Overview

Dataset statistics

Number of variables9
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory85.0 B

Variable types

Text1
Numeric7
DateTime1

Dataset

Description대구광역시 달서구_주민등록_월별인구현황_20190630
Author대구광역시 달서구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3074876&dataSetDetailId=30748761b57b96c3f5d3&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
동명 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 17:18:42.961309
Analysis finished2023-12-10 17:18:53.970088
Duration11.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-11T02:18:54.092837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.4545455
Min length3

Characters and Unicode

Total characters98
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row성당동
2nd row두류1·2동
3rd row두류3동
4th row본 리 동
5th row감 삼 동
ValueCountFrequency (%)
동 8
21.6%
본 2
 
5.4%
성당동 1
 
2.7%
신 1
 
2.7%
송현1동 1
 
2.7%
원 1
 
2.7%
도 1
 
2.7%
상인3동 1
 
2.7%
상인2동 1
 
2.7%
상인1동 1
 
2.7%
Other values (19) 19
51.4%
2023-12-11T02:18:54.536621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 22
22.4%
18
18.4%
1 6
 
6.1%
2 6
 
6.1%
성 3
 
3.1%
인 3
 
3.1%
상 3
 
3.1%
당 2
 
2.0%
송 2
 
2.0%
월 2
 
2.0%
Other values (22) 31
31.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
66.3%
Space Separator 18
 
18.4%
Decimal Number 14
 
14.3%
Other Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 22
33.8%
성 3
 
4.6%
인 3
 
4.6%
상 3
 
4.6%
당 2
 
3.1%
송 2
 
3.1%
월 2
 
3.1%
곡 2
 
3.1%
이 2
 
3.1%
산 2
 
3.1%
Other values (17) 22
33.8%
Decimal Number
ValueCountFrequency (%)
1 6
42.9%
2 6
42.9%
3 2
 
14.3%
Space Separator
ValueCountFrequency (%)
18
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
66.3%
Common 33
33.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
33.8%
성 3
 
4.6%
인 3
 
4.6%
상 3
 
4.6%
당 2
 
3.1%
송 2
 
3.1%
월 2
 
3.1%
곡 2
 
3.1%
이 2
 
3.1%
산 2
 
3.1%
Other values (17) 22
33.8%
Common
ValueCountFrequency (%)
18
54.5%
1 6
 
18.2%
2 6
 
18.2%
3 2
 
6.1%
· 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
66.3%
ASCII 32
32.7%
None 1
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 22
33.8%
성 3
 
4.6%
인 3
 
4.6%
상 3
 
4.6%
당 2
 
3.1%
송 2
 
3.1%
월 2
 
3.1%
곡 2
 
3.1%
이 2
 
3.1%
산 2
 
3.1%
Other values (17) 22
33.8%
ASCII
ValueCountFrequency (%)
18
56.2%
1 6
 
18.8%
2 6
 
18.8%
3 2
 
6.2%
None
ValueCountFrequency (%)
· 1
100.0%

통
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.954545
Minimum16
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T02:18:54.740663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile21
Q127.5
median33.5
Q342
95-th percentile52.9
Maximum84
Range68
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation14.538872
Coefficient of variation (CV)0.40436811
Kurtosis4.7653783
Mean35.954545
Median Absolute Deviation (MAD)8
Skewness1.6995349
Sum791
Variance211.37879
MonotonicityNot monotonic
2023-12-11T02:18:54.927830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
33 3
13.6%
44 2
 
9.1%
29 2
 
9.1%
39 2
 
9.1%
21 2
 
9.1%
51 1
 
4.5%
22 1
 
4.5%
37 1
 
4.5%
43 1
 
4.5%
36 1
 
4.5%
Other values (6) 6
27.3%
ValueCountFrequency (%)
16 1
 
4.5%
21 2
9.1%
22 1
 
4.5%
23 1
 
4.5%
27 1
 
4.5%
29 2
9.1%
33 3
13.6%
34 1
 
4.5%
36 1
 
4.5%
37 1
 
4.5%
ValueCountFrequency (%)
84 1
 
4.5%
53 1
 
4.5%
51 1
 
4.5%
44 2
9.1%
43 1
 
4.5%
39 2
9.1%
37 1
 
4.5%
36 1
 
4.5%
34 1
 
4.5%
33 3
13.6%

반
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.31818
Minimum95
Maximum542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T02:18:55.175472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile125.1
Q1197.25
median227.5
Q3287.75
95-th percentile367.85
Maximum542
Range447
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation99.998755
Coefficient of variation (CV)0.39948658
Kurtosis2.1477691
Mean250.31818
Median Absolute Deviation (MAD)60
Skewness1.1113802
Sum5507
Variance9999.7511
MonotonicityNot monotonic
2023-12-11T02:18:55.402932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
287 1
 
4.5%
320 1
 
4.5%
146 1
 
4.5%
242 1
 
4.5%
210 1
 
4.5%
363 1
 
4.5%
124 1
 
4.5%
219 1
 
4.5%
368 1
 
4.5%
542 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
95 1
4.5%
124 1
4.5%
146 1
4.5%
164 1
4.5%
165 1
4.5%
195 1
4.5%
204 1
4.5%
208 1
4.5%
210 1
4.5%
219 1
4.5%
ValueCountFrequency (%)
542 1
4.5%
368 1
4.5%
365 1
4.5%
363 1
4.5%
320 1
4.5%
288 1
4.5%
287 1
4.5%
275 1
4.5%
272 1
4.5%
242 1
4.5%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10474.364
Minimum4372
Maximum27740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T02:18:55.636042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4372
5-th percentile6101.3
Q17389.75
median9189
Q311972.75
95-th percentile15889.7
Maximum27740
Range23368
Interquartile range (IQR)4583

Descriptive statistics

Standard deviation4871.4186
Coefficient of variation (CV)0.46508014
Kurtosis6.9720253
Mean10474.364
Median Absolute Deviation (MAD)2369.5
Skewness2.2028542
Sum230436
Variance23730719
MonotonicityNot monotonic
2023-12-11T02:18:55.825097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10873 1
 
4.5%
13641 1
 
4.5%
6278 1
 
4.5%
9292 1
 
4.5%
9792 1
 
4.5%
13710 1
 
4.5%
6092 1
 
4.5%
8757 1
 
4.5%
13965 1
 
4.5%
27740 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
4372 1
4.5%
6092 1
4.5%
6278 1
4.5%
6653 1
4.5%
6687 1
4.5%
7019 1
4.5%
8502 1
4.5%
8549 1
4.5%
8631 1
4.5%
8757 1
4.5%
ValueCountFrequency (%)
27740 1
4.5%
15991 1
4.5%
13965 1
4.5%
13710 1
4.5%
13641 1
4.5%
12155 1
4.5%
11426 1
4.5%
11225 1
4.5%
10873 1
4.5%
9792 1
4.5%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26384.818
Minimum9565
Maximum77259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T02:18:56.064067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9565
5-th percentile12092.05
Q117431.25
median21396
Q332250.25
95-th percentile44395.55
Maximum77259
Range67694
Interquartile range (IQR)14819

Descriptive statistics

Standard deviation14879.514
Coefficient of variation (CV)0.56394227
Kurtosis5.6695317
Mean26384.818
Median Absolute Deviation (MAD)6370.5
Skewness2.0435539
Sum580466
Variance2.2139995 × 108
MonotonicityNot monotonic
2023-12-11T02:18:56.305010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
24969 1
 
4.5%
44585 1
 
4.5%
13993 1
 
4.5%
19848 1
 
4.5%
20154 1
 
4.5%
37879 1
 
4.5%
11992 1
 
4.5%
20072 1
 
4.5%
40796 1
 
4.5%
77259 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
9565 1
4.5%
11992 1
4.5%
13993 1
4.5%
15309 1
4.5%
16065 1
4.5%
17428 1
4.5%
17441 1
4.5%
18323 1
4.5%
19848 1
4.5%
20072 1
4.5%
ValueCountFrequency (%)
77259 1
4.5%
44585 1
4.5%
40796 1
4.5%
37879 1
4.5%
35884 1
4.5%
32783 1
4.5%
30652 1
4.5%
28050 1
4.5%
24969 1
4.5%
24781 1
4.5%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12852.545
Minimum4615
Maximum37956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T02:18:56.593028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4615
5-th percentile5815.6
Q18342.75
median10564.5
Q316117.25
95-th percentile21676.2
Maximum37956
Range33341
Interquartile range (IQR)7774.5

Descriptive statistics

Standard deviation7318.4272
Coefficient of variation (CV)0.56941462
Kurtosis5.7746779
Mean12852.545
Median Absolute Deviation (MAD)3109.5
Skewness2.0597794
Sum282756
Variance53559377
MonotonicityNot monotonic
2023-12-11T02:18:56.843805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
12154 1
 
4.5%
21762 1
 
4.5%
6891 1
 
4.5%
9249 1
 
4.5%
9989 1
 
4.5%
18413 1
 
4.5%
5759 1
 
4.5%
9925 1
 
4.5%
20046 1
 
4.5%
37956 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
4615 1
4.5%
5759 1
4.5%
6891 1
4.5%
7531 1
4.5%
7796 1
4.5%
8319 1
4.5%
8414 1
4.5%
8589 1
4.5%
9249 1
4.5%
9925 1
4.5%
ValueCountFrequency (%)
37956 1
4.5%
21762 1
4.5%
20046 1
4.5%
18413 1
4.5%
16517 1
4.5%
16369 1
4.5%
15362 1
4.5%
13750 1
4.5%
12210 1
4.5%
12154 1
4.5%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13115.773
Minimum4886
Maximum38945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T02:18:57.116745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4886
5-th percentile6206.05
Q18478.75
median10632
Q315530.75
95-th percentile22556.45
Maximum38945
Range34059
Interquartile range (IQR)7052

Descriptive statistics

Standard deviation7491.7078
Coefficient of variation (CV)0.57119835
Kurtosis5.9732698
Mean13115.773
Median Absolute Deviation (MAD)3287.5
Skewness2.1240171
Sum288547
Variance56125685
MonotonicityNot monotonic
2023-12-11T02:18:57.389967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
12695 1
 
4.5%
22656 1
 
4.5%
6967 1
 
4.5%
9983 1
 
4.5%
10037 1
 
4.5%
19385 1
 
4.5%
6166 1
 
4.5%
10034 1
 
4.5%
20665 1
 
4.5%
38945 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
4886 1
4.5%
6166 1
4.5%
6967 1
4.5%
7598 1
4.5%
8141 1
4.5%
8431 1
4.5%
8622 1
4.5%
8883 1
4.5%
9983 1
4.5%
10034 1
4.5%
ValueCountFrequency (%)
38945 1
4.5%
22656 1
4.5%
20665 1
4.5%
19385 1
4.5%
16253 1
4.5%
15681 1
4.5%
15080 1
4.5%
14173 1
4.5%
12695 1
4.5%
12039 1
4.5%

외국인수
Real number (ℝ)

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.5
Minimum64
Maximum3686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T02:18:57.675282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile67.7
Q1121.75
median164
Q3393.25
95-th percentile1095.75
Maximum3686
Range3622
Interquartile range (IQR)271.5

Descriptive statistics

Standard deviation770.54073
Coefficient of variation (CV)1.8500378
Kurtosis17.136923
Mean416.5
Median Absolute Deviation (MAD)81
Skewness4.0006913
Sum9163
Variance593733.02
MonotonicityNot monotonic
2023-12-11T02:18:58.003211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
128 2
 
9.1%
120 1
 
4.5%
135 1
 
4.5%
616 1
 
4.5%
81 1
 
4.5%
67 1
 
4.5%
113 1
 
4.5%
85 1
 
4.5%
358 1
 
4.5%
1121 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
64 1
4.5%
67 1
4.5%
81 1
4.5%
85 1
4.5%
113 1
4.5%
120 1
4.5%
127 1
4.5%
128 2
9.1%
135 1
4.5%
161 1
4.5%
ValueCountFrequency (%)
3686 1
4.5%
1121 1
4.5%
616 1
4.5%
532 1
4.5%
408 1
4.5%
405 1
4.5%
358 1
4.5%
271 1
4.5%
210 1
4.5%
180 1
4.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum2019-06-30 00:00:00
Maximum2019-06-30 00:00:00
2023-12-11T02:18:58.294323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:58.547491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T02:18:52.488700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:43.524968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:45.127643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:46.525232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:47.797181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:49.218692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:50.622886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:52.652321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:43.762429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:45.323717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:46.718670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:47.975996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:49.413425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:51.451439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:52.808375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:44.005108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:45.501750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:46.890166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:48.163268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:49.600858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:51.624720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:52.961436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:44.224494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:45.674085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:47.050518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:48.358447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:49.784283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:51.801635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:53.139875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:44.447322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:45.894061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:47.234911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:48.574079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:50.005256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:51.983405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:53.308750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:44.668434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:46.130376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:47.422313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:48.778021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:50.205728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:52.144200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:53.474831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:44.896413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:46.326107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:47.609834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:49.000472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:50.417364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:18:52.304388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:18:58.750452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명통반세대수계남여외국인수
동명1.0001.0001.0001.0001.0001.0001.0001.000
통1.0001.0000.8490.8730.8170.8170.8230.000
반1.0000.8491.0000.8770.8110.8110.7990.683
세대수1.0000.8730.8771.0000.6710.6710.7260.473
계1.0000.8170.8110.6711.0001.0000.9990.000
남1.0000.8170.8110.6711.0001.0000.9990.000
여1.0000.8230.7990.7260.9990.9991.0000.000
외국인수1.0000.0000.6830.4730.0000.0000.0001.000
2023-12-11T02:18:59.002594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통반세대수계남여외국인수
통1.0000.9500.9290.8690.8410.8630.075
반0.9501.0000.9640.9400.9270.9370.145
세대수0.9290.9641.0000.9620.9510.9570.090
계0.8690.9400.9621.0000.9940.9990.116
남0.8410.9270.9510.9941.0000.9930.124
여0.8630.9370.9570.9990.9931.0000.103
외국인수0.0750.1450.0900.1160.1240.1031.000

Missing values

2023-12-11T02:18:53.678383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:18:53.891395image/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

동명통반세대수계남여외국인수데이터기준일자
0성당동44287108732496912154126951202019-06-30
1두류1·2동33208854916065779681411282019-06-30
2두류3동16954372956546154886642019-06-30
3본 리 동2919586312263811140112272712019-06-30
4감 삼 동39272114262805013750141731272019-06-30
5죽 전 동27165701915309753175981802019-06-30
6장 기 동21164668717441841486224052019-06-30
7용산1동39288121553278316369162531612019-06-30
8용산2동33275112253065215362150802102019-06-30
9이곡1동2923590862478112210120395322019-06-30
동명통반세대수계남여외국인수데이터기준일자
12월성1동44320136414458521762226561672019-06-30
13월성2동342208502183238319888311212019-06-30
14진 천 동84542277407725937956389453582019-06-30
15상인1동5136813965407962004620665852019-06-30
16상인2동362198757200729925100341132019-06-30
17상인3동2112460921199257596166672019-06-30
18도 원 동4336313710378791841319385812019-06-30
19송현1동332109792201549989100371282019-06-30
20송현2동37242929219848924999836162019-06-30
21본 동22146627813993689169671352019-06-30