Overview

Dataset statistics

Number of variables11
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory100.9 B

Variable types

Text1
Categorical1
Numeric9

Dataset

Description지역별 교통약자 인구현황입니다. 총인구, 교통약자, 장애인, 고령자, 임산부, 영유아동반자, 어린이 단위( 명, %)
URLhttps://www.data.go.kr/data/15106043/fileData.do

Alerts

총인구 is highly overall correlated with 교통약자 전체 and 5 other fieldsHigh correlation
교통약자 전체 is highly overall correlated with 총인구 and 5 other fieldsHigh correlation
장애인 전체 is highly overall correlated with 총인구 and 5 other fieldsHigh correlation
장애인-타유형 중복 제외 is highly overall correlated with 총인구 and 5 other fieldsHigh correlation
고령자 is highly overall correlated with 총인구 and 5 other fieldsHigh correlation
임산부 is highly overall correlated with 성별High correlation
영유아동반자 is highly overall correlated with 총인구 and 5 other fieldsHigh correlation
어린이 is highly overall correlated with 총인구 and 5 other fieldsHigh correlation
성별 is highly overall correlated with 임산부High correlation
총인구 has unique valuesUnique
교통약자 전체 has unique valuesUnique
장애인 전체 has unique valuesUnique
장애인-타유형 중복 제외 has unique valuesUnique
고령자 has unique valuesUnique
영유아동반자 has unique valuesUnique
어린이 has unique valuesUnique
임산부 has 17 (50.0%) zerosZeros

Reproduction

Analysis started2023-12-12 01:07:05.000077
Analysis finished2023-12-12 01:07:14.310282
Duration9.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T10:07:14.437129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.6470588
Min length3

Characters and Unicode

Total characters158
Distinct characters31
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

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row서울특별시
3rd row부산광역시
4th row부산광역시
5th row대구광역시
ValueCountFrequency (%)
서울특별시 2
 
5.9%
강원도 2
 
5.9%
경상남도 2
 
5.9%
경상북도 2
 
5.9%
전라남도 2
 
5.9%
전라북도 2
 
5.9%
충청남도 2
 
5.9%
충청북도 2
 
5.9%
경기도 2
 
5.9%
부산광역시 2
 
5.9%
Other values (7) 14
41.2%
2023-12-12T10:07:14.851990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 18
 
11.4%
시 16
 
10.1%
광 14
 
8.9%
역 12
 
7.6%
경 6
 
3.8%
전 6
 
3.8%
북 6
 
3.8%
남 6
 
3.8%
별 6
 
3.8%
특 6
 
3.8%
Other values (21) 62
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 158
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 18
 
11.4%
시 16
 
10.1%
광 14
 
8.9%
역 12
 
7.6%
경 6
 
3.8%
전 6
 
3.8%
북 6
 
3.8%
남 6
 
3.8%
별 6
 
3.8%
특 6
 
3.8%
Other values (21) 62
39.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 158
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 18
 
11.4%
시 16
 
10.1%
광 14
 
8.9%
역 12
 
7.6%
경 6
 
3.8%
전 6
 
3.8%
북 6
 
3.8%
남 6
 
3.8%
별 6
 
3.8%
특 6
 
3.8%
Other values (21) 62
39.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 158
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 18
 
11.4%
시 16
 
10.1%
광 14
 
8.9%
역 12
 
7.6%
경 6
 
3.8%
전 6
 
3.8%
북 6
 
3.8%
남 6
 
3.8%
별 6
 
3.8%
특 6
 
3.8%
Other values (21) 62
39.2%

성별
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
남자
17 
여자
17 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남자
2nd row여자
3rd row남자
4th row여자
5th row남자

Common Values

ValueCountFrequency (%)
남자 17
50.0%
여자 17
50.0%

Length

2023-12-12T10:07:14.987098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:07:15.096575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남자 17
50.0%
여자 17
50.0%

총인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1518788.5
Minimum185678
Maximum6827298
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T10:07:15.198784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum185678
5-th percentile284604.25
Q1727867.75
median916401.5
Q31475280.2
95-th percentile5537774.9
Maximum6827298
Range6641620
Interquartile range (IQR)747412.5

Descriptive statistics

Standard deviation1665984.7
Coefficient of variation (CV)1.0969169
Kurtosis5.0742708
Mean1518788.5
Median Absolute Deviation (MAD)355605.5
Skewness2.4045614
Sum51638809
Variance2.775505 × 1012
MonotonicityNot monotonic
2023-12-12T10:07:15.343034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4618040 1
 
2.9%
922221 1
 
2.9%
810672 1
 
2.9%
786755 1
 
2.9%
1083366 1
 
2.9%
1035891 1
 
2.9%
888994 1
 
2.9%
897861 1
 
2.9%
910582 1
 
2.9%
774332 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
185678 1
2.9%
186217 1
2.9%
337582 1
2.9%
339177 1
2.9%
545375 1
2.9%
576217 1
2.9%
713015 1
2.9%
724626 1
2.9%
727625 1
2.9%
728596 1
2.9%
ValueCountFrequency (%)
6827298 1
2.9%
6738152 1
2.9%
4891418 1
2.9%
4618040 1
2.9%
1711336 1
2.9%
1668338 1
2.9%
1645845 1
2.9%
1639044 1
2.9%
1476373 1
2.9%
1472002 1
2.9%

교통약자 전체
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean456133.53
Minimum53077
Maximum1948081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T10:07:15.509611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53077
5-th percentile85082
Q1216317
median337197.5
Q3480786.25
95-th percentile1530558.6
Maximum1948081
Range1895004
Interquartile range (IQR)264469.25

Descriptive statistics

Standard deviation453970.51
Coefficient of variation (CV)0.99525793
Kurtosis4.8080864
Mean456133.53
Median Absolute Deviation (MAD)130331
Skewness2.2914275
Sum15508540
Variance2.0608923 × 1011
MonotonicityNot monotonic
2023-12-12T10:07:15.689714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1236670 1
 
2.9%
311508 1
 
2.9%
245116 1
 
2.9%
271963 1
 
2.9%
337682 1
 
2.9%
376835 1
 
2.9%
292258 1
 
2.9%
337464 1
 
2.9%
370856 1
 
2.9%
246958 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
53077 1
2.9%
57314 1
2.9%
100034 1
2.9%
111809 1
2.9%
152831 1
2.9%
158337 1
2.9%
196352 1
2.9%
198356 1
2.9%
215377 1
2.9%
219137 1
2.9%
ValueCountFrequency (%)
1948081 1
2.9%
1801307 1
2.9%
1384771 1
2.9%
1236670 1
2.9%
570318 1
2.9%
561312 1
2.9%
504796 1
2.9%
499825 1
2.9%
497145 1
2.9%
431710 1
2.9%

교통약자 비율
Real number (ℝ)

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31273529
Minimum0.264
Maximum0.407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T10:07:15.871067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.264
5-th percentile0.26695
Q10.2875
median0.305
Q30.3325
95-th percentile0.3781
Maximum0.407
Range0.143
Interquartile range (IQR)0.045

Descriptive statistics

Standard deviation0.036187482
Coefficient of variation (CV)0.11571282
Kurtosis0.10682016
Mean0.31273529
Median Absolute Deviation (MAD)0.023
Skewness0.78764842
Sum10.633
Variance0.0013095339
MonotonicityNot monotonic
2023-12-12T10:07:16.030369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.268 2
 
5.9%
0.29 2
 
5.9%
0.364 2
 
5.9%
0.308 2
 
5.9%
0.331 1
 
2.9%
0.295 1
 
2.9%
0.341 1
 
2.9%
0.3 1
 
2.9%
0.302 1
 
2.9%
0.326 1
 
2.9%
Other values (20) 20
58.8%
ValueCountFrequency (%)
0.264 1
2.9%
0.265 1
2.9%
0.268 2
5.9%
0.274 1
2.9%
0.275 1
2.9%
0.283 1
2.9%
0.286 1
2.9%
0.287 1
2.9%
0.289 1
2.9%
0.29 2
5.9%
ValueCountFrequency (%)
0.407 1
2.9%
0.382 1
2.9%
0.376 1
2.9%
0.364 2
5.9%
0.346 1
2.9%
0.341 1
2.9%
0.338 1
2.9%
0.333 1
2.9%
0.331 1
2.9%
0.329 1
2.9%

장애인 전체
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77785.294
Minimum5099
Maximum344091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T10:07:16.185679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5099
5-th percentile13357.6
Q140092.25
median60236
Q380435
95-th percentile229848.25
Maximum344091
Range338992
Interquartile range (IQR)40342.75

Descriptive statistics

Standard deviation70290.234
Coefficient of variation (CV)0.90364426
Kurtosis6.0942399
Mean77785.294
Median Absolute Deviation (MAD)20490
Skewness2.3091366
Sum2644700
Variance4.9407171 × 109
MonotonicityNot monotonic
2023-12-12T10:07:16.313474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
227302 1
 
2.9%
74616 1
 
2.9%
56117 1
 
2.9%
41722 1
 
2.9%
77050 1
 
2.9%
57699 1
 
2.9%
71784 1
 
2.9%
60273 1
 
2.9%
65252 1
 
2.9%
58810 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
5099 1
2.9%
7531 1
2.9%
16495 1
2.9%
20282 1
2.9%
20381 1
2.9%
30191 1
2.9%
30270 1
2.9%
31048 1
2.9%
39549 1
2.9%
41722 1
2.9%
ValueCountFrequency (%)
344091 1
2.9%
234577 1
2.9%
227302 1
2.9%
164821 1
2.9%
109468 1
2.9%
103824 1
2.9%
102009 1
2.9%
88447 1
2.9%
80529 1
2.9%
80153 1
2.9%

장애인-타유형 중복 제외
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36401.265
Minimum1972
Maximum198881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T10:07:16.482869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1972
5-th percentile5531.5
Q116031
median24694.5
Q340537.25
95-th percentile102147.7
Maximum198881
Range196909
Interquartile range (IQR)24506.25

Descriptive statistics

Standard deviation37879.207
Coefficient of variation (CV)1.0406014
Kurtosis10.154833
Mean36401.265
Median Absolute Deviation (MAD)12314
Skewness2.8801253
Sum1237643
Variance1.4348343 × 109
MonotonicityNot monotonic
2023-12-12T10:07:16.623605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
119543 1
 
2.9%
37957 1
 
2.9%
31177 1
 
2.9%
15395 1
 
2.9%
40307 1
 
2.9%
19405 1
 
2.9%
36703 1
 
2.9%
18923 1
 
2.9%
18845 1
 
2.9%
29890 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1972 1
2.9%
4446 1
2.9%
6116 1
2.9%
8501 1
2.9%
11523 1
2.9%
12226 1
2.9%
12535 1
2.9%
14804 1
2.9%
15395 1
2.9%
17939 1
2.9%
ValueCountFrequency (%)
198881 1
2.9%
119543 1
2.9%
92781 1
2.9%
60991 1
2.9%
60580 1
2.9%
53351 1
2.9%
51455 1
2.9%
49771 1
2.9%
40614 1
2.9%
40307 1
2.9%

고령자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260324.5
Minimum16513
Maximum1046998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T10:07:17.102469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16513
5-th percentile38119.75
Q1122207.75
median186688.5
Q3259676.5
95-th percentile853955.4
Maximum1046998
Range1030485
Interquartile range (IQR)137468.75

Descriptive statistics

Standard deviation247078.82
Coefficient of variation (CV)0.94911858
Kurtosis3.5823682
Mean260324.5
Median Absolute Deviation (MAD)72925.5
Skewness2.0037645
Sum8851033
Variance6.1047944 × 1010
MonotonicityNot monotonic
2023-12-12T10:07:17.237844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
707297 1
 
2.9%
185709 1
 
2.9%
131637 1
 
2.9%
169879 1
 
2.9%
182374 1
 
2.9%
237605 1
 
2.9%
168828 1
 
2.9%
228791 1
 
2.9%
259489 1
 
2.9%
145508 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
16513 1
2.9%
20950 1
2.9%
47365 1
2.9%
63280 1
2.9%
70034 1
2.9%
82927 1
2.9%
91588 1
2.9%
97485 1
2.9%
121703 1
2.9%
123722 1
2.9%
ValueCountFrequency (%)
1046998 1
2.9%
890150 1
2.9%
834466 1
2.9%
707297 1
2.9%
383983 1
2.9%
348640 1
2.9%
342057 1
2.9%
297902 1
2.9%
259739 1
2.9%
259489 1
2.9%

임산부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7661.7647
Minimum0
Maximum76100
Zeros17
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T10:07:17.415710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1800
Q38350
95-th percentile26065
Maximum76100
Range76100
Interquartile range (IQR)8350

Descriptive statistics

Standard deviation14923.259
Coefficient of variation (CV)1.9477574
Kurtosis14.338344
Mean7661.7647
Median Absolute Deviation (MAD)1800
Skewness3.567799
Sum260500
Variance2.2270365 × 108
MonotonicityNot monotonic
2023-12-12T10:07:17.576214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 17
50.0%
7400 2
 
5.9%
8200 1
 
2.9%
3700 1
 
2.9%
15600 1
 
2.9%
12000 1
 
2.9%
8400 1
 
2.9%
7500 1
 
2.9%
11000 1
 
2.9%
76100 1
 
2.9%
Other values (7) 7
20.6%
ValueCountFrequency (%)
0 17
50.0%
3600 1
 
2.9%
3700 1
 
2.9%
6100 1
 
2.9%
7400 2
 
5.9%
7500 1
 
2.9%
8000 1
 
2.9%
8200 1
 
2.9%
8400 1
 
2.9%
10700 1
 
2.9%
ValueCountFrequency (%)
76100 1
2.9%
45500 1
2.9%
15600 1
2.9%
14900 1
2.9%
14400 1
2.9%
12000 1
2.9%
11000 1
2.9%
10700 1
2.9%
8400 1
2.9%
8200 1
2.9%

영유아동반자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57153.176
Minimum12390
Maximum293242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T10:07:17.692279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12390
5-th percentile13761.45
Q127529
median31959
Q354801
95-th percentile200700.9
Maximum293242
Range280852
Interquartile range (IQR)27272

Descriptive statistics

Standard deviation66146.054
Coefficient of variation (CV)1.1573469
Kurtosis7.632849
Mean57153.176
Median Absolute Deviation (MAD)11482
Skewness2.8129159
Sum1943208
Variance4.3753004 × 109
MonotonicityNot monotonic
2023-12-12T10:07:17.807132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
158248 1
 
2.9%
32557 1
 
2.9%
31361 1
 
2.9%
29739 1
 
2.9%
43078 1
 
2.9%
41023 1
 
2.9%
30290 1
 
2.9%
28879 1
 
2.9%
31214 1
 
2.9%
27248 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
12390 1
2.9%
12817 1
2.9%
14270 1
2.9%
15093 1
2.9%
22763 1
2.9%
24106 1
2.9%
25905 1
2.9%
26892 1
2.9%
27248 1
2.9%
28372 1
2.9%
ValueCountFrequency (%)
293242 1
2.9%
279542 1
2.9%
158248 1
2.9%
149277 1
2.9%
64302 1
2.9%
60625 1
2.9%
57823 1
2.9%
57509 1
2.9%
54835 1
2.9%
54699 1
2.9%

어린이
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94592.824
Minimum18402
Maximum474718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T10:07:17.928953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18402
5-th percentile22643.3
Q145835
median55861
Q390756.5
95-th percentile321959.3
Maximum474718
Range456316
Interquartile range (IQR)44921.5

Descriptive statistics

Standard deviation106357.13
Coefficient of variation (CV)1.1243679
Kurtosis7.6572011
Mean94592.824
Median Absolute Deviation (MAD)17726.5
Skewness2.8018216
Sum3216156
Variance1.1311839 × 1010
MonotonicityNot monotonic
2023-12-12T10:07:18.055627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
251582 1
 
2.9%
55285 1
 
2.9%
50941 1
 
2.9%
48750 1
 
2.9%
71923 1
 
2.9%
67802 1
 
2.9%
56437 1
 
2.9%
53371 1
 
2.9%
52908 1
 
2.9%
44312 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
18402 1
2.9%
19301 1
2.9%
24443 1
2.9%
26053 1
2.9%
38046 1
2.9%
40752 1
2.9%
42137 1
2.9%
44312 1
2.9%
45137 1
2.9%
47929 1
2.9%
ValueCountFrequency (%)
474718 1
2.9%
452660 1
2.9%
251582 1
2.9%
239264 1
2.9%
114793 1
2.9%
108123 1
2.9%
96034 1
2.9%
95559 1
2.9%
90814 1
2.9%
90584 1
2.9%

Interactions

2023-12-12T10:07:13.138901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:05.363591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:06.373277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:07.408924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.349802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.246317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:10.176697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.222759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:12.130110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:13.225904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:05.449080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:06.501261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:07.514830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.447496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.346604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:10.259050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.319516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:12.219369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:13.327581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:05.568578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:06.621364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:07.647795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.547037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.472367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:10.348302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.412216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:12.319996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:13.465604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:05.672322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:06.746299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:07.743506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.650700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.572115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:10.429176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.515529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:12.485327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:13.556422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:05.805300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:06.843290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:07.860594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.737327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.664192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:10.515695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.623512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:12.628813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:13.652457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:05.946460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:06.965375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:07.984192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.848344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.759144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:10.607187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.734000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:12.754538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:13.756409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:06.061527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:07.090458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.080158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.938858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.852071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:10.982324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.819012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:12.846631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:13.845652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:06.161442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:07.193186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.180085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.053742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.957636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.067329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.931269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:12.946031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:13.951355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:06.260904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:07.293460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:08.274793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:09.143678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:10.072732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:11.146846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:12.036711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:07:13.055633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:07:18.141377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역성별총인구교통약자 전체교통약자 비율장애인 전체장애인-타유형 중복 제외고령자임산부영유아동반자어린이
지역1.0000.0000.9330.9200.0000.5890.0000.4390.0000.8880.862
성별0.0001.0000.0000.0000.4790.0000.5760.3130.5280.0000.000
총인구0.9330.0001.0000.9960.0000.8500.9360.9320.7250.9530.989
교통약자 전체0.9200.0000.9961.0000.0000.8720.9350.9640.7150.9400.984
교통약자 비율0.0000.4790.0000.0001.0000.0000.0000.0000.0000.0000.000
장애인 전체0.5890.0000.8500.8720.0001.0000.8480.8620.7040.8640.915
장애인-타유형 중복 제외0.0000.5760.9360.9350.0000.8481.0000.9080.5990.8370.945
고령자0.4390.3130.9320.9640.0000.8620.9081.0000.8580.9410.907
임산부0.0000.5280.7250.7150.0000.7040.5990.8581.0000.9030.699
영유아동반자0.8880.0000.9530.9400.0000.8640.8370.9410.9031.0001.000
어린이0.8620.0000.9890.9840.0000.9150.9450.9070.6991.0001.000
2023-12-12T10:07:18.274321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총인구교통약자 전체교통약자 비율장애인 전체장애인-타유형 중복 제외고령자임산부영유아동반자어린이성별
총인구1.0000.987-0.0420.9350.8500.9540.2610.9800.9790.000
교통약자 전체0.9871.0000.0510.9210.7970.9860.3470.9530.9570.000
교통약자 비율-0.0420.0511.000-0.054-0.2520.1350.409-0.138-0.1340.308
장애인 전체0.9350.921-0.0541.0000.9370.8880.0290.9330.9350.018
장애인-타유형 중복 제외0.8500.797-0.2520.9371.0000.733-0.1980.8840.8770.410
고령자0.9540.9860.1350.8880.7331.0000.4180.9070.9090.221
임산부0.2610.3470.4090.029-0.1980.4181.0000.1800.1850.607
영유아동반자0.9800.953-0.1380.9330.8840.9070.1801.0000.9910.000
어린이0.9790.957-0.1340.9350.8770.9090.1850.9911.0000.000
성별0.0000.0000.3080.0180.4100.2210.6070.0000.0001.000

Missing values

2023-12-12T10:07:14.091539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:07:14.253787image/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

지역성별총인구교통약자 전체교통약자 비율장애인 전체장애인-타유형 중복 제외고령자임산부영유아동반자어린이
0서울특별시남자461804012366700.2682273021195437072970158248251582
1서울특별시여자489141813847710.2831648216058089015045500149277239264
2부산광역시남자16390445047960.3081038245335129790205750996034
3부산광역시여자17113365703180.3337262726286383983144005483590814
4대구광역시남자11756323369310.287739554061417781804380474695
5대구광역시여자12097803819720.3165332719734239242107004150470792
6인천광역시남자14763733951430.268884474977119199005782395559
7인천광역시여자14720024273810.296019923757243441149005469990584
8광주광역시남자7130151963520.27539549235229158802952251720
9광주광역시여자7285962191370.301302701253512170380002837248527
지역성별총인구교통약자 전체교통약자 비율장애인 전체장애인-타유형 중복 제외고령자임산부영유아동반자어린이
24전라북도남자8889942922580.329717843670316882803029056437
25전라북도여자8978613374640.376602731892322879175002887953371
26전라남도남자9222213115080.338746163795718570903255755285
27전라남도여자9105823708560.407652521884525948984003121452908
28경상북도남자13236614317100.3261020095145525425504713778863
29경상북도여자13029484971450.3828052924867342057120004472273499
30경상남도남자16683384998250.310946860991259739064302114793
31경상남도여자16458455613120.34180153283243486401560060625108123
32제주특별자치도남자3391771000340.29520381115234736501509326053
33제주특별자치도여자3375821118090.3311649561166328037001427024443