Overview

Dataset statistics

Number of variables5
Number of observations138
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory44.0 B

Variable types

Categorical2
Numeric3

Dataset

Description광주광역시 서구의 관내 독거노인(65세 이상 1인 가구)의 행정동, 연령, 계, 남성, 여성 등에 관한 현황에 대한 내용입니다.
Author광주광역시 서구
URLhttps://www.data.go.kr/data/15090275/fileData.do

Alerts

계 is highly overall correlated with 남 and 1 other fieldsHigh correlation
남 is highly overall correlated with 계 and 1 other fieldsHigh correlation
여 is highly overall correlated with 계 and 1 other fieldsHigh correlation
남 has 26 (18.8%) zerosZeros
여 has 2 (1.4%) zerosZeros

Reproduction

Analysis started2023-12-12 08:47:28.467778
Analysis finished2023-12-12 08:47:29.717420
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Categorical

Distinct18
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
상무2동
10 
화정3동
 
9
화정1동
 
8
광천동
 
8
치평동
 
8
Other values (13)
95 

Length

Max length4
Median length4
Mean length3.5144928
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상무2동 10
 
7.2%
화정3동 9
 
6.5%
화정1동 8
 
5.8%
광천동 8
 
5.8%
치평동 8
 
5.8%
농성1동 8
 
5.8%
화정4동 8
 
5.8%
서창동 8
 
5.8%
상무1동 8
 
5.8%
양동 8
 
5.8%
Other values (8) 55
39.9%

Length

2023-12-12T17:47:29.812098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상무2동 10
 
7.2%
화정3동 9
 
6.5%
화정4동 8
 
5.8%
상무1동 8
 
5.8%
서창동 8
 
5.8%
양동 8
 
5.8%
농성1동 8
 
5.8%
치평동 8
 
5.8%
광천동 8
 
5.8%
화정1동 8
 
5.8%
Other values (8) 55
39.9%

연령
Categorical

Distinct11
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
65 - 69
18 
70 - 74
18 
75 - 79
18 
80 - 84
18 
85 - 89
18 
Other values (6)
48 

Length

Max length9
Median length7
Mean length7.2028986
Min length7

Unique

Unique2 ?
Unique (%)1.4%

Sample

1st row65 - 69
2nd row70 - 74
3rd row75 - 79
4th row80 - 84
5th row85 - 89

Common Values

ValueCountFrequency (%)
65 - 69 18
13.0%
70 - 74 18
13.0%
75 - 79 18
13.0%
80 - 84 18
13.0%
85 - 89 18
13.0%
90 - 94 18
13.0%
95 - 99 16
11.6%
100 - 104 10
7.2%
105 - 109 2
 
1.4%
130 - 134 1
 
0.7%

Length

2023-12-12T17:47:29.968339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
138
33.3%
65 18
 
4.3%
80 18
 
4.3%
94 18
 
4.3%
89 18
 
4.3%
85 18
 
4.3%
84 18
 
4.3%
90 18
 
4.3%
79 18
 
4.3%
75 18
 
4.3%
Other values (13) 114
27.5%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.601449
Minimum1
Maximum498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:47:30.140529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111.25
median71.5
Q3129.75
95-th percentile240.8
Maximum498
Range497
Interquartile range (IQR)118.5

Descriptive statistics

Standard deviation90.15587
Coefficient of variation (CV)1.0532049
Kurtosis4.4631069
Mean85.601449
Median Absolute Deviation (MAD)59
Skewness1.7703611
Sum11813
Variance8128.0809
MonotonicityNot monotonic
2023-12-12T17:47:30.332322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13
 
9.4%
4 5
 
3.6%
5 4
 
2.9%
3 4
 
2.9%
42 3
 
2.2%
15 3
 
2.2%
2 3
 
2.2%
23 3
 
2.2%
79 2
 
1.4%
97 2
 
1.4%
Other values (85) 96
69.6%
ValueCountFrequency (%)
1 13
9.4%
2 3
 
2.2%
3 4
 
2.9%
4 5
 
3.6%
5 4
 
2.9%
6 1
 
0.7%
7 1
 
0.7%
10 2
 
1.4%
11 2
 
1.4%
12 1
 
0.7%
ValueCountFrequency (%)
498 1
0.7%
452 1
0.7%
358 1
0.7%
349 1
0.7%
272 1
0.7%
264 1
0.7%
251 1
0.7%
239 1
0.7%
236 1
0.7%
231 1
0.7%

남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.253623
Minimum0
Maximum231
Zeros26
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:47:30.486451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.25
median13.5
Q334.75
95-th percentile87.15
Maximum231
Range231
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation33.774059
Coefficient of variation (CV)1.3373946
Kurtosis11.475739
Mean25.253623
Median Absolute Deviation (MAD)13.5
Skewness2.771455
Sum3485
Variance1140.687
MonotonicityNot monotonic
2023-12-12T17:47:30.659354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
 
18.8%
3 10
 
7.2%
1 5
 
3.6%
18 4
 
2.9%
9 4
 
2.9%
32 4
 
2.9%
2 4
 
2.9%
4 4
 
2.9%
28 3
 
2.2%
16 3
 
2.2%
Other values (48) 71
51.4%
ValueCountFrequency (%)
0 26
18.8%
1 5
 
3.6%
2 4
 
2.9%
3 10
 
7.2%
4 4
 
2.9%
5 3
 
2.2%
6 2
 
1.4%
7 2
 
1.4%
8 2
 
1.4%
9 4
 
2.9%
ValueCountFrequency (%)
231 1
0.7%
167 1
0.7%
128 1
0.7%
108 1
0.7%
96 1
0.7%
90 1
0.7%
88 1
0.7%
87 1
0.7%
79 1
0.7%
76 1
0.7%

여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.347826
Minimum0
Maximum285
Zeros2
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:47:30.808187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18.5
median49.5
Q391.25
95-th percentile167.05
Maximum285
Range285
Interquartile range (IQR)82.75

Descriptive statistics

Standard deviation59.178396
Coefficient of variation (CV)0.98062183
Kurtosis2.3386399
Mean60.347826
Median Absolute Deviation (MAD)41.5
Skewness1.3916545
Sum8328
Variance3502.0825
MonotonicityNot monotonic
2023-12-12T17:47:31.001907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
8.7%
40 5
 
3.6%
4 5
 
3.6%
5 4
 
2.9%
3 4
 
2.9%
61 4
 
2.9%
2 3
 
2.2%
20 3
 
2.2%
69 3
 
2.2%
10 3
 
2.2%
Other values (75) 92
66.7%
ValueCountFrequency (%)
0 2
 
1.4%
1 12
8.7%
2 3
 
2.2%
3 4
 
2.9%
4 5
3.6%
5 4
 
2.9%
6 1
 
0.7%
7 2
 
1.4%
8 2
 
1.4%
10 3
 
2.2%
ValueCountFrequency (%)
285 1
0.7%
267 1
0.7%
262 1
0.7%
221 1
0.7%
216 1
0.7%
174 1
0.7%
173 1
0.7%
166 1
0.7%
163 1
0.7%
151 1
0.7%

Interactions

2023-12-12T17:47:29.240752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:47:28.681183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:47:28.955579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:47:29.333718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:47:28.787847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:47:29.042428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:47:29.448776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:47:28.868445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:47:29.154383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:47:31.122905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동연령계남여
행정동1.0000.0000.0000.0000.635
연령0.0001.0000.5830.5460.550
계0.0000.5831.0000.9540.879
남0.0000.5460.9541.0000.793
여0.6350.5500.8790.7931.000
2023-12-12T17:47:31.234811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동연령
행정동1.0000.000
연령0.0001.000
2023-12-12T17:47:31.334893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계남여행정동연령
계1.0000.9620.9910.0000.318
남0.9621.0000.9250.0000.290
여0.9910.9251.0000.2520.284
행정동0.0000.0000.2521.0000.000
연령0.3180.2900.2840.0001.000

Missing values

2023-12-12T17:47:29.576689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:47:29.675620image/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

행정동연령계남여
0양동65 - 691074364
1양동70 - 741284088
2양동75 - 791152788
3양동80 - 84752451
4양동85 - 8949940
5양동90 - 941156
6양동95 - 99404
7양동100 - 104101
8양3동65 - 69903258
9양3동70 - 74922468
행정동연령계남여
128풍암동80 - 8413131100
129풍암동85 - 8963954
130풍암동90 - 9420317
131풍암동95 - 99303
132동천동65 - 6915647109
133동천동70 - 74973067
134동천동75 - 79771661
135동천동80 - 84431231
136동천동85 - 8914410
137동천동90 - 941028