Overview

Dataset statistics

Number of variables7
Number of observations569
Missing cells102
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.4 KiB
Average record size in memory58.2 B

Variable types

Text4
Categorical1
Numeric2

Dataset

Description전북특별자치도 전주시 버스정류장 자료로 정류장 ID, 정류장명, 정류장 번호, 위도, 경도 등을 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=14&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15080859

Alerts

위도 is highly overall correlated with 지역(구)High correlation
지역(구) is highly overall correlated with 위도High correlation
지역(동) has 35 (6.2%) missing valuesMissing
행정동 has 67 (11.8%) missing valuesMissing
위도 has unique valuesUnique

Reproduction

Analysis started2024-03-14 00:07:36.594752
Analysis finished2024-03-14 00:07:37.941219
Duration1.35 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

설치 지점명
Text

Distinct407
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-03-14T09:07:38.102595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length14
Mean length7.0228471
Min length2

Characters and Unicode

Total characters3996
Distinct characters301
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique246 ?
Unique (%)43.2%

Sample

1st row우아롯데아파트
2nd row여의동우체국전북은행
3rd row기린대로경제통상진흥원
4th row팔복근린상가
5th row추천대교
ValueCountFrequency (%)
효자휴먼시아 5
 
0.8%
롯데마트 4
 
0.7%
서신중흥아파트 3
 
0.5%
만성시티프라디움 3
 
0.5%
고속버스터미널.불교회관 2
 
0.3%
기린대로금암광장 2
 
0.3%
풍남중 2
 
0.3%
새전주요양병원 2
 
0.3%
안심마을.한국농수산대학 2
 
0.3%
남고사입구 2
 
0.3%
Other values (413) 572
95.5%
2024-03-14T09:07:38.407592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 161
 
4.0%
트 139
 
3.5%
파 136
 
3.4%
전 93
 
2.3%
주 81
 
2.0%
교 78
 
2.0%
대 69
 
1.7%
서 67
 
1.7%
마 64
 
1.6%
. 62
 
1.6%
Other values (291) 3046
76.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3820
95.6%
Other Punctuation 62
 
1.6%
Decimal Number 57
 
1.4%
Space Separator 30
 
0.8%
Uppercase Letter 24
 
0.6%
Lowercase Letter 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 161
 
4.2%
트 139
 
3.6%
파 136
 
3.6%
전 93
 
2.4%
주 81
 
2.1%
교 78
 
2.0%
대 69
 
1.8%
서 67
 
1.8%
마 64
 
1.7%
동 61
 
1.6%
Other values (271) 2871
75.2%
Uppercase Letter
ValueCountFrequency (%)
T 7
29.2%
K 4
16.7%
S 2
 
8.3%
C 2
 
8.3%
H 2
 
8.3%
L 2
 
8.3%
J 2
 
8.3%
V 2
 
8.3%
I 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
2 19
33.3%
1 14
24.6%
3 8
14.0%
6 6
 
10.5%
4 6
 
10.5%
5 4
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 62
100.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3820
95.6%
Common 151
 
3.8%
Latin 25
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 161
 
4.2%
트 139
 
3.6%
파 136
 
3.6%
전 93
 
2.4%
주 81
 
2.1%
교 78
 
2.0%
대 69
 
1.8%
서 67
 
1.8%
마 64
 
1.7%
동 61
 
1.6%
Other values (271) 2871
75.2%
Common
ValueCountFrequency (%)
. 62
41.1%
30
19.9%
2 19
 
12.6%
1 14
 
9.3%
3 8
 
5.3%
6 6
 
4.0%
4 6
 
4.0%
5 4
 
2.6%
( 1
 
0.7%
) 1
 
0.7%
Latin
ValueCountFrequency (%)
T 7
28.0%
K 4
16.0%
S 2
 
8.0%
C 2
 
8.0%
H 2
 
8.0%
L 2
 
8.0%
J 2
 
8.0%
V 2
 
8.0%
e 1
 
4.0%
I 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3820
95.6%
ASCII 176
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 161
 
4.2%
트 139
 
3.6%
파 136
 
3.6%
전 93
 
2.4%
주 81
 
2.1%
교 78
 
2.0%
대 69
 
1.8%
서 67
 
1.8%
마 64
 
1.7%
동 61
 
1.6%
Other values (271) 2871
75.2%
ASCII
ValueCountFrequency (%)
. 62
35.2%
30
17.0%
2 19
 
10.8%
1 14
 
8.0%
3 8
 
4.5%
T 7
 
4.0%
6 6
 
3.4%
4 6
 
3.4%
K 4
 
2.3%
5 4
 
2.3%
Other values (10) 16
 
9.1%

지역(구)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
덕진구
298 
완산구
271 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
덕진구 298
52.4%
완산구 271
47.6%

Length

2024-03-14T09:07:38.518377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:07:38.598500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
덕진구 298
52.4%
완산구 271
47.6%

지역(동)
Text

MISSING 

Distinct60
Distinct (%)11.2%
Missing35
Missing (%)6.2%
Memory size4.6 KiB
2024-03-14T09:07:38.729439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2509363
Min length2

Characters and Unicode

Total characters1736
Distinct characters59
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

Unique16 ?
Unique (%)3.0%

Sample

1st row우아동
2nd row여의동
3rd row팔복동
4th row팔복동
5th row팔복동
ValueCountFrequency (%)
효자동 65
 
12.0%
인후동 41
 
7.6%
삼천동 39
 
7.2%
송천동 37
 
6.8%
서신동 32
 
5.9%
덕진동 27
 
5.0%
우아동 25
 
4.6%
장동 24
 
4.4%
평화동 24
 
4.4%
중화산동 23
 
4.2%
Other values (41) 206
37.9%
2024-03-14T09:07:39.002138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 540
31.1%
86
 
5.0%
천 77
 
4.4%
효 68
 
3.9%
자 68
 
3.9%
서 64
 
3.7%
송 53
 
3.1%
화 47
 
2.7%
중 43
 
2.5%
인 43
 
2.5%
Other values (49) 647
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1637
94.3%
Space Separator 86
 
5.0%
Decimal Number 13
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 540
33.0%
천 77
 
4.7%
효 68
 
4.2%
자 68
 
4.2%
서 64
 
3.9%
송 53
 
3.2%
화 47
 
2.9%
중 43
 
2.6%
인 43
 
2.6%
후 41
 
2.5%
Other values (45) 593
36.2%
Decimal Number
ValueCountFrequency (%)
2 8
61.5%
3 4
30.8%
1 1
 
7.7%
Space Separator
ValueCountFrequency (%)
86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1637
94.3%
Common 99
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 540
33.0%
천 77
 
4.7%
효 68
 
4.2%
자 68
 
4.2%
서 64
 
3.9%
송 53
 
3.2%
화 47
 
2.9%
중 43
 
2.6%
인 43
 
2.6%
후 41
 
2.5%
Other values (45) 593
36.2%
Common
ValueCountFrequency (%)
86
86.9%
2 8
 
8.1%
3 4
 
4.0%
1 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1637
94.3%
ASCII 99
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 540
33.0%
천 77
 
4.7%
효 68
 
4.2%
자 68
 
4.2%
서 64
 
3.9%
송 53
 
3.2%
화 47
 
2.9%
중 43
 
2.6%
인 43
 
2.6%
후 41
 
2.5%
Other values (45) 593
36.2%
ASCII
ValueCountFrequency (%)
86
86.9%
2 8
 
8.1%
3 4
 
4.0%
1 1
 
1.0%

세부주소
Text

Distinct521
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-03-14T09:07:39.215122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length10.205624
Min length5

Characters and Unicode

Total characters5807
Distinct characters70
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique490 ?
Unique (%)86.1%

Sample

1st row우아동 2동 887
2nd row여의동2가 696
3rd row팔복동2가 550
4th row팔복동 2가 250-21
5th row팔복동 2가 14-5
ValueCountFrequency (%)
2가 47
 
3.8%
서신동 34
 
2.8%
인후동1가 25
 
2.0%
장동 25
 
2.0%
1가 24
 
2.0%
삼천동1가 24
 
2.0%
효자동3가 23
 
1.9%
만성동 22
 
1.8%
효자동2가 21
 
1.7%
금암동 20
 
1.6%
Other values (584) 963
78.4%
2024-03-14T09:07:39.554807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
662
 
11.4%
1 597
 
10.3%
동 583
 
10.0%
2 426
 
7.3%
- 380
 
6.5%
가 353
 
6.1%
3 318
 
5.5%
7 209
 
3.6%
5 199
 
3.4%
8 196
 
3.4%
Other values (60) 1884
32.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2633
45.3%
Other Letter 2122
36.5%
Space Separator 662
 
11.4%
Dash Punctuation 380
 
6.5%
Close Punctuation 5
 
0.1%
Open Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 583
27.5%
가 353
16.6%
천 80
 
3.8%
자 72
 
3.4%
효 72
 
3.4%
서 67
 
3.2%
산 59
 
2.8%
송 56
 
2.6%
화 53
 
2.5%
중 48
 
2.3%
Other values (46) 679
32.0%
Decimal Number
ValueCountFrequency (%)
1 597
22.7%
2 426
16.2%
3 318
12.1%
7 209
 
7.9%
5 199
 
7.6%
8 196
 
7.4%
6 190
 
7.2%
4 172
 
6.5%
9 165
 
6.3%
0 161
 
6.1%
Space Separator
ValueCountFrequency (%)
662
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 380
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3685
63.5%
Hangul 2122
36.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 583
27.5%
가 353
16.6%
천 80
 
3.8%
자 72
 
3.4%
효 72
 
3.4%
서 67
 
3.2%
산 59
 
2.8%
송 56
 
2.6%
화 53
 
2.5%
중 48
 
2.3%
Other values (46) 679
32.0%
Common
ValueCountFrequency (%)
662
18.0%
1 597
16.2%
2 426
11.6%
- 380
10.3%
3 318
8.6%
7 209
 
5.7%
5 199
 
5.4%
8 196
 
5.3%
6 190
 
5.2%
4 172
 
4.7%
Other values (4) 336
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3685
63.5%
Hangul 2122
36.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
662
18.0%
1 597
16.2%
2 426
11.6%
- 380
10.3%
3 318
8.6%
7 209
 
5.7%
5 199
 
5.4%
8 196
 
5.3%
6 190
 
5.2%
4 172
 
4.7%
Other values (4) 336
9.1%
Hangul
ValueCountFrequency (%)
동 583
27.5%
가 353
16.6%
천 80
 
3.8%
자 72
 
3.4%
효 72
 
3.4%
서 67
 
3.2%
산 59
 
2.8%
송 56
 
2.6%
화 53
 
2.5%
중 48
 
2.3%
Other values (46) 679
32.0%

행정동
Text

MISSING 

Distinct73
Distinct (%)14.5%
Missing67
Missing (%)11.8%
Memory size4.6 KiB
2024-03-14T09:07:39.717616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.8386454
Min length2

Characters and Unicode

Total characters4939
Distinct characters45
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

Unique21 ?
Unique (%)4.2%

Sample

1st row우아2동
2nd row여의동
3rd row팔복동
4th row팔복동
5th row팔복동
ValueCountFrequency (%)
여의동 49
 
9.7%
서신동 32
 
6.4%
효자4동 30
 
6.0%
덕진동 27
 
5.4%
송천1동 25
 
5.0%
평화2동 22
 
4.4%
인후3동 17
 
3.4%
풍남동 16
 
3.2%
금암1동 16
 
3.2%
우아1동 16
 
3.2%
Other values (30) 253
50.3%
2024-03-14T09:07:40.011980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3122
63.2%
동 513
 
10.4%
1 101
 
2.0%
2 94
 
1.9%
천 68
 
1.4%
자 64
 
1.3%
효 64
 
1.3%
서 59
 
1.2%
의 49
 
1.0%
여 49
 
1.0%
Other values (35) 756
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 3122
63.2%
Other Letter 1544
31.3%
Decimal Number 273
 
5.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 513
33.2%
천 68
 
4.4%
자 64
 
4.1%
효 64
 
4.1%
서 59
 
3.8%
의 49
 
3.2%
여 49
 
3.2%
화 48
 
3.1%
송 47
 
3.0%
인 44
 
2.8%
Other values (29) 539
34.9%
Decimal Number
ValueCountFrequency (%)
1 101
37.0%
2 94
34.4%
3 38
 
13.9%
4 30
 
11.0%
5 10
 
3.7%
Space Separator
ValueCountFrequency (%)
3122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3395
68.7%
Hangul 1544
31.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 513
33.2%
천 68
 
4.4%
자 64
 
4.1%
효 64
 
4.1%
서 59
 
3.8%
의 49
 
3.2%
여 49
 
3.2%
화 48
 
3.1%
송 47
 
3.0%
인 44
 
2.8%
Other values (29) 539
34.9%
Common
ValueCountFrequency (%)
3122
92.0%
1 101
 
3.0%
2 94
 
2.8%
3 38
 
1.1%
4 30
 
0.9%
5 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3395
68.7%
Hangul 1544
31.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3122
92.0%
1 101
 
3.0%
2 94
 
2.8%
3 38
 
1.1%
4 30
 
0.9%
5 10
 
0.3%
Hangul
ValueCountFrequency (%)
동 513
33.2%
천 68
 
4.4%
자 64
 
4.1%
효 64
 
4.1%
서 59
 
3.8%
의 49
 
3.2%
여 49
 
3.2%
화 48
 
3.1%
송 47
 
3.0%
인 44
 
2.8%
Other values (29) 539
34.9%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct569
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.830296
Minimum35.762943
Maximum35.896154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-03-14T09:07:40.132803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.762943
5-th percentile35.793184
Q135.812636
median35.83251
Q335.845494
95-th percentile35.872947
Maximum35.896154
Range0.13321128
Interquartile range (IQR)0.03285836

Descriptive statistics

Standard deviation0.024175146
Coefficient of variation (CV)0.00067471243
Kurtosis-0.47016078
Mean35.830296
Median Absolute Deviation (MAD)0.01643341
Skewness-0.035937479
Sum20387.439
Variance0.0005844377
MonotonicityNot monotonic
2024-03-14T09:07:40.261093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.83251021 1
 
0.2%
35.79650315 1
 
0.2%
35.84327127 1
 
0.2%
35.83139685 1
 
0.2%
35.82626336 1
 
0.2%
35.79492566 1
 
0.2%
35.78977178 1
 
0.2%
35.79261956 1
 
0.2%
35.79612176 1
 
0.2%
35.82666764 1
 
0.2%
Other values (559) 559
98.2%
ValueCountFrequency (%)
35.76294285 1
0.2%
35.76465935 1
0.2%
35.76955585 1
0.2%
35.77152806 1
0.2%
35.77317756 1
0.2%
35.77449441 1
0.2%
35.77627977 1
0.2%
35.77981483 1
0.2%
35.78259605 1
0.2%
35.78315647 1
0.2%
ValueCountFrequency (%)
35.89615413 1
0.2%
35.8793652 1
0.2%
35.8790768 1
0.2%
35.8788305 1
0.2%
35.8779065 1
0.2%
35.8770714 1
0.2%
35.87700746 1
0.2%
35.8768187 1
0.2%
35.8762709 1
0.2%
35.87625094 1
0.2%

경도
Real number (ℝ)

Distinct568
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.1228
Minimum127.03095
Maximum127.19801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-03-14T09:07:40.387258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.03095
5-th percentile127.06734
Q1127.10768
median127.12473
Q3127.14597
95-th percentile127.16461
Maximum127.19801
Range0.16705856
Interquartile range (IQR)0.03828313

Descriptive statistics

Standard deviation0.029286984
Coefficient of variation (CV)0.00023038341
Kurtosis-0.12186209
Mean127.1228
Median Absolute Deviation (MAD)0.01915113
Skewness-0.43736857
Sum72332.875
Variance0.00085772746
MonotonicityNot monotonic
2024-03-14T09:07:40.522198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.15724898 2
 
0.4%
127.16782411 1
 
0.2%
127.13461747 1
 
0.2%
127.10908478 1
 
0.2%
127.11589385 1
 
0.2%
127.12302775 1
 
0.2%
127.1262537 1
 
0.2%
127.11696879 1
 
0.2%
127.11423809 1
 
0.2%
127.11042711 1
 
0.2%
Other values (558) 558
98.1%
ValueCountFrequency (%)
127.03095478 1
0.2%
127.03972137 1
0.2%
127.05430995 1
0.2%
127.05466606 1
0.2%
127.05724821 1
0.2%
127.05744365 1
0.2%
127.05764054 1
0.2%
127.0576893 1
0.2%
127.0578618 1
0.2%
127.05834319 1
0.2%
ValueCountFrequency (%)
127.19801334 1
0.2%
127.19623694 1
0.2%
127.19370554 1
0.2%
127.19301438 1
0.2%
127.19046187 1
0.2%
127.18657383 1
0.2%
127.17858018 1
0.2%
127.17349211 1
0.2%
127.17344848 1
0.2%
127.1709477 1
0.2%

Interactions

2024-03-14T09:07:37.504479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:07:37.352038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:07:37.593917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:07:37.426259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:07:40.608933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역(구)지역(동)행정동위도경도
지역(구)1.0001.0000.9990.9680.584
지역(동)1.0001.0000.9920.9590.969
행정동0.9990.9921.0000.9350.926
위도0.9680.9590.9351.0000.499
경도0.5840.9690.9260.4991.000
2024-03-14T09:07:40.690655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도지역(구)
위도1.000-0.0970.839
경도-0.0971.0000.448
지역(구)0.8390.4481.000

Missing values

2024-03-14T09:07:37.709054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:07:37.801777image/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.
2024-03-14T09:07:37.898145image/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

설치 지점명지역(구)지역(동)세부주소행정동위도경도
0우아롯데아파트덕진구우아동우아동 2동 887우아2동35.83251127.167824
1여의동우체국전북은행덕진구여의동여의동2가 696여의동35.871416127.076584
2기린대로경제통상진흥원덕진구팔복동팔복동2가 550팔복동35.859761127.092528
3팔복근린상가덕진구팔복동팔복동 2가 250-21팔복동35.853288127.105168
4추천대교덕진구팔복동팔복동 2가 14-5팔복동35.851201127.109413
5추천대교덕진구팔복동팔복동 2가 11-2팔복동35.851578127.109638
6우아우성아파트덕진구우아동우아동 3가 594-23우아1동35.855203127.155648
7완산고입구완산구효자동효자동2가 876-2효자4동35.798911127.091205
8전주박물관전주역사박물관완산구효자동효자동2가 759-19효자4동35.800037127.093467
9전라북도교육청 동암고완산구효자동효자동 2가 368-1효자4동35.802895127.103138
설치 지점명지역(구)지역(동)세부주소행정동위도경도
559중화산영무예다음현대아파트완산구<NA>중화산동1가 329<NA>35.811257127.131714
560중화산동오페라하우스완산구<NA>중화산동1가 331<NA>35.811317127.131969
561혁신도시입구완산구상림동상림동 66<NA>35.824959127.071243
562아래삼거리덕진구<NA>금상동 227-2<NA>35.854228127.196237
563전일고 롯데마트완산구<NA>효자동2가 1233-3<NA>35.81614127.101663
564진북전자상가덕진구<NA>진북동 320-72<NA>35.831726127.139342
565가리내로 백제교덕진구<NA>덕진동1가 1121-19<NA>35.834871127.125919
566세병호입구덕진구<NA>송천동2가 1298<NA>35.87883127.132656
567만성골드클래스후문덕진구만성동만성동 1386<NA>35.849275127.078368
568면허시험장월드컵경기장덕진구성덕동여의동 1326-1<NA>35.862997127.068746