Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory69.3 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
소비인구(명) is highly imbalanced (51.1%)Imbalance

Reproduction

Analysis started2023-12-10 11:53:54.528168
Analysis finished2023-12-10 11:53:57.619918
Duration3.09 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

2023-12-10T20:53:57.716558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:57.839471image/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

2023-12-10T20:53:57.980643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:58.107693image/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

2023-12-10T20:53:58.254905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:58.384193image/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

2023-12-10T20:53:58.526477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:58.718148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청운효자동 100
100.0%

기준일자
Real number (ℝ)

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200921
Minimum20200801
Maximum20201023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:58.933182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200801
5-th percentile20200803
Q120200819
median20200924
Q320201009
95-th percentile20201021
Maximum20201023
Range222
Interquartile range (IQR)190.25

Descriptive statistics

Standard deviation83.824691
Coefficient of variation (CV)4.1495481 × 10-6
Kurtosis-1.4872858
Mean20200921
Median Absolute Deviation (MAD)91
Skewness-0.1843506
Sum2.0200921 × 109
Variance7026.5789
MonotonicityNot monotonic
2023-12-10T20:53:59.175969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201009 4
 
4.0%
20201017 4
 
4.0%
20201016 3
 
3.0%
20200801 3
 
3.0%
20200924 3
 
3.0%
20200803 3
 
3.0%
20200806 3
 
3.0%
20200817 3
 
3.0%
20201023 3
 
3.0%
20200904 3
 
3.0%
Other values (45) 68
68.0%
ValueCountFrequency (%)
20200801 3
3.0%
20200803 3
3.0%
20200804 2
2.0%
20200806 3
3.0%
20200807 2
2.0%
20200808 3
3.0%
20200810 1
 
1.0%
20200811 1
 
1.0%
20200813 2
2.0%
20200815 1
 
1.0%
ValueCountFrequency (%)
20201023 3
3.0%
20201022 1
 
1.0%
20201021 2
2.0%
20201020 3
3.0%
20201019 2
2.0%
20201017 4
4.0%
20201016 3
3.0%
20201015 1
 
1.0%
20201014 2
2.0%
20201012 1
 
1.0%

성별
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
63 
M
37 

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 (%)
F 63
63.0%
M 37
37.0%

Length

2023-12-10T20:53:59.378947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:59.501013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 63
63.0%
m 37
37.0%

연령대
Real number (ℝ)

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.65
Minimum20
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:59.633921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q128.75
median40
Q350
95-th percentile60
Maximum70
Range50
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation13.243161
Coefficient of variation (CV)0.33400154
Kurtosis-1.0865958
Mean39.65
Median Absolute Deviation (MAD)10
Skewness0.22681466
Sum3965
Variance175.38131
MonotonicityNot monotonic
2023-12-10T20:53:59.779650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
25 18
18.0%
30 15
15.0%
50 15
15.0%
45 14
14.0%
35 8
8.0%
55 8
8.0%
20 7
 
7.0%
60 6
 
6.0%
40 5
 
5.0%
65 3
 
3.0%
ValueCountFrequency (%)
20 7
 
7.0%
25 18
18.0%
30 15
15.0%
35 8
8.0%
40 5
 
5.0%
45 14
14.0%
50 15
15.0%
55 8
8.0%
60 6
 
6.0%
65 3
 
3.0%
ValueCountFrequency (%)
70 1
 
1.0%
65 3
 
3.0%
60 6
 
6.0%
55 8
8.0%
50 15
15.0%
45 14
14.0%
40 5
 
5.0%
35 8
8.0%
30 15
15.0%
25 18
18.0%

소비인구(명)
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
22.479082395
78 
29.97210986
17 
37.465137325
 
3
44.95816479
 
2

Length

Max length12
Median length12
Mean length11.81
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22.479082395
2nd row22.479082395
3rd row22.479082395
4th row22.479082395
5th row22.479082395

Common Values

ValueCountFrequency (%)
22.479082395 78
78.0%
29.97210986 17
 
17.0%
37.465137325 3
 
3.0%
44.95816479 2
 
2.0%

Length

2023-12-10T20:53:59.972327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:00.123631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22.479082395 78
78.0%
29.97210986 17
 
17.0%
37.465137325 3
 
3.0%
44.95816479 2
 
2.0%

Interactions

2023-12-10T20:53:56.637742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:56.309712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:56.778121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:56.497394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:54:00.239286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자성별연령대소비인구(명)
기준일자1.0000.0000.0000.000
성별0.0001.0000.4360.070
연령대0.0000.4361.0000.000
소비인구(명)0.0000.0700.0001.000
2023-12-10T20:54:00.453831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소비인구(명)성별
소비인구(명)1.0000.043
성별0.0431.000
2023-12-10T20:54:00.608521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자연령대성별소비인구(명)
기준일자1.000-0.1390.0000.000
연령대-0.1391.0000.3090.000
성별0.0000.3091.0000.043
소비인구(명)0.0000.0000.0431.000

Missing values

2023-12-10T20:53:57.337966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:53:57.537568image/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)
01111051500서울특별시종로구청운효자동20200930M2522.479082
11111051500서울특별시종로구청운효자동20201023F2522.479082
21111051500서울특별시종로구청운효자동20200830M3022.479082
31111051500서울특별시종로구청운효자동20200929M4522.479082
41111051500서울특별시종로구청운효자동20201015M2522.479082
51111051500서울특별시종로구청운효자동20201022M2022.479082
61111051500서울특별시종로구청운효자동20200808M3022.479082
71111051500서울특별시종로구청운효자동20200829M4022.479082
81111051500서울특별시종로구청운효자동20200919F5022.479082
91111051500서울특별시종로구청운효자동20200929F6022.479082
행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
901111051500서울특별시종로구청운효자동20200926F2022.479082
911111051500서울특별시종로구청운효자동20200926F2537.465137
921111051500서울특별시종로구청운효자동20200927F5022.479082
931111051500서울특별시종로구청운효자동20200928F6529.97211
941111051500서울특별시종로구청운효자동20201003F3022.479082
951111051500서울특별시종로구청운효자동20201004F2522.479082
961111051500서울특별시종로구청운효자동20201005M2022.479082
971111051500서울특별시종로구청운효자동20201005M5522.479082
981111051500서울특별시종로구청운효자동20201023F5522.479082
991111051500서울특별시종로구청운효자동20201023F5029.97211