.9%
로 5
 
1.9%
마 4
 
1.5%
Other values (32) 52
19.5%
Common
ValueCountFrequency (%)
78
39.4%
1 23
 
11.6%
- 18
 
9.1%
3 18
 
9.1%
2 12
 
6.1%
0 9
 
4.5%
4 9
 
4.5%
8 9
 
4.5%
5 8
 
4.0%
6 7
 
3.5%
Other values (2) 7
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 266
57.3%
ASCII 198
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
78
39.4%
1 23
 
11.6%
- 18
 
9.1%
3 18
 
9.1%
2 12
 
6.1%
0 9
 
4.5%
4 9
 
4.5%
8 9
 
4.5%
5 8
 
4.0%
6 7
 
3.5%
Other values (2) 7
 
3.5%
Hangul
ValueCountFrequency (%)
대 27
10.2%
서 27
10.2%
광 26
9.8%
역 26
9.8%
시 26
9.8%
구 26
9.8%
전 26
9.8%
동 21
7.9%
로 5
 
1.9%
마 4
 
1.5%
Other values (32) 52
19.5%

위도
Real number (ℝ)

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.32889
Minimum36.25218
Maximum36.36554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:43:46.429027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.25218
5-th percentile36.300075
Q136.308871
median36.331434
Q336.350904
95-th percentile36.363279
Maximum36.36554
Range0.1133599
Interquartile range (IQR)0.042032725

Descriptive statistics

Standard deviation0.026233186
Coefficient of variation (CV)0.00072210259
Kurtosis1.2671558
Mean36.32889
Median Absolute Deviation (MAD)0.020313
Skewness-0.82539728
Sum944.55115
Variance0.00068818004
MonotonicityNot monotonic
2023-12-12T18:43:46.570438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
36.3073144 1
 
3.8%
36.3300851 1
 
3.8%
36.2521802 1
 
3.8%
36.2986814 1
 
3.8%
36.3043243 1
 
3.8%
36.3075796 1
 
3.8%
36.3048901 1
 
3.8%
36.3655401 1
 
3.8%
36.3609634 1
 
3.8%
36.3640506 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
36.2521802 1
3.8%
36.2986814 1
3.8%
36.3042548 1
3.8%
36.3043243 1
3.8%
36.3048901 1
3.8%
36.3073144 1
3.8%
36.3075796 1
3.8%
36.3127458 1
3.8%
36.315432 1
3.8%
36.3175408 1
3.8%
ValueCountFrequency (%)
36.3655401 1
3.8%
36.3640506 1
3.8%
36.3609634 1
3.8%
36.3577178 1
3.8%
36.353825 1
3.8%
36.3525304 1
3.8%
36.3509641 1
3.8%
36.3507232 1
3.8%
36.346648 1
3.8%
36.3370657 1
3.8%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.37184
Minimum127.33282
Maximum127.39879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T18:43:46.811617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.33282
5-th percentile127.33771
Q1127.36413
median127.37433
Q3127.38334
95-th percentile127.39512
Maximum127.39879
Range0.0659652
Interquartile range (IQR)0.01920355

Descriptive statistics

Standard deviation0.018003384
Coefficient of variation (CV)0.00014134509
Kurtosis0.070588817
Mean127.37184
Median Absolute Deviation (MAD)0.0096859
Skewness-0.855527
Sum3311.6678
Variance0.00032412183
MonotonicityNot monotonic
2023-12-12T18:43:46.951357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
127.3789456 1
 
3.8%
127.3851695 1
 
3.8%
127.3410378 1
 
3.8%
127.3387875 1
 
3.8%
127.3373481 1
 
3.8%
127.3328219 1
 
3.8%
127.3551254 1
 
3.8%
127.3713292 1
 
3.8%
127.3716334 1
 
3.8%
127.3725781 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
127.3328219 1
3.8%
127.3373481 1
3.8%
127.3387875 1
3.8%
127.3410378 1
3.8%
127.3551254 1
3.8%
127.3627022 1
3.8%
127.3629017 1
3.8%
127.3678274 1
3.8%
127.3713292 1
3.8%
127.3716334 1
3.8%
ValueCountFrequency (%)
127.3987871 1
3.8%
127.395389 1
3.8%
127.3943286 1
3.8%
127.3860181 1
3.8%
127.3851695 1
3.8%
127.3843906 1
3.8%
127.3836459 1
3.8%
127.382409 1
3.8%
127.3819103 1
3.8%
127.3805867 1
3.8%

초소면적
Categorical

Distinct9
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Memory size340.0 B
18
11 
10
7 
27
2 
40
 
1
8
 
1
Other values (4)
4 

Length

Max length14
Median length2
Mean length2.4230769
Min length1

Unique

Unique6 ?
Unique (%)23.1%

Sample

1st row18
2nd row18
3rd row10
4th row10
5th row40

Common Values

ValueCountFrequency (%)
18 11
42.3%
10 7
26.9%
27 2
 
7.7%
40 1
 
3.8%
8 1
 
3.8%
20 1
 
3.8%
18(남성), 15(여성) 1
 
3.8%
21 1
 
3.8%
23 1
 
3.8%

Length

2023-12-12T18:43:47.100350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:43:47.564931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
18 11
40.7%
10 7
25.9%
27 2
 
7.4%
40 1
 
3.7%
8 1
 
3.7%
20 1
 
3.7%
18(남성 1
 
3.7%
15(여성 1
 
3.7%
21 1
 
3.7%
23 1
 
3.7%

개수
Categorical

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
1
20 
2
6 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 20
76.9%
2 6
 
23.1%

Length

2023-12-12T18:43:47.718451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:43:47.842816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 20
76.9%
2 6
 
23.1%

남녀공용
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
공용
15 
별도
8 
여성방범대 없음
3 

Length

Max length8
Median length2
Mean length2.6923077
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공용
2nd row공용
3rd row별도
4th row별도
5th row별도

Common Values

ValueCountFrequency (%)
공용 15
57.7%
별도 8
30.8%
여성방범대 없음 3
 
11.5%

Length

2023-12-12T18:43:47.971460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:43:48.109529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공용 15
51.7%
별도 8
27.6%
여성방범대 3
 
10.3%
없음 3
 
10.3%

Interactions

2023-12-12T18:43:43.242043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.002197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.454416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.834777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:43.345402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.128801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.557363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.932854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:43.445509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.237857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.645718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:43.054368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:43.553181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.340602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:42.732871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:43:43.157220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:43:48.214516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분행정동 코드법정동명법정동 코드초소위치위도경도초소면적개수남녀공용
구분1.0001.0001.0001.0001.0001.0001.0000.9091.0001.000
행정동 코드1.0001.0000.8420.8901.0000.4780.7600.0000.0000.887
법정동명1.0000.8421.0001.0001.0001.0000.8980.6690.7020.863
법정동 코드1.0000.8901.0001.0001.0000.8350.6500.6720.2900.343
초소위치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0000.4781.0000.8351.0001.0000.7260.0000.2480.589
경도1.0000.7600.8980.6501.0000.7261.0000.0000.0000.860
초소면적0.9090.0000.6690.6721.0000.0000.0001.0000.5600.000
개수1.0000.0000.7020.2901.0000.2480.0000.5601.0000.000
남녀공용1.0000.8870.8630.3431.0000.5890.8600.0000.0001.000
2023-12-12T18:43:48.383540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
초소면적개수남녀공용
초소면적1.0000.4650.000
개수0.4651.0000.000
남녀공용0.0000.0001.000
2023-12-12T18:43:48.511409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드법정동 코드위도경도초소면적개수남녀공용
행정동 코드1.0000.8840.305-0.2110.0000.0000.537
법정동 코드0.8841.0000.132-0.5430.4090.2920.078
위도0.3050.1321.0000.4410.0000.2170.428
경도-0.211-0.5430.4411.0000.0000.0000.488
초소면적0.0000.4090.0000.0001.0000.4650.000
개수0.0000.2920.2170.0000.4651.0000.000
남녀공용0.5370.0780.4280.4880.0000.0001.000

Missing values

2023-12-12T18:43:43.787628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:43:44.010315image/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

구분행정동 코드법정동명법정동 코드초소위치위도경도초소면적개수남녀공용
0복수동3017051000복수동3017010100대전광역시 서구 복수동 산2-336.307314127.378946181공용
1도마1동3017052000도마동3017010300대전광역시 서구 도마동 134-4536.315432127.38191181공용
2도마2동3017053000도마동3017010300대전광역시 서구 도마동 155-2836.312746127.373168101별도
3도마2동3017053000도마동3017010300대전광역시 서구 도마동 109-236.317541127.373229101별도
4정림동3017053500정림동3017010400대전광역시 서구 정림동 402-636.304255127.362702402별도
5변동3017054000변동3017010200대전광역시 서구 변동 47-3236.324517127.380279271별도
6변동3017054000변동3017010200대전광역시 서구 변동 35-836.325619127.382409181별도
7용문동3017055000용문동3017010500대전광역시 서구 용문동 256-2936.336837127.394329181공용
8탄방동3017055500탄방동3017010600대전광역시 서구 탄방동 1041-136.346648127.395389182공용
9둔산1동3017063000둔산동3017011200대전광역시 서구 둔산중로 6536.35253127.386018181여성방범대 없음
구분행정동 코드법정동명법정동 코드초소위치위도경도초소면적개수남녀공용
16갈마1동3017058100갈마동3017011100대전광역시 서구 갈마동 316-336.350723127.367827201공용
17월평1동3017058600월평동3017011300대전광역시 서구 한밭대로 58036.357718127.362902101공용
18월평2동3017058700월평동3017011300대전광역시 서구 월평동 218-836.364051127.372578182공용
19월평3동3017058800월평동3017011300대전광역시 서구 월평동 30236.360963127.37163318(남성), 15(여성)2공용
20만년동3017065000만년동3017012800대전광역시 서구 만년동 3136.36554127.371329211공용
21가수원동3017059000가수원동3017011400대전광역시 서구 가수원동 191-136.30489127.355125182별도
22관저1동3017059600관저동3017011600대전광역시 서구 관저동 1644-136.30758127.332822181공용
23관저1동3017059600관저동3017011600대전광역시 서구 관저동 999-136.304324127.337348181공용
24관저2동3017059700관저동3017011600대전광역시 서구 관저동 134336.298681127.338787232공용
25기성동3017060000흑석동3017011700대전광역시 서구 흑석동 311-1636.25218127.341038101여성방범대 없음