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

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
성별 is highly overall correlated with 연령대High correlation
연령대 is highly overall correlated with 성별High correlation
행정동코드 is highly imbalanced (91.9%)Imbalance
행정동명 is highly imbalanced (91.9%)Imbalance

Reproduction

Analysis started2023-12-10 13:38:03.744293
Analysis finished2023-12-10 13:38:04.822342
Duration1.08 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1111061500
99 
1111056000
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1111056000
2nd row1111061500
3rd row1111061500
4th row1111061500
5th row1111061500

Common Values

ValueCountFrequency (%)
1111061500 99
99.0%
1111056000 1
 
1.0%

Length

2023-12-10T22:38:04.891937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:04.989018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1111061500 99
99.0%
1111056000 1
 
1.0%

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

2023-12-10T22:38:05.094646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:05.195564image/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

2023-12-10T22:38:05.306664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:05.414825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로구 100
100.0%

행정동명
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로1.2.3.4가동
99 
평창동
 
1

Length

Max length11
Median length11
Mean length10.92
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row평창동
2nd row종로1.2.3.4가동
3rd row종로1.2.3.4가동
4th row종로1.2.3.4가동
5th row종로1.2.3.4가동

Common Values

ValueCountFrequency (%)
종로1.2.3.4가동 99
99.0%
평창동 1
 
1.0%

Length

2023-12-10T22:38:05.569057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:05.687934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로1.2.3.4가동 99
99.0%
평창동 1
 
1.0%

기준일자
Real number (ℝ)

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190806
Minimum20190801
Maximum20190926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:05.792717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190801
5-th percentile20190801
Q120190802
median20190805
Q320190808
95-th percentile20190812
Maximum20190926
Range125
Interquartile range (IQR)6

Descriptive statistics

Standard deviation12.636583
Coefficient of variation (CV)6.2585827 × 10-7
Kurtosis82.926268
Mean20190806
Median Absolute Deviation (MAD)3
Skewness8.719303
Sum2.0190806 × 109
Variance159.68323
MonotonicityNot monotonic
2023-12-10T22:38:05.920396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
20190801 19
19.0%
20190802 14
14.0%
20190805 13
13.0%
20190803 11
11.0%
20190806 9
9.0%
20190812 9
9.0%
20190808 7
 
7.0%
20190807 5
 
5.0%
20190809 4
 
4.0%
20190810 3
 
3.0%
Other values (4) 6
 
6.0%
ValueCountFrequency (%)
20190801 19
19.0%
20190802 14
14.0%
20190803 11
11.0%
20190804 1
 
1.0%
20190805 13
13.0%
20190806 9
9.0%
20190807 5
 
5.0%
20190808 7
 
7.0%
20190809 4
 
4.0%
20190810 3
 
3.0%
ValueCountFrequency (%)
20190926 1
 
1.0%
20190813 3
 
3.0%
20190812 9
9.0%
20190811 1
 
1.0%
20190810 3
 
3.0%
20190809 4
 
4.0%
20190808 7
7.0%
20190807 5
 
5.0%
20190806 9
9.0%
20190805 13
13.0%

성별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
56 
M
42 
X
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 56
56.0%
M 42
42.0%
X 2
 
2.0%

Length

2023-12-10T22:38:06.077791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:06.232275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 56
56.0%
m 42
42.0%
x 2
 
2.0%

연령대
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20
20 
25
18 
50
14 
45
12 
55
10 
Other values (6)
26 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row15
3rd row45
4th row25
5th row30

Common Values

ValueCountFrequency (%)
20 20
20.0%
25 18
18.0%
50 14
14.0%
45 12
12.0%
55 10
10.0%
30 8
 
8.0%
15 6
 
6.0%
40 5
 
5.0%
35 3
 
3.0%
60 2
 
2.0%

Length

2023-12-10T22:38:06.365078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20 20
20.0%
25 18
18.0%
50 14
14.0%
45 12
12.0%
55 10
10.0%
30 8
 
8.0%
15 6
 
6.0%
40 5
 
5.0%
35 3
 
3.0%
60 2
 
2.0%

소비인구(명)
Real number (ℝ)

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.02
Minimum22
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:06.499871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q130
median37
Q360.75
95-th percentile148.35
Maximum177
Range155
Interquartile range (IQR)30.75

Descriptive statistics

Standard deviation36.52147
Coefficient of variation (CV)0.70206594
Kurtosis2.9354172
Mean52.02
Median Absolute Deviation (MAD)15
Skewness1.8031988
Sum5202
Variance1333.8178
MonotonicityNot monotonic
2023-12-10T22:38:06.640442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
22 24
24.0%
30 17
17.0%
37 12
12.0%
59 8
 
8.0%
52 8
 
8.0%
66 7
 
7.0%
44 6
 
6.0%
163 3
 
3.0%
103 3
 
3.0%
74 2
 
2.0%
Other values (8) 10
10.0%
ValueCountFrequency (%)
22 24
24.0%
30 17
17.0%
37 12
12.0%
44 6
 
6.0%
52 8
 
8.0%
59 8
 
8.0%
66 7
 
7.0%
74 2
 
2.0%
81 2
 
2.0%
89 2
 
2.0%
ValueCountFrequency (%)
177 1
 
1.0%
163 3
3.0%
155 1
 
1.0%
148 1
 
1.0%
126 1
 
1.0%
118 1
 
1.0%
103 3
3.0%
96 1
 
1.0%
89 2
2.0%
81 2
2.0%

Interactions

2023-12-10T22:38:04.298999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:04.071504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:04.419357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:04.172172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:38:06.741077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드행정동명기준일자성별연령대소비인구(명)
행정동코드1.0000.6930.6890.0000.3330.000
행정동명0.6931.0000.6890.0000.3330.000
기준일자0.6890.6891.0000.0000.4990.000
성별0.0000.0000.0001.0000.8070.000
연령대0.3330.3330.4990.8071.0000.000
소비인구(명)0.0000.0000.0000.0000.0001.000
2023-12-10T22:38:06.876985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명성별행정동코드연령대
행정동명1.0000.0000.4870.303
성별0.0001.0000.0000.656
행정동코드0.4870.0001.0000.303
연령대0.3030.6560.3031.000
2023-12-10T22:38:07.019213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자소비인구(명)행정동코드행정동명성별연령대
기준일자1.000-0.1920.4870.4870.0000.303
소비인구(명)-0.1921.0000.0000.0000.0000.000
행정동코드0.4870.0001.0000.4870.0000.303
행정동명0.4870.0000.4871.0000.0000.303
성별0.0000.0000.0000.0001.0000.656
연령대0.3030.0000.3030.3030.6561.000

Missing values

2023-12-10T22:38:04.571058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:38:04.771032image/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)
01111056000서울특별시종로구평창동20190926F4030
11111061500서울특별시종로구종로1.2.3.4가동20190801F1530
21111061500서울특별시종로구종로1.2.3.4가동20190806F4566
31111061500서울특별시종로구종로1.2.3.4가동20190801F25155
41111061500서울특별시종로구종로1.2.3.4가동20190801F3022
51111061500서울특별시종로구종로1.2.3.4가동20190801F3522
61111061500서울특별시종로구종로1.2.3.4가동20190802M4537
71111061500서울특별시종로구종로1.2.3.4가동20190801F45126
81111061500서울특별시종로구종로1.2.3.4가동20190801F50163
91111061500서울특별시종로구종로1.2.3.4가동20190801F5544
행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
901111061500서울특별시종로구종로1.2.3.4가동20190812F3022
911111061500서울특별시종로구종로1.2.3.4가동20190812F4522
921111061500서울특별시종로구종로1.2.3.4가동20190812F5037
931111061500서울특별시종로구종로1.2.3.4가동20190812F5537
941111061500서울특별시종로구종로1.2.3.4가동20190812M2059
951111061500서울특별시종로구종로1.2.3.4가동20190812M2537
961111061500서울특별시종로구종로1.2.3.4가동20190812M5022
971111061500서울특별시종로구종로1.2.3.4가동20190813F2066
981111061500서울특별시종로구종로1.2.3.4가동20190813F2522
991111061500서울특별시종로구종로1.2.3.4가동20190813F4030