Overview

Dataset statistics

Number of variables3
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory28.2 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description경기도 용인시에 등록된 읍면동별 전기차 등록현황에 대한 데이터입니다. 구분, 읍면동, 등록수 데이터를 제공합니다.
Author경기도 용인시
URLhttps://www.data.go.kr/data/15106940/fileData.do

Alerts

읍면동 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:45:56.348859
Analysis finished2023-12-12 23:45:56.610840
Duration0.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
기흥구
18 
처인구
16 
수지구
7 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기흥구 18
43.9%
처인구 16
39.0%
수지구 7
 
17.1%

Length

2023-12-13T08:45:56.660408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:45:56.747973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기흥구 18
43.9%
처인구 16
39.0%
수지구 7
 
17.1%

읍면동
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-13T08:45:56.935691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9756098
Min length2

Characters and Unicode

Total characters122
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row김량장동
2nd row역북동
3rd row삼가동
4th row남동
5th row유방동
ValueCountFrequency (%)
김량장동 1
 
2.4%
지곡동 1
 
2.4%
고매동 1
 
2.4%
농서동 1
 
2.4%
서천동 1
 
2.4%
영덕동 1
 
2.4%
언남동 1
 
2.4%
마북동 1
 
2.4%
청덕동 1
 
2.4%
동백동 1
 
2.4%
Other values (31) 31
75.6%
2023-12-13T08:45:57.278567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 37
30.3%
갈 4
 
3.3%
읍 4
 
3.3%
덕 3
 
2.5%
상 3
 
2.5%
남 3
 
2.5%
면 3
 
2.5%
고 3
 
2.5%
천 3
 
2.5%
곡 2
 
1.6%
Other values (47) 57
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 37
30.3%
갈 4
 
3.3%
읍 4
 
3.3%
덕 3
 
2.5%
상 3
 
2.5%
남 3
 
2.5%
면 3
 
2.5%
고 3
 
2.5%
천 3
 
2.5%
곡 2
 
1.6%
Other values (47) 57
46.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 122
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 37
30.3%
갈 4
 
3.3%
읍 4
 
3.3%
덕 3
 
2.5%
상 3
 
2.5%
남 3
 
2.5%
면 3
 
2.5%
고 3
 
2.5%
천 3
 
2.5%
곡 2
 
1.6%
Other values (47) 57
46.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 122
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 37
30.3%
갈 4
 
3.3%
읍 4
 
3.3%
덕 3
 
2.5%
상 3
 
2.5%
남 3
 
2.5%
면 3
 
2.5%
고 3
 
2.5%
천 3
 
2.5%
곡 2
 
1.6%
Other values (47) 57
46.7%

등록수
Real number (ℝ)

Distinct38
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.29268
Minimum7
Maximum832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T08:45:57.401313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile20
Q164
median105
Q3183
95-th percentile351
Maximum832
Range825
Interquartile range (IQR)119

Descriptive statistics

Standard deviation144.33871
Coefficient of variation (CV)0.99343411
Kurtosis12.215128
Mean145.29268
Median Absolute Deviation (MAD)64
Skewness2.9449888
Sum5957
Variance20833.662
MonotonicityNot monotonic
2023-12-13T08:45:57.769501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
64 2
 
4.9%
95 2
 
4.9%
105 2
 
4.9%
97 1
 
2.4%
384 1
 
2.4%
85 1
 
2.4%
124 1
 
2.4%
69 1
 
2.4%
158 1
 
2.4%
244 1
 
2.4%
Other values (28) 28
68.3%
ValueCountFrequency (%)
7 1
2.4%
13 1
2.4%
20 1
2.4%
22 1
2.4%
29 1
2.4%
34 1
2.4%
36 1
2.4%
41 1
2.4%
42 1
2.4%
49 1
2.4%
ValueCountFrequency (%)
832 1
2.4%
384 1
2.4%
351 1
2.4%
299 1
2.4%
297 1
2.4%
249 1
2.4%
244 1
2.4%
222 1
2.4%
193 1
2.4%
192 1
2.4%

Interactions

2023-12-13T08:45:56.440725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:45:57.848848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분읍면동등록수
구분1.0001.0000.681
읍면동1.0001.0001.000
등록수0.6811.0001.000
2023-12-13T08:45:57.917212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록수구분
등록수1.0000.348
구분0.3481.000

Missing values

2023-12-13T08:45:56.524412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:45:56.585625image/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

구분읍면동등록수
0처인구김량장동64
1처인구역북동103
2처인구삼가동150
3처인구남동64
4처인구유방동34
5처인구고림동95
6처인구마평동22
7처인구운학동13
8처인구호동7
9처인구포곡읍142
구분읍면동등록수
31기흥구중동244
32기흥구상하동97
33기흥구보정동193
34수지구풍덕천동222
35수지구죽전동249
36수지구동천동299
37수지구고기동49
38수지구신봉동177
39수지구성복동297
40수지구상현동351