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 10:33:11.537849
Analysis finished2023-12-10 10:33:13.262807
Duration1.72 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
1114055000
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1114055000
2nd row1114055000
3rd row1114055000
4th row1114055000
5th row1114055000

Common Values

ValueCountFrequency (%)
1114055000 100
100.0%

Length

2023-12-10T19:33:13.396576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:33:13.678000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1114055000 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

2023-12-10T19:33:13.908535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:33:14.165635image/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
중구
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 100
100.0%

Length

2023-12-10T19:33:14.365252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:33:14.593567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 100
100.0%

행정동명
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
명동 100
100.0%

Length

2023-12-10T19:33:14.772909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:33:15.040695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
명동 100
100.0%

기준일자
Real number (ℝ)

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190809
Minimum20190801
Maximum20190818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:33:15.357825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190801
5-th percentile20190801
Q120190804
median20190809
Q320190814
95-th percentile20190817
Maximum20190818
Range17
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.2705011
Coefficient of variation (CV)2.6103466 × 10-7
Kurtosis-1.2496913
Mean20190809
Median Absolute Deviation (MAD)5
Skewness0.0083572983
Sum2.0190809 × 109
Variance27.778182
MonotonicityNot monotonic
2023-12-10T19:33:15.602559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
20190810 8
 
8.0%
20190803 8
 
8.0%
20190802 7
 
7.0%
20190817 7
 
7.0%
20190814 7
 
7.0%
20190801 7
 
7.0%
20190808 6
 
6.0%
20190809 6
 
6.0%
20190806 6
 
6.0%
20190811 6
 
6.0%
Other values (8) 32
32.0%
ValueCountFrequency (%)
20190801 7
7.0%
20190802 7
7.0%
20190803 8
8.0%
20190804 4
4.0%
20190805 4
4.0%
20190806 6
6.0%
20190807 3
 
3.0%
20190808 6
6.0%
20190809 6
6.0%
20190810 8
8.0%
ValueCountFrequency (%)
20190818 3
 
3.0%
20190817 7
7.0%
20190816 5
5.0%
20190815 5
5.0%
20190814 7
7.0%
20190813 6
6.0%
20190812 2
 
2.0%
20190811 6
6.0%
20190810 8
8.0%
20190809 6
6.0%

성별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
61 
M
29 
X
10 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 61
61.0%
M 29
29.0%
X 10
 
10.0%

Length

2023-12-10T19:33:15.854883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:33:16.360029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 61
61.0%
m 29
29.0%
x 10
 
10.0%

연령대
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
25
20 
30
16 
35
14 
40
13 
20
11 
Other values (6)
26 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row30
2nd row35
3rd row45
4th row30
5th row35

Common Values

ValueCountFrequency (%)
25 20
20.0%
30 16
16.0%
35 14
14.0%
40 13
13.0%
20 11
11.0%
xx 10
10.0%
45 9
9.0%
50 4
 
4.0%
15 1
 
1.0%
60 1
 
1.0%

Length

2023-12-10T19:33:16.679553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25 20
20.0%
30 16
16.0%
35 14
14.0%
40 13
13.0%
20 11
11.0%
xx 10
10.0%
45 9
9.0%
50 4
 
4.0%
15 1
 
1.0%
60 1
 
1.0%

소비인구(명)
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.8
Minimum22
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:33:16.978915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q122
median30
Q337
95-th percentile59
Maximum66
Range44
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.93247
Coefficient of variation (CV)0.35303165
Kurtosis-0.018673437
Mean33.8
Median Absolute Deviation (MAD)8
Skewness0.8982523
Sum3380
Variance142.38384
MonotonicityNot monotonic
2023-12-10T19:33:17.231749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
22 34
34.0%
30 23
23.0%
37 19
19.0%
44 9
 
9.0%
52 8
 
8.0%
59 5
 
5.0%
66 2
 
2.0%
ValueCountFrequency (%)
22 34
34.0%
30 23
23.0%
37 19
19.0%
44 9
 
9.0%
52 8
 
8.0%
59 5
 
5.0%
66 2
 
2.0%
ValueCountFrequency (%)
66 2
 
2.0%
59 5
 
5.0%
52 8
 
8.0%
44 9
 
9.0%
37 19
19.0%
30 23
23.0%
22 34
34.0%

Interactions

2023-12-10T19:33:12.368264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:33:11.968420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:33:12.544607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:33:12.165256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:33:17.446104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자성별연령대소비인구(명)
기준일자1.0000.1640.0000.000
성별0.1641.0000.8310.360
연령대0.0000.8311.0000.000
소비인구(명)0.0000.3600.0001.000
2023-12-10T19:33:17.743304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대성별
연령대1.0000.692
성별0.6921.000
2023-12-10T19:33:17.975088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자소비인구(명)성별연령대
기준일자1.000-0.0250.0840.000
소비인구(명)-0.0251.0000.2530.000
성별0.0840.2531.0000.692
연령대0.0000.0000.6921.000

Missing values

2023-12-10T19:33:12.779601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:33:13.157516image/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)
01114055000서울특별시중구명동20190801F3030
11114055000서울특별시중구명동20190801F3530
21114055000서울특별시중구명동20190801F4530
31114055000서울특별시중구명동20190801M3022
41114055000서울특별시중구명동20190801M3537
51114055000서울특별시중구명동20190801Xxx66
61114055000서울특별시중구명동20190802F3037
71114055000서울특별시중구명동20190802F3559
81114055000서울특별시중구명동20190802F4022
91114055000서울특별시중구명동20190802F4552
행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
901114055000서울특별시중구명동20190817F3522
911114055000서울특별시중구명동20190817F4037
921114055000서울특별시중구명동20190817M2030
931114055000서울특별시중구명동20190817M3044
941114055000서울특별시중구명동20190817M4522
951114055000서울특별시중구명동20190817Xxx30
961114055000서울특별시중구명동20190818F2530
971114055000서울특별시중구명동20190818F3552
981114055000서울특별시중구명동20190818F4537
991114055000서울특별시중구명동20190801F2030