Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells8025
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory654.3 KiB
Average record size in memory67.0 B

Variable types

Numeric3
Text4

Dataset

Description전북특별자치도 군산시에 설치된 도로시설물 가로등 현황에 대하여 가로등표찰관리번호, 소재 읍면동, 소재지번주소, 도로명 주소, 위경도 등의 위치정보를 제공합니다.
Author전북특별자치도 군산시
URLhttps://www.data.go.kr/data/15060101/fileData.do

Alerts

도로명주소 has 7867 (78.7%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2024-04-13 12:37:24.768060
Analysis finished2024-04-13 12:37:31.779374
Duration7.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8436.385
Minimum1
Maximum16955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-13T21:37:32.005507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile824.95
Q14223.25
median8466.5
Q312592.25
95-th percentile16079.05
Maximum16955
Range16954
Interquartile range (IQR)8369

Descriptive statistics

Standard deviation4882.5812
Coefficient of variation (CV)0.57875278
Kurtosis-1.1924807
Mean8436.385
Median Absolute Deviation (MAD)4187
Skewness0.0043971487
Sum84363850
Variance23839600
MonotonicityNot monotonic
2024-04-13T21:37:32.444526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
995 1
 
< 0.1%
6145 1
 
< 0.1%
1693 1
 
< 0.1%
5641 1
 
< 0.1%
8183 1
 
< 0.1%
7428 1
 
< 0.1%
7924 1
 
< 0.1%
2271 1
 
< 0.1%
2936 1
 
< 0.1%
1100 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
16955 1
< 0.1%
16954 1
< 0.1%
16951 1
< 0.1%
16948 1
< 0.1%
16946 1
< 0.1%
16945 1
< 0.1%
16944 1
< 0.1%
16943 1
< 0.1%
16942 1
< 0.1%
16940 1
< 0.1%

가로등표찰
Text

Distinct9959
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-13T21:37:33.445630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length8.8779
Min length7

Characters and Unicode

Total characters88779
Distinct characters206
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

Unique9926 ?
Unique (%)99.3%

Sample

1st row고군산로01-05
2nd row해망로10-37
3rd row가도로07-33
4th row창오교차로02-03
5th row외항안길05-16
ValueCountFrequency (%)
서만지하차도01-통로등 8
 
0.1%
대야번영로04-통로등 3
 
< 0.1%
대야번영로03-통로등 3
 
< 0.1%
양촌1길01-14 2
 
< 0.1%
칠성로01-43 2
 
< 0.1%
칠성로02-08 2
 
< 0.1%
신시도길01-10 2
 
< 0.1%
칠성로02-07 2
 
< 0.1%
칠성로02-06 2
 
< 0.1%
칠성로01-30 2
 
< 0.1%
Other values (9949) 9972
99.7%
2024-04-13T21:37:34.833895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13617
15.3%
1 10184
 
11.5%
- 9824
 
11.1%
로 6844
 
7.7%
2 5420
 
6.1%
3 3167
 
3.6%
길 2666
 
3.0%
4 2201
 
2.5%
5 1688
 
1.9%
6 1489
 
1.7%
Other values (196) 31679
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41626
46.9%
Other Letter 37070
41.8%
Dash Punctuation 9824
 
11.1%
Uppercase Letter 194
 
0.2%
Open Punctuation 33
 
< 0.1%
Close Punctuation 32
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 6844
 
18.5%
길 2666
 
7.2%
산 1121
 
3.0%
안 884
 
2.4%
항 882
 
2.4%
교 824
 
2.2%
동 819
 
2.2%
차 799
 
2.2%
해 722
 
1.9%
대 718
 
1.9%
Other values (168) 20791
56.1%
Uppercase Letter
ValueCountFrequency (%)
A 82
42.3%
C 44
22.7%
I 30
 
15.5%
B 18
 
9.3%
D 5
 
2.6%
H 3
 
1.5%
E 3
 
1.5%
G 2
 
1.0%
F 1
 
0.5%
L 1
 
0.5%
Other values (5) 5
 
2.6%
Decimal Number
ValueCountFrequency (%)
0 13617
32.7%
1 10184
24.5%
2 5420
 
13.0%
3 3167
 
7.6%
4 2201
 
5.3%
5 1688
 
4.1%
6 1489
 
3.6%
7 1373
 
3.3%
8 1309
 
3.1%
9 1178
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 9824
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51515
58.0%
Hangul 37070
41.8%
Latin 194
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 6844
 
18.5%
길 2666
 
7.2%
산 1121
 
3.0%
안 884
 
2.4%
항 882
 
2.4%
교 824
 
2.2%
동 819
 
2.2%
차 799
 
2.2%
해 722
 
1.9%
대 718
 
1.9%
Other values (168) 20791
56.1%
Latin
ValueCountFrequency (%)
A 82
42.3%
C 44
22.7%
I 30
 
15.5%
B 18
 
9.3%
D 5
 
2.6%
H 3
 
1.5%
E 3
 
1.5%
G 2
 
1.0%
F 1
 
0.5%
L 1
 
0.5%
Other values (5) 5
 
2.6%
Common
ValueCountFrequency (%)
0 13617
26.4%
1 10184
19.8%
- 9824
19.1%
2 5420
 
10.5%
3 3167
 
6.1%
4 2201
 
4.3%
5 1688
 
3.3%
6 1489
 
2.9%
7 1373
 
2.7%
8 1309
 
2.5%
Other values (3) 1243
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51709
58.2%
Hangul 37070
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13617
26.3%
1 10184
19.7%
- 9824
19.0%
2 5420
 
10.5%
3 3167
 
6.1%
4 2201
 
4.3%
5 1688
 
3.3%
6 1489
 
2.9%
7 1373
 
2.7%
8 1309
 
2.5%
Other values (18) 1437
 
2.8%
Hangul
ValueCountFrequency (%)
로 6844
 
18.5%
길 2666
 
7.2%
산 1121
 
3.0%
안 884
 
2.4%
항 882
 
2.4%
교 824
 
2.2%
동 819
 
2.2%
차 799
 
2.2%
해 722
 
1.9%
대 718
 
1.9%
Other values (168) 20791
56.1%

읍면동
Text

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-13T21:37:35.675100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.2298
Min length2

Characters and Unicode

Total characters32298
Distinct characters78
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row옥도면
2nd row해망동
3rd row오식도동
4th row성산면
5th row소룡동
ValueCountFrequency (%)
오식도동 1354
13.5%
소룡동 1289
12.9%
비응도동 764
 
7.6%
산북동 665
 
6.6%
미장동 566
 
5.6%
수송동 522
 
5.2%
조촌동 519
 
5.2%
나운동 489
 
4.9%
대야면 324
 
3.2%
미룡동 312
 
3.1%
Other values (59) 3216
32.1%
2024-04-13T21:37:36.905166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8341
25.8%
도 2224
 
6.9%
룡 1621
 
5.0%
면 1512
 
4.7%
오 1374
 
4.3%
식 1354
 
4.2%
소 1289
 
4.0%
산 1104
 
3.4%
미 958
 
3.0%
응 765
 
2.4%
Other values (68) 11756
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32215
99.7%
Decimal Number 62
 
0.2%
Space Separator 21
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8341
25.9%
도 2224
 
6.9%
룡 1621
 
5.0%
면 1512
 
4.7%
오 1374
 
4.3%
식 1354
 
4.2%
소 1289
 
4.0%
산 1104
 
3.4%
미 958
 
3.0%
응 765
 
2.4%
Other values (64) 11673
36.2%
Decimal Number
ValueCountFrequency (%)
1 35
56.5%
2 15
24.2%
3 12
 
19.4%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32215
99.7%
Common 83
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8341
25.9%
도 2224
 
6.9%
룡 1621
 
5.0%
면 1512
 
4.7%
오 1374
 
4.3%
식 1354
 
4.2%
소 1289
 
4.0%
산 1104
 
3.4%
미 958
 
3.0%
응 765
 
2.4%
Other values (64) 11673
36.2%
Common
ValueCountFrequency (%)
1 35
42.2%
21
25.3%
2 15
18.1%
3 12
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32215
99.7%
ASCII 83
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8341
25.9%
도 2224
 
6.9%
룡 1621
 
5.0%
면 1512
 
4.7%
오 1374
 
4.3%
식 1354
 
4.2%
소 1289
 
4.0%
산 1104
 
3.4%
미 958
 
3.0%
응 765
 
2.4%
Other values (64) 11673
36.2%
ASCII
ValueCountFrequency (%)
1 35
42.2%
21
25.3%
2 15
18.1%
3 12
 
14.5%

지번주소
Text

Distinct4517
Distinct (%)45.2%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2024-04-13T21:37:38.171044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length18.082416
Min length14

Characters and Unicode

Total characters180788
Distinct characters114
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

Unique3202 ?
Unique (%)32.0%

Sample

1st row전라북도 군산시 옥도면 신시도리 258
2nd row전라북도 군산시 해망동 1000-30
3rd row전라북도 군산시 오식도동 658-1
4th row전라북도 군산시 성산면 창오리 210-8
5th row전라북도 군산시 소룡동 46
ValueCountFrequency (%)
군산시 9998
24.0%
전라북도 9826
23.6%
오식도동 1354
 
3.3%
소룡동 1291
 
3.1%
비응도동 765
 
1.8%
산북동 663
 
1.6%
미장동 566
 
1.4%
수송동 522
 
1.3%
조촌동 519
 
1.2%
나운동 489
 
1.2%
Other values (4115) 15635
37.6%
2024-04-13T21:37:39.882619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31631
17.5%
도 12203
 
6.7%
산 11304
 
6.3%
북 10763
 
6.0%
시 10017
 
5.5%
전 9998
 
5.5%
군 9998
 
5.5%
라 9826
 
5.4%
동 8420
 
4.7%
1 6504
 
3.6%
Other values (104) 60124
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106925
59.1%
Decimal Number 37212
 
20.6%
Space Separator 31631
 
17.5%
Dash Punctuation 5020
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 12203
11.4%
산 11304
10.6%
북 10763
10.1%
시 10017
9.4%
전 9998
9.4%
군 9998
9.4%
라 9826
9.2%
동 8420
 
7.9%
룡 1623
 
1.5%
리 1615
 
1.5%
Other values (92) 21158
19.8%
Decimal Number
ValueCountFrequency (%)
1 6504
17.5%
5 4242
11.4%
8 4055
10.9%
2 3991
10.7%
6 3752
10.1%
3 3542
9.5%
4 3121
8.4%
7 2921
7.8%
9 2623
7.0%
0 2461
 
6.6%
Space Separator
ValueCountFrequency (%)
31631
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5020
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106925
59.1%
Common 73863
40.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 12203
11.4%
산 11304
10.6%
북 10763
10.1%
시 10017
9.4%
전 9998
9.4%
군 9998
9.4%
라 9826
9.2%
동 8420
 
7.9%
룡 1623
 
1.5%
리 1615
 
1.5%
Other values (92) 21158
19.8%
Common
ValueCountFrequency (%)
31631
42.8%
1 6504
 
8.8%
- 5020
 
6.8%
5 4242
 
5.7%
8 4055
 
5.5%
2 3991
 
5.4%
6 3752
 
5.1%
3 3542
 
4.8%
4 3121
 
4.2%
7 2921
 
4.0%
Other values (2) 5084
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106925
59.1%
ASCII 73863
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31631
42.8%
1 6504
 
8.8%
- 5020
 
6.8%
5 4242
 
5.7%
8 4055
 
5.5%
2 3991
 
5.4%
6 3752
 
5.1%
3 3542
 
4.8%
4 3121
 
4.2%
7 2921
 
4.0%
Other values (2) 5084
 
6.9%
Hangul
ValueCountFrequency (%)
도 12203
11.4%
산 11304
10.6%
북 10763
10.1%
시 10017
9.4%
전 9998
9.4%
군 9998
9.4%
라 9826
9.2%
동 8420
 
7.9%
룡 1623
 
1.5%
리 1615
 
1.5%
Other values (92) 21158
19.8%

도로명주소
Text

MISSING 

Distinct1311
Distinct (%)61.5%
Missing7867
Missing (%)78.7%
Memory size156.2 KiB
2024-04-13T21:37:40.922986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length31
Mean length19.642288
Min length1

Characters and Unicode

Total characters41897
Distinct characters514
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1052 ?
Unique (%)49.3%

Sample

1st row전라북도 군산시 새만금로 1563 선착장
2nd row전라북도 군산시 탑천로 627
3rd row전라북도 군산시 칠성7길 103
4th row전라북도 군산시 자유로 7 BTX코리아
5th row없음
ValueCountFrequency (%)
군산시 1967
21.0%
전라북도 1966
20.9%
외항로 120
 
1.3%
해망로 112
 
1.2%
없음 83
 
0.9%
대학로 61
 
0.6%
자유로 57
 
0.6%
백토로 55
 
0.6%
상리교차로 51
 
0.5%
번영로 50
 
0.5%
Other values (1424) 4865
51.8%
2024-04-13T21:37:42.431168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7994
19.1%
산 2449
 
5.8%
군 2146
 
5.1%
북 2095
 
5.0%
도 2054
 
4.9%
시 2030
 
4.8%
전 2029
 
4.8%
라 1990
 
4.7%
로 1391
 
3.3%
1 1091
 
2.6%
Other values (504) 16628
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27968
66.8%
Space Separator 7994
 
19.1%
Decimal Number 5405
 
12.9%
Uppercase Letter 234
 
0.6%
Open Punctuation 78
 
0.2%
Close Punctuation 78
 
0.2%
Lowercase Letter 58
 
0.1%
Other Punctuation 40
 
0.1%
Other Symbol 35
 
0.1%
Dash Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2449
 
8.8%
군 2146
 
7.7%
북 2095
 
7.5%
도 2054
 
7.3%
시 2030
 
7.3%
전 2029
 
7.3%
라 1990
 
7.1%
로 1391
 
5.0%
길 680
 
2.4%
동 299
 
1.1%
Other values (442) 10805
38.6%
Uppercase Letter
ValueCountFrequency (%)
G 24
 
10.3%
S 24
 
10.3%
O 19
 
8.1%
T 16
 
6.8%
C 16
 
6.8%
K 13
 
5.6%
B 13
 
5.6%
A 13
 
5.6%
M 13
 
5.6%
R 12
 
5.1%
Other values (14) 71
30.3%
Lowercase Letter
ValueCountFrequency (%)
a 9
15.5%
i 5
 
8.6%
e 5
 
8.6%
l 5
 
8.6%
t 4
 
6.9%
y 4
 
6.9%
o 4
 
6.9%
n 3
 
5.2%
c 3
 
5.2%
m 3
 
5.2%
Other values (10) 13
22.4%
Decimal Number
ValueCountFrequency (%)
1 1091
20.2%
2 806
14.9%
3 645
11.9%
4 621
11.5%
6 464
8.6%
5 441
8.2%
0 376
 
7.0%
7 337
 
6.2%
8 321
 
5.9%
9 303
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 34
85.0%
/ 4
 
10.0%
& 2
 
5.0%
Space Separator
ValueCountFrequency (%)
7994
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27997
66.8%
Common 13602
32.5%
Latin 292
 
0.7%
Han 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2449
 
8.7%
군 2146
 
7.7%
북 2095
 
7.5%
도 2054
 
7.3%
시 2030
 
7.3%
전 2029
 
7.2%
라 1990
 
7.1%
로 1391
 
5.0%
길 680
 
2.4%
동 299
 
1.1%
Other values (439) 10834
38.7%
Latin
ValueCountFrequency (%)
G 24
 
8.2%
S 24
 
8.2%
O 19
 
6.5%
T 16
 
5.5%
C 16
 
5.5%
K 13
 
4.5%
B 13
 
4.5%
A 13
 
4.5%
M 13
 
4.5%
R 12
 
4.1%
Other values (34) 129
44.2%
Common
ValueCountFrequency (%)
7994
58.8%
1 1091
 
8.0%
2 806
 
5.9%
3 645
 
4.7%
4 621
 
4.6%
6 464
 
3.4%
5 441
 
3.2%
0 376
 
2.8%
7 337
 
2.5%
8 321
 
2.4%
Other values (7) 506
 
3.7%
Han
ValueCountFrequency (%)
金 2
33.3%
和 2
33.3%
家 1
16.7%
選 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27962
66.7%
ASCII 13894
33.2%
None 35
 
0.1%
CJK 4
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7994
57.5%
1 1091
 
7.9%
2 806
 
5.8%
3 645
 
4.6%
4 621
 
4.5%
6 464
 
3.3%
5 441
 
3.2%
0 376
 
2.7%
7 337
 
2.4%
8 321
 
2.3%
Other values (51) 798
 
5.7%
Hangul
ValueCountFrequency (%)
산 2449
 
8.8%
군 2146
 
7.7%
북 2095
 
7.5%
도 2054
 
7.3%
시 2030
 
7.3%
전 2029
 
7.3%
라 1990
 
7.1%
로 1391
 
5.0%
길 680
 
2.4%
동 299
 
1.1%
Other values (438) 10799
38.6%
None
ValueCountFrequency (%)
㈜ 35
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
金 2
100.0%
CJK
ValueCountFrequency (%)
和 2
50.0%
家 1
25.0%
選 1
25.0%

위도
Real number (ℝ)

Distinct9528
Distinct (%)96.0%
Missing78
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean35.961705
Minimum35.733575
Maximum36.031002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-13T21:37:42.830329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.733575
5-th percentile35.930881
Q135.953335
median35.96315
Q335.973256
95-th percentile35.990429
Maximum36.031002
Range0.2974267
Interquartile range (IQR)0.019920375

Descriptive statistics

Standard deviation0.023079807
Coefficient of variation (CV)0.00064178844
Kurtosis16.846383
Mean35.961705
Median Absolute Deviation (MAD)0.00998355
Skewness-2.6260397
Sum356812.04
Variance0.00053267747
MonotonicityNot monotonic
2024-04-13T21:37:43.263441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.0105692 45
 
0.4%
35.946468 4
 
< 0.1%
35.958553 3
 
< 0.1%
35.954254 3
 
< 0.1%
35.958912 3
 
< 0.1%
35.953148 3
 
< 0.1%
35.997719 3
 
< 0.1%
35.997841 3
 
< 0.1%
35.9595075 3
 
< 0.1%
35.969093 3
 
< 0.1%
Other values (9518) 9849
98.5%
(Missing) 78
 
0.8%
ValueCountFrequency (%)
35.733575 1
< 0.1%
35.7338541 1
< 0.1%
35.7345157 1
< 0.1%
35.7346578 1
< 0.1%
35.7999147 1
< 0.1%
35.8001845 1
< 0.1%
35.8004543 1
< 0.1%
35.8009807 1
< 0.1%
35.8013288 1
< 0.1%
35.8015507 1
< 0.1%
ValueCountFrequency (%)
36.0310017 1
< 0.1%
36.0307041 1
< 0.1%
36.0303545 1
< 0.1%
36.0302065 1
< 0.1%
36.0301713 1
< 0.1%
36.0300817 1
< 0.1%
36.0299167 1
< 0.1%
36.0298893 1
< 0.1%
36.0298825 1
< 0.1%
36.0298311 1
< 0.1%

경도
Real number (ℝ)

Distinct9612
Distinct (%)96.9%
Missing78
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean126.67224
Minimum126.41121
Maximum126.88773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-13T21:37:43.687318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.41121
5-th percentile126.53277
Q1126.58765
median126.69078
Q3126.73048
95-th percentile126.81629
Maximum126.88773
Range0.4765266
Interquartile range (IQR)0.1428309

Descriptive statistics

Standard deviation0.087511594
Coefficient of variation (CV)0.00069085061
Kurtosis-0.46552577
Mean126.67224
Median Absolute Deviation (MAD)0.0532077
Skewness-0.21555042
Sum1256842
Variance0.0076582791
MonotonicityNot monotonic
2024-04-13T21:37:44.138222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7590252 45
 
0.4%
126.569038 4
 
< 0.1%
126.75853 4
 
< 0.1%
126.546097 3
 
< 0.1%
126.7180789 3
 
< 0.1%
126.7471903 3
 
< 0.1%
126.533821 3
 
< 0.1%
126.561707 3
 
< 0.1%
126.816925 3
 
< 0.1%
126.814575 3
 
< 0.1%
Other values (9602) 9848
98.5%
(Missing) 78
 
0.8%
ValueCountFrequency (%)
126.4112064 1
< 0.1%
126.4115371 1
< 0.1%
126.4117966 1
< 0.1%
126.4119259 1
< 0.1%
126.4120195 1
< 0.1%
126.4121323 1
< 0.1%
126.4123314 1
< 0.1%
126.4124164 1
< 0.1%
126.4125912 1
< 0.1%
126.4126006 1
< 0.1%
ValueCountFrequency (%)
126.887733 1
< 0.1%
126.887245 1
< 0.1%
126.886894 1
< 0.1%
126.886765 1
< 0.1%
126.88665 1
< 0.1%
126.886574 1
< 0.1%
126.886444 1
< 0.1%
126.886391 1
< 0.1%
126.885933 1
< 0.1%
126.885551 1
< 0.1%

Interactions

2024-04-13T21:37:29.832066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:37:28.209527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:37:29.034224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:37:30.104556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:37:28.494905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:37:29.308433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:37:30.367710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:37:28.764896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:37:29.568760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T21:37:44.403578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번읍면동위도경도
순번1.0000.8310.3480.721
읍면동0.8311.0000.9210.986
위도0.3480.9211.0000.733
경도0.7210.9860.7331.000
2024-04-13T21:37:44.654077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번위도경도
순번1.0000.154-0.068
위도0.1541.0000.422
경도-0.0680.4221.000

Missing values

2024-04-13T21:37:30.717730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T21:37:31.137315image/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.
2024-04-13T21:37:31.611073image/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

순번가로등표찰읍면동지번주소도로명주소위도경도
994995고군산로01-05옥도면전라북도 군산시 옥도면 신시도리 258전라북도 군산시 새만금로 1563 선착장35.822472126.474983
1646016461해망로10-37해망동전라북도 군산시 해망동 1000-30<NA>35.992375126.704521
267268가도로07-33오식도동전라북도 군산시 오식도동 658-1<NA>35.965824126.567347
1500415005창오교차로02-03성산면전라북도 군산시 성산면 창오리 210-8<NA>35.983414126.819084
1249812499외항안길05-16소룡동전라북도 군산시 소룡동 46<NA>35.969902126.628914
1067610677수송안8길01-10수송동전라북도 군산시 수송동 893<NA>35.963236126.71934
1383813839자유로06-48오식도동전라북도 군산시 오식도동 766<NA>35.961075126.574425
800801경포천동길01-08경암동전라북도 군산시 경암동 620<NA>35.977063126.726518
26172618궁멀길02-18구암동전라북도 군산시 구암동 263-9<NA>35.984431126.746972
72367237비응2길01-52비응도동전라북도 군산시 비응도동 64<NA>35.939503126.530289
순번가로등표찰읍면동지번주소도로명주소위도경도
1630116302해망로04-28경암동전라북도 군산시 경암동 627-42전라북도 군산시 해망로 7635.98231126.724329
1636816369해망로09-01해망동전라북도 군산시 해망동 1010-38<NA>35.988248126.696994
18481849구암로02-21구암동전라북도 군산시 구암동 272-13<NA>35.981432126.747196
1676116762호덕교차로01-03개정면전라북도 군산시 개정면 아동리 679-4<NA>35.986973126.767448
1575115752칠성안3길02-06산북동전라북도 군산시 산북동 3601-3전라북도 군산시 동아로 11 군산근로자종합복지관35.961021126.67524
73817382비응동로01-42비응도동전라북도 군산시 비응도동 40<NA>35.940646126.534112
60316032미장안4길01-20미장동전라북도 군산시 미장동 573<NA>35.966662126.727129
1393113932자유로09-07오식도동전라북도 군산시 오식도동 525<NA>35.959648126.547188
13121313공단대로12-24수송동전라북도 군산시 수송동 21-37<NA>35.959373126.720406
47444745뜰아름01-04나포면전라북도 군산시 나포면 주곡리 1141<NA>36.007603126.825638