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

Categorical7
Numeric1

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
행정동코드 is highly overall correlated with 행정동명High correlation
행정동명 is highly overall correlated with 행정동코드High correlation
성별 is highly overall correlated with 연령대High correlation
연령대 is highly overall correlated with 성별High correlation
소비인구(명) is highly imbalanced (52.3%)Imbalance

Reproduction

Analysis started2023-12-10 12:02:25.763604
Analysis finished2023-12-10 12:02:26.661298
Duration0.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1111051500
68 
1111053000
19 
1111055000
13 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1111051500
2nd row1111051500
3rd row1111051500
4th row1111051500
5th row1111051500

Common Values

ValueCountFrequency (%)
1111051500 68
68.0%
1111053000 19
 
19.0%
1111055000 13
 
13.0%

Length

2023-12-10T21:02:26.768225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:02:26.915511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1111051500 68
68.0%
1111053000 19
 
19.0%
1111055000 13
 
13.0%

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

2023-12-10T21:02:27.091604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:02:27.259966image/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

2023-12-10T21:02:27.421062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:02:27.571747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로구 100
100.0%

행정동명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
청운효자동
68 
사직동
19 
부암동
13 

Length

Max length5
Median length5
Mean length4.36
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청운효자동 68
68.0%
사직동 19
 
19.0%
부암동 13
 
13.0%

Length

2023-12-10T21:02:27.752486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:02:27.917960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청운효자동 68
68.0%
사직동 19
 
19.0%
부암동 13
 
13.0%

기준일자
Real number (ℝ)

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190942
Minimum20190805
Maximum20191031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:02:28.098312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190805
5-th percentile20190809
Q120190903
median20190928
Q320191015
95-th percentile20191029
Maximum20191031
Range226
Interquartile range (IQR)112.25

Descriptive statistics

Standard deviation81.623191
Coefficient of variation (CV)4.0425649 × 10-6
Kurtosis-1.2389527
Mean20190942
Median Absolute Deviation (MAD)85.5
Skewness-0.50391812
Sum2.0190942 × 109
Variance6662.3454
MonotonicityNot monotonic
2023-12-10T21:02:28.346301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
20191010 7
 
7.0%
20190923 6
 
6.0%
20191001 5
 
5.0%
20191025 4
 
4.0%
20191016 4
 
4.0%
20190925 4
 
4.0%
20191007 4
 
4.0%
20190909 3
 
3.0%
20190810 3
 
3.0%
20191030 3
 
3.0%
Other values (34) 57
57.0%
ValueCountFrequency (%)
20190805 3
3.0%
20190806 2
2.0%
20190809 1
 
1.0%
20190810 3
3.0%
20190812 1
 
1.0%
20190813 2
2.0%
20190816 3
3.0%
20190820 1
 
1.0%
20190822 1
 
1.0%
20190823 2
2.0%
ValueCountFrequency (%)
20191031 2
2.0%
20191030 3
3.0%
20191029 1
 
1.0%
20191028 2
2.0%
20191025 4
4.0%
20191024 2
2.0%
20191023 3
3.0%
20191022 1
 
1.0%
20191021 2
2.0%
20191019 1
 
1.0%

성별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
M
62 
F
34 
X
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 62
62.0%
F 34
34.0%
X 4
 
4.0%

Length

2023-12-10T21:02:28.567619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:02:28.770449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 62
62.0%
f 34
34.0%
x 4
 
4.0%

연령대
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
40
21 
35
20 
30
17 
25
14 
50
12 
Other values (5)
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row35
2nd row50
3rd row30
4th row25
5th row40

Common Values

ValueCountFrequency (%)
40 21
21.0%
35 20
20.0%
30 17
17.0%
25 14
14.0%
50 12
12.0%
45 5
 
5.0%
55 5
 
5.0%
xx 4
 
4.0%
60 1
 
1.0%
70 1
 
1.0%

Length

2023-12-10T21:02:28.952491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:02:29.155321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
40 21
21.0%
35 20
20.0%
30 17
17.0%
25 14
14.0%
50 12
12.0%
45 5
 
5.0%
55 5
 
5.0%
xx 4
 
4.0%
60 1
 
1.0%
70 1
 
1.0%

소비인구(명)
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
22
76 
30
21 
37
 
2
44
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row22
2nd row22
3rd row44
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 76
76.0%
30 21
 
21.0%
37 2
 
2.0%
44 1
 
1.0%

Length

2023-12-10T21:02:29.395460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:02:29.649096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22 76
76.0%
30 21
 
21.0%
37 2
 
2.0%
44 1
 
1.0%

Interactions

2023-12-10T21:02:26.208737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:02:29.843217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드행정동명기준일자성별연령대소비인구(명)
행정동코드1.0001.0000.4300.6170.4940.078
행정동명1.0001.0000.4300.6170.4940.078
기준일자0.4300.4301.0000.0000.0000.000
성별0.6170.6170.0001.0000.8660.000
연령대0.4940.4940.0000.8661.0000.000
소비인구(명)0.0780.0780.0000.0000.0001.000
2023-12-10T21:02:30.086245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드연령대성별소비인구(명)행정동명
행정동코드1.0000.3280.2840.0711.000
연령대0.3281.0000.7650.0000.328
성별0.2840.7651.0000.0000.284
소비인구(명)0.0710.0000.0001.0000.071
행정동명1.0000.3280.2840.0711.000
2023-12-10T21:02:30.269092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자행정동코드행정동명성별연령대소비인구(명)
기준일자1.0000.1960.1960.0000.0000.000
행정동코드0.1961.0001.0000.2840.3280.071
행정동명0.1961.0001.0000.2840.3280.071
성별0.0000.2840.2841.0000.7650.000
연령대0.0000.3280.3280.7651.0000.000
소비인구(명)0.0000.0710.0710.0000.0001.000

Missing values

2023-12-10T21:02:26.389389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:02:26.583387image/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)
01111051500서울특별시종로구청운효자동20191004M3522
11111051500서울특별시종로구청운효자동20190923F5022
21111051500서울특별시종로구청운효자동20191010M3044
31111051500서울특별시종로구청운효자동20190823M2522
41111051500서울특별시종로구청운효자동20191012M4022
51111051500서울특별시종로구청운효자동20190816M4022
61111051500서울특별시종로구청운효자동20190805M2522
71111051500서울특별시종로구청운효자동20190919M3022
81111051500서울특별시종로구청운효자동20191014F6022
91111051500서울특별시종로구청운효자동20191007M2537
행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
901111055000서울특별시종로구부암동20190925F5030
911111055000서울특별시종로구부암동20190918M4522
921111055000서울특별시종로구부암동20190903F4022
931111055000서울특별시종로구부암동20190831M5022
941111055000서울특별시종로구부암동20190822F4022
951111055000서울특별시종로구부암동20190816F5530
961111055000서울특별시종로구부암동20190810F5030
971111055000서울특별시종로구부암동20190806F5022
981111055000서울특별시종로구부암동20190806F4022
991111055000서울특별시종로구부암동20190805F5030