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 memory92.6 B

Variable types

Categorical3
Numeric7

Dataset

Description대구광역시 달서구_주민등록 월별 인구 현황_20230531
Author대구광역시 달서구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15059790&dataSetDetailId=15059790197bfc908ebdb&provdMethod=FILE

Alerts

담당부서 has constant value "대구광역시 달서구 총무과"Constant
기준일자 has constant value "2023-05-31"Constant
통 is highly overall correlated with 반 and 5 other fieldsHigh correlation
반 is highly overall correlated with 통 and 5 other fieldsHigh correlation
세대수 is highly overall correlated with 통 and 5 other fieldsHigh correlation
인구_남여_외국인 is highly overall correlated with 통 and 5 other fieldsHigh correlation
남 is highly overall correlated with 통 and 5 other fieldsHigh correlation
여 is highly overall correlated with 통 and 5 other fieldsHigh correlation
외국인 is highly overall correlated with 동명High correlation
동명 is highly overall correlated with 통 and 6 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 started2023-07-10 05:56:48.321835
Analysis finished2023-07-10 05:57:05.000812
Duration16.68 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

동명
Categorical

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
성당동
 
1
두류1.2동
 
1
두류3동
 
1
본리동
 
1
감삼동
 
1
Other values (18)
18 

Length

Max length6
Median length4
Mean length3.6086957
Min length2

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row성당동
2nd row두류1.2동
3rd row두류3동
4th row본리동
5th row감삼동

Common Values

ValueCountFrequency (%)
성당동 1
 
4.3%
두류1.2동 1
 
4.3%
두류3동 1
 
4.3%
본리동 1
 
4.3%
감삼동 1
 
4.3%
죽전동 1
 
4.3%
장기동 1
 
4.3%
용산1동 1
 
4.3%
용산2동 1
 
4.3%
이곡1동 1
 
4.3%
Other values (13) 13
56.5%

Length

2023-07-10T14:57:05.149686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
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%

통
Real number (ℝ)

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.478261
Minimum18
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2023-07-10T14:57:05.528995image/svg+xmlMatplotlib v3.7.1, 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 deviation11.40522
Coefficient of variation (CV)0.32147067
Kurtosis0.58869604
Mean35.478261
Median Absolute Deviation (MAD)8
Skewness0.74698166
Sum816
Variance130.07905
MonotonicityNot monotonic
2023-07-10T14:57:05.778484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
33 3
 
13.0%
29 2
 
8.7%
43 2
 
8.7%
53 1
 
4.3%
23 1
 
4.3%
18 1
 
4.3%
31 1
 
4.3%
45 1
 
4.3%
28 1
 
4.3%
22 1
 
4.3%
Other values (9) 9
39.1%
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%
31 1
 
4.3%
33 3
13.0%
34 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%
40 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%
Mean246.73913
Minimum104
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2023-07-10T14:57:06.017753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile127.8
Q1192
median221
Q3300
95-th percentile367.7
Maximum440
Range336
Interquartile range (IQR)108

Descriptive statistics

Standard deviation84.772539
Coefficient of variation (CV)0.34357152
Kurtosis-0.18137842
Mean246.73913
Median Absolute Deviation (MAD)60
Skewness0.48732581
Sum5675
Variance7186.3834
MonotonicityNot monotonic
2023-07-10T14:57:06.241295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
287 1
 
4.3%
305 1
 
4.3%
153 1
 
4.3%
104 1
 
4.3%
196 1
 
4.3%
310 1
 
4.3%
161 1
 
4.3%
183 1
 
4.3%
295 1
 
4.3%
275 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
104 1
4.3%
125 1
4.3%
153 1
4.3%
161 1
4.3%
183 1
4.3%
188 1
4.3%
196 1
4.3%
210 1
4.3%
212 1
4.3%
217 1
4.3%
ValueCountFrequency (%)
440 1
4.3%
368 1
4.3%
365 1
4.3%
363 1
4.3%
310 1
4.3%
305 1
4.3%
295 1
4.3%
287 1
4.3%
275 1
4.3%
242 1
4.3%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10295.391
Minimum4284
Maximum21427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2023-07-10T14:57:06.545613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4284
5-th percentile6042.4
Q17568
median9193
Q312403.5
95-th percentile17216.8
Maximum21427
Range17143
Interquartile range (IQR)4835.5

Descriptive statistics

Standard deviation3911.319
Coefficient of variation (CV)0.3799097
Kurtosis1.8724036
Mean10295.391
Median Absolute Deviation (MAD)2140
Skewness1.1437903
Sum236794
Variance15298417
MonotonicityNot monotonic
2023-07-10T14:57:06.851736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10596 1
 
4.3%
13364 1
 
4.3%
6030 1
 
4.3%
4284 1
 
4.3%
8014 1
 
4.3%
12412 1
 
4.3%
6761 1
 
4.3%
7122 1
 
4.3%
12395 1
 
4.3%
11300 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
4284 1
4.3%
6030 1
4.3%
6154 1
4.3%
6761 1
4.3%
7053 1
4.3%
7122 1
4.3%
8014 1
4.3%
8259 1
4.3%
8755 1
4.3%
9164 1
4.3%
ValueCountFrequency (%)
21427 1
4.3%
17598 1
4.3%
13786 1
4.3%
13364 1
4.3%
12947 1
4.3%
12412 1
4.3%
12395 1
4.3%
11300 1
4.3%
11042 1
4.3%
10596 1
4.3%

인구_남여_외국인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23701.913
Minimum8535
Maximum51651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2023-07-10T14:57:07.133472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8535
5-th percentile10804.7
Q116020.5
median20018
Q331852.5
95-th percentile40363.4
Maximum51651
Range43116
Interquartile range (IQR)15832

Descriptive statistics

Standard deviation10899.13
Coefficient of variation (CV)0.45984176
Kurtosis0.29297171
Mean23701.913
Median Absolute Deviation (MAD)7454
Skewness0.82692305
Sum545144
Variance1.1879102 × 108
MonotonicityNot monotonic
2023-07-10T14:57:07.377800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
22519 1
 
4.3%
40936 1
 
4.3%
12089 1
 
4.3%
8535 1
 
4.3%
20018 1
 
4.3%
29112 1
 
4.3%
12916 1
 
4.3%
16893 1
 
4.3%
29774 1
 
4.3%
27472 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
8535 1
4.3%
10662 1
4.3%
12089 1
4.3%
12916 1
4.3%
15171 1
4.3%
15911 1
4.3%
16130 1
4.3%
16893 1
4.3%
17709 1
4.3%
18564 1
4.3%
ValueCountFrequency (%)
51651 1
4.3%
40936 1
4.3%
35210 1
4.3%
34680 1
4.3%
34003 1
4.3%
33931 1
4.3%
29774 1
4.3%
29112 1
4.3%
27472 1
4.3%
22519 1
4.3%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11409.043
Minimum4093
Maximum25053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2023-07-10T14:57:07.644808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4093
5-th percentile5115.9
Q17615
median9674
Q315100.5
95-th percentile19670
Maximum25053
Range20960
Interquartile range (IQR)7485.5

Descriptive statistics

Standard deviation5290.3244
Coefficient of variation (CV)0.4636957
Kurtosis0.35827133
Mean11409.043
Median Absolute Deviation (MAD)3893
Skewness0.8340705
Sum262408
Variance27987533
MonotonicityNot monotonic
2023-07-10T14:57:07.896574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10868 1
 
4.3%
19982 1
 
4.3%
5781 1
 
4.3%
4093 1
 
4.3%
9674 1
 
4.3%
14092 1
 
4.3%
6299 1
 
4.3%
8060 1
 
4.3%
14779 1
 
4.3%
13628 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
4093 1
4.3%
5042 1
4.3%
5781 1
4.3%
6299 1
4.3%
7096 1
4.3%
7352 1
4.3%
7878 1
4.3%
8060 1
4.3%
8232 1
4.3%
9030 1
4.3%
ValueCountFrequency (%)
25053 1
4.3%
19982 1
4.3%
16862 1
4.3%
16676 1
4.3%
16349 1
4.3%
15422 1
4.3%
14779 1
4.3%
14092 1
4.3%
13628 1
4.3%
10924 1
4.3%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11828.565
Minimum4386
Maximum26274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2023-07-10T14:57:08.155739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4386
5-th percentile5603.2
Q17817
median9977
Q314958
95-th percentile20546.5
Maximum26274
Range21888
Interquartile range (IQR)7141

Descriptive statistics

Standard deviation5489.2703
Coefficient of variation (CV)0.46406899
Kurtosis0.57876224
Mean11828.565
Median Absolute Deviation (MAD)3645
Skewness0.92631712
Sum272057
Variance30132088
MonotonicityNot monotonic
2023-07-10T14:57:08.341167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
11512 1
 
4.3%
20859 1
 
4.3%
5983 1
 
4.3%
4386 1
 
4.3%
9977 1
 
4.3%
14875 1
 
4.3%
6471 1
 
4.3%
8391 1
 
4.3%
14799 1
 
4.3%
13622 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
4386 1
4.3%
5561 1
4.3%
5983 1
4.3%
6471 1
4.3%
7678 1
4.3%
7734 1
4.3%
7900 1
4.3%
8391 1
4.3%
8950 1
4.3%
9129 1
4.3%
ValueCountFrequency (%)
26274 1
4.3%
20859 1
4.3%
17734 1
4.3%
17575 1
4.3%
17201 1
4.3%
15041 1
4.3%
14875 1
4.3%
14799 1
4.3%
13622 1
4.3%
11512 1
4.3%

외국인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean464.30435
Minimum54
Maximum4747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2023-07-10T14:57:08.521212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile56.3
Q1108
median196
Q3423.5
95-th percentile880.7
Maximum4747
Range4693
Interquartile range (IQR)315.5

Descriptive statistics

Standard deviation958.37416
Coefficient of variation (CV)2.0641077
Kurtosis20.383673
Mean464.30435
Median Absolute Deviation (MAD)129
Skewness4.4150212
Sum10679
Variance918481.04
MonotonicityNot monotonic
2023-07-10T14:57:08.735065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
139 1
 
4.3%
95 1
 
4.3%
325 1
 
4.3%
56 1
 
4.3%
367 1
 
4.3%
145 1
 
4.3%
146 1
 
4.3%
442 1
 
4.3%
196 1
 
4.3%
222 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
54 1
4.3%
56 1
4.3%
59 1
4.3%
79 1
4.3%
84 1
4.3%
95 1
4.3%
121 1
4.3%
139 1
4.3%
141 1
4.3%
145 1
4.3%
ValueCountFrequency (%)
4747 1
4.3%
915 1
4.3%
572 1
4.3%
527 1
4.3%
518 1
4.3%
442 1
4.3%
405 1
4.3%
367 1
4.3%
325 1
4.3%
324 1
4.3%

담당부서
Categorical

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size312.0 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

2023-07-10T14:57:09.256405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T14:57:09.481555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 23
33.3%
달서구 23
33.3%
총무과 23
33.3%

기준일자
Categorical

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size312.0 B
2023-05-31
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-31
2nd row2023-05-31
3rd row2023-05-31
4th row2023-05-31
5th row2023-05-31

Common Values

ValueCountFrequency (%)
2023-05-31 23
100.0%

Length

2023-07-10T14:57:09.654399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T14:57:09.835428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-31 23
100.0%

Interactions

2023-07-10T14:57:02.576114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:52.307836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:54.138759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:55.999243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:57.476908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:58.977922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:01.083107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:02.807136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:52.804952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:54.344306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:56.235878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:57.689081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:59.178439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:01.292227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:03.039515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:53.154277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:54.624492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:56.472411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:57.949166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:59.393101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:01.548188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:03.522251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:53.349364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:54.856809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:56.677191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:58.190506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:59.779522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:01.768026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:03.726907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:53.575980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:55.084380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:56.887594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:58.395421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:00.173296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:01.969436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:03.942394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:53.761445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:55.301721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:57.073535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:58.598686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:00.453006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:02.162976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:04.148921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:53.956813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:55.522270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:57.279339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:56:58.778361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:00.798218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T14:57:02.369727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-10T14:57:09.961672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
통반세대수인구_남여_외국인남여외국인
통1.0000.9490.9340.8430.8240.8280.301
반0.9491.0000.9320.8790.8640.8640.279
세대수0.9340.9321.0000.9380.9200.9130.365
인구_남여_외국인0.8430.8790.9381.0000.9970.9940.175
남0.8240.8640.9200.9971.0000.9980.107
여0.8280.8640.9130.9940.9981.0000.069
외국인0.3010.2790.3650.1750.1070.0691.000
2023-07-10T14:57:10.213146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
통반세대수인구_남여_외국인남여외국인
통1.0000.9370.8950.8040.7610.7960.045
반0.9371.0000.9120.8560.8220.8440.098
세대수0.8950.9121.0000.9480.9280.941-0.060
인구_남여_외국인0.8040.8560.9481.0000.9910.992-0.066
남0.7610.8220.9280.9911.0000.994-0.127
여0.7960.8440.9410.9920.9941.000-0.124
외국인0.0450.098-0.060-0.066-0.127-0.1241.000
2023-07-10T14:57:10.441361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
통반세대수인구_남여_외국인남여외국인
통1.0000.8300.7510.6390.5910.6310.016
반0.8301.0000.7940.7230.6920.7310.059
세대수0.7510.7941.0000.8180.7870.810-0.067
인구_남여_외국인0.6390.7230.8181.0000.9530.960-0.059
남0.5910.6920.7870.9531.0000.960-0.091
여0.6310.7310.8100.9600.9601.000-0.083
외국인0.0160.059-0.067-0.059-0.091-0.0831.000
2023-07-10T14:57:10.682285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
동명통반세대수인구_남여_외국인남여외국인
동명1.0001.0001.0001.0001.0001.0001.0001.000
통1.0001.0000.8280.7300.6900.4430.4590.341
반1.0000.8281.0000.7300.8470.7890.8850.508
세대수1.0000.7300.7301.0000.8520.7830.8820.815
인구_남여_외국인1.0000.6900.8470.8521.0000.9870.9790.000
남1.0000.4430.7890.7830.9871.0000.9670.000
여1.0000.4590.8850.8820.9790.9671.0000.000
외국인1.0000.3410.5080.8150.0000.0000.0001.000
2023-07-10T14:57:10.929307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
통반세대수인구_남여_외국인남여외국인동명
통1.0000.9370.8950.8040.7610.7960.0451.000
반0.9371.0000.9120.8560.8220.8440.0981.000
세대수0.8950.9121.0000.9480.9280.941-0.0601.000
인구_남여_외국인0.8040.8560.9481.0000.9910.992-0.0661.000
남0.7610.8220.9280.9911.0000.994-0.1271.000
여0.7960.8440.9410.9920.9941.000-0.1241.000
외국인0.0450.098-0.060-0.066-0.127-0.1241.0001.000
동명1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-07-10T14:57:04.456766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-10T14:57:04.851181image/svg+xmlMatplotlib v3.7.1, 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
0성당동4428710596225191086811512139대구광역시 달서구 총무과2023-05-31
1두류1.2동3321287551517173527678141대구광역시 달서구 총무과2023-05-31
2두류3동18104428485354093438656대구광역시 달서구 총무과2023-05-31
3본리동3119680142001896749977367대구광역시 달서구 총무과2023-05-31
4감삼동4531012412291121409214875145대구광역시 달서구 총무과2023-05-31
5죽전동2816167611291662996471146대구광역시 달서구 총무과2023-05-31
6장기동2218371221689380608391442대구광역시 달서구 총무과2023-05-31
7용산1동4029512395297741477914799196대구광역시 달서구 총무과2023-05-31
8용산2동3327511300274721362813622222대구광역시 달서구 총무과2023-05-31
9이곡1동292359164224421092410946572대구광역시 달서구 총무과2023-05-31
동명통반세대수인구_남여_외국인남여외국인담당부서기준일자
13월성2동3422082591591170967900915대구광역시 달서구 총무과2023-05-31
14진천동6544021427516512505326274324대구광역시 달서구 총무과2023-05-31
15유천동291881104233931166761720154대구광역시 달서구 총무과2023-05-31
16상인1동513681294734680168621773484대구광역시 달서구 총무과2023-05-31
17상인2동3622191721881692369459121대구광역시 달서구 총무과2023-05-31
18상인3동211256154106625042556159대구광역시 달서구 총무과2023-05-31
19도원동433631378634003163491757579대구광역시 달서구 총무과2023-05-31
20송현1동3321099661856490309129405대구광역시 달서구 총무과2023-05-31
21송현2동3724291931770982328950527대구광역시 달서구 총무과2023-05-31
22본동2315360301208957815983325대구광역시 달서구 총무과2023-05-31