Overview

Dataset statistics

Number of variables5
Number of observations333
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory42.4 B

Variable types

Categorical2
Text1
Numeric2

Dataset

Description광주광역시 서구의 행정동별(양동, 양3동, 농성1동, 농성2동 등) 1인 가구수의 5세별 남자, 여자에 대한 현황입니다.
Author광주광역시 서구
URLhttps://www.data.go.kr/data/15086008/fileData.do

Alerts

남 is highly overall correlated with 여High correlation
여 is highly overall correlated with 남High correlation
남 has 28 (8.4%) zerosZeros
여 has 10 (3.0%) zerosZeros

Reproduction

Analysis started2024-04-13 11:16:30.098536
Analysis finished2024-04-13 11:16:34.069197
Duration3.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정기관
Categorical

Distinct18
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
상무2동
 
21
금호1동
 
20
상무1동
 
20
풍암동
 
20
화정1동
 
19
Other values (13)
233 

Length

Max length4
Median length4
Mean length3.5255255
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상무2동 21
 
6.3%
금호1동 20
 
6.0%
상무1동 20
 
6.0%
풍암동 20
 
6.0%
화정1동 19
 
5.7%
금호2동 19
 
5.7%
화정4동 19
 
5.7%
화정3동 19
 
5.7%
동천동 19
 
5.7%
농성2동 18
 
5.4%
Other values (8) 139
41.7%

Length

2024-04-13T20:16:34.326139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상무2동 21
 
6.3%
상무1동 20
 
6.0%
풍암동 20
 
6.0%
금호1동 20
 
6.0%
화정1동 19
 
5.7%
금호2동 19
 
5.7%
화정4동 19
 
5.7%
화정3동 19
 
5.7%
동천동 19
 
5.7%
치평동 18
 
5.4%
Other values (8) 139
41.7%

연령
Categorical

Distinct23
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
20 - 24
 
18
60 - 64
 
18
30 - 34
 
18
35 - 39
 
18
40 - 44
 
18
Other values (18)
243 

Length

Max length9
Median length7
Mean length7.03003
Min length5

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row20 - 24
2nd row25 - 29
3rd row30 - 34
4th row35 - 39
5th row40 - 44

Common Values

ValueCountFrequency (%)
20 - 24 18
 
5.4%
60 - 64 18
 
5.4%
30 - 34 18
 
5.4%
35 - 39 18
 
5.4%
40 - 44 18
 
5.4%
45 - 49 18
 
5.4%
65 - 69 18
 
5.4%
55 - 59 18
 
5.4%
50 - 54 18
 
5.4%
75 - 79 18
 
5.4%
Other values (13) 153
45.9%

Length

2024-04-13T20:16:34.769684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
333
33.3%
20 18
 
1.8%
80 18
 
1.8%
75 18
 
1.8%
79 18
 
1.8%
70 18
 
1.8%
74 18
 
1.8%
29 18
 
1.8%
84 18
 
1.8%
50 18
 
1.8%
Other values (37) 504
50.5%

계
Text

Distinct210
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-04-13T20:16:36.172936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.4084084
Min length1

Characters and Unicode

Total characters802
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)44.4%

Sample

1st row15
2nd row42
3rd row50
4th row32
5th row56
ValueCountFrequency (%)
1 24
 
7.2%
2 8
 
2.4%
3 8
 
2.4%
6 6
 
1.8%
13 5
 
1.5%
4 4
 
1.2%
56 4
 
1.2%
77 4
 
1.2%
295 4
 
1.2%
182 3
 
0.9%
Other values (200) 263
79.0%
2024-04-13T20:16:38.239724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 175
21.8%
2 108
13.5%
3 82
10.2%
4 80
10.0%
5 71
8.9%
0 66
 
8.2%
6 57
 
7.1%
7 55
 
6.9%
8 54
 
6.7%
9 51
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 799
99.6%
Other Punctuation 3
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 175
21.9%
2 108
13.5%
3 82
10.3%
4 80
10.0%
5 71
8.9%
0 66
 
8.3%
6 57
 
7.1%
7 55
 
6.9%
8 54
 
6.8%
9 51
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 802
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 175
21.8%
2 108
13.5%
3 82
10.2%
4 80
10.0%
5 71
8.9%
0 66
 
8.2%
6 57
 
7.1%
7 55
 
6.9%
8 54
 
6.7%
9 51
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 175
21.8%
2 108
13.5%
3 82
10.2%
4 80
10.0%
5 71
8.9%
0 66
 
8.2%
6 57
 
7.1%
7 55
 
6.9%
8 54
 
6.7%
9 51
 
6.4%

남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct160
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.408408
Minimum0
Maximum638
Zeros28
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-04-13T20:16:38.719255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median46
Q3111
95-th percentile311.2
Maximum638
Range638
Interquartile range (IQR)103

Descriptive statistics

Standard deviation106.41644
Coefficient of variation (CV)1.2758479
Kurtosis6.3017729
Mean83.408408
Median Absolute Deviation (MAD)44
Skewness2.2568138
Sum27775
Variance11324.459
MonotonicityNot monotonic
2024-04-13T20:16:39.190781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
8.4%
1 18
 
5.4%
2 17
 
5.1%
8 5
 
1.5%
4 5
 
1.5%
3 5
 
1.5%
6 5
 
1.5%
9 5
 
1.5%
27 4
 
1.2%
111 4
 
1.2%
Other values (150) 237
71.2%
ValueCountFrequency (%)
0 28
8.4%
1 18
5.4%
2 17
5.1%
3 5
 
1.5%
4 5
 
1.5%
5 1
 
0.3%
6 5
 
1.5%
7 3
 
0.9%
8 5
 
1.5%
9 5
 
1.5%
ValueCountFrequency (%)
638 1
0.3%
624 1
0.3%
564 1
0.3%
499 1
0.3%
487 1
0.3%
451 1
0.3%
384 1
0.3%
363 1
0.3%
360 1
0.3%
359 1
0.3%

여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct172
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.255255
Minimum0
Maximum666
Zeros10
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-04-13T20:16:39.751026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q118
median62
Q3124
95-th percentile276.4
Maximum666
Range666
Interquartile range (IQR)106

Descriptive statistics

Standard deviation98.776785
Coefficient of variation (CV)1.119217
Kurtosis8.1371573
Mean88.255255
Median Absolute Deviation (MAD)50
Skewness2.3561097
Sum29389
Variance9756.8533
MonotonicityNot monotonic
2024-04-13T20:16:40.246095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
6.0%
0 10
 
3.0%
2 9
 
2.7%
3 8
 
2.4%
4 6
 
1.8%
11 6
 
1.8%
47 5
 
1.5%
18 5
 
1.5%
62 5
 
1.5%
51 5
 
1.5%
Other values (162) 254
76.3%
ValueCountFrequency (%)
0 10
3.0%
1 20
6.0%
2 9
2.7%
3 8
 
2.4%
4 6
 
1.8%
5 3
 
0.9%
6 2
 
0.6%
7 1
 
0.3%
9 2
 
0.6%
10 1
 
0.3%
ValueCountFrequency (%)
666 1
0.3%
597 1
0.3%
596 1
0.3%
487 1
0.3%
474 1
0.3%
397 1
0.3%
356 1
0.3%
347 1
0.3%
344 1
0.3%
334 1
0.3%

Interactions

2024-04-13T20:16:33.026411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:16:32.506613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:16:33.279607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:16:32.770010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T20:16:40.546431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관연령남여
행정기관1.0000.0000.5520.564
연령0.0001.0000.5120.477
남0.5520.5121.0000.841
여0.5640.4770.8411.000
2024-04-13T20:16:40.813815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령행정기관
연령1.0000.000
행정기관0.0001.000
2024-04-13T20:16:41.072201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남여행정기관연령
남1.0000.8370.2430.211
여0.8371.0000.2180.201
행정기관0.2430.2181.0000.000
연령0.2110.2010.0001.000

Missing values

2024-04-13T20:16:33.613484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T20:16:33.934841image/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

행정기관연령계남여
0양동20 - 241569
1양동25 - 29422418
2양동30 - 34503416
3양동35 - 3932239
4양동40 - 44563422
5양동45 - 49574215
6양동50 - 54704624
7양동55 - 59885533
8양동60 - 641116447
9양동65 - 691255669
행정기관연령계남여
323동천동50 - 5418310974
324동천동55 - 5919675121
325동천동60 - 6424583162
326동천동65 - 6921062148
327동천동70 - 741324191
328동천동75 - 79951976
329동천동80 - 84641351
330동천동85 - 8930822
331동천동90 - 9413211
332동천동95 - 99101