Overview

Dataset statistics

Number of variables16
Number of observations654
Missing cells664
Missing cells (%)6.3%
Duplicate rows157
Duplicate rows (%)24.0%
Total size in memory82.5 KiB
Average record size in memory129.2 B

Variable types

Unsupported10
Categorical5
Text1

Dataset

Description경상남도 사천시 공간정보시스템의 데이터베이스 테이블 중 과속방지턱 테이블의 내용입니다. 실제 과속방지턱과 차이가 있을 수 있습니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15063661

Alerts

Dataset has 157 (24.0%) duplicate rowsDuplicates
Unnamed: 9 is highly overall correlated with 사천시 과속방지턱 추출 자료 and 2 other fieldsHigh correlation
사천시 과속방지턱 추출 자료 is highly overall correlated with Unnamed: 4 and 2 other fieldsHigh correlation
Unnamed: 6 is highly overall correlated with Unnamed: 4 and 2 other fieldsHigh correlation
Unnamed: 4 is highly overall correlated with 사천시 과속방지턱 추출 자료 and 2 other fieldsHigh correlation
Unnamed: 8 is highly overall correlated with 사천시 과속방지턱 추출 자료 and 3 other fieldsHigh correlation
사천시 과속방지턱 추출 자료 is highly imbalanced (97.9%)Imbalance
Unnamed: 6 is highly imbalanced (97.9%)Imbalance
Unnamed: 8 is highly imbalanced (87.6%)Imbalance
Unnamed: 9 is highly imbalanced (62.9%)Imbalance
Unnamed: 0 has 654 (100.0%) 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: 3 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: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 12 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 13 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

Reproduction

Analysis started2023-12-10 23:08:44.335901
Analysis finished2023-12-10 23:08:45.222595
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing654
Missing (%)100.0%
Memory size5.9 KiB

사천시 과속방지턱 추출 자료
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
과속방지턱
652 
* 작성 일자 : 2020.08.03
 
1
지형지물부호
 
1

Length

Max length20
Median length5
Mean length5.0244648
Min length5

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
과속방지턱 652
99.7%
* 작성 일자 : 2020.08.03 1
 
0.2%
지형지물부호 1
 
0.2%

Length

2023-12-11T08:08:45.283188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:08:45.377937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
과속방지턱 652
99.1%
2
 
0.3%
작성 1
 
0.2%
일자 1
 
0.2%
2020.08.03 1
 
0.2%
지형지물부호 1
 
0.2%

Unnamed: 2
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size5.2 KiB

Unnamed: 3
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size5.2 KiB

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
사천읍
101 
용현면
67 
곤양면
51 
벌용동
49 
정동면
48 
Other values (12)
338 

Length

Max length7
Median length3
Mean length3.1039755
Min length3

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
사천읍 101
15.4%
용현면 67
10.2%
곤양면 51
7.8%
벌용동 49
 
7.5%
정동면 48
 
7.3%
서포면 45
 
6.9%
남양동 45
 
6.9%
사남면 43
 
6.6%
축동면 42
 
6.4%
동서동 39
 
6.0%
Other values (7) 124
19.0%

Length

2023-12-11T08:08:45.486445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사천읍 101
15.2%
용현면 67
10.1%
곤양면 51
 
7.7%
벌용동 49
 
7.4%
정동면 48
 
7.2%
서포면 45
 
6.8%
남양동 45
 
6.8%
사남면 43
 
6.5%
축동면 42
 
6.3%
동서동 39
 
5.9%
Other values (8) 135
20.3%

Unnamed: 5
Text

Distinct352
Distinct (%)53.9%
Missing1
Missing (%)0.2%
Memory size5.2 KiB
2023-12-11T08:08:45.702973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9908116
Min length4

Characters and Unicode

Total characters6524
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

Unique190 ?
Unique (%)29.1%

Sample

1st row도엽번호
2nd row357162596C
3rd row357162575D
4th row347040506A
5th row347040515D
ValueCountFrequency (%)
358131748c 12
 
1.8%
358131740b 7
 
1.1%
358131758a 6
 
0.9%
348010717d 6
 
0.9%
358131729b 5
 
0.8%
358131740c 5
 
0.8%
358131766b 5
 
0.8%
348010717b 5
 
0.8%
348010717a 5
 
0.8%
358131710c 5
 
0.8%
Other values (342) 592
90.7%
2023-12-11T08:08:46.066614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1142
17.5%
1 1061
16.3%
8 724
11.1%
0 614
9.4%
5 577
8.8%
7 544
8.3%
4 435
 
6.7%
2 368
 
5.6%
6 272
 
4.2%
B 195
 
3.0%
Other values (8) 592
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5868
89.9%
Uppercase Letter 652
 
10.0%
Other Letter 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1142
19.5%
1 1061
18.1%
8 724
12.3%
0 614
10.5%
5 577
9.8%
7 544
9.3%
4 435
 
7.4%
2 368
 
6.3%
6 272
 
4.6%
9 131
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
B 195
29.9%
C 157
24.1%
D 153
23.5%
A 147
22.5%
Other Letter
ValueCountFrequency (%)
도 1
25.0%
엽 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5868
89.9%
Latin 652
 
10.0%
Hangul 4
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1142
19.5%
1 1061
18.1%
8 724
12.3%
0 614
10.5%
5 577
9.8%
7 544
9.3%
4 435
 
7.4%
2 368
 
6.3%
6 272
 
4.6%
9 131
 
2.2%
Latin
ValueCountFrequency (%)
B 195
29.9%
C 157
24.1%
D 153
23.5%
A 147
22.5%
Hangul
ValueCountFrequency (%)
도 1
25.0%
엽 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6520
99.9%
Hangul 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1142
17.5%
1 1061
16.3%
8 724
11.1%
0 614
9.4%
5 577
8.8%
7 544
8.3%
4 435
 
6.7%
2 368
 
5.6%
6 272
 
4.2%
B 195
 
3.0%
Other values (4) 588
9.0%
Hangul
ValueCountFrequency (%)
도 1
25.0%
엽 1
25.0%
번 1
25.0%
호 1
25.0%

Unnamed: 6
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
사천시
652 
<NA>
 
1
관리기관
 
1

Length

Max length4
Median length3
Mean length3.0030581
Min length3

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
사천시 652
99.7%
<NA> 1
 
0.2%
관리기관 1
 
0.2%

Length

2023-12-11T08:08:46.195267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:08:46.279859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사천시 652
99.7%
na 1
 
0.2%
관리기관 1
 
0.2%

Unnamed: 7
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size5.2 KiB

Unnamed: 8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
<NA>
619 
RD20110002
 
4
RD20110013
 
4
RD20110005
 
4
RD20110004
 
3
Other values (12)
 
20

Length

Max length10
Median length4
Mean length4.3119266
Min length4

Unique

Unique5 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 619
94.6%
RD20110002 4
 
0.6%
RD20110013 4
 
0.6%
RD20110005 4
 
0.6%
RD20110004 3
 
0.5%
RD20110017 3
 
0.5%
RD20110001 2
 
0.3%
RD20110008 2
 
0.3%
RD20110007 2
 
0.3%
RD20110019 2
 
0.3%
Other values (7) 9
 
1.4%

Length

2023-12-11T08:08:46.401065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 619
94.6%
rd20110013 4
 
0.6%
rd20110005 4
 
0.6%
rd20110002 4
 
0.6%
rd20110004 3
 
0.5%
rd20110017 3
 
0.5%
rd20110019 2
 
0.3%
rd20110010 2
 
0.3%
rd20110020 2
 
0.3%
rd20110007 2
 
0.3%
Other values (7) 9
 
1.4%

Unnamed: 9
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
19600101
493 
<NA>
62 
20110101
 
33
19000101
 
28
20080101
 
9
Other values (10)
 
29

Length

Max length8
Median length8
Mean length7.6146789
Min length4

Unique

Unique6 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
19600101 493
75.4%
<NA> 62
 
9.5%
20110101 33
 
5.0%
19000101 28
 
4.3%
20080101 9
 
1.4%
20051231 8
 
1.2%
20001231 6
 
0.9%
20100101 6
 
0.9%
19920430 3
 
0.5%
설치일자 1
 
0.2%
Other values (5) 5
 
0.8%

Length

2023-12-11T08:08:46.527430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19600101 493
75.4%
na 62
 
9.5%
20110101 33
 
5.0%
19000101 28
 
4.3%
20080101 9
 
1.4%
20051231 8
 
1.2%
20001231 6
 
0.9%
20100101 6
 
0.9%
19920430 3
 
0.5%
설치일자 1
 
0.2%
Other values (5) 5
 
0.8%

Unnamed: 10
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size5.2 KiB

Unnamed: 11
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size5.2 KiB

Unnamed: 12
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size5.2 KiB

Unnamed: 13
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size5.2 KiB

Unnamed: 14
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size5.2 KiB

Unnamed: 15
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.2%
Memory size5.2 KiB

Correlations

2023-12-11T08:08:46.610915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사천시 과속방지턱 추출 자료Unnamed: 4Unnamed: 6Unnamed: 8Unnamed: 9
사천시 과속방지턱 추출 자료1.0001.0000.7051.0001.000
Unnamed: 41.0001.0001.0000.9670.728
Unnamed: 60.7051.0001.0001.0001.000
Unnamed: 81.0000.9671.0001.0001.000
Unnamed: 91.0000.7281.0001.0001.000
2023-12-11T08:08:46.709412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 8Unnamed: 9사천시 과속방지턱 추출 자료Unnamed: 6Unnamed: 4
Unnamed: 81.0000.7710.7590.7590.735
Unnamed: 90.7711.0000.9900.9900.351
사천시 과속방지턱 추출 자료0.7590.9901.0000.4980.989
Unnamed: 60.7590.9900.4981.0000.989
Unnamed: 40.7350.3510.9890.9891.000
2023-12-11T08:08:46.796581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사천시 과속방지턱 추출 자료Unnamed: 4Unnamed: 6Unnamed: 8Unnamed: 9
사천시 과속방지턱 추출 자료1.0000.9890.4980.7590.990
Unnamed: 40.9891.0000.9890.7350.351
Unnamed: 60.4980.9891.0000.7590.990
Unnamed: 80.7590.7350.7591.0000.771
Unnamed: 90.9900.3510.9900.7711.000

Missing values

2023-12-11T08:08:44.741438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:08:44.918076image/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.
2023-12-11T08:08:45.085716image/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 과속방지턱 추출 자료Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15
0<NA>* 작성 일자 : 2020.08.03NaNNaN<NA><NA><NA>NaN<NA><NA>NaNNaNNaNNaNNaNNaN
1<NA>지형지물부호관리번호행정읍면동코드행정읍면동도엽번호관리기관도로구간번호공사번호설치일자폭원연장위도경도X좌표Y좌표
2<NA>과속방지턱80002148240370서포면357162596C사천시478104<NA>196001013.13.735.000694127.975393106464.616206267651.573931
3<NA>과속방지턱80002748240370서포면357162575D사천시490718<NA>196001016.53.935.010704127.974868106428.044842268762.732241
4<NA>과속방지턱90600148240370서포면347040506A사천시478104<NA>196001013.253.6734.999412127.975649106486.447375267509.141294
5<NA>과속방지턱91500148240370서포면347040515D사천시478110<NA>196001015.983.9134.992296127.974085106335.582345266721.16549
6<NA>과속방지턱91500248240370서포면347040515D사천시478113<NA>196001015.413.8434.991713127.974563106378.560702266655.948566
7<NA>과속방지턱80002248240370서포면357162587D사천시471504<NA>196001015.83.835.005459127.984165107270.752638268172.14439
8<NA>과속방지턱80002448240370서포면357162596A사천시478104<NA>196001016.23.835.004646127.976958106611.938847268088.642417
9<NA>과속방지턱80002348240370서포면357162586D사천시471506<NA>196001016.43.435.006678127.979829106876.342797268311.418245
Unnamed: 0사천시 과속방지턱 추출 자료Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15
644<NA>과속방지턱19400148240370서포면357162475B사천시194036<NA>190001016.653.5935.014038127.924814101863.008763269180.639327
645<NA>과속방지턱19400248240370서포면357162475B사천시194036<NA>190001016.683.5835.014546127.923081101705.452964269238.728516
646<NA>과속방지턱19400348240320사남면358131775C사천시194062<NA>190001016.423.9535.061042128.071816115329.518831274260.895697
647<NA>과속방지턱19400448240320사남면358131796C사천시194066<NA>190001015.723.935.050776128.075791115681.525758273118.612131
648<NA>과속방지턱19400548240320사남면358131796C사천시194066<NA>190001016.113.9635.05097128.075301115637.066328273140.550388
649<NA>과속방지턱19400648240550벌용동348010735D사천시194001<NA>190001016.133.6134.930516128.073396115339.325112259778.274335
650<NA>과속방지턱19400748240550벌용동348010735D사천시194001<NA>190001016.183.5734.930527128.073945115389.494775259779.126391
651<NA>과속방지턱19400848240550벌용동348010726C사천시194002<NA>190001016.733.5834.936578128.076935115668.84577260447.927554
652<NA>과속방지턱19400948240550벌용동348010726C사천시194002<NA>190001016.433.5734.935089128.076403115618.763085260283.16596
653<NA>과속방지턱19401048240370서포면358132191B사천시194049<NA>190001016.543.3835.00476128.003114108999.783676268077.080082

Duplicate rows

Most frequently occurring

사천시 과속방지턱 추출 자료Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 8Unnamed: 9# duplicates
133과속방지턱정동면358131748C사천시<NA>1960010112
85과속방지턱사천읍358131740B사천시<NA>196001017
48과속방지턱벌용동348010717A사천시<NA>196001015
73과속방지턱사천읍358131729B사천시<NA>196001015
86과속방지턱사천읍358131740C사천시<NA>196001015
136과속방지턱정동면358131758A사천시<NA>196001015
150과속방지턱축동면358131710C사천시<NA>196001015
1과속방지턱(확인 불가)348010718B사천시<NA><NA>4
3과속방지턱곤명면357161488B사천시<NA>196001014
12과속방지턱곤양면357162072A사천시<NA>196001014