Overview

Dataset statistics

Number of variables13
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory120.5 B

Variable types

Text1
Numeric9
Categorical2
DateTime1

Dataset

Description세종특별자치시 지역별 세대원수별 세대수 정보를 제공합니다.데이터는 읍면동, 1인, 2인, 3인, 4인, 5인, 6인, 7인, 8인, 9인, 10인이상 으로 구성되어 있습니다.
Author세종특별자치시
URLhttps://www.data.go.kr/data/15064337/fileData.do

Alerts

데이터기준일 has constant value ""Constant
계 is highly overall correlated with 1인 and 8 other fieldsHigh correlation
1인 is highly overall correlated with 계 and 6 other fieldsHigh correlation
2인 is highly overall correlated with 계 and 8 other fieldsHigh correlation
3인 is highly overall correlated with 계 and 8 other fieldsHigh correlation
4인 is highly overall correlated with 계 and 8 other fieldsHigh correlation
5인 is highly overall correlated with 계 and 8 other fieldsHigh correlation
6인 is highly overall correlated with 계 and 7 other fieldsHigh correlation
7인 is highly overall correlated with 계 and 8 other fieldsHigh correlation
8인 is highly overall correlated with 계 and 7 other fieldsHigh correlation
9인 is highly overall correlated with 계 and 6 other fieldsHigh correlation
읍면동 has unique valuesUnique
계 has unique valuesUnique
1인 has unique valuesUnique
2인 has unique valuesUnique
3인 has unique valuesUnique
4인 has unique valuesUnique
5인 has unique valuesUnique
7인 has 1 (4.2%) zerosZeros
8인 has 6 (25.0%) zerosZeros

Reproduction

Analysis started2024-04-21 01:29:39.535287
Analysis finished2024-04-21 01:29:48.843434
Duration9.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-04-21T10:29:48.961291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0416667
Min length3

Characters and Unicode

Total characters73
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row조치원읍
2nd row연기면
3rd row연동면
4th row부강면
5th row금남면
ValueCountFrequency (%)
조치원읍 1
 
4.2%
연기면 1
 
4.2%
대평동 1
 
4.2%
보람동 1
 
4.2%
반곡동 1
 
4.2%
소담동 1
 
4.2%
고운동 1
 
4.2%
종촌동 1
 
4.2%
아름동 1
 
4.2%
해밀동 1
 
4.2%
Other values (14) 14
58.3%
2024-04-21T10:29:49.241652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 16
21.9%
면 9
 
12.3%
연 3
 
4.1%
담 2
 
2.7%
정 2
 
2.7%
소 2
 
2.7%
전 2
 
2.7%
종 1
 
1.4%
어 1
 
1.4%
진 1
 
1.4%
Other values (34) 34
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 16
21.9%
면 9
 
12.3%
연 3
 
4.1%
담 2
 
2.7%
정 2
 
2.7%
소 2
 
2.7%
전 2
 
2.7%
종 1
 
1.4%
어 1
 
1.4%
진 1
 
1.4%
Other values (34) 34
46.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 16
21.9%
면 9
 
12.3%
연 3
 
4.1%
담 2
 
2.7%
정 2
 
2.7%
소 2
 
2.7%
전 2
 
2.7%
종 1
 
1.4%
어 1
 
1.4%
진 1
 
1.4%
Other values (34) 34
46.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 16
21.9%
면 9
 
12.3%
연 3
 
4.1%
담 2
 
2.7%
정 2
 
2.7%
소 2
 
2.7%
전 2
 
2.7%
종 1
 
1.4%
어 1
 
1.4%
진 1
 
1.4%
Other values (34) 34
46.6%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6720.9583
Minimum1123
Maximum20317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-21T10:29:49.353850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1123
5-th percentile1512.2
Q13573.25
median5587
Q39861.75
95-th percentile12700.05
Maximum20317
Range19194
Interquartile range (IQR)6288.5

Descriptive statistics

Standard deviation4558.4551
Coefficient of variation (CV)0.67824481
Kurtosis1.8857703
Mean6720.9583
Median Absolute Deviation (MAD)2733.5
Skewness1.1825671
Sum161303
Variance20779513
MonotonicityNot monotonic
2024-04-21T10:29:49.456412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20317 1
 
4.2%
10242 1
 
4.2%
10826 1
 
4.2%
4392 1
 
4.2%
6972 1
 
4.2%
12060 1
 
4.2%
8423 1
 
4.2%
12813 1
 
4.2%
10785 1
 
4.2%
7939 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1123 1
4.2%
1490 1
4.2%
1638 1
4.2%
1865 1
4.2%
2956 1
4.2%
3115 1
4.2%
3726 1
4.2%
4107 1
4.2%
4392 1
4.2%
4508 1
4.2%
ValueCountFrequency (%)
20317 1
4.2%
12813 1
4.2%
12060 1
4.2%
10826 1
4.2%
10785 1
4.2%
10242 1
4.2%
9735 1
4.2%
8423 1
4.2%
7939 1
4.2%
6972 1
4.2%

1인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2330.0417
Minimum555
Maximum9873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-21T10:29:49.574653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum555
5-th percentile899.3
Q11221.25
median2083
Q32701.5
95-th percentile4142.4
Maximum9873
Range9318
Interquartile range (IQR)1480.25

Descriptive statistics

Standard deviation1847.5688
Coefficient of variation (CV)0.79293382
Kurtosis12.465819
Mean2330.0417
Median Absolute Deviation (MAD)806
Skewness3.142505
Sum55921
Variance3413510.7
MonotonicityNot monotonic
2024-04-21T10:29:49.685910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
9873 1
 
4.2%
3391 1
 
4.2%
2766 1
 
4.2%
1099 1
 
4.2%
1621 1
 
4.2%
4275 1
 
4.2%
2246 1
 
4.2%
2443 1
 
4.2%
2912 1
 
4.2%
1272 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
555 1
4.2%
899 1
4.2%
901 1
4.2%
1076 1
4.2%
1099 1
4.2%
1180 1
4.2%
1235 1
4.2%
1272 1
4.2%
1621 1
4.2%
1629 1
4.2%
ValueCountFrequency (%)
9873 1
4.2%
4275 1
4.2%
3391 1
4.2%
2912 1
4.2%
2884 1
4.2%
2766 1
4.2%
2680 1
4.2%
2593 1
4.2%
2554 1
4.2%
2443 1
4.2%

2인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1465.2917
Minimum315
Maximum4589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-21T10:29:49.797132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum315
5-th percentile359.75
Q1741.75
median1227.5
Q32001.5
95-th percentile2862.65
Maximum4589
Range4274
Interquartile range (IQR)1259.75

Descriptive statistics

Standard deviation1004.8948
Coefficient of variation (CV)0.68579847
Kurtosis2.6199226
Mean1465.2917
Median Absolute Deviation (MAD)635.5
Skewness1.4025179
Sum35167
Variance1009813.5
MonotonicityNot monotonic
2024-04-21T10:29:49.902842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4589 1
 
4.2%
2126 1
 
4.2%
2385 1
 
4.2%
1123 1
 
4.2%
1490 1
 
4.2%
2549 1
 
4.2%
1949 1
 
4.2%
2918 1
 
4.2%
2374 1
 
4.2%
1566 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
315 1
4.2%
341 1
4.2%
466 1
4.2%
499 1
4.2%
678 1
4.2%
693 1
4.2%
758 1
4.2%
865 1
4.2%
866 1
4.2%
929 1
4.2%
ValueCountFrequency (%)
4589 1
4.2%
2918 1
4.2%
2549 1
4.2%
2385 1
4.2%
2374 1
4.2%
2126 1
4.2%
1960 1
4.2%
1949 1
4.2%
1566 1
4.2%
1490 1
4.2%

3인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1284.3333
Minimum134
Maximum3130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-21T10:29:50.206276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum134
5-th percentile148.75
Q1337.25
median1033.5
Q32103.5
95-th percentile2710.35
Maximum3130
Range2996
Interquartile range (IQR)1766.25

Descriptive statistics

Standard deviation978.01123
Coefficient of variation (CV)0.7614933
Kurtosis-1.356591
Mean1284.3333
Median Absolute Deviation (MAD)827
Skewness0.32989875
Sum30824
Variance956505.97
MonotonicityNot monotonic
2024-04-21T10:29:50.319152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2748 1
 
4.2%
2041 1
 
4.2%
2467 1
 
4.2%
1017 1
 
4.2%
1640 1
 
4.2%
2497 1
 
4.2%
1824 1
 
4.2%
3130 1
 
4.2%
2387 1
 
4.2%
2027 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
134 1
4.2%
148 1
4.2%
153 1
4.2%
170 1
4.2%
307 1
4.2%
320 1
4.2%
343 1
4.2%
429 1
4.2%
528 1
4.2%
778 1
4.2%
ValueCountFrequency (%)
3130 1
4.2%
2748 1
4.2%
2497 1
4.2%
2467 1
4.2%
2387 1
4.2%
2291 1
4.2%
2041 1
4.2%
2027 1
4.2%
1824 1
4.2%
1640 1
4.2%

4인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1272.7917
Minimum62
Maximum3354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-21T10:29:50.423909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile72.5
Q1185.75
median1049
Q32179.75
95-th percentile2588.95
Maximum3354
Range3292
Interquartile range (IQR)1994

Descriptive statistics

Standard deviation1059.1815
Coefficient of variation (CV)0.83217194
Kurtosis-1.370341
Mean1272.7917
Median Absolute Deviation (MAD)914.5
Skewness0.25918201
Sum30547
Variance1121865.5
MonotonicityNot monotonic
2024-04-21T10:29:50.537126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2206 1
 
4.2%
2119 1
 
4.2%
2566 1
 
4.2%
902 1
 
4.2%
1766 1
 
4.2%
2171 1
 
4.2%
1925 1
 
4.2%
3354 1
 
4.2%
2488 1
 
4.2%
2485 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
62 1
4.2%
71 1
4.2%
81 1
4.2%
98 1
4.2%
171 1
4.2%
173 1
4.2%
190 1
4.2%
225 1
4.2%
232 1
4.2%
885 1
4.2%
ValueCountFrequency (%)
3354 1
4.2%
2593 1
4.2%
2566 1
4.2%
2488 1
4.2%
2485 1
4.2%
2206 1
4.2%
2171 1
4.2%
2119 1
4.2%
1925 1
4.2%
1766 1
4.2%

5인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.875
Minimum16
Maximum787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-21T10:29:50.650728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile27.75
Q163.75
median303.5
Q3488.25
95-th percentile683.95
Maximum787
Range771
Interquartile range (IQR)424.5

Descriptive statistics

Standard deviation238.58939
Coefficient of variation (CV)0.78002252
Kurtosis-1.1235773
Mean305.875
Median Absolute Deviation (MAD)224
Skewness0.30407375
Sum7341
Variance56924.897
MonotonicityNot monotonic
2024-04-21T10:29:50.760586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
706 1
 
4.2%
485 1
 
4.2%
559 1
 
4.2%
209 1
 
4.2%
383 1
 
4.2%
479 1
 
4.2%
408 1
 
4.2%
787 1
 
4.2%
539 1
 
4.2%
498 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
16 1
4.2%
27 1
4.2%
32 1
4.2%
35 1
4.2%
56 1
4.2%
60 1
4.2%
65 1
4.2%
82 1
4.2%
99 1
4.2%
203 1
4.2%
ValueCountFrequency (%)
787 1
4.2%
706 1
4.2%
559 1
4.2%
539 1
4.2%
530 1
4.2%
498 1
4.2%
485 1
4.2%
479 1
4.2%
476 1
4.2%
408 1
4.2%

6인
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.875
Minimum2
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-21T10:29:50.872239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q121.25
median43
Q365
95-th percentile133.6
Maximum148
Range146
Interquartile range (IQR)43.75

Descriptive statistics

Standard deviation38.048382
Coefficient of variation (CV)0.79474426
Kurtosis2.1493312
Mean47.875
Median Absolute Deviation (MAD)22
Skewness1.3240534
Sum1149
Variance1447.6793
MonotonicityNot monotonic
2024-04-21T10:29:50.986648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
65 4
16.7%
5 2
 
8.3%
69 2
 
8.3%
148 1
 
4.2%
66 1
 
4.2%
35 1
 
4.2%
55 1
 
4.2%
51 1
 
4.2%
145 1
 
4.2%
40 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
2 1
4.2%
5 2
8.3%
9 1
4.2%
11 1
4.2%
19 1
4.2%
22 1
4.2%
25 1
4.2%
28 1
4.2%
35 1
4.2%
39 1
4.2%
ValueCountFrequency (%)
148 1
 
4.2%
145 1
 
4.2%
69 2
8.3%
66 1
 
4.2%
65 4
16.7%
55 1
 
4.2%
51 1
 
4.2%
46 1
 
4.2%
40 1
 
4.2%
39 1
 
4.2%

7인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.958333
Minimum0
Maximum37
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-21T10:29:51.096610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.15
Q15.75
median9
Q314.75
95-th percentile22
Maximum37
Range37
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.1051737
Coefficient of variation (CV)0.73963562
Kurtosis3.3909557
Mean10.958333
Median Absolute Deviation (MAD)4
Skewness1.5704456
Sum263
Variance65.693841
MonotonicityNot monotonic
2024-04-21T10:29:51.204095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
6 3
12.5%
17 3
12.5%
4 2
 
8.3%
7 2
 
8.3%
9 2
 
8.3%
5 2
 
8.3%
22 2
 
8.3%
37 1
 
4.2%
0 1
 
4.2%
3 1
 
4.2%
Other values (5) 5
20.8%
ValueCountFrequency (%)
0 1
 
4.2%
3 1
 
4.2%
4 2
8.3%
5 2
8.3%
6 3
12.5%
7 2
8.3%
9 2
8.3%
10 1
 
4.2%
11 1
 
4.2%
12 1
 
4.2%
ValueCountFrequency (%)
37 1
 
4.2%
22 2
8.3%
17 3
12.5%
14 1
 
4.2%
13 1
 
4.2%
12 1
 
4.2%
11 1
 
4.2%
10 1
 
4.2%
9 2
8.3%
7 2
8.3%

8인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2916667
Minimum0
Maximum8
Zeros6
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-21T10:29:51.322266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2
Q34
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.136213
Coefficient of variation (CV)0.93216565
Kurtosis0.43941362
Mean2.2916667
Median Absolute Deviation (MAD)2
Skewness0.86881196
Sum55
Variance4.5634058
MonotonicityNot monotonic
2024-04-21T10:29:51.421444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6
25.0%
1 5
20.8%
2 3
12.5%
3 3
12.5%
5 3
12.5%
4 3
12.5%
8 1
 
4.2%
ValueCountFrequency (%)
0 6
25.0%
1 5
20.8%
2 3
12.5%
3 3
12.5%
4 3
12.5%
5 3
12.5%
8 1
 
4.2%
ValueCountFrequency (%)
8 1
 
4.2%
5 3
12.5%
4 3
12.5%
3 3
12.5%
2 3
12.5%
1 5
20.8%
0 6
25.0%

9인
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
1
11 
0
10 
6
 
1
3
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)12.5%

Sample

1st row6
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 11
45.8%
0 10
41.7%
6 1
 
4.2%
3 1
 
4.2%
5 1
 
4.2%

Length

2024-04-21T10:29:51.542277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:29:51.652992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 11
45.8%
0 10
41.7%
6 1
 
4.2%
3 1
 
4.2%
5 1
 
4.2%

10인이상
Categorical

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
15 
1
7 
2
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 15
62.5%
1 7
29.2%
2 2
 
8.3%

Length

2024-04-21T10:29:51.763880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:29:51.861996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15
62.5%
1 7
29.2%
2 2
 
8.3%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2024-02-29 00:00:00
Maximum2024-02-29 00:00:00
2024-04-21T10:29:51.959116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:52.053218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-21T10:29:47.807765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:41.432245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.284395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.003474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.728746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.539082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.256986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.247252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.019668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.896790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:41.595186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.369928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.084347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.829634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.619626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.343525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.335227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.110614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.970896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:41.668176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.435550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.151753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.915851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.696553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.648182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.407564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.197272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:48.037629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:41.741780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.503334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.224411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.010269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.761362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.723417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.490423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.265037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:48.124283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:41.817342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.599979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.292404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.111216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.835033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.812892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.568154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.344294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:48.213838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:41.906456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.681465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.394134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.198361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.914984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.898036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.662033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.425333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:48.305956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.001425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.769016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.477597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.279103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.005009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.984240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.762740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.515023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:48.395923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.093684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.848951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.554486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.364225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.088171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.068815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.849551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.620016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:48.484727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.191002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:42.924406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:43.639152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:44.455571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:45.167013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.158755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:46.931622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:29:47.706511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:29:52.135838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동계1인2인3인4인5인6인7인8인9인10인이상
읍면동1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
계1.0001.0000.7040.9550.8950.9170.8220.7870.7410.6130.8420.644
1인1.0000.7041.0000.7740.8440.5630.7150.4640.6490.0000.8010.468
2인1.0000.9550.7741.0000.7960.8340.7220.6940.7660.6530.8150.630
3인1.0000.8950.8440.7961.0000.9270.9530.9000.8290.8130.9950.593
4인1.0000.9170.5630.8340.9271.0000.8970.8980.7530.6380.8830.000
5인1.0000.8220.7150.7220.9530.8971.0000.8910.7780.8150.9770.631
6인1.0000.7870.4640.6940.9000.8980.8911.0000.6950.5520.6500.516
7인1.0000.7410.6490.7660.8290.7530.7780.6951.0000.6590.7770.607
8인1.0000.6130.0000.6530.8130.6380.8150.5520.6591.0000.7070.000
9인1.0000.8420.8010.8150.9950.8830.9770.6500.7770.7071.0000.484
10인이상1.0000.6440.4680.6300.5930.0000.6310.5160.6070.0000.4841.000
2024-04-21T10:29:52.283749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
9인10인이상
9인1.0000.389
10인이상0.3891.000
2024-04-21T10:29:52.387761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계1인2인3인4인5인6인7인8인9인10인이상
계1.0000.7530.9360.9810.9480.9610.9570.8600.6090.6710.454
1인0.7531.0000.6680.6600.6060.6330.6380.5210.2440.4220.385
2인0.9360.6681.0000.9430.8970.9100.8950.8830.6810.6330.444
3인0.9810.6600.9431.0000.9650.9750.9620.8500.6080.7770.355
4인0.9480.6060.8970.9651.0000.9760.9340.8520.6710.7260.000
5인0.9610.6330.9100.9750.9761.0000.9580.8480.6750.6680.371
6인0.9570.6380.8950.9620.9340.9581.0000.8750.5960.4880.208
7인0.8600.5210.8830.8500.8520.8480.8751.0000.6830.6060.441
8인0.6090.2440.6810.6080.6710.6750.5960.6831.0000.5180.000
9인0.6710.4220.6330.7770.7260.6680.4880.6060.5181.0000.389
10인이상0.4540.3850.4440.3550.0000.3710.2080.4410.0000.3891.000

Missing values

2024-04-21T10:29:48.603415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:29:48.774119image/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

읍면동계1인2인3인4인5인6인7인8인9인10인이상데이터기준일
0조치원읍203179873458927482206706148372622024-02-29
1연기면14909013411487116940002024-02-29
2연동면16388994661706235500012024-02-29
3부강면31151642866343190561170002024-02-29
4금남면476725541332528232822891012024-02-29
5장군면41072593929320173651962002024-02-29
6연서면372619201018429225992572012024-02-29
7전의면29561629758307171602253102024-02-29
8전동면186510764991539827561002024-02-29
9소정면11235553151348132231002024-02-29
읍면동계1인2인3인4인5인6인7인8인9인10인이상데이터기준일
14어진동526826806787788852033940102024-02-29
15해밀동45081235865105010272844051102024-02-29
16아름동7939127215662027248549869173112024-02-29
17종촌동10785291223742387248853965134122024-02-29
18고운동128132443291831303354787145228512024-02-29
19소담동8423224619491824192540851145102024-02-29
20반곡동12060427525492497217147969171112024-02-29
21보람동6972162114901640176638355124102024-02-29
22대평동43921099112310179022093560102024-02-29
23다정동10826276623852467256655965115112024-02-29