Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory68.3 B

Variable types

Categorical6
Numeric2

Alerts

행정동코드 has constant value ""Constant
시도명 has constant value ""Constant
시군구명 has constant value ""Constant
행정동명 has constant value ""Constant
성별 is highly overall correlated with 연령대High correlation
연령대 is highly overall correlated with 성별High correlation

Reproduction

Analysis started2023-12-10 13:10:38.257538
Analysis finished2023-12-10 13:10:39.961806
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1111053000
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1111053000
2nd row1111053000
3rd row1111053000
4th row1111053000
5th row1111053000

Common Values

ValueCountFrequency (%)
1111053000 100
100.0%

Length

2023-12-10T22:10:40.113225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:10:40.332996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1111053000 100
100.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 100
100.0%

Length

2023-12-10T22:10:40.587225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:10:40.745005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 100
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로구
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row종로구
3rd row종로구
4th row종로구
5th row종로구

Common Values

ValueCountFrequency (%)
종로구 100
100.0%

Length

2023-12-10T22:10:40.954623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:10:41.138115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로구 100
100.0%

행정동명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
사직동
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사직동
2nd row사직동
3rd row사직동
4th row사직동
5th row사직동

Common Values

ValueCountFrequency (%)
사직동 100
100.0%

Length

2023-12-10T22:10:41.348157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:10:41.501949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사직동 100
100.0%

기준일자
Real number (ℝ)

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190803
Minimum20190801
Maximum20190806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:41.751504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190801
5-th percentile20190801
Q120190802
median20190803
Q320190804
95-th percentile20190806
Maximum20190806
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5333333
Coefficient of variation (CV)7.5942166 × 10-8
Kurtosis-1.0954227
Mean20190803
Median Absolute Deviation (MAD)1
Skewness0.10214527
Sum2.0190803 × 109
Variance2.3511111
MonotonicityIncreasing
2023-12-10T22:10:41.977373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20190803 20
20.0%
20190802 19
19.0%
20190804 19
19.0%
20190801 18
18.0%
20190805 18
18.0%
20190806 6
 
6.0%
ValueCountFrequency (%)
20190801 18
18.0%
20190802 19
19.0%
20190803 20
20.0%
20190804 19
19.0%
20190805 18
18.0%
20190806 6
 
6.0%
ValueCountFrequency (%)
20190806 6
 
6.0%
20190805 18
18.0%
20190804 19
19.0%
20190803 20
20.0%
20190802 19
19.0%
20190801 18
18.0%

성별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
51 
M
44 
X
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 51
51.0%
M 44
44.0%
X 5
 
5.0%

Length

2023-12-10T22:10:42.168690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:10:42.329533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 51
51.0%
m 44
44.0%
x 5
 
5.0%

연령대
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
40
11 
20
11 
25
11 
30
11 
35
11 
Other values (7)
45 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row15
3rd row20
4th row25
5th row30

Common Values

ValueCountFrequency (%)
40 11
11.0%
20 11
11.0%
25 11
11.0%
30 11
11.0%
35 11
11.0%
45 11
11.0%
50 9
9.0%
15 6
6.0%
55 6
6.0%
60 6
6.0%
Other values (2) 7
7.0%

Length

2023-12-10T22:10:42.487940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
40 11
11.0%
20 11
11.0%
25 11
11.0%
30 11
11.0%
35 11
11.0%
45 11
11.0%
50 9
9.0%
15 6
6.0%
55 6
6.0%
60 6
6.0%
Other values (2) 7
7.0%

소비인구(명)
Real number (ℝ)

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.74
Minimum22
Maximum192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:42.636609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q137
median59
Q389
95-th percentile140.4
Maximum192
Range170
Interquartile range (IQR)52

Descriptive statistics

Standard deviation37.136282
Coefficient of variation (CV)0.54821792
Kurtosis0.71159024
Mean67.74
Median Absolute Deviation (MAD)22
Skewness1.0037724
Sum6774
Variance1379.1034
MonotonicityNot monotonic
2023-12-10T22:10:42.814917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
30 11
11.0%
52 11
11.0%
44 10
10.0%
37 8
 
8.0%
22 8
 
8.0%
74 7
 
7.0%
103 6
 
6.0%
66 6
 
6.0%
89 6
 
6.0%
81 5
 
5.0%
Other values (11) 22
22.0%
ValueCountFrequency (%)
22 8
8.0%
30 11
11.0%
37 8
8.0%
44 10
10.0%
52 11
11.0%
59 5
5.0%
66 6
6.0%
74 7
7.0%
81 5
5.0%
89 6
6.0%
ValueCountFrequency (%)
192 1
 
1.0%
170 1
 
1.0%
155 1
 
1.0%
148 2
 
2.0%
140 2
 
2.0%
133 1
 
1.0%
126 1
 
1.0%
118 2
 
2.0%
111 2
 
2.0%
103 6
6.0%

Interactions

2023-12-10T22:10:39.031606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:38.672240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:39.184837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:38.880034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:10:42.953100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자성별연령대소비인구(명)
기준일자1.0000.0000.0000.000
성별0.0001.0000.9200.385
연령대0.0000.9201.0000.537
소비인구(명)0.0000.3850.5371.000
2023-12-10T22:10:43.083573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대성별
연령대1.0000.653
성별0.6531.000
2023-12-10T22:10:43.208688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자소비인구(명)성별연령대
기준일자1.000-0.1940.0000.000
소비인구(명)-0.1941.0000.2400.254
성별0.0000.2401.0000.653
연령대0.0000.2540.6531.000

Missing values

2023-12-10T22:10:39.447932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:10:39.815558image/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

행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
01111053000서울특별시종로구사직동20190801M4066
11111053000서울특별시종로구사직동20190801F1552
21111053000서울특별시종로구사직동20190801F2059
31111053000서울특별시종로구사직동20190801F2596
41111053000서울특별시종로구사직동20190801F3081
51111053000서울특별시종로구사직동20190801F3589
61111053000서울특별시종로구사직동20190801F4089
71111053000서울특별시종로구사직동20190801F4566
81111053000서울특별시종로구사직동20190801F5052
91111053000서울특별시종로구사직동20190801F5544
행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
901111053000서울특별시종로구사직동20190805M4544
911111053000서울특별시종로구사직동20190805M5022
921111053000서울특별시종로구사직동20190805M6030
931111053000서울특별시종로구사직동20190805Xxx81
941111053000서울특별시종로구사직동20190806F2074
951111053000서울특별시종로구사직동20190806F25103
961111053000서울특별시종로구사직동20190806F3066
971111053000서울특별시종로구사직동20190806F3552
981111053000서울특별시종로구사직동20190806F4022
991111053000서울특별시종로구사직동20190806F4566