Overview

Dataset statistics

Number of variables17
Number of observations362
Missing cells57
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.0 KiB
Average record size in memory144.4 B

Variable types

Categorical8
Numeric7
Text2

Dataset

Description경상남도 사천시 공간정보시스템 데이터베이스 테이블 중 정류장 테이블의 자료입니다. 현재 정류장자료가 아니며 구축 당시에 자료로 실제와 차이가 있습니다.
Author경상남도 사천시
URLhttps://www.data.go.kr/data/15063665/fileData.do

Alerts

지형지물부호 has constant value ""Constant
관리기관 has constant value ""Constant
BIS코드번호 has constant value ""Constant
공사번호 is highly overall correlated with 관리번호 and 9 other fieldsHigh correlation
행정읍면동 is highly overall correlated with 관리번호 and 5 other fieldsHigh correlation
관리번호 is highly overall correlated with 행정읍면동 and 2 other fieldsHigh correlation
행정읍면동코드 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
도로구간번호 is highly overall correlated with 공사번호High correlation
위도 is highly overall correlated with 행정읍면동코드 and 4 other fieldsHigh correlation
경도 is highly overall correlated with X좌표 and 1 other fieldsHigh correlation
X좌표 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with 행정읍면동코드 and 4 other fieldsHigh correlation
정류장종류 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
정류장유형 is highly overall correlated with 공사번호 and 1 other fieldsHigh correlation
승강장재질 is highly overall correlated with 공사번호 and 1 other fieldsHigh correlation
공사번호 is highly imbalanced (91.6%)Imbalance
정류장종류 is highly imbalanced (52.7%)Imbalance
정류장명 has 57 (15.7%) missing valuesMissing
관리번호 has unique valuesUnique
경도 has unique valuesUnique
X좌표 has unique valuesUnique
Y좌표 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:32:23.560698
Analysis finished2023-12-12 09:32:30.918232
Duration7.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
정류장
362 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정류장 362
100.0%

Length

2023-12-12T18:32:31.004647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:31.435073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정류장 362
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct362
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461936.47
Minimum1001
Maximum999001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T18:32:31.626697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile100017.05
Q1174804.25
median327001
Q3747002.75
95-th percentile900011.95
Maximum999001
Range998000
Interquartile range (IQR)572198.5

Descriptive statistics

Standard deviation311741.16
Coefficient of variation (CV)0.67485722
Kurtosis-1.7111656
Mean461936.47
Median Absolute Deviation (MAD)226988.5
Skewness0.14415595
Sum1.67221 × 108
Variance9.7182551 × 1010
MonotonicityNot monotonic
2023-12-12T18:32:31.809338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100028 1
 
0.3%
708004 1
 
0.3%
717002 1
 
0.3%
718001 1
 
0.3%
718002 1
 
0.3%
718003 1
 
0.3%
726002 1
 
0.3%
726001 1
 
0.3%
730002 1
 
0.3%
730001 1
 
0.3%
Other values (352) 352
97.2%
ValueCountFrequency (%)
1001 1
0.3%
1002 1
0.3%
100001 1
0.3%
100002 1
0.3%
100003 1
0.3%
100004 1
0.3%
100005 1
0.3%
100006 1
0.3%
100007 1
0.3%
100008 1
0.3%
ValueCountFrequency (%)
999001 1
0.3%
990006 1
0.3%
990005 1
0.3%
990004 1
0.3%
990003 1
0.3%
990002 1
0.3%
990001 1
0.3%
990000 1
0.3%
989001 1
0.3%
900021 1
0.3%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
사천시
362 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사천시 362
100.0%

Length

2023-12-12T18:32:31.971645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:32.103227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사천시 362
100.0%

행정읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48240412
Minimum48240250
Maximum48240595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T18:32:32.219552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48240250
5-th percentile48240250
Q148240322
median48240360
Q348240550
95-th percentile48240595
Maximum48240595
Range345
Interquartile range (IQR)227.5

Descriptive statistics

Standard deviation117.92601
Coefficient of variation (CV)2.4445481 × 10-6
Kurtosis-1.4904693
Mean48240412
Median Absolute Deviation (MAD)110
Skewness0.28651357
Sum1.7463029 × 1010
Variance13906.544
MonotonicityNot monotonic
2023-12-12T18:32:32.353457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
48240550 45
12.4%
48240250 44
12.2%
48240320 32
8.8%
48240330 32
8.8%
48240370 32
8.8%
48240595 27
7.5%
48240570 27
7.5%
48240340 24
6.6%
48240360 22
 
6.1%
48240350 20
 
5.5%
Other values (4) 57
15.7%
ValueCountFrequency (%)
48240250 44
12.2%
48240310 15
 
4.1%
48240320 32
8.8%
48240330 32
8.8%
48240340 24
6.6%
48240350 20
5.5%
48240360 22
6.1%
48240370 32
8.8%
48240510 19
5.2%
48240520 14
 
3.9%
ValueCountFrequency (%)
48240595 27
7.5%
48240570 27
7.5%
48240550 45
12.4%
48240530 9
 
2.5%
48240520 14
 
3.9%
48240510 19
5.2%
48240370 32
8.8%
48240360 22
6.1%
48240350 20
5.5%
48240340 24
6.6%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
벌용동
45 
사천읍
44 
사남면
32 
용현면
32 
서포면
32 
Other values (9)
177 

Length

Max length4
Median length3
Mean length3.0248619
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
벌용동 45
12.4%
사천읍 44
12.2%
사남면 32
8.8%
용현면 32
8.8%
서포면 32
8.8%
남양동 27
7.5%
향촌동 27
7.5%
축동면 24
6.6%
곤명면 22
 
6.1%
곤양면 20
 
5.5%
Other values (4) 57
15.7%

Length

2023-12-12T18:32:32.522513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
벌용동 45
12.4%
사천읍 44
12.2%
사남면 32
8.8%
용현면 32
8.8%
서포면 32
8.8%
남양동 27
7.5%
향촌동 27
7.5%
축동면 24
6.6%
곤명면 22
 
6.1%
곤양면 20
 
5.5%
Other values (4) 57
15.7%

도엽번호
Text

Distinct249
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-12T18:32:32.794698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3620
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique153 ?
Unique (%)42.3%

Sample

1st row358131293C
2nd row358131199D
3rd row358131180A
4th row358131293C
5th row358131293D
ValueCountFrequency (%)
348010727a 7
 
1.9%
348010727b 4
 
1.1%
348010718c 4
 
1.1%
358131766d 3
 
0.8%
358131824b 3
 
0.8%
348010736c 3
 
0.8%
358131728d 3
 
0.8%
348010728a 3
 
0.8%
358131288c 3
 
0.8%
358132293a 3
 
0.8%
Other values (239) 326
90.1%
2023-12-12T18:32:33.193482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 651
18.0%
1 575
15.9%
8 412
11.4%
0 379
10.5%
5 283
7.8%
7 269
7.4%
4 268
7.4%
2 220
 
6.1%
6 126
 
3.5%
A 103
 
2.8%
Other values (4) 334
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3258
90.0%
Uppercase Letter 362
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 651
20.0%
1 575
17.6%
8 412
12.6%
0 379
11.6%
5 283
8.7%
7 269
8.3%
4 268
8.2%
2 220
 
6.8%
6 126
 
3.9%
9 75
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
A 103
28.5%
B 101
27.9%
C 83
22.9%
D 75
20.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3258
90.0%
Latin 362
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 651
20.0%
1 575
17.6%
8 412
12.6%
0 379
11.6%
5 283
8.7%
7 269
8.3%
4 268
8.2%
2 220
 
6.8%
6 126
 
3.9%
9 75
 
2.3%
Latin
ValueCountFrequency (%)
A 103
28.5%
B 101
27.9%
C 83
22.9%
D 75
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 651
18.0%
1 575
15.9%
8 412
11.4%
0 379
10.5%
5 283
7.8%
7 269
7.4%
4 268
7.4%
2 220
 
6.1%
6 126
 
3.5%
A 103
 
2.8%
Other values (4) 334
9.2%

도로구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct278
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297756.93
Minimum1027
Maximum999021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T18:32:33.385274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1027
5-th percentile100315.7
Q1150103
median303107
Q3399003.5
95-th percentile491901
Maximum999021
Range997994
Interquartile range (IQR)248900.5

Descriptive statistics

Standard deviation160311.37
Coefficient of variation (CV)0.53839677
Kurtosis1.9585998
Mean297756.93
Median Absolute Deviation (MAD)109450
Skewness0.86373703
Sum1.0778801 × 108
Variance2.5699734 × 1010
MonotonicityNot monotonic
2023-12-12T18:32:33.555956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350302 5
 
1.4%
230036 5
 
1.4%
491901 4
 
1.1%
351406 3
 
0.8%
479808 3
 
0.8%
351305 3
 
0.8%
351301 3
 
0.8%
301024 3
 
0.8%
350810 3
 
0.8%
352237 3
 
0.8%
Other values (268) 327
90.3%
ValueCountFrequency (%)
1027 1
0.3%
100010 1
0.3%
100147 1
0.3%
100155 1
0.3%
100157 1
0.3%
100179 2
0.6%
100220 1
0.3%
100227 1
0.3%
100244 1
0.3%
100259 1
0.3%
ValueCountFrequency (%)
999021 1
0.3%
900227 1
0.3%
900220 1
0.3%
900216 2
0.6%
900204 1
0.3%
706302 1
0.3%
634007 1
0.3%
608101 1
0.3%
501505 2
0.6%
501501 1
0.3%

공사번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
<NA>
355 
1999000106
 
3
RD20110022
 
2
2001000006
 
2

Length

Max length10
Median length4
Mean length4.1160221
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 355
98.1%
1999000106 3
 
0.8%
RD20110022 2
 
0.6%
2001000006 2
 
0.6%

Length

2023-12-12T18:32:33.717245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:33.874250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 355
98.1%
1999000106 3
 
0.8%
rd20110022 2
 
0.6%
2001000006 2
 
0.6%

정류장종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
일반버스
285 
기타
59 
시외버스
 
15
택시
 
3

Length

Max length4
Median length4
Mean length3.6574586
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반버스 285
78.7%
기타 59
 
16.3%
시외버스 15
 
4.1%
택시 3
 
0.8%

Length

2023-12-12T18:32:34.014241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:34.200515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반버스 285
78.7%
기타 59
 
16.3%
시외버스 15
 
4.1%
택시 3
 
0.8%

정류장명
Text

MISSING 

Distinct215
Distinct (%)70.5%
Missing57
Missing (%)15.7%
Memory size3.0 KiB
2023-12-12T18:32:34.559468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.3836066
Min length2

Characters and Unicode

Total characters1032
Distinct characters202
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)44.9%

Sample

1st row하탑
2nd row용산
3rd row신촌
4th row하탑
5th row탑리공단
ValueCountFrequency (%)
구랑마을 4
 
1.3%
하탑 4
 
1.3%
구암1리 3
 
1.0%
동치 3
 
1.0%
병둔 3
 
1.0%
신촌 3
 
1.0%
버스터미널 3
 
1.0%
한주빌라트 3
 
1.0%
용수 3
 
1.0%
신기 3
 
1.0%
Other values (207) 277
89.6%
2023-12-12T18:32:35.130922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 38
 
3.7%
동 34
 
3.3%
마 33
 
3.2%
을 33
 
3.2%
리 29
 
2.8%
천 29
 
2.8%
장 27
 
2.6%
구 25
 
2.4%
정 24
 
2.3%
포 20
 
1.9%
Other values (192) 740
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1004
97.3%
Decimal Number 12
 
1.2%
Uppercase Letter 6
 
0.6%
Space Separator 4
 
0.4%
Other Punctuation 4
 
0.4%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 38
 
3.8%
동 34
 
3.4%
마 33
 
3.3%
을 33
 
3.3%
리 29
 
2.9%
천 29
 
2.9%
장 27
 
2.7%
구 25
 
2.5%
정 24
 
2.4%
포 20
 
2.0%
Other values (178) 712
70.9%
Uppercase Letter
ValueCountFrequency (%)
B 1
16.7%
U 1
16.7%
S 1
16.7%
A 1
16.7%
P 1
16.7%
T 1
16.7%
Decimal Number
ValueCountFrequency (%)
1 6
50.0%
2 3
25.0%
5 3
25.0%
Other Punctuation
ValueCountFrequency (%)
· 2
50.0%
, 2
50.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1004
97.3%
Common 22
 
2.1%
Latin 6
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 38
 
3.8%
동 34
 
3.4%
마 33
 
3.3%
을 33
 
3.3%
리 29
 
2.9%
천 29
 
2.9%
장 27
 
2.7%
구 25
 
2.5%
정 24
 
2.4%
포 20
 
2.0%
Other values (178) 712
70.9%
Common
ValueCountFrequency (%)
1 6
27.3%
4
18.2%
2 3
13.6%
5 3
13.6%
· 2
 
9.1%
, 2
 
9.1%
( 1
 
4.5%
) 1
 
4.5%
Latin
ValueCountFrequency (%)
B 1
16.7%
U 1
16.7%
S 1
16.7%
A 1
16.7%
P 1
16.7%
T 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1004
97.3%
ASCII 26
 
2.5%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 38
 
3.8%
동 34
 
3.4%
마 33
 
3.3%
을 33
 
3.3%
리 29
 
2.9%
천 29
 
2.9%
장 27
 
2.7%
구 25
 
2.5%
정 24
 
2.4%
포 20
 
2.0%
Other values (178) 712
70.9%
ASCII
ValueCountFrequency (%)
1 6
23.1%
4
15.4%
2 3
11.5%
5 3
11.5%
, 2
 
7.7%
B 1
 
3.8%
U 1
 
3.8%
S 1
 
3.8%
( 1
 
3.8%
) 1
 
3.8%
Other values (3) 3
11.5%
None
ValueCountFrequency (%)
· 2
100.0%

정류장유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
표지설치
186 
지붕설치
176 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지붕설치
2nd row지붕설치
3rd row지붕설치
4th row표지설치
5th row지붕설치

Common Values

ValueCountFrequency (%)
표지설치 186
51.4%
지붕설치 176
48.6%

Length

2023-12-12T18:32:35.318093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:35.494395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
표지설치 186
51.4%
지붕설치 176
48.6%

승강장재질
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
미분류
133 
철재류
92 
FRP
72 
적벽돌조적(스라브조적)
60 
나무
 
4

Length

Max length12
Median length3
Mean length4.4779006
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row적벽돌조적(스라브조적)
2nd row적벽돌조적(스라브조적)
3rd row적벽돌조적(스라브조적)
4th row철재류
5th rowFRP

Common Values

ValueCountFrequency (%)
미분류 133
36.7%
철재류 92
25.4%
FRP 72
19.9%
적벽돌조적(스라브조적) 60
16.6%
나무 4
 
1.1%
기타 1
 
0.3%

Length

2023-12-12T18:32:35.619103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:35.767003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 133
36.7%
철재류 92
25.4%
frp 72
19.9%
적벽돌조적(스라브조적 60
16.6%
나무 4
 
1.1%
기타 1
 
0.3%

BIS코드번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
362 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 362
100.0%

Length

2023-12-12T18:32:35.947275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:36.090536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 362
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct358
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.017137
Minimum34.925277
Maximum35.153787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T18:32:36.266678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.925277
5-th percentile34.930304
Q134.946562
median35.012797
Q335.079043
95-th percentile35.111169
Maximum35.153787
Range0.22851
Interquartile range (IQR)0.13248075

Descriptive statistics

Standard deviation0.065472339
Coefficient of variation (CV)0.0018697228
Kurtosis-1.392
Mean35.017137
Median Absolute Deviation (MAD)0.066375
Skewness0.1021437
Sum12676.204
Variance0.0042866272
MonotonicityNot monotonic
2023-12-12T18:32:36.446869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.939505 2
 
0.6%
35.061322 2
 
0.6%
35.08735 2
 
0.6%
35.097608 2
 
0.6%
35.101674 1
 
0.3%
34.935975 1
 
0.3%
34.947136 1
 
0.3%
34.944731 1
 
0.3%
34.94461 1
 
0.3%
34.942908 1
 
0.3%
Other values (348) 348
96.1%
ValueCountFrequency (%)
34.925277 1
0.3%
34.92545 1
0.3%
34.92769 1
0.3%
34.927782 1
0.3%
34.927889 1
0.3%
34.928244 1
0.3%
34.929528 1
0.3%
34.929545 1
0.3%
34.929704 1
0.3%
34.929834 1
0.3%
ValueCountFrequency (%)
35.153787 1
0.3%
35.149292 1
0.3%
35.145496 1
0.3%
35.143453 1
0.3%
35.140754 1
0.3%
35.13767 1
0.3%
35.132989 1
0.3%
35.132176 1
0.3%
35.131794 1
0.3%
35.13177 1
0.3%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct362
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.05997
Minimum127.89969
Maximum128.16291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T18:32:36.620340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.89969
5-th percentile127.94807
Q1128.04439
median128.07458
Q3128.08812
95-th percentile128.1326
Maximum128.16291
Range0.263222
Interquartile range (IQR)0.04372625

Descriptive statistics

Standard deviation0.052001675
Coefficient of variation (CV)0.00040607283
Kurtosis0.61137216
Mean128.05997
Median Absolute Deviation (MAD)0.018649
Skewness-1.0096775
Sum46357.709
Variance0.0027041742
MonotonicityNot monotonic
2023-12-12T18:32:36.802875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.061185 1
 
0.3%
128.084639 1
 
0.3%
128.078248 1
 
0.3%
128.08402 1
 
0.3%
128.087093 1
 
0.3%
128.086875 1
 
0.3%
128.077106 1
 
0.3%
128.076865 1
 
0.3%
128.09421 1
 
0.3%
128.094134 1
 
0.3%
Other values (352) 352
97.2%
ValueCountFrequency (%)
127.899687 1
0.3%
127.905068 1
0.3%
127.907674 1
0.3%
127.913896 1
0.3%
127.919995 1
0.3%
127.921386 1
0.3%
127.925341 1
0.3%
127.928552 1
0.3%
127.934692 1
0.3%
127.938747 1
0.3%
ValueCountFrequency (%)
128.162909 1
0.3%
128.15652 1
0.3%
128.154335 1
0.3%
128.154266 1
0.3%
128.144701 1
0.3%
128.142706 1
0.3%
128.142614 1
0.3%
128.142588 1
0.3%
128.141073 1
0.3%
128.139759 1
0.3%

X좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct362
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114204.06
Minimum99735.138
Maximum123614.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T18:32:36.995469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99735.138
5-th percentile103980.49
Q1112790.34
median115464.69
Q3116853.64
95-th percentile120793.91
Maximum123614.26
Range23879.127
Interquartile range (IQR)4063.2967

Descriptive statistics

Standard deviation4726.6944
Coefficient of variation (CV)0.041388146
Kurtosis0.59155187
Mean114204.06
Median Absolute Deviation (MAD)1758.2065
Skewness-0.99411616
Sum41341871
Variance22341640
MonotonicityNot monotonic
2023-12-12T18:32:37.178775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114402.151 1
 
0.3%
116384.467 1
 
0.3%
115798.448 1
 
0.3%
116326.855 1
 
0.3%
116605.175 1
 
0.3%
116585.174 1
 
0.3%
115690.981 1
 
0.3%
115668.886 1
 
0.3%
117250.29 1
 
0.3%
117243.353 1
 
0.3%
Other values (352) 352
97.2%
ValueCountFrequency (%)
99735.138 1
0.3%
100220.806 1
0.3%
100443.105 1
0.3%
101007.394 1
0.3%
101489.044 1
0.3%
101659.642 1
0.3%
102022.22 1
0.3%
102351.645 1
0.3%
102900.257 1
0.3%
103133.349 1
0.3%
ValueCountFrequency (%)
123614.265 1
0.3%
123033.381 1
0.3%
122888.764 1
0.3%
122882.469 1
0.3%
121910.937 1
0.3%
121823.244 1
0.3%
121820.873 1
0.3%
121731.356 1
0.3%
121681.97 1
0.3%
121521.807 1
0.3%

Y좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct362
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269401.29
Minimum259222.05
Maximum284653.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T18:32:37.393488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum259222.05
5-th percentile259742.54
Q1261553.27
median268939.65
Q3276238.68
95-th percentile279837.97
Maximum284653.14
Range25431.092
Interquartile range (IQR)14685.407

Descriptive statistics

Standard deviation7276.4671
Coefficient of variation (CV)0.027009771
Kurtosis-1.3848252
Mean269401.29
Median Absolute Deviation (MAD)7332.1985
Skewness0.10296664
Sum97523267
Variance52946973
MonotonicityNot monotonic
2023-12-12T18:32:37.587535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
278778.021 1
 
0.3%
261724.86 1
 
0.3%
261490.077 1
 
0.3%
261613.244 1
 
0.3%
261343.891 1
 
0.3%
261330.623 1
 
0.3%
261150.057 1
 
0.3%
261142.509 1
 
0.3%
260778.208 1
 
0.3%
260786.192 1
 
0.3%
Other values (352) 352
97.2%
ValueCountFrequency (%)
259222.052 1
0.3%
259241.405 1
0.3%
259468.893 1
0.3%
259474.73 1
0.3%
259489.081 1
0.3%
259534.758 1
0.3%
259669.802 1
0.3%
259671.489 1
0.3%
259674.286 1
0.3%
259687.076 1
0.3%
ValueCountFrequency (%)
284653.144 1
0.3%
284211.928 1
0.3%
283785.324 1
0.3%
283492.733 1
0.3%
283170.813 1
0.3%
282893.785 1
0.3%
282395.13 1
0.3%
282237.112 1
0.3%
282196.679 1
0.3%
282193.228 1
0.3%

Interactions

2023-12-12T18:32:29.609093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:24.575004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:25.444968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:26.350519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:27.161428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:27.959584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:28.761270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:29.738124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:24.694039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:25.592792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:26.446579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:27.269089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:28.061294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:28.879887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:29.878848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:24.829329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:25.735524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:26.589362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:27.400735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:28.177646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:29.001712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:29.990933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:24.947707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:25.872782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:26.697013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:27.535927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:28.284434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:29.121555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:30.095836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:25.058345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:25.991979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:26.796476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:27.647767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:28.378325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:29.256892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:30.203698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:25.187171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:26.112135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:26.938131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:27.774884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:28.510098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:29.393112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:30.309074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:25.305656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:26.229191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:27.052164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:27.859263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:28.637669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:29.503346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:32:37.725825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동코드행정읍면동도로구간번호공사번호정류장종류정류장유형승강장재질위도경도X좌표Y좌표
관리번호1.0000.8140.8910.7041.0000.7770.3970.6520.8900.8290.8270.894
행정읍면동코드0.8141.0001.0000.6941.0000.5190.3110.6200.7690.7070.6980.775
행정읍면동0.8911.0001.0000.7351.0000.7560.4550.5070.8650.8120.8100.870
도로구간번호0.7040.6940.7351.0001.0000.4820.1550.3670.4830.6690.6590.492
공사번호1.0001.0001.0001.0001.0000.0000.5070.952NaN1.0001.000NaN
정류장종류0.7770.5190.7560.4820.0001.0000.3280.3360.7470.5270.5180.762
정류장유형0.3970.3110.4550.1550.5070.3281.0000.9730.2470.3520.3020.260
승강장재질0.6520.6200.5070.3670.9520.3360.9731.0000.4280.4780.4620.434
위도0.8900.7690.8650.483NaN0.7470.2470.4281.0000.6730.6701.000
경도0.8290.7070.8120.6691.0000.5270.3520.4780.6731.0001.0000.705
X좌표0.8270.6980.8100.6591.0000.5180.3020.4620.6701.0001.0000.701
Y좌표0.8940.7750.8700.492NaN0.7620.2600.4341.0000.7050.7011.000
2023-12-12T18:32:37.898689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류장유형공사번호승강장재질정류장종류행정읍면동
정류장유형1.0000.6710.8480.2180.350
공사번호0.6711.0000.7070.0001.000
승강장재질0.8480.7071.0000.2210.275
정류장종류0.2180.0000.2211.0000.534
행정읍면동0.3501.0000.2750.5341.000
2023-12-12T18:32:38.038988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동코드도로구간번호위도경도X좌표Y좌표행정읍면동공사번호정류장종류정류장유형승강장재질
관리번호1.0000.4440.268-0.356-0.349-0.362-0.3540.6351.0000.5880.3010.412
행정읍면동코드0.4441.000-0.012-0.718-0.146-0.167-0.7170.9900.8940.4220.2680.250
도로구간번호0.268-0.0121.0000.210-0.435-0.4360.2120.4210.8660.3300.1560.192
위도-0.356-0.7180.2101.000-0.153-0.1311.0000.5841.0000.5510.1870.240
경도-0.349-0.146-0.435-0.1531.0000.999-0.1580.4980.8940.3400.2670.274
X좌표-0.362-0.167-0.436-0.1310.9991.000-0.1360.4970.8940.3500.2350.267
Y좌표-0.354-0.7170.2121.000-0.158-0.1361.0000.5921.0000.5690.2000.245
행정읍면동0.6350.9900.4210.5840.4980.4970.5921.0001.0000.5340.3500.275
공사번호1.0000.8940.8661.0000.8940.8941.0001.0001.0000.0000.6710.707
정류장종류0.5880.4220.3300.5510.3400.3500.5690.5340.0001.0000.2180.221
정류장유형0.3010.2680.1560.1870.2670.2350.2000.3500.6710.2181.0000.848
승강장재질0.4120.2500.1920.2400.2740.2670.2450.2750.7070.2210.8481.000

Missing values

2023-12-12T18:32:30.500455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:32:30.813283image/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.

Sample

지형지물부호관리번호관리기관행정읍면동코드행정읍면동도엽번호도로구간번호공사번호정류장종류정류장명정류장유형승강장재질BIS코드번호위도경도X좌표Y좌표
0정류장100028사천시48240340축동면358131293C352242<NA>일반버스하탑지붕설치적벽돌조적(스라브조적)035.101674128.061185114402.151278778.021
1정류장100020사천시48240340축동면358131199D352502<NA>일반버스용산지붕설치적벽돌조적(스라브조적)035.101904128.042726112719.291278819.57
2정류장100019사천시48240340축동면358131180A352504<NA>일반버스신촌지붕설치적벽돌조적(스라브조적)035.113649128.046433113069.752280119.422
3정류장100027사천시48240340축동면358131293C352602<NA>일반버스하탑표지설치철재류035.101641128.061522114432.791278774.043
4정류장100029사천시48240340축동면358131293D352602<NA>일반버스탑리공단지붕설치FRP035.102022128.063658114627.99278814.546
5정류장100021사천시48240340축동면358131286A352704<NA>일반버스예동지붕설치FRP035.107882128.075244115690.414279454.849
6정류장100026사천시48240340축동면358131288C352802<NA>일반버스동치지붕설치FRP035.106564128.087214116780.413279298.464
7정류장100001사천시48240320사남면348010319B350804<NA>일반버스송암표지설치철재류034.992801128.142706121731.356266631.992
8정류장100073사천시48240320사남면358132398B350810<NA>일반버스<NA>표지설치철재류035.003974128.137673121282.535267875.631
9정류장100072사천시48240320사남면358132377D350810<NA>일반버스사남지붕설치적벽돌조적(스라브조적)035.011095128.133229120883.68268669.107
지형지물부호관리번호관리기관행정읍면동코드행정읍면동도엽번호도로구간번호공사번호정류장종류정류장명정류장유형승강장재질BIS코드번호위도경도X좌표Y좌표
352정류장100018사천시48240340축동면358131180A352242<NA>일반버스<NA>표지설치철재류035.113889128.046575113083.01280145.881
353정류장990002사천시48240350곤양면358131635C900216<NA>일반버스멀구리지붕설치철재류035.08735128.01681110340.378277227.879
354정류장990003사천시48240350곤양면358131635C900216<NA>일반버스멀구리지붕설치철재류035.087193128.016712110331.223277210.543
355정류장990004사천시48240350곤양면358131657A900204<NA>일반버스검정지붕설치철재류035.078192128.026848111245.868276202.798
356정류장990005사천시48240350곤양면358131613B900227<NA>일반버스갑사지붕설치철재류035.098866128.009911109723.921278511.76
357정류장990006사천시48240350곤양면358131624D900220<NA>일반버스덕골지붕설치철재류035.091647128.013818110072.244277707.261
358정류장999001사천시48240320사남면358131776C999021<NA>일반버스리가아파트지붕설치철재류035.062055128.076778115783.167274369.131
359정류장194001사천시48240320사남면358131775C194062<NA>일반버스명칭없음표지설치미분류035.061322128.070905115246.631274292.752
360정류장194002사천시48240350곤양면357161964B194010<NA>일반버스원동지붕설치적벽돌조적(스라브조적)035.068941127.919995101489.044275276.791
361정류장194003사천시48240370서포면358132191B194046<NA>일반버스중촌지붕설치적벽돌조적(스라브조적)035.003527128.003435109027.705267940.067