Overview

Dataset statistics

Number of variables7
Number of observations2051
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.3 KiB
Average record size in memory57.1 B

Variable types

Numeric1
Categorical2
Text4

Dataset

Description지역경찰청별 소속 관서를 표시하였습니다. 지방청, 경찰서, 관서명, 지구대,파출소, 주소 정보를 가지고 있습니다.
URLhttps://www.data.go.kr/data/15054711/fileData.do

Alerts

연번 is highly overall correlated with 시도청High correlation
시도청 is highly overall correlated with 연번High correlation
구분 is highly imbalanced (53.7%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:38:36.938008
Analysis finished2023-12-12 17:38:38.095998
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2051
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1026
Minimum1
Maximum2051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2023-12-13T02:38:38.174722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile103.5
Q1513.5
median1026
Q31538.5
95-th percentile1948.5
Maximum2051
Range2050
Interquartile range (IQR)1025

Descriptive statistics

Standard deviation592.21702
Coefficient of variation (CV)0.57720957
Kurtosis-1.2
Mean1026
Median Absolute Deviation (MAD)513
Skewness0
Sum2104326
Variance350721
MonotonicityStrictly increasing
2023-12-13T02:38:38.329481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1284 1
 
< 0.1%
1378 1
 
< 0.1%
1377 1
 
< 0.1%
1376 1
 
< 0.1%
1375 1
 
< 0.1%
1374 1
 
< 0.1%
1373 1
 
< 0.1%
1372 1
 
< 0.1%
1371 1
 
< 0.1%
Other values (2041) 2041
99.5%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2051 1
< 0.1%
2050 1
< 0.1%
2049 1
< 0.1%
2048 1
< 0.1%
2047 1
< 0.1%
2046 1
< 0.1%
2045 1
< 0.1%
2044 1
< 0.1%
2043 1
< 0.1%
2042 1
< 0.1%

시도청
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
경기남부청
257 
서울청
243 
경북청
228 
전남청
207 
경남청
175 
Other values (13)
941 

Length

Max length5
Median length3
Mean length3.3500731
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기남부청 257
12.5%
서울청 243
11.8%
경북청 228
11.1%
전남청 207
10.1%
경남청 175
8.5%
전북청 162
7.9%
충남청 117
 
5.7%
강원청 106
 
5.2%
경기북부청 102
 
5.0%
부산청 94
 
4.6%
Other values (8) 360
17.6%

Length

2023-12-13T02:38:38.751837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기남부청 257
12.5%
서울청 243
11.8%
경북청 228
11.1%
전남청 207
10.1%
경남청 175
8.5%
전북청 162
7.9%
충남청 117
 
5.7%
강원청 106
 
5.2%
경기북부청 102
 
5.0%
부산청 94
 
4.6%
Other values (8) 360
17.6%

경찰서
Text

Distinct258
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
2023-12-13T02:38:39.161377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.9078498
Min length2

Characters and Unicode

Total characters5964
Distinct characters140
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row서울중부
2nd row서울중부
3rd row서울중부
4th row서울중부
5th row서울중부
ValueCountFrequency (%)
목포 27
 
1.3%
경주 22
 
1.1%
군산 21
 
1.0%
익산 19
 
0.9%
여수 18
 
0.9%
안동 17
 
0.8%
순천 17
 
0.8%
구미 17
 
0.8%
평택 17
 
0.8%
인천중부 16
 
0.8%
Other values (248) 1860
90.7%
2023-12-13T02:38:39.709880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 472
 
7.9%
서 411
 
6.9%
산 322
 
5.4%
울 296
 
5.0%
주 289
 
4.8%
천 282
 
4.7%
동 178
 
3.0%
남 162
 
2.7%
성 161
 
2.7%
양 135
 
2.3%
Other values (130) 3256
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5964
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 472
 
7.9%
서 411
 
6.9%
산 322
 
5.4%
울 296
 
5.0%
주 289
 
4.8%
천 282
 
4.7%
동 178
 
3.0%
남 162
 
2.7%
성 161
 
2.7%
양 135
 
2.3%
Other values (130) 3256
54.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5964
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 472
 
7.9%
서 411
 
6.9%
산 322
 
5.4%
울 296
 
5.0%
주 289
 
4.8%
천 282
 
4.7%
동 178
 
3.0%
남 162
 
2.7%
성 161
 
2.7%
양 135
 
2.3%
Other values (130) 3256
54.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5964
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 472
 
7.9%
서 411
 
6.9%
산 322
 
5.4%
울 296
 
5.0%
주 289
 
4.8%
천 282
 
4.7%
동 178
 
3.0%
남 162
 
2.7%
성 161
 
2.7%
양 135
 
2.3%
Other values (130) 3256
54.6%

관서명
Text

Distinct1664
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
2023-12-13T02:38:40.101828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.1379815
Min length2

Characters and Unicode

Total characters4385
Distinct characters325
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1451 ?
Unique (%)70.7%

Sample

1st row을지
2nd row광희
3rd row약수
4th row신당
5th row장충
ValueCountFrequency (%)
중앙 37
 
1.8%
읍내 19
 
0.9%
역전 11
 
0.5%
서부 10
 
0.5%
동부 10
 
0.5%
기동순찰대 8
 
0.4%
서면 8
 
0.4%
남면 8
 
0.4%
남부 6
 
0.3%
덕산 6
 
0.3%
Other values (1654) 1928
94.0%
2023-12-13T02:38:40.589955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 166
 
3.8%
동 133
 
3.0%
남 92
 
2.1%
성 90
 
2.1%
신 85
 
1.9%
대 84
 
1.9%
서 84
 
1.9%
천 76
 
1.7%
부 71
 
1.6%
양 67
 
1.5%
Other values (315) 3437
78.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4311
98.3%
Decimal Number 72
 
1.6%
Other Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 166
 
3.9%
동 133
 
3.1%
남 92
 
2.1%
성 90
 
2.1%
신 85
 
2.0%
대 84
 
1.9%
서 84
 
1.9%
천 76
 
1.8%
부 71
 
1.6%
양 67
 
1.6%
Other values (308) 3363
78.0%
Decimal Number
ValueCountFrequency (%)
2 26
36.1%
1 20
27.8%
3 19
26.4%
4 5
 
6.9%
5 2
 
2.8%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4311
98.3%
Common 73
 
1.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 166
 
3.9%
동 133
 
3.1%
남 92
 
2.1%
성 90
 
2.1%
신 85
 
2.0%
대 84
 
1.9%
서 84
 
1.9%
천 76
 
1.8%
부 71
 
1.6%
양 67
 
1.6%
Other values (308) 3363
78.0%
Common
ValueCountFrequency (%)
2 26
35.6%
1 20
27.4%
3 19
26.0%
4 5
 
6.8%
5 2
 
2.7%
· 1
 
1.4%
Latin
ValueCountFrequency (%)
T 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4311
98.3%
ASCII 73
 
1.7%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 166
 
3.9%
동 133
 
3.1%
남 92
 
2.1%
성 90
 
2.1%
신 85
 
2.0%
대 84
 
1.9%
서 84
 
1.9%
천 76
 
1.8%
부 71
 
1.6%
양 67
 
1.6%
Other values (308) 3363
78.0%
ASCII
ValueCountFrequency (%)
2 26
35.6%
1 20
27.4%
3 19
26.0%
4 5
 
6.8%
5 2
 
2.7%
T 1
 
1.4%
None
ValueCountFrequency (%)
· 1
100.0%

구분
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
파출소
1417 
지구대
626 
기동순찰대
 
6
폐지
 
2

Length

Max length5
Median length3
Mean length3.0048757
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
파출소 1417
69.1%
지구대 626
30.5%
기동순찰대 6
 
0.3%
폐지 2
 
0.1%

Length

2023-12-13T02:38:40.719695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:38:40.822800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
파출소 1417
69.1%
지구대 626
30.5%
기동순찰대 6
 
0.3%
폐지 2
 
0.1%

전화번호
Text

Distinct2043
Distinct (%)99.7%
Missing2
Missing (%)0.1%
Memory size16.2 KiB
2023-12-13T02:38:41.055909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.970229
Min length9

Characters and Unicode

Total characters24527
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

Unique2037 ?
Unique (%)99.4%

Sample

1st row02-2279-1908
2nd row02-2233-1444
3rd row02-2234-8112
4th row02-2252-0435
5th row02-2274-9003
ValueCountFrequency (%)
031-639-1456 2
 
0.1%
02-536-8477 2
 
0.1%
031-432-0112 2
 
0.1%
031-8086-0116 2
 
0.1%
031-770-9805 2
 
0.1%
042-582-0112 2
 
0.1%
063-582-3112 1
 
< 0.1%
063-562-8112 1
 
< 0.1%
063-563-6112 1
 
< 0.1%
063-582-8712 1
 
< 0.1%
Other values (2033) 2033
99.2%
2023-12-13T02:38:41.453592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 4098
16.7%
1 3822
15.6%
0 3714
15.1%
2 2751
11.2%
3 2599
10.6%
5 1871
7.6%
4 1545
 
6.3%
6 1501
 
6.1%
8 991
 
4.0%
7 947
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20429
83.3%
Dash Punctuation 4098
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3822
18.7%
0 3714
18.2%
2 2751
13.5%
3 2599
12.7%
5 1871
9.2%
4 1545
7.6%
6 1501
 
7.3%
8 991
 
4.9%
7 947
 
4.6%
9 688
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 4098
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24527
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 4098
16.7%
1 3822
15.6%
0 3714
15.1%
2 2751
11.2%
3 2599
10.6%
5 1871
7.6%
4 1545
 
6.3%
6 1501
 
6.1%
8 991
 
4.0%
7 947
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4098
16.7%
1 3822
15.6%
0 3714
15.1%
2 2751
11.2%
3 2599
10.6%
5 1871
7.6%
4 1545
 
6.3%
6 1501
 
6.1%
8 991
 
4.0%
7 947
 
3.9%

주소
Text

Distinct2048
Distinct (%)> 99.9%
Missing2
Missing (%)0.1%
Memory size16.2 KiB
2023-12-13T02:38:41.789745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length20.38897
Min length9

Characters and Unicode

Total characters41777
Distinct characters421
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2047 ?
Unique (%)99.9%

Sample

1st row서울특별시 중구 을지로 234
2nd row서울특별시 중구 퇴계로 375-1
3rd row서울특별시 중구 동호로 5길 15, 약수지구대
4th row서울특별시 중구 다산로 248 (신당동, 신당파출소)
5th row서울특별시 중구 동호로 261
ValueCountFrequency (%)
경기도 323
 
3.4%
서울특별시 236
 
2.4%
경상북도 228
 
2.4%
전라남도 206
 
2.1%
경상남도 165
 
1.7%
전라북도 162
 
1.7%
충청남도 116
 
1.2%
강원도 107
 
1.1%
부산광역시 94
 
1.0%
충청북도 83
 
0.9%
Other values (4063) 7915
82.1%
2023-12-13T02:38:42.213058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8420
 
20.2%
로 1756
 
4.2%
시 1554
 
3.7%
도 1540
 
3.7%
1 1363
 
3.3%
구 995
 
2.4%
경 820
 
2.0%
2 793
 
1.9%
남 785
 
1.9%
면 762
 
1.8%
Other values (411) 22989
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26792
64.1%
Space Separator 8420
 
20.2%
Decimal Number 6228
 
14.9%
Dash Punctuation 207
 
0.5%
Open Punctuation 49
 
0.1%
Close Punctuation 49
 
0.1%
Other Punctuation 32
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1756
 
6.6%
시 1554
 
5.8%
도 1540
 
5.7%
구 995
 
3.7%
경 820
 
3.1%
남 785
 
2.9%
면 762
 
2.8%
길 651
 
2.4%
북 633
 
2.4%
군 625
 
2.3%
Other values (393) 16671
62.2%
Decimal Number
ValueCountFrequency (%)
1 1363
21.9%
2 793
12.7%
3 689
11.1%
5 584
9.4%
4 575
9.2%
7 514
 
8.3%
6 488
 
7.8%
9 422
 
6.8%
0 402
 
6.5%
8 398
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 27
84.4%
" 2
 
6.2%
. 2
 
6.2%
· 1
 
3.1%
Space Separator
ValueCountFrequency (%)
8420
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 207
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26792
64.1%
Common 14985
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1756
 
6.6%
시 1554
 
5.8%
도 1540
 
5.7%
구 995
 
3.7%
경 820
 
3.1%
남 785
 
2.9%
면 762
 
2.8%
길 651
 
2.4%
북 633
 
2.4%
군 625
 
2.3%
Other values (393) 16671
62.2%
Common
ValueCountFrequency (%)
8420
56.2%
1 1363
 
9.1%
2 793
 
5.3%
3 689
 
4.6%
5 584
 
3.9%
4 575
 
3.8%
7 514
 
3.4%
6 488
 
3.3%
9 422
 
2.8%
0 402
 
2.7%
Other values (8) 735
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26792
64.1%
ASCII 14984
35.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8420
56.2%
1 1363
 
9.1%
2 793
 
5.3%
3 689
 
4.6%
5 584
 
3.9%
4 575
 
3.8%
7 514
 
3.4%
6 488
 
3.3%
9 422
 
2.8%
0 402
 
2.7%
Other values (7) 734
 
4.9%
Hangul
ValueCountFrequency (%)
로 1756
 
6.6%
시 1554
 
5.8%
도 1540
 
5.7%
구 995
 
3.7%
경 820
 
3.1%
남 785
 
2.9%
면 762
 
2.8%
길 651
 
2.4%
북 633
 
2.4%
군 625
 
2.3%
Other values (393) 16671
62.2%
None
ValueCountFrequency (%)
· 1
100.0%

Interactions

2023-12-13T02:38:37.614381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:38:42.302477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시도청구분
연번1.0000.9820.302
시도청0.9821.0000.328
구분0.3020.3281.000
2023-12-13T02:38:42.388850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시도청
구분1.0000.184
시도청0.1841.000
2023-12-13T02:38:42.458589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시도청구분
연번1.0000.9050.184
시도청0.9051.0000.184
구분0.1840.1841.000

Missing values

2023-12-13T02:38:37.771937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:38:37.922399image/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.
2023-12-13T02:38:38.046685image/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

연번시도청경찰서관서명구분전화번호주소
01서울청서울중부을지지구대02-2279-1908서울특별시 중구 을지로 234
12서울청서울중부광희지구대02-2233-1444서울특별시 중구 퇴계로 375-1
23서울청서울중부약수지구대02-2234-8112서울특별시 중구 동호로 5길 15, 약수지구대
34서울청서울중부신당파출소02-2252-0435서울특별시 중구 다산로 248 (신당동, 신당파출소)
45서울청서울중부장충파출소02-2274-9003서울특별시 중구 동호로 261
56서울청서울중부충무파출소02-2278-7710서울특별시 중구 퇴계로 178
67서울청서울중부을지로3가파출소02-2266-2404서울특별시 중구 충무로56-1
78서울청서울종로종로2가지구대02-3701-4301서울특별시 종로구 종로17길 4 종로2가파출소
89서울청서울종로관수파출소02-3701-4302서울특별시 종로구 삼일대로 386
910서울청서울종로세검정파출소02-3701-4507서울특별시 종로구 세검정로 226
연번시도청경찰서관서명구분전화번호주소
20412042제주청제주서부외도파출소064-742-0519제주특별자치도 내도동 723
20422043제주청서귀포중동지구대064-733-0112제주특별자치도 서귀포시 태평로 552
20432044제주청서귀포남원파출소064-764-0112제주특별자치도 서귀포시 남원읍 태위로 702
20442045제주청서귀포성산파출소064-782-2112제주특별자치도 서귀포시 성산읍 성산중앙로 53 성산파출소
20452046제주청서귀포표선파출소064-787-0112제주특별자치도 서귀포시 표선면 표선동서로 217
20462047제주청서귀포안덕파출소064-794-9112제주특별자치도 서귀포시 안덕면 화순로 116
20472048제주청서귀포대정파출소064-794-2622제주특별자치도 서귀포시 대정읍 상모로 310
20482049제주청서귀포중문파출소064-738-0112제주특별자치도 서귀포시 천제연로 165
20492050제주청서귀포대신파출소064-739-2226제주특별자치도 서귀포시 신서귀로51번길 18
20502051제주청서귀포효돈파출소064-767-0112제주특별자치도 서귀포시 효돈로 171-1