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
행정동코드 is highly overall correlated with 기준일자 and 3 other fieldsHigh correlation
행정동명 is highly overall correlated with 기준일자 and 3 other fieldsHigh correlation
기준일자 is highly overall correlated with 소비인구(명) and 2 other fieldsHigh correlation
소비인구(명) is highly overall correlated with 기준일자 and 2 other fieldsHigh correlation
성별 is highly overall correlated with 연령대High correlation
연령대 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 13:16:12.254063
Analysis finished2023-12-10 13:16:13.905158
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1111053000
70 
1111061500
30 

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 70
70.0%
1111061500 30
30.0%

Length

2023-12-10T22:16:14.020789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:16:14.201122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1111053000 70
70.0%
1111061500 30
30.0%

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

2023-12-10T22:16:14.363794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:16:14.584865image/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

2023-12-10T22:16:14.727749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:16:14.969284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로구 100
100.0%

행정동명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
사직동
70 
종로1.2.3.4가동
30 

Length

Max length11
Median length3
Mean length5.4
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사직동 70
70.0%
종로1.2.3.4가동 30
30.0%

Length

2023-12-10T22:16:15.262967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:16:15.434005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사직동 70
70.0%
종로1.2.3.4가동 30
30.0%

기준일자
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190890
Minimum20190801
Maximum20191031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:16:15.636070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190801
5-th percentile20190801
Q120190802
median20190828
Q320191003
95-th percentile20191026
Maximum20191031
Range230
Interquartile range (IQR)201

Descriptive statistics

Standard deviation92.995207
Coefficient of variation (CV)4.6058002 × 10-6
Kurtosis-1.6226238
Mean20190890
Median Absolute Deviation (MAD)26.5
Skewness0.3817866
Sum2.019089 × 109
Variance8648.1086
MonotonicityNot monotonic
2023-12-10T22:16:15.876626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190802 17
 
17.0%
20190801 15
 
15.0%
20191003 3
 
3.0%
20190823 3
 
3.0%
20191002 3
 
3.0%
20191030 3
 
3.0%
20191018 3
 
3.0%
20191001 2
 
2.0%
20190814 2
 
2.0%
20191007 2
 
2.0%
Other values (40) 47
47.0%
ValueCountFrequency (%)
20190801 15
15.0%
20190802 17
17.0%
20190803 2
 
2.0%
20190805 1
 
1.0%
20190806 2
 
2.0%
20190809 1
 
1.0%
20190814 2
 
2.0%
20190815 1
 
1.0%
20190816 1
 
1.0%
20190819 1
 
1.0%
ValueCountFrequency (%)
20191031 1
 
1.0%
20191030 3
3.0%
20191029 1
 
1.0%
20191026 1
 
1.0%
20191025 1
 
1.0%
20191024 1
 
1.0%
20191021 1
 
1.0%
20191019 1
 
1.0%
20191018 3
3.0%
20191017 1
 
1.0%

성별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
M
58 
X
34 
F
8 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 58
58.0%
X 34
34.0%
F 8
 
8.0%

Length

2023-12-10T22:16:16.136300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:16:16.287032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 58
58.0%
x 34
34.0%
f 8
 
8.0%

연령대
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
xx
34 
55
16 
45
15 
50
9 
60
5 
Other values (7)
21 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row55
2nd row45
3rd row55
4th row55
5th row50

Common Values

ValueCountFrequency (%)
xx 34
34.0%
55 16
16.0%
45 15
15.0%
50 9
 
9.0%
60 5
 
5.0%
30 5
 
5.0%
40 4
 
4.0%
25 4
 
4.0%
20 3
 
3.0%
70 2
 
2.0%
Other values (2) 3
 
3.0%

Length

2023-12-10T22:16:16.456222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xx 34
34.0%
55 16
16.0%
45 15
15.0%
50 9
 
9.0%
60 5
 
5.0%
30 5
 
5.0%
40 4
 
4.0%
25 4
 
4.0%
20 3
 
3.0%
70 2
 
2.0%
Other values (2) 3
 
3.0%

소비인구(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.29
Minimum22
Maximum251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:16:16.619192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q122
median30
Q344
95-th percentile96
Maximum251
Range229
Interquartile range (IQR)22

Descriptive statistics

Standard deviation32.915347
Coefficient of variation (CV)0.7971748
Kurtosis16.786183
Mean41.29
Median Absolute Deviation (MAD)8
Skewness3.4471477
Sum4129
Variance1083.4201
MonotonicityNot monotonic
2023-12-10T22:16:16.813706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
22 44
44.0%
37 15
 
15.0%
30 13
 
13.0%
52 5
 
5.0%
44 4
 
4.0%
74 3
 
3.0%
66 3
 
3.0%
89 3
 
3.0%
59 3
 
3.0%
96 2
 
2.0%
Other values (4) 5
 
5.0%
ValueCountFrequency (%)
22 44
44.0%
30 13
 
13.0%
37 15
 
15.0%
44 4
 
4.0%
52 5
 
5.0%
59 3
 
3.0%
66 3
 
3.0%
74 3
 
3.0%
81 1
 
1.0%
89 3
 
3.0%
ValueCountFrequency (%)
251 1
 
1.0%
133 2
 
2.0%
126 1
 
1.0%
96 2
 
2.0%
89 3
3.0%
81 1
 
1.0%
74 3
3.0%
66 3
3.0%
59 3
3.0%
52 5
5.0%

Interactions

2023-12-10T22:16:13.254324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:16:12.844400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:16:13.384983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:16:13.065605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:16:16.976089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드행정동명기준일자성별연령대소비인구(명)
행정동코드1.0000.9990.7460.2860.8270.948
행정동명0.9991.0000.7460.2860.8270.948
기준일자0.7460.7461.0000.3650.0000.000
성별0.2860.2860.3651.0000.9550.000
연령대0.8270.8270.0000.9551.0000.776
소비인구(명)0.9480.9480.0000.0000.7761.000
2023-12-10T22:16:17.150441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드성별연령대행정동명
행정동코드1.0000.4610.6330.976
성별0.4611.0000.7200.461
연령대0.6330.7201.0000.633
행정동명0.9760.4610.6331.000
2023-12-10T22:16:17.309895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자소비인구(명)행정동코드행정동명성별연령대
기준일자1.000-0.5170.6320.6320.2170.000
소비인구(명)-0.5171.0000.7810.7810.0000.404
행정동코드0.6320.7811.0000.9760.4610.633
행정동명0.6320.7810.9761.0000.4610.633
성별0.2170.0000.4610.4611.0000.720
연령대0.0000.4040.6330.6330.7201.000

Missing values

2023-12-10T22:16:13.595622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:16:13.824476image/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)
01111053000서울특별시종로구사직동20190928M5537
11111053000서울특별시종로구사직동20190925M4522
21111053000서울특별시종로구사직동20191003M5530
31111053000서울특별시종로구사직동20190805M5522
41111053000서울특별시종로구사직동20190823M5022
51111053000서울특별시종로구사직동20190806M4522
61111053000서울특별시종로구사직동20190806Xxx22
71111053000서울특별시종로구사직동20191003Xxx30
81111053000서울특별시종로구사직동20191002F4522
91111053000서울특별시종로구사직동20190928Xxx22
행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
901111061500서울특별시종로구종로1.2.3.4가동20190801M5066
911111061500서울특별시종로구종로1.2.3.4가동20190801M4574
921111061500서울특별시종로구종로1.2.3.4가동20190801M4059
931111061500서울특별시종로구종로1.2.3.4가동20190802M4096
941111061500서울특별시종로구종로1.2.3.4가동20190801M3574
951111061500서울특별시종로구종로1.2.3.4가동20190801M30126
961111061500서울특별시종로구종로1.2.3.4가동20190801M25133
971111061500서울특별시종로구종로1.2.3.4가동20190801M2052
981111061500서울특별시종로구종로1.2.3.4가동20190801F3044
991111061500서울특별시종로구종로1.2.3.4가동20190801F2522