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 2 other fieldsHigh correlation
행정동명 is highly overall correlated with 기준일자 and 2 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 성별High correlation

Reproduction

Analysis started2023-12-10 11:54:09.117069
Analysis finished2023-12-10 11:54:11.156539
Duration2.04 seconds
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
1111051500
53 
1111053000
47 

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 53
53.0%
1111053000 47
47.0%

Length

2023-12-10T20:54:11.260503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:11.462277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1111051500 53
53.0%
1111053000 47
47.0%

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

2023-12-10T20:54:11.652246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:11.828763image/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

2023-12-10T20:54:12.020176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:12.283843image/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
청운효자동
53 
사직동
47 

Length

Max length5
Median length5
Mean length4.06
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청운효자동 53
53.0%
사직동 47
47.0%

Length

2023-12-10T20:54:12.487826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:12.702271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청운효자동 53
53.0%
사직동 47
47.0%

기준일자
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190865
Minimum20190801
Maximum20191031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:12.930640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190801
5-th percentile20190801
Q120190802
median20190804
Q320190924
95-th percentile20191019
Maximum20191031
Range230
Interquartile range (IQR)122.25

Descriptive statistics

Standard deviation83.306721
Coefficient of variation (CV)4.1259611 × 10-6
Kurtosis-0.81415572
Mean20190865
Median Absolute Deviation (MAD)3.5
Skewness0.90066144
Sum2.0190865 × 109
Variance6940.0097
MonotonicityNot monotonic
2023-12-10T20:54:13.164993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
20190801 19
19.0%
20190802 18
18.0%
20190803 13
 
13.0%
20190917 3
 
3.0%
20190928 3
 
3.0%
20191019 3
 
3.0%
20190826 2
 
2.0%
20191022 2
 
2.0%
20190816 2
 
2.0%
20190906 2
 
2.0%
Other values (28) 33
33.0%
ValueCountFrequency (%)
20190801 19
19.0%
20190802 18
18.0%
20190803 13
13.0%
20190806 1
 
1.0%
20190807 1
 
1.0%
20190808 1
 
1.0%
20190810 1
 
1.0%
20190816 2
 
2.0%
20190819 1
 
1.0%
20190822 1
 
1.0%
ValueCountFrequency (%)
20191031 1
 
1.0%
20191024 1
 
1.0%
20191022 2
2.0%
20191019 3
3.0%
20191017 1
 
1.0%
20191016 1
 
1.0%
20191012 2
2.0%
20191011 2
2.0%
20191008 1
 
1.0%
20191005 1
 
1.0%

성별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
M
55 
F
43 
X
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 55
55.0%
F 43
43.0%
X 2
 
2.0%

Length

2023-12-10T20:54:13.379046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:13.548028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 55
55.0%
f 43
43.0%
x 2
 
2.0%

연령대
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
45
20 
40
19 
20
15 
35
11 
25
9 
Other values (6)
26 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row20
2nd row20
3rd row25
4th row45
5th row45

Common Values

ValueCountFrequency (%)
45 20
20.0%
40 19
19.0%
20 15
15.0%
35 11
11.0%
25 9
9.0%
30 9
9.0%
50 7
 
7.0%
55 5
 
5.0%
15 2
 
2.0%
xx 2
 
2.0%

Length

2023-12-10T20:54:13.713437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45 20
20.0%
40 19
19.0%
20 15
15.0%
35 11
11.0%
25 9
9.0%
30 9
9.0%
50 7
 
7.0%
55 5
 
5.0%
15 2
 
2.0%
xx 2
 
2.0%

소비인구(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.14
Minimum22
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:13.888779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q122
median26
Q344
95-th percentile118.4
Maximum222
Range200
Interquartile range (IQR)22

Descriptive statistics

Standard deviation35.608142
Coefficient of variation (CV)0.84499626
Kurtosis8.0313395
Mean42.14
Median Absolute Deviation (MAD)4
Skewness2.64764
Sum4214
Variance1267.9398
MonotonicityNot monotonic
2023-12-10T20:54:14.117360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
22 50
50.0%
30 12
 
12.0%
37 9
 
9.0%
44 6
 
6.0%
52 4
 
4.0%
66 3
 
3.0%
59 2
 
2.0%
74 2
 
2.0%
118 2
 
2.0%
81 2
 
2.0%
Other values (8) 8
 
8.0%
ValueCountFrequency (%)
22 50
50.0%
30 12
 
12.0%
37 9
 
9.0%
44 6
 
6.0%
52 4
 
4.0%
59 2
 
2.0%
66 3
 
3.0%
74 2
 
2.0%
81 2
 
2.0%
89 1
 
1.0%
ValueCountFrequency (%)
222 1
1.0%
170 1
1.0%
148 1
1.0%
133 1
1.0%
126 1
1.0%
118 2
2.0%
103 1
1.0%
96 1
1.0%
89 1
1.0%
81 2
2.0%

Interactions

2023-12-10T20:54:10.131580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:09.671270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:10.326799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:09.898746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:54:14.289487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드행정동명기준일자성별연령대소비인구(명)
행정동코드1.0000.9990.9130.1350.2320.637
행정동명0.9991.0000.9130.1350.2320.637
기준일자0.9130.9131.0000.0000.0000.000
성별0.1350.1350.0001.0000.8230.345
연령대0.2320.2320.0000.8231.0000.079
소비인구(명)0.6370.6370.0000.3450.0791.000
2023-12-10T20:54:14.476969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대행정동코드성별행정동명
연령대1.0000.2100.6790.210
행정동코드0.2101.0000.2220.980
성별0.6790.2221.0000.222
행정동명0.2100.9800.2221.000
2023-12-10T20:54:14.685109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자소비인구(명)행정동코드행정동명성별연령대
기준일자1.000-0.6480.7560.7560.0000.000
소비인구(명)-0.6481.0000.6190.6190.1540.012
행정동코드0.7560.6191.0000.9800.2220.210
행정동명0.7560.6190.9801.0000.2220.210
성별0.0000.1540.2220.2221.0000.679
연령대0.0000.0120.2100.2100.6791.000

Missing values

2023-12-10T20:54:10.905172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:54:11.069874image/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)
01111051500서울특별시종로구청운효자동20190831M2022
11111051500서울특별시종로구청운효자동20190923M2022
21111051500서울특별시종로구청운효자동20190806M2530
31111051500서울특별시종로구청운효자동20190928M4522
41111051500서울특별시종로구청운효자동20190927F4537
51111051500서울특별시종로구청운효자동20190924M2022
61111051500서울특별시종로구청운효자동20191017F5530
71111051500서울특별시종로구청운효자동20191011F4522
81111051500서울특별시종로구청운효자동20190904M2522
91111051500서울특별시종로구청운효자동20191019M3522
행정동코드시도명시군구명행정동명기준일자성별연령대소비인구(명)
901111053000서울특별시종로구사직동20190801M5037
911111053000서울특별시종로구사직동20190801M4522
921111053000서울특별시종로구사직동20190801M4044
931111053000서울특별시종로구사직동20190803F5022
941111053000서울특별시종로구사직동20190801M3552
951111053000서울특별시종로구사직동20190801M3059
961111053000서울특별시종로구사직동20190801M2574
971111053000서울특별시종로구사직동20190801M2030
981111053000서울특별시종로구사직동20190801F6037
991111053000서울특별시종로구사직동20190802Xxx37