Overview

Dataset statistics

Number of variables7
Number of observations36
Missing cells35
Missing cells (%)13.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory59.7 B

Variable types

Text2
Unsupported5

Alerts

3. 용도지구 현황(연도별) has 23 (63.9%) missing valuesMissing
Unnamed: 1 has 6 (16.7%) missing valuesMissing
Unnamed: 2 has 1 (2.8%) missing valuesMissing
Unnamed: 3 has 1 (2.8%) missing valuesMissing
Unnamed: 4 has 1 (2.8%) missing valuesMissing
Unnamed: 5 has 2 (5.6%) missing valuesMissing
Unnamed: 6 has 1 (2.8%) 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

Reproduction

Analysis started2024-03-14 02:06:52.822530
Analysis finished2024-03-14 02:06:53.252480
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

3. 용도지구 현황(연도별)
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing23
Missing (%)63.9%
Memory size420.0 B
2024-03-14T11:06:53.331453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length10
Mean length7.8461538
Min length2

Characters and Unicode

Total characters102
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row(기준 : 2013. 12월말, 단위 : : ㎢)
2nd row구분
3rd row총 계
4th row경관 지구
5th row미관 지구
ValueCountFrequency (%)
지구 7
20.6%
3
 
8.8%
방 2
 
5.9%
구 2
 
5.9%
지 2
 
5.9%
기준 1
 
2.9%
개발진흥 1
 
2.9%
취락 1
 
2.9%
시설보호 1
 
2.9%
보존 1
 
2.9%
Other values (13) 13
38.2%
2024-03-14T11:06:53.577268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
22.5%
구 11
 
10.8%
지 10
 
9.8%
7
 
6.9%
: 3
 
2.9%
도 2
 
2.0%
관 2
 
2.0%
보 2
 
2.0%
방 2
 
2.0%
2 2
 
2.0%
Other values (37) 38
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58
56.9%
Space Separator 23
 
22.5%
Control 7
 
6.9%
Decimal Number 6
 
5.9%
Other Punctuation 5
 
4.9%
Open Punctuation 1
 
1.0%
Other Symbol 1
 
1.0%
Close Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 11
19.0%
지 10
 
17.2%
도 2
 
3.4%
관 2
 
3.4%
보 2
 
3.4%
방 2
 
3.4%
특 1
 
1.7%
시 1
 
1.7%
화 1
 
1.7%
제 1
 
1.7%
Other values (25) 25
43.1%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
0 1
16.7%
3 1
16.7%
Other Punctuation
ValueCountFrequency (%)
: 3
60.0%
. 1
 
20.0%
, 1
 
20.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Control
ValueCountFrequency (%)
7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Symbol
ValueCountFrequency (%)
㎢ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58
56.9%
Common 44
43.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 11
19.0%
지 10
 
17.2%
도 2
 
3.4%
관 2
 
3.4%
보 2
 
3.4%
방 2
 
3.4%
특 1
 
1.7%
시 1
 
1.7%
화 1
 
1.7%
제 1
 
1.7%
Other values (25) 25
43.1%
Common
ValueCountFrequency (%)
23
52.3%
7
 
15.9%
: 3
 
6.8%
2 2
 
4.5%
1 2
 
4.5%
( 1
 
2.3%
0 1
 
2.3%
3 1
 
2.3%
. 1
 
2.3%
, 1
 
2.3%
Other values (2) 2
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58
56.9%
ASCII 43
42.2%
CJK Compat 1
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23
53.5%
7
 
16.3%
: 3
 
7.0%
2 2
 
4.7%
1 2
 
4.7%
( 1
 
2.3%
0 1
 
2.3%
3 1
 
2.3%
. 1
 
2.3%
, 1
 
2.3%
Hangul
ValueCountFrequency (%)
구 11
19.0%
지 10
 
17.2%
도 2
 
3.4%
관 2
 
3.4%
보 2
 
3.4%
방 2
 
3.4%
특 1
 
1.7%
시 1
 
1.7%
화 1
 
1.7%
제 1
 
1.7%
Other values (25) 25
43.1%
CJK Compat
ValueCountFrequency (%)
㎢ 1
100.0%

Unnamed: 1
Text

MISSING 

Distinct27
Distinct (%)90.0%
Missing6
Missing (%)16.7%
Memory size420.0 B
2024-03-14T11:06:53.743779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.6
Min length2

Characters and Unicode

Total characters138
Distinct characters47
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

Unique25 ?
Unique (%)83.3%

Sample

1st row소 계
2nd row자 연
3rd row수 변
4th row시가지
5th row소 계
ValueCountFrequency (%)
소 6
 
13.6%
계 6
 
13.6%
최 2
 
4.5%
자 2
 
4.5%
연 2
 
4.5%
단 1
 
2.3%
소계 1
 
2.3%
주거 1
 
2.3%
산업 1
 
2.3%
관광 1
 
2.3%
Other values (21) 21
47.7%
2024-03-14T11:06:54.000447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61
44.2%
계 8
 
5.8%
소 7
 
5.1%
시 6
 
4.3%
설 5
 
3.6%
자 3
 
2.2%
문 2
 
1.4%
항 2
 
1.4%
최 2
 
1.4%
공 2
 
1.4%
Other values (37) 40
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
55.8%
Space Separator 61
44.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 8
 
10.4%
소 7
 
9.1%
시 6
 
7.8%
설 5
 
6.5%
자 3
 
3.9%
문 2
 
2.6%
항 2
 
2.6%
최 2
 
2.6%
공 2
 
2.6%
화 2
 
2.6%
Other values (36) 38
49.4%
Space Separator
ValueCountFrequency (%)
61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
55.8%
Common 61
44.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 8
 
10.4%
소 7
 
9.1%
시 6
 
7.8%
설 5
 
6.5%
자 3
 
3.9%
문 2
 
2.6%
항 2
 
2.6%
최 2
 
2.6%
공 2
 
2.6%
화 2
 
2.6%
Other values (36) 38
49.4%
Common
ValueCountFrequency (%)
61
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
55.8%
ASCII 61
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61
100.0%
Hangul
ValueCountFrequency (%)
계 8
 
10.4%
소 7
 
9.1%
시 6
 
7.8%
설 5
 
6.5%
자 3
 
3.9%
문 2
 
2.6%
항 2
 
2.6%
최 2
 
2.6%
공 2
 
2.6%
화 2
 
2.6%
Other values (36) 38
49.4%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.8%
Memory size420.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.8%
Memory size420.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.8%
Memory size420.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)5.6%
Memory size420.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)2.8%
Memory size420.0 B

Correlations

2024-03-14T11:06:54.068936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
3. 용도지구 현황(연도별)Unnamed: 1
3. 용도지구 현황(연도별)1.0001.000
Unnamed: 11.0001.000

Missing values

2024-03-14T11:06:52.950725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:06:53.078005image/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.
2024-03-14T11:06:53.181861image/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

3. 용도지구 현황(연도별)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
0(기준 : 2013. 12월말, 단위 : : ㎢)<NA>NaNNaNNaNNaNNaN
1구분<NA>20092010201120122013
2총 계<NA>103.76109.8138.09138.36146.51
3경관 지구소 계1.141.1313.8113.9313.81
4<NA>자 연1.141.131.131.251.13
5<NA>수 변--12.6812.6812.68
6<NA>시가지-----
7미관 지구소 계7.927.949.849.869.81
8<NA>중 심0.540.540.570.570.57
9<NA>역사문화0.010.011.281.281.28
3. 용도지구 현황(연도별)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
26<NA>자 연22.3833.4338.8650.4359.66
27<NA>집 단-----
28개발진흥 지구소계45.2540.5638.8637.6336.53
29<NA>주거16.4811.9110.219.588.57
30<NA>산업7.567.329.099.419.83
31<NA>유통0.030.030.09NaN0.05
32<NA>관광18.9219.9719.0318.2617.57
33<NA>복합0.270.860.070.070.07
34<NA>특정1.990.470.370.310.44
35특정용도제한지구<NA>-----