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 memory92.7 B

Variable types

Text1
Numeric7
Categorical1
DateTime1

Dataset

Description대구광역시 달서구 월별 주민등록 인구 동별, 성별, 외국인 포함 인구수, 세대수, 통반수 현황월별 총인구수(총무과 제공)
Author대구광역시 달서구
URLhttps://www.data.go.kr/data/15059790/fileData.do

Alerts

담당부서 has constant value ""Constant
기준일자 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
인구_남여_외국인 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
동명 has unique valuesUnique
반 has unique valuesUnique
세대수 has unique valuesUnique
인구_남여_외국인 has unique valuesUnique
남 has unique valuesUnique
여 has unique valuesUnique
외국인 has unique valuesUnique

Reproduction

Analysis started2024-03-23 05:33:55.898546
Analysis finished2024-03-23 05:34:04.899761
Duration9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-03-23T14:34:05.103935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.6086957
Min length2

Characters and Unicode

Total characters83
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 row성당동
2nd row두류1·2동
3rd row두류3동
4th row본리동
5th row감삼동
ValueCountFrequency (%)
성당동 1
 
4.3%
월성1동 1
 
4.3%
송현2동 1
 
4.3%
송현1동 1
 
4.3%
도원동 1
 
4.3%
상인3동 1
 
4.3%
상인2동 1
 
4.3%
상인1동 1
 
4.3%
유천동 1
 
4.3%
진천동 1
 
4.3%
Other values (13) 13
56.5%
2024-03-23T14:34:05.671561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
27.7%
1 6
 
7.2%
2 6
 
7.2%
성 3
 
3.6%
인 3
 
3.6%
상 3
 
3.6%
당 2
 
2.4%
송 2
 
2.4%
천 2
 
2.4%
월 2
 
2.4%
Other values (22) 31
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
81.9%
Decimal Number 14
 
16.9%
Other Punctuation 1
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
33.8%
성 3
 
4.4%
인 3
 
4.4%
상 3
 
4.4%
당 2
 
2.9%
송 2
 
2.9%
천 2
 
2.9%
월 2
 
2.9%
곡 2
 
2.9%
이 2
 
2.9%
Other values (18) 24
35.3%
Decimal Number
ValueCountFrequency (%)
1 6
42.9%
2 6
42.9%
3 2
 
14.3%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
81.9%
Common 15
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
33.8%
성 3
 
4.4%
인 3
 
4.4%
상 3
 
4.4%
당 2
 
2.9%
송 2
 
2.9%
천 2
 
2.9%
월 2
 
2.9%
곡 2
 
2.9%
이 2
 
2.9%
Other values (18) 24
35.3%
Common
ValueCountFrequency (%)
1 6
40.0%
2 6
40.0%
3 2
 
13.3%
· 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
81.9%
ASCII 14
 
16.9%
None 1
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
33.8%
성 3
 
4.4%
인 3
 
4.4%
상 3
 
4.4%
당 2
 
2.9%
송 2
 
2.9%
천 2
 
2.9%
월 2
 
2.9%
곡 2
 
2.9%
이 2
 
2.9%
Other values (18) 24
35.3%
ASCII
ValueCountFrequency (%)
1 6
42.9%
2 6
42.9%
3 2
 
14.3%
None
ValueCountFrequency (%)
· 1
100.0%

통
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.608696
Minimum18
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:34:05.908985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21.1
Q128.5
median33
Q343
95-th percentile52.8
Maximum65
Range47
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation11.396727
Coefficient of variation (CV)0.32005459
Kurtosis0.56091886
Mean35.608696
Median Absolute Deviation (MAD)8
Skewness0.71610786
Sum819
Variance129.88538
MonotonicityNot monotonic
2024-03-23T14:34:06.225435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
33 4
17.4%
29 2
 
8.7%
43 2
 
8.7%
44 1
 
4.3%
34 1
 
4.3%
23 1
 
4.3%
37 1
 
4.3%
21 1
 
4.3%
36 1
 
4.3%
51 1
 
4.3%
Other values (8) 8
34.8%
ValueCountFrequency (%)
18 1
 
4.3%
21 1
 
4.3%
22 1
 
4.3%
23 1
 
4.3%
25 1
 
4.3%
28 1
 
4.3%
29 2
8.7%
33 4
17.4%
34 1
 
4.3%
36 1
 
4.3%
ValueCountFrequency (%)
65 1
4.3%
53 1
4.3%
51 1
4.3%
45 1
4.3%
44 1
4.3%
43 2
8.7%
41 1
4.3%
37 1
4.3%
36 1
4.3%
34 1
4.3%

반
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.13043
Minimum107
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:34:06.536324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile127.8
Q1199
median221
Q3302
95-th percentile372.2
Maximum440
Range333
Interquartile range (IQR)103

Descriptive statistics

Standard deviation84.00774
Coefficient of variation (CV)0.33720384
Kurtosis-0.16423103
Mean249.13043
Median Absolute Deviation (MAD)54
Skewness0.48158752
Sum5730
Variance7057.3004
MonotonicityNot monotonic
2024-03-23T14:34:06.870408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
287 1
 
4.3%
212 1
 
4.3%
153 1
 
4.3%
248 1
 
4.3%
210 1
 
4.3%
363 1
 
4.3%
125 1
 
4.3%
221 1
 
4.3%
373 1
 
4.3%
188 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
107 1
4.3%
125 1
4.3%
153 1
4.3%
178 1
4.3%
183 1
4.3%
188 1
4.3%
210 1
4.3%
212 1
4.3%
213 1
4.3%
216 1
4.3%
ValueCountFrequency (%)
440 1
4.3%
373 1
4.3%
365 1
4.3%
363 1
4.3%
314 1
4.3%
305 1
4.3%
299 1
4.3%
287 1
4.3%
275 1
4.3%
248 1
4.3%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10244.435
Minimum4387
Maximum21274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:34:07.571498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4387
5-th percentile6077.3
Q17589.5
median9095
Q312548.5
95-th percentile16756.4
Maximum21274
Range16887
Interquartile range (IQR)4959

Descriptive statistics

Standard deviation3850.5448
Coefficient of variation (CV)0.37586698
Kurtosis1.8373618
Mean10244.435
Median Absolute Deviation (MAD)2163
Skewness1.1455644
Sum235622
Variance14826695
MonotonicityNot monotonic
2024-03-23T14:34:07.861546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10443 1
 
4.3%
8617 1
 
4.3%
6323 1
 
4.3%
9013 1
 
4.3%
9674 1
 
4.3%
13754 1
 
4.3%
6050 1
 
4.3%
9050 1
 
4.3%
12958 1
 
4.3%
11031 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
4387 1
4.3%
6050 1
4.3%
6323 1
4.3%
6843 1
4.3%
6991 1
4.3%
7079 1
4.3%
8100 1
4.3%
8125 1
4.3%
8617 1
4.3%
9013 1
4.3%
ValueCountFrequency (%)
21274 1
4.3%
17090 1
4.3%
13754 1
4.3%
13370 1
4.3%
12958 1
4.3%
12811 1
4.3%
12286 1
4.3%
11258 1
4.3%
11031 1
4.3%
10443 1
4.3%

인구_남여_외국인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23350.391
Minimum8743
Maximum50672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:34:08.108284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8743
5-th percentile10506.4
Q115623
median20079
Q331560.5
95-th percentile40154.8
Maximum50672
Range41929
Interquartile range (IQR)15937.5

Descriptive statistics

Standard deviation10739.327
Coefficient of variation (CV)0.45992064
Kurtosis0.22457912
Mean23350.391
Median Absolute Deviation (MAD)7110
Skewness0.8337606
Sum537059
Variance1.1533314 × 108
MonotonicityNot monotonic
2024-03-23T14:34:08.465535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
21884 1
 
4.3%
14862 1
 
4.3%
12616 1
 
4.3%
17056 1
 
4.3%
17793 1
 
4.3%
33181 1
 
4.3%
10272 1
 
4.3%
18370 1
 
4.3%
34172 1
 
4.3%
33665 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
8743 1
4.3%
10272 1
4.3%
12616 1
4.3%
12969 1
4.3%
14862 1
4.3%
15541 1
4.3%
15705 1
4.3%
16522 1
4.3%
17056 1
4.3%
17793 1
4.3%
ValueCountFrequency (%)
50672 1
4.3%
40788 1
4.3%
34456 1
4.3%
34172 1
4.3%
33665 1
4.3%
33181 1
4.3%
29940 1
4.3%
29029 1
4.3%
26807 1
4.3%
21937 1
4.3%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11211.435
Minimum4182
Maximum24582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:34:08.660572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4182
5-th percentile4978.5
Q17421
median9667
Q314676
95-th percentile19591.6
Maximum24582
Range20400
Interquartile range (IQR)7255

Descriptive statistics

Standard deviation5208.3363
Coefficient of variation (CV)0.46455573
Kurtosis0.33312348
Mean11211.435
Median Absolute Deviation (MAD)3586
Skewness0.8558183
Sum257863
Variance27126767
MonotonicityNot monotonic
2024-03-23T14:34:08.975755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10530 1
 
4.3%
7199 1
 
4.3%
6081 1
 
4.3%
7932 1
 
4.3%
8651 1
 
4.3%
15882 1
 
4.3%
4856 1
 
4.3%
8968 1
 
4.3%
16555 1
 
4.3%
16544 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
4182 1
4.3%
4856 1
4.3%
6081 1
4.3%
6312 1
4.3%
6861 1
4.3%
7199 1
4.3%
7643 1
4.3%
7844 1
4.3%
7932 1
4.3%
8651 1
4.3%
ValueCountFrequency (%)
24582 1
4.3%
19929 1
4.3%
16555 1
4.3%
16544 1
4.3%
15882 1
4.3%
14817 1
4.3%
14535 1
4.3%
14401 1
4.3%
13268 1
4.3%
10624 1
4.3%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11646.87
Minimum4497
Maximum25744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:34:09.286887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4497
5-th percentile5450
Q17604.5
median10012
Q314887
95-th percentile20438.4
Maximum25744
Range21247
Interquartile range (IQR)7282.5

Descriptive statistics

Standard deviation5406.603
Coefficient of variation (CV)0.46421083
Kurtosis0.50026459
Mean11646.87
Median Absolute Deviation (MAD)3502
Skewness0.93455794
Sum267878
Variance29231356
MonotonicityNot monotonic
2024-03-23T14:34:09.557979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
11216 1
 
4.3%
7539 1
 
4.3%
6224 1
 
4.3%
8644 1
 
4.3%
8750 1
 
4.3%
17221 1
 
4.3%
5364 1
 
4.3%
9261 1
 
4.3%
17517 1
 
4.3%
17056 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
4497 1
4.3%
5364 1
4.3%
6224 1
4.3%
6510 1
4.3%
7516 1
4.3%
7539 1
4.3%
7670 1
4.3%
8205 1
4.3%
8644 1
4.3%
8750 1
4.3%
ValueCountFrequency (%)
25744 1
4.3%
20763 1
4.3%
17517 1
4.3%
17221 1
4.3%
17056 1
4.3%
15259 1
4.3%
14515 1
4.3%
14414 1
4.3%
13285 1
4.3%
11216 1
4.3%

외국인
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492.08696
Minimum52
Maximum5124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:34:09.800877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile64.1
Q1112
median214
Q3436.5
95-th percentile970.7
Maximum5124
Range5072
Interquartile range (IQR)324.5

Descriptive statistics

Standard deviation1036.0093
Coefficient of variation (CV)2.1053378
Kurtosis20.430735
Mean492.08696
Median Absolute Deviation (MAD)136
Skewness4.4240728
Sum11318
Variance1073315.2
MonotonicityNot monotonic
2024-03-23T14:34:10.011251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
138 1
 
4.3%
124 1
 
4.3%
311 1
 
4.3%
480 1
 
4.3%
392 1
 
4.3%
78 1
 
4.3%
52 1
 
4.3%
141 1
 
4.3%
100 1
 
4.3%
65 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
52 1
4.3%
64 1
4.3%
65 1
4.3%
78 1
4.3%
96 1
4.3%
100 1
4.3%
124 1
4.3%
138 1
4.3%
141 1
4.3%
146 1
4.3%
ValueCountFrequency (%)
5124 1
4.3%
1010 1
4.3%
617 1
4.3%
546 1
4.3%
480 1
4.3%
473 1
4.3%
400 1
4.3%
392 1
4.3%
346 1
4.3%
311 1
4.3%

담당부서
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
대구광역시 달서구 총무과
23 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 달서구 총무과
2nd row대구광역시 달서구 총무과
3rd row대구광역시 달서구 총무과
4th row대구광역시 달서구 총무과
5th row대구광역시 달서구 총무과

Common Values

ValueCountFrequency (%)
대구광역시 달서구 총무과 23
100.0%

Length

2024-03-23T14:34:10.260842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T14:34:10.520996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 23
33.3%
달서구 23
33.3%
총무과 23
33.3%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
Minimum2024-02-29 00:00:00
Maximum2024-02-29 00:00:00
2024-03-23T14:34:10.733634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:10.901635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-23T14:34:03.406087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:56.395432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:57.909126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:58.866271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:59.793503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:00.841832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:02.124977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:03.545711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:56.545059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:58.034843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:58.977261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:59.887699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:01.054744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:02.249516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:03.688746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:56.707728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:58.162496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:59.146920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:00.030335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:01.207519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:02.414461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:03.825779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:56.897162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:58.300467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:59.297634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:00.165181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:01.358089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:02.598081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:03.960657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:57.512650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:58.458112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:59.429517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:00.284164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:01.496778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:02.789132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:04.170452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:57.667194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:58.617413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:59.571439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:00.439267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:01.729695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:02.962218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:04.328321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:57.788240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:58.733436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:59.672473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:00.551620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:01.932446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:03.171634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:34:11.026978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명통반세대수인구_남여_외국인남여외국인
동명1.0001.0001.0001.0001.0001.0001.0001.000
통1.0001.0000.8580.8770.7690.5830.6320.263
반1.0000.8581.0000.8290.8960.8200.8200.000
세대수1.0000.8770.8291.0000.7850.7230.7690.768
인구_남여_외국인1.0000.7690.8960.7851.0000.9910.9880.195
남1.0000.5830.8200.7230.9911.0000.9740.000
여1.0000.6320.8200.7690.9880.9741.0000.000
외국인1.0000.2630.0000.7680.1950.0000.0001.000
2024-03-23T14:34:11.252457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통반세대수인구_남여_외국인남여외국인
통1.0000.9330.8750.7900.7680.8030.079
반0.9331.0000.9030.8500.8320.8490.141
세대수0.8750.9031.0000.9500.9400.950-0.009
인구_남여_외국인0.7900.8500.9501.0000.9930.9850.005
남0.7680.8320.9400.9931.0000.993-0.065
여0.8030.8490.9500.9850.9931.000-0.086
외국인0.0790.141-0.0090.005-0.065-0.0861.000

Missing values

2024-03-23T14:34:04.521421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:34:04.805131image/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

동명통반세대수인구_남여_외국인남여외국인담당부서기준일자
0성당동4428710443218841053011216138대구광역시 달서구 총무과2024-02-29
1두류1·2동3321286171486271997539124대구광역시 달서구 총무과2024-02-29
2두류3동18107438787434182449764대구광역시 달서구 총무과2024-02-29
3본리동33213810020079966710012400대구광역시 달서구 총무과2024-02-29
4감삼동4531412811299401453515259146대구광역시 달서구 총무과2024-02-29
5죽전동2817868431296963126510147대구광역시 달서구 총무과2024-02-29
6장기동2218370791652278448205473대구광역시 달서구 총무과2024-02-29
7용산1동4129912286290291440114414214대구광역시 달서구 총무과2024-02-29
8용산2동3327511258268071326813285254대구광역시 달서구 총무과2024-02-29
9이곡1동292359095219371062410696617대구광역시 달서구 총무과2024-02-29
동명통반세대수인구_남여_외국인남여외국인담당부서기준일자
13월성2동34220812515541686176701010대구광역시 달서구 총무과2024-02-29
14진천동6544021274506722458225744346대구광역시 달서구 총무과2024-02-29
15유천동291881103133665165441705665대구광역시 달서구 총무과2024-02-29
16상인1동5137312958341721655517517100대구광역시 달서구 총무과2024-02-29
17상인2동3622190501837089689261141대구광역시 달서구 총무과2024-02-29
18상인3동211256050102724856536452대구광역시 달서구 총무과2024-02-29
19도원동433631375433181158821722178대구광역시 달서구 총무과2024-02-29
20송현1동3321096741779386518750392대구광역시 달서구 총무과2024-02-29
21송현2동3724890131705679328644480대구광역시 달서구 총무과2024-02-29
22본동2315363231261660816224311대구광역시 달서구 총무과2024-02-29