Overview

Dataset statistics

Number of variables4
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory37.9 B

Variable types

Text1
Numeric3

Alerts

관용대수(대) is highly overall correlated with 자가용대수(대) and 1 other fieldsHigh correlation
자가용대수(대) is highly overall correlated with 관용대수(대) and 1 other fieldsHigh correlation
영업용대수(대) is highly overall correlated with 관용대수(대) and 1 other fieldsHigh correlation
시군구명 has unique valuesUnique
자가용대수(대) has unique valuesUnique
관용대수(대) has 4 (8.7%) zerosZeros
영업용대수(대) has 4 (8.7%) zerosZeros

Reproduction

Analysis started2024-03-12 23:14:32.273198
Analysis finished2024-03-12 23:14:33.075750
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2024-03-13T08:14:33.211774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length4.8478261
Min length3

Characters and Unicode

Total characters223
Distinct characters62
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

Unique46 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row고양시 덕양구
3rd row고양시 일산동구
4th row고양시 일산서구
5th row과천시
ValueCountFrequency (%)
부천시 4
 
6.1%
수원시 4
 
6.1%
용인시 3
 
4.5%
고양시 3
 
4.5%
성남시 3
 
4.5%
안양시 2
 
3.0%
안산시 2
 
3.0%
이천시 1
 
1.5%
여주군 1
 
1.5%
상록구 1
 
1.5%
Other values (42) 42
63.6%
2024-03-13T08:14:33.483247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 43
19.3%
구 21
 
9.4%
20
 
9.0%
양 9
 
4.0%
천 9
 
4.0%
안 8
 
3.6%
원 7
 
3.1%
주 6
 
2.7%
수 6
 
2.7%
부 5
 
2.2%
Other values (52) 89
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 203
91.0%
Space Separator 20
 
9.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 43
21.2%
구 21
 
10.3%
양 9
 
4.4%
천 9
 
4.4%
안 8
 
3.9%
원 7
 
3.4%
주 6
 
3.0%
수 6
 
3.0%
부 5
 
2.5%
산 5
 
2.5%
Other values (51) 84
41.4%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 203
91.0%
Common 20
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 43
21.2%
구 21
 
10.3%
양 9
 
4.4%
천 9
 
4.4%
안 8
 
3.9%
원 7
 
3.4%
주 6
 
3.0%
수 6
 
3.0%
부 5
 
2.5%
산 5
 
2.5%
Other values (51) 84
41.4%
Common
ValueCountFrequency (%)
20
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 203
91.0%
ASCII 20
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 43
21.2%
구 21
 
10.3%
양 9
 
4.4%
천 9
 
4.4%
안 8
 
3.9%
원 7
 
3.4%
주 6
 
3.0%
수 6
 
3.0%
부 5
 
2.5%
산 5
 
2.5%
Other values (51) 84
41.4%
ASCII
ValueCountFrequency (%)
20
100.0%

관용대수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.673913
Minimum0
Maximum37
Zeros4
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-03-13T08:14:33.579444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q317
95-th percentile26.5
Maximum37
Range37
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.116662
Coefficient of variation (CV)0.85410683
Kurtosis0.1193981
Mean10.673913
Median Absolute Deviation (MAD)6
Skewness0.80348138
Sum491
Variance83.113527
MonotonicityNot monotonic
2024-03-13T08:14:33.671892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 6
 
13.0%
0 4
 
8.7%
8 4
 
8.7%
12 3
 
6.5%
17 3
 
6.5%
1 3
 
6.5%
19 2
 
4.3%
4 2
 
4.3%
7 2
 
4.3%
11 2
 
4.3%
Other values (15) 15
32.6%
ValueCountFrequency (%)
0 4
8.7%
1 3
6.5%
2 6
13.0%
3 1
 
2.2%
4 2
 
4.3%
5 1
 
2.2%
6 1
 
2.2%
7 2
 
4.3%
8 4
8.7%
11 2
 
4.3%
ValueCountFrequency (%)
37 1
 
2.2%
29 1
 
2.2%
27 1
 
2.2%
25 1
 
2.2%
24 1
 
2.2%
22 1
 
2.2%
21 1
 
2.2%
19 2
4.3%
18 1
 
2.2%
17 3
6.5%

자가용대수(대)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1281.8043
Minimum2
Maximum9571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-03-13T08:14:33.778291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile17.75
Q1192.5
median529.5
Q32344.5
95-th percentile3642.75
Maximum9571
Range9569
Interquartile range (IQR)2152

Descriptive statistics

Standard deviation1732.9877
Coefficient of variation (CV)1.3519908
Kurtosis10.672661
Mean1281.8043
Median Absolute Deviation (MAD)368.5
Skewness2.7605452
Sum58963
Variance3003246.3
MonotonicityNot monotonic
2024-03-13T08:14:33.895219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
589 1
 
2.2%
553 1
 
2.2%
2843 1
 
2.2%
244 1
 
2.2%
218 1
 
2.2%
2508 1
 
2.2%
873 1
 
2.2%
41 1
 
2.2%
1851 1
 
2.2%
900 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
2 1
2.2%
7 1
2.2%
10 1
2.2%
41 1
2.2%
86 1
2.2%
140 1
2.2%
144 1
2.2%
155 1
2.2%
163 1
2.2%
165 1
2.2%
ValueCountFrequency (%)
9571 1
2.2%
4186 1
2.2%
3666 1
2.2%
3573 1
2.2%
3296 1
2.2%
3065 1
2.2%
2843 1
2.2%
2791 1
2.2%
2508 1
2.2%
2497 1
2.2%

영업용대수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1081.4565
Minimum0
Maximum4825
Zeros4
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-03-13T08:14:34.207688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1138.5
median800.5
Q31544
95-th percentile3380.75
Maximum4825
Range4825
Interquartile range (IQR)1405.5

Descriptive statistics

Standard deviation1125.2068
Coefficient of variation (CV)1.0404549
Kurtosis2.0618631
Mean1081.4565
Median Absolute Deviation (MAD)671
Skewness1.4532957
Sum49747
Variance1266090.3
MonotonicityNot monotonic
2024-03-13T08:14:34.318874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 4
 
8.7%
1027 2
 
4.3%
366 1
 
2.2%
129 1
 
2.2%
1159 1
 
2.2%
704 1
 
2.2%
738 1
 
2.2%
313 1
 
2.2%
1337 1
 
2.2%
828 1
 
2.2%
Other values (32) 32
69.6%
ValueCountFrequency (%)
0 4
8.7%
1 1
 
2.2%
8 1
 
2.2%
47 1
 
2.2%
60 1
 
2.2%
67 1
 
2.2%
79 1
 
2.2%
129 1
 
2.2%
130 1
 
2.2%
164 1
 
2.2%
ValueCountFrequency (%)
4825 1
2.2%
3854 1
2.2%
3536 1
2.2%
2915 1
2.2%
2564 1
2.2%
2547 1
2.2%
2331 1
2.2%
2005 1
2.2%
1988 1
2.2%
1784 1
2.2%

Interactions

2024-03-13T08:14:32.762140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:32.364375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:32.569039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:32.831837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:32.440455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:32.637910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:32.889601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:32.503170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:32.700623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:14:34.399647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명관용대수(대)자가용대수(대)영업용대수(대)
시군구명1.0001.0001.0001.000
관용대수(대)1.0001.0000.7920.456
자가용대수(대)1.0000.7921.0000.658
영업용대수(대)1.0000.4560.6581.000
2024-03-13T08:14:34.487531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관용대수(대)자가용대수(대)영업용대수(대)
관용대수(대)1.0000.6970.525
자가용대수(대)0.6971.0000.691
영업용대수(대)0.5250.6911.000

Missing values

2024-03-13T08:14:32.965446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:14:33.042000image/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

시군구명관용대수(대)자가용대수(대)영업용대수(대)
0가평군18589366
1고양시 덕양구13847508
2고양시 일산동구18851988
3고양시 일산서구1306426
4과천시8860
5광명시242147
6광주시2432961242
7구리시2506877
8군포시1427459
9김포시1241861139
시군구명관용대수(대)자가용대수(대)영업용대수(대)
36용인시 수지구214479
37용인시 처인구2524973854
38의왕시31848
39의정부시273212005
40이천시824882547
41파주시1630652915
42평택시2135734825
43포천시2936661282
44하남시11779130
45화성시2295712331