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_20180930
Author대구광역시 북구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3038385&dataSetDetailId=30383851e1585292f5ab&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 started2023-09-29 01:00:34.446859
Analysis finished2023-09-29 01:01:14.738342
Duration40.29 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
2023-09-29T01:01:15.061473image/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%
2023-09-29T01:01:16.859754image/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
2023-09-29T01:01:17.534032image/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
2023-09-29T01:01:18.248914image/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
2023-09-29T01:01:18.964676image/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
2023-09-29T01:01:19.780308image/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%
Mean7672.6957
Minimum2074
Maximum13405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-09-29T01:01:20.635863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2074
5-th percentile2591.1
Q14864
median7460
Q310326
95-th percentile12474.4
Maximum13405
Range11331
Interquartile range (IQR)5462

Descriptive statistics

Standard deviation3296.1901
Coefficient of variation (CV)0.42960001
Kurtosis-1.0996099
Mean7672.6957
Median Absolute Deviation (MAD)2804
Skewness-0.11040679
Sum176472
Variance10864869
MonotonicityNot monotonic
2023-09-29T01:01:21.245934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2519 1
 
4.3%
9800 1
 
4.3%
9575 1
 
4.3%
10438 1
 
4.3%
10579 1
 
4.3%
7444 1
 
4.3%
13405 1
 
4.3%
10214 1
 
4.3%
9477 1
 
4.3%
12606 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2074 1
4.3%
2519 1
4.3%
3240 1
4.3%
4092 1
4.3%
4289 1
4.3%
4656 1
4.3%
5072 1
4.3%
5312 1
4.3%
6422 1
4.3%
7111 1
4.3%
ValueCountFrequency (%)
13405 1
4.3%
12606 1
4.3%
11290 1
4.3%
10579 1
4.3%
10519 1
4.3%
10438 1
4.3%
10214 1
4.3%
9800 1
4.3%
9575 1
4.3%
9477 1
4.3%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19134.957
Minimum4510
Maximum38383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-09-29T01:01:21.853583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4510
5-th percentile5304.7
Q19669
median20039
Q326497
95-th percentile31568.9
Maximum38383
Range33873
Interquartile range (IQR)16828

Descriptive statistics

Standard deviation9750.2743
Coefficient of variation (CV)0.50955299
Kurtosis-1.0821556
Mean19134.957
Median Absolute Deviation (MAD)8766
Skewness0.055737414
Sum440104
Variance95067849
MonotonicityNot monotonic
2023-09-29T01:01:22.435467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5098 1
 
4.3%
23808 1
 
4.3%
25074 1
 
4.3%
30479 1
 
4.3%
26980 1
 
4.3%
18356 1
 
4.3%
38383 1
 
4.3%
26014 1
 
4.3%
23264 1
 
4.3%
31690 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
4510 1
4.3%
5098 1
4.3%
7165 1
4.3%
7433 1
4.3%
8893 1
4.3%
9043 1
4.3%
10295 1
4.3%
11900 1
4.3%
13649 1
4.3%
18356 1
4.3%
ValueCountFrequency (%)
38383 1
4.3%
31690 1
4.3%
30479 1
4.3%
28835 1
4.3%
28805 1
4.3%
26980 1
4.3%
26014 1
4.3%
25074 1
4.3%
23808 1
4.3%
23264 1
4.3%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9551.1739
Minimum2409
Maximum19014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-09-29T01:01:23.218637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2409
5-th percentile2694
Q14922
median9764
Q313104
95-th percentile15679.6
Maximum19014
Range16605
Interquartile range (IQR)8182

Descriptive statistics

Standard deviation4775.3848
Coefficient of variation (CV)0.49997883
Kurtosis-1.0595444
Mean9551.1739
Median Absolute Deviation (MAD)4513
Skewness0.072521825
Sum219677
Variance22804300
MonotonicityNot monotonic
2023-09-29T01:01:24.052942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2576 1
 
4.3%
11602 1
 
4.3%
12472 1
 
4.3%
14920 1
 
4.3%
13404 1
 
4.3%
9154 1
 
4.3%
19014 1
 
4.3%
12804 1
 
4.3%
11754 1
 
4.3%
15764 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2409 1
4.3%
2576 1
4.3%
3756 1
4.3%
3890 1
4.3%
4586 1
4.3%
4703 1
4.3%
5141 1
4.3%
5925 1
4.3%
7141 1
4.3%
9154 1
4.3%
ValueCountFrequency (%)
19014 1
4.3%
15764 1
4.3%
14920 1
4.3%
14726 1
4.3%
14277 1
4.3%
13404 1
4.3%
12804 1
4.3%
12472 1
4.3%
11754 1
4.3%
11602 1
4.3%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9583.7826
Minimum2101
Maximum19369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-09-29T01:01:24.928049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2101
5-th percentile2597.3
Q14747
median10275
Q313393
95-th percentile15889.3
Maximum19369
Range17268
Interquartile range (IQR)8646

Descriptive statistics

Standard deviation4980.5498
Coefficient of variation (CV)0.51968519
Kurtosis-1.1027135
Mean9583.7826
Median Absolute Deviation (MAD)4253
Skewness0.041489709
Sum220427
Variance24805877
MonotonicityNot monotonic
2023-09-29T01:01:25.613622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2522 1
 
4.3%
12206 1
 
4.3%
12602 1
 
4.3%
15559 1
 
4.3%
13576 1
 
4.3%
9202 1
 
4.3%
19369 1
 
4.3%
13210 1
 
4.3%
11510 1
 
4.3%
15926 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2101 1
4.3%
2522 1
4.3%
3275 1
4.3%
3677 1
4.3%
4307 1
4.3%
4340 1
4.3%
5154 1
4.3%
5975 1
4.3%
6508 1
4.3%
9202 1
4.3%
ValueCountFrequency (%)
19369 1
4.3%
15926 1
4.3%
15559 1
4.3%
14528 1
4.3%
14109 1
4.3%
13576 1
4.3%
13210 1
4.3%
12602 1
4.3%
12206 1
4.3%
11510 1
4.3%

전월대비세대증감
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.913043
Minimum-63
Maximum337
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)39.1%
Memory size339.0 B
2023-09-29T01:01:26.231754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-63
5-th percentile-16.5
Q1-5
median2
Q311.5
95-th percentile38.4
Maximum337
Range400
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation72.481918
Coefficient of variation (CV)4.8603035
Kurtosis19.923062
Mean14.913043
Median Absolute Deviation (MAD)7
Skewness4.2945258
Sum343
Variance5253.6285
MonotonicityNot monotonic
2023-09-29T01:01:26.873961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
-5 3
 
13.0%
2 2
 
8.7%
1 2
 
8.7%
4 1
 
4.3%
-12 1
 
4.3%
-17 1
 
4.3%
11 1
 
4.3%
41 1
 
4.3%
337 1
 
4.3%
13 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
-63 1
 
4.3%
-17 1
 
4.3%
-12 1
 
4.3%
-6 1
 
4.3%
-5 3
13.0%
-4 1
 
4.3%
-1 1
 
4.3%
1 2
8.7%
2 2
8.7%
3 1
 
4.3%
ValueCountFrequency (%)
337 1
4.3%
41 1
4.3%
15 1
4.3%
14 1
4.3%
13 1
4.3%
12 1
4.3%
11 1
4.3%
5 1
4.3%
4 1
4.3%
3 1
4.3%

전월대비인구증감
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.695652
Minimum-128
Maximum824
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)78.3%
Memory size339.0 B
2023-09-29T01:01:27.780203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-128
5-th percentile-88.3
Q1-33.5
median-10
Q3-2
95-th percentile18.4
Maximum824
Range952
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation180.5246
Coefficient of variation (CV)15.435189
Kurtosis21.067097
Mean11.695652
Median Absolute Deviation (MAD)13
Skewness4.4896677
Sum269
Variance32589.13
MonotonicityNot monotonic
2023-09-29T01:01:28.603405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-2 3
 
13.0%
3 2
 
8.7%
-14 1
 
4.3%
-63 1
 
4.3%
-23 1
 
4.3%
-44 1
 
4.3%
-62 1
 
4.3%
-64 1
 
4.3%
-91 1
 
4.3%
20 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
-128 1
4.3%
-91 1
4.3%
-64 1
4.3%
-63 1
4.3%
-62 1
4.3%
-44 1
4.3%
-23 1
4.3%
-22 1
4.3%
-20 1
4.3%
-18 1
4.3%
ValueCountFrequency (%)
824 1
 
4.3%
20 1
 
4.3%
4 1
 
4.3%
3 2
8.7%
-2 3
13.0%
-5 1
 
4.3%
-6 1
 
4.3%
-9 1
 
4.3%
-10 1
 
4.3%
-14 1
 
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2018-09-30
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-09-30
2nd row2018-09-30
3rd row2018-09-30
4th row2018-09-30
5th row2018-09-30

Common Values

ValueCountFrequency (%)
2018-09-30 23
100.0%

Length

2023-09-29T01:01:29.181121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:01:29.573936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-09-30 23
100.0%

Interactions

2023-09-29T01:01:05.176955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:36.754581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:41.097133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:44.832679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:49.302238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:52.488439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:56.440487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:00.791545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:06.095519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:37.131920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:41.536651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:45.587311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:49.609959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:52.979759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:57.041814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:01.198352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:07.651523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:37.751464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:41.954019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:45.974283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:49.960984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:53.359977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:57.610116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:01.588314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:08.986541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:38.142559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:42.236139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:46.271898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:50.210729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:53.919502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:58.142868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:02.278018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:09.926694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:38.607372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:42.614648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:46.829444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:50.836125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:54.452066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:58.636068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:02.916088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:10.601006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:39.217247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:43.030884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:47.231311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:51.194731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:54.886517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:59.119053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:03.490933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:11.439809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:40.035912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:43.595930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:47.876764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:51.574968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:55.249218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:59.666722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:04.435144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:11.868445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:40.619521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:44.323632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:48.750426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:52.152559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:00:55.859420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:00.215818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:01:04.791223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-29T01:01:30.020326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분통반세대수계남여전월대비세대증감전월대비인구증감
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
통1.0001.0000.9370.6830.7820.7820.8100.6290.793
반1.0000.9371.0000.6950.6980.6980.8210.6820.370
세대수1.0000.6830.6951.0000.9140.9140.8760.0000.506
계1.0000.7820.6980.9141.0001.0000.9960.7520.995
남1.0000.7820.6980.9141.0001.0000.9960.7520.995
여1.0000.8100.8210.8760.9960.9961.0000.7520.981
전월대비세대증감1.0000.6290.6820.0000.7520.7520.7521.0000.930
전월대비인구증감1.0000.7930.3700.5060.9950.9950.9810.9301.000
2023-09-29T01:01:30.596075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통반세대수계남여전월대비세대증감전월대비인구증감
통1.0000.9470.9550.9220.9150.9190.028-0.183
반0.9471.0000.9620.9580.9510.962-0.072-0.293
세대수0.9550.9621.0000.9810.9800.9790.028-0.291
계0.9220.9580.9811.0000.9980.9990.016-0.322
남0.9150.9510.9800.9981.0000.9970.033-0.315
여0.9190.9620.9790.9990.9971.000-0.001-0.331
전월대비세대증감0.028-0.0720.0280.0160.033-0.0011.0000.615
전월대비인구증감-0.183-0.293-0.291-0.322-0.315-0.3310.6151.000

Missing values

2023-09-29T01:01:13.002336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-29T01:01:14.067383image/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
0고성동191102519509825762522-1-22018-09-30
1칠성동382569800238081160212206-432018-09-30
2침산1동18872074451024092101-6-182018-09-30
3침산2동251587111200399764102753-102018-09-30
4침산3동30204746021175102791089612-22018-09-30
5노원동3518664221364971416508242018-09-30
6산격1동27159531210295514151545-202018-09-30
7산격2동21142465611900592559751-222018-09-30
8산격3동2412750728893458643071432018-09-30
9산격4동21126428990434703434015-52018-09-30
구분통반세대수계남여전월대비세대증감전월대비인구증감데이터기준일자
13검단동169532407433375636772-92018-09-30
14무태조야동422441129028835147261410913-142018-09-30
15관문동50261126063169015764159263378242018-09-30
16태전1동32205947723264117541151041202018-09-30
17태전2동3724210214260141280413210-5-912018-09-30
18구암동463181340538383190141936911-642018-09-30
19관음동2919274441835691549202-17-622018-09-30
20읍내동41237105792698013404135761-442018-09-30
21동천동3525210438304791492015559-5-232018-09-30
22국우동342109575250741247212602-12-632018-09-30