Overview

Dataset statistics

Number of variables15
Number of observations46
Missing cells529
Missing cells (%)76.7%
Duplicate rows1
Duplicate rows (%)2.2%
Total size in memory5.5 KiB
Average record size in memory122.9 B

Variable types

Text5
Unsupported10

Dataset

Description경상남도 진주시 일원 가로수 위치별 식재 현황에 대한 데이터입니다. (세부 구분 항목: 노선, 수종, 흉고직경 규격 등)
Author경상남도 진주시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15109454

Alerts

Unnamed: 11 has constant value ""Constant
Unnamed: 14 has constant value ""Constant
Dataset has 1 (2.2%) duplicate rowsDuplicates
흙깍기 has 30 (65.2%) missing valuesMissing
Unnamed: 1 has 24 (52.2%) missing valuesMissing
Unnamed: 2 has 37 (80.4%) missing valuesMissing
터파기 has 30 (65.2%) missing valuesMissing
Unnamed: 4 has 25 (54.3%) missing valuesMissing
Unnamed: 5 has 26 (56.5%) missing valuesMissing
무대 has 30 (65.2%) missing valuesMissing
Unnamed: 7 has 31 (67.4%) missing valuesMissing
Unnamed: 8 has 40 (87.0%) missing valuesMissing
도쟈운반 has 38 (82.6%) missing valuesMissing
Unnamed: 10 has 40 (87.0%) missing valuesMissing
Unnamed: 11 has 45 (97.8%) missing valuesMissing
덤프운반 has 44 (95.7%) missing valuesMissing
Unnamed: 13 has 44 (95.7%) missing valuesMissing
Unnamed: 14 has 45 (97.8%) missing valuesMissing
Unnamed: 1 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
터파기 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: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
무대 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: 8 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: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 00:03:59.404853
Analysis finished2023-12-11 00:04:00.265545
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

흙깍기
Text

MISSING 

Distinct15
Distinct (%)93.8%
Missing30
Missing (%)65.2%
Memory size500.0 B
2023-12-11T09:04:00.374951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8.5
Mean length6.8125
Min length2

Characters and Unicode

Total characters109
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)87.5%

Sample

1st row토사
2nd row흙쌓기
3rd row노체
4th row층따기
5th row배수공터파기
ValueCountFrequency (%)
계 2
 
12.5%
토사 1
 
6.2%
흙쌓기 1
 
6.2%
노체 1
 
6.2%
층따기 1
 
6.2%
배수공터파기 1
 
6.2%
구조물공터파기 1
 
6.2%
상하수도공터파기 1
 
6.2%
조경공터파기 1
 
6.2%
교통안전시설공터파기 1
 
6.2%
Other values (5) 5
31.2%
2023-12-11T09:04:00.978308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
16.5%
기 12
 
11.0%
공 10
 
9.2%
메 5
 
4.6%
되 5
 
4.6%
파 5
 
4.6%
터 5
 
4.6%
우 5
 
4.6%
수 4
 
3.7%
조 4
 
3.7%
Other values (22) 36
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
83.5%
Space Separator 18
 
16.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 12
 
13.2%
공 10
 
11.0%
메 5
 
5.5%
되 5
 
5.5%
파 5
 
5.5%
터 5
 
5.5%
우 5
 
5.5%
수 4
 
4.4%
조 4
 
4.4%
하 2
 
2.2%
Other values (21) 34
37.4%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
83.5%
Common 18
 
16.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 12
 
13.2%
공 10
 
11.0%
메 5
 
5.5%
되 5
 
5.5%
파 5
 
5.5%
터 5
 
5.5%
우 5
 
5.5%
수 4
 
4.4%
조 4
 
4.4%
하 2
 
2.2%
Other values (21) 34
37.4%
Common
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
83.5%
ASCII 18
 
16.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
100.0%
Hangul
ValueCountFrequency (%)
기 12
 
13.2%
공 10
 
11.0%
메 5
 
5.5%
되 5
 
5.5%
파 5
 
5.5%
터 5
 
5.5%
우 5
 
5.5%
수 4
 
4.4%
조 4
 
4.4%
하 2
 
2.2%
Other values (21) 34
37.4%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing24
Missing (%)52.2%
Memory size500.0 B

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)80.4%
Memory size500.0 B

터파기
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)65.2%
Memory size500.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing25
Missing (%)54.3%
Memory size500.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing26
Missing (%)56.5%
Memory size500.0 B

무대
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)65.2%
Memory size500.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)67.4%
Memory size500.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing40
Missing (%)87.0%
Memory size500.0 B

도쟈운반
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing38
Missing (%)82.6%
Memory size500.0 B
2023-12-11T09:04:01.155238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length4.125
Min length2

Characters and Unicode

Total characters33
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st row토사
2nd row콘크리트유용
3rd row토공
4th row배수공
5th row구조물공
ValueCountFrequency (%)
토사 1
12.5%
콘크리트유용 1
12.5%
토공 1
12.5%
배수공 1
12.5%
구조물공 1
12.5%
포장공 1
12.5%
부대공 1
12.5%
계 1
12.5%
2023-12-11T09:04:01.476134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
27.3%
공 5
15.2%
토 2
 
6.1%
구 1
 
3.0%
대 1
 
3.0%
부 1
 
3.0%
장 1
 
3.0%
포 1
 
3.0%
물 1
 
3.0%
조 1
 
3.0%
Other values (10) 10
30.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24
72.7%
Space Separator 9
 
27.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 5
20.8%
토 2
 
8.3%
구 1
 
4.2%
대 1
 
4.2%
부 1
 
4.2%
장 1
 
4.2%
포 1
 
4.2%
물 1
 
4.2%
조 1
 
4.2%
수 1
 
4.2%
Other values (9) 9
37.5%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24
72.7%
Common 9
 
27.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 5
20.8%
토 2
 
8.3%
구 1
 
4.2%
대 1
 
4.2%
부 1
 
4.2%
장 1
 
4.2%
포 1
 
4.2%
물 1
 
4.2%
조 1
 
4.2%
수 1
 
4.2%
Other values (9) 9
37.5%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24
72.7%
ASCII 9
 
27.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9
100.0%
Hangul
ValueCountFrequency (%)
공 5
20.8%
토 2
 
8.3%
구 1
 
4.2%
대 1
 
4.2%
부 1
 
4.2%
장 1
 
4.2%
포 1
 
4.2%
물 1
 
4.2%
조 1
 
4.2%
수 1
 
4.2%
Other values (9) 9
37.5%

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing40
Missing (%)87.0%
Memory size500.0 B

Unnamed: 11
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing45
Missing (%)97.8%
Memory size500.0 B
2023-12-11T09:04:01.567806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row발파
ValueCountFrequency (%)
발파 1
100.0%
2023-12-11T09:04:01.773755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
발 1
50.0%
파 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
발 1
50.0%
파 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
발 1
50.0%
파 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
발 1
50.0%
파 1
50.0%

덤프운반
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing44
Missing (%)95.7%
Memory size500.0 B
2023-12-11T09:04:01.915082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters6
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row토사
2nd row노견피토
ValueCountFrequency (%)
토사 1
50.0%
노견피토 1
50.0%
2023-12-11T09:04:02.188378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
토 2
33.3%
사 1
16.7%
노 1
16.7%
견 1
16.7%
피 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
토 2
33.3%
사 1
16.7%
노 1
16.7%
견 1
16.7%
피 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
토 2
33.3%
사 1
16.7%
노 1
16.7%
견 1
16.7%
피 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
토 2
33.3%
사 1
16.7%
노 1
16.7%
견 1
16.7%
피 1
16.7%

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing44
Missing (%)95.7%
Memory size500.0 B

Unnamed: 14
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing45
Missing (%)97.8%
Memory size500.0 B
2023-12-11T09:04:02.274650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row발파
ValueCountFrequency (%)
발파 1
100.0%
2023-12-11T09:04:02.468926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
발 1
50.0%
파 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
발 1
50.0%
파 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
발 1
50.0%
파 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
발 1
50.0%
파 1
50.0%

Correlations

2023-12-11T09:04:02.559494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
흙깍기도쟈운반덤프운반
흙깍기1.0001.000NaN
도쟈운반1.0001.0000.000
덤프운반NaN0.0001.000

Missing values

2023-12-11T09:03:59.637770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:03:59.870720image/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.
2023-12-11T09:04:00.073112image/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

흙깍기Unnamed: 1Unnamed: 2터파기Unnamed: 4Unnamed: 5무대Unnamed: 7Unnamed: 8도쟈운반Unnamed: 10Unnamed: 11덤프운반Unnamed: 13Unnamed: 14
0토사리핑발파토사리핑발파토사리핑발파토사리핑발파토사리핑발파
1<NA>NaNNaNNaNNaNNaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
2<NA>NaNNaN측구터파기NaNNaN횡무대NaNNaN<NA>NaN<NA><NA>NaN<NA>
3<NA>NaNNaN토사리핑발파토사리핑발파<NA>NaN<NA><NA>NaN<NA>
4<NA>NaNNaN000NaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
5<NA>NaNNaN구조물터파기NaNNaN종무대NaNNaN<NA>NaN<NA><NA>NaN<NA>
6<NA>NaNNaN토사리핑발파토사리핑발파<NA>NaN<NA><NA>NaN<NA>
7<NA>NaNNaNNaNNaNNaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
8<NA>NaNNaNNaNNaNNaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
9흙쌓기NaNNaNNaN사토NaNNaN사토순성토<NA>NaN<NA><NA>NaN<NA>
흙깍기Unnamed: 1Unnamed: 2터파기Unnamed: 4Unnamed: 5무대Unnamed: 7Unnamed: 8도쟈운반Unnamed: 10Unnamed: 11덤프운반Unnamed: 13Unnamed: 14
36<NA>540NaNNaN성토부NaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
37구조물공되메우기NaNNaNNaN절토부NaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
38<NA>0NaNNaNNaNNaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
39상하수도공되메우기NaNNaNNaN법면보호공NaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
40<NA>160NaNNaN줄떼NaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
41조경공되메우기NaNNaNNaN평떼NaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
42<NA>153NaNNaN면고르기(리핑)NaN0NaNNaN<NA>NaN<NA><NA>NaN<NA>
43교통안전시설공되메우기NaNNaNNaN면고르기(발파)NaN0NaNNaN<NA>NaN<NA><NA>NaN<NA>
44<NA>40.7NaNNaNNaNNaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>
45계893.7NaNNaN벌 개 제 근NaNNaNNaNNaN<NA>NaN<NA><NA>NaN<NA>

Duplicate rows

Most frequently occurring

흙깍기도쟈운반Unnamed: 11덤프운반Unnamed: 14# duplicates
0<NA><NA><NA><NA><NA>29