Overview

Dataset statistics

Number of variables5
Number of observations6105
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory250.5 KiB
Average record size in memory42.0 B

Variable types

Text3
Numeric2

Dataset

Description경남 창원시의 구청별 읍면동별 연령별(만나이) 인구현황입니다 단위는 1세 단위입니다. 행정기관, 연령(만), 합계, 남자, 여자 항목이 있습니다.
URLhttps://www.data.go.kr/data/15004972/fileData.do

Alerts

남 is highly overall correlated with 녀High correlation
녀 is highly overall correlated with 남High correlation
남 has 854 (14.0%) zerosZeros
녀 has 650 (10.6%) zerosZeros

Reproduction

Analysis started2023-12-12 05:05:46.147168
Analysis finished2023-12-12 05:05:47.242083
Duration1.09 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정기관
Text

Distinct55
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size47.8 KiB
2023-12-12T14:05:47.493287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1454545
Min length2

Characters and Unicode

Total characters19203
Distinct characters68
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

Unique0 ?
Unique (%)0.0%

Sample

1st row동읍
2nd row동읍
3rd row동읍
4th row동읍
5th row동읍
ValueCountFrequency (%)
동읍 111
 
1.8%
합포동 111
 
1.8%
내서읍 111
 
1.8%
회원1동 111
 
1.8%
회원2동 111
 
1.8%
석전동 111
 
1.8%
회성동 111
 
1.8%
양덕1동 111
 
1.8%
양덕2동 111
 
1.8%
합성1동 111
 
1.8%
Other values (45) 4995
81.8%
2023-12-12T14:05:47.991676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 5772
30.1%
면 666
 
3.5%
2 555
 
2.9%
산 555
 
2.9%
1 555
 
2.9%
웅 444
 
2.3%
성 444
 
2.3%
암 444
 
2.3%
회 333
 
1.7%
진 333
 
1.7%
Other values (58) 9102
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18093
94.2%
Decimal Number 1110
 
5.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 5772
31.9%
면 666
 
3.7%
산 555
 
3.1%
웅 444
 
2.5%
성 444
 
2.5%
암 444
 
2.5%
회 333
 
1.8%
진 333
 
1.8%
덕 333
 
1.8%
합 333
 
1.8%
Other values (56) 8436
46.6%
Decimal Number
ValueCountFrequency (%)
2 555
50.0%
1 555
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18093
94.2%
Common 1110
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 5772
31.9%
면 666
 
3.7%
산 555
 
3.1%
웅 444
 
2.5%
성 444
 
2.5%
암 444
 
2.5%
회 333
 
1.8%
진 333
 
1.8%
덕 333
 
1.8%
합 333
 
1.8%
Other values (56) 8436
46.6%
Common
ValueCountFrequency (%)
2 555
50.0%
1 555
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18093
94.2%
ASCII 1110
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 5772
31.9%
면 666
 
3.7%
산 555
 
3.1%
웅 444
 
2.5%
성 444
 
2.5%
암 444
 
2.5%
회 333
 
1.8%
진 333
 
1.8%
덕 333
 
1.8%
합 333
 
1.8%
Other values (56) 8436
46.6%
ASCII
ValueCountFrequency (%)
2 555
50.0%
1 555
50.0%

연령(만)
Text

Distinct112
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size47.8 KiB
2023-12-12T14:05:48.412847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0355446
Min length2

Characters and Unicode

Total characters18532
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0세
2nd row1세
3rd row2세
4th row3세
5th row4세
ValueCountFrequency (%)
0세 55
 
0.9%
54세 55
 
0.9%
82세 55
 
0.9%
81세 55
 
0.9%
80세 55
 
0.9%
79세 55
 
0.9%
78세 55
 
0.9%
77세 55
 
0.9%
76세 55
 
0.9%
75세 55
 
0.9%
Other values (102) 5609
91.1%
2023-12-12T14:05:48.978283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 6105
32.9%
1 1815
 
9.8%
0 1210
 
6.5%
5 1155
 
6.2%
4 1155
 
6.2%
2 1155
 
6.2%
3 1155
 
6.2%
6 1155
 
6.2%
7 1155
 
6.2%
8 1155
 
6.2%
Other values (4) 1317
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12265
66.2%
Other Letter 6213
33.5%
Space Separator 54
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1815
14.8%
0 1210
9.9%
5 1155
9.4%
4 1155
9.4%
2 1155
9.4%
3 1155
9.4%
6 1155
9.4%
7 1155
9.4%
8 1155
9.4%
9 1155
9.4%
Other Letter
ValueCountFrequency (%)
세 6105
98.3%
이 54
 
0.9%
상 54
 
0.9%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12319
66.5%
Hangul 6213
33.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1815
14.7%
0 1210
9.8%
5 1155
9.4%
4 1155
9.4%
2 1155
9.4%
3 1155
9.4%
6 1155
9.4%
7 1155
9.4%
8 1155
9.4%
9 1155
9.4%
Hangul
ValueCountFrequency (%)
세 6105
98.3%
이 54
 
0.9%
상 54
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12319
66.5%
Hangul 6213
33.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 6105
98.3%
이 54
 
0.9%
상 54
 
0.9%
ASCII
ValueCountFrequency (%)
1 1815
14.7%
0 1210
9.8%
5 1155
9.4%
4 1155
9.4%
2 1155
9.4%
3 1155
9.4%
6 1155
9.4%
7 1155
9.4%
8 1155
9.4%
9 1155
9.4%

합계
Text

Distinct869
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size47.8 KiB
2023-12-12T14:05:49.373358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length2.37543
Min length1

Characters and Unicode

Total characters14502
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)4.5%

Sample

1st row52
2nd row67
3rd row46
4th row81
5th row75
ValueCountFrequency (%)
0 619
 
10.0%
1 126
 
2.0%
2 63
 
1.0%
4 52
 
0.8%
3 45
 
0.7%
5 39
 
0.6%
9 38
 
0.6%
7 37
 
0.6%
53 36
 
0.6%
12 35
 
0.6%
Other values (829) 5087
82.4%
2023-12-12T14:05:49.941118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2668
18.4%
2 1773
12.2%
0 1638
11.3%
3 1470
10.1%
5 1283
8.8%
4 1282
8.8%
6 1123
7.7%
7 1045
 
7.2%
8 1007
 
6.9%
9 997
 
6.9%
Other values (3) 216
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14286
98.5%
Other Letter 144
 
1.0%
Space Separator 72
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2668
18.7%
2 1773
12.4%
0 1638
11.5%
3 1470
10.3%
5 1283
9.0%
4 1282
9.0%
6 1123
7.9%
7 1045
 
7.3%
8 1007
 
7.0%
9 997
 
7.0%
Other Letter
ValueCountFrequency (%)
월 72
50.0%
일 72
50.0%
Space Separator
ValueCountFrequency (%)
72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14358
99.0%
Hangul 144
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2668
18.6%
2 1773
12.3%
0 1638
11.4%
3 1470
10.2%
5 1283
8.9%
4 1282
8.9%
6 1123
7.8%
7 1045
 
7.3%
8 1007
 
7.0%
9 997
 
6.9%
Hangul
ValueCountFrequency (%)
월 72
50.0%
일 72
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14358
99.0%
Hangul 144
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2668
18.6%
2 1773
12.3%
0 1638
11.4%
3 1470
10.2%
5 1283
8.9%
4 1282
8.9%
6 1123
7.8%
7 1045
 
7.3%
8 1007
 
7.0%
9 997
 
6.9%
Hangul
ValueCountFrequency (%)
월 72
50.0%
일 72
50.0%

남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct478
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.131204
Minimum0
Maximum757
Zeros854
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size53.8 KiB
2023-12-12T14:05:50.181578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median50
Q3110
95-th percentile316
Maximum757
Range757
Interquartile range (IQR)100

Descriptive statistics

Standard deviation105.17493
Coefficient of variation (CV)1.2354452
Kurtosis4.2042457
Mean85.131204
Median Absolute Deviation (MAD)44
Skewness1.9578182
Sum519726
Variance11061.767
MonotonicityNot monotonic
2023-12-12T14:05:50.341100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 854
 
14.0%
1 149
 
2.4%
2 94
 
1.5%
3 64
 
1.0%
6 61
 
1.0%
7 59
 
1.0%
9 57
 
0.9%
5 53
 
0.9%
4 53
 
0.9%
31 52
 
0.9%
Other values (468) 4609
75.5%
ValueCountFrequency (%)
0 854
14.0%
1 149
 
2.4%
2 94
 
1.5%
3 64
 
1.0%
4 53
 
0.9%
5 53
 
0.9%
6 61
 
1.0%
7 59
 
1.0%
8 46
 
0.8%
9 57
 
0.9%
ValueCountFrequency (%)
757 1
< 0.1%
672 1
< 0.1%
659 1
< 0.1%
653 1
< 0.1%
648 1
< 0.1%
644 1
< 0.1%
632 1
< 0.1%
620 1
< 0.1%
618 1
< 0.1%
615 1
< 0.1%

녀
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct464
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.954136
Minimum0
Maximum732
Zeros650
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size53.8 KiB
2023-12-12T14:05:50.513597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median48
Q3108
95-th percentile299.8
Maximum732
Range732
Interquartile range (IQR)94

Descriptive statistics

Standard deviation100.3221
Coefficient of variation (CV)1.2093683
Kurtosis4.9835893
Mean82.954136
Median Absolute Deviation (MAD)40
Skewness2.0645278
Sum506435
Variance10064.524
MonotonicityNot monotonic
2023-12-12T14:05:50.685268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 650
 
10.6%
1 125
 
2.0%
2 87
 
1.4%
5 75
 
1.2%
4 73
 
1.2%
3 66
 
1.1%
38 61
 
1.0%
9 61
 
1.0%
8 57
 
0.9%
14 57
 
0.9%
Other values (454) 4793
78.5%
ValueCountFrequency (%)
0 650
10.6%
1 125
 
2.0%
2 87
 
1.4%
3 66
 
1.1%
4 73
 
1.2%
5 75
 
1.2%
6 46
 
0.8%
7 52
 
0.9%
8 57
 
0.9%
9 61
 
1.0%
ValueCountFrequency (%)
732 1
< 0.1%
683 1
< 0.1%
680 1
< 0.1%
679 1
< 0.1%
667 1
< 0.1%
656 2
< 0.1%
617 1
< 0.1%
607 1
< 0.1%
584 1
< 0.1%
581 1
< 0.1%

Interactions

2023-12-12T14:05:46.694664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:05:46.433791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:05:46.855423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:05:46.554593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:05:50.806649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관남녀
행정기관1.0000.6850.662
남0.6851.0000.945
녀0.6620.9451.000
2023-12-12T14:05:50.932519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남녀
남1.0000.972
녀0.9721.000

Missing values

2023-12-12T14:05:47.091006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:05:47.195487image/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

행정기관연령(만)합계남녀
0동읍0세522329
1동읍1세672938
2동읍2세462323
3동읍3세814239
4동읍4세754035
5동읍5세924547
6동읍6세1286464
7동읍7세1186058
8동읍8세1165858
9동읍9세1668878
행정기관연령(만)합계남녀
6095웅동2동101세000
6096웅동2동102세000
6097웅동2동103세000
6098웅동2동104세000
6099웅동2동105세000
6100웅동2동106세000
6101웅동2동107세000
6102웅동2동108세000
6103웅동2동109세000
6104웅동2동110세 이상000