Overview

Dataset statistics

Number of variables6
Number of observations193
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory51.7 B

Variable types

Categorical2
Numeric3
DateTime1

Dataset

Description2022. 8. 22. 기준 나주시 1인가구 현황 관련, 나주시 행정동별, 연령별(단위: 10세) 1인가구 남녀인구수 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15120238/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
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 17 (8.8%) zerosZeros
여 has 10 (5.2%) zerosZeros

Reproduction

Analysis started2023-12-12 14:43:16.481981
Analysis finished2023-12-12 14:43:17.747891
Duration1.27 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Categorical

Distinct20
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
남평읍
 
11
영강동
 
11
이창동
 
11
빛가람동
 
10
동강면
 
10
Other values (15)
140 

Length

Max length4
Median length3
Mean length3.0518135
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남평읍 11
 
5.7%
영강동 11
 
5.7%
이창동 11
 
5.7%
빛가람동 10
 
5.2%
동강면 10
 
5.2%
다시면 10
 
5.2%
공산면 10
 
5.2%
송월동 10
 
5.2%
성북동 10
 
5.2%
영산동 10
 
5.2%
Other values (10) 90
46.6%

Length

2023-12-12T23:43:17.818000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남평읍 11
 
5.7%
이창동 11
 
5.7%
영강동 11
 
5.7%
송월동 10
 
5.2%
영산동 10
 
5.2%
성북동 10
 
5.2%
봉황면 10
 
5.2%
공산면 10
 
5.2%
다시면 10
 
5.2%
동강면 10
 
5.2%
Other values (10) 90
46.6%

연령
Categorical

Distinct12
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
20 - 29
20 
30 - 39
20 
40 - 49
20 
50 - 59
20 
60 - 69
20 
Other values (7)
93 

Length

Max length9
Median length7
Mean length7.1139896
Min length5

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0 - 9
2nd row10 - 19
3rd row20 - 29
4th row30 - 39
5th row40 - 49

Common Values

ValueCountFrequency (%)
20 - 29 20
10.4%
30 - 39 20
10.4%
40 - 49 20
10.4%
50 - 59 20
10.4%
60 - 69 20
10.4%
70 - 79 20
10.4%
80 - 89 20
10.4%
90 - 99 20
10.4%
10 - 19 14
7.3%
100 - 109 14
7.3%
Other values (2) 5
 
2.6%

Length

2023-12-12T23:43:17.969391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
193
33.3%
20 20
 
3.5%
60 20
 
3.5%
99 20
 
3.5%
90 20
 
3.5%
89 20
 
3.5%
80 20
 
3.5%
70 20
 
3.5%
69 20
 
3.5%
79 20
 
3.5%
Other values (15) 206
35.6%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.98964
Minimum1
Maximum2061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T23:43:18.137856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q127
median107
Q3208
95-th percentile385.2
Maximum2061
Range2060
Interquartile range (IQR)181

Descriptive statistics

Standard deviation226.55576
Coefficient of variation (CV)1.452377
Kurtosis32.246547
Mean155.98964
Median Absolute Deviation (MAD)84
Skewness4.7751181
Sum30106
Variance51327.51
MonotonicityNot monotonic
2023-12-12T23:43:18.290225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
9.3%
2 9
 
4.7%
24 4
 
2.1%
298 3
 
1.6%
194 3
 
1.6%
111 3
 
1.6%
27 3
 
1.6%
32 2
 
1.0%
226 2
 
1.0%
107 2
 
1.0%
Other values (117) 144
74.6%
ValueCountFrequency (%)
1 18
9.3%
2 9
4.7%
4 1
 
0.5%
6 2
 
1.0%
10 2
 
1.0%
11 1
 
0.5%
13 2
 
1.0%
14 1
 
0.5%
15 1
 
0.5%
17 2
 
1.0%
ValueCountFrequency (%)
2061 1
0.5%
1469 1
0.5%
1073 1
0.5%
871 1
0.5%
667 1
0.5%
589 1
0.5%
555 1
0.5%
403 1
0.5%
389 1
0.5%
387 1
0.5%

남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.176166
Minimum0
Maximum1286
Zeros17
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T23:43:18.479452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median42
Q3109
95-th percentile233.4
Maximum1286
Range1286
Interquartile range (IQR)102

Descriptive statistics

Standard deviation133.35381
Coefficient of variation (CV)1.6427705
Kurtosis38.131672
Mean81.176166
Median Absolute Deviation (MAD)38
Skewness5.1381368
Sum15667
Variance17783.24
MonotonicityNot monotonic
2023-12-12T23:43:18.652177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
8.8%
1 11
 
5.7%
4 5
 
2.6%
3 5
 
2.6%
5 4
 
2.1%
42 4
 
2.1%
6 4
 
2.1%
15 4
 
2.1%
76 4
 
2.1%
29 4
 
2.1%
Other values (95) 131
67.9%
ValueCountFrequency (%)
0 17
8.8%
1 11
5.7%
2 2
 
1.0%
3 5
 
2.6%
4 5
 
2.6%
5 4
 
2.1%
6 4
 
2.1%
7 2
 
1.0%
9 3
 
1.6%
10 2
 
1.0%
ValueCountFrequency (%)
1286 1
0.5%
694 1
0.5%
685 1
0.5%
461 1
0.5%
359 1
0.5%
356 1
0.5%
251 2
1.0%
248 1
0.5%
234 1
0.5%
233 1
0.5%

여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct107
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.813472
Minimum0
Maximum784
Zeros10
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T23:43:18.799855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q110
median37
Q3109
95-th percentile228.8
Maximum784
Range784
Interquartile range (IQR)99

Descriptive statistics

Standard deviation104.21445
Coefficient of variation (CV)1.3929905
Kurtosis21.074912
Mean74.813472
Median Absolute Deviation (MAD)35
Skewness3.7847179
Sum14439
Variance10860.653
MonotonicityNot monotonic
2023-12-12T23:43:18.955478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13
 
6.7%
0 10
 
5.2%
2 7
 
3.6%
5 5
 
2.6%
36 5
 
2.6%
10 5
 
2.6%
7 4
 
2.1%
31 3
 
1.6%
9 3
 
1.6%
13 3
 
1.6%
Other values (97) 135
69.9%
ValueCountFrequency (%)
0 10
5.2%
1 13
6.7%
2 7
3.6%
3 1
 
0.5%
4 1
 
0.5%
5 5
 
2.6%
6 2
 
1.0%
7 4
 
2.1%
8 2
 
1.0%
9 3
 
1.6%
ValueCountFrequency (%)
784 1
0.5%
775 1
0.5%
410 1
0.5%
379 1
0.5%
321 1
0.5%
308 1
0.5%
290 1
0.5%
243 1
0.5%
233 1
0.5%
230 1
0.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2023-08-22 00:00:00
Maximum2023-08-22 00:00:00
2023-12-12T23:43:19.079335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:19.186903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T23:43:17.236417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:16.716145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:16.960766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:17.334219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:16.786838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:17.040304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:17.446782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:16.870001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:17.125298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:43:19.267191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동연령계남여
행정동1.0000.0000.2890.4410.517
연령0.0001.0000.3400.5290.490
계0.2890.3401.0000.9740.897
남0.4410.5290.9741.0000.828
여0.5170.4900.8970.8281.000
2023-12-12T23:43:19.382855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동연령
행정동1.0000.000
연령0.0001.000
2023-12-12T23:43:19.478749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계남여행정동연령
계1.0000.9150.9330.1130.148
남0.9151.0000.7310.2120.230
여0.9330.7311.0000.2430.260
행정동0.1130.2120.2431.0000.000
연령0.1480.2300.2600.0001.000

Missing values

2023-12-12T23:43:17.577785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:43:17.695479image/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

행정동연령계남여데이터기준일자
0남평읍0 - 91102023-08-22
1남평읍10 - 196332023-08-22
2남평읍20 - 2912273492023-08-22
3남평읍30 - 39229150792023-08-22
4남평읍40 - 493572481092023-08-22
5남평읍50 - 595893562332023-08-22
6남평읍60 - 696673593082023-08-22
7남평읍70 - 793841412432023-08-22
8남평읍80 - 89338482902023-08-22
9남평읍90 - 99605552023-08-22
행정동연령계남여데이터기준일자
183빛가람동10 - 1910462023-08-22
184빛가람동20 - 2914696857842023-08-22
185빛가람동30 - 39206112867752023-08-22
186빛가람동40 - 4910736943792023-08-22
187빛가람동50 - 598714614102023-08-22
188빛가람동60 - 695552343212023-08-22
189빛가람동70 - 79203691342023-08-22
190빛가람동80 - 896112492023-08-22
191빛가람동90 - 994042023-08-22
192빛가람동100 - 1091012023-08-22