Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows123
Duplicate rows (%)1.2%
Total size in memory400.4 KiB
Average record size in memory41.0 B

Variable types

DateTime1
Text1
Categorical1
Numeric1

Dataset

Description서울시의 2016년도 부터 2023년도 까지의 서울특별시의 월별 행정동별 자동차 신규 등록 대수에 대한 현황자료 입니다.
Author서울특별시
URLhttps://www.data.go.kr/data/15109858/fileData.do

Alerts

Dataset has 123 (1.2%) duplicate rowsDuplicates

Reproduction

Analysis started2024-04-13 12:37:50.118299
Analysis finished2024-04-13 12:37:53.288747
Duration3.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2016-01-01 00:00:00
Maximum2022-11-01 00:00:00
2024-04-13T21:37:53.426440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:37:53.666435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

행정동명
Text

Distinct427
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-13T21:37:54.856082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length13.8509
Min length11

Characters and Unicode

Total characters138509
Distinct characters194
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 동대문구 회기동
2nd row서울특별시 강북구 인수동
3rd row서울특별시 강북구 수유1동
4th row서울특별시 도봉구 창1동
5th row서울특별시 금천구 가산동
ValueCountFrequency (%)
서울특별시 10000
33.3%
송파구 620
 
2.1%
강남구 535
 
1.8%
강서구 525
 
1.8%
관악구 500
 
1.7%
노원구 460
 
1.5%
성북구 460
 
1.5%
강동구 447
 
1.5%
양천구 441
 
1.5%
영등포구 432
 
1.4%
Other values (442) 15580
51.9%
2024-04-13T21:37:56.288037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20000
14.4%
서 11503
 
8.3%
동 11461
 
8.3%
구 10660
 
7.7%
시 10137
 
7.3%
특 10000
 
7.2%
별 10000
 
7.2%
울 10000
 
7.2%
1 2431
 
1.8%
2 2213
 
1.6%
Other values (184) 40104
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111409
80.4%
Space Separator 20000
 
14.4%
Decimal Number 6884
 
5.0%
Other Punctuation 216
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 11503
 
10.3%
동 11461
 
10.3%
구 10660
 
9.6%
시 10137
 
9.1%
특 10000
 
9.0%
별 10000
 
9.0%
울 10000
 
9.0%
강 1940
 
1.7%
성 1212
 
1.1%
포 1045
 
0.9%
Other values (172) 33451
30.0%
Decimal Number
ValueCountFrequency (%)
1 2431
35.3%
2 2213
32.1%
3 1031
15.0%
4 566
 
8.2%
5 250
 
3.6%
6 161
 
2.3%
7 124
 
1.8%
8 75
 
1.1%
9 20
 
0.3%
0 13
 
0.2%
Space Separator
ValueCountFrequency (%)
20000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111409
80.4%
Common 27100
 
19.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 11503
 
10.3%
동 11461
 
10.3%
구 10660
 
9.6%
시 10137
 
9.1%
특 10000
 
9.0%
별 10000
 
9.0%
울 10000
 
9.0%
강 1940
 
1.7%
성 1212
 
1.1%
포 1045
 
0.9%
Other values (172) 33451
30.0%
Common
ValueCountFrequency (%)
20000
73.8%
1 2431
 
9.0%
2 2213
 
8.2%
3 1031
 
3.8%
4 566
 
2.1%
5 250
 
0.9%
. 216
 
0.8%
6 161
 
0.6%
7 124
 
0.5%
8 75
 
0.3%
Other values (2) 33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111409
80.4%
ASCII 27100
 
19.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20000
73.8%
1 2431
 
9.0%
2 2213
 
8.2%
3 1031
 
3.8%
4 566
 
2.1%
5 250
 
0.9%
. 216
 
0.8%
6 161
 
0.6%
7 124
 
0.5%
8 75
 
0.3%
Other values (2) 33
 
0.1%
Hangul
ValueCountFrequency (%)
서 11503
 
10.3%
동 11461
 
10.3%
구 10660
 
9.6%
시 10137
 
9.1%
특 10000
 
9.0%
별 10000
 
9.0%
울 10000
 
9.0%
강 1940
 
1.7%
성 1212
 
1.1%
포 1045
 
0.9%
Other values (172) 33451
30.0%

차종
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
승용
4040 
화물
3656 
승합
1722 
특수
582 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row승용
2nd row화물
3rd row승합
4th row화물
5th row화물

Common Values

ValueCountFrequency (%)
승용 4040
40.4%
화물 3656
36.6%
승합 1722
17.2%
특수 582
 
5.8%

Length

2024-04-13T21:37:56.693490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:37:57.010267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
승용 4040
40.4%
화물 3656
36.6%
승합 1722
17.2%
특수 582
 
5.8%

등록대수
Real number (ℝ)

Distinct150
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.5808
Minimum1
Maximum509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-13T21:37:57.380369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q327
95-th percentile61
Maximum509
Range508
Interquartile range (IQR)25

Descriptive statistics

Standard deviation24.801
Coefficient of variation (CV)1.4106866
Kurtosis53.115586
Mean17.5808
Median Absolute Deviation (MAD)5
Skewness4.6671055
Sum175808
Variance615.08958
MonotonicityNot monotonic
2024-04-13T21:37:57.823148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1953
19.5%
2 1011
 
10.1%
3 689
 
6.9%
4 573
 
5.7%
5 444
 
4.4%
6 348
 
3.5%
7 285
 
2.9%
8 219
 
2.2%
9 160
 
1.6%
10 152
 
1.5%
Other values (140) 4166
41.7%
ValueCountFrequency (%)
1 1953
19.5%
2 1011
10.1%
3 689
 
6.9%
4 573
 
5.7%
5 444
 
4.4%
6 348
 
3.5%
7 285
 
2.9%
8 219
 
2.2%
9 160
 
1.6%
10 152
 
1.5%
ValueCountFrequency (%)
509 1
< 0.1%
460 1
< 0.1%
442 1
< 0.1%
364 1
< 0.1%
346 1
< 0.1%
306 2
< 0.1%
276 1
< 0.1%
265 2
< 0.1%
257 1
< 0.1%
242 1
< 0.1%

Interactions

2024-04-13T21:37:52.576728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T21:37:58.084897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월차종등록대수
연월1.0000.1600.057
차종0.1601.0000.325
등록대수0.0570.3251.000
2024-04-13T21:37:58.237302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록대수차종
등록대수1.0000.200
차종0.2001.000

Missing values

2024-04-13T21:37:52.925755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T21:37:53.214769image/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

연월행정동명차종등록대수
845702021-07서울특별시 동대문구 회기동승용9
428642019-03서울특별시 강북구 인수동화물11
464022019-06서울특별시 강북구 수유1동승합2
382982018-10서울특별시 도봉구 창1동화물6
732842021-08서울특별시 금천구 가산동화물15
766192021-12서울특별시 강북구 미아동승용10
74052016-07서울특별시 강동구 암사2동승용33
637592020-10서울특별시 동작구 노량진1동화물2
383042018-10서울특별시 노원구 상계3.4동화물9
7742016-01서울특별시 성동구 왕십리2동화물8
연월행정동명차종등록대수
450842019-05서울특별시 용산구 남영동화물1
967212022-07서울특별시 강서구 공항동승용42
579262020-05서울특별시 노원구 상계6.7동화물10
676512021-02서울특별시 광진구 자양2동화물3
480022019-07서울특별시 강남구 논현2동화물10
45602016-05서울특별시 영등포구 대림2동승합1
663342021-01서울특별시 성동구 금호2.3가동승용42
559122020-03서울특별시 양천구 신월7동승합4
942582022-05서울특별시 중구 황학동화물4
810102021-03서울특별시 강서구 방화2동화물4

Duplicate rows

Most frequently occurring

연월행정동명차종등록대수# duplicates
02021-01서울특별시 강동구 성내1동승용482
12021-01서울특별시 관악구 미성동승용262
22021-01서울특별시 관악구 미성동화물22
32021-01서울특별시 도봉구 쌍문4동화물62
42021-01서울특별시 서초구 반포3동화물32
52021-01서울특별시 성북구 돈암2동화물22
62021-01서울특별시 송파구 거여2동화물32
72021-01서울특별시 중랑구 신내2동승용242
82021-02서울특별시 강동구 고덕2동화물32
92021-02서울특별시 광진구 중곡4동승용312