Overview

Dataset statistics

Number of variables9
Number of observations50
Missing cells42
Missing cells (%)9.3%
Duplicate rows6
Duplicate rows (%)12.0%
Total size in memory3.6 KiB
Average record size in memory74.6 B

Variable types

Text1
Categorical1
Unsupported7

Dataset

Description대전광역시 급수관로 부설 총괄 현황입니다. 각 관경 및 관재질별 급수관로 총 연장과 지역구별 부설 현황을 나타낸 자료입니다.
URLhttps://www.data.go.kr/data/15081158/fileData.do

Alerts

Dataset has 6 (12.0%) duplicate rowsDuplicates
급수관 총괄 has 37 (74.0%) missing valuesMissing
Unnamed: 3 has 1 (2.0%) missing valuesMissing
Unnamed: 4 has 1 (2.0%) missing valuesMissing
Unnamed: 5 has 1 (2.0%) missing valuesMissing
Unnamed: 7 has 1 (2.0%) missing valuesMissing
Unnamed: 8 has 1 (2.0%) missing valuesMissing
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: 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
Unnamed: 6 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

Reproduction

Analysis started2023-12-12 18:00:29.620417
Analysis finished2023-12-12 18:00:30.137051
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

급수관 총괄
Text

MISSING 

Distinct7
Distinct (%)53.8%
Missing37
Missing (%)74.0%
Memory size532.0 B
2023-12-13T03:00:30.241152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.3076923
Min length2

Characters and Unicode

Total characters30
Distinct characters11
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 (%)7.7%

Sample

1st row구별
2nd row합계
3rd row합계
4th row동구
5th row동구
ValueCountFrequency (%)
합계 2
15.4%
동구 2
15.4%
중구 2
15.4%
서구 2
15.4%
유성구 2
15.4%
대덕구 2
15.4%
구별 1
7.7%
2023-12-13T03:00:30.535276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 11
36.7%
합 2
 
6.7%
계 2
 
6.7%
동 2
 
6.7%
중 2
 
6.7%
서 2
 
6.7%
유 2
 
6.7%
성 2
 
6.7%
대 2
 
6.7%
덕 2
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 11
36.7%
합 2
 
6.7%
계 2
 
6.7%
동 2
 
6.7%
중 2
 
6.7%
서 2
 
6.7%
유 2
 
6.7%
성 2
 
6.7%
대 2
 
6.7%
덕 2
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 11
36.7%
합 2
 
6.7%
계 2
 
6.7%
동 2
 
6.7%
중 2
 
6.7%
서 2
 
6.7%
유 2
 
6.7%
성 2
 
6.7%
대 2
 
6.7%
덕 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 11
36.7%
합 2
 
6.7%
계 2
 
6.7%
동 2
 
6.7%
중 2
 
6.7%
서 2
 
6.7%
유 2
 
6.7%
성 2
 
6.7%
대 2
 
6.7%
덕 2
 
6.7%

Unnamed: 1
Categorical

Distinct10
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
Ø15
8 
계
6 
Ø20
6 
Ø25
6 
Ø30
6 
Other values (5)
18 

Length

Max length4
Median length3
Mean length2.76
Min length1

Unique

Unique2 ?
Unique (%)4.0%

Sample

1st row<NA>
2nd row관경
3rd row계
4th rowØ15
5th rowØ15

Common Values

ValueCountFrequency (%)
Ø15 8
16.0%
계 6
12.0%
Ø20 6
12.0%
Ø25 6
12.0%
Ø30 6
12.0%
Ø40 6
12.0%
Ø50 6
12.0%
Ø13 4
8.0%
<NA> 1
 
2.0%
관경 1
 
2.0%

Length

2023-12-13T03:00:30.678064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:00:30.820135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ø15 8
16.0%
계 6
12.0%
ø20 6
12.0%
ø25 6
12.0%
ø30 6
12.0%
ø40 6
12.0%
ø50 6
12.0%
ø13 4
8.0%
na 1
 
2.0%
관경 1
 
2.0%

Unnamed: 2
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size532.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.0%
Memory size532.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.0%
Memory size532.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.0%
Memory size532.0 B

Unnamed: 6
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size532.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.0%
Memory size532.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.0%
Memory size532.0 B

Correlations

2023-12-13T03:00:30.908007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수관 총괄Unnamed: 1
급수관 총괄1.0000.376
Unnamed: 10.3761.000

Missing values

2023-12-13T03:00:29.745352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:00:29.905050image/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.
2023-12-13T03:00:30.049925image/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

급수관 총괄Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0<NA><NA>단위 : mNaNNaNNaNㄴㅇNaNNaN
1구별관경합 계닥타일주철관도복장강관PVC(경화염화비닐관)PE(폴리에틸렌관)STS(스테인레스관)기타
2합계계1265472.31221.5713825.07120010.47106999.21020747.773668.23
3합계Ø151424.7150001419.710
4<NA>Ø15484949.0943.451542.5450900.88342.7431975.49144.03
5<NA>Ø20206682.51201627.2616274.97688.86187829.77241.65
6<NA>Ø25166483.2138.441497.918588.26501.27155616.73240.6
7<NA>Ø3035018.664311.575725.872824.9526122.2730
8<NA>Ø40132987.158.693060.0619834.1534598.2475227.12258.89
9<NA>Ø50237926.98101.995785.7318686.3468043.18142556.682753.06
급수관 총괄Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
40<NA>Ø4021141.428.69233.58166.92984.5917717.6630
41<NA>Ø5057339.9222.322040.76208.8719303.6434302.331462
42대덕구계197476.9974561.8320198.7821270.93151322.99115.46
43대덕구Ø130000000
44<NA>Ø1580168.637934.9812691.12101.7366400.5433.26
45<NA>Ø2035884.740899.142536.36228.0732199.5221.65
46<NA>Ø2524169.940391.181180.97202.2822395.510
47<NA>Ø303097.810279.23346.89179.952291.740
48<NA>Ø4021710.710503.031722.918397.3811087.390
49<NA>Ø5032445.1601554.271720.5312161.5216948.2960.55

Duplicate rows

Most frequently occurring

급수관 총괄Unnamed: 1# duplicates
0<NA>Ø156
1<NA>Ø206
2<NA>Ø256
3<NA>Ø306
4<NA>Ø406
5<NA>Ø506