Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells50373
Missing cells (%)36.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory132.0 B

Variable types

Numeric6
Text2
Unsupported5
Categorical1

Dataset

Description경기도_주민등록인구통계읍면동출생자수정제기본
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=OMAC35ZELYK6LHHHTEEF34684032&infSeq=1

Alerts

기준연월 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
시군코드 is highly overall correlated with 행정동코드High correlation
행정동코드 is highly overall correlated with 기준연월 and 2 other fieldsHigh correlation
총인구수 is highly overall correlated with 남성인구수 and 1 other fieldsHigh correlation
남성인구수 is highly overall correlated with 총인구수 and 1 other fieldsHigh correlation
여성인구수 is highly overall correlated with 총인구수 and 1 other fieldsHigh correlation
생성일자 is highly overall correlated with 기준연월 and 1 other fieldsHigh correlation
총인구수 has 123 (1.2%) missing valuesMissing
남성인구수 has 123 (1.2%) missing valuesMissing
여성인구수 has 123 (1.2%) missing valuesMissing
동단위분석시작연월 has 10000 (100.0%) missing valuesMissing
동단위분석종료연월 has 10000 (100.0%) missing valuesMissing
동단위분석시작년도 has 10000 (100.0%) missing valuesMissing
동단위분석종료년도 has 10000 (100.0%) missing valuesMissing
마트완료여부 has 10000 (100.0%) missing valuesMissing
동단위분석시작연월 is an unsupported type, check if it needs cleaning or further analysisUnsupported
동단위분석종료연월 is an unsupported type, check if it needs cleaning or further analysisUnsupported
동단위분석시작년도 is an unsupported type, check if it needs cleaning or further analysisUnsupported
동단위분석종료년도 is an unsupported type, check if it needs cleaning or further analysisUnsupported
마트완료여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported
총인구수 has 487 (4.9%) zerosZeros
남성인구수 has 977 (9.8%) zerosZeros
여성인구수 has 1017 (10.2%) zerosZeros

Reproduction

Analysis started2024-04-17 14:18:37.516149
Analysis finished2024-04-17 14:18:41.984429
Duration4.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201701.19
Minimum201601
Maximum201810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:18:42.035362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201601
5-th percentile201602
Q1201609
median201706
Q3201802
95-th percentile201809
Maximum201810
Range209
Interquartile range (IQR)193

Descriptive statistics

Standard deviation79.288262
Coefficient of variation (CV)0.00039309764
Kurtosis-1.4159878
Mean201701.19
Median Absolute Deviation (MAD)96
Skewness0.081456351
Sum2.0170119 × 109
Variance6286.6284
MonotonicityNot monotonic
2024-04-17T23:18:42.130811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
201712 322
 
3.2%
201709 316
 
3.2%
201804 314
 
3.1%
201704 314
 
3.1%
201707 309
 
3.1%
201711 308
 
3.1%
201705 307
 
3.1%
201803 306
 
3.1%
201708 304
 
3.0%
201610 303
 
3.0%
Other values (24) 6897
69.0%
ValueCountFrequency (%)
201601 294
2.9%
201602 264
2.6%
201603 281
2.8%
201604 292
2.9%
201605 294
2.9%
201606 272
2.7%
201607 302
3.0%
201608 270
2.7%
201609 294
2.9%
201610 303
3.0%
ValueCountFrequency (%)
201810 282
2.8%
201809 295
2.9%
201808 283
2.8%
201807 282
2.8%
201806 283
2.8%
201805 299
3.0%
201804 314
3.1%
201803 306
3.1%
201802 290
2.9%
201801 298
3.0%

시군코드
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4135.4632
Minimum4111
Maximum4183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:18:42.233876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4111
5-th percentile4111
Q14119
median4128
Q34150
95-th percentile4180
Maximum4183
Range72
Interquartile range (IQR)31

Descriptive statistics

Standard deviation20.017455
Coefficient of variation (CV)0.0048404385
Kurtosis-0.61689421
Mean4135.4632
Median Absolute Deviation (MAD)15
Skewness0.62343593
Sum41354632
Variance400.69852
MonotonicityNot monotonic
2024-04-17T23:18:42.348575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4113 883
 
8.8%
4111 725
 
7.2%
4128 678
 
6.8%
4119 612
 
6.1%
4146 563
 
5.6%
4117 556
 
5.6%
4127 517
 
5.2%
4159 456
 
4.6%
4122 413
 
4.1%
4121 316
 
3.2%
Other values (21) 4281
42.8%
ValueCountFrequency (%)
4111 725
7.2%
4113 883
8.8%
4115 290
 
2.9%
4117 556
5.6%
4119 612
6.1%
4121 316
 
3.2%
4122 413
4.1%
4125 148
 
1.5%
4127 517
5.2%
4128 678
6.8%
ValueCountFrequency (%)
4183 217
2.2%
4182 101
 
1.0%
4180 187
1.9%
4167 212
2.1%
4165 247
2.5%
4163 185
1.8%
4161 159
 
1.6%
4159 456
4.6%
4157 241
2.4%
4155 253
2.5%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct1177
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7291752 × 109
Minimum41111560
Maximum4.183041 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:18:42.464118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41111560
5-th percentile41135662
Q141480350
median4.117358 × 109
Q34.141054 × 109
95-th percentile4.165041 × 109
Maximum4.183041 × 109
Range4.1419294 × 109
Interquartile range (IQR)4.0995736 × 109

Descriptive statistics

Standard deviation1.9443853 × 109
Coefficient of variation (CV)0.71244429
Kurtosis-1.5657254
Mean2.7291752 × 109
Median Absolute Deviation (MAD)32676000
Skewness-0.65899902
Sum2.7291752 × 1013
Variance3.7806342 × 1018
MonotonicityNot monotonic
2024-04-17T23:18:42.574096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4180033000 18
 
0.2%
4128754000 18
 
0.2%
4146552000 18
 
0.2%
4157056000 17
 
0.2%
4127151500 17
 
0.2%
4183040000 17
 
0.2%
4119056000 17
 
0.2%
4146136000 16
 
0.2%
4143056000 16
 
0.2%
4159041000 16
 
0.2%
Other values (1167) 9830
98.3%
ValueCountFrequency (%)
41111560 4
< 0.1%
41111566 6
0.1%
41111571 8
0.1%
41111572 4
< 0.1%
41111573 3
 
< 0.1%
41111580 5
0.1%
41111591 9
0.1%
41111597 7
0.1%
41111598 4
< 0.1%
41111600 7
0.1%
ValueCountFrequency (%)
4183041000 9
0.1%
4183040000 17
0.2%
4183039500 13
0.1%
4183038000 12
0.1%
4183037000 14
0.1%
4183036000 12
0.1%
4183035000 7
0.1%
4183034000 13
0.1%
4183033000 11
0.1%
4183032000 12
0.1%

시군명
Text

Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T23:18:42.741397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.3877
Min length3

Characters and Unicode

Total characters33877
Distinct characters62
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

Unique0 ?
Unique (%)0.0%

Sample

1st row파주시
2nd row평택시
3rd row안양시
4th row수원시
5th row안산시
ValueCountFrequency (%)
성남시 883
 
8.2%
수원시 725
 
6.7%
고양시 678
 
6.3%
부천시 612
 
5.7%
용인시 563
 
5.2%
안양시 556
 
5.2%
안산시 517
 
4.8%
화성시 456
 
4.2%
평택시 413
 
3.8%
광명시 316
 
2.9%
Other values (41) 5048
46.9%
2024-04-17T23:18:43.040206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 9796
28.9%
양 1998
 
5.9%
성 1592
 
4.7%
천 1537
 
4.5%
안 1460
 
4.3%
남 1411
 
4.2%
주 1181
 
3.5%
구 904
 
2.7%
부 902
 
2.7%
원 842
 
2.5%
Other values (52) 12254
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33110
97.7%
Space Separator 767
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 9796
29.6%
양 1998
 
6.0%
성 1592
 
4.8%
천 1537
 
4.6%
안 1460
 
4.4%
남 1411
 
4.3%
주 1181
 
3.6%
구 904
 
2.7%
부 902
 
2.7%
원 842
 
2.5%
Other values (51) 11487
34.7%
Space Separator
ValueCountFrequency (%)
767
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33110
97.7%
Common 767
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 9796
29.6%
양 1998
 
6.0%
성 1592
 
4.8%
천 1537
 
4.6%
안 1460
 
4.4%
남 1411
 
4.3%
주 1181
 
3.6%
구 904
 
2.7%
부 902
 
2.7%
원 842
 
2.5%
Other values (51) 11487
34.7%
Common
ValueCountFrequency (%)
767
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33110
97.7%
ASCII 767
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 9796
29.6%
양 1998
 
6.0%
성 1592
 
4.8%
천 1537
 
4.6%
안 1460
 
4.4%
남 1411
 
4.3%
주 1181
 
3.6%
구 904
 
2.7%
부 902
 
2.7%
원 842
 
2.5%
Other values (51) 11487
34.7%
ASCII
ValueCountFrequency (%)
767
100.0%

행정동명
Text

Distinct560
Distinct (%)5.6%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2024-04-17T23:18:43.313052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.3814526
Min length2

Characters and Unicode

Total characters33801
Distinct characters201
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row금촌2동
2nd row비전1동
3rd row박달1동
4th row영통3동
5th row원곡본동
ValueCountFrequency (%)
중앙동 145
 
1.5%
금곡동 54
 
0.5%
풍산동 43
 
0.4%
부곡동 40
 
0.4%
고등동 38
 
0.4%
능곡동 36
 
0.4%
신장1동 36
 
0.4%
위례동 36
 
0.4%
정자2동 35
 
0.4%
정자1동 35
 
0.4%
Other values (550) 9498
95.0%
2024-04-17T23:18:43.690810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7844
23.2%
면 1818
 
5.4%
1 1408
 
4.2%
2 1338
 
4.0%
읍 636
 
1.9%
3 636
 
1.9%
산 622
 
1.8%
정 532
 
1.6%
양 518
 
1.5%
신 498
 
1.5%
Other values (191) 17951
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30105
89.1%
Decimal Number 3696
 
10.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7844
26.1%
면 1818
 
6.0%
읍 636
 
2.1%
산 622
 
2.1%
정 532
 
1.8%
양 518
 
1.7%
신 498
 
1.7%
곡 460
 
1.5%
원 452
 
1.5%
안 437
 
1.5%
Other values (182) 16288
54.1%
Decimal Number
ValueCountFrequency (%)
1 1408
38.1%
2 1338
36.2%
3 636
17.2%
4 165
 
4.5%
7 41
 
1.1%
5 40
 
1.1%
6 39
 
1.1%
9 18
 
0.5%
8 11
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30105
89.1%
Common 3696
 
10.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7844
26.1%
면 1818
 
6.0%
읍 636
 
2.1%
산 622
 
2.1%
정 532
 
1.8%
양 518
 
1.7%
신 498
 
1.7%
곡 460
 
1.5%
원 452
 
1.5%
안 437
 
1.5%
Other values (182) 16288
54.1%
Common
ValueCountFrequency (%)
1 1408
38.1%
2 1338
36.2%
3 636
17.2%
4 165
 
4.5%
7 41
 
1.1%
5 40
 
1.1%
6 39
 
1.1%
9 18
 
0.5%
8 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30105
89.1%
ASCII 3696
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7844
26.1%
면 1818
 
6.0%
읍 636
 
2.1%
산 622
 
2.1%
정 532
 
1.8%
양 518
 
1.7%
신 498
 
1.7%
곡 460
 
1.5%
원 452
 
1.5%
안 437
 
1.5%
Other values (182) 16288
54.1%
ASCII
ValueCountFrequency (%)
1 1408
38.1%
2 1338
36.2%
3 636
17.2%
4 165
 
4.5%
7 41
 
1.1%
5 40
 
1.1%
6 39
 
1.1%
9 18
 
0.5%
8 11
 
0.3%

총인구수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct104
Distinct (%)1.1%
Missing123
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean14.704364
Minimum0
Maximum122
Zeros487
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:18:43.809790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q320
95-th percentile41
Maximum122
Range122
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.717093
Coefficient of variation (CV)0.93285866
Kurtosis7.9917374
Mean14.704364
Median Absolute Deviation (MAD)7
Skewness2.1520587
Sum145235
Variance188.15864
MonotonicityNot monotonic
2024-04-17T23:18:43.949199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 530
 
5.3%
0 487
 
4.9%
2 469
 
4.7%
3 413
 
4.1%
4 407
 
4.1%
5 385
 
3.9%
6 373
 
3.7%
8 372
 
3.7%
11 364
 
3.6%
7 360
 
3.6%
Other values (94) 5717
57.2%
ValueCountFrequency (%)
0 487
4.9%
1 530
5.3%
2 469
4.7%
3 413
4.1%
4 407
4.1%
5 385
3.9%
6 373
3.7%
7 360
3.6%
8 372
3.7%
9 354
3.5%
ValueCountFrequency (%)
122 2
< 0.1%
121 1
 
< 0.1%
119 1
 
< 0.1%
114 1
 
< 0.1%
113 2
< 0.1%
112 1
 
< 0.1%
110 1
 
< 0.1%
109 1
 
< 0.1%
104 1
 
< 0.1%
103 3
< 0.1%

남성인구수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct61
Distinct (%)0.6%
Missing123
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean7.5417637
Minimum0
Maximum69
Zeros977
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:18:44.072855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q310
95-th percentile22
Maximum69
Range69
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.2886727
Coefficient of variation (CV)0.9664414
Kurtosis7.3827212
Mean7.5417637
Median Absolute Deviation (MAD)4
Skewness2.0814991
Sum74490
Variance53.124749
MonotonicityNot monotonic
2024-04-17T23:18:44.188606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 977
 
9.8%
1 873
 
8.7%
2 742
 
7.4%
5 731
 
7.3%
4 717
 
7.2%
3 710
 
7.1%
6 630
 
6.3%
7 592
 
5.9%
8 526
 
5.3%
9 507
 
5.1%
Other values (51) 2872
28.7%
ValueCountFrequency (%)
0 977
9.8%
1 873
8.7%
2 742
7.4%
3 710
7.1%
4 717
7.2%
5 731
7.3%
6 630
6.3%
7 592
5.9%
8 526
5.3%
9 507
5.1%
ValueCountFrequency (%)
69 1
< 0.1%
64 2
< 0.1%
63 2
< 0.1%
62 2
< 0.1%
58 1
< 0.1%
57 2
< 0.1%
56 2
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%

여성인구수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct60
Distinct (%)0.6%
Missing123
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean7.1626
Minimum0
Maximum67
Zeros1017
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T23:18:44.319578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q310
95-th percentile20
Maximum67
Range67
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.9766061
Coefficient of variation (CV)0.97403262
Kurtosis8.0602619
Mean7.1626
Median Absolute Deviation (MAD)4
Skewness2.152101
Sum70745
Variance48.673032
MonotonicityNot monotonic
2024-04-17T23:18:44.438751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1017
 
10.2%
1 903
 
9.0%
2 804
 
8.0%
3 769
 
7.7%
4 736
 
7.4%
5 692
 
6.9%
6 640
 
6.4%
7 615
 
6.2%
8 556
 
5.6%
9 490
 
4.9%
Other values (50) 2655
26.6%
ValueCountFrequency (%)
0 1017
10.2%
1 903
9.0%
2 804
8.0%
3 769
7.7%
4 736
7.4%
5 692
6.9%
6 640
6.4%
7 615
6.2%
8 556
5.6%
9 490
4.9%
ValueCountFrequency (%)
67 1
 
< 0.1%
65 1
 
< 0.1%
64 1
 
< 0.1%
57 3
< 0.1%
56 1
 
< 0.1%
55 3
< 0.1%
54 2
< 0.1%
53 2
< 0.1%
52 3
< 0.1%
50 3
< 0.1%

동단위분석시작연월
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

동단위분석종료연월
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

동단위분석시작년도
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

동단위분석종료년도
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

마트완료여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

생성일자
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20181120
3633 
20180323
3435 
20190131
2932 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20180323
2nd row20180323
3rd row20181120
4th row20190131
5th row20180323

Common Values

ValueCountFrequency (%)
20181120 3633
36.3%
20180323 3435
34.4%
20190131 2932
29.3%

Length

2024-04-17T23:18:44.542956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:18:44.616180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20181120 3633
36.3%
20180323 3435
34.4%
20190131 2932
29.3%

Interactions

2024-04-17T23:18:40.908568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:38.519295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:38.967641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.440248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.906035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.422168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.988542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:38.587507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.042086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.516615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.007450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.501051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:41.078374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:38.664364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.124483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.598431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.088921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.588737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:41.151500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:38.732140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.196778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.669418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.165697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.664615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:41.228769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:38.817332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.275940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.742858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.248728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.746138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:41.307359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:38.890422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.359335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:39.823578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.334981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T23:18:40.822023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T23:18:44.675838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월시군코드행정동코드시군명총인구수남성인구수여성인구수생성일자
기준연월1.0000.0001.0000.4240.0880.0710.0891.000
시군코드0.0001.0000.0001.0000.3360.3090.3040.000
행정동코드1.0000.0001.0000.5030.0910.0790.1001.000
시군명0.4241.0000.5031.0000.5030.4660.4560.533
총인구수0.0880.3360.0910.5031.0000.9500.9480.104
남성인구수0.0710.3090.0790.4660.9501.0000.8630.085
여성인구수0.0890.3040.1000.4560.9480.8631.0000.104
생성일자1.0000.0001.0000.5330.1040.0850.1041.000
2024-04-17T23:18:44.771589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월시군코드행정동코드총인구수남성인구수여성인구수생성일자
기준연월1.000-0.0010.681-0.089-0.081-0.0851.000
시군코드-0.0011.0000.540-0.195-0.186-0.1820.000
행정동코드0.6810.5401.000-0.159-0.148-0.1511.000
총인구수-0.089-0.195-0.1591.0000.9520.9480.062
남성인구수-0.081-0.186-0.1480.9521.0000.8100.050
여성인구수-0.085-0.182-0.1510.9480.8101.0000.062
생성일자1.0000.0001.0000.0620.0500.0621.000

Missing values

2024-04-17T23:18:41.422349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T23:18:41.828644image/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.
2024-04-17T23:18:41.936590image/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

기준연월시군코드행정동코드시군명행정동명총인구수남성인구수여성인구수동단위분석시작연월동단위분석종료연월동단위분석시작년도동단위분석종료년도마트완료여부생성일자
1346201603414841480520파주시금촌2동412417<NA><NA><NA><NA><NA>20180323
4838201610412241220620평택시비전1동623527<NA><NA><NA><NA><NA>20180323
727520170241174117162100안양시박달1동1358<NA><NA><NA><NA><NA>20181120
1451720180141114111758500수원시영통3동1697<NA><NA><NA><NA><NA>20190131
4866201610412741273540안산시원곡본동231112<NA><NA><NA><NA><NA>20180323
1818520181041834183040000양평군용문면945<NA><NA><NA><NA><NA>20190131
1760720181041274127159000안산시성포동1376<NA><NA><NA><NA><NA>20190131
1862520180941134113356000성남시은행2동761<NA><NA><NA><NA><NA>20190131
880220170441824182031000가평군설악면303<NA><NA><NA><NA><NA>20181120
1658920180741134113351000성남시성남동19811<NA><NA><NA><NA><NA>20190131
기준연월시군코드행정동코드시군명행정동명총인구수남성인구수여성인구수동단위분석시작연월동단위분석종료연월동단위분석시작년도동단위분석종료년도마트완료여부생성일자
792220170341674167052000여주시중앙동15510<NA><NA><NA><NA><NA>20181120
1569320180441114111753000수원시매탄3동24915<NA><NA><NA><NA><NA>20190131
842320170341394139053100시흥시신현동862<NA><NA><NA><NA><NA>20181120
5056201610415041500520이천시중리동1284<NA><NA><NA><NA><NA>20180323
1441520171041154115058000의정부시자금동16610<NA><NA><NA><NA><NA>20181120
5922201611418041800340연천군미산면000<NA><NA><NA><NA><NA>20180323
1154220171141574157025000김포시통진읍18108<NA><NA><NA><NA><NA>20181120
832020170341274127355500안산시백운동<NA><NA><NA><NA><NA><NA><NA><NA>20181120
937420170641194119067000부천시중2동21129<NA><NA><NA><NA><NA>20181120
1867920180941174117162100안양시박달1동927<NA><NA><NA><NA><NA>20190131