Overview

Dataset statistics

Number of variables8
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory73.3 B

Variable types

Text1
Numeric4
Categorical3

Dataset

Description경상북도 구미시 인구현황 데이터로 각 읍면동 별 세대수, 남자 인구 수,여자 인구 수에 대한 데이터를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15053579/fileData.do

Alerts

관리기관명 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
데이터기준일자 has constant value ""Constant
세대수 is highly overall correlated with 계 and 2 other fieldsHigh correlation
계 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
남 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
여 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
구분 has unique valuesUnique
세대수 has unique valuesUnique
계 has unique valuesUnique
남 has unique valuesUnique
여 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:25:44.979914
Analysis finished2023-12-12 15:25:47.296640
Duration2.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-13T00:25:47.457498image/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%
2023-12-13T00:25:47.898631image/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%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.92
Minimum926
Maximum23098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:25:48.047241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum926
5-th percentile1035
Q12132
median5836
Q311817
95-th percentile18893.6
Maximum23098
Range22172
Interquartile range (IQR)9685

Descriptive statistics

Standard deviation6447.4234
Coefficient of variation (CV)0.86311813
Kurtosis-0.11485127
Mean7469.92
Median Absolute Deviation (MAD)4667
Skewness0.87944657
Sum186748
Variance41569269
MonotonicityNot monotonic
2023-12-13T00:25:48.187813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6769 1
 
4.0%
15298 1
 
4.0%
19353 1
 
4.0%
11817 1
 
4.0%
23098 1
 
4.0%
7500 1
 
4.0%
13467 1
 
4.0%
2157 1
 
4.0%
2134 1
 
4.0%
5549 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
926 1
4.0%
1034 1
4.0%
1039 1
4.0%
1169 1
4.0%
1495 1
4.0%
1537 1
4.0%
2132 1
4.0%
2134 1
4.0%
2157 1
4.0%
2586 1
4.0%
ValueCountFrequency (%)
23098 1
4.0%
19353 1
4.0%
17056 1
4.0%
15298 1
4.0%
13467 1
4.0%
11932 1
4.0%
11817 1
4.0%
10858 1
4.0%
10531 1
4.0%
7500 1
4.0%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16261.28
Minimum1668
Maximum47262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:25:48.312828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1668
5-th percentile1838
Q13833
median13500
Q327263
95-th percentile44373.6
Maximum47262
Range45594
Interquartile range (IQR)23430

Descriptive statistics

Standard deviation14736.417
Coefficient of variation (CV)0.90622735
Kurtosis-0.50203375
Mean16261.28
Median Absolute Deviation (MAD)10453
Skewness0.8252814
Sum406532
Variance2.1716198 × 108
MonotonicityNot monotonic
2023-12-13T00:25:48.453681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
14144 1
 
4.0%
38136 1
 
4.0%
45871 1
 
4.0%
16932 1
 
4.0%
47262 1
 
4.0%
18215 1
 
4.0%
30295 1
 
4.0%
4221 1
 
4.0%
4273 1
 
4.0%
12950 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1668 1
4.0%
1800 1
4.0%
1990 1
4.0%
2107 1
4.0%
2858 1
4.0%
3047 1
4.0%
3833 1
4.0%
4221 1
4.0%
4273 1
4.0%
4431 1
4.0%
ValueCountFrequency (%)
47262 1
4.0%
45871 1
4.0%
38384 1
4.0%
38136 1
4.0%
30295 1
4.0%
28053 1
4.0%
27263 1
4.0%
23329 1
4.0%
18215 1
4.0%
16932 1
4.0%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8382.52
Minimum860
Maximum24760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:25:48.585497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum860
5-th percentile929.4
Q12038
median6774
Q313628
95-th percentile23258
Maximum24760
Range23900
Interquartile range (IQR)11590

Descriptive statistics

Standard deviation7590.9015
Coefficient of variation (CV)0.90556318
Kurtosis-0.33404572
Mean8382.52
Median Absolute Deviation (MAD)5156
Skewness0.87426508
Sum209563
Variance57621785
MonotonicityNot monotonic
2023-12-13T00:25:48.722191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
7004 1
 
4.0%
19177 1
 
4.0%
24208 1
 
4.0%
9469 1
 
4.0%
24760 1
 
4.0%
9528 1
 
4.0%
15878 1
 
4.0%
2307 1
 
4.0%
2371 1
 
4.0%
6774 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
860 1
4.0%
906 1
4.0%
1023 1
4.0%
1083 1
4.0%
1476 1
4.0%
1618 1
4.0%
2038 1
4.0%
2307 1
4.0%
2371 1
4.0%
2497 1
4.0%
ValueCountFrequency (%)
24760 1
4.0%
24208 1
4.0%
19458 1
4.0%
19177 1
4.0%
15878 1
4.0%
14182 1
4.0%
13628 1
4.0%
11461 1
4.0%
9528 1
4.0%
9469 1
4.0%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7878.76
Minimum808
Maximum22502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:25:48.873740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum808
5-th percentile908.6
Q11795
median6858
Q313081
95-th percentile21122.2
Maximum22502
Range21694
Interquartile range (IQR)11286

Descriptive statistics

Standard deviation7161.878
Coefficient of variation (CV)0.90901082
Kurtosis-0.66344708
Mean7878.76
Median Absolute Deviation (MAD)5429
Skewness0.77911106
Sum196969
Variance51292497
MonotonicityNot monotonic
2023-12-13T00:25:49.001486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
7140 1
 
4.0%
18959 1
 
4.0%
21663 1
 
4.0%
7463 1
 
4.0%
22502 1
 
4.0%
8687 1
 
4.0%
14417 1
 
4.0%
1914 1
 
4.0%
1902 1
 
4.0%
6176 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
808 1
4.0%
894 1
4.0%
967 1
4.0%
1024 1
4.0%
1382 1
4.0%
1429 1
4.0%
1795 1
4.0%
1902 1
4.0%
1914 1
4.0%
1934 1
4.0%
ValueCountFrequency (%)
22502 1
4.0%
21663 1
4.0%
18959 1
4.0%
18926 1
4.0%
14425 1
4.0%
14417 1
4.0%
13081 1
4.0%
11868 1
4.0%
8687 1
4.0%
8112 1
4.0%

관리기관명
Categorical

CONSTANT 

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

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 구미시청 25
100.0%

Length

2023-12-13T00:25:49.129508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:49.212713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 25
50.0%
구미시청 25
50.0%

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
054-480-6767
25 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row054-480-6767
2nd row054-480-6767
3rd row054-480-6767
4th row054-480-6767
5th row054-480-6767

Common Values

ValueCountFrequency (%)
054-480-6767 25
100.0%

Length

2023-12-13T00:25:49.306891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:49.398897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
054-480-6767 25
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-07-31
25 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-31
2nd row2023-07-31
3rd row2023-07-31
4th row2023-07-31
5th row2023-07-31

Common Values

ValueCountFrequency (%)
2023-07-31 25
100.0%

Length

2023-12-13T00:25:49.500335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:49.596934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-31 25
100.0%

Interactions

2023-12-13T00:25:46.585713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:45.258449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:45.694657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:46.140608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:46.694419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:45.368691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:45.834508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:46.242801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:46.794173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:45.469963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:45.944269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:46.345794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:46.897050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:45.583931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:46.037808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:46.480365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:25:49.655067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세대수계남여
구분1.0001.0001.0001.0001.000
세대수1.0001.0000.9790.8980.875
계1.0000.9791.0000.9810.969
남1.0000.8980.9811.0000.951
여1.0000.8750.9690.9511.000
2023-12-13T00:25:49.754492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수계남여
세대수1.0000.9920.9890.988
계0.9921.0000.9980.997
남0.9890.9981.0000.995
여0.9880.9970.9951.000

Missing values

2023-12-13T00:25:47.050416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:25:47.226585image/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.

Sample

구분세대수계남여관리기관명관리기관전화번호데이터기준일자
0선산읍67691414470047140경상북도 구미시청054-480-67672023-07-31
1고아읍15298381361917718959경상북도 구미시청054-480-67672023-07-31
2산동읍10531272631418213081경상북도 구미시청054-480-67672023-07-31
3무을면10391800906894경상북도 구미시청054-480-67672023-07-31
4옥성면9261668860808경상북도 구미시청054-480-67672023-07-31
5도개면1169210710831024경상북도 구미시청054-480-67672023-07-31
6해평면2132383320381795경상북도 구미시청054-480-67672023-07-31
7장천면1537285814761382경상북도 구미시청054-480-67672023-07-31
8송정동10858233291146111868경상북도 구미시청054-480-67672023-07-31
9원평동4106585132082643경상북도 구미시청054-480-67672023-07-31
구분세대수계남여관리기관명관리기관전화번호데이터기준일자
15신평1동2586443124971934경상북도 구미시청054-480-67672023-07-31
16신평2동1495304716181429경상북도 구미시청054-480-67672023-07-31
17비산동55491295067746176경상북도 구미시청054-480-67672023-07-31
18공단동2134427323711902경상북도 구미시청054-480-67672023-07-31
19광평동2157422123071914경상북도 구미시청054-480-67672023-07-31
20상모사곡동13467302951587814417경상북도 구미시청054-480-67672023-07-31
21임오동75001821595288687경상북도 구미시청054-480-67672023-07-31
22인동동23098472622476022502경상북도 구미시청054-480-67672023-07-31
23진미동118171693294697463경상북도 구미시청054-480-67672023-07-31
24양포동19353458712420821663경상북도 구미시청054-480-67672023-07-31