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 started2024-04-17 10:31:03.452592
Analysis finished2024-04-17 10:31:04.049899
Duration0.6 seconds
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

2024-04-17T19:31:04.105857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:31:04.179907image/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

2024-04-17T19:31:04.267873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:31:04.353379image/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

2024-04-17T19:31:04.432203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:31:04.512231image/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

2024-04-17T19:31:04.597014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:31:04.674283image/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
2024-04-17T19:31:04.744135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190801
5-th percentile20190801
Q120190802
median20190803
Q320190804
95-th percentile20190805
Maximum20190806
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4595904
Coefficient of variation (CV)7.2289862 × 10-8
Kurtosis-1.2535557
Mean20190803
Median Absolute Deviation (MAD)1
Skewness0.11264915
Sum2.0190803 × 109
Variance2.130404
MonotonicityNot monotonic
2024-04-17T19:31:04.835552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20190801 21
21.0%
20190802 21
21.0%
20190805 20
20.0%
20190803 20
20.0%
20190804 17
17.0%
20190806 1
 
1.0%
ValueCountFrequency (%)
20190801 21
21.0%
20190802 21
21.0%
20190803 20
20.0%
20190804 17
17.0%
20190805 20
20.0%
20190806 1
 
1.0%
ValueCountFrequency (%)
20190806 1
 
1.0%
20190805 20
20.0%
20190804 17
17.0%
20190803 20
20.0%
20190802 21
21.0%
20190801 21
21.0%

성별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
M
49 
F
46 
X
5 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 49
49.0%
F 46
46.0%
X 5
 
5.0%

Length

2024-04-17T19:31:04.931521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:31:05.009539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 49
49.0%
f 46
46.0%
x 5
 
5.0%

연령대
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20
11 
25
10 
30
10 
35
10 
40
10 
Other values (8)
49 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
20 11
11.0%
25 10
10.0%
30 10
10.0%
35 10
10.0%
40 10
10.0%
45 10
10.0%
55 10
10.0%
50 9
9.0%
70 6
6.0%
xx 5
5.0%
Other values (3) 9
9.0%

Length

2024-04-17T19:31:05.096999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20 11
11.0%
25 10
10.0%
30 10
10.0%
35 10
10.0%
40 10
10.0%
45 10
10.0%
55 10
10.0%
50 9
9.0%
70 6
6.0%
xx 5
5.0%
Other values (3) 9
9.0%

소비인구(명)
Real number (ℝ)

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.49
Minimum22
Maximum214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T19:31:05.181871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q130
median48
Q366
95-th percentile113.6
Maximum214
Range192
Interquartile range (IQR)36

Descriptive statistics

Standard deviation38.066527
Coefficient of variation (CV)0.67386311
Kurtosis5.9130624
Mean56.49
Median Absolute Deviation (MAD)18
Skewness2.1797975
Sum5649
Variance1449.0605
MonotonicityNot monotonic
2024-04-17T19:31:05.278384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
22 18
18.0%
30 14
14.0%
66 13
13.0%
37 10
10.0%
59 10
10.0%
44 8
8.0%
52 6
 
6.0%
89 5
 
5.0%
81 4
 
4.0%
111 3
 
3.0%
Other values (5) 9
9.0%
ValueCountFrequency (%)
22 18
18.0%
30 14
14.0%
37 10
10.0%
44 8
8.0%
52 6
 
6.0%
59 10
10.0%
66 13
13.0%
74 3
 
3.0%
81 4
 
4.0%
89 5
 
5.0%
ValueCountFrequency (%)
214 2
 
2.0%
170 2
 
2.0%
163 1
 
1.0%
111 3
 
3.0%
96 1
 
1.0%
89 5
 
5.0%
81 4
 
4.0%
74 3
 
3.0%
66 13
13.0%
59 10
10.0%

Interactions

2024-04-17T19:31:03.745820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:31:03.608732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:31:03.814883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:31:03.673619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T19:31:05.349264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자성별연령대소비인구(명)
기준일자1.0000.0000.0000.000
성별0.0001.0000.8080.551
연령대0.0000.8081.0000.527
소비인구(명)0.0000.5510.5271.000
2024-04-17T19:31:05.425331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별연령대
성별1.0000.632
연령대0.6321.000
2024-04-17T19:31:05.494499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자소비인구(명)성별연령대
기준일자1.000-0.1420.0000.000
소비인구(명)-0.1421.0000.4300.266
성별0.0000.4301.0000.632
연령대0.0000.2660.6321.000

Missing values

2024-04-17T19:31:03.910255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T19:31:04.005796image/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)
01111051500서울특별시종로구청운효자동20190805Xxx214
11111051500서울특별시종로구청운효자동20190801F2037
21111051500서울특별시종로구청운효자동20190801F2566
31111051500서울특별시종로구청운효자동20190801F3066
41111051500서울특별시종로구청운효자동20190801F3566
51111051500서울특별시종로구청운효자동20190801F4052
61111051500서울특별시종로구청운효자동20190801F4544
71111051500서울특별시종로구청운효자동20190801F5081
81111051500서울특별시종로구청운효자동20190801F5566
91111051500서울특별시종로구청운효자동20190801F6037
행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
901111051500서울특별시종로구청운효자동20190805M2589
911111051500서울특별시종로구청운효자동20190805M3089
921111051500서울특별시종로구청운효자동20190805M3566
931111051500서울특별시종로구청운효자동20190805M40111
941111051500서울특별시종로구청운효자동20190805M4544
951111051500서울특별시종로구청운효자동20190805M5052
961111051500서울특별시종로구청운효자동20190805M5574
971111051500서울특별시종로구청운효자동20190805M6037
981111051500서울특별시종로구청운효자동20190805M7022
991111051500서울특별시종로구청운효자동20190806F2037