Overview

Dataset statistics

Number of variables7
Number of observations257
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 KiB
Average record size in memory59.5 B

Variable types

Categorical1
Numeric3
Text2
DateTime1

Dataset

Description충청남도 금산군 인구현황에 관한 데이터로써 행정리 별 남자 인구 수, 여자 인구 수, 남녀 합계 인구 수, 세대 수를 안내하고 있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=425&beforeMenuCd=DOM_000000201001001000&publicdatapk=15030427

Alerts

기준일자 has constant value ""Constant
세대수 is highly overall correlated with 남자 and 1 other fieldsHigh correlation
남자 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
여자 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-01-09 20:11:29.717548
Analysis finished2024-01-09 20:11:31.786486
Duration2.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct10
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
금산읍
42 
제원면
28 
부리면
28 
진산면
28 
추부면
27 
Other values (5)
104 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금산읍
2nd row금산읍
3rd row금산읍
4th row금산읍
5th row금산읍

Common Values

ValueCountFrequency (%)
금산읍 42
16.3%
제원면 28
10.9%
부리면 28
10.9%
진산면 28
10.9%
추부면 27
10.5%
복수면 23
8.9%
금성면 22
8.6%
군북면 20
7.8%
남이면 20
7.8%
남일면 19
7.4%

Length

2024-01-10T05:11:31.869434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:11:32.012594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금산읍 42
16.3%
제원면 28
10.9%
부리면 28
10.9%
진산면 28
10.9%
추부면 27
10.5%
복수면 23
8.9%
금성면 22
8.6%
군북면 20
7.8%
남이면 20
7.8%
남일면 19
7.4%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.51751
Minimum18
Maximum822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:11:32.192142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27.8
Q153
median72
Q3109
95-th percentile267.4
Maximum822
Range804
Interquartile range (IQR)56

Descriptive statistics

Standard deviation100.66102
Coefficient of variation (CV)1.0114906
Kurtosis19.392111
Mean99.51751
Median Absolute Deviation (MAD)26
Skewness3.9267571
Sum25576
Variance10132.641
MonotonicityNot monotonic
2024-01-10T05:11:32.385207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 7
 
2.7%
81 6
 
2.3%
58 6
 
2.3%
48 6
 
2.3%
65 6
 
2.3%
55 5
 
1.9%
84 5
 
1.9%
46 5
 
1.9%
36 4
 
1.6%
64 4
 
1.6%
Other values (115) 203
79.0%
ValueCountFrequency (%)
18 1
 
0.4%
21 1
 
0.4%
22 3
1.2%
23 1
 
0.4%
24 1
 
0.4%
25 2
0.8%
26 2
0.8%
27 2
0.8%
28 1
 
0.4%
29 3
1.2%
ValueCountFrequency (%)
822 1
0.4%
665 1
0.4%
597 1
0.4%
587 1
0.4%
569 1
0.4%
426 1
0.4%
408 1
0.4%
351 1
0.4%
290 1
0.4%
287 1
0.4%

계(%)
Text

Distinct236
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-01-10T05:11:32.794095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length11.552529
Min length2

Characters and Unicode

Total characters2969
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique216 ?
Unique (%)84.0%

Sample

1st row 236(1)
2nd row 295(1)
3rd row 233(1)
4th row 223(1)
5th row 367(2)
ValueCountFrequency (%)
44(2 3
 
1.2%
120(4 3
 
1.2%
119(4 3
 
1.2%
62(2 3
 
1.2%
141(4 3
 
1.2%
55(2 3
 
1.2%
118(4 3
 
1.2%
116 2
 
0.8%
65(2 2
 
0.8%
95(4 2
 
0.8%
Other values (213) 230
89.5%
2024-01-10T05:11:33.391567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1546
52.1%
( 235
 
7.9%
) 235
 
7.9%
1 206
 
6.9%
2 135
 
4.5%
3 110
 
3.7%
4 104
 
3.5%
5 94
 
3.2%
6 73
 
2.5%
0 63
 
2.1%
Other values (4) 168
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 1546
52.1%
Decimal Number 946
31.9%
Open Punctuation 235
 
7.9%
Close Punctuation 235
 
7.9%
Other Punctuation 7
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 206
21.8%
2 135
14.3%
3 110
11.6%
4 104
11.0%
5 94
9.9%
6 73
 
7.7%
0 63
 
6.7%
9 61
 
6.4%
8 54
 
5.7%
7 46
 
4.9%
Space Separator
ValueCountFrequency (%)
1546
100.0%
Open Punctuation
ValueCountFrequency (%)
( 235
100.0%
Close Punctuation
ValueCountFrequency (%)
) 235
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2969
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1546
52.1%
( 235
 
7.9%
) 235
 
7.9%
1 206
 
6.9%
2 135
 
4.5%
3 110
 
3.7%
4 104
 
3.5%
5 94
 
3.2%
6 73
 
2.5%
0 63
 
2.1%
Other values (4) 168
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1546
52.1%
( 235
 
7.9%
) 235
 
7.9%
1 206
 
6.9%
2 135
 
4.5%
3 110
 
3.7%
4 104
 
3.5%
5 94
 
3.2%
6 73
 
2.5%
0 63
 
2.1%
Other values (4) 168
 
5.7%

남자
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.393
Minimum13
Maximum961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:11:33.601874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile25.6
Q148
median70
Q3113
95-th percentile290.6
Maximum961
Range948
Interquartile range (IQR)65

Descriptive statistics

Standard deviation124.42677
Coefficient of variation (CV)1.1919072
Kurtosis22.774147
Mean104.393
Median Absolute Deviation (MAD)26
Skewness4.3175121
Sum26829
Variance15482.021
MonotonicityNot monotonic
2024-01-10T05:11:33.779603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 7
 
2.7%
66 5
 
1.9%
61 5
 
1.9%
72 5
 
1.9%
52 5
 
1.9%
57 5
 
1.9%
60 4
 
1.6%
40 4
 
1.6%
50 4
 
1.6%
48 4
 
1.6%
Other values (124) 209
81.3%
ValueCountFrequency (%)
13 1
 
0.4%
15 1
 
0.4%
16 1
 
0.4%
17 1
 
0.4%
18 3
1.2%
19 2
0.8%
21 1
 
0.4%
22 1
 
0.4%
23 1
 
0.4%
24 1
 
0.4%
ValueCountFrequency (%)
961 1
0.4%
954 1
0.4%
781 1
0.4%
727 1
0.4%
556 1
0.4%
520 1
0.4%
515 1
0.4%
403 1
0.4%
362 1
0.4%
330 1
0.4%

여자
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.63424
Minimum14
Maximum1054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:11:33.999366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile25
Q146
median65
Q3106
95-th percentile302.6
Maximum1054
Range1040
Interquartile range (IQR)60

Descriptive statistics

Standard deviation131.16413
Coefficient of variation (CV)1.2779763
Kurtosis24.256434
Mean102.63424
Median Absolute Deviation (MAD)26
Skewness4.4852031
Sum26377
Variance17204.03
MonotonicityNot monotonic
2024-01-10T05:11:34.188257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 6
 
2.3%
50 6
 
2.3%
63 6
 
2.3%
31 5
 
1.9%
43 5
 
1.9%
59 5
 
1.9%
65 5
 
1.9%
61 5
 
1.9%
54 5
 
1.9%
91 4
 
1.6%
Other values (120) 205
79.8%
ValueCountFrequency (%)
14 1
 
0.4%
15 1
 
0.4%
16 1
 
0.4%
17 2
0.8%
18 1
 
0.4%
20 1
 
0.4%
21 4
1.6%
22 1
 
0.4%
25 4
1.6%
26 3
1.2%
ValueCountFrequency (%)
1054 1
0.4%
941 1
0.4%
841 1
0.4%
824 1
0.4%
563 1
0.4%
540 1
0.4%
511 1
0.4%
407 1
0.4%
347 1
0.4%
326 1
0.4%

행정리명
Text

Distinct253
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-01-10T05:11:34.570217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.3346304
Min length3

Characters and Unicode

Total characters1114
Distinct characters114
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

Unique249 ?
Unique (%)96.9%

Sample

1st row신대1리
2nd row신대2리
3rd row중도1리
4th row중도2리
5th row중도3리
ValueCountFrequency (%)
신대1리 2
 
0.8%
신대2리 2
 
0.8%
읍내2리 2
 
0.8%
읍내1리 2
 
0.8%
하금3리 1
 
0.4%
역평2리 1
 
0.4%
역평1리 1
 
0.4%
건천2리 1
 
0.4%
건천1리 1
 
0.4%
상금리 1
 
0.4%
Other values (243) 243
94.6%
2024-01-10T05:11:35.142986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 257
23.1%
108
 
9.7%
1 95
 
8.5%
2 91
 
8.2%
3 32
 
2.9%
곡 26
 
2.3%
상 22
 
2.0%
신 19
 
1.7%
대 15
 
1.3%
마 14
 
1.3%
Other values (104) 435
39.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 761
68.3%
Decimal Number 245
 
22.0%
Space Separator 108
 
9.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 257
33.8%
곡 26
 
3.4%
상 22
 
2.9%
신 19
 
2.5%
대 15
 
2.0%
마 14
 
1.8%
내 14
 
1.8%
도 13
 
1.7%
지 13
 
1.7%
전 12
 
1.6%
Other values (93) 356
46.8%
Decimal Number
ValueCountFrequency (%)
1 95
38.8%
2 91
37.1%
3 32
 
13.1%
4 8
 
3.3%
5 5
 
2.0%
9 3
 
1.2%
6 3
 
1.2%
8 3
 
1.2%
7 3
 
1.2%
0 2
 
0.8%
Space Separator
ValueCountFrequency (%)
108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 761
68.3%
Common 353
31.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 257
33.8%
곡 26
 
3.4%
상 22
 
2.9%
신 19
 
2.5%
대 15
 
2.0%
마 14
 
1.8%
내 14
 
1.8%
도 13
 
1.7%
지 13
 
1.7%
전 12
 
1.6%
Other values (93) 356
46.8%
Common
ValueCountFrequency (%)
108
30.6%
1 95
26.9%
2 91
25.8%
3 32
 
9.1%
4 8
 
2.3%
5 5
 
1.4%
9 3
 
0.8%
6 3
 
0.8%
8 3
 
0.8%
7 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 761
68.3%
ASCII 353
31.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 257
33.8%
곡 26
 
3.4%
상 22
 
2.9%
신 19
 
2.5%
대 15
 
2.0%
마 14
 
1.8%
내 14
 
1.8%
도 13
 
1.7%
지 13
 
1.7%
전 12
 
1.6%
Other values (93) 356
46.8%
ASCII
ValueCountFrequency (%)
108
30.6%
1 95
26.9%
2 91
25.8%
3 32
 
9.1%
4 8
 
2.3%
5 5
 
1.4%
9 3
 
0.8%
6 3
 
0.8%
8 3
 
0.8%
7 3
 
0.8%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2018-12-31 00:00:00
Maximum2018-12-31 00:00:00
2024-01-10T05:11:35.328092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:35.477625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T05:11:31.169344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:30.005187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:30.810795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:31.302083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:30.143166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:30.937947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:31.419789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:30.695382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:31.055261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:11:35.581835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세대수남자여자
구분1.0000.5450.4430.409
세대수0.5451.0000.9460.948
남자0.4430.9461.0000.984
여자0.4090.9480.9841.000
2024-01-10T05:11:35.724891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수남자여자구분
세대수1.0000.9730.9690.194
남자0.9731.0000.9670.219
여자0.9690.9671.0000.199
구분0.1940.2190.1991.000

Missing values

2024-01-10T05:11:31.562688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:11:31.721916image/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

구분세대수계(%)남자여자행정리명기준일자
0금산읍116236(1)121115신대1리2018-12-31
1금산읍135295(1)152143신대2리2018-12-31
2금산읍131233(1)112121중도1리2018-12-31
3금산읍103223(1)113110중도2리2018-12-31
4금산읍158367(2)167200중도3리2018-12-31
5금산읍179375(2)193182중도4리2018-12-31
6금산읍119243(1)117126중도5리2018-12-31
7금산읍185431(2)207224중도6리2018-12-31
8금산읍351810(3)403407중도7리2018-12-31
9금산읍290709(3)362347중도8리2018-12-31
구분세대수계(%)남자여자행정리명기준일자
247추부면78164(3)7985장대2리2018-12-31
248추부면54102(2)5448요광1리2018-12-31
249추부면70137(2)7265요광2리2018-12-31
250추부면4779(1)4237요광3리2018-12-31
251추부면192371(6)199172신평1리2018-12-31
252추부면4892(1)4943신평2리2018-12-31
253추부면165317(5)158159성당1리2018-12-31
254추부면119211(3)107104성당2리2018-12-31
255추부면98168(3)9375서대1리2018-12-31
256추부면77131(2)6665서대2리2018-12-31