Overview

Dataset statistics

Number of variables15
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory139.3 B

Variable types

Text1
Numeric14

Dataset

Description강원도 원주시의 읍면동별 1인가구 현황에 대한 자료를 제공하고 있습니다. 원주시의 지역별 1인가구 수를 제공하며, 2010년은 월별, 2015년, 2020년은 연도별 자료를 제공합니다.
Author강원도 원주시
URLhttps://www.data.go.kr/data/15100341/fileData.do

Alerts

2010-01 is highly overall correlated with 2010-02 and 12 other fieldsHigh correlation
2010-02 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-03 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-04 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-05 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-06 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-07 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-08 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-09 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-10 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-11 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2010-12 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2015 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
2020 is highly overall correlated with 2010-01 and 12 other fieldsHigh correlation
행정구역 has unique valuesUnique
2010-01 has unique valuesUnique
2010-02 has unique valuesUnique
2010-03 has unique valuesUnique
2010-04 has unique valuesUnique
2010-05 has unique valuesUnique
2010-06 has unique valuesUnique
2010-07 has unique valuesUnique
2010-08 has unique valuesUnique
2010-09 has unique valuesUnique
2010-10 has unique valuesUnique
2010-12 has unique valuesUnique
2015 has unique valuesUnique
2020 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:17:30.485895
Analysis finished2023-12-12 15:17:49.361673
Duration18.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-13T00:17:49.540668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.24
Min length3

Characters and Unicode

Total characters81
Distinct characters48
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row문막읍
2nd row소초면
3rd row호저면
4th row지정면
5th row부론면
ValueCountFrequency (%)
문막읍 1
 
4.0%
명륜2동 1
 
4.0%
무실동 1
 
4.0%
행구동 1
 
4.0%
봉산동 1
 
4.0%
태장2동 1
 
4.0%
태장1동 1
 
4.0%
우산동 1
 
4.0%
단계동 1
 
4.0%
학성동 1
 
4.0%
Other values (15) 15
60.0%
2023-12-13T00:17:49.999794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
19.8%
8
 
9.9%
3
 
3.7%
2
 
2.5%
2
 
2.5%
1 2
 
2.5%
2 2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
Other values (38) 40
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
95.1%
Decimal Number 4
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
16
20.8%
8
 
10.4%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (36) 36
46.8%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
95.1%
Common 4
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
16
20.8%
8
 
10.4%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (36) 36
46.8%
Common
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
95.1%
ASCII 4
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
16
20.8%
8
 
10.4%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (36) 36
46.8%
ASCII
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

2010-01
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1526
Minimum361
Maximum4363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:50.131053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum361
5-th percentile512.4
Q1917
median1336
Q31983
95-th percentile2915.4
Maximum4363
Range4002
Interquartile range (IQR)1066

Descriptive statistics

Standard deviation947.35773
Coefficient of variation (CV)0.62081109
Kurtosis1.8685299
Mean1526
Median Absolute Deviation (MAD)585
Skewness1.2929395
Sum38150
Variance897486.67
MonotonicityNot monotonic
2023-12-13T00:17:50.282540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2598 1
 
4.0%
1416 1
 
4.0%
1983 1
 
4.0%
2399 1
 
4.0%
610 1
 
4.0%
1462 1
 
4.0%
2962 1
 
4.0%
935 1
 
4.0%
1848 1
 
4.0%
2729 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
361 1
4.0%
506 1
4.0%
538 1
4.0%
610 1
4.0%
731 1
4.0%
751 1
4.0%
917 1
4.0%
935 1
4.0%
1034 1
4.0%
1130 1
4.0%
ValueCountFrequency (%)
4363 1
4.0%
2962 1
4.0%
2729 1
4.0%
2598 1
4.0%
2444 1
4.0%
2399 1
4.0%
1983 1
4.0%
1848 1
4.0%
1462 1
4.0%
1416 1
4.0%

2010-02
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1531.52
Minimum355
Maximum4391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:50.408311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum355
5-th percentile508.2
Q1918
median1349
Q32000
95-th percentile2933.8
Maximum4391
Range4036
Interquartile range (IQR)1082

Descriptive statistics

Standard deviation954.38358
Coefficient of variation (CV)0.62316103
Kurtosis1.878134
Mean1531.52
Median Absolute Deviation (MAD)591
Skewness1.2939537
Sum38288
Variance910848.01
MonotonicityNot monotonic
2023-12-13T00:17:50.563716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2602 1
 
4.0%
1403 1
 
4.0%
2000 1
 
4.0%
2391 1
 
4.0%
610 1
 
4.0%
1479 1
 
4.0%
2981 1
 
4.0%
937 1
 
4.0%
1850 1
 
4.0%
2745 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
355 1
4.0%
502 1
4.0%
533 1
4.0%
610 1
4.0%
735 1
4.0%
758 1
4.0%
918 1
4.0%
937 1
4.0%
1037 1
4.0%
1127 1
4.0%
ValueCountFrequency (%)
4391 1
4.0%
2981 1
4.0%
2745 1
4.0%
2602 1
4.0%
2472 1
4.0%
2391 1
4.0%
2000 1
4.0%
1850 1
4.0%
1479 1
4.0%
1410 1
4.0%

2010-03
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1535.4
Minimum362
Maximum4441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:50.731074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum362
5-th percentile506.2
Q1941
median1364
Q31982
95-th percentile2942
Maximum4441
Range4079
Interquartile range (IQR)1041

Descriptive statistics

Standard deviation960.79295
Coefficient of variation (CV)0.62576068
Kurtosis2.0235887
Mean1535.4
Median Absolute Deviation (MAD)610
Skewness1.3218694
Sum38385
Variance923123.08
MonotonicityNot monotonic
2023-12-13T00:17:50.880214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2588 1
 
4.0%
1400 1
 
4.0%
1982 1
 
4.0%
2385 1
 
4.0%
592 1
 
4.0%
1479 1
 
4.0%
2984 1
 
4.0%
956 1
 
4.0%
1862 1
 
4.0%
2774 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
362 1
4.0%
499 1
4.0%
535 1
4.0%
592 1
4.0%
736 1
4.0%
754 1
4.0%
941 1
4.0%
956 1
4.0%
1005 1
4.0%
1127 1
4.0%
ValueCountFrequency (%)
4441 1
4.0%
2984 1
4.0%
2774 1
4.0%
2588 1
4.0%
2465 1
4.0%
2385 1
4.0%
1982 1
4.0%
1862 1
4.0%
1479 1
4.0%
1419 1
4.0%

2010-04
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1540.64
Minimum367
Maximum4484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:51.053247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum367
5-th percentile504.8
Q1939
median1379
Q31994
95-th percentile2953.4
Maximum4484
Range4117
Interquartile range (IQR)1055

Descriptive statistics

Standard deviation967.37587
Coefficient of variation (CV)0.6279052
Kurtosis2.1172787
Mean1540.64
Median Absolute Deviation (MAD)615
Skewness1.3384759
Sum38516
Variance935816.07
MonotonicityNot monotonic
2023-12-13T00:17:51.225801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2615 1
 
4.0%
1403 1
 
4.0%
1994 1
 
4.0%
2396 1
 
4.0%
592 1
 
4.0%
1494 1
 
4.0%
3005 1
 
4.0%
951 1
 
4.0%
1873 1
 
4.0%
2747 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
367 1
4.0%
499 1
4.0%
528 1
4.0%
592 1
4.0%
733 1
4.0%
760 1
4.0%
939 1
4.0%
951 1
4.0%
1006 1
4.0%
1133 1
4.0%
ValueCountFrequency (%)
4484 1
4.0%
3005 1
4.0%
2747 1
4.0%
2615 1
4.0%
2453 1
4.0%
2396 1
4.0%
1994 1
4.0%
1873 1
4.0%
1494 1
4.0%
1407 1
4.0%

2010-05
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1543.48
Minimum371
Maximum4510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:51.356500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum371
5-th percentile506.4
Q1925
median1385
Q32037
95-th percentile2967.4
Maximum4510
Range4139
Interquartile range (IQR)1112

Descriptive statistics

Standard deviation971.56802
Coefficient of variation (CV)0.6294659
Kurtosis2.1752639
Mean1543.48
Median Absolute Deviation (MAD)626
Skewness1.3522127
Sum38587
Variance943944.43
MonotonicityNot monotonic
2023-12-13T00:17:51.493940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2614 1
 
4.0%
1408 1
 
4.0%
2037 1
 
4.0%
2385 1
 
4.0%
590 1
 
4.0%
1498 1
 
4.0%
3025 1
 
4.0%
961 1
 
4.0%
1855 1
 
4.0%
2737 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
371 1
4.0%
501 1
4.0%
528 1
4.0%
590 1
4.0%
738 1
4.0%
759 1
4.0%
925 1
4.0%
961 1
4.0%
1005 1
4.0%
1135 1
4.0%
ValueCountFrequency (%)
4510 1
4.0%
3025 1
4.0%
2737 1
4.0%
2614 1
4.0%
2464 1
4.0%
2385 1
4.0%
2037 1
4.0%
1855 1
4.0%
1498 1
4.0%
1408 1
4.0%

2010-06
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1873.48
Minimum506
Maximum7300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:51.641148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum506
5-th percentile540.6
Q1975
median1395
Q32438
95-th percentile4245
Maximum7300
Range6794
Interquartile range (IQR)1463

Descriptive statistics

Standard deviation1479.0633
Coefficient of variation (CV)0.78947375
Kurtosis7.0583634
Mean1873.48
Median Absolute Deviation (MAD)642
Skewness2.3620853
Sum46837
Variance2187628.2
MonotonicityNot monotonic
2023-12-13T00:17:51.798649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2628 1
 
4.0%
1413 1
 
4.0%
2037 1
 
4.0%
2438 1
 
4.0%
603 1
 
4.0%
1496 1
 
4.0%
3049 1
 
4.0%
975 1
 
4.0%
1865 1
 
4.0%
2760 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
506 1
4.0%
525 1
4.0%
603 1
4.0%
743 1
4.0%
754 1
4.0%
931 1
4.0%
975 1
4.0%
997 1
4.0%
1134 1
4.0%
1244 1
4.0%
ValueCountFrequency (%)
7300 1
4.0%
4544 1
4.0%
3049 1
4.0%
2760 1
4.0%
2628 1
4.0%
2478 1
4.0%
2438 1
4.0%
2270 1
4.0%
2037 1
4.0%
1865 1
4.0%

2010-07
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1558.36
Minimum363
Maximum4526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:51.978354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum363
5-th percentile517.2
Q1933
median1360
Q32077
95-th percentile3021.4
Maximum4526
Range4163
Interquartile range (IQR)1144

Descriptive statistics

Standard deviation983.8201
Coefficient of variation (CV)0.6313176
Kurtosis1.985717
Mean1558.36
Median Absolute Deviation (MAD)605
Skewness1.3222575
Sum38959
Variance967901.99
MonotonicityNot monotonic
2023-12-13T00:17:52.117097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2641 1
 
4.0%
1414 1
 
4.0%
2077 1
 
4.0%
2470 1
 
4.0%
603 1
 
4.0%
1489 1
 
4.0%
3081 1
 
4.0%
977 1
 
4.0%
1869 1
 
4.0%
2783 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
363 1
4.0%
515 1
4.0%
526 1
4.0%
603 1
4.0%
749 1
4.0%
755 1
4.0%
933 1
4.0%
977 1
4.0%
1001 1
4.0%
1118 1
4.0%
ValueCountFrequency (%)
4526 1
4.0%
3081 1
4.0%
2783 1
4.0%
2641 1
4.0%
2478 1
4.0%
2470 1
4.0%
2077 1
4.0%
1869 1
4.0%
1489 1
4.0%
1414 1
4.0%

2010-08
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1562
Minimum369
Maximum4537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:52.261777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum369
5-th percentile521.8
Q1921
median1356
Q32081
95-th percentile3027.6
Maximum4537
Range4168
Interquartile range (IQR)1160

Descriptive statistics

Standard deviation985.49357
Coefficient of variation (CV)0.63091778
Kurtosis1.9970269
Mean1562
Median Absolute Deviation (MAD)601
Skewness1.3253308
Sum39050
Variance971197.58
MonotonicityNot monotonic
2023-12-13T00:17:52.399945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2640 1
 
4.0%
1432 1
 
4.0%
2081 1
 
4.0%
2489 1
 
4.0%
613 1
 
4.0%
1480 1
 
4.0%
3089 1
 
4.0%
979 1
 
4.0%
1857 1
 
4.0%
2782 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
369 1
4.0%
520 1
4.0%
529 1
4.0%
613 1
4.0%
737 1
4.0%
755 1
4.0%
921 1
4.0%
979 1
4.0%
1010 1
4.0%
1124 1
4.0%
ValueCountFrequency (%)
4537 1
4.0%
3089 1
4.0%
2782 1
4.0%
2640 1
4.0%
2489 1
4.0%
2484 1
4.0%
2081 1
4.0%
1857 1
4.0%
1480 1
4.0%
1432 1
4.0%

2010-09
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564
Minimum369
Maximum4546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:52.547181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum369
5-th percentile517.8
Q1933
median1356
Q32119
95-th percentile3023.4
Maximum4546
Range4177
Interquartile range (IQR)1186

Descriptive statistics

Standard deviation987.05859
Coefficient of variation (CV)0.63111163
Kurtosis2.0032019
Mean1564
Median Absolute Deviation (MAD)598
Skewness1.3248072
Sum39100
Variance974284.67
MonotonicityNot monotonic
2023-12-13T00:17:52.652189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2664 1
 
4.0%
1428 1
 
4.0%
2119 1
 
4.0%
2470 1
 
4.0%
623 1
 
4.0%
1464 1
 
4.0%
3084 1
 
4.0%
993 1
 
4.0%
1858 1
 
4.0%
2781 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
369 1
4.0%
516 1
4.0%
525 1
4.0%
623 1
4.0%
731 1
4.0%
758 1
4.0%
933 1
4.0%
993 1
4.0%
1006 1
4.0%
1103 1
4.0%
ValueCountFrequency (%)
4546 1
4.0%
3084 1
4.0%
2781 1
4.0%
2664 1
4.0%
2478 1
4.0%
2470 1
4.0%
2119 1
4.0%
1858 1
4.0%
1464 1
4.0%
1428 1
4.0%

2010-10
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1640.2
Minimum391
Maximum4665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:52.785001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum391
5-th percentile522.2
Q11039
median1441
Q32195
95-th percentile3189.8
Maximum4665
Range4274
Interquartile range (IQR)1156

Descriptive statistics

Standard deviation1019.6085
Coefficient of variation (CV)0.62163667
Kurtosis1.7633244
Mean1640.2
Median Absolute Deviation (MAD)639
Skewness1.263443
Sum41005
Variance1039601.4
MonotonicityNot monotonic
2023-12-13T00:17:52.907448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2772 1
 
4.0%
1478 1
 
4.0%
2195 1
 
4.0%
2673 1
 
4.0%
643 1
 
4.0%
1573 1
 
4.0%
3265 1
 
4.0%
1064 1
 
4.0%
1972 1
 
4.0%
2889 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
391 1
4.0%
519 1
4.0%
535 1
4.0%
643 1
4.0%
766 1
4.0%
802 1
4.0%
1039 1
4.0%
1064 1
4.0%
1076 1
4.0%
1189 1
4.0%
ValueCountFrequency (%)
4665 1
4.0%
3265 1
4.0%
2889 1
4.0%
2772 1
4.0%
2673 1
4.0%
2507 1
4.0%
2195 1
4.0%
1972 1
4.0%
1573 1
4.0%
1480 1
4.0%

2010-11
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1640
Minimum388
Maximum4670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:53.048519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum388
5-th percentile521.4
Q11036
median1433
Q32207
95-th percentile3177.8
Maximum4670
Range4282
Interquartile range (IQR)1171

Descriptive statistics

Standard deviation1021.9547
Coefficient of variation (CV)0.62314309
Kurtosis1.7462427
Mean1640
Median Absolute Deviation (MAD)635
Skewness1.2620202
Sum41000
Variance1044391.3
MonotonicityNot monotonic
2023-12-13T00:17:53.177661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1433 2
 
8.0%
2754 1
 
4.0%
2488 1
 
4.0%
2207 1
 
4.0%
2763 1
 
4.0%
644 1
 
4.0%
1583 1
 
4.0%
3254 1
 
4.0%
1069 1
 
4.0%
1960 1
 
4.0%
Other values (14) 14
56.0%
ValueCountFrequency (%)
388 1
4.0%
518 1
4.0%
535 1
4.0%
644 1
4.0%
765 1
4.0%
798 1
4.0%
1036 1
4.0%
1069 1
4.0%
1078 1
4.0%
1182 1
4.0%
ValueCountFrequency (%)
4670 1
4.0%
3254 1
4.0%
2873 1
4.0%
2763 1
4.0%
2754 1
4.0%
2488 1
4.0%
2207 1
4.0%
1960 1
4.0%
1583 1
4.0%
1497 1
4.0%

2010-12
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1641.2
Minimum389
Maximum4654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:53.289656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum389
5-th percentile525.4
Q11033
median1416
Q32194
95-th percentile3189.4
Maximum4654
Range4265
Interquartile range (IQR)1161

Descriptive statistics

Standard deviation1024.2675
Coefficient of variation (CV)0.62409672
Kurtosis1.6356279
Mean1641.2
Median Absolute Deviation (MAD)622
Skewness1.2467756
Sum41030
Variance1049124
MonotonicityNot monotonic
2023-12-13T00:17:53.394678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2781 1
 
4.0%
1452 1
 
4.0%
2194 1
 
4.0%
2794 1
 
4.0%
652 1
 
4.0%
1582 1
 
4.0%
3268 1
 
4.0%
1070 1
 
4.0%
1966 1
 
4.0%
2875 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
389 1
4.0%
523 1
4.0%
535 1
4.0%
652 1
4.0%
759 1
4.0%
794 1
4.0%
1033 1
4.0%
1066 1
4.0%
1070 1
4.0%
1178 1
4.0%
ValueCountFrequency (%)
4654 1
4.0%
3268 1
4.0%
2875 1
4.0%
2794 1
4.0%
2781 1
4.0%
2502 1
4.0%
2194 1
4.0%
1966 1
4.0%
1582 1
4.0%
1498 1
4.0%

2015
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1475.76
Minimum278
Maximum4406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:53.504488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile348.6
Q1621
median1038
Q32475
95-th percentile3274.6
Maximum4406
Range4128
Interquartile range (IQR)1854

Descriptive statistics

Standard deviation1131.3351
Coefficient of variation (CV)0.76661187
Kurtosis0.11132279
Mean1475.76
Median Absolute Deviation (MAD)625
Skewness0.95778068
Sum36894
Variance1279919.2
MonotonicityNot monotonic
2023-12-13T00:17:53.622390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2065 1
 
4.0%
948 1
 
4.0%
2653 1
 
4.0%
3356 1
 
4.0%
444 1
 
4.0%
1038 1
 
4.0%
2154 1
 
4.0%
650 1
 
4.0%
2949 1
 
4.0%
2568 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
278 1
4.0%
345 1
4.0%
363 1
4.0%
413 1
4.0%
444 1
4.0%
500 1
4.0%
621 1
4.0%
639 1
4.0%
650 1
4.0%
791 1
4.0%
ValueCountFrequency (%)
4406 1
4.0%
3356 1
4.0%
2949 1
4.0%
2653 1
4.0%
2633 1
4.0%
2568 1
4.0%
2475 1
4.0%
2154 1
4.0%
2065 1
4.0%
1314 1
4.0%

2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2077.6
Minimum311
Maximum5785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T00:17:53.734304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum311
5-th percentile473.8
Q1770
median1477
Q33118
95-th percentile5288
Maximum5785
Range5474
Interquartile range (IQR)2348

Descriptive statistics

Standard deviation1595.2234
Coefficient of variation (CV)0.76782029
Kurtosis0.052025213
Mean2077.6
Median Absolute Deviation (MAD)964
Skewness0.93972818
Sum51940
Variance2544737.8
MonotonicityNot monotonic
2023-12-13T00:17:53.846816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3207 1
 
4.0%
1089 1
 
4.0%
5584 1
 
4.0%
4104 1
 
4.0%
770 1
 
4.0%
1048 1
 
4.0%
3118 1
 
4.0%
1410 1
 
4.0%
3119 1
 
4.0%
3961 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
311 1
4.0%
464 1
4.0%
513 1
4.0%
584 1
4.0%
621 1
4.0%
672 1
4.0%
770 1
4.0%
937 1
4.0%
1001 1
4.0%
1048 1
4.0%
ValueCountFrequency (%)
5785 1
4.0%
5584 1
4.0%
4104 1
4.0%
3961 1
4.0%
3207 1
4.0%
3119 1
4.0%
3118 1
4.0%
3076 1
4.0%
2866 1
4.0%
2800 1
4.0%

Interactions

2023-12-13T00:17:47.618485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:30.919419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.067394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:33.513237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.606564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.473964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.562321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:38.084579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:39.426070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.922576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.151711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.354556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.497432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:45.900251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:47.738466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.012074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.154343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:33.605468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.681030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.551656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.667490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:38.242339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:39.843444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.028939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.241003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.460502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.619741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.025734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:47.849184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.103014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.245894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:33.698658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.751489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.644349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.815552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:38.363927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:39.953180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.147320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.323494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.553562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.712815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.140081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:47.981035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.191323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.329343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:33.798987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.823477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.755494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.907320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:38.482213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.057332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.266669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.418191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.649358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.817533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.252526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.078373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.258629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.402401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:33.885567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.877361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.825537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.989304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:38.562156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.137469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.346637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.495995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.732277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.903694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.330275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.163506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.334110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.478940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:33.963348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.930382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.895632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:37.088449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:38.649108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.207203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.430680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.570876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.810484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.999688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.409184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.271447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.415559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.580893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.058468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.999836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.979892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:37.200141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:38.762789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.277118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.511283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.662993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.892322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:45.122019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.511904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.350677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.509052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.658575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.138720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.055283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.058604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:37.301451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:38.847897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.352953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.575788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.748270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.963270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:45.201262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.616981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.435836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.603769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.740653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.203491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.107251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.122268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:37.453311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:38.929352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.427803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.651845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.832936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.043585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:45.293765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.719615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.515672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.685705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.820953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.271512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.162282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.180805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:37.550636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:39.017818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.496481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.730875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.918379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.128840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:45.436309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.812716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.610413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.762150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:32.895617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.333356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.215773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.242697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:37.647449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:39.098824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.568933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.806744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.993403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.209354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:45.516928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.893068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.698808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.833173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:33.267228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.397785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.277563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.310412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:37.739829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:39.171619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.662401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.887357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.086473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.273696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:45.606565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:46.989668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.803051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.905789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:33.358128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.460042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.344176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.390550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:37.836257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:39.261663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.750091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:41.971228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.188359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.346054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:45.697504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:47.085680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:48.898745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:31.975847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:33.436553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:34.531694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:35.408652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:36.485861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:37.945069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:39.347181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:40.832227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:42.061394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:43.267350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:44.414937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:45.789380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:17:47.512420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:17:53.959882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역2010-012010-022010-032010-042010-052010-062010-072010-082010-092010-102010-112010-1220152020
행정구역1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2010-011.0001.0001.0000.9960.9960.9980.8901.0001.0001.0000.9980.9980.9980.9510.721
2010-021.0001.0001.0000.9960.9960.9980.8901.0001.0001.0000.9980.9980.9980.9510.721
2010-031.0000.9960.9961.0001.0000.9980.9090.9960.9960.9960.9930.9930.9930.9620.687
2010-041.0000.9960.9961.0001.0000.9980.9090.9960.9960.9960.9930.9930.9930.9620.687
2010-051.0000.9980.9980.9980.9981.0000.9090.9980.9980.9980.9960.9960.9960.9620.654
2010-061.0000.8900.8900.9090.9090.9091.0000.8900.8900.8900.9090.9090.9090.8740.630
2010-071.0001.0001.0000.9960.9960.9980.8901.0001.0001.0000.9980.9980.9980.9510.721
2010-081.0001.0001.0000.9960.9960.9980.8901.0001.0001.0000.9980.9980.9980.9510.721
2010-091.0001.0001.0000.9960.9960.9980.8901.0001.0001.0000.9980.9980.9980.9510.721
2010-101.0000.9980.9980.9930.9930.9960.9090.9980.9980.9981.0001.0001.0000.9600.734
2010-111.0000.9980.9980.9930.9930.9960.9090.9980.9980.9981.0001.0001.0000.9600.734
2010-121.0000.9980.9980.9930.9930.9960.9090.9980.9980.9981.0001.0001.0000.9600.734
20151.0000.9510.9510.9620.9620.9620.8740.9510.9510.9510.9600.9600.9601.0000.864
20201.0000.7210.7210.6870.6870.6540.6300.7210.7210.7210.7340.7340.7340.8641.000
2023-12-13T00:17:54.429165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2010-012010-022010-032010-042010-052010-062010-072010-082010-092010-102010-112010-1220152020
2010-011.0000.9980.9980.9950.9980.7800.9960.9950.9960.9870.9880.9870.9220.844
2010-020.9981.0000.9990.9980.9980.7780.9950.9940.9950.9850.9860.9840.9200.843
2010-030.9980.9991.0000.9970.9970.7910.9950.9950.9950.9860.9870.9850.9190.844
2010-040.9950.9980.9971.0000.9980.7790.9950.9950.9950.9860.9820.9810.9230.843
2010-050.9980.9980.9970.9981.0000.7820.9980.9980.9980.9890.9860.9850.9190.838
2010-060.7800.7780.7910.7790.7821.0000.7950.7950.7950.7860.7820.7820.6980.613
2010-070.9960.9950.9950.9950.9980.7951.0000.9991.0000.9910.9870.9860.9220.842
2010-080.9950.9940.9950.9950.9980.7950.9991.0000.9990.9920.9880.9880.9250.846
2010-090.9960.9950.9950.9950.9980.7951.0000.9991.0000.9910.9870.9860.9220.842
2010-100.9870.9850.9860.9860.9890.7860.9910.9920.9911.0000.9970.9960.9280.835
2010-110.9880.9860.9870.9820.9860.7820.9870.9880.9870.9971.0000.9990.9340.841
2010-120.9870.9840.9850.9810.9850.7820.9860.9880.9860.9960.9991.0000.9370.847
20150.9220.9200.9190.9230.9190.6980.9220.9250.9220.9280.9340.9371.0000.882
20200.8440.8430.8440.8430.8380.6130.8420.8460.8420.8350.8410.8470.8821.000

Missing values

2023-12-13T00:17:49.058807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:17:49.282072image/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

행정구역2010-012010-022010-032010-042010-052010-062010-072010-082010-092010-102010-112010-1220152020
0문막읍25982602258826152614262826412640266427722754278120653207
1소초면1416140314001403140814131414143214281478145514529481089
2호저면731735736733738743749737731766765759413464
3지정면5385335355285285255265295255195185233452800
4부론면506502499499501506515520516535535535363513
5귀래면3613553623673712270363369369391388389278311
6흥업면13521349136413791385138013781391140114411433142924753076
7판부면13361365138514071392139514051405141714421409141012731477
8신림면751758754760759754755755758802798794500584
9중앙동917918941939925931933921933103910361033621621
행정구역2010-012010-022010-032010-042010-052010-062010-072010-082010-092010-102010-112010-1220152020
15일산동14031410141914041388137213601356135614181433141611671799
16학성동10341037100510061005997100110101006107610781066639672
17단계동27292745277427472737276027832782278128892873287525683961
18우산동18481850186218731855186518691857185819721960196629493119
19태장1동9359379569519619759779799931064106910706501410
20태장2동29622981298430053025304930813089308432653254326821543118
21봉산동14621479147914941498149614891480146415731583158210381048
22행구동610610592592590603603613623643644652444770
23무실동23992391238523962385243824702489247026732763279433564104
24반곡관설동19832000198219942037203720772081211921952207219426535584