Overview

Dataset statistics

Number of variables10
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory93.7 B

Variable types

Text1
Numeric8
Categorical1

Dataset

Description대구광역시_북구_월별인구현황_20180331
Author대구광역시 북구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3038385&dataSetDetailId=3038385181f360b032bc&provdMethod=FILE

Alerts

데이터기준일자 has constant value ""Constant
통 is highly overall correlated with 반 and 4 other fieldsHigh correlation
반 is highly overall correlated with 통 and 4 other fieldsHigh correlation
세대수 is highly overall correlated with 통 and 4 other fieldsHigh correlation
계 is highly overall correlated with 통 and 4 other fieldsHigh correlation
남 is highly overall correlated with 통 and 4 other fieldsHigh correlation
여 is highly overall correlated with 통 and 4 other fieldsHigh correlation
전월대비세대증감 is highly overall correlated with 전월대비인구증감High correlation
전월대비인구증감 is highly overall correlated with 전월대비세대증감High correlation
구분 has unique valuesUnique
반 has unique valuesUnique
세대수 has unique valuesUnique
계 has unique valuesUnique
남 has unique valuesUnique
여 has unique valuesUnique

Reproduction

Analysis started2023-12-10 20:04:40.421091
Analysis finished2023-12-10 20:04:52.646013
Duration12.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T05:04:52.941958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.6521739
Min length3

Characters and Unicode

Total characters84
Distinct characters34
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

Unique23 ?
Unique (%)100.0%

Sample

1st row고성동
2nd row칠성동
3rd row침산1동
4th row침산2동
5th row침산3동
ValueCountFrequency (%)
고성동 1
 
4.0%
복현1동 1
 
4.0%
동천동 1
 
4.0%
읍내동 1
 
4.0%
관음동 1
 
4.0%
구암동 1
 
4.0%
태전2동 1
 
4.0%
태전1동 1
 
4.0%
관문동 1
 
4.0%
무태조야동 1
 
4.0%
Other values (15) 15
60.0%
2023-12-11T05:04:53.637054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
28.6%
산 7
 
8.3%
격 4
 
4.8%
1 4
 
4.8%
2 4
 
4.8%
침 3
 
3.6%
태 3
 
3.6%
현 3
 
3.6%
복 2
 
2.4%
전 2
 
2.4%
Other values (24) 28
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71
84.5%
Decimal Number 11
 
13.1%
Space Separator 2
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
33.8%
산 7
 
9.9%
격 4
 
5.6%
침 3
 
4.2%
태 3
 
4.2%
현 3
 
4.2%
복 2
 
2.8%
전 2
 
2.8%
관 2
 
2.8%
성 2
 
2.8%
Other values (19) 19
26.8%
Decimal Number
ValueCountFrequency (%)
1 4
36.4%
2 4
36.4%
3 2
18.2%
4 1
 
9.1%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71
84.5%
Common 13
 
15.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
33.8%
산 7
 
9.9%
격 4
 
5.6%
침 3
 
4.2%
태 3
 
4.2%
현 3
 
4.2%
복 2
 
2.8%
전 2
 
2.8%
관 2
 
2.8%
성 2
 
2.8%
Other values (19) 19
26.8%
Common
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 2
15.4%
2
15.4%
4 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71
84.5%
ASCII 13
 
15.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
33.8%
산 7
 
9.9%
격 4
 
5.6%
침 3
 
4.2%
태 3
 
4.2%
현 3
 
4.2%
복 2
 
2.8%
전 2
 
2.8%
관 2
 
2.8%
성 2
 
2.8%
Other values (19) 19
26.8%
ASCII
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 2
15.4%
2
15.4%
4 1
 
7.7%

통
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.434783
Minimum15
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:04:53.791408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.2
Q122.5
median32
Q337
95-th percentile45.6
Maximum47
Range32
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation9.3896756
Coefficient of variation (CV)0.30851791
Kurtosis-0.93600339
Mean30.434783
Median Absolute Deviation (MAD)7
Skewness-0.016349245
Sum700
Variance88.166008
MonotonicityNot monotonic
2023-12-11T05:04:54.001297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
35 2
 
8.7%
21 2
 
8.7%
32 2
 
8.7%
37 2
 
8.7%
19 1
 
4.3%
16 1
 
4.3%
34 1
 
4.3%
40 1
 
4.3%
29 1
 
4.3%
46 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
15 1
4.3%
16 1
4.3%
18 1
4.3%
19 1
4.3%
21 2
8.7%
24 1
4.3%
25 1
4.3%
27 1
4.3%
29 1
4.3%
30 1
4.3%
ValueCountFrequency (%)
47 1
4.3%
46 1
4.3%
42 1
4.3%
40 1
4.3%
38 1
4.3%
37 2
8.7%
35 2
8.7%
34 1
4.3%
32 2
8.7%
30 1
4.3%

반
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.69565
Minimum79
Maximum318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:04:54.195260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile87.8
Q1134.5
median198
Q3243
95-th percentile270.4
Maximum318
Range239
Interquartile range (IQR)108.5

Descriptive statistics

Standard deviation65.168617
Coefficient of variation (CV)0.34536364
Kurtosis-0.80626578
Mean188.69565
Median Absolute Deviation (MAD)49
Skewness-0.073445058
Sum4340
Variance4246.9486
MonotonicityNot monotonic
2023-12-11T05:04:54.396545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
110 1
 
4.3%
256 1
 
4.3%
210 1
 
4.3%
252 1
 
4.3%
231 1
 
4.3%
192 1
 
4.3%
318 1
 
4.3%
242 1
 
4.3%
205 1
 
4.3%
247 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
79 1
4.3%
87 1
4.3%
95 1
4.3%
110 1
4.3%
126 1
4.3%
127 1
4.3%
142 1
4.3%
158 1
4.3%
159 1
4.3%
186 1
4.3%
ValueCountFrequency (%)
318 1
4.3%
272 1
4.3%
256 1
4.3%
252 1
4.3%
247 1
4.3%
244 1
4.3%
242 1
4.3%
231 1
4.3%
210 1
4.3%
205 1
4.3%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7633.6087
Minimum2093
Maximum13319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:04:54.578489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2093
5-th percentile2609.8
Q14821
median7502
Q310298.5
95-th percentile11890.1
Maximum13319
Range11226
Interquartile range (IQR)5477.5

Descriptive statistics

Standard deviation3264.8067
Coefficient of variation (CV)0.42768851
Kurtosis-1.1625061
Mean7633.6087
Median Absolute Deviation (MAD)2869
Skewness-0.13725563
Sum175573
Variance10658963
MonotonicityNot monotonic
2023-12-11T05:04:54.757371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2542 1
 
4.3%
9692 1
 
4.3%
9526 1
 
4.3%
10400 1
 
4.3%
10550 1
 
4.3%
7502 1
 
4.3%
13319 1
 
4.3%
10197 1
 
4.3%
9370 1
 
4.3%
11966 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2093 1
4.3%
2542 1
4.3%
3220 1
4.3%
4044 1
4.3%
4253 1
4.3%
4633 1
4.3%
5009 1
4.3%
5350 1
4.3%
6313 1
4.3%
7034 1
4.3%
ValueCountFrequency (%)
13319 1
4.3%
11966 1
4.3%
11207 1
4.3%
11059 1
4.3%
10550 1
4.3%
10400 1
4.3%
10197 1
4.3%
9692 1
4.3%
9526 1
4.3%
9370 1
4.3%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19185.696
Minimum4620
Maximum38480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:04:54.944330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4620
5-th percentile5347.2
Q19801.5
median19933
Q326688.5
95-th percentile30549.5
Maximum38480
Range33860
Interquartile range (IQR)16887

Descriptive statistics

Standard deviation9732.2029
Coefficient of variation (CV)0.50726349
Kurtosis-1.0889699
Mean19185.696
Median Absolute Deviation (MAD)8930
Skewness0.048708812
Sum441271
Variance94715774
MonotonicityNot monotonic
2023-12-11T05:04:55.113436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5147 1
 
4.3%
23690 1
 
4.3%
25319 1
 
4.3%
30596 1
 
4.3%
27158 1
 
4.3%
18666 1
 
4.3%
38480 1
 
4.3%
26219 1
 
4.3%
23239 1
 
4.3%
30131 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
4620 1
4.3%
5147 1
4.3%
7149 1
4.3%
7497 1
4.3%
8928 1
4.3%
9101 1
4.3%
10502 1
4.3%
11961 1
4.3%
13487 1
4.3%
18666 1
4.3%
ValueCountFrequency (%)
38480 1
4.3%
30596 1
4.3%
30131 1
4.3%
30059 1
4.3%
28863 1
4.3%
27158 1
4.3%
26219 1
4.3%
25319 1
4.3%
23690 1
4.3%
23239 1
4.3%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9583.6957
Minimum2475
Maximum19101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:04:55.298333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2475
5-th percentile2722.2
Q14978
median9741
Q313242
95-th percentile14995.7
Maximum19101
Range16626
Interquartile range (IQR)8264

Descriptive statistics

Standard deviation4770.9826
Coefficient of variation (CV)0.49782284
Kurtosis-1.0651041
Mean9583.6957
Median Absolute Deviation (MAD)4500
Skewness0.067352682
Sum220425
Variance22762275
MonotonicityNot monotonic
2023-12-11T05:04:55.492689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2602 1
 
4.3%
11547 1
 
4.3%
12603 1
 
4.3%
14993 1
 
4.3%
13512 1
 
4.3%
9289 1
 
4.3%
19101 1
 
4.3%
12972 1
 
4.3%
11707 1
 
4.3%
14996 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2475 1
4.3%
2602 1
4.3%
3804 1
4.3%
3888 1
4.3%
4599 1
4.3%
4715 1
4.3%
5241 1
4.3%
5966 1
4.3%
7055 1
4.3%
9289 1
4.3%
ValueCountFrequency (%)
19101 1
4.3%
14996 1
4.3%
14993 1
4.3%
14888 1
4.3%
14731 1
4.3%
13512 1
4.3%
12972 1
4.3%
12603 1
4.3%
11707 1
4.3%
11547 1
4.3%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9602
Minimum2145
Maximum19379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:04:55.680446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2145
5-th percentile2616.6
Q14823.5
median10199
Q313446.5
95-th percentile15559.8
Maximum19379
Range17234
Interquartile range (IQR)8623

Descriptive statistics

Standard deviation4966.2842
Coefficient of variation (CV)0.51721351
Kurtosis-1.1102888
Mean9602
Median Absolute Deviation (MAD)4204
Skewness0.032352088
Sum220846
Variance24663978
MonotonicityNot monotonic
2023-12-11T05:04:55.877535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2545 1
 
4.3%
12143 1
 
4.3%
12716 1
 
4.3%
15603 1
 
4.3%
13646 1
 
4.3%
9377 1
 
4.3%
19379 1
 
4.3%
13247 1
 
4.3%
11532 1
 
4.3%
15135 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2145 1
4.3%
2545 1
4.3%
3261 1
4.3%
3693 1
4.3%
4329 1
4.3%
4386 1
4.3%
5261 1
4.3%
5995 1
4.3%
6432 1
4.3%
9377 1
4.3%
ValueCountFrequency (%)
19379 1
4.3%
15603 1
4.3%
15171 1
4.3%
15135 1
4.3%
14132 1
4.3%
13646 1
4.3%
13247 1
4.3%
12716 1
4.3%
12143 1
4.3%
11532 1
4.3%

전월대비세대증감
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.826087
Minimum-45
Maximum81
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)34.8%
Memory size339.0 B
2023-12-11T05:04:56.084184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-45
5-th percentile-11.9
Q1-4.5
median5
Q324
95-th percentile62.6
Maximum81
Range126
Interquartile range (IQR)28.5

Descriptive statistics

Standard deviation27.332385
Coefficient of variation (CV)2.3111943
Kurtosis1.3036778
Mean11.826087
Median Absolute Deviation (MAD)14
Skewness0.75500717
Sum272
Variance747.05929
MonotonicityNot monotonic
2023-12-11T05:04:56.304496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5 3
 
13.0%
-9 2
 
8.7%
1 2
 
8.7%
-45 1
 
4.3%
-5 1
 
4.3%
25 1
 
4.3%
10 1
 
4.3%
23 1
 
4.3%
50 1
 
4.3%
-4 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
-45 1
 
4.3%
-12 1
 
4.3%
-11 1
 
4.3%
-9 2
8.7%
-5 1
 
4.3%
-4 1
 
4.3%
-1 1
 
4.3%
1 2
8.7%
5 3
13.0%
10 1
 
4.3%
ValueCountFrequency (%)
81 1
4.3%
64 1
4.3%
50 1
4.3%
35 1
4.3%
30 1
4.3%
25 1
4.3%
23 1
4.3%
17 1
4.3%
16 1
4.3%
10 1
4.3%

전월대비인구증감
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.26086957
Minimum-162
Maximum162
Zeros0
Zeros (%)0.0%
Negative14
Negative (%)60.9%
Memory size339.0 B
2023-12-11T05:04:56.470252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-162
5-th percentile-62.5
Q1-37
median-14
Q328.5
95-th percentile149.3
Maximum162
Range324
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation70.531887
Coefficient of variation (CV)-270.37223
Kurtosis1.6657369
Mean-0.26086957
Median Absolute Deviation (MAD)24
Skewness0.62226116
Sum-6
Variance4974.747
MonotonicityNot monotonic
2023-12-11T05:04:56.662047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
-37 2
 
8.7%
-36 1
 
4.3%
-162 1
 
4.3%
-38 1
 
4.3%
34 1
 
4.3%
-63 1
 
4.3%
-3 1
 
4.3%
-58 1
 
4.3%
23 1
 
4.3%
154 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
-162 1
4.3%
-63 1
4.3%
-58 1
4.3%
-43 1
4.3%
-38 1
4.3%
-37 2
8.7%
-36 1
4.3%
-28 1
4.3%
-22 1
4.3%
-20 1
4.3%
ValueCountFrequency (%)
162 1
4.3%
154 1
4.3%
107 1
4.3%
43 1
4.3%
40 1
4.3%
34 1
4.3%
23 1
4.3%
2 1
4.3%
1 1
4.3%
-3 1
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2018-03-31
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-03-31
2nd row2018-03-31
3rd row2018-03-31
4th row2018-03-31
5th row2018-03-31

Common Values

ValueCountFrequency (%)
2018-03-31 23
100.0%

Length

2023-12-11T05:04:56.852945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:04:56.983442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-03-31 23
100.0%

Interactions

2023-12-11T05:04:49.774296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:40.762792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:41.843769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.625327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:43.476225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:44.486370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:45.735718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:47.828101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:50.015646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:40.880396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:41.947778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.737563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:43.613357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:44.628639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:45.910869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:48.057163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:50.261415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:41.014648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.036419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.837006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:43.727495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:44.761957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:46.588331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:48.237712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:50.503615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:41.129761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.123233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.935730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:43.854146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:44.904649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:46.780770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:48.454616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:50.779531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:41.269387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.211392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:43.061476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:43.974850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:45.054398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:47.005916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:48.704485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:51.060475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:41.408424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.312980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:43.147074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:44.090632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:45.219873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:47.205958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:48.953802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:51.359087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:41.558886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.409594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:43.263167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:44.212979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:45.394584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:47.409414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:49.237648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:51.615957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:41.695226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:42.514158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:43.374258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:44.355530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:45.567762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:47.606370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:04:49.506080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T05:04:57.084506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분통반세대수계남여전월대비세대증감전월대비인구증감
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
통1.0001.0000.9150.8030.5770.5770.4500.0000.326
반1.0000.9151.0000.7670.7520.7520.8540.2340.520
세대수1.0000.8030.7671.0000.8640.8640.7970.5370.451
계1.0000.5770.7520.8641.0001.0000.9960.2490.468
남1.0000.5770.7520.8641.0001.0000.9960.2490.468
여1.0000.4500.8540.7970.9960.9961.0000.3110.467
전월대비세대증감1.0000.0000.2340.5370.2490.2490.3111.0000.871
전월대비인구증감1.0000.3260.5200.4510.4680.4680.4670.8711.000
2023-12-11T05:04:57.279397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통반세대수계남여전월대비세대증감전월대비인구증감
통1.0000.9350.9520.9110.9160.9060.193-0.099
반0.9351.0000.9570.9560.9500.9580.082-0.162
세대수0.9520.9571.0000.9740.9800.9720.124-0.176
계0.9110.9560.9741.0000.9960.9990.109-0.169
남0.9160.9500.9800.9961.0000.9940.092-0.178
여0.9060.9580.9720.9990.9941.0000.089-0.190
전월대비세대증감0.1930.0820.1240.1090.0920.0891.0000.854
전월대비인구증감-0.099-0.162-0.176-0.169-0.178-0.1900.8541.000

Missing values

2023-12-11T05:04:51.978006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T05:04:52.478827image/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

구분통반세대수계남여전월대비세대증감전월대비인구증감데이터기준일자
0고성동191102542514726022545-9-362018-03-31
1칠성동382569692236901154712143811622018-03-31
2침산1동18872093462024752145-11-282018-03-31
3침산2동2515870341993397341019930402018-03-31
4침산3동302047383210821026610816-1-142018-03-31
5노원동3518663131348770556432641072018-03-31
6산격1동27159535010502524152611-222018-03-31
7산격2동2114246331196159665995-12-372018-03-31
8산격3동2412750098928459943291722018-03-31
9산격4동2112642539101471543861612018-03-31
구분통반세대수계남여전월대비세대증감전월대비인구증감데이터기준일자
13검단동169532207497380436935-202018-03-31
14무태조야동4224411207288631473114132-4-432018-03-31
15관문동4724711966301311499615135501542018-03-31
16태전1동322059370232391170711532-9-372018-03-31
17태전2동372421019726219129721324723232018-03-31
18구암동46318133193848019101193795-582018-03-31
19관음동291927502186669289937710-32018-03-31
20읍내동40231105502715813512136465-632018-03-31
21동천동352521040030596149931560325342018-03-31
22국우동342109526253191260312716-5-382018-03-31