Overview

Dataset statistics

Number of variables8
Number of observations494
Missing cells144
Missing cells (%)3.6%
Duplicate rows3
Duplicate rows (%)0.6%
Total size in memory32.9 KiB
Average record size in memory68.3 B

Variable types

Text2
Numeric4
Categorical2

Dataset

Description김포시에 설치돼있는 버스정보안내단말기(정류소명, 정류소번호, 소재지주소, 단말기종류, 위도, 경도, 설치년도, 데이터기준일자)등의 데이터를 제공하고 있습니다.
Author경기도 김포시
URLhttps://www.data.go.kr/data/15034887/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 3 (0.6%) duplicate rowsDuplicates
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
설치년도 is highly overall correlated with 단말기종류High correlation
단말기종류 is highly overall correlated with 설치년도High correlation
위도 has 71 (14.4%) missing valuesMissing
경도 has 71 (14.4%) missing valuesMissing
위도 is highly skewed (γ1 = 20.5669638)Skewed

Reproduction

Analysis started2024-04-06 08:39:57.213738
Analysis finished2024-04-06 08:40:04.872246
Duration7.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정류소명
Text

Distinct387
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-06T17:40:05.368810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length18
Mean length8.0040486
Min length2

Characters and Unicode

Total characters3954
Distinct characters312
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique287 ?
Unique (%)58.1%

Sample

1st row김포대학후문
2nd row김포대학후문(서울)
3rd row약암호텔(서울)
4th row청룡회관(서울)
5th row청룡회관(강화)
ValueCountFrequency (%)
구래역 4
 
0.8%
현대아파트 3
 
0.6%
누산삼거리 3
 
0.6%
풍경마을(서울 3
 
0.6%
사우역.김포고(서울 3
 
0.6%
장곡 3
 
0.6%
한옥마을 2
 
0.4%
청송1단지.중흥s클래스 2
 
0.4%
수자인.호반아파트 2
 
0.4%
풍무중학교 2
 
0.4%
Other values (386) 482
94.7%
2024-04-06T17:40:06.897162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 168
 
4.2%
( 167
 
4.2%
마 115
 
2.9%
리 98
 
2.5%
을 93
 
2.4%
서 88
 
2.2%
트 84
 
2.1%
울 82
 
2.1%
아 78
 
2.0%
지 73
 
1.8%
Other values (302) 2908
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3370
85.2%
Close Punctuation 172
 
4.4%
Open Punctuation 171
 
4.3%
Other Punctuation 95
 
2.4%
Decimal Number 92
 
2.3%
Uppercase Letter 32
 
0.8%
Space Separator 16
 
0.4%
Lowercase Letter 4
 
0.1%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 115
 
3.4%
리 98
 
2.9%
을 93
 
2.8%
서 88
 
2.6%
트 84
 
2.5%
울 82
 
2.4%
아 78
 
2.3%
지 73
 
2.2%
포 70
 
2.1%
교 65
 
1.9%
Other values (275) 2524
74.9%
Decimal Number
ValueCountFrequency (%)
2 29
31.5%
1 25
27.2%
3 19
20.7%
5 7
 
7.6%
4 7
 
7.6%
9 2
 
2.2%
8 2
 
2.2%
7 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
C 10
31.2%
L 6
18.8%
H 4
 
12.5%
K 4
 
12.5%
S 4
 
12.5%
E 2
 
6.2%
D 2
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 73
76.8%
, 20
 
21.1%
: 2
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
s 2
50.0%
e 1
25.0%
a 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 168
97.7%
] 4
 
2.3%
Open Punctuation
ValueCountFrequency (%)
( 167
97.7%
[ 4
 
2.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3370
85.2%
Common 548
 
13.9%
Latin 36
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 115
 
3.4%
리 98
 
2.9%
을 93
 
2.8%
서 88
 
2.6%
트 84
 
2.5%
울 82
 
2.4%
아 78
 
2.3%
지 73
 
2.2%
포 70
 
2.1%
교 65
 
1.9%
Other values (275) 2524
74.9%
Common
ValueCountFrequency (%)
) 168
30.7%
( 167
30.5%
. 73
13.3%
2 29
 
5.3%
1 25
 
4.6%
, 20
 
3.6%
3 19
 
3.5%
16
 
2.9%
5 7
 
1.3%
4 7
 
1.3%
Other values (7) 17
 
3.1%
Latin
ValueCountFrequency (%)
C 10
27.8%
L 6
16.7%
H 4
 
11.1%
K 4
 
11.1%
S 4
 
11.1%
s 2
 
5.6%
E 2
 
5.6%
D 2
 
5.6%
e 1
 
2.8%
a 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3370
85.2%
ASCII 584
 
14.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 168
28.8%
( 167
28.6%
. 73
12.5%
2 29
 
5.0%
1 25
 
4.3%
, 20
 
3.4%
3 19
 
3.3%
16
 
2.7%
C 10
 
1.7%
5 7
 
1.2%
Other values (17) 50
 
8.6%
Hangul
ValueCountFrequency (%)
마 115
 
3.4%
리 98
 
2.9%
을 93
 
2.8%
서 88
 
2.6%
트 84
 
2.5%
울 82
 
2.4%
아 78
 
2.3%
지 73
 
2.2%
포 70
 
2.1%
교 65
 
1.9%
Other values (275) 2524
74.9%

정류소번호
Real number (ℝ)

Distinct466
Distinct (%)94.7%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean35546.591
Minimum35005
Maximum62082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-06T17:40:07.452730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35005
5-th percentile35035.55
Q135200.75
median35464.5
Q335658.25
95-th percentile35920.45
Maximum62082
Range27077
Interquartile range (IQR)457.5

Descriptive statistics

Standard deviation1717.6778
Coefficient of variation (CV)0.048321872
Kurtosis230.98663
Mean35546.591
Median Absolute Deviation (MAD)231.5
Skewness15.033643
Sum17488923
Variance2950417.2
MonotonicityNot monotonic
2024-04-06T17:40:07.979520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35194 4
 
0.8%
35246 3
 
0.6%
35650 3
 
0.6%
35918 3
 
0.6%
35501 2
 
0.4%
35247 2
 
0.4%
35685 2
 
0.4%
35493 2
 
0.4%
35196 2
 
0.4%
35195 2
 
0.4%
Other values (456) 467
94.5%
ValueCountFrequency (%)
35005 1
0.2%
35006 1
0.2%
35008 1
0.2%
35009 1
0.2%
35010 1
0.2%
35012 1
0.2%
35013 1
0.2%
35014 1
0.2%
35015 1
0.2%
35016 1
0.2%
ValueCountFrequency (%)
62082 1
0.2%
62034 1
0.2%
35992 1
0.2%
35990 1
0.2%
35989 1
0.2%
35984 1
0.2%
35983 1
0.2%
35982 1
0.2%
35981 1
0.2%
35980 1
0.2%

소재지주소
Text

Distinct386
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-06T17:40:08.659141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length18
Mean length8.0020243
Min length2

Characters and Unicode

Total characters3953
Distinct characters312
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique286 ?
Unique (%)57.9%

Sample

1st row김포대학후문
2nd row김포대학후문(서울)
3rd row약암호텔(서울)
4th row청룡회관(서울)
5th row청룡회관(강화)
ValueCountFrequency (%)
구래역 4
 
0.8%
장곡 3
 
0.6%
사우역.김포고(서울 3
 
0.6%
현대아파트 3
 
0.6%
사우역.김포고(김포초교 3
 
0.6%
풍경마을(서울 3
 
0.6%
누산삼거리 3
 
0.6%
가오대입구 2
 
0.4%
신안실크밸리 2
 
0.4%
당곡고개 2
 
0.4%
Other values (385) 481
94.5%
2024-04-06T17:40:09.804816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 168
 
4.2%
( 167
 
4.2%
마 115
 
2.9%
리 98
 
2.5%
을 93
 
2.4%
서 88
 
2.2%
트 84
 
2.1%
울 82
 
2.1%
아 78
 
2.0%
지 73
 
1.8%
Other values (302) 2907
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3370
85.3%
Close Punctuation 172
 
4.4%
Open Punctuation 171
 
4.3%
Other Punctuation 95
 
2.4%
Decimal Number 91
 
2.3%
Uppercase Letter 32
 
0.8%
Space Separator 16
 
0.4%
Lowercase Letter 4
 
0.1%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 115
 
3.4%
리 98
 
2.9%
을 93
 
2.8%
서 88
 
2.6%
트 84
 
2.5%
울 82
 
2.4%
아 78
 
2.3%
지 73
 
2.2%
포 70
 
2.1%
교 65
 
1.9%
Other values (275) 2524
74.9%
Decimal Number
ValueCountFrequency (%)
2 28
30.8%
1 25
27.5%
3 19
20.9%
5 7
 
7.7%
4 7
 
7.7%
9 2
 
2.2%
8 2
 
2.2%
7 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
C 10
31.2%
L 6
18.8%
H 4
 
12.5%
K 4
 
12.5%
S 4
 
12.5%
D 2
 
6.2%
E 2
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 73
76.8%
, 20
 
21.1%
: 2
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
s 2
50.0%
e 1
25.0%
a 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 168
97.7%
] 4
 
2.3%
Open Punctuation
ValueCountFrequency (%)
( 167
97.7%
[ 4
 
2.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3370
85.3%
Common 547
 
13.8%
Latin 36
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 115
 
3.4%
리 98
 
2.9%
을 93
 
2.8%
서 88
 
2.6%
트 84
 
2.5%
울 82
 
2.4%
아 78
 
2.3%
지 73
 
2.2%
포 70
 
2.1%
교 65
 
1.9%
Other values (275) 2524
74.9%
Common
ValueCountFrequency (%)
) 168
30.7%
( 167
30.5%
. 73
13.3%
2 28
 
5.1%
1 25
 
4.6%
, 20
 
3.7%
3 19
 
3.5%
16
 
2.9%
5 7
 
1.3%
4 7
 
1.3%
Other values (7) 17
 
3.1%
Latin
ValueCountFrequency (%)
C 10
27.8%
L 6
16.7%
H 4
 
11.1%
K 4
 
11.1%
S 4
 
11.1%
s 2
 
5.6%
D 2
 
5.6%
E 2
 
5.6%
e 1
 
2.8%
a 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3370
85.3%
ASCII 583
 
14.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 168
28.8%
( 167
28.6%
. 73
12.5%
2 28
 
4.8%
1 25
 
4.3%
, 20
 
3.4%
3 19
 
3.3%
16
 
2.7%
C 10
 
1.7%
5 7
 
1.2%
Other values (17) 50
 
8.6%
Hangul
ValueCountFrequency (%)
마 115
 
3.4%
리 98
 
2.9%
을 93
 
2.8%
서 88
 
2.6%
트 84
 
2.5%
울 82
 
2.4%
아 78
 
2.3%
지 73
 
2.2%
포 70
 
2.1%
교 65
 
1.9%
Other values (275) 2524
74.9%

단말기종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
LED
349 
LCD
107 
LCD+LED
38 

Length

Max length7
Median length3
Mean length3.3076923
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLED
2nd rowLCD+LED
3rd rowLED
4th rowLED
5th rowLED

Common Values

ValueCountFrequency (%)
LED 349
70.6%
LCD 107
 
21.7%
LCD+LED 38
 
7.7%

Length

2024-04-06T17:40:10.465808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:40:10.785961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
led 349
70.6%
lcd 107
 
21.7%
lcd+led 38
 
7.7%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct336
Distinct (%)79.4%
Missing71
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean926.96784
Minimum37.591445
Maximum376220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-06T17:40:11.141718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.591445
5-th percentile37.600556
Q137.627917
median37.645342
Q337.658479
95-th percentile37.715647
Maximum376220
Range376182.41
Interquartile range (IQR)0.0305617

Descriptive statistics

Standard deviation18290.612
Coefficient of variation (CV)19.731657
Kurtosis423
Mean926.96784
Median Absolute Deviation (MAD)0.01558639
Skewness20.566964
Sum392107.4
Variance3.3454648 × 108
MonotonicityNot monotonic
2024-04-06T17:40:11.640502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.60055638 21
 
4.3%
37.6472 6
 
1.2%
37.6452 5
 
1.0%
37.63496626 4
 
0.8%
37.64337162 4
 
0.8%
37.6247 3
 
0.6%
37.6538 3
 
0.6%
37.6493 3
 
0.6%
37.65506605 3
 
0.6%
37.64748117 3
 
0.6%
Other values (326) 368
74.5%
(Missing) 71
 
14.4%
ValueCountFrequency (%)
37.59144496 1
0.2%
37.59149871 1
0.2%
37.5918 1
0.2%
37.59215426 1
0.2%
37.59225908 1
0.2%
37.5926 1
0.2%
37.5927 1
0.2%
37.595 1
0.2%
37.5953 1
0.2%
37.5971 1
0.2%
ValueCountFrequency (%)
376220.0 1
0.2%
37.73027432 1
0.2%
37.73026233 2
0.4%
37.72932661 1
0.2%
37.72906581 2
0.4%
37.72815121 1
0.2%
37.72581469 1
0.2%
37.72552349 1
0.2%
37.72471589 1
0.2%
37.7246302 1
0.2%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct356
Distinct (%)84.2%
Missing71
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean126.65348
Minimum126.53029
Maximum126.78549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-06T17:40:12.071554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.53029
5-th percentile126.55516
Q1126.62201
median126.65964
Q3126.69685
95-th percentile126.73118
Maximum126.78549
Range0.2552015
Interquartile range (IQR)0.0748346

Descriptive statistics

Standard deviation0.055100695
Coefficient of variation (CV)0.00043505079
Kurtosis-0.50289704
Mean126.65348
Median Absolute Deviation (MAD)0.0374126
Skewness-0.13199677
Sum53574.421
Variance0.0030360866
MonotonicityNot monotonic
2024-04-06T17:40:12.512608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6773762 4
 
0.8%
126.7051128 4
 
0.8%
126.6629 4
 
0.8%
126.5677845 3
 
0.6%
126.6409426 3
 
0.6%
126.6976 3
 
0.6%
126.6851 3
 
0.6%
126.6345807 3
 
0.6%
126.7158 3
 
0.6%
126.5302892 2
 
0.4%
Other values (346) 391
79.1%
(Missing) 71
 
14.4%
ValueCountFrequency (%)
126.5302892 2
0.4%
126.5363436 1
0.2%
126.5371025 1
0.2%
126.5373424 1
0.2%
126.5375655 1
0.2%
126.5376437 1
0.2%
126.5434586 1
0.2%
126.5478957 2
0.4%
126.5504303 1
0.2%
126.5509237 1
0.2%
ValueCountFrequency (%)
126.7854907 1
0.2%
126.784584 1
0.2%
126.7710717 2
0.4%
126.7707 2
0.4%
126.7683906 1
0.2%
126.7661164 1
0.2%
126.7624 1
0.2%
126.7622 1
0.2%
126.7598 1
0.2%
126.7597 1
0.2%

설치년도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.1457
Minimum2010
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-06T17:40:12.875895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2012
Q12017
median2019
Q32021
95-th percentile2023
Maximum2023
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.001523
Coefficient of variation (CV)0.0014865311
Kurtosis0.78503787
Mean2019.1457
Median Absolute Deviation (MAD)2
Skewness-0.86535336
Sum997458
Variance9.0091401
MonotonicityNot monotonic
2024-04-06T17:40:13.249492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2017 99
20.0%
2021 82
16.6%
2023 70
14.2%
2018 68
13.8%
2022 52
10.5%
2019 43
8.7%
2020 32
 
6.5%
2012 14
 
2.8%
2016 11
 
2.2%
2011 9
 
1.8%
Other values (2) 14
 
2.8%
ValueCountFrequency (%)
2010 7
 
1.4%
2011 9
 
1.8%
2012 14
 
2.8%
2015 7
 
1.4%
2016 11
 
2.2%
2017 99
20.0%
2018 68
13.8%
2019 43
8.7%
2020 32
 
6.5%
2021 82
16.6%
ValueCountFrequency (%)
2023 70
14.2%
2022 52
10.5%
2021 82
16.6%
2020 32
 
6.5%
2019 43
8.7%
2018 68
13.8%
2017 99
20.0%
2016 11
 
2.2%
2015 7
 
1.4%
2012 14
 
2.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-03-12
494 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-12
2nd row2024-03-12
3rd row2024-03-12
4th row2024-03-12
5th row2024-03-12

Common Values

ValueCountFrequency (%)
2024-03-12 494
100.0%

Length

2024-04-06T17:40:13.605555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:40:14.000376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-12 494
100.0%

Interactions

2024-04-06T17:40:02.730883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:39:58.430394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:39:59.649344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:40:01.536586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:40:03.068722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:39:58.711176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:39:59.939475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:40:01.828074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:40:03.422773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:39:59.016459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:40:00.353300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:40:02.109867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:40:03.675020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:39:59.367024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:40:00.629118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:40:02.393243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:40:14.246280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소번호단말기종류위도경도설치년도
정류소번호1.0000.000NaNNaN0.000
단말기종류0.0001.0000.0860.3000.893
위도NaN0.0861.0000.0000.000
경도NaN0.3000.0001.0000.426
설치년도0.0000.8930.0000.4261.000
2024-04-06T17:40:14.613856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소번호위도경도설치년도단말기종류
정류소번호1.000-0.2550.173-0.0470.000
위도-0.2551.000-0.698-0.0620.142
경도0.173-0.6981.0000.0750.186
설치년도-0.047-0.0620.0751.0000.614
단말기종류0.0000.1420.1860.6141.000

Missing values

2024-04-06T17:40:04.042406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:40:04.398077image/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.
2024-04-06T17:40:04.693218image/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

정류소명정류소번호소재지주소단말기종류위도경도설치년도데이터기준일자
0김포대학후문35005김포대학후문LED37.729066126.54789620232024-03-12
1김포대학후문(서울)35006김포대학후문(서울)LCD+LED37.729066126.54789620202024-03-12
2약암호텔(서울)35008약암호텔(서울)LED37.641945126.5504320152024-03-12
3청룡회관(서울)35009청룡회관(서울)LED37.729327126.55152120182024-03-12
4청룡회관(강화)35010청룡회관(강화)LED37.730274126.55092420182024-03-12
5대명초교35012대명초교LCD37.645342126.55268720192024-03-12
6군하리,한우마을35013군하리,한우마을LCD+LED37.714953126.5556920212024-03-12
7군하리,한우마을35014군하리,한우마을LED37.715649126.55515520222024-03-12
8고막리(시청)35015고막리(시청)LED37.728151126.55559320182024-03-12
9조각공원.청소년수련원35016조각공원.청소년수련원LED37.720094126.55764420122024-03-12
정류소명정류소번호소재지주소단말기종류위도경도설치년도데이터기준일자
484김포우리병원35582김포우리병원LED<NA><NA>20232024-03-12
485김포우리병원35591김포우리병원LED<NA><NA>20232024-03-12
486구)마을회관62082구)마을회관LED<NA><NA>20232024-03-12
487호반베르디움5차35895호반베르디움5차LED<NA><NA>20232024-03-12
488수기마을1단지35315수기마을1단지LED<NA><NA>20222024-03-12
489삼성래미안35696삼성래미안LED<NA><NA>20232024-03-12
490대포일반산업단지35989대포일반산업단지LED<NA><NA>20232024-03-12
491대포일반산업단지35990대포일반산업단지LED<NA><NA>20232024-03-12
492신곡교35116신곡교LED<NA><NA>20232024-03-12
493인향근린공원35446인향근린공원LED<NA><NA>20232024-03-12

Duplicate rows

Most frequently occurring

정류소명정류소번호소재지주소단말기종류위도경도설치년도데이터기준일자# duplicates
0장기역.고용복지플러스센터35508장기역.고용복지플러스센터LED37.6436126.669920202024-03-122
1풍무역(서울)35271풍무역(서울)LED37.600556126.734220192024-03-122
2힐스테이트리버시티남측도로<NA>힐스테이트리버시티남측도로LED<NA><NA>20232024-03-122