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
Categorical1

Dataset

Description대구광역시 달서구_주민등록_월별인구현황_20190531
Author대구광역시 달서구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3074876&dataSetDetailId=307487618b8842597313&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 started2023-09-29 01:12:43.878299
Analysis finished2023-09-29 01:13:21.242116
Duration37.36 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
2023-09-29T01:13:21.483511image/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%
2023-09-29T01:13:22.877486image/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
2023-09-29T01:13:23.787311image/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
2023-09-29T01:13:24.369358image/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
2023-09-29T01:13:24.983812image/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
2023-09-29T01:13:25.555558image/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%
Mean10459.727
Minimum4392
Maximum27652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-09-29T01:13:26.273741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4392
5-th percentile6099.75
Q17403
median9187
Q311954.75
95-th percentile15880.75
Maximum27652
Range23260
Interquartile range (IQR)4551.75

Descriptive statistics

Standard deviation4857.5378
Coefficient of variation (CV)0.46440386
Kurtosis6.9184792
Mean10459.727
Median Absolute Deviation (MAD)2378.5
Skewness2.194172
Sum230114
Variance23595673
MonotonicityNot monotonic
2023-09-29T01:13:27.305108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10860 1
 
4.5%
13623 1
 
4.5%
6304 1
 
4.5%
9314 1
 
4.5%
9773 1
 
4.5%
13695 1
 
4.5%
6089 1
 
4.5%
8733 1
 
4.5%
13976 1
 
4.5%
27652 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
4392 1
4.5%
6089 1
4.5%
6304 1
4.5%
6563 1
4.5%
6645 1
4.5%
7037 1
4.5%
8501 1
4.5%
8549 1
4.5%
8630 1
4.5%
8733 1
4.5%
ValueCountFrequency (%)
27652 1
4.5%
15981 1
4.5%
13976 1
4.5%
13695 1
4.5%
13623 1
4.5%
12139 1
4.5%
11402 1
4.5%
11196 1
4.5%
10860 1
4.5%
9773 1
4.5%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26380
Minimum9613
Maximum77067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-09-29T01:13:28.151829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9613
5-th percentile12104.8
Q117245.75
median21398.5
Q332276.25
95-th percentile44388.7
Maximum77067
Range67454
Interquartile range (IQR)15030.5

Descriptive statistics

Standard deviation14855.641
Coefficient of variation (CV)0.5631403
Kurtosis5.6008341
Mean26380
Median Absolute Deviation (MAD)6334.5
Skewness2.0326849
Sum580360
Variance2.2069007 × 108
MonotonicityNot monotonic
2023-09-29T01:13:28.691812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
24978 1
 
4.5%
44574 1
 
4.5%
14058 1
 
4.5%
19709 1
 
4.5%
20167 1
 
4.5%
37943 1
 
4.5%
12002 1
 
4.5%
20073 1
 
4.5%
40868 1
 
4.5%
77067 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
9613 1
4.5%
12002 1
4.5%
14058 1
4.5%
15373 1
4.5%
16074 1
4.5%
17171 1
4.5%
17470 1
4.5%
18382 1
4.5%
19709 1
4.5%
20073 1
4.5%
ValueCountFrequency (%)
77067 1
4.5%
44574 1
4.5%
40868 1
4.5%
37943 1
4.5%
35942 1
4.5%
32816 1
4.5%
30657 1
4.5%
28042 1
4.5%
24978 1
4.5%
24751 1
4.5%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12855.136
Minimum4620
Maximum37854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-09-29T01:13:29.668000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4620
5-th percentile5815.4
Q18299.25
median10570.5
Q316132.75
95-th percentile21681.25
Maximum37854
Range33234
Interquartile range (IQR)7833.5

Descriptive statistics

Standard deviation7305.3453
Coefficient of variation (CV)0.56828221
Kurtosis5.6964832
Mean12855.136
Median Absolute Deviation (MAD)3096
Skewness2.0460413
Sum282813
Variance53368070
MonotonicityNot monotonic
2023-09-29T01:13:30.392073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
12169 1
 
4.5%
21765 1
 
4.5%
6925 1
 
4.5%
9282 1
 
4.5%
9996 1
 
4.5%
18443 1
 
4.5%
5757 1
 
4.5%
9933 1
 
4.5%
20090 1
 
4.5%
37854 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
4620 1
4.5%
5757 1
4.5%
6925 1
4.5%
7562 1
4.5%
7802 1
4.5%
8283 1
4.5%
8348 1
4.5%
8598 1
4.5%
9282 1
4.5%
9933 1
4.5%
ValueCountFrequency (%)
37854 1
4.5%
21765 1
4.5%
20090 1
4.5%
18443 1
4.5%
16554 1
4.5%
16392 1
4.5%
15355 1
4.5%
13754 1
4.5%
12186 1
4.5%
12169 1
4.5%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13115.864
Minimum4929
Maximum38855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-09-29T01:13:31.116415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4929
5-th percentile6219.9
Q18462.75
median10626
Q315545.75
95-th percentile22539.8
Maximum38855
Range33926
Interquartile range (IQR)7083

Descriptive statistics

Standard deviation7476.0111
Coefficient of variation (CV)0.56999762
Kurtosis5.9251662
Mean13115.864
Median Absolute Deviation (MAD)3264
Skewness2.1170212
Sum288549
Variance55890741
MonotonicityNot monotonic
2023-09-29T01:13:31.955099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
12682 1
 
4.5%
22637 1
 
4.5%
6997 1
 
4.5%
10003 1
 
4.5%
10042 1
 
4.5%
19419 1
 
4.5%
6179 1
 
4.5%
10029 1
 
4.5%
20693 1
 
4.5%
38855 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
4929 1
4.5%
6179 1
4.5%
6997 1
4.5%
7635 1
4.5%
8143 1
4.5%
8454 1
4.5%
8489 1
4.5%
8914 1
4.5%
10003 1
4.5%
10029 1
4.5%
ValueCountFrequency (%)
38855 1
4.5%
22637 1
4.5%
20693 1
4.5%
19419 1
4.5%
16264 1
4.5%
15697 1
4.5%
15092 1
4.5%
14163 1
4.5%
12682 1
4.5%
12023 1
4.5%

외국인수
Real number (ℝ)

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean409
Minimum64
Maximum3691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-09-29T01:13:32.575030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile66.75
Q1125.5
median166
Q3388.75
95-th percentile1091.1
Maximum3691
Range3627
Interquartile range (IQR)263.25

Descriptive statistics

Standard deviation770.198
Coefficient of variation (CV)1.8831247
Kurtosis17.48976
Mean409
Median Absolute Deviation (MAD)83
Skewness4.0557098
Sum8998
Variance593204.95
MonotonicityNot monotonic
2023-09-29T01:13:33.275681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
129 2
 
9.1%
127 1
 
4.5%
136 1
 
4.5%
424 1
 
4.5%
81 1
 
4.5%
66 1
 
4.5%
111 1
 
4.5%
85 1
 
4.5%
358 1
 
4.5%
1120 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
64 1
4.5%
66 1
4.5%
81 1
4.5%
85 1
4.5%
111 1
4.5%
125 1
4.5%
127 1
4.5%
129 2
9.1%
136 1
4.5%
160 1
4.5%
ValueCountFrequency (%)
3691 1
4.5%
1120 1
4.5%
542 1
4.5%
424 1
4.5%
418 1
4.5%
399 1
4.5%
358 1
4.5%
275 1
4.5%
210 1
4.5%
176 1
4.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2019-05-31
22 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-05-31
2nd row2019-05-31
3rd row2019-05-31
4th row2019-05-31
5th row2019-05-31

Common Values

ValueCountFrequency (%)
2019-05-31 22
100.0%

Length

2023-09-29T01:13:34.185304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-29T01:13:34.846539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-05-31 22
100.0%

Interactions

2023-09-29T01:13:14.650891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:51.130471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:55.986324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:00.094403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:03.340347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:06.943924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:10.853878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:15.669991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:51.837301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:56.715397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:00.911315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:03.721700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:07.466182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:11.494030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:16.858222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:52.292274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:57.154711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:01.290759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:04.093086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:07.947649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:11.902443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:17.732990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:52.882286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:57.544297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:01.623428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:04.719822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:08.425016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:12.346684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:18.286103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:53.723326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:58.080108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:02.121282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:05.550925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:08.919559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:12.680671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:18.899301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:54.601833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:58.586596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:02.462778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:05.983835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:09.950653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:13.144026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:19.418730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:55.226693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:12:59.483889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:02.840094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:06.388205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:10.317094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:13:13.809179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-29T01:13:35.252840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명통반세대수계남여외국인수
동명1.0001.0001.0001.0001.0001.0001.0001.000
통1.0001.0000.8490.8730.8170.8170.8230.152
반1.0000.8491.0000.8770.8110.8110.7990.000
세대수1.0000.8730.8771.0000.6710.6710.7260.266
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
외국인수1.0000.1520.0000.2660.0000.0000.0001.000
2023-09-29T01:13:36.054599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통반세대수계남여외국인수
통1.0000.9500.9320.8720.8520.8630.071
반0.9501.0000.9670.9440.9350.9370.146
세대수0.9320.9671.0000.9620.9560.9560.088
계0.8720.9440.9621.0000.9980.9980.120
남0.8520.9350.9560.9981.0000.9940.132
여0.8630.9370.9560.9980.9941.0000.107
외국인수0.0710.1460.0880.1200.1320.1071.000

Missing values

2023-09-29T01:13:20.110704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-29T01:13:20.928773image/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
0성당동44287108602497812169126821272019-05-31
1두류1·2동33208854916074780281431292019-05-31
2두류3동16954392961346204929642019-05-31
3본 리 동2919586302263011145112102752019-05-31
4감 삼 동39272114022804213754141631252019-05-31
5죽 전 동27165703715373756276351762019-05-31
6장 기 동21164656317171828384893992019-05-31
7용산1동39288121393281616392162641602019-05-31
8용산2동33275111963065715355150922102019-05-31
9이곡1동2923590602475112186120235422019-05-31
동명통반세대수계남여외국인수데이터기준일자
12월성1동44320136234457421765226371722019-05-31
13월성2동342208501183828348891411202019-05-31
14진 천 동84542276527706737854388553582019-05-31
15상인1동5136813976408682009020693852019-05-31
16상인2동362198733200739933100291112019-05-31
17상인3동2112460891200257576179662019-05-31
18도 원 동4336313695379431844319419812019-05-31
19송현1동332109773201679996100421292019-05-31
20송현2동372429314197099282100034242019-05-31
21본 동22146630414058692569971362019-05-31