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

Categorical2
Numeric3
Text2

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 20:11:23.447187
Analysis finished2024-01-09 20:11:24.947636
Duration1.5 second
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

2024-01-10T05:11:25.024149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:11:25.161347image/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 

Distinct127
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.38521
Minimum18
Maximum833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:11:25.362358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26.8
Q153
median71
Q3107
95-th percentile268.2
Maximum833
Range815
Interquartile range (IQR)54

Descriptive statistics

Standard deviation101.03899
Coefficient of variation (CV)1.0065127
Kurtosis18.928314
Mean100.38521
Median Absolute Deviation (MAD)22
Skewness3.8491707
Sum25799
Variance10208.878
MonotonicityNot monotonic
2024-01-10T05:11:25.551440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 7
 
2.7%
59 7
 
2.7%
64 7
 
2.7%
85 6
 
2.3%
83 5
 
1.9%
40 5
 
1.9%
54 5
 
1.9%
60 5
 
1.9%
51 5
 
1.9%
44 4
 
1.6%
Other values (117) 201
78.2%
ValueCountFrequency (%)
18 1
 
0.4%
21 2
0.8%
22 1
 
0.4%
23 4
1.6%
24 2
0.8%
26 3
1.2%
27 2
0.8%
29 1
 
0.4%
30 1
 
0.4%
31 1
 
0.4%
ValueCountFrequency (%)
833 1
0.4%
686 1
0.4%
587 1
0.4%
575 1
0.4%
442 1
0.4%
435 1
0.4%
416 1
0.4%
384 1
0.4%
382 1
0.4%
313 1
0.4%

계
Text

Distinct223
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-01-10T05:11:25.956390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.7276265
Min length5

Characters and Unicode

Total characters1472
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)74.3%

Sample

1st row221(1)
2nd row261(1)
3rd row211(1)
4th row210(1)
5th row366(2)
ValueCountFrequency (%)
58(2 3
 
1.2%
105(4 3
 
1.2%
91(3 2
 
0.8%
120(4 2
 
0.8%
101(5 2
 
0.8%
104(3 2
 
0.8%
40(1 2
 
0.8%
159(5 2
 
0.8%
129(5 2
 
0.8%
97(4 2
 
0.8%
Other values (213) 235
91.4%
2024-01-10T05:11:26.538152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 257
17.5%
) 257
17.5%
1 196
13.3%
2 136
9.2%
4 111
7.5%
3 107
7.3%
5 95
 
6.5%
6 71
 
4.8%
9 64
 
4.3%
0 63
 
4.3%
Other values (2) 115
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 958
65.1%
Open Punctuation 257
 
17.5%
Close Punctuation 257
 
17.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 196
20.5%
2 136
14.2%
4 111
11.6%
3 107
11.2%
5 95
9.9%
6 71
 
7.4%
9 64
 
6.7%
0 63
 
6.6%
7 59
 
6.2%
8 56
 
5.8%
Open Punctuation
ValueCountFrequency (%)
( 257
100.0%
Close Punctuation
ValueCountFrequency (%)
) 257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
( 257
17.5%
) 257
17.5%
1 196
13.3%
2 136
9.2%
4 111
7.5%
3 107
7.3%
5 95
 
6.5%
6 71
 
4.8%
9 64
 
4.3%
0 63
 
4.3%
Other values (2) 115
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 257
17.5%
) 257
17.5%
1 196
13.3%
2 136
9.2%
4 111
7.5%
3 107
7.3%
5 95
 
6.5%
6 71
 
4.8%
9 64
 
4.3%
0 63
 
4.3%
Other values (2) 115
7.8%

남자
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.949416
Minimum14
Maximum994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:11:26.759983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile22
Q146
median67
Q3106
95-th percentile267.8
Maximum994
Range980
Interquartile range (IQR)60

Descriptive statistics

Standard deviation119.12861
Coefficient of variation (CV)1.191889
Kurtosis23.312309
Mean99.949416
Median Absolute Deviation (MAD)25
Skewness4.3204712
Sum25687
Variance14191.626
MonotonicityNot monotonic
2024-01-10T05:11:26.965401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 10
 
3.9%
48 6
 
2.3%
66 5
 
1.9%
70 5
 
1.9%
79 5
 
1.9%
45 4
 
1.6%
61 4
 
1.6%
75 4
 
1.6%
26 4
 
1.6%
33 4
 
1.6%
Other values (119) 206
80.2%
ValueCountFrequency (%)
14 1
0.4%
15 2
0.8%
16 2
0.8%
18 2
0.8%
19 1
0.4%
20 2
0.8%
21 2
0.8%
22 2
0.8%
23 2
0.8%
24 1
0.4%
ValueCountFrequency (%)
994 1
0.4%
865 1
0.4%
720 1
0.4%
659 1
0.4%
484 1
0.4%
480 1
0.4%
453 1
0.4%
445 1
0.4%
406 1
0.4%
390 1
0.4%

여자
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.229572
Minimum13
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:11:27.146420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22.6
Q145
median60
Q3101
95-th percentile286.6
Maximum999
Range986
Interquartile range (IQR)56

Descriptive statistics

Standard deviation126.52701
Coefficient of variation (CV)1.2880745
Kurtosis24.108453
Mean98.229572
Median Absolute Deviation (MAD)23
Skewness4.4556539
Sum25245
Variance16009.084
MonotonicityNot monotonic
2024-01-10T05:11:27.351017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 7
 
2.7%
52 7
 
2.7%
43 7
 
2.7%
45 6
 
2.3%
58 6
 
2.3%
60 6
 
2.3%
47 6
 
2.3%
49 5
 
1.9%
64 5
 
1.9%
48 5
 
1.9%
Other values (119) 197
76.7%
ValueCountFrequency (%)
13 2
0.8%
14 1
 
0.4%
15 1
 
0.4%
16 1
 
0.4%
18 2
0.8%
19 3
1.2%
20 2
0.8%
21 1
 
0.4%
23 1
 
0.4%
24 3
1.2%
ValueCountFrequency (%)
999 1
0.4%
963 1
0.4%
783 1
0.4%
761 1
0.4%
497 1
0.4%
495 1
0.4%
430 1
0.4%
425 1
0.4%
423 2
0.8%
323 1
0.4%

행정리명
Text

Distinct255
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-01-10T05:11:27.781864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.3346304
Min length3

Characters and Unicode

Total characters1114
Distinct characters115
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

Unique253 ?
Unique (%)98.4%

Sample

1st row신대1리
2nd row신대2리
3rd row중도1리
4th row중도2리
5th row중도3리
ValueCountFrequency (%)
신대1리 2
 
0.8%
신대2리 2
 
0.8%
요광3리 1
 
0.4%
흑암2리 1
 
0.4%
목소리 1
 
0.4%
구석2리 1
 
0.4%
서대1리 1
 
0.4%
매곡2리 1
 
0.4%
하금3리 1
 
0.4%
상금리 1
 
0.4%
Other values (245) 245
95.3%
2024-01-10T05:11:28.452065image/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 (105) 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%
지 13
 
1.7%
도 13
 
1.7%
전 12
 
1.6%
천 12
 
1.6%
Other values (94) 358
47.0%
Decimal Number
ValueCountFrequency (%)
1 95
38.8%
2 91
37.1%
3 32
 
13.1%
4 8
 
3.3%
5 5
 
2.0%
8 3
 
1.2%
6 3
 
1.2%
7 3
 
1.2%
9 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%
지 13
 
1.7%
도 13
 
1.7%
전 12
 
1.6%
천 12
 
1.6%
Other values (94) 358
47.0%
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%
8 3
 
0.8%
6 3
 
0.8%
7 3
 
0.8%
9 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%
지 13
 
1.7%
도 13
 
1.7%
전 12
 
1.6%
천 12
 
1.6%
Other values (94) 358
47.0%
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%
8 3
 
0.8%
6 3
 
0.8%
7 3
 
0.8%
9 3
 
0.8%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2021-05-31
257 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-05-31
2nd row2021-05-31
3rd row2021-05-31
4th row2021-05-31
5th row2021-05-31

Common Values

ValueCountFrequency (%)
2021-05-31 257
100.0%

Length

2024-01-10T05:11:28.685747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:11:28.810378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-05-31 257
100.0%

Interactions

2024-01-10T05:11:24.370435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:23.688562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:24.034361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:24.491418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:23.811112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:24.152861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:24.603631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:23.921225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:11:24.259726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:11:28.891436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세대수남자여자
구분1.0000.4230.3930.440
세대수0.4231.0000.9870.929
남자0.3930.9871.0000.950
여자0.4400.9290.9501.000
2024-01-10T05:11:29.031502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수남자여자구분
세대수1.0000.9740.9670.206
남자0.9741.0000.9650.190
여자0.9670.9651.0000.239
구분0.2060.1900.2391.000

Missing values

2024-01-10T05:11:24.743694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:11:24.883433image/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

구분세대수계남자여자행정리명기준일자
0금산읍115221(1)118103신대1리2021-05-31
1금산읍130261(1)132129신대2리2021-05-31
2금산읍120211(1)105106중도1리2021-05-31
3금산읍102210(1)11793중도2리2021-05-31
4금산읍174366(2)171195중도3리2021-05-31
5금산읍178361(2)186175중도4리2021-05-31
6금산읍130269(1)131138중도5리2021-05-31
7금산읍175366(2)174192중도6리2021-05-31
8금산읍382829(4)406423중도7리2021-05-31
9금산읍293650(3)327323중도8리2021-05-31
구분세대수계남자여자행정리명기준일자
247추부면74147(2)7077장대2리2021-05-31
248추부면5699(2)5247요광1리2021-05-31
249추부면61109(2)5950요광2리2021-05-31
250추부면5088(1)4840요광3리2021-05-31
251추부면204365(6)206159신평1리2021-05-31
252추부면5196(2)5640신평2리2021-05-31
253추부면168297(5)151146성당1리2021-05-31
254추부면129220(4)111109성당2리2021-05-31
255추부면92153(3)8865서대1리2021-05-31
256추부면78120(2)6753서대2리2021-05-31