Overview

Dataset statistics

Number of variables7
Number of observations290
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 KiB
Average record size in memory61.5 B

Variable types

Numeric5
Categorical2

Dataset

Description증평군 법정리별 인구현황 (2008년~ )
Author충청북도 증평군
URLhttps://www.data.go.kr/data/3045216/fileData.do

Alerts

법정리 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
읍면 is highly overall correlated with 법정리High correlation
세대수 is highly overall correlated with 인구합계 and 3 other fieldsHigh correlation
인구합계 is highly overall correlated with 세대수 and 3 other fieldsHigh correlation
남성인구 is highly overall correlated with 세대수 and 3 other fieldsHigh correlation
여성인구 is highly overall correlated with 세대수 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 10:38:58.587485
Analysis finished2023-12-12 10:39:02.273209
Duration3.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계연도
Real number (ℝ)

Distinct10
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.5
Minimum2008
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-12T19:39:02.333343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12010
median2012.5
Q32015
95-th percentile2017
Maximum2017
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8772464
Coefficient of variation (CV)0.0014296876
Kurtosis-1.2246366
Mean2012.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum583625
Variance8.2785467
MonotonicityDecreasing
2023-12-12T19:39:02.472207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2017 29
10.0%
2016 29
10.0%
2015 29
10.0%
2014 29
10.0%
2013 29
10.0%
2012 29
10.0%
2011 29
10.0%
2010 29
10.0%
2009 29
10.0%
2008 29
10.0%
ValueCountFrequency (%)
2008 29
10.0%
2009 29
10.0%
2010 29
10.0%
2011 29
10.0%
2012 29
10.0%
2013 29
10.0%
2014 29
10.0%
2015 29
10.0%
2016 29
10.0%
2017 29
10.0%
ValueCountFrequency (%)
2017 29
10.0%
2016 29
10.0%
2015 29
10.0%
2014 29
10.0%
2013 29
10.0%
2012 29
10.0%
2011 29
10.0%
2010 29
10.0%
2009 29
10.0%
2008 29
10.0%

읍면
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
증평읍
210 
도안면
80 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
증평읍 210
72.4%
도안면 80
 
27.6%

Length

2023-12-12T19:39:02.610219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:39:02.717095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
증평읍 210
72.4%
도안면 80
 
27.6%

법정리
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
증평읍합계
 
10
증평리
 
10
교동리
 
10
중동리
 
10
대동리
 
10
Other values (25)
240 

Length

Max length5
Median length3
Mean length3.0689655
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row증평읍합계
2nd row증평리
3rd row교동리
4th row중동리
5th row대동리

Common Values

ValueCountFrequency (%)
증평읍합계 10
 
3.4%
증평리 10
 
3.4%
교동리 10
 
3.4%
중동리 10
 
3.4%
대동리 10
 
3.4%
신동리 10
 
3.4%
창동리 10
 
3.4%
장동리 10
 
3.4%
증천리 10
 
3.4%
내성리 10
 
3.4%
Other values (20) 190
65.5%

Length

2023-12-12T19:39:02.846682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
증평읍합계 10
 
3.4%
증평리 10
 
3.4%
도당리 10
 
3.4%
석곡리 10
 
3.4%
송정리 10
 
3.4%
연촌리 10
 
3.4%
노암리 10
 
3.4%
화성리 10
 
3.4%
도안면합계 10
 
3.4%
남하리 10
 
3.4%
Other values (20) 190
65.5%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1002.7241
Minimum7
Maximum15671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-12T19:39:02.995786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile92.45
Q1154.25
median275
Q3884
95-th percentile2519.95
Maximum15671
Range15664
Interquartile range (IQR)729.75

Descriptive statistics

Standard deviation2468.6457
Coefficient of variation (CV)2.4619391
Kurtosis21.788447
Mean1002.7241
Median Absolute Deviation (MAD)161
Skewness4.6723114
Sum290790
Variance6094211.8
MonotonicityNot monotonic
2023-12-12T19:39:03.182158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 5
 
1.7%
92 4
 
1.4%
16 4
 
1.4%
97 4
 
1.4%
159 4
 
1.4%
126 3
 
1.0%
182 3
 
1.0%
101 3
 
1.0%
104 3
 
1.0%
266 3
 
1.0%
Other values (209) 254
87.6%
ValueCountFrequency (%)
7 1
 
0.3%
8 1
 
0.3%
12 1
 
0.3%
14 1
 
0.3%
16 4
1.4%
17 2
0.7%
89 1
 
0.3%
92 4
1.4%
93 1
 
0.3%
94 3
1.0%
ValueCountFrequency (%)
15671 1
0.3%
15238 1
0.3%
14767 1
0.3%
13703 1
0.3%
13379 1
0.3%
13158 1
0.3%
12923 1
0.3%
12549 1
0.3%
12280 1
0.3%
11469 1
0.3%

인구합계
Real number (ℝ)

HIGH CORRELATION 

Distinct253
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2399.8966
Minimum13
Maximum35665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-12T19:39:03.714091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile172.45
Q1314
median599.5
Q32117.25
95-th percentile7339.6
Maximum35665
Range35652
Interquartile range (IQR)1803.25

Descriptive statistics

Standard deviation5966.8602
Coefficient of variation (CV)2.4862989
Kurtosis20.449234
Mean2399.8966
Median Absolute Deviation (MAD)359.5
Skewness4.5348484
Sum695970
Variance35603421
MonotonicityNot monotonic
2023-12-12T19:39:03.920675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
389 4
 
1.4%
314 3
 
1.0%
28 2
 
0.7%
211 2
 
0.7%
222 2
 
0.7%
190 2
 
0.7%
385 2
 
0.7%
401 2
 
0.7%
213 2
 
0.7%
532 2
 
0.7%
Other values (243) 267
92.1%
ValueCountFrequency (%)
13 1
0.3%
15 1
0.3%
25 2
0.7%
27 1
0.3%
28 2
0.7%
30 1
0.3%
31 2
0.7%
146 1
0.3%
148 1
0.3%
155 1
0.3%
ValueCountFrequency (%)
35665 1
0.3%
35193 1
0.3%
34492 1
0.3%
32535 1
0.3%
32302 1
0.3%
31986 1
0.3%
31501 1
0.3%
30941 1
0.3%
30680 1
0.3%
29736 1
0.3%

남성인구
Real number (ℝ)

HIGH CORRELATION 

Distinct238
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1229.531
Minimum10
Maximum18342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-12T19:39:04.130160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile92.9
Q1163.25
median326.5
Q31101.25
95-th percentile3667.55
Maximum18342
Range18332
Interquartile range (IQR)938

Descriptive statistics

Standard deviation3052.0693
Coefficient of variation (CV)2.4823036
Kurtosis20.591295
Mean1229.531
Median Absolute Deviation (MAD)206.5
Skewness4.5525494
Sum356564
Variance9315126.7
MonotonicityNot monotonic
2023-12-12T19:39:04.346504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 3
 
1.0%
211 3
 
1.0%
208 3
 
1.0%
115 3
 
1.0%
98 3
 
1.0%
107 3
 
1.0%
207 3
 
1.0%
96 3
 
1.0%
103 3
 
1.0%
451 3
 
1.0%
Other values (228) 260
89.7%
ValueCountFrequency (%)
10 2
0.7%
16 1
0.3%
17 2
0.7%
18 2
0.7%
19 1
0.3%
20 2
0.7%
78 1
0.3%
81 2
0.7%
88 1
0.3%
92 1
0.3%
ValueCountFrequency (%)
18342 1
0.3%
18020 1
0.3%
17671 1
0.3%
16668 1
0.3%
16454 1
0.3%
16336 1
0.3%
16197 1
0.3%
15942 1
0.3%
15770 1
0.3%
15067 1
0.3%

여성인구
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1170.3655
Minimum3
Maximum17323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-12T19:39:04.578028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile78.9
Q1150.25
median278.5
Q31039.75
95-th percentile3673.85
Maximum17323
Range17320
Interquartile range (IQR)889.5

Descriptive statistics

Standard deviation2915.4228
Coefficient of variation (CV)2.4910361
Kurtosis20.288292
Mean1170.3655
Median Absolute Deviation (MAD)160.5
Skewness4.5144514
Sum339406
Variance8499689.9
MonotonicityNot monotonic
2023-12-12T19:39:04.785917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146 4
 
1.4%
177 4
 
1.4%
97 4
 
1.4%
10 3
 
1.0%
1182 3
 
1.0%
144 3
 
1.0%
11 3
 
1.0%
93 3
 
1.0%
208 3
 
1.0%
182 3
 
1.0%
Other values (220) 257
88.6%
ValueCountFrequency (%)
3 1
 
0.3%
5 1
 
0.3%
8 1
 
0.3%
9 1
 
0.3%
10 3
1.0%
11 3
1.0%
67 1
 
0.3%
68 1
 
0.3%
74 1
 
0.3%
77 1
 
0.3%
ValueCountFrequency (%)
17323 1
0.3%
17173 1
0.3%
16821 1
0.3%
15867 1
0.3%
15848 1
0.3%
15650 1
0.3%
15304 1
0.3%
14999 1
0.3%
14910 1
0.3%
14669 1
0.3%

Interactions

2023-12-12T19:39:01.571008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:58.946497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:59.586612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:00.343379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:00.987442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:01.660363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:59.090665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:59.716775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:00.459400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:01.103304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:01.774761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:59.200137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:59.880724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:00.590521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:01.224916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:01.895915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:59.328236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:00.050555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:00.740310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:01.337560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:01.992231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:59.454925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:00.194166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:00.870305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:39:01.446159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:39:04.918483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계연도읍면법정리세대수인구합계남성인구여성인구
집계연도1.0000.0000.0000.0000.0000.0000.000
읍면0.0001.0001.0000.2560.1850.1720.185
법정리0.0001.0001.0000.8750.9700.9300.982
세대수0.0000.2560.8751.0000.8700.9220.860
인구합계0.0000.1850.9700.8701.0000.9900.997
남성인구0.0000.1720.9300.9220.9901.0000.981
여성인구0.0000.1850.9820.8600.9970.9811.000
2023-12-12T19:39:05.064905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정리읍면
법정리1.0000.950
읍면0.9501.000
2023-12-12T19:39:05.208393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계연도세대수인구합계남성인구여성인구읍면법정리
집계연도1.0000.018-0.025-0.027-0.0190.0000.000
세대수0.0181.0000.9900.9910.9780.1830.545
인구합계-0.0250.9901.0000.9970.9940.2260.757
남성인구-0.0270.9910.9971.0000.9850.2090.660
여성인구-0.0190.9780.9940.9851.0000.2260.799
읍면0.0000.1830.2260.2090.2261.0000.950
법정리0.0000.5450.7570.6600.7990.9501.000

Missing values

2023-12-12T19:39:02.116980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:39:02.230904image/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

집계연도읍면법정리세대수인구합계남성인구여성인구
02017증평읍증평읍합계15671356651834217323
12017증평읍증평리7691766865901
22017증평읍교동리360719363356
32017증평읍중동리219430209221
42017증평읍대동리124269134135
52017증평읍신동리1056222310891134
62017증평읍창동리1526357917591820
72017증평읍장동리3276928146334648
82017증평읍증천리87220231073950
92017증평읍내성리3501102572530
집계연도읍면법정리세대수인구합계남성인구여성인구
2802008증평읍죽리95236115121
2812008증평읍남하리231555269286
2822008도안면도안면합계1012244312601183
2832008도안면화성리356906451455
2842008도안면노암리155385208177
2852008도안면연촌리1225178
2862008도안면송정리167392208184
2872008도안면광덕리94222109113
2882008도안면석곡리9621111497
2892008도안면도당리132302153149