Overview

Dataset statistics

Number of variables5
Number of observations3267
Missing cells0
Missing cells (%)0.0%
Duplicate rows61
Duplicate rows (%)1.9%
Total size in memory137.3 KiB
Average record size in memory43.0 B

Variable types

Categorical2
Numeric3

Dataset

Description경기도 용인시 읍면동별, 성별, 연령별 1인가구 현황입니다. 읍면동, 연령, 1인가구수 남, 1인가구수 여 데이터를 제공합니다.※ 데이터기준일자 : 2023-10-20
Author경기도 용인시
URLhttps://www.data.go.kr/data/15124558/fileData.do

Alerts

ͱ has constant value ""Constant
Dataset has 61 (1.9%) duplicate rowsDuplicates
1ΰ is highly overall correlated with 1ΰ .1High correlation
1ΰ .1 is highly overall correlated with 1ΰ High correlation
1ΰ has 269 (8.2%) zerosZeros
1ΰ .1 has 123 (3.8%) zerosZeros

Reproduction

Analysis started2023-12-12 20:05:33.573870
Analysis finished2023-12-12 20:05:35.171885
Duration1.6 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

鵿
Categorical

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size25.7 KiB
<NA>
1037 
2
345 
1
339 
ϵ
260 
3
253 
Other values (12)
1033 

Length

Max length4
Median length1
Mean length2.0884604
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1037
31.7%
2 345
 
10.6%
1 339
 
10.4%
ϵ 260
 
8.0%
3 253
 
7.7%
õ 92
 
2.8%
dzõ1 89
 
2.7%
dzõ2 89
 
2.7%
߾ӵ 89
 
2.7%
Ű 88
 
2.7%
Other values (7) 586
17.9%

Length

2023-12-13T05:05:35.248021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1037
31.7%
2 345
 
10.6%
1 339
 
10.4%
ϵ 260
 
8.0%
3 253
 
7.7%
õ 92
 
2.8%
dzõ2 89
 
2.7%
߾ӵ 89
 
2.7%
dzõ1 89
 
2.7%
ű 88
 
2.7%
Other values (7) 586
17.9%

Unnamed: 1
Real number (ℝ)

Distinct97
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.323232
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-13T05:05:35.429599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15
Q133
median54
Q376
95-th percentile93
Maximum99
Range97
Interquartile range (IQR)43

Descriptive statistics

Standard deviation25.16216
Coefficient of variation (CV)0.46319336
Kurtosis-1.1380499
Mean54.323232
Median Absolute Deviation (MAD)21
Skewness-0.032202459
Sum177474
Variance633.13431
MonotonicityNot monotonic
2023-12-13T05:05:35.665777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 42
 
1.3%
63 38
 
1.2%
65 38
 
1.2%
72 38
 
1.2%
71 38
 
1.2%
70 38
 
1.2%
69 38
 
1.2%
68 38
 
1.2%
67 38
 
1.2%
66 38
 
1.2%
Other values (87) 2883
88.2%
ValueCountFrequency (%)
2 2
 
0.1%
4 5
 
0.2%
5 3
 
0.1%
6 4
 
0.1%
7 7
 
0.2%
8 14
 
0.4%
9 12
 
0.4%
10 42
1.3%
11 10
 
0.3%
12 17
0.5%
ValueCountFrequency (%)
99 15
 
0.5%
98 22
0.7%
97 23
0.7%
96 26
0.8%
95 32
1.0%
94 34
1.0%
93 37
1.1%
92 38
1.2%
91 38
1.2%
90 38
1.2%

1ΰ
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct144
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.842057
Minimum0
Maximum237
Zeros269
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-13T05:05:35.849434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median14
Q333
95-th percentile71
Maximum237
Range237
Interquartile range (IQR)29

Descriptive statistics

Standard deviation26.481923
Coefficient of variation (CV)1.1593493
Kurtosis8.9724505
Mean22.842057
Median Absolute Deviation (MAD)12
Skewness2.3820335
Sum74625
Variance701.29225
MonotonicityNot monotonic
2023-12-13T05:05:36.005123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 269
 
8.2%
1 232
 
7.1%
2 134
 
4.1%
4 110
 
3.4%
3 108
 
3.3%
6 106
 
3.2%
5 101
 
3.1%
9 88
 
2.7%
8 88
 
2.7%
10 87
 
2.7%
Other values (134) 1944
59.5%
ValueCountFrequency (%)
0 269
8.2%
1 232
7.1%
2 134
4.1%
3 108
3.3%
4 110
3.4%
5 101
 
3.1%
6 106
 
3.2%
7 79
 
2.4%
8 88
 
2.7%
9 88
 
2.7%
ValueCountFrequency (%)
237 1
< 0.1%
229 1
< 0.1%
224 1
< 0.1%
208 1
< 0.1%
185 1
< 0.1%
181 1
< 0.1%
173 1
< 0.1%
170 2
0.1%
162 1
< 0.1%
161 1
< 0.1%

1ΰ .1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.203245
Minimum0
Maximum260
Zeros123
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-13T05:05:36.158003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median18
Q328
95-th percentile46.7
Maximum260
Range260
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.147009
Coefficient of variation (CV)0.84872551
Kurtosis16.874744
Mean20.203245
Median Absolute Deviation (MAD)10
Skewness2.487196
Sum66004
Variance294.01992
MonotonicityNot monotonic
2023-12-13T05:05:36.339338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 219
 
6.7%
0 123
 
3.8%
22 105
 
3.2%
20 104
 
3.2%
2 101
 
3.1%
15 97
 
3.0%
18 95
 
2.9%
13 91
 
2.8%
23 90
 
2.8%
14 87
 
2.7%
Other values (94) 2155
66.0%
ValueCountFrequency (%)
0 123
3.8%
1 219
6.7%
2 101
3.1%
3 72
 
2.2%
4 68
 
2.1%
5 73
 
2.2%
6 59
 
1.8%
7 58
 
1.8%
8 71
 
2.2%
9 78
 
2.4%
ValueCountFrequency (%)
260 1
 
< 0.1%
134 1
 
< 0.1%
128 1
 
< 0.1%
124 1
 
< 0.1%
115 1
 
< 0.1%
113 1
 
< 0.1%
112 1
 
< 0.1%
107 1
 
< 0.1%
106 3
0.1%
105 2
0.1%

ͱ
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.7 KiB
2023-10-20
3267 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-20
2nd row2023-10-20
3rd row2023-10-20
4th row2023-10-20
5th row2023-10-20

Common Values

ValueCountFrequency (%)
2023-10-20 3267
100.0%

Length

2023-12-13T05:05:36.482388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:05:36.572609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-20 3267
100.0%

Interactions

2023-12-13T05:05:34.393175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:33.816831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:34.112901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:34.486461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:33.918101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:34.209826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:34.585220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:34.012026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:34.304674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:05:36.631825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
鵿Unnamed: 11ΰ1ΰ .1
鵿1.0000.0000.4410.421
Unnamed: 10.0001.0000.6380.391
1ΰ0.4410.6381.0000.692
1ΰ .10.4210.3910.6921.000
2023-12-13T05:05:36.731369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 11ΰ1ΰ .1鵿
Unnamed: 11.000-0.3530.0220.000
1ΰ-0.3531.0000.7280.169
1ΰ .10.0220.7281.0000.231
鵿0.0000.1690.2311.000

Missing values

2023-12-13T05:05:35.025541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:05:35.126099image/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

鵿Unnamed: 11ΰ1ΰ .1ͱ
0<NA>11012023-10-20
1<NA>15102023-10-20
2<NA>16012023-10-20
3<NA>17112023-10-20
4<NA>18102023-10-20
5<NA>19542023-10-20
6<NA>20712023-10-20
7<NA>211082023-10-20
8<NA>2222152023-10-20
9<NA>2344292023-10-20
鵿Unnamed: 11ΰ1ΰ .1ͱ
3257<NA>90292023-10-20
3258<NA>91342023-10-20
3259<NA>92132023-10-20
3260<NA>93092023-10-20
3261<NA>94132023-10-20
3262<NA>95142023-10-20
3263<NA>96032023-10-20
3264<NA>97032023-10-20
3265<NA>98012023-10-20
3266<NA>99102023-10-20

Duplicate rows

Most frequently occurring

鵿Unnamed: 11ΰ1ΰ .1ͱ# duplicates
29<NA>10012023-10-209
15ϵ10012023-10-205
58<NA>98012023-10-205
26<NA>8012023-10-204
36<NA>16102023-10-204
50<NA>94032023-10-204
54<NA>96022023-10-204
4210102023-10-203
17ϵ14012023-10-203
28<NA>9012023-10-203