Overview

Dataset statistics

Number of variables19
Number of observations364
Missing cells787
Missing cells (%)11.4%
Duplicate rows35
Duplicate rows (%)9.6%
Total size in memory54.5 KiB
Average record size in memory153.4 B

Variable types

Unsupported9
Categorical7
Text3

Dataset

Description경상남도 사천시 공간정보시스템 데이터베이스 테이블 중 정류장 테이블의 자료입니다. 현재 정류장자료가 아니며 구축 당시에 자료로 실제와 차이가 있습니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15063665

Alerts

Dataset has 35 (9.6%) duplicate rowsDuplicates
Unnamed: 13 is highly overall correlated with 사천시 정류장 추출 자료 and 3 other fieldsHigh correlation
Unnamed: 12 is highly overall correlated with 사천시 정류장 추출 자료 and 5 other fieldsHigh correlation
Unnamed: 3 is highly overall correlated with Unnamed: 5 and 4 other fieldsHigh correlation
Unnamed: 5 is highly overall correlated with 사천시 정류장 추출 자료 and 3 other fieldsHigh correlation
Unnamed: 10 is highly overall correlated with 사천시 정류장 추출 자료 and 5 other fieldsHigh correlation
사천시 정류장 추출 자료 is highly overall correlated with Unnamed: 5 and 4 other fieldsHigh correlation
Unnamed: 9 is highly overall correlated with 사천시 정류장 추출 자료 and 3 other fieldsHigh correlation
사천시 정류장 추출 자료 is highly imbalanced (96.6%)Imbalance
Unnamed: 3 is highly imbalanced (96.6%)Imbalance
Unnamed: 9 is highly imbalanced (77.8%)Imbalance
Unnamed: 10 is highly imbalanced (61.5%)Imbalance
Unnamed: 0 has 364 (100.0%) missing valuesMissing
Unnamed: 8 has 356 (97.8%) missing valuesMissing
Unnamed: 11 has 58 (15.9%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 14 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 16 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 17 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 18 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 23:49:33.824230
Analysis finished2023-12-10 23:49:35.401221
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing364
Missing (%)100.0%
Memory size3.3 KiB

사천시 정류장 추출 자료
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
정류장
362 
* 작성 일자 : 2020.08.03
 
1
지형지물부호
 
1

Length

Max length20
Median length3
Mean length3.0549451
Min length3

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row* 작성 일자 : 2020.08.03
2nd row지형지물부호
3rd row정류장
4th row정류장
5th row정류장

Common Values

ValueCountFrequency (%)
정류장 362
99.5%
* 작성 일자 : 2020.08.03 1
 
0.3%
지형지물부호 1
 
0.3%

Length

2023-12-11T08:49:35.476901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:49:35.609074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정류장 362
98.4%
2
 
0.5%
작성 1
 
0.3%
일자 1
 
0.3%
2020.08.03 1
 
0.3%
지형지물부호 1
 
0.3%

Unnamed: 2
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.0 KiB

Unnamed: 3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
사천시
362 
<NA>
 
1
관리기관
 
1

Length

Max length4
Median length3
Mean length3.0054945
Min length3

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
사천시 362
99.5%
<NA> 1
 
0.3%
관리기관 1
 
0.3%

Length

2023-12-11T08:49:35.751110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:49:35.868258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사천시 362
99.5%
na 1
 
0.3%
관리기관 1
 
0.3%

Unnamed: 4
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.0 KiB

Unnamed: 5
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
벌용동
45 
사천읍
44 
사남면
32 
용현면
32 
서포면
32 
Other values (11)
179 

Length

Max length5
Median length3
Mean length3.032967
Min length3

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
벌용동 45
12.4%
사천읍 44
12.1%
사남면 32
8.8%
용현면 32
8.8%
서포면 32
8.8%
남양동 27
7.4%
향촌동 27
7.4%
축동면 24
6.6%
곤명면 22
 
6.0%
곤양면 20
 
5.5%
Other values (6) 59
16.2%

Length

2023-12-11T08:49:36.023658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
벌용동 45
12.4%
사천읍 44
12.1%
사남면 32
8.8%
용현면 32
8.8%
서포면 32
8.8%
남양동 27
7.4%
향촌동 27
7.4%
축동면 24
6.6%
곤명면 22
 
6.0%
곤양면 20
 
5.5%
Other values (6) 59
16.2%

Unnamed: 6
Text

Distinct250
Distinct (%)68.9%
Missing1
Missing (%)0.3%
Memory size3.0 KiB
2023-12-11T08:49:36.348585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9834711
Min length4

Characters and Unicode

Total characters3624
Distinct characters18
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)42.4%

Sample

1st row도엽번호
2nd row358131293C
3rd row358131199D
4th row358131180A
5th row358131293C
ValueCountFrequency (%)
348010727a 7
 
1.9%
348010727b 4
 
1.1%
348010718c 4
 
1.1%
358131738a 3
 
0.8%
358131766d 3
 
0.8%
348010736c 3
 
0.8%
348010728a 3
 
0.8%
358131288c 3
 
0.8%
358131824b 3
 
0.8%
358132293a 3
 
0.8%
Other values (240) 327
90.1%
2023-12-11T08:49:36.750425image/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 (8) 338
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3258
89.9%
Uppercase Letter 362
 
10.0%
Other Letter 4
 
0.1%

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%
Other Letter
ValueCountFrequency (%)
도 1
25.0%
엽 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3258
89.9%
Latin 362
 
10.0%
Hangul 4
 
0.1%

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%
Hangul
ValueCountFrequency (%)
도 1
25.0%
엽 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3620
99.9%
Hangul 4
 
0.1%

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%
Hangul
ValueCountFrequency (%)
도 1
25.0%
엽 1
25.0%
번 1
25.0%
호 1
25.0%

Unnamed: 7
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.0 KiB

Unnamed: 8
Text

MISSING 

Distinct4
Distinct (%)50.0%
Missing356
Missing (%)97.8%
Memory size3.0 KiB
2023-12-11T08:49:36.905646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.25
Min length4

Characters and Unicode

Total characters74
Distinct characters11
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row공사번호
2nd row1999000106
3rd row1999000106
4th row1999000106
5th rowRD20110022
ValueCountFrequency (%)
1999000106 3
37.5%
rd20110022 2
25.0%
2001000006 2
25.0%
공사번호 1
 
12.5%
2023-12-11T08:49:37.217837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32
43.2%
1 12
 
16.2%
9 9
 
12.2%
2 8
 
10.8%
6 5
 
6.8%
R 2
 
2.7%
D 2
 
2.7%
공 1
 
1.4%
사 1
 
1.4%
번 1
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
89.2%
Uppercase Letter 4
 
5.4%
Other Letter 4
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32
48.5%
1 12
 
18.2%
9 9
 
13.6%
2 8
 
12.1%
6 5
 
7.6%
Other Letter
ValueCountFrequency (%)
공 1
25.0%
사 1
25.0%
번 1
25.0%
호 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
R 2
50.0%
D 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66
89.2%
Latin 4
 
5.4%
Hangul 4
 
5.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32
48.5%
1 12
 
18.2%
9 9
 
13.6%
2 8
 
12.1%
6 5
 
7.6%
Hangul
ValueCountFrequency (%)
공 1
25.0%
사 1
25.0%
번 1
25.0%
호 1
25.0%
Latin
ValueCountFrequency (%)
R 2
50.0%
D 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70
94.6%
Hangul 4
 
5.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32
45.7%
1 12
 
17.1%
9 9
 
12.9%
2 8
 
11.4%
6 5
 
7.1%
R 2
 
2.9%
D 2
 
2.9%
Hangul
ValueCountFrequency (%)
공 1
25.0%
사 1
25.0%
번 1
25.0%
호 1
25.0%

Unnamed: 9
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
19600101
326 
20100101
 
9
19000101
 
8
<NA>
 
7
20000127
 
3
Other values (7)
 
11

Length

Max length8
Median length8
Mean length7.9120879
Min length4

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row설치일자
3rd row19600101
4th row19600101
5th row19600101

Common Values

ValueCountFrequency (%)
19600101 326
89.6%
20100101 9
 
2.5%
19000101 8
 
2.2%
<NA> 7
 
1.9%
20000127 3
 
0.8%
20001231 2
 
0.5%
20110101 2
 
0.5%
20020207 2
 
0.5%
19870101 2
 
0.5%
설치일자 1
 
0.3%
Other values (2) 2
 
0.5%

Length

2023-12-11T08:49:37.350926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19600101 326
89.6%
20100101 9
 
2.5%
19000101 8
 
2.2%
na 7
 
1.9%
20000127 3
 
0.8%
20001231 2
 
0.5%
20110101 2
 
0.5%
20020207 2
 
0.5%
19870101 2
 
0.5%
설치일자 1
 
0.3%
Other values (2) 2
 
0.5%

Unnamed: 10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
일반버스
285 
기타
59 
시외버스
 
15
택시
 
3
<NA>
 
1

Length

Max length5
Median length4
Mean length3.6620879
Min length2

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
일반버스 285
78.3%
기타 59
 
16.2%
시외버스 15
 
4.1%
택시 3
 
0.8%
<NA> 1
 
0.3%
정류장종류 1
 
0.3%

Length

2023-12-11T08:49:37.470848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:49:37.585266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반버스 285
78.3%
기타 59
 
16.2%
시외버스 15
 
4.1%
택시 3
 
0.8%
na 1
 
0.3%
정류장종류 1
 
0.3%

Unnamed: 11
Text

MISSING 

Distinct216
Distinct (%)70.6%
Missing58
Missing (%)15.9%
Memory size3.0 KiB
2023-12-11T08:49:37.913328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.3856209
Min length2

Characters and Unicode

Total characters1036
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

Unique138 ?
Unique (%)45.1%

Sample

1st row정류장명
2nd row하탑
3rd row용산
4th row신촌
5th row하탑
ValueCountFrequency (%)
구랑마을 4
 
1.3%
하탑 4
 
1.3%
한주빌라트 3
 
1.0%
버스터미널 3
 
1.0%
병둔 3
 
1.0%
동치 3
 
1.0%
신기 3
 
1.0%
신촌 3
 
1.0%
용수 3
 
1.0%
구암1리 3
 
1.0%
Other values (208) 278
89.7%
2023-12-11T08:49:38.439126image/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%
천 29
 
2.8%
리 29
 
2.8%
장 28
 
2.7%
구 25
 
2.4%
정 25
 
2.4%
포 20
 
1.9%
Other values (192) 742
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1008
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%
천 29
 
2.9%
리 29
 
2.9%
장 28
 
2.8%
구 25
 
2.5%
정 25
 
2.5%
포 20
 
2.0%
Other values (178) 714
70.8%
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%
5 3
25.0%
2 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 1008
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%
천 29
 
2.9%
리 29
 
2.9%
장 28
 
2.8%
구 25
 
2.5%
정 25
 
2.5%
포 20
 
2.0%
Other values (178) 714
70.8%
Common
ValueCountFrequency (%)
1 6
27.3%
4
18.2%
5 3
13.6%
2 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 1008
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%
천 29
 
2.9%
리 29
 
2.9%
장 28
 
2.8%
구 25
 
2.5%
정 25
 
2.5%
포 20
 
2.0%
Other values (178) 714
70.8%
ASCII
ValueCountFrequency (%)
1 6
23.1%
4
15.4%
5 3
11.5%
2 3
11.5%
, 2
 
7.7%
( 1
 
3.8%
) 1
 
3.8%
B 1
 
3.8%
U 1
 
3.8%
S 1
 
3.8%
Other values (3) 3
11.5%
None
ValueCountFrequency (%)
· 2
100.0%

Unnamed: 12
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
표지설치
186 
지붕설치
176 
<NA>
 
1
정류장유형
 
1

Length

Max length5
Median length4
Mean length4.0027473
Min length4

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
표지설치 186
51.1%
지붕설치 176
48.4%
<NA> 1
 
0.3%
정류장유형 1
 
0.3%

Length

2023-12-11T08:49:38.581627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:49:38.687313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
표지설치 186
51.1%
지붕설치 176
48.4%
na 1
 
0.3%
정류장유형 1
 
0.3%

Unnamed: 13
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
미분류
133 
철재류
92 
FRP
72 
적벽돌조적(스라브조적)
60 
나무
 
4
Other values (3)
 
3

Length

Max length12
Median length3
Mean length4.478022
Min length2

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row승강장재질
3rd row적벽돌조적(스라브조적)
4th row적벽돌조적(스라브조적)
5th row적벽돌조적(스라브조적)

Common Values

ValueCountFrequency (%)
미분류 133
36.5%
철재류 92
25.3%
FRP 72
19.8%
적벽돌조적(스라브조적) 60
16.5%
나무 4
 
1.1%
<NA> 1
 
0.3%
승강장재질 1
 
0.3%
기타 1
 
0.3%

Length

2023-12-11T08:49:38.846067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:49:38.970734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 133
36.5%
철재류 92
25.3%
frp 72
19.8%
적벽돌조적(스라브조적 60
16.5%
나무 4
 
1.1%
na 1
 
0.3%
승강장재질 1
 
0.3%
기타 1
 
0.3%

Unnamed: 14
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.0 KiB

Unnamed: 15
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.0 KiB

Unnamed: 16
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.0 KiB

Unnamed: 17
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.0 KiB

Unnamed: 18
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.0 KiB

Correlations

2023-12-11T08:49:39.072251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사천시 정류장 추출 자료Unnamed: 3Unnamed: 5Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 12Unnamed: 13
사천시 정류장 추출 자료1.0000.7031.0001.0001.0001.0001.0001.000
Unnamed: 30.7031.0001.0001.0001.0001.0001.0001.000
Unnamed: 51.0001.0001.0001.0000.7460.9390.9480.763
Unnamed: 81.0001.0001.0001.0001.0000.6000.8110.979
Unnamed: 91.0001.0000.7461.0001.0000.7370.8260.694
Unnamed: 101.0001.0000.9390.6000.7371.0000.7300.687
Unnamed: 121.0001.0000.9480.8110.8260.7301.0000.918
Unnamed: 131.0001.0000.7630.9790.6940.6870.9181.000
2023-12-11T08:49:39.205222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 13Unnamed: 12Unnamed: 3Unnamed: 5Unnamed: 10사천시 정류장 추출 자료Unnamed: 9
Unnamed: 131.0000.9240.9930.4730.5320.9930.434
Unnamed: 120.9241.0000.9990.7370.7210.9990.707
Unnamed: 30.9930.9991.0000.9820.9960.4970.987
Unnamed: 50.4730.7370.9821.0000.6750.9820.401
Unnamed: 100.5320.7210.9960.6751.0000.9960.517
사천시 정류장 추출 자료0.9930.9990.4970.9820.9961.0000.987
Unnamed: 90.4340.7070.9870.4010.5170.9871.000
2023-12-11T08:49:39.335451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사천시 정류장 추출 자료Unnamed: 3Unnamed: 5Unnamed: 9Unnamed: 10Unnamed: 12Unnamed: 13
사천시 정류장 추출 자료1.0000.4970.9820.9870.9960.9990.993
Unnamed: 30.4971.0000.9820.9870.9960.9990.993
Unnamed: 50.9820.9821.0000.4010.6750.7370.473
Unnamed: 90.9870.9870.4011.0000.5170.7070.434
Unnamed: 100.9960.9960.6750.5171.0000.7210.532
Unnamed: 120.9990.9990.7370.7070.7211.0000.924
Unnamed: 130.9930.9930.4730.4340.5320.9241.000

Missing values

2023-12-11T08:49:34.607087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:49:34.879495image/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.
2023-12-11T08:49:35.156384image/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

Unnamed: 0사천시 정류장 추출 자료Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18
0<NA>* 작성 일자 : 2020.08.03NaN<NA>NaN<NA><NA>NaN<NA><NA><NA><NA><NA><NA>NaNNaNNaNNaNNaN
1<NA>지형지물부호관리번호관리기관행정읍면동코드행정읍면동도엽번호도로구간번호공사번호설치일자정류장종류정류장명정류장유형승강장재질BIS코드번호위도경도X좌표Y좌표
2<NA>정류장100028사천시48240340축동면358131293C352242<NA>19600101일반버스하탑지붕설치적벽돌조적(스라브조적)035.101674128.061185114402.151278778.021
3<NA>정류장100020사천시48240340축동면358131199D352502<NA>19600101일반버스용산지붕설치적벽돌조적(스라브조적)035.101904128.042726112719.291278819.57
4<NA>정류장100019사천시48240340축동면358131180A352504<NA>19600101일반버스신촌지붕설치적벽돌조적(스라브조적)035.113649128.046433113069.752280119.422
5<NA>정류장100027사천시48240340축동면358131293C352602<NA>19600101일반버스하탑표지설치철재류035.101641128.061522114432.791278774.043
6<NA>정류장100029사천시48240340축동면358131293D352602<NA>19600101일반버스탑리공단지붕설치FRP035.102022128.063658114627.99278814.546
7<NA>정류장100021사천시48240340축동면358131286A352704<NA>19600101일반버스예동지붕설치FRP035.107882128.075244115690.414279454.849
8<NA>정류장100026사천시48240340축동면358131288C352802<NA>19600101일반버스동치지붕설치FRP035.106564128.087214116780.413279298.464
9<NA>정류장100001사천시48240320사남면348010319B350804<NA>19600101일반버스송암표지설치철재류034.992801128.142706121731.356266631.992
Unnamed: 0사천시 정류장 추출 자료Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18
354<NA>정류장100018사천시48240340축동면358131180A352242<NA>19600101일반버스<NA>표지설치철재류035.113889128.046575113083.01280145.881
355<NA>정류장990002사천시48240350곤양면358131635C900216<NA><NA>일반버스멀구리지붕설치철재류035.08735128.01681110340.378277227.879
356<NA>정류장990003사천시48240350곤양면358131635C900216<NA><NA>일반버스멀구리지붕설치철재류035.087193128.016712110331.223277210.543
357<NA>정류장990004사천시48240350곤양면358131657A900204<NA><NA>일반버스검정지붕설치철재류035.078192128.026848111245.868276202.798
358<NA>정류장990005사천시48240350곤양면358131613B900227<NA><NA>일반버스갑사지붕설치철재류035.098866128.009911109723.921278511.76
359<NA>정류장990006사천시48240350곤양면358131624D900220<NA><NA>일반버스덕골지붕설치철재류035.091647128.013818110072.244277707.261
360<NA>정류장999001사천시48240320사남면358131776C999021<NA><NA>일반버스리가아파트지붕설치철재류035.062055128.076778115783.167274369.131
361<NA>정류장194001사천시48240320사남면358131775C194062<NA>19000101일반버스명칭없음표지설치미분류035.061322128.070905115246.631274292.752
362<NA>정류장194002사천시48240350곤양면357161964B194010<NA>19000101일반버스원동지붕설치적벽돌조적(스라브조적)035.068941127.919995101489.044275276.791
363<NA>정류장194003사천시48240370서포면358132191B194046<NA>19000101일반버스중촌지붕설치적벽돌조적(스라브조적)035.003527128.003435109027.705267940.067

Duplicate rows

Most frequently occurring

사천시 정류장 추출 자료Unnamed: 3Unnamed: 5Unnamed: 6Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13# duplicates
12정류장사천시벌용동348010727B<NA>19600101일반버스<NA>표지설치미분류4
14정류장사천시벌용동348010728A<NA>19600101일반버스<NA>표지설치미분류3
0정류장사천시곤명면357161536C<NA>19600101시외버스완사지붕설치FRP2
1정류장사천시곤양면358131635C<NA><NA>일반버스멀구리지붕설치철재류2
2정류장사천시남양동348010253C<NA>19600101일반버스포도단지표지설치미분류2
3정류장사천시남양동348010262B<NA>19600101일반버스송포표지설치미분류2
4정류장사천시동서동348010734C<NA>19600101일반버스대방초교표지설치미분류2
5정류장사천시벌용동348010706D<NA>19600101일반버스자동차학원표지설치미분류2
6정류장사천시벌용동348010707D<NA>19600101일반버스<NA>표지설치미분류2
7정류장사천시벌용동348010708A<NA>19600101일반버스<NA>표지설치미분류2