Overview

Dataset statistics

Number of variables9
Number of observations25
Missing cells36
Missing cells (%)16.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory82.3 B

Variable types

Categorical3
Text2
Numeric3
DateTime1

Dataset

Description경상북도 구미시의 행정구역 현황으로 읍면동별로 법정동수, 행정동수, 통수, 리수, 반수, 해당 법정동 정보 등을 제공합니다.
URLhttps://www.data.go.kr/data/15119988/fileData.do

Alerts

시군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
행정동수 is highly overall correlated with 통수 and 2 other fieldsHigh correlation
법정동수 is highly overall correlated with 통수 and 2 other fieldsHigh correlation
통수 is highly overall correlated with 반수 and 2 other fieldsHigh correlation
리수 is highly overall correlated with 반수High correlation
반수 is highly overall correlated with 통수 and 3 other fieldsHigh correlation
통수 has 8 (32.0%) missing valuesMissing
리수 has 17 (68.0%) missing valuesMissing
비고 has 11 (44.0%) missing valuesMissing
읍면동명 has unique valuesUnique
반수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:22:24.068985
Analysis finished2023-12-12 16:22:25.573261
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
경상북도 구미시
25 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도 구미시
2nd row경상북도 구미시
3rd row경상북도 구미시
4th row경상북도 구미시
5th row경상북도 구미시

Common Values

ValueCountFrequency (%)
경상북도 구미시 25
100.0%

Length

2023-12-13T01:22:25.629593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:22:25.750269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 25
50.0%
구미시 25
50.0%

읍면동명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-13T01:22:25.953390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.32
Min length3

Characters and Unicode

Total characters83
Distinct characters43
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 (%)100.0%

Sample

1st row선산읍
2nd row고아읍
3rd row산동읍
4th row무을면
5th row옥성면
ValueCountFrequency (%)
선산읍 1
 
4.0%
형곡1동 1
 
4.0%
진미동 1
 
4.0%
인동동 1
 
4.0%
임오동 1
 
4.0%
상모사곡동 1
 
4.0%
광평동 1
 
4.0%
공단동 1
 
4.0%
비산동 1
 
4.0%
신평2동 1
 
4.0%
Other values (15) 15
60.0%
2023-12-13T01:22:26.305868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 19
22.9%
면 5
 
6.0%
평 5
 
6.0%
산 4
 
4.8%
곡 3
 
3.6%
읍 3
 
3.6%
1 2
 
2.4%
형 2
 
2.4%
2 2
 
2.4%
신 2
 
2.4%
Other values (33) 36
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
95.2%
Decimal Number 4
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 19
24.1%
면 5
 
6.3%
평 5
 
6.3%
산 4
 
5.1%
곡 3
 
3.8%
읍 3
 
3.8%
형 2
 
2.5%
신 2
 
2.5%
원 2
 
2.5%
선 2
 
2.5%
Other values (31) 32
40.5%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
95.2%
Common 4
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 19
24.1%
면 5
 
6.3%
평 5
 
6.3%
산 4
 
5.1%
곡 3
 
3.8%
읍 3
 
3.8%
형 2
 
2.5%
신 2
 
2.5%
원 2
 
2.5%
선 2
 
2.5%
Other values (31) 32
40.5%
Common
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
95.2%
ASCII 4
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 19
24.1%
면 5
 
6.3%
평 5
 
6.3%
산 4
 
5.1%
곡 3
 
3.8%
읍 3
 
3.8%
형 2
 
2.5%
신 2
 
2.5%
원 2
 
2.5%
선 2
 
2.5%
Other values (31) 32
40.5%
ASCII
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

법정동수
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
11 
1
7 
2
3 
5
2 
4
 
1

Length

Max length4
Median length1
Mean length2.32
Min length1

Unique

Unique2 ?
Unique (%)8.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 11
44.0%
1 7
28.0%
2 3
 
12.0%
5 2
 
8.0%
4 1
 
4.0%
3 1
 
4.0%

Length

2023-12-13T01:22:26.430829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:22:26.531355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 11
44.0%
1 7
28.0%
2 3
 
12.0%
5 2
 
8.0%
4 1
 
4.0%
3 1
 
4.0%

행정동수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
1
17 
<NA>
8 

Length

Max length4
Median length1
Mean length1.96
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 17
68.0%
<NA> 8
32.0%

Length

2023-12-13T01:22:26.652745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:22:26.758821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 17
68.0%
na 8
32.0%

통수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)70.6%
Missing8
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean29.117647
Minimum7
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T01:22:26.843450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q123
median27
Q340
95-th percentile59
Maximum71
Range64
Interquartile range (IQR)17

Descriptive statistics

Standard deviation17.828637
Coefficient of variation (CV)0.61229662
Kurtosis0.5157418
Mean29.117647
Median Absolute Deviation (MAD)13
Skewness0.79218865
Sum495
Variance317.86029
MonotonicityNot monotonic
2023-12-13T01:22:26.939581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
23 3
 
12.0%
7 2
 
8.0%
40 2
 
8.0%
27 2
 
8.0%
32 1
 
4.0%
49 1
 
4.0%
25 1
 
4.0%
9 1
 
4.0%
8 1
 
4.0%
28 1
 
4.0%
Other values (2) 2
 
8.0%
(Missing) 8
32.0%
ValueCountFrequency (%)
7 2
8.0%
8 1
 
4.0%
9 1
 
4.0%
23 3
12.0%
25 1
 
4.0%
27 2
8.0%
28 1
 
4.0%
32 1
 
4.0%
40 2
8.0%
49 1
 
4.0%
ValueCountFrequency (%)
71 1
 
4.0%
56 1
 
4.0%
49 1
 
4.0%
40 2
8.0%
32 1
 
4.0%
28 1
 
4.0%
27 2
8.0%
25 1
 
4.0%
23 3
12.0%
9 1
 
4.0%

리수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)87.5%
Missing17
Missing (%)68.0%
Infinite0
Infinite (%)0.0%
Mean25.375
Minimum15
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T01:22:27.053445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q115.75
median21
Q330.75
95-th percentile45.1
Maximum50
Range35
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.489281
Coefficient of variation (CV)0.49218842
Kurtosis0.94081645
Mean25.375
Median Absolute Deviation (MAD)6
Skewness1.2435254
Sum203
Variance155.98214
MonotonicityNot monotonic
2023-12-13T01:22:27.147950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
15 2
 
8.0%
29 1
 
4.0%
50 1
 
4.0%
36 1
 
4.0%
18 1
 
4.0%
16 1
 
4.0%
24 1
 
4.0%
(Missing) 17
68.0%
ValueCountFrequency (%)
15 2
8.0%
16 1
4.0%
18 1
4.0%
24 1
4.0%
29 1
4.0%
36 1
4.0%
50 1
4.0%
ValueCountFrequency (%)
50 1
4.0%
36 1
4.0%
29 1
4.0%
24 1
4.0%
18 1
4.0%
16 1
4.0%
15 2
8.0%

반수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.84
Minimum41
Maximum519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T01:22:27.253688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile46.8
Q159
median173
Q3284
95-th percentile506.2
Maximum519
Range478
Interquartile range (IQR)225

Descriptive statistics

Standard deviation150.48856
Coefficient of variation (CV)0.74558343
Kurtosis-0.20696686
Mean201.84
Median Absolute Deviation (MAD)114
Skewness0.85861924
Sum5046
Variance22646.807
MonotonicityNot monotonic
2023-12-13T01:22:27.358387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
242 1
 
4.0%
518 1
 
4.0%
459 1
 
4.0%
123 1
 
4.0%
519 1
 
4.0%
202 1
 
4.0%
294 1
 
4.0%
53 1
 
4.0%
170 1
 
4.0%
166 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
41 1
4.0%
46 1
4.0%
50 1
4.0%
53 1
4.0%
54 1
4.0%
58 1
4.0%
59 1
4.0%
68 1
4.0%
93 1
4.0%
123 1
4.0%
ValueCountFrequency (%)
519 1
4.0%
518 1
4.0%
459 1
4.0%
407 1
4.0%
326 1
4.0%
294 1
4.0%
284 1
4.0%
269 1
4.0%
242 1
4.0%
202 1
4.0%

비고
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing11
Missing (%)44.0%
Memory size332.0 B
2023-12-13T01:22:27.526537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16.5
Mean length7.5
Min length3

Characters and Unicode

Total characters105
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

Unique14 ?
Unique (%)100.0%

Sample

1st row송정동
2nd row원평동
3rd row지산동
4th row도량동
5th row봉곡동+부곡동+선기동+수점동+남통동
ValueCountFrequency (%)
송정동 1
 
7.1%
원평동 1
 
7.1%
지산동 1
 
7.1%
도량동 1
 
7.1%
봉곡동+부곡동+선기동+수점동+남통동 1
 
7.1%
형곡동 1
 
7.1%
신평동 1
 
7.1%
비산동+공단동 1
 
7.1%
광평동 1
 
7.1%
사곡동+상모동 1
 
7.1%
Other values (4) 4
28.6%
2023-12-13T01:22:27.828660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 30
28.6%
+ 16
15.2%
평 5
 
4.8%
곡 4
 
3.8%
상 2
 
1.9%
의 2
 
1.9%
수 2
 
1.9%
임 2
 
1.9%
신 2
 
1.9%
구 2
 
1.9%
Other values (37) 38
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
84.8%
Math Symbol 16
 
15.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 30
33.7%
평 5
 
5.6%
곡 4
 
4.5%
상 2
 
2.2%
의 2
 
2.2%
수 2
 
2.2%
임 2
 
2.2%
신 2
 
2.2%
구 2
 
2.2%
산 2
 
2.2%
Other values (36) 36
40.4%
Math Symbol
ValueCountFrequency (%)
+ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
84.8%
Common 16
 
15.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 30
33.7%
평 5
 
5.6%
곡 4
 
4.5%
상 2
 
2.2%
의 2
 
2.2%
수 2
 
2.2%
임 2
 
2.2%
신 2
 
2.2%
구 2
 
2.2%
산 2
 
2.2%
Other values (36) 36
40.4%
Common
ValueCountFrequency (%)
+ 16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
84.8%
ASCII 16
 
15.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 30
33.7%
평 5
 
5.6%
곡 4
 
4.5%
상 2
 
2.2%
의 2
 
2.2%
수 2
 
2.2%
임 2
 
2.2%
신 2
 
2.2%
구 2
 
2.2%
산 2
 
2.2%
Other values (36) 36
40.4%
ASCII
ValueCountFrequency (%)
+ 16
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum2023-08-01 00:00:00
Maximum2023-08-01 00:00:00
2023-12-13T01:22:27.925931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:28.011751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T01:22:24.892417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:24.351150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:24.588434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:24.980680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:24.433966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:24.675396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:25.080191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:24.512666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:24.780504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:22:28.086108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명법정동수통수리수반수비고
읍면동명1.0001.0001.0001.0001.0001.000
법정동수1.0001.0000.751NaN0.8721.000
통수1.0000.7511.000NaN0.9311.000
리수1.000NaNNaN1.0001.000NaN
반수1.0000.8720.9311.0001.0001.000
비고1.0001.0001.000NaN1.0001.000
2023-12-13T01:22:28.185130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동수법정동수
행정동수1.0001.000
법정동수1.0001.000
2023-12-13T01:22:28.288179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통수리수반수법정동수행정동수
통수1.000NaN0.9140.5121.000
리수NaN1.0000.9220.0000.000
반수0.9140.9221.0000.6161.000
법정동수0.5120.0000.6161.0001.000
행정동수1.0000.0001.0001.0001.000

Missing values

2023-12-13T01:22:25.233723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:22:25.397496image/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.
2023-12-13T01:22:25.507931image/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

시군명읍면동명법정동수행정동수통수리수반수비고데이터기준일자
0경상북도 구미시선산읍<NA><NA><NA>29242<NA>2023-08-01
1경상북도 구미시고아읍<NA><NA><NA>50518<NA>2023-08-01
2경상북도 구미시산동읍<NA><NA><NA>36269<NA>2023-08-01
3경상북도 구미시무을면<NA><NA><NA>1868<NA>2023-08-01
4경상북도 구미시옥성면<NA><NA><NA>1650<NA>2023-08-01
5경상북도 구미시도개면<NA><NA><NA>1559<NA>2023-08-01
6경상북도 구미시해평면<NA><NA><NA>2493<NA>2023-08-01
7경상북도 구미시장천면<NA><NA><NA>1554<NA>2023-08-01
8경상북도 구미시송정동1132<NA>284송정동2023-08-01
9경상북도 구미시원평동1123<NA>181원평동2023-08-01
시군명읍면동명법정동수행정동수통수리수반수비고데이터기준일자
15경상북도 구미시신평1동119<NA>58신평동2023-08-01
16경상북도 구미시신평2동<NA>18<NA>46<NA>2023-08-01
17경상북도 구미시비산동2128<NA>166비산동+공단동2023-08-01
18경상북도 구미시공단동<NA>123<NA>170<NA>2023-08-01
19경상북도 구미시광평동117<NA>53광평동2023-08-01
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21경상북도 구미시임오동2127<NA>202임은동+오태동2023-08-01
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24경상북도 구미시양포동5156<NA>459구포동+금전동+양호동+거의동+옥계동2023-08-01