Overview

Dataset statistics

Number of variables5
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory50.0 B

Variable types

Text1
Numeric4

Alerts

가구 is highly overall correlated with 인원 and 2 other fieldsHigh correlation
인원 is highly overall correlated with 가구 and 2 other fieldsHigh correlation
남 is highly overall correlated with 가구 and 2 other fieldsHigh correlation
여 is highly overall correlated with 가구 and 2 other fieldsHigh correlation
동별 has unique valuesUnique
가구 has unique valuesUnique
인원 has unique valuesUnique
남 has unique valuesUnique
여 has unique valuesUnique

Reproduction

Analysis started2024-03-18 03:30:17.642093
Analysis finished2024-03-18 03:30:20.643038
Duration3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동별
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-18T12:30:20.752302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9545455
Min length3

Characters and Unicode

Total characters87
Distinct characters20
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

Unique22 ?
Unique (%)100.0%

Sample

1st row부평1동
2nd row부평2동
3rd row부평3동
4th row부평4동
5th row부평5동
ValueCountFrequency (%)
부평1동 1
 
4.5%
부평2동 1
 
4.5%
십정1동 1
 
4.5%
일신동 1
 
4.5%
부개3동 1
 
4.5%
부개2동 1
 
4.5%
부개1동 1
 
4.5%
삼산2동 1
 
4.5%
삼산1동 1
 
4.5%
갈산2동 1
 
4.5%
Other values (12) 12
54.5%
2024-03-18T12:30:20.996626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 22
25.3%
부 9
10.3%
산 8
 
9.2%
1 7
 
8.0%
2 7
 
8.0%
평 6
 
6.9%
곡 4
 
4.6%
3 3
 
3.4%
개 3
 
3.4%
4 2
 
2.3%
Other values (10) 16
18.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
75.9%
Decimal Number 21
 
24.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 22
33.3%
부 9
13.6%
산 8
 
12.1%
평 6
 
9.1%
곡 4
 
6.1%
개 3
 
4.5%
청 2
 
3.0%
천 2
 
3.0%
갈 2
 
3.0%
삼 2
 
3.0%
Other values (4) 6
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
75.9%
Common 21
 
24.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
33.3%
부 9
13.6%
산 8
 
12.1%
평 6
 
9.1%
곡 4
 
6.1%
개 3
 
4.5%
청 2
 
3.0%
천 2
 
3.0%
갈 2
 
3.0%
삼 2
 
3.0%
Other values (4) 6
 
9.1%
Common
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
75.9%
ASCII 21
 
24.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 22
33.3%
부 9
13.6%
산 8
 
12.1%
평 6
 
9.1%
곡 4
 
6.1%
개 3
 
4.5%
청 2
 
3.0%
천 2
 
3.0%
갈 2
 
3.0%
삼 2
 
3.0%
Other values (4) 6
 
9.1%
ASCII
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%

가구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean929.72727
Minimum288
Maximum2606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:30:21.091896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum288
5-th percentile357.95
Q1642.75
median794.5
Q31033
95-th percentile1644.45
Maximum2606
Range2318
Interquartile range (IQR)390.25

Descriptive statistics

Standard deviation518.62567
Coefficient of variation (CV)0.5578256
Kurtosis4.2051222
Mean929.72727
Median Absolute Deviation (MAD)204
Skewness1.7098113
Sum20454
Variance268972.59
MonotonicityNot monotonic
2024-03-18T12:30:21.179844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
782 1
 
4.5%
773 1
 
4.5%
1482 1
 
4.5%
807 1
 
4.5%
356 1
 
4.5%
750 1
 
4.5%
619 1
 
4.5%
1044 1
 
4.5%
818 1
 
4.5%
2606 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
288 1
4.5%
356 1
4.5%
395 1
4.5%
419 1
4.5%
604 1
4.5%
619 1
4.5%
714 1
4.5%
750 1
4.5%
759 1
4.5%
773 1
4.5%
ValueCountFrequency (%)
2606 1
4.5%
1653 1
4.5%
1482 1
4.5%
1381 1
4.5%
1264 1
4.5%
1044 1
4.5%
1000 1
4.5%
997 1
4.5%
943 1
4.5%
818 1
4.5%

인원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1353
Minimum437
Maximum3594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:30:21.265191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437
5-th percentile557.5
Q1954
median1249.5
Q31502.5
95-th percentile2370.1
Maximum3594
Range3157
Interquartile range (IQR)548.5

Descriptive statistics

Standard deviation705.40485
Coefficient of variation (CV)0.52136352
Kurtosis3.8579426
Mean1353
Median Absolute Deviation (MAD)293.5
Skewness1.6037151
Sum29766
Variance497596
MonotonicityNot monotonic
2024-03-18T12:30:21.354468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1134 1
 
4.5%
1292 1
 
4.5%
2087 1
 
4.5%
1207 1
 
4.5%
557 1
 
4.5%
1182 1
 
4.5%
952 1
 
4.5%
1495 1
 
4.5%
1330 1
 
4.5%
3594 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
437 1
4.5%
557 1
4.5%
567 1
4.5%
684 1
4.5%
865 1
4.5%
952 1
4.5%
960 1
4.5%
1106 1
4.5%
1134 1
4.5%
1182 1
4.5%
ValueCountFrequency (%)
3594 1
4.5%
2385 1
4.5%
2087 1
4.5%
2028 1
4.5%
1608 1
4.5%
1505 1
4.5%
1495 1
4.5%
1417 1
4.5%
1374 1
4.5%
1330 1
4.5%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean604.5
Minimum169
Maximum1541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:30:21.470557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum169
5-th percentile254.6
Q1419
median541.5
Q3680
95-th percentile1099.85
Maximum1541
Range1372
Interquartile range (IQR)261

Descriptive statistics

Standard deviation315.74112
Coefficient of variation (CV)0.52231781
Kurtosis2.5837852
Mean604.5
Median Absolute Deviation (MAD)137
Skewness1.3983291
Sum13299
Variance99692.452
MonotonicityNot monotonic
2024-03-18T12:30:21.596429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
520 1
 
4.5%
589 1
 
4.5%
1040 1
 
4.5%
563 1
 
4.5%
253 1
 
4.5%
487 1
 
4.5%
408 1
 
4.5%
674 1
 
4.5%
516 1
 
4.5%
1541 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
169 1
4.5%
253 1
4.5%
285 1
4.5%
294 1
4.5%
388 1
4.5%
408 1
4.5%
452 1
4.5%
469 1
4.5%
487 1
4.5%
516 1
4.5%
ValueCountFrequency (%)
1541 1
4.5%
1103 1
4.5%
1040 1
4.5%
902 1
4.5%
688 1
4.5%
682 1
4.5%
674 1
4.5%
649 1
4.5%
627 1
4.5%
589 1
4.5%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean748.5
Minimum268
Maximum2053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:30:21.722189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum268
5-th percentile283.1
Q1517
median699
Q3820
95-th percentile1274.2
Maximum2053
Range1785
Interquartile range (IQR)303

Descriptive statistics

Standard deviation393.90786
Coefficient of variation (CV)0.52626301
Kurtosis4.8766053
Mean748.5
Median Absolute Deviation (MAD)173
Skewness1.7694612
Sum16467
Variance155163.4
MonotonicityNot monotonic
2024-03-18T12:30:21.828345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
614 1
 
4.5%
703 1
 
4.5%
1047 1
 
4.5%
644 1
 
4.5%
304 1
 
4.5%
695 1
 
4.5%
544 1
 
4.5%
821 1
 
4.5%
814 1
 
4.5%
2053 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
268 1
4.5%
282 1
4.5%
304 1
4.5%
390 1
4.5%
477 1
4.5%
508 1
4.5%
544 1
4.5%
614 1
4.5%
637 1
4.5%
644 1
4.5%
ValueCountFrequency (%)
2053 1
4.5%
1282 1
4.5%
1126 1
4.5%
1047 1
4.5%
926 1
4.5%
821 1
4.5%
817 1
4.5%
814 1
4.5%
768 1
4.5%
747 1
4.5%

Interactions

2024-03-18T12:30:20.123994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.199485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.584434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.848932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:20.194547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.350901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.643022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.909089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:20.275107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.448312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.709498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.976527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:20.363663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.518996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:19.778243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:30:20.045795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T12:30:21.904744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동별가구인원남여
동별1.0001.0001.0001.0001.000
가구1.0001.0000.9670.9800.954
인원1.0000.9671.0000.9810.994
남1.0000.9800.9811.0000.963
여1.0000.9540.9940.9631.000
2024-03-18T12:30:22.014834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가구인원남여
가구1.0000.9860.9800.974
인원0.9861.0000.9900.990
남0.9800.9901.0000.967
여0.9740.9900.9671.000

Missing values

2024-03-18T12:30:20.498075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T12:30:20.600898image/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

동별가구인원남여
0부평1동7821134520614
1부평2동9431417649768
2부평3동10001374627747
3부평4동1653238511031282
4부평5동138120289021126
5부평6동714960452508
6산곡1동604865388477
7산곡2동419684294390
8산곡3동7591106469637
9산곡4동288437169268
동별가구인원남여
12갈산1동7731292589703
13갈산2동12641608682926
14삼산1동2606359415412053
15삼산2동8181330516814
16부개1동10441495674821
17부개2동619952408544
18부개3동7501182487695
19일신동356557253304
20십정1동8071207563644
21십정2동1482208710401047