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
DateTime1

Dataset

Description대구광역시_북구_월별인구현황_20181130
Author대구광역시 북구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3038385&dataSetDetailId=303838518790cd835420&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
구분 has unique valuesUnique
반 has unique valuesUnique
세대수 has unique valuesUnique
계 has unique valuesUnique
남 has unique valuesUnique
여 has unique valuesUnique
전월대비인구증감 has 1 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-10 20:05:54.751465
Analysis finished2023-12-10 20:06:04.366161
Duration9.61 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
2023-12-11T05:06:04.488134image/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%
2023-12-11T05:06:04.814249image/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%
Mean30.608696
Minimum15
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:06:04.940398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.2
Q122.5
median32
Q337
95-th percentile45.6
Maximum50
Range35
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation9.6941366
Coefficient of variation (CV)0.31671185
Kurtosis-0.74005859
Mean30.608696
Median Absolute Deviation (MAD)7
Skewness0.10368631
Sum704
Variance93.976285
MonotonicityNot monotonic
2023-12-11T05:06:05.062381image/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%
41 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 (%)
50 1
4.3%
46 1
4.3%
42 1
4.3%
41 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%
Mean189.6087
Minimum79
Maximum318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:06:05.187350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile87.8
Q1134.5
median198
Q3243
95-th percentile271.8
Maximum318
Range239
Interquartile range (IQR)108.5

Descriptive statistics

Standard deviation66.041818
Coefficient of variation (CV)0.34830585
Kurtosis-0.87627219
Mean189.6087
Median Absolute Deviation (MAD)54
Skewness-0.074886933
Sum4361
Variance4361.5217
MonotonicityNot monotonic
2023-12-11T05:06:05.335540image/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%
237 1
 
4.3%
192 1
 
4.3%
318 1
 
4.3%
242 1
 
4.3%
205 1
 
4.3%
261 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%
273 1
4.3%
261 1
4.3%
256 1
4.3%
252 1
4.3%
244 1
4.3%
242 1
4.3%
237 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%
Mean7696.9565
Minimum2085
Maximum13390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:06:05.484761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2085
5-th percentile2562.1
Q14866.5
median7486
Q310328.5
95-th percentile12618
Maximum13390
Range11305
Interquartile range (IQR)5462

Descriptive statistics

Standard deviation3315.8064
Coefficient of variation (CV)0.43079448
Kurtosis-1.1039083
Mean7696.9565
Median Absolute Deviation (MAD)2828
Skewness-0.10733005
Sum177030
Variance10994572
MonotonicityNot monotonic
2023-12-11T05:06:05.639493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2485 1
 
4.3%
9827 1
 
4.3%
9585 1
 
4.3%
10441 1
 
4.3%
10852 1
 
4.3%
7477 1
 
4.3%
13390 1
 
4.3%
10225 1
 
4.3%
9508 1
 
4.3%
12764 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2085 1
4.3%
2485 1
4.3%
3256 1
4.3%
4122 1
4.3%
4293 1
4.3%
4658 1
4.3%
5075 1
4.3%
5313 1
4.3%
6429 1
4.3%
7115 1
4.3%
ValueCountFrequency (%)
13390 1
4.3%
12764 1
4.3%
11304 1
4.3%
10852 1
4.3%
10441 1
4.3%
10432 1
4.3%
10225 1
4.3%
9827 1
4.3%
9585 1
4.3%
9508 1
4.3%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19131.957
Minimum4512
Maximum38243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:06:05.801129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4512
5-th percentile5268.6
Q19617
median20005
Q326722.5
95-th percentile31929.8
Maximum38243
Range33731
Interquartile range (IQR)17105.5

Descriptive statistics

Standard deviation9771.1578
Coefficient of variation (CV)0.51072444
Kurtosis-1.1027883
Mean19131.957
Median Absolute Deviation (MAD)8621
Skewness0.055282305
Sum440035
Variance95475525
MonotonicityNot monotonic
2023-12-11T05:06:05.935622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5051 1
 
4.3%
23834 1
 
4.3%
24990 1
 
4.3%
30389 1
 
4.3%
27566 1
 
4.3%
18278 1
 
4.3%
38243 1
 
4.3%
25879 1
 
4.3%
23198 1
 
4.3%
32101 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
4512 1
4.3%
5051 1
4.3%
7227 1
4.3%
7418 1
4.3%
8854 1
4.3%
8993 1
4.3%
10241 1
4.3%
11851 1
4.3%
13634 1
4.3%
18278 1
4.3%
ValueCountFrequency (%)
38243 1
4.3%
32101 1
4.3%
30389 1
4.3%
28782 1
4.3%
28626 1
4.3%
27566 1
4.3%
25879 1
4.3%
24990 1
4.3%
23834 1
4.3%
23198 1
4.3%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9548.0435
Minimum2405
Maximum18946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:06:06.068663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2405
5-th percentile2668.1
Q14895
median9738
Q313215
95-th percentile15852.4
Maximum18946
Range16541
Interquartile range (IQR)8320

Descriptive statistics

Standard deviation4787.2292
Coefficient of variation (CV)0.50138326
Kurtosis-1.0808772
Mean9548.0435
Median Absolute Deviation (MAD)4430
Skewness0.072328328
Sum219605
Variance22917563
MonotonicityNot monotonic
2023-12-11T05:06:06.198409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2546 1
 
4.3%
11630 1
 
4.3%
12447 1
 
4.3%
14884 1
 
4.3%
13702 1
 
4.3%
9113 1
 
4.3%
18946 1
 
4.3%
12728 1
 
4.3%
11713 1
 
4.3%
15960 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2405 1
4.3%
2546 1
4.3%
3767 1
4.3%
3920 1
4.3%
4556 1
4.3%
4668 1
4.3%
5122 1
4.3%
5888 1
4.3%
7122 1
4.3%
9113 1
4.3%
ValueCountFrequency (%)
18946 1
4.3%
15960 1
4.3%
14884 1
4.3%
14708 1
4.3%
14168 1
4.3%
13702 1
4.3%
12728 1
4.3%
12447 1
4.3%
11713 1
4.3%
11630 1
4.3%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9583.913
Minimum2107
Maximum19297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T05:06:06.345673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2107
5-th percentile2585.2
Q14722
median10267
Q313507.5
95-th percentile16077.4
Maximum19297
Range17190
Interquartile range (IQR)8785.5

Descriptive statistics

Standard deviation4989.5614
Coefficient of variation (CV)0.5206184
Kurtosis-1.1227693
Mean9583.913
Median Absolute Deviation (MAD)4191
Skewness0.040839313
Sum220430
Variance24895723
MonotonicityNot monotonic
2023-12-11T05:06:06.484099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2505 1
 
4.3%
12204 1
 
4.3%
12543 1
 
4.3%
15505 1
 
4.3%
13864 1
 
4.3%
9165 1
 
4.3%
19297 1
 
4.3%
13151 1
 
4.3%
11485 1
 
4.3%
16141 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2107 1
4.3%
2505 1
4.3%
3307 1
4.3%
3651 1
4.3%
4298 1
4.3%
4325 1
4.3%
5119 1
4.3%
5963 1
4.3%
6512 1
4.3%
9165 1
4.3%
ValueCountFrequency (%)
19297 1
4.3%
16141 1
4.3%
15505 1
4.3%
14458 1
4.3%
14074 1
4.3%
13864 1
4.3%
13151 1
4.3%
12543 1
4.3%
12204 1
4.3%
11485 1
4.3%

전월대비세대증감
Real number (ℝ)

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3913043
Minimum-32
Maximum127
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)30.4%
Memory size339.0 B
2023-12-11T05:06:06.639249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-32
5-th percentile-18
Q1-2
median6
Q312.5
95-th percentile35.6
Maximum127
Range159
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation29.439829
Coefficient of variation (CV)3.1347967
Kurtosis12.145601
Mean9.3913043
Median Absolute Deviation (MAD)7
Skewness2.977456
Sum216
Variance866.70356
MonotonicityNot monotonic
2023-12-11T05:06:06.762073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-18 2
 
8.7%
6 2
 
8.7%
3 2
 
8.7%
37 1
 
4.3%
17 1
 
4.3%
-8 1
 
4.3%
127 1
 
4.3%
10 1
 
4.3%
-1 1
 
4.3%
15 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
-32 1
4.3%
-18 2
8.7%
-8 1
4.3%
-6 1
4.3%
-3 1
4.3%
-1 1
4.3%
2 1
4.3%
3 2
8.7%
6 2
8.7%
8 1
4.3%
ValueCountFrequency (%)
127 1
4.3%
37 1
4.3%
23 1
4.3%
17 1
4.3%
15 1
4.3%
13 1
4.3%
12 1
4.3%
11 1
4.3%
10 1
4.3%
9 1
4.3%

전월대비인구증감
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.2173913
Minimum-87
Maximum277
Zeros1
Zeros (%)4.3%
Negative16
Negative (%)69.6%
Memory size339.0 B
2023-12-11T05:06:06.902569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-87
5-th percentile-73.4
Q1-43
median-18
Q34.5
95-th percentile134
Maximum277
Range364
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation77.112053
Coefficient of variation (CV)-14.77981
Kurtosis8.3114141
Mean-5.2173913
Median Absolute Deviation (MAD)27
Skewness2.6603089
Sum-120
Variance5946.2688
MonotonicityNot monotonic
2023-12-11T05:06:07.051423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
-28 2
 
8.7%
-15 1
 
4.3%
-12 1
 
4.3%
-18 1
 
4.3%
-59 1
 
4.3%
277 1
 
4.3%
-56 1
 
4.3%
-45 1
 
4.3%
-87 1
 
4.3%
-75 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
-87 1
4.3%
-75 1
4.3%
-59 1
4.3%
-57 1
4.3%
-56 1
4.3%
-45 1
4.3%
-41 1
4.3%
-40 1
4.3%
-28 2
8.7%
-25 1
4.3%
ValueCountFrequency (%)
277 1
4.3%
146 1
4.3%
26 1
4.3%
22 1
4.3%
19 1
4.3%
9 1
4.3%
0 1
4.3%
-12 1
4.3%
-15 1
4.3%
-16 1
4.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
Minimum2018-11-30 00:00:00
Maximum2018-11-30 00:00:00
2023-12-11T05:06:07.212720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:07.344928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T05:06:02.947204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:55.390091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:57.027722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:58.174505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:59.214833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:00.290071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:01.295688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:02.172200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:03.045534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:55.536855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:57.241039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:58.339535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:59.353871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:00.415487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:01.430111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:02.278541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:03.134846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:55.663885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:57.357561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:58.459459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:59.497334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:00.535917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:01.542054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:02.358866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:03.364256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:55.793969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:57.494577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:58.576788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:59.622370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:00.663440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:01.639653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:02.442060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:03.473553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:55.965788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:57.628150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:58.706680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:59.753391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:00.783683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:01.751283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:02.539594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:03.570451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:56.498168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:57.754508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:58.839973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:59.886475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:00.903858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:01.845492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:02.629555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:03.668372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:56.647119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:57.886421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:58.962875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:00.019357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:01.033619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:01.943511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:02.736660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:03.771326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:56.824706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:58.027825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:05:59.082341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:00.156944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:01.161345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:02.061643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:06:02.848176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T05:06:07.460499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분통반세대수계남여전월대비세대증감전월대비인구증감
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
통1.0001.0000.9370.6830.7820.7820.8100.7210.743
반1.0000.9371.0000.6950.6980.6980.8210.2240.000
세대수1.0000.6830.6951.0000.9140.9140.8760.4440.000
계1.0000.7820.6980.9141.0001.0000.9960.6110.646
남1.0000.7820.6980.9141.0001.0000.9960.6110.646
여1.0000.8100.8210.8760.9960.9961.0000.7000.523
전월대비세대증감1.0000.7210.2240.4440.6110.6110.7001.0000.897
전월대비인구증감1.0000.7430.0000.0000.6460.6460.5230.8971.000
2023-12-11T05:06:07.630787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통반세대수계남여전월대비세대증감전월대비인구증감
통1.0000.9470.9530.9220.9150.9190.3930.029
반0.9471.0000.9590.9580.9510.9620.250-0.115
세대수0.9530.9591.0000.9830.9820.9800.355-0.064
계0.9220.9580.9831.0000.9980.9990.297-0.102
남0.9150.9510.9820.9981.0000.9970.285-0.117
여0.9190.9620.9800.9990.9971.0000.278-0.114
전월대비세대증감0.3930.2500.3550.2970.2850.2781.0000.477
전월대비인구증감0.029-0.115-0.064-0.102-0.117-0.1140.4771.000

Missing values

2023-12-11T05:06:04.173152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T05:06:04.317135image/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
0고성동191102485505125462505-18-152018-11-30
1칠성동3825698272383411630122048262018-11-30
2침산1동18872085451224052107602018-11-30
3침산2동25158711520005973810267-6-252018-11-30
4침산3동3020474862119610280109161392018-11-30
5노원동35186642913634712265126-162018-11-30
6산격1동27159531310241512251199-172018-11-30
7산격2동2114246581185158885963-3-282018-11-30
8산격3동241275075885445564298-32-572018-11-30
9산격4동2112642938993466843253-282018-11-30
구분통반세대수계남여전월대비세대증감전월대비인구증감데이터기준일자
13검단동1695325674183767365111-122018-11-30
14무태조야동422441130428782147081407423192018-11-30
15관문동5026112764321011596016141371462018-11-30
16태전1동3220595082319811713114852-752018-11-30
17태전2동372421022525879127281315115-872018-11-30
18구암동4631813390382431894619297-1-452018-11-30
19관음동291927477182789113916510-562018-11-30
20읍내동41237108522756613702138641272772018-11-30
21동천동3525210441303891488415505-8-592018-11-30
22국우동34210958524990124471254317-182018-11-30