Overview

Dataset statistics

Number of variables9
Number of observations317
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.0 KiB
Average record size in memory77.4 B

Variable types

Categorical4
Text1
Numeric4

Dataset

Description진안군 행정리별 인구 현황에 대한 데이터로, 연도, 기준일, 읍면명, 행정리명, 세대수, 인구수, 남자수, 여자수 정보를 제공합니다.
Author전북특별자치도 진안군
URLhttps://www.data.go.kr/data/15031883/fileData.do

Alerts

연도 has constant value ""Constant
기준일 has constant value ""Constant
데이터기준일자 has constant value ""Constant
세대수 is highly overall correlated with 인구수 and 2 other fieldsHigh correlation
인구수 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
남자 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
여자 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation

Reproduction

Analysis started2024-03-14 12:42:31.477010
Analysis finished2024-03-14 12:42:36.341276
Duration4.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023
317 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 317
100.0%

Length

2024-03-14T21:42:36.543554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:42:37.062181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 317
100.0%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
10월 31일
317 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10월 31일
2nd row10월 31일
3rd row10월 31일
4th row10월 31일
5th row10월 31일

Common Values

ValueCountFrequency (%)
10월 31일 317
100.0%

Length

2024-03-14T21:42:37.379727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:42:37.687898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10월 317
50.0%
31일 317
50.0%

읍면명
Categorical

Distinct11
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
전북특별자치도 진안군 진안읍
71 
전북특별자치도 진안군 부귀면
42 
전북특별자치도 진안군 백운면
33 
전북특별자치도 진안군 성수면
32 
전북특별자치도 진안군 동향면
28 
Other values (6)
111 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북특별자치도 진안군 진안읍
2nd row전북특별자치도 진안군 진안읍
3rd row전북특별자치도 진안군 진안읍
4th row전북특별자치도 진안군 진안읍
5th row전북특별자치도 진안군 진안읍

Common Values

ValueCountFrequency (%)
전북특별자치도 진안군 진안읍 71
22.4%
전북특별자치도 진안군 부귀면 42
13.2%
전북특별자치도 진안군 백운면 33
10.4%
전북특별자치도 진안군 성수면 32
10.1%
전북특별자치도 진안군 동향면 28
 
8.8%
전북특별자치도 진안군 주천면 25
 
7.9%
전북특별자치도 진안군 마령면 22
 
6.9%
전북특별자치도 진안군 상전면 17
 
5.4%
전북특별자치도 진안군 용담면 16
 
5.0%
전북특별자치도 진안군 안천면 16
 
5.0%

Length

2024-03-14T21:42:38.025440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북특별자치도 317
33.3%
진안군 317
33.3%
진안읍 71
 
7.5%
부귀면 42
 
4.4%
백운면 33
 
3.5%
성수면 32
 
3.4%
동향면 28
 
2.9%
주천면 25
 
2.6%
마령면 22
 
2.3%
상전면 17
 
1.8%
Other values (3) 47
 
4.9%

행정리명
Text

Distinct301
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-03-14T21:42:39.620559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.3943218
Min length2

Characters and Unicode

Total characters759
Distinct characters172
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

Unique286 ?
Unique (%)90.2%

Sample

1st row노계1동
2nd row노계2동
3rd row학천1동
4th row학천2동
5th row우화1동
ValueCountFrequency (%)
양지 3
 
0.9%
신기 2
 
0.6%
지사 2
 
0.6%
새마을 2
 
0.6%
방화 2
 
0.6%
추동 2
 
0.6%
교동 2
 
0.6%
중평 2
 
0.6%
금평 2
 
0.6%
괴정 2
 
0.6%
Other values (291) 296
93.4%
2024-03-14T21:42:41.521440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 58
 
7.6%
원 31
 
4.1%
신 19
 
2.5%
상 19
 
2.5%
산 16
 
2.1%
평 16
 
2.1%
화 15
 
2.0%
중 14
 
1.8%
곡 14
 
1.8%
대 13
 
1.7%
Other values (162) 544
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 734
96.7%
Decimal Number 25
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 58
 
7.9%
원 31
 
4.2%
신 19
 
2.6%
상 19
 
2.6%
산 16
 
2.2%
평 16
 
2.2%
화 15
 
2.0%
중 14
 
1.9%
곡 14
 
1.9%
대 13
 
1.8%
Other values (156) 519
70.7%
Decimal Number
ValueCountFrequency (%)
2 9
36.0%
1 9
36.0%
3 3
 
12.0%
4 2
 
8.0%
6 1
 
4.0%
5 1
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 734
96.7%
Common 25
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 58
 
7.9%
원 31
 
4.2%
신 19
 
2.6%
상 19
 
2.6%
산 16
 
2.2%
평 16
 
2.2%
화 15
 
2.0%
중 14
 
1.9%
곡 14
 
1.9%
대 13
 
1.8%
Other values (156) 519
70.7%
Common
ValueCountFrequency (%)
2 9
36.0%
1 9
36.0%
3 3
 
12.0%
4 2
 
8.0%
6 1
 
4.0%
5 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 734
96.7%
ASCII 25
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 58
 
7.9%
원 31
 
4.2%
신 19
 
2.6%
상 19
 
2.6%
산 16
 
2.2%
평 16
 
2.2%
화 15
 
2.0%
중 14
 
1.9%
곡 14
 
1.9%
대 13
 
1.8%
Other values (156) 519
70.7%
ASCII
ValueCountFrequency (%)
2 9
36.0%
1 9
36.0%
3 3
 
12.0%
4 2
 
8.0%
6 1
 
4.0%
5 1
 
4.0%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.845426
Minimum9
Maximum298
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-14T21:42:41.933592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile14
Q123
median34
Q350
95-th percentile92.8
Maximum298
Range289
Interquartile range (IQR)27

Descriptive statistics

Standard deviation35.750428
Coefficient of variation (CV)0.83440478
Kurtosis19.872324
Mean42.845426
Median Absolute Deviation (MAD)13
Skewness3.7508738
Sum13582
Variance1278.0931
MonotonicityNot monotonic
2024-03-14T21:42:42.361854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 11
 
3.5%
19 10
 
3.2%
17 10
 
3.2%
22 10
 
3.2%
34 9
 
2.8%
35 9
 
2.8%
20 9
 
2.8%
26 9
 
2.8%
43 8
 
2.5%
28 8
 
2.5%
Other values (78) 224
70.7%
ValueCountFrequency (%)
9 3
 
0.9%
10 1
 
0.3%
11 4
 
1.3%
12 2
 
0.6%
13 3
 
0.9%
14 6
1.9%
15 6
1.9%
16 5
1.6%
17 10
3.2%
18 3
 
0.9%
ValueCountFrequency (%)
298 1
0.3%
297 1
0.3%
240 1
0.3%
185 1
0.3%
176 1
0.3%
171 1
0.3%
159 2
0.6%
141 1
0.3%
122 1
0.3%
110 1
0.3%

인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.337539
Minimum11
Maximum887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-14T21:42:42.791092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile22.8
Q139
median57
Q384
95-th percentile174
Maximum887
Range876
Interquartile range (IQR)45

Descriptive statistics

Standard deviation81.158364
Coefficient of variation (CV)1.0494045
Kurtosis40.978945
Mean77.337539
Median Absolute Deviation (MAD)22
Skewness5.3779181
Sum24516
Variance6586.68
MonotonicityNot monotonic
2024-03-14T21:42:43.233653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 8
 
2.5%
55 8
 
2.5%
72 7
 
2.2%
88 7
 
2.2%
36 7
 
2.2%
40 6
 
1.9%
26 6
 
1.9%
45 6
 
1.9%
50 6
 
1.9%
35 6
 
1.9%
Other values (115) 250
78.9%
ValueCountFrequency (%)
11 1
 
0.3%
13 1
 
0.3%
15 2
 
0.6%
17 2
 
0.6%
18 1
 
0.3%
19 2
 
0.6%
21 5
1.6%
22 2
 
0.6%
23 5
1.6%
24 6
1.9%
ValueCountFrequency (%)
887 1
0.3%
634 1
0.3%
491 1
0.3%
419 1
0.3%
383 1
0.3%
331 1
0.3%
320 1
0.3%
297 1
0.3%
269 1
0.3%
230 1
0.3%

남자
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.492114
Minimum6
Maximum435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-14T21:42:43.647917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11.8
Q121
median30
Q344
95-th percentile89.2
Maximum435
Range429
Interquartile range (IQR)23

Descriptive statistics

Standard deviation39.471237
Coefficient of variation (CV)0.99947138
Kurtosis40.532965
Mean39.492114
Median Absolute Deviation (MAD)12
Skewness5.2705275
Sum12519
Variance1557.9786
MonotonicityNot monotonic
2024-03-14T21:42:44.086518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 11
 
3.5%
26 11
 
3.5%
23 10
 
3.2%
34 10
 
3.2%
27 9
 
2.8%
21 9
 
2.8%
25 9
 
2.8%
13 9
 
2.8%
43 8
 
2.5%
22 8
 
2.5%
Other values (73) 223
70.3%
ValueCountFrequency (%)
6 1
 
0.3%
7 4
1.3%
9 2
 
0.6%
10 5
1.6%
11 4
1.3%
12 7
2.2%
13 9
2.8%
14 5
1.6%
15 8
2.5%
16 4
1.3%
ValueCountFrequency (%)
435 1
0.3%
307 1
0.3%
230 1
0.3%
201 1
0.3%
171 1
0.3%
168 1
0.3%
165 1
0.3%
142 1
0.3%
123 1
0.3%
121 1
0.3%

여자
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.845426
Minimum4
Maximum452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-14T21:42:44.499911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10
Q118
median28
Q342
95-th percentile86
Maximum452
Range448
Interquartile range (IQR)24

Descriptive statistics

Standard deviation42.018133
Coefficient of variation (CV)1.1102566
Kurtosis40.281314
Mean37.845426
Median Absolute Deviation (MAD)12
Skewness5.3890951
Sum11997
Variance1765.5235
MonotonicityNot monotonic
2024-03-14T21:42:44.929214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 12
 
3.8%
18 12
 
3.8%
22 10
 
3.2%
21 10
 
3.2%
19 10
 
3.2%
23 9
 
2.8%
27 9
 
2.8%
13 9
 
2.8%
17 8
 
2.5%
25 8
 
2.5%
Other values (75) 220
69.4%
ValueCountFrequency (%)
4 1
 
0.3%
5 1
 
0.3%
6 1
 
0.3%
7 3
 
0.9%
8 1
 
0.3%
9 8
2.5%
10 6
1.9%
11 7
2.2%
12 5
1.6%
13 9
2.8%
ValueCountFrequency (%)
452 1
0.3%
327 1
0.3%
261 1
0.3%
218 1
0.3%
212 1
0.3%
163 1
0.3%
155 2
0.6%
148 1
0.3%
122 1
0.3%
113 1
0.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-10-31
317 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-31
2nd row2023-10-31
3rd row2023-10-31
4th row2023-10-31
5th row2023-10-31

Common Values

ValueCountFrequency (%)
2023-10-31 317
100.0%

Length

2024-03-14T21:42:45.338826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:42:45.642034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-31 317
100.0%

Interactions

2024-03-14T21:42:34.691371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:31.808775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:32.791018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:33.738879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:34.936723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:32.060035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:33.039238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:33.988616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:35.167108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:32.303649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:33.271931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:34.220600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:35.397812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:32.542303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:33.507632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:34.454262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:42:45.820292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면명세대수인구수남자여자
읍면명1.0000.1210.1370.1380.076
세대수0.1211.0000.9140.9370.917
인구수0.1370.9141.0000.9980.999
남자0.1380.9370.9981.0000.995
여자0.0760.9170.9990.9951.000
2024-03-14T21:42:46.085737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구수남자여자읍면명
세대수1.0000.9660.9500.9500.054
인구수0.9661.0000.9840.9820.064
남자0.9500.9841.0000.9350.064
여자0.9500.9820.9351.0000.034
읍면명0.0540.0640.0640.0341.000

Missing values

2024-03-14T21:42:35.727210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:42:36.173539image/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

연도기준일읍면명행정리명세대수인구수남자여자데이터기준일자
0202310월 31일전북특별자치도 진안군 진안읍노계1동5710957522023-10-31
1202310월 31일전북특별자치도 진안군 진안읍노계2동1222301231072023-10-31
2202310월 31일전북특별자치도 진안군 진안읍학천1동1593201651552023-10-31
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