Overview

Dataset statistics

Number of variables11
Number of observations39
Missing cells4
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory94.4 B

Variable types

Categorical3
Text5
Numeric3

Alerts

소재지우편번호 is highly overall correlated with WGS84위도 and 3 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
지방청명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
구분명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
소재지우편번호 has 1 (2.6%) missing valuesMissing
소재지도로명주소 has 1 (2.6%) missing valuesMissing
WGS84위도 has 1 (2.6%) missing valuesMissing
WGS84경도 has 1 (2.6%) missing valuesMissing
명칭 has unique valuesUnique
소재지지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:30:46.714187
Analysis finished2023-12-10 21:30:48.221659
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
성남시
8 
부천시
5 
안양시
5 
안성시
3 
의정부시
3 
Other values (11)
15 

Length

Max length4
Median length3
Mean length3.0769231
Min length3

Unique

Unique7 ?
Unique (%)17.9%

Sample

1st row고양시
2nd row고양시
3rd row과천시
4th row과천시
5th row광명시

Common Values

ValueCountFrequency (%)
성남시 8
20.5%
부천시 5
12.8%
안양시 5
12.8%
안성시 3
 
7.7%
의정부시 3
 
7.7%
고양시 2
 
5.1%
과천시 2
 
5.1%
수원시 2
 
5.1%
화성시 2
 
5.1%
광명시 1
 
2.6%
Other values (6) 6
15.4%

Length

2023-12-11T06:30:48.285086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 8
20.5%
부천시 5
12.8%
안양시 5
12.8%
안성시 3
 
7.7%
의정부시 3
 
7.7%
고양시 2
 
5.1%
과천시 2
 
5.1%
수원시 2
 
5.1%
화성시 2
 
5.1%
광명시 1
 
2.6%
Other values (6) 6
15.4%

명칭
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-11T06:30:48.481053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2307692
Min length6

Characters and Unicode

Total characters243
Distinct characters65
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

Unique39 ?
Unique (%)100.0%

Sample

1st row송포치안센터
2nd row마두2치안센터
3rd row대공원치안센터
4th row선암치안센터
5th row철산1치안센터
ValueCountFrequency (%)
송포치안센터 1
 
2.6%
영화치안센터 1
 
2.6%
풍도치안센터 1
 
2.6%
미양치안센터 1
 
2.6%
서운치안센터 1
 
2.6%
고삼치안센터 1
 
2.6%
냉천치안센터 1
 
2.6%
운동장치안센터 1
 
2.6%
관악치안센터 1
 
2.6%
목감치안센터 1
 
2.6%
Other values (29) 29
74.4%
2023-12-11T06:30:48.816695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
안 41
16.9%
치 39
16.0%
센 39
16.0%
터 39
16.0%
정 3
 
1.2%
부 3
 
1.2%
삼 3
 
1.2%
장 3
 
1.2%
흥 2
 
0.8%
2 2
 
0.8%
Other values (55) 69
28.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 238
97.9%
Decimal Number 5
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 41
17.2%
치 39
16.4%
센 39
16.4%
터 39
16.4%
정 3
 
1.3%
부 3
 
1.3%
삼 3
 
1.3%
장 3
 
1.3%
흥 2
 
0.8%
내 2
 
0.8%
Other values (52) 64
26.9%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
3 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 238
97.9%
Common 5
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 41
17.2%
치 39
16.4%
센 39
16.4%
터 39
16.4%
정 3
 
1.3%
부 3
 
1.3%
삼 3
 
1.3%
장 3
 
1.3%
흥 2
 
0.8%
내 2
 
0.8%
Other values (52) 64
26.9%
Common
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
3 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 238
97.9%
ASCII 5
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
안 41
17.2%
치 39
16.4%
센 39
16.4%
터 39
16.4%
정 3
 
1.3%
부 3
 
1.3%
삼 3
 
1.3%
장 3
 
1.3%
흥 2
 
0.8%
내 2
 
0.8%
Other values (52) 64
26.9%
ASCII
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
3 1
20.0%

지방청명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size444.0 B
경기남부청
34 
경기북부청
5 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기북부청
2nd row경기북부청
3rd row경기남부청
4th row경기남부청
5th row경기남부청

Common Values

ValueCountFrequency (%)
경기남부청 34
87.2%
경기북부청 5
 
12.8%

Length

2023-12-11T06:30:48.932572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:30:49.019334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기남부청 34
87.2%
경기북부청 5
 
12.8%

경찰서명
Text

Distinct21
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-11T06:30:49.168841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.1538462
Min length2

Characters and Unicode

Total characters123
Distinct characters33
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

Unique12 ?
Unique (%)30.8%

Sample

1st row일산서부
2nd row일산동부
3rd row과천
4th row과천
5th row광명
ValueCountFrequency (%)
분당 4
 
10.3%
안양만안 4
 
10.3%
부천소사 4
 
10.3%
의정부 3
 
7.7%
안성 3
 
7.7%
성남수정 3
 
7.7%
수원중부 2
 
5.1%
화성서부 2
 
5.1%
과천 2
 
5.1%
군포 1
 
2.6%
Other values (11) 11
28.2%
2023-12-11T06:30:49.526833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 14
 
11.4%
안 14
 
11.4%
성 9
 
7.3%
천 8
 
6.5%
정 7
 
5.7%
양 6
 
4.9%
수 5
 
4.1%
당 4
 
3.3%
원 4
 
3.3%
남 4
 
3.3%
Other values (23) 48
39.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 14
 
11.4%
안 14
 
11.4%
성 9
 
7.3%
천 8
 
6.5%
정 7
 
5.7%
양 6
 
4.9%
수 5
 
4.1%
당 4
 
3.3%
원 4
 
3.3%
남 4
 
3.3%
Other values (23) 48
39.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 123
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 14
 
11.4%
안 14
 
11.4%
성 9
 
7.3%
천 8
 
6.5%
정 7
 
5.7%
양 6
 
4.9%
수 5
 
4.1%
당 4
 
3.3%
원 4
 
3.3%
남 4
 
3.3%
Other values (23) 48
39.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 123
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 14
 
11.4%
안 14
 
11.4%
성 9
 
7.3%
천 8
 
6.5%
정 7
 
5.7%
양 6
 
4.9%
수 5
 
4.1%
당 4
 
3.3%
원 4
 
3.3%
남 4
 
3.3%
Other values (23) 48
39.0%

구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size444.0 B
지구대
24 
파출소
15 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row파출소
2nd row지구대
3rd row지구대
4th row지구대
5th row지구대

Common Values

ValueCountFrequency (%)
지구대 24
61.5%
파출소 15
38.5%

Length

2023-12-11T06:30:49.642535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:30:49.739177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지구대 24
61.5%
파출소 15
38.5%

지역경찰관서명
Text

Distinct33
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-11T06:30:49.915208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0769231
Min length2

Characters and Unicode

Total characters81
Distinct characters51
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

Unique27 ?
Unique (%)69.2%

Sample

1st row가좌
2nd row마두
3rd row과천
4th row과천
5th row철산
ValueCountFrequency (%)
화서문 2
 
5.1%
신곡 2
 
5.1%
송내 2
 
5.1%
안양 2
 
5.1%
야탑 2
 
5.1%
과천 2
 
5.1%
대부 1
 
2.6%
내리 1
 
2.6%
평택 1
 
2.6%
아미 1
 
2.6%
Other values (23) 23
59.0%
2023-12-11T06:30:50.210522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
내 5
 
6.2%
서 4
 
4.9%
신 4
 
4.9%
곡 3
 
3.7%
능 2
 
2.5%
금 2
 
2.5%
정 2
 
2.5%
산 2
 
2.5%
평 2
 
2.5%
가 2
 
2.5%
Other values (41) 53
65.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
98.8%
Decimal Number 1
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
내 5
 
6.2%
서 4
 
5.0%
신 4
 
5.0%
곡 3
 
3.8%
능 2
 
2.5%
금 2
 
2.5%
정 2
 
2.5%
산 2
 
2.5%
평 2
 
2.5%
가 2
 
2.5%
Other values (40) 52
65.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80
98.8%
Common 1
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
내 5
 
6.2%
서 4
 
5.0%
신 4
 
5.0%
곡 3
 
3.8%
능 2
 
2.5%
금 2
 
2.5%
정 2
 
2.5%
산 2
 
2.5%
평 2
 
2.5%
가 2
 
2.5%
Other values (40) 52
65.0%
Common
ValueCountFrequency (%)
4 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80
98.8%
ASCII 1
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
내 5
 
6.2%
서 4
 
5.0%
신 4
 
5.0%
곡 3
 
3.8%
능 2
 
2.5%
금 2
 
2.5%
정 2
 
2.5%
산 2
 
2.5%
평 2
 
2.5%
가 2
 
2.5%
Other values (40) 52
65.0%
ASCII
ValueCountFrequency (%)
4 1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)100.0%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean14554.474
Minimum10223
Maximum18557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-11T06:30:50.361026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10223
5-th percentile11487.7
Q113514
median14016
Q315768
95-th percentile18012.4
Maximum18557
Range8334
Interquartile range (IQR)2254

Descriptive statistics

Standard deviation2064.1162
Coefficient of variation (CV)0.14182005
Kurtosis-0.12378172
Mean14554.474
Median Absolute Deviation (MAD)731
Skewness0.2059767
Sum553070
Variance4260575.7
MonotonicityNot monotonic
2023-12-11T06:30:50.469162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
10223 1
 
2.6%
14002 1
 
2.6%
15654 1
 
2.6%
17601 1
 
2.6%
17607 1
 
2.6%
17505 1
 
2.6%
14030 1
 
2.6%
13941 1
 
2.6%
13962 1
 
2.6%
13991 1
 
2.6%
Other values (28) 28
71.8%
ValueCountFrequency (%)
10223 1
2.6%
10415 1
2.6%
11677 1
2.6%
11695 1
2.6%
12529 1
2.6%
13134 1
2.6%
13259 1
2.6%
13340 1
2.6%
13387 1
2.6%
13511 1
2.6%
ValueCountFrequency (%)
18557 1
2.6%
18553 1
2.6%
17917 1
2.6%
17607 1
2.6%
17601 1
2.6%
17505 1
2.6%
17400 1
2.6%
16315 1
2.6%
16271 1
2.6%
15806 1
2.6%

소재지지번주소
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-11T06:30:50.760729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length21.307692
Min length18

Characters and Unicode

Total characters831
Distinct characters99
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

Unique39 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 일산서구 대화동 2314번지
2nd row경기도 고양시 일산동구 마두동 786번지
3rd row경기도 과천시 막계동 산7-1번지
4th row경기도 과천시 과천동 362-2번지
5th row경기도 광명시 철산동 56-28번지
ValueCountFrequency (%)
경기도 39
 
21.4%
성남시 8
 
4.4%
부천시 5
 
2.7%
안양시 5
 
2.7%
분당구 4
 
2.2%
만안구 4
 
2.2%
안성시 3
 
1.6%
수정구 3
 
1.6%
의정부시 3
 
1.6%
안양동 3
 
1.6%
Other values (99) 105
57.7%
2023-12-11T06:30:51.219927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
143
 
17.2%
도 40
 
4.8%
지 40
 
4.8%
경 39
 
4.7%
기 39
 
4.7%
번 39
 
4.7%
시 39
 
4.7%
동 34
 
4.1%
1 34
 
4.1%
- 26
 
3.1%
Other values (89) 358
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 516
62.1%
Decimal Number 146
 
17.6%
Space Separator 143
 
17.2%
Dash Punctuation 26
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 40
 
7.8%
지 40
 
7.8%
경 39
 
7.6%
기 39
 
7.6%
번 39
 
7.6%
시 39
 
7.6%
동 34
 
6.6%
안 21
 
4.1%
구 18
 
3.5%
양 14
 
2.7%
Other values (77) 193
37.4%
Decimal Number
ValueCountFrequency (%)
1 34
23.3%
5 18
12.3%
2 14
9.6%
4 14
9.6%
3 13
 
8.9%
8 12
 
8.2%
6 11
 
7.5%
9 10
 
6.8%
7 10
 
6.8%
0 10
 
6.8%
Space Separator
ValueCountFrequency (%)
143
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 516
62.1%
Common 315
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 40
 
7.8%
지 40
 
7.8%
경 39
 
7.6%
기 39
 
7.6%
번 39
 
7.6%
시 39
 
7.6%
동 34
 
6.6%
안 21
 
4.1%
구 18
 
3.5%
양 14
 
2.7%
Other values (77) 193
37.4%
Common
ValueCountFrequency (%)
143
45.4%
1 34
 
10.8%
- 26
 
8.3%
5 18
 
5.7%
2 14
 
4.4%
4 14
 
4.4%
3 13
 
4.1%
8 12
 
3.8%
6 11
 
3.5%
9 10
 
3.2%
Other values (2) 20
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 516
62.1%
ASCII 315
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
143
45.4%
1 34
 
10.8%
- 26
 
8.3%
5 18
 
5.7%
2 14
 
4.4%
4 14
 
4.4%
3 13
 
4.1%
8 12
 
3.8%
6 11
 
3.5%
9 10
 
3.2%
Other values (2) 20
 
6.3%
Hangul
ValueCountFrequency (%)
도 40
 
7.8%
지 40
 
7.8%
경 39
 
7.6%
기 39
 
7.6%
번 39
 
7.6%
시 39
 
7.6%
동 34
 
6.6%
안 21
 
4.1%
구 18
 
3.5%
양 14
 
2.7%
Other values (77) 193
37.4%

소재지도로명주소
Text

MISSING 

Distinct38
Distinct (%)100.0%
Missing1
Missing (%)2.6%
Memory size444.0 B
2023-12-11T06:30:51.517705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length19.447368
Min length14

Characters and Unicode

Total characters739
Distinct characters85
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

Unique38 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 일산서구 대화로 166
2nd row경기도 고양시 일산동구 강송로 165
3rd row경기도 과천시 대공원광장로 104
4th row경기도 과천시 뒷골2로 4-8
5th row경기도 광명시 광복로38번길 3
ValueCountFrequency (%)
경기도 38
 
21.6%
성남시 8
 
4.5%
부천시 5
 
2.8%
안양시 5
 
2.8%
분당구 4
 
2.3%
만안구 4
 
2.3%
안성시 3
 
1.7%
수정구 3
 
1.7%
19 3
 
1.7%
의정부시 2
 
1.1%
Other values (91) 101
57.4%
2023-12-11T06:30:51.949132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138
18.7%
경 41
 
5.5%
도 39
 
5.3%
시 39
 
5.3%
기 38
 
5.1%
로 33
 
4.5%
1 26
 
3.5%
길 22
 
3.0%
안 21
 
2.8%
구 18
 
2.4%
Other values (75) 324
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 463
62.7%
Space Separator 138
 
18.7%
Decimal Number 132
 
17.9%
Dash Punctuation 6
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 41
 
8.9%
도 39
 
8.4%
시 39
 
8.4%
기 38
 
8.2%
로 33
 
7.1%
길 22
 
4.8%
안 21
 
4.5%
구 18
 
3.9%
번 17
 
3.7%
성 16
 
3.5%
Other values (63) 179
38.7%
Decimal Number
ValueCountFrequency (%)
1 26
19.7%
5 17
12.9%
6 16
12.1%
9 15
11.4%
3 15
11.4%
2 10
 
7.6%
7 10
 
7.6%
4 9
 
6.8%
0 7
 
5.3%
8 7
 
5.3%
Space Separator
ValueCountFrequency (%)
138
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 463
62.7%
Common 276
37.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 41
 
8.9%
도 39
 
8.4%
시 39
 
8.4%
기 38
 
8.2%
로 33
 
7.1%
길 22
 
4.8%
안 21
 
4.5%
구 18
 
3.9%
번 17
 
3.7%
성 16
 
3.5%
Other values (63) 179
38.7%
Common
ValueCountFrequency (%)
138
50.0%
1 26
 
9.4%
5 17
 
6.2%
6 16
 
5.8%
9 15
 
5.4%
3 15
 
5.4%
2 10
 
3.6%
7 10
 
3.6%
4 9
 
3.3%
0 7
 
2.5%
Other values (2) 13
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 463
62.7%
ASCII 276
37.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
50.0%
1 26
 
9.4%
5 17
 
6.2%
6 16
 
5.8%
9 15
 
5.4%
3 15
 
5.4%
2 10
 
3.6%
7 10
 
3.6%
4 9
 
3.3%
0 7
 
2.5%
Other values (2) 13
 
4.7%
Hangul
ValueCountFrequency (%)
경 41
 
8.9%
도 39
 
8.4%
시 39
 
8.4%
기 38
 
8.2%
로 33
 
7.1%
길 22
 
4.8%
안 21
 
4.5%
구 18
 
3.9%
번 17
 
3.7%
성 16
 
3.5%
Other values (63) 179
38.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)100.0%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean37.379332
Minimum36.941307
Maximum37.750682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-11T06:30:52.099890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.941307
5-th percentile36.989438
Q137.312049
median37.405138
Q337.481619
95-th percentile37.680737
Maximum37.750682
Range0.80937508
Interquartile range (IQR)0.16957087

Descriptive statistics

Standard deviation0.19438245
Coefficient of variation (CV)0.0052002655
Kurtosis0.33394606
Mean37.379332
Median Absolute Deviation (MAD)0.078541491
Skewness-0.55065823
Sum1420.4146
Variance0.037784537
MonotonicityNot monotonic
2023-12-11T06:30:52.234018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
37.670320986 1
 
2.6%
37.3966079824 1
 
2.6%
37.1128025578 1
 
2.6%
36.9760582794 1
 
2.6%
36.941307103 1
 
2.6%
37.0831022839 1
 
2.6%
37.3929994062 1
 
2.6%
37.4038348724 1
 
2.6%
37.4170803051 1
 
2.6%
37.4046902226 1
 
2.6%
Other values (28) 28
71.8%
ValueCountFrequency (%)
36.941307103 1
2.6%
36.9760582794 1
2.6%
36.9917996768 1
2.6%
37.0614464141 1
2.6%
37.0831022839 1
2.6%
37.1128025578 1
2.6%
37.1769946597 1
2.6%
37.2232492653 1
2.6%
37.2909042538 1
2.6%
37.2932109229 1
2.6%
ValueCountFrequency (%)
37.7506821827 1
2.6%
37.7397602721 1
2.6%
37.670320986 1
2.6%
37.6532713067 1
2.6%
37.5271436104 1
2.6%
37.5146079714 1
2.6%
37.4880173459 1
2.6%
37.4841268852 1
2.6%
37.4837044349 1
2.6%
37.4836549832 1
2.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)100.0%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean126.98604
Minimum126.39266
Maximum127.6469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-11T06:30:52.368521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.39266
5-th percentile126.6119
Q1126.8236
median127.0006
Q3127.14187
95-th percentile127.2903
Maximum127.6469
Range1.2542491
Interquartile range (IQR)0.31826823

Descriptive statistics

Standard deviation0.23790566
Coefficient of variation (CV)0.0018734789
Kurtosis1.1659494
Mean126.98604
Median Absolute Deviation (MAD)0.14196231
Skewness0.14568043
Sum4825.4693
Variance0.056599104
MonotonicityNot monotonic
2023-12-11T06:30:52.498653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
126.7372106212 1
 
2.6%
126.9175953549 1
 
2.6%
126.3926552177 1
 
2.6%
127.2156607163 1
 
2.6%
127.2596023925 1
 
2.6%
127.2621265824 1
 
2.6%
126.924036285 1
 
2.6%
126.9497534184 1
 
2.6%
126.9076765704 1
 
2.6%
126.9178685561 1
 
2.6%
Other values (28) 28
71.8%
ValueCountFrequency (%)
126.3926552177 1
2.6%
126.5574205565 1
2.6%
126.621512871 1
2.6%
126.7372106212 1
2.6%
126.7635254815 1
2.6%
126.7777954046 1
2.6%
126.7837891363 1
2.6%
126.7838757199 1
2.6%
126.7965751165 1
2.6%
126.8110474311 1
2.6%
ValueCountFrequency (%)
127.6469043619 1
2.6%
127.4499579593 1
2.6%
127.2621265824 1
2.6%
127.2596023925 1
2.6%
127.2156607163 1
2.6%
127.1684516018 1
2.6%
127.1484918477 1
2.6%
127.1454013222 1
2.6%
127.1429902462 1
2.6%
127.1421302736 1
2.6%

Interactions

2023-12-11T06:30:47.664244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:47.210590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:47.421408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:47.738392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:47.273904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:47.494747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:47.822610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:47.346522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:47.577940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:30:52.598025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명명칭지방청명경찰서명구분명지역경찰관서명소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
시군명1.0001.0001.0001.0000.8921.0000.9921.0001.0000.9320.962
명칭1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지방청명1.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.000
경찰서명1.0001.0001.0001.0000.8841.0001.0001.0001.0000.9770.977
구분명0.8921.0000.0000.8841.0001.0000.8051.0001.0000.4230.595
지역경찰관서명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.963
소재지우편번호0.9921.0001.0001.0000.8051.0001.0001.0001.0000.9590.855
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
WGS84위도0.9321.0001.0000.9770.4231.0000.9591.0001.0001.0000.782
WGS84경도0.9621.0000.0000.9770.5950.9630.8551.0001.0000.7821.000
2023-12-11T06:30:52.715830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명구분명지방청명
시군명1.0000.5860.788
구분명0.5861.0000.000
지방청명0.7880.0001.000
2023-12-11T06:30:52.804883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명지방청명구분명
소재지우편번호1.000-0.737-0.1870.8530.8820.529
WGS84위도-0.7371.000-0.2120.6400.8820.276
WGS84경도-0.187-0.2121.0000.7340.0000.535
시군명0.8530.6400.7341.0000.7880.586
지방청명0.8820.8820.0000.7881.0000.000
구분명0.5290.2760.5350.5860.0001.000

Missing values

2023-12-11T06:30:47.931387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:30:48.064620image/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.
2023-12-11T06:30:48.165604image/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

시군명명칭지방청명경찰서명구분명지역경찰관서명소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
0고양시송포치안센터경기북부청일산서부파출소가좌10223경기도 고양시 일산서구 대화동 2314번지경기도 고양시 일산서구 대화로 16637.670321126.737211
1고양시마두2치안센터경기북부청일산동부지구대마두10415경기도 고양시 일산동구 마두동 786번지경기도 고양시 일산동구 강송로 16537.653271126.783789
2과천시대공원치안센터경기남부청과천지구대과천13829경기도 과천시 막계동 산7-1번지경기도 과천시 대공원광장로 10437.436192127.012746
3과천시선암치안센터경기남부청과천지구대과천13814경기도 과천시 과천동 362-2번지경기도 과천시 뒷골2로 4-837.452879127.002633
4광명시철산1치안센터경기남부청광명지구대철산14209경기도 광명시 철산동 56-28번지경기도 광명시 광복로38번길 337.488017126.863591
5군포시산본치안센터경기남부청군포파출소금정15806경기도 군포시 산본동 90-1번지경기도 군포시 군포로761번길 6937.371393126.938927
6부천시역삼치안센터경기남부청부천소사지구대범박14676경기도 부천시 괴안동 114-1번지경기도 부천시 경인로 500-137.483655126.811047
7부천시오정치안센터경기남부청부천오정지구대내동14437경기도 부천시 오정동 578-6번지경기도 부천시 부천로478번길 3437.527144126.783876
8부천시송일치안센터경기남부청부천소사지구대송내14721경기도 부천시 송내동 352-1번지경기도 부천시 중동로 4537.484127126.763525
9부천시소삼치안센터경기남부청부천소사지구대소사14703경기도 부천시 소사본동 159-58번지경기도 부천시 은성로51번길 1037.475513126.796575
시군명명칭지방청명경찰서명구분명지역경찰관서명소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
29안양시중앙치안센터경기남부청안양만안지구대안양14002경기도 안양시 만안구 안양동 711-94번지경기도 안양시 만안구 냉천로193번길 3637.396608126.917595
30안양시만안치안센터경기남부청안양만안지구대안양13991경기도 안양시 만안구 안양동 842-10번지경기도 안양시 만안구 안양로372번길 9-1037.40469126.917869
31양평군봉상치안센터경기남부청양평파출소단월12529경기도 양평군 단월면 봉상리 589-1번지경기도 양평군 단월면 경강로 352637.514608127.646904
32의정부시북부치안센터경기북부청의정부지구대가능11677경기도 의정부시 가능동 640번지경기도 의정부시 신촌로63번길 837.750682127.041711
33의정부시동부치안센터경기북부청의정부지구대신곡11695경기도 의정부시 의정부동 196-15번지경기도 의정부시 행복로 9-137.73976127.04824
34의정부시장암치안센터경기북부청의정부지구대신곡<NA>경기도 의정부시 장곡로 205번길 21<NA><NA><NA>
35이천시단월치안센터경기남부청이천파출소아미17400경기도 이천시 단월동 520-4번지경기도 이천시 단월로 5337.223249127.449958
36평택시성내치안센터경기남부청평택지구대평택17917경기도 평택시 평택동 55-41번지경기도 평택시 평택로 5536.9918127.085558
37화성시국화도치안센터경기남부청화성서부파출소우정18557경기도 화성시 우정읍 국화리 19번지경기도 화성시 우정읍 국화길 2637.061446126.557421
38화성시제부치안센터경기남부청화성서부파출소서신18553경기도 화성시 서신면 제부리 289-3번지경기도 화성시 서신면 해안길 421-537.176995126.621513