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 13:18:28.912474
Analysis finished2023-12-10 13:18:30.101159
Duration1.19 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
1111051500
100 

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 100
100.0%

Length

2023-12-10T22:18:30.208353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:18:30.483621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1111051500 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

2023-12-10T22:18:30.922421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:18:31.129058image/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

2023-12-10T22:18:31.398538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:18:31.619593image/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
청운효자동
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청운효자동 100
100.0%

Length

2023-12-10T22:18:31.864818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:18:32.082533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청운효자동 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
2023-12-10T22:18:32.206892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190801
5-th percentile20190801
Q120190802
median20190803
Q320190805
95-th percentile20190806
Maximum20190806
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7184677
Coefficient of variation (CV)8.5111409 × 10-8
Kurtosis-1.292105
Mean20190803
Median Absolute Deviation (MAD)1.5
Skewness0.041062384
Sum2.0190803 × 109
Variance2.9531313
MonotonicityIncreasing
2023-12-10T22:18:32.404221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20190801 18
18.0%
20190802 18
18.0%
20190804 17
17.0%
20190805 17
17.0%
20190803 15
15.0%
20190806 15
15.0%
ValueCountFrequency (%)
20190801 18
18.0%
20190802 18
18.0%
20190803 15
15.0%
20190804 17
17.0%
20190805 17
17.0%
20190806 15
15.0%
ValueCountFrequency (%)
20190806 15
15.0%
20190805 17
17.0%
20190804 17
17.0%
20190803 15
15.0%
20190802 18
18.0%
20190801 18
18.0%

성별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
50 
M
45 
X
 
5

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 50
50.0%
M 45
45.0%
X 5
 
5.0%

Length

2023-12-10T22:18:32.600008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:18:32.821996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 50
50.0%
m 45
45.0%
x 5
 
5.0%

연령대
Categorical

HIGH CORRELATION 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row20
3rd row25
4th row30
5th row35

Common Values

ValueCountFrequency (%)
20 12
12.0%
25 12
12.0%
30 12
12.0%
35 12
12.0%
40 12
12.0%
45 12
12.0%
50 10
10.0%
55 10
10.0%
xx 5
5.0%
15 3
 
3.0%

Length

2023-12-10T22:18:33.032117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:18:33.308487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 12
12.0%
25 12
12.0%
30 12
12.0%
35 12
12.0%
40 12
12.0%
45 12
12.0%
50 10
10.0%
55 10
10.0%
xx 5
5.0%
15 3
 
3.0%

소비인구(명)
Real number (ℝ)

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.6
Minimum22
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:18:33.574679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile30
Q152
median74
Q3112.75
95-th percentile170.35
Maximum273
Range251
Interquartile range (IQR)60.75

Descriptive statistics

Standard deviation47.952839
Coefficient of variation (CV)0.54122843
Kurtosis2.094684
Mean88.6
Median Absolute Deviation (MAD)29
Skewness1.2071463
Sum8860
Variance2299.4747
MonotonicityNot monotonic
2023-12-10T22:18:33.787128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
74 9
 
9.0%
66 9
 
9.0%
89 8
 
8.0%
52 7
 
7.0%
59 7
 
7.0%
126 6
 
6.0%
30 6
 
6.0%
44 5
 
5.0%
103 5
 
5.0%
37 5
 
5.0%
Other values (15) 33
33.0%
ValueCountFrequency (%)
22 3
 
3.0%
30 6
6.0%
37 5
5.0%
44 5
5.0%
52 7
7.0%
59 7
7.0%
66 9
9.0%
74 9
9.0%
81 3
 
3.0%
89 8
8.0%
ValueCountFrequency (%)
273 1
 
1.0%
251 1
 
1.0%
199 1
 
1.0%
178 1
 
1.0%
177 1
 
1.0%
170 2
2.0%
163 1
 
1.0%
155 2
2.0%
148 3
3.0%
133 4
4.0%

Interactions

2023-12-10T22:18:29.478803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:18:29.195550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:18:29.620529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:18:29.335066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:18:33.959536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자성별연령대소비인구(명)
기준일자1.0000.0000.0000.209
성별0.0001.0000.7960.459
연령대0.0000.7961.0000.507
소비인구(명)0.2090.4590.5071.000
2023-12-10T22:18:34.147352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별연령대
성별1.0000.659
연령대0.6591.000
2023-12-10T22:18:34.366225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자소비인구(명)성별연령대
기준일자1.000-0.1480.0000.000
소비인구(명)-0.1481.0000.2190.255
성별0.0000.2191.0000.659
연령대0.0000.2550.6591.000

Missing values

2023-12-10T22:18:29.808446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:18:30.019166image/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)
01111051500서울특별시종로구청운효자동20190801F1544
11111051500서울특별시종로구청운효자동20190801F20126
21111051500서울특별시종로구청운효자동20190801F25273
31111051500서울특별시종로구청운효자동20190801F30155
41111051500서울특별시종로구청운효자동20190801F35148
51111051500서울특별시종로구청운효자동20190801F40111
61111051500서울특별시종로구청운효자동20190801F4596
71111051500서울특별시종로구청운효자동20190801F5089
81111051500서울특별시종로구청운효자동20190801F5530
91111051500서울특별시종로구청운효자동20190801M2052
행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
901111051500서울특별시종로구청운효자동20190806F4566
911111051500서울특별시종로구청운효자동20190806F5074
921111051500서울특별시종로구청운효자동20190806F5522
931111051500서울특별시종로구청운효자동20190806M2074
941111051500서울특별시종로구청운효자동20190806M25118
951111051500서울특별시종로구청운효자동20190806M30103
961111051500서울특별시종로구청운효자동20190806M35111
971111051500서울특별시종로구청운효자동20190806M4066
981111051500서울특별시종로구청운효자동20190806M45133
991111051500서울특별시종로구청운효자동20190806M5066