Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells9812
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory123.0 B

Variable types

Numeric11
Categorical1
Text1

Alerts

전월 인구수 is highly overall correlated with 당월 인구수 and 5 other fieldsHigh correlation
당월 인구수 is highly overall correlated with 전월 인구수 and 5 other fieldsHigh correlation
인구증감 is highly overall correlated with 인구증감 (남자) and 1 other fieldsHigh correlation
전월 인구수 (남자) is highly overall correlated with 전월 인구수 and 5 other fieldsHigh correlation
당월 인구수 (남자) is highly overall correlated with 전월 인구수 and 5 other fieldsHigh correlation
인구증감 (남자) is highly overall correlated with 인구증감 and 1 other fieldsHigh correlation
전월 인구수 (여자) is highly overall correlated with 전월 인구수 and 5 other fieldsHigh correlation
당월 인구수 (여자) is highly overall correlated with 전월 인구수 and 5 other fieldsHigh correlation
인구증감 (여자) is highly overall correlated with 인구증감 and 1 other fieldsHigh correlation
행정구역구분명 is highly overall correlated with 전월 인구수 and 5 other fieldsHigh correlation
행정구역구분명 is highly imbalanced (74.5%)Imbalance
전월 인구수 (남자) has 2453 (24.5%) missing valuesMissing
인구증감 (남자) has 2453 (24.5%) missing valuesMissing
전월 인구수 (여자) has 2453 (24.5%) missing valuesMissing
인구증감 (여자) has 2453 (24.5%) missing valuesMissing
전월 인구수 is highly skewed (γ1 = 21.73773219)Skewed
당월 인구수 is highly skewed (γ1 = 21.74551993)Skewed
인구증감 is highly skewed (γ1 = 93.4058153)Skewed
전월 인구수 (남자) is highly skewed (γ1 = 20.82959691)Skewed
당월 인구수 (남자) is highly skewed (γ1 = 21.75949658)Skewed
인구증감 (남자) is highly skewed (γ1 = 81.8275569)Skewed
전월 인구수 (여자) is highly skewed (γ1 = 20.8010307)Skewed
당월 인구수 (여자) is highly skewed (γ1 = 21.73012303)Skewed
인구증감 (여자) is highly skewed (γ1 = 81.87436298)Skewed
인구증감 has 128 (1.3%) zerosZeros
인구증감 (남자) has 169 (1.7%) zerosZeros
인구증감 (여자) has 169 (1.7%) zerosZeros

Reproduction

Analysis started2024-04-11 02:48:54.420533
Analysis finished2024-04-11 02:49:17.702288
Duration23.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.914
Minimum2010
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-11T11:49:17.770851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12013
median2017
Q32020
95-th percentile2023
Maximum2024
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.9857989
Coefficient of variation (CV)0.0019761868
Kurtosis-1.1779002
Mean2016.914
Median Absolute Deviation (MAD)3
Skewness-0.0049261327
Sum20169140
Variance15.886593
MonotonicityNot monotonic
2024-04-11T11:49:17.905184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2023 759
 
7.6%
2016 747
 
7.5%
2013 743
 
7.4%
2014 739
 
7.4%
2018 737
 
7.4%
2011 730
 
7.3%
2022 727
 
7.3%
2020 726
 
7.3%
2021 726
 
7.3%
2017 717
 
7.2%
Other values (5) 2649
26.5%
ValueCountFrequency (%)
2010 341
3.4%
2011 730
7.3%
2012 696
7.0%
2013 743
7.4%
2014 739
7.4%
2015 711
7.1%
2016 747
7.5%
2017 717
7.2%
2018 737
7.4%
2019 714
7.1%
ValueCountFrequency (%)
2024 187
 
1.9%
2023 759
7.6%
2022 727
7.3%
2021 726
7.3%
2020 726
7.3%
2019 714
7.1%
2018 737
7.4%
2017 717
7.2%
2016 747
7.5%
2015 711
7.1%


Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4995
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-11T11:49:18.073803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4888131
Coefficient of variation (CV)0.53678177
Kurtosis-1.231864
Mean6.4995
Median Absolute Deviation (MAD)3
Skewness-0.0085971257
Sum64995
Variance12.171817
MonotonicityNot monotonic
2024-04-11T11:49:18.211419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 887
8.9%
12 877
8.8%
10 871
8.7%
2 863
8.6%
6 834
8.3%
5 823
8.2%
9 822
8.2%
8 821
8.2%
7 818
8.2%
3 806
8.1%
Other values (2) 1578
15.8%
ValueCountFrequency (%)
1 887
8.9%
2 863
8.6%
3 806
8.1%
4 777
7.8%
5 823
8.2%
6 834
8.3%
7 818
8.2%
8 821
8.2%
9 822
8.2%
10 871
8.7%
ValueCountFrequency (%)
12 877
8.8%
11 801
8.0%
10 871
8.7%
9 822
8.2%
8 821
8.2%
7 818
8.2%
6 834
8.3%
5 823
8.2%
4 777
7.8%
3 806
8.1%

행정구역구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
읍면동
9155 
시군
 
520
 
307
 
18

Length

Max length3
Median length3
Mean length2.883
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row읍면동
2nd row시군
3rd row읍면동
4th row읍면동
5th row읍면동

Common Values

ValueCountFrequency (%)
읍면동 9155
91.5%
시군 520
 
5.2%
307
 
3.1%
18
 
0.2%

Length

2024-04-11T11:49:18.380873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T11:49:18.531607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
읍면동 9155
91.5%
시군 520
 
5.2%
307
 
3.1%
18
 
0.2%
Distinct1011
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-11T11:49:18.859020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length12.9288
Min length3

Characters and Unicode

Total characters129288
Distinct characters214
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

Unique167 ?
Unique (%)1.7%

Sample

1st row경기도 양주시 회천1동
2nd row경기도 동두천시
3rd row경기도 포천시 군내면
4th row경기도 용인시 처인구 유림동
5th row경기도 수원시 장안구 조원2동
ValueCountFrequency (%)
경기도 10000
30.0%
성남시 903
 
2.7%
수원시 772
 
2.3%
고양시 720
 
2.2%
용인시 628
 
1.9%
안양시 577
 
1.7%
부천시 489
 
1.5%
안산시 474
 
1.4%
화성시 431
 
1.3%
평택시 368
 
1.1%
Other values (676) 17996
53.9%
2024-04-11T11:49:19.409475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24507
19.0%
10281
 
8.0%
10167
 
7.9%
10014
 
7.7%
9732
 
7.5%
7853
 
6.1%
4533
 
3.5%
2788
 
2.2%
2434
 
1.9%
1879
 
1.5%
Other values (204) 45100
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 101362
78.4%
Space Separator 24507
 
19.0%
Decimal Number 3419
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10281
 
10.1%
10167
 
10.0%
10014
 
9.9%
9732
 
9.6%
7853
 
7.7%
4533
 
4.5%
2788
 
2.8%
2434
 
2.4%
1879
 
1.9%
1769
 
1.7%
Other values (194) 39912
39.4%
Decimal Number
ValueCountFrequency (%)
1 1291
37.8%
2 1248
36.5%
3 555
16.2%
4 142
 
4.2%
6 53
 
1.6%
7 42
 
1.2%
5 40
 
1.2%
9 27
 
0.8%
8 21
 
0.6%
Space Separator
ValueCountFrequency (%)
24507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 101362
78.4%
Common 27926
 
21.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10281
 
10.1%
10167
 
10.0%
10014
 
9.9%
9732
 
9.6%
7853
 
7.7%
4533
 
4.5%
2788
 
2.8%
2434
 
2.4%
1879
 
1.9%
1769
 
1.7%
Other values (194) 39912
39.4%
Common
ValueCountFrequency (%)
24507
87.8%
1 1291
 
4.6%
2 1248
 
4.5%
3 555
 
2.0%
4 142
 
0.5%
6 53
 
0.2%
7 42
 
0.2%
5 40
 
0.1%
9 27
 
0.1%
8 21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 101362
78.4%
ASCII 27926
 
21.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24507
87.8%
1 1291
 
4.6%
2 1248
 
4.5%
3 555
 
2.0%
4 142
 
0.5%
6 53
 
0.2%
7 42
 
0.2%
5 40
 
0.1%
9 27
 
0.1%
8 21
 
0.1%
Hangul
ValueCountFrequency (%)
10281
 
10.1%
10167
 
10.0%
10014
 
9.9%
9732
 
9.6%
7853
 
7.7%
4533
 
4.5%
2788
 
2.8%
2434
 
2.4%
1879
 
1.9%
1769
 
1.7%
Other values (194) 39912
39.4%

전월 인구수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9039
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77584.224
Minimum0
Maximum13600800
Zeros30
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-11T11:49:20.002876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3634
Q112278.75
median22347.5
Q335532.5
95-th percentile291461.2
Maximum13600800
Range13600800
Interquartile range (IQR)23253.75

Descriptive statistics

Standard deviation560267.57
Coefficient of variation (CV)7.2214111
Kurtosis495.86984
Mean77584.224
Median Absolute Deviation (MAD)11144
Skewness21.737732
Sum7.7584224 × 108
Variance3.1389975 × 1011
MonotonicityNot monotonic
2024-04-11T11:49:20.205098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
0.3%
19127 4
 
< 0.1%
13394 4
 
< 0.1%
4575 4
 
< 0.1%
21497 4
 
< 0.1%
26397 4
 
< 0.1%
14351 4
 
< 0.1%
9812 3
 
< 0.1%
19703 3
 
< 0.1%
24848 3
 
< 0.1%
Other values (9029) 9937
99.4%
ValueCountFrequency (%)
0 30
0.3%
56 1
 
< 0.1%
57 1
 
< 0.1%
144 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
162 1
 
< 0.1%
166 1
 
< 0.1%
167 2
 
< 0.1%
169 1
 
< 0.1%
ValueCountFrequency (%)
13600800 1
< 0.1%
13581496 1
< 0.1%
13557973 1
< 0.1%
13530519 1
< 0.1%
13338020 1
< 0.1%
13288975 1
< 0.1%
13207219 1
< 0.1%
13061074 1
< 0.1%
13012486 1
< 0.1%
12925761 1
< 0.1%

당월 인구수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9013
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77708.499
Minimum0
Maximum13603546
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-11T11:49:20.393052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3643.45
Q112319
median22357
Q335523
95-th percentile291415.75
Maximum13603546
Range13603546
Interquartile range (IQR)23204

Descriptive statistics

Standard deviation562178.65
Coefficient of variation (CV)7.2344551
Kurtosis496.06689
Mean77708.499
Median Absolute Deviation (MAD)11126
Skewness21.74552
Sum7.7708499 × 108
Variance3.1604483 × 1011
MonotonicityNot monotonic
2024-04-11T11:49:20.580081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
0.2%
14361 5
 
0.1%
26189 4
 
< 0.1%
13263 4
 
< 0.1%
24193 4
 
< 0.1%
4542 4
 
< 0.1%
26751 4
 
< 0.1%
4198 4
 
< 0.1%
15544 4
 
< 0.1%
14026 4
 
< 0.1%
Other values (9003) 9938
99.4%
ValueCountFrequency (%)
0 25
0.2%
56 2
 
< 0.1%
144 1
 
< 0.1%
150 1
 
< 0.1%
155 1
 
< 0.1%
161 1
 
< 0.1%
165 1
 
< 0.1%
167 2
 
< 0.1%
169 1
 
< 0.1%
171 1
 
< 0.1%
ValueCountFrequency (%)
13603546 1
< 0.1%
13585967 1
< 0.1%
13565450 1
< 0.1%
13542284 1
< 0.1%
13351891 1
< 0.1%
13311254 1
< 0.1%
13218912 1
< 0.1%
13176011 1
< 0.1%
13077153 1
< 0.1%
13027338 1
< 0.1%

인구증감
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1242
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.2753
Minimum-45987
Maximum616160
Zeros128
Zeros (%)1.3%
Negative5737
Negative (%)57.4%
Memory size166.0 KiB
2024-04-11T11:49:20.758590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-45987
5-th percentile-152
Q1-44
median-8
Q324
95-th percentile441.05
Maximum616160
Range662147
Interquartile range (IQR)68

Descriptive statistics

Standard deviation6304.3439
Coefficient of variation (CV)50.728857
Kurtosis9122.6007
Mean124.2753
Median Absolute Deviation (MAD)34
Skewness93.405815
Sum1242753
Variance39744752
MonotonicityNot monotonic
2024-04-11T11:49:20.939524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128
 
1.3%
2 123
 
1.2%
-3 105
 
1.1%
-6 104
 
1.0%
-7 103
 
1.0%
1 102
 
1.0%
-4 98
 
1.0%
-12 95
 
0.9%
-16 94
 
0.9%
-2 93
 
0.9%
Other values (1232) 8955
89.5%
ValueCountFrequency (%)
-45987 1
< 0.1%
-36168 1
< 0.1%
-32047 1
< 0.1%
-24804 1
< 0.1%
-23979 1
< 0.1%
-19207 1
< 0.1%
-14882 1
< 0.1%
-9187 1
< 0.1%
-5531 1
< 0.1%
-1640 1
< 0.1%
ValueCountFrequency (%)
616160 1
< 0.1%
67855 1
< 0.1%
24828 1
< 0.1%
22397 1
< 0.1%
22279 1
< 0.1%
18770 1
< 0.1%
18350 1
< 0.1%
16840 1
< 0.1%
16397 1
< 0.1%
16079 1
< 0.1%

전월 인구수 (남자)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct6519
Distinct (%)86.4%
Missing2453
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean40181.994
Minimum0
Maximum6844167
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-11T11:49:21.115520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1880.7
Q16282.5
median11150
Q317689
95-th percentile143544.8
Maximum6844167
Range6844167
Interquartile range (IQR)11406.5

Descriptive statistics

Standard deviation299211.87
Coefficient of variation (CV)7.4464166
Kurtosis452.50542
Mean40181.994
Median Absolute Deviation (MAD)5487
Skewness20.829597
Sum3.0325351 × 108
Variance8.9527742 × 1010
MonotonicityNot monotonic
2024-04-11T11:49:21.311263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.3%
7043 6
 
0.1%
3526 5
 
0.1%
7411 5
 
0.1%
93 5
 
0.1%
12823 4
 
< 0.1%
2495 4
 
< 0.1%
7937 4
 
< 0.1%
4778 4
 
< 0.1%
7176 4
 
< 0.1%
Other values (6509) 7477
74.8%
(Missing) 2453
 
24.5%
ValueCountFrequency (%)
0 29
0.3%
28 1
 
< 0.1%
29 1
 
< 0.1%
78 1
 
< 0.1%
83 1
 
< 0.1%
85 1
 
< 0.1%
86 1
 
< 0.1%
89 1
 
< 0.1%
90 1
 
< 0.1%
92 1
 
< 0.1%
ValueCountFrequency (%)
6844167 1
< 0.1%
6835968 1
< 0.1%
6823931 1
< 0.1%
6810105 1
< 0.1%
6711047 1
< 0.1%
6684759 1
< 0.1%
6644899 1
< 0.1%
6569721 1
< 0.1%
6545752 1
< 0.1%
6501263 1
< 0.1%

당월 인구수 (남자)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8321
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39066.333
Minimum0
Maximum6845505
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-11T11:49:21.524760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1913.8
Q16341.25
median11220
Q317692.5
95-th percentile143619
Maximum6845505
Range6845505
Interquartile range (IQR)11351.25

Descriptive statistics

Standard deviation282841.48
Coefficient of variation (CV)7.2400315
Kurtosis496.50419
Mean39066.333
Median Absolute Deviation (MAD)5452.5
Skewness21.759497
Sum3.9066333 × 108
Variance7.9999305 × 1010
MonotonicityNot monotonic
2024-04-11T11:49:21.723921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
0.2%
7054 5
 
0.1%
7070 5
 
0.1%
3998 5
 
0.1%
11307 4
 
< 0.1%
11277 4
 
< 0.1%
7315 4
 
< 0.1%
8255 4
 
< 0.1%
9937 4
 
< 0.1%
13405 4
 
< 0.1%
Other values (8311) 9936
99.4%
ValueCountFrequency (%)
0 25
0.2%
28 2
 
< 0.1%
78 1
 
< 0.1%
83 2
 
< 0.1%
86 1
 
< 0.1%
88 1
 
< 0.1%
90 1
 
< 0.1%
91 1
 
< 0.1%
92 2
 
< 0.1%
93 4
 
< 0.1%
ValueCountFrequency (%)
6845505 1
< 0.1%
6838396 1
< 0.1%
6827298 1
< 0.1%
6816449 1
< 0.1%
6717812 1
< 0.1%
6696531 1
< 0.1%
6650459 1
< 0.1%
6629105 1
< 0.1%
6577501 1
< 0.1%
6553196 1
< 0.1%

인구증감 (남자)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct823
Distinct (%)10.9%
Missing2453
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean71.247781
Minimum-22373
Maximum306380
Zeros169
Zeros (%)1.7%
Negative4366
Negative (%)43.7%
Memory size166.0 KiB
2024-04-11T11:49:21.909739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-22373
5-th percentile-86
Q1-25
median-5
Q312
95-th percentile210
Maximum306380
Range328753
Interquartile range (IQR)37

Descriptive statistics

Standard deviation3598.5606
Coefficient of variation (CV)50.507687
Kurtosis6960.6765
Mean71.247781
Median Absolute Deviation (MAD)19
Skewness81.827557
Sum537707
Variance12949638
MonotonicityNot monotonic
2024-04-11T11:49:22.084213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 169
 
1.7%
1 139
 
1.4%
-3 137
 
1.4%
-2 132
 
1.3%
2 132
 
1.3%
-1 127
 
1.3%
-8 127
 
1.3%
4 120
 
1.2%
-9 117
 
1.2%
-12 116
 
1.2%
Other values (813) 6231
62.3%
(Missing) 2453
 
24.5%
ValueCountFrequency (%)
-22373 1
< 0.1%
-17922 1
< 0.1%
-12408 1
< 0.1%
-11940 1
< 0.1%
-9466 1
< 0.1%
-4522 1
< 0.1%
-2927 1
< 0.1%
-838 1
< 0.1%
-824 1
< 0.1%
-726 1
< 0.1%
ValueCountFrequency (%)
306380 1
< 0.1%
33568 1
< 0.1%
13211 1
< 0.1%
11772 1
< 0.1%
10951 1
< 0.1%
9357 1
< 0.1%
9135 1
< 0.1%
8471 1
< 0.1%
8111 1
< 0.1%
7908 1
< 0.1%

전월 인구수 (여자)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct6499
Distinct (%)86.1%
Missing2453
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean39771.041
Minimum0
Maximum6756633
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-11T11:49:22.277201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1631.3
Q15874.5
median10968
Q317737.5
95-th percentile146297.1
Maximum6756633
Range6756633
Interquartile range (IQR)11863

Descriptive statistics

Standard deviation295582.75
Coefficient of variation (CV)7.4321101
Kurtosis451.61566
Mean39771.041
Median Absolute Deviation (MAD)5689
Skewness20.801031
Sum3.0015204 × 108
Variance8.7369164 × 1010
MonotonicityNot monotonic
2024-04-11T11:49:22.471030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.3%
12804 5
 
0.1%
82 5
 
0.1%
5300 5
 
0.1%
13858 4
 
< 0.1%
13977 4
 
< 0.1%
6388 4
 
< 0.1%
11762 4
 
< 0.1%
7392 4
 
< 0.1%
14120 4
 
< 0.1%
Other values (6489) 7479
74.8%
(Missing) 2453
 
24.5%
ValueCountFrequency (%)
0 29
0.3%
28 2
 
< 0.1%
66 1
 
< 0.1%
68 1
 
< 0.1%
74 1
 
< 0.1%
75 2
 
< 0.1%
76 2
 
< 0.1%
77 3
 
< 0.1%
80 2
 
< 0.1%
81 3
 
< 0.1%
ValueCountFrequency (%)
6756633 1
< 0.1%
6745528 1
< 0.1%
6734042 1
< 0.1%
6720414 1
< 0.1%
6626973 1
< 0.1%
6604216 1
< 0.1%
6562320 1
< 0.1%
6491353 1
< 0.1%
6466734 1
< 0.1%
6424498 1
< 0.1%

당월 인구수 (여자)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8331
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38642.166
Minimum0
Maximum6758041
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-11T11:49:22.655111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1679.95
Q15974
median11063
Q317697
95-th percentile146094.05
Maximum6758041
Range6758041
Interquartile range (IQR)11723

Descriptive statistics

Standard deviation279343.2
Coefficient of variation (CV)7.2289736
Kurtosis495.58311
Mean38642.166
Median Absolute Deviation (MAD)5693.5
Skewness21.730123
Sum3.8642166 × 108
Variance7.8032621 × 1010
MonotonicityNot monotonic
2024-04-11T11:49:22.864591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
0.2%
6404 5
 
0.1%
82 5
 
0.1%
2812 5
 
0.1%
3227 5
 
0.1%
7307 5
 
0.1%
11446 4
 
< 0.1%
10426 4
 
< 0.1%
1270 4
 
< 0.1%
15777 4
 
< 0.1%
Other values (8321) 9934
99.3%
ValueCountFrequency (%)
0 25
0.2%
28 2
 
< 0.1%
66 1
 
< 0.1%
67 1
 
< 0.1%
72 1
 
< 0.1%
74 1
 
< 0.1%
75 2
 
< 0.1%
76 1
 
< 0.1%
77 4
 
< 0.1%
78 1
 
< 0.1%
ValueCountFrequency (%)
6758041 1
< 0.1%
6747571 1
< 0.1%
6738152 1
< 0.1%
6725835 1
< 0.1%
6634079 1
< 0.1%
6614723 1
< 0.1%
6568453 1
< 0.1%
6546906 1
< 0.1%
6499652 1
< 0.1%
6474142 1
< 0.1%

인구증감 (여자)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct788
Distinct (%)10.4%
Missing2453
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean71.405592
Minimum-23614
Maximum309780
Zeros169
Zeros (%)1.7%
Negative4465
Negative (%)44.6%
Memory size166.0 KiB
2024-04-11T11:49:23.053387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23614
5-th percentile-82
Q1-25
median-6
Q311
95-th percentile211.7
Maximum309780
Range333394
Interquartile range (IQR)36

Descriptive statistics

Standard deviation3637.7282
Coefficient of variation (CV)50.944584
Kurtosis6966.672
Mean71.405592
Median Absolute Deviation (MAD)18
Skewness81.874363
Sum538898
Variance13233066
MonotonicityNot monotonic
2024-04-11T11:49:23.240834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 169
 
1.7%
-2 156
 
1.6%
-1 148
 
1.5%
1 146
 
1.5%
-4 138
 
1.4%
-7 134
 
1.3%
-12 124
 
1.2%
-5 124
 
1.2%
-8 120
 
1.2%
-6 119
 
1.2%
Other values (778) 6169
61.7%
(Missing) 2453
 
24.5%
ValueCountFrequency (%)
-23614 1
< 0.1%
-18246 1
< 0.1%
-12864 1
< 0.1%
-11571 1
< 0.1%
-9741 1
< 0.1%
-4665 1
< 0.1%
-2604 1
< 0.1%
-815 1
< 0.1%
-802 1
< 0.1%
-774 1
< 0.1%
ValueCountFrequency (%)
309780 1
< 0.1%
34287 1
< 0.1%
11617 1
< 0.1%
11446 1
< 0.1%
10507 1
< 0.1%
9413 1
< 0.1%
9215 1
< 0.1%
8369 1
< 0.1%
8299 1
< 0.1%
8286 1
< 0.1%

Interactions

2024-04-11T11:49:15.584352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:48:59.804197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:01.448316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:02.904073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:04.340276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:06.194792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:07.651454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:09.118203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:10.561499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:12.032786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:14.019907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:15.706579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:00.010020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:01.584424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:03.037146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:04.487565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:06.324470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:07.771812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:09.251738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:10.693100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:12.592099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:14.166819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:15.845176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:00.158103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:01.706938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:03.165265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:04.616154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:06.440296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:07.905185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:09.370827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:10.831944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:12.718827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:14.302117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:15.984278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:00.307100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:01.835074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:03.294638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:04.756559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:06.583926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:08.046463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:09.509412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:10.967381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:12.897015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:14.452565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:16.113073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:00.429643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:01.963175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:03.430913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:04.890656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:06.729414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:08.191450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:09.640024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:11.103050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:13.054076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:14.580954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:16.248884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:00.571385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:02.090309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:03.551562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:05.018342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:06.858522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:08.319616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:09.774804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:11.226280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:13.196057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:14.721963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:16.402347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:00.704987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:02.228778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:03.686038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:05.157247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:06.991529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:08.445730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:09.911772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:11.351995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:13.345423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:14.883764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:16.545374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:00.877083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:02.372195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:03.818564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:05.287675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:07.122782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:08.571766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:10.041843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:11.474556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:13.470159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:15.041224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:16.683410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:01.013433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:02.499132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:03.953446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:05.420231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:07.241499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:08.701713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:10.171956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:11.599464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:13.604747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:15.170681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:16.827195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:01.154968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:02.634190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:04.086412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:05.931702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:07.382758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:08.844329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:10.302701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:11.754207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:13.743567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:15.304896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:16.972776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:01.313684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:02.777324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:04.217913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:06.065668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:07.511023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:08.987663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:10.434291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:11.906541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:13.888687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:49:15.441278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-11T11:49:23.379731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도행정구역구분명전월 인구수당월 인구수인구증감전월 인구수 (남자)당월 인구수 (남자)인구증감 (남자)전월 인구수 (여자)당월 인구수 (여자)인구증감 (여자)
연도1.0000.1130.0380.0000.0000.0180.0000.0000.0110.0000.0000.011
0.1131.0000.0000.0000.0000.0300.0000.0000.0370.0000.0000.037
행정구역구분명0.0380.0001.0000.6760.6760.1951.0000.6760.2131.0000.6760.213
전월 인구수0.0000.0000.6761.0001.0000.5010.9991.0000.1750.9991.0000.175
당월 인구수0.0000.0000.6761.0001.0000.5010.9991.0000.1750.9991.0000.175
인구증감0.0180.0300.1950.5010.5011.0000.1750.5011.0000.1750.5011.000
전월 인구수 (남자)0.0000.0001.0000.9990.9990.1751.0000.9990.1750.9990.9990.175
당월 인구수 (남자)0.0000.0000.6761.0001.0000.5010.9991.0000.1750.9991.0000.175
인구증감 (남자)0.0110.0370.2130.1750.1751.0000.1750.1751.0000.1750.1751.000
전월 인구수 (여자)0.0000.0001.0000.9990.9990.1750.9990.9990.1751.0000.9990.175
당월 인구수 (여자)0.0000.0000.6761.0001.0000.5010.9991.0000.1750.9991.0000.175
인구증감 (여자)0.0110.0370.2130.1750.1751.0000.1750.1751.0000.1750.1751.000
2024-04-11T11:49:23.569787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도전월 인구수당월 인구수인구증감전월 인구수 (남자)당월 인구수 (남자)인구증감 (남자)전월 인구수 (여자)당월 인구수 (여자)인구증감 (여자)행정구역구분명
연도1.000-0.1010.0010.001-0.1280.0100.001-0.0830.0090.000-0.0840.024
-0.1011.0000.0030.0030.0170.0080.0030.0150.0090.0030.0090.000
전월 인구수0.0010.0031.0000.9990.0030.9990.998-0.0400.9990.998-0.0270.707
당월 인구수0.0010.0030.9991.0000.0110.9970.999-0.0310.9970.999-0.0180.707
인구증감-0.1280.0170.0030.0111.000-0.0400.0110.939-0.0400.0110.9350.185
전월 인구수 (남자)0.0100.0080.9990.997-0.0401.0000.998-0.0390.9950.993-0.0271.000
당월 인구수 (남자)0.0010.0030.9980.9990.0110.9981.000-0.0300.9930.995-0.0190.707
인구증감 (남자)-0.0830.015-0.040-0.0310.939-0.039-0.0301.000-0.041-0.0320.7800.203
전월 인구수 (여자)0.0090.0090.9990.997-0.0400.9950.993-0.0411.0000.999-0.0261.000
당월 인구수 (여자)0.0000.0030.9980.9990.0110.9930.995-0.0320.9991.000-0.0170.707
인구증감 (여자)-0.0840.009-0.027-0.0180.935-0.027-0.0190.780-0.026-0.0171.0000.203
행정구역구분명0.0240.0000.7070.7070.1851.0000.7070.2031.0000.7070.2031.000

Missing values

2024-04-11T11:49:17.167510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-11T11:49:17.409610image/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.
2024-04-11T11:49:17.610573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도행정구역구분명행정구역명전월 인구수당월 인구수인구증감전월 인구수 (남자)당월 인구수 (남자)인구증감 (남자)전월 인구수 (여자)당월 인구수 (여자)인구증감 (여자)
7427820142읍면동경기도 양주시 회천1동774777569409741151836503641-9
464120238시군경기도 동두천시8931689174-1424452944450-794478744724-63
3128620201읍면동경기도 포천시 군내면87458716-2947004683-1740454033-12
7495820141읍면동경기도 용인시 처인구 유림동3293032798-1321671916666-531621116132-79
2321520212읍면동경기도 수원시 장안구 조원2동1910319057-4692149188-2698899869-20
5803920165읍면동경기도 포천시 소흘읍4732447309-15240672406922325723240-17
1465720224읍면동경기도 수원시 권선구 서둔동3788037909291929619291-5185841861834
8724220124읍면동경기도 부천시 원미구 상동2112421108-16<NA>10423<NA><NA>10685<NA>
2959720203읍면동경기도 고양시 일산서구 송산동515115176925825412255231112609926246147
68067201412읍면동경기도 성남시 수정구 신흥2동2968529657-281498714972-151469814685-13
연도행정구역구분명행정구역명전월 인구수당월 인구수인구증감전월 인구수 (남자)당월 인구수 (남자)인구증감 (남자)전월 인구수 (여자)당월 인구수 (여자)인구증감 (여자)
4835820178경기도 고양시 일산동구293742293729-13143434143428-6150308150301-7
4317520185읍면동경기도 안성시 금광면82058185-2040614050-1141444135-9
2031420217읍면동경기도 양평군 강상면1050610504-252115211052955293-2
39344201811읍면동경기도 부천시 역곡3동2375423669-851161911577-421213512092-43
6049120161읍면동경기도 포천시 영중면55245522-229562962625682560-8
7885520136경기도 부천시 오정구191043190707-336<NA>97084<NA><NA>93623<NA>
6348020158읍면동경기도 이천시 마장면873688016548484899513888390214
18611202110읍면동경기도 이천시 장호원읍1457614584874117410-1716571749
2406820211읍면동경기도 파주시 금촌2동3315833156-21626216230-32168961692630
6464220156읍면동경기도 용인시 기흥구 동백동766957698128637631377871563906439194130