Overview

Dataset statistics

Number of variables7
Number of observations26
Missing cells12
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory61.9 B

Variable types

Categorical1
Text5
Numeric1

Dataset

Description강원도내 자동차 운전전문학원 현황에 대한 데이터로서 지역, 학원명, 전화번호, FAX, 우편번호, 소재지 등의 항목을 제공합니다.
Author경찰청 강원특별자치도경찰청
URLhttps://www.data.go.kr/data/15007077/fileData.do

Alerts

구분 is highly imbalanced (65.1%)Imbalance
지역 has 2 (7.7%) missing valuesMissing
학원명 has 2 (7.7%) missing valuesMissing
전화번호 has 2 (7.7%) missing valuesMissing
팩스번호 has 2 (7.7%) missing valuesMissing
우편번호 has 2 (7.7%) missing valuesMissing
소 재 지 has 2 (7.7%) missing valuesMissing

Reproduction

Analysis started2024-03-14 16:03:52.078362
Analysis finished2024-03-14 16:03:53.479261
Duration1.4 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

IMBALANCE 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size336.0 B
전문학원
23 
일반학원
 
1
<NA>
 
1
 
1

Length

Max length4
Median length4
Mean length3.9230769
Min length2

Unique

Unique3 ?
Unique (%)11.5%

Sample

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

Common Values

ValueCountFrequency (%)
전문학원 23
88.5%
일반학원 1
 
3.8%
<NA> 1
 
3.8%
1
 
3.8%

Length

2024-03-15T01:03:53.622509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:03:53.821147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전문학원 23
92.0%
일반학원 1
 
4.0%
na 1
 
4.0%

지역
Text

MISSING 

Distinct14
Distinct (%)58.3%
Missing2
Missing (%)7.7%
Memory size336.0 B
2024-03-15T01:03:54.439620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters48
Distinct characters25
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

Unique9 ?
Unique (%)37.5%

Sample

1st row춘천
2nd row춘천
3rd row강릉
4th row강릉
5th row강릉
ValueCountFrequency (%)
원주 5
20.8%
강릉 4
16.7%
춘천 2
 
8.3%
동해 2
 
8.3%
홍천 2
 
8.3%
속초 1
 
4.2%
양구 1
 
4.2%
삼척 1
 
4.2%
영월 1
 
4.2%
평창 1
 
4.2%
Other values (4) 4
16.7%
2024-03-15T01:03:55.320707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 6
 
12.5%
주 5
 
10.4%
강 4
 
8.3%
릉 4
 
8.3%
천 4
 
8.3%
춘 2
 
4.2%
동 2
 
4.2%
해 2
 
4.2%
홍 2
 
4.2%
성 2
 
4.2%
Other values (15) 15
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 6
 
12.5%
주 5
 
10.4%
강 4
 
8.3%
릉 4
 
8.3%
천 4
 
8.3%
춘 2
 
4.2%
동 2
 
4.2%
해 2
 
4.2%
홍 2
 
4.2%
성 2
 
4.2%
Other values (15) 15
31.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 6
 
12.5%
주 5
 
10.4%
강 4
 
8.3%
릉 4
 
8.3%
천 4
 
8.3%
춘 2
 
4.2%
동 2
 
4.2%
해 2
 
4.2%
홍 2
 
4.2%
성 2
 
4.2%
Other values (15) 15
31.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 6
 
12.5%
주 5
 
10.4%
강 4
 
8.3%
릉 4
 
8.3%
천 4
 
8.3%
춘 2
 
4.2%
동 2
 
4.2%
해 2
 
4.2%
홍 2
 
4.2%
성 2
 
4.2%
Other values (15) 15
31.2%

학원명
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing2
Missing (%)7.7%
Memory size336.0 B
2024-03-15T01:03:56.155718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0833333
Min length2

Characters and Unicode

Total characters50
Distinct characters39
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

Unique24 ?
Unique (%)100.0%

Sample

1st row성문
2nd row신촌
3rd row신신
4th row영동
5th row용봉
ValueCountFrequency (%)
신촌 1
 
4.2%
신신 1
 
4.2%
황지 1
 
4.2%
신진 1
 
4.2%
동송 1
 
4.2%
설악산 1
 
4.2%
미래 1
 
4.2%
나래 1
 
4.2%
홍천 1
 
4.2%
동남 1
 
4.2%
Other values (14) 14
58.3%
2024-03-15T01:03:57.372791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 4
 
8.0%
동 4
 
8.0%
악 2
 
4.0%
지 2
 
4.0%
문 2
 
4.0%
산 2
 
4.0%
래 2
 
4.0%
설 1
 
2.0%
미 1
 
2.0%
송 1
 
2.0%
Other values (29) 29
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 4
 
8.0%
동 4
 
8.0%
악 2
 
4.0%
지 2
 
4.0%
문 2
 
4.0%
산 2
 
4.0%
래 2
 
4.0%
설 1
 
2.0%
미 1
 
2.0%
송 1
 
2.0%
Other values (29) 29
58.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 4
 
8.0%
동 4
 
8.0%
악 2
 
4.0%
지 2
 
4.0%
문 2
 
4.0%
산 2
 
4.0%
래 2
 
4.0%
설 1
 
2.0%
미 1
 
2.0%
송 1
 
2.0%
Other values (29) 29
58.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 4
 
8.0%
동 4
 
8.0%
악 2
 
4.0%
지 2
 
4.0%
문 2
 
4.0%
산 2
 
4.0%
래 2
 
4.0%
설 1
 
2.0%
미 1
 
2.0%
송 1
 
2.0%
Other values (29) 29
58.0%

전화번호
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing2
Missing (%)7.7%
Memory size336.0 B
2024-03-15T01:03:58.122832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters288
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

Unique24 ?
Unique (%)100.0%

Sample

1st row033-241-9100
2nd row033-261-3388
3rd row033-652-4444
4th row033-648-4444
5th row033-641-6666
ValueCountFrequency (%)
033-261-3388 1
 
4.2%
033-652-4444 1
 
4.2%
033-553-7777 1
 
4.2%
033-342-7932 1
 
4.2%
033-455-1500 1
 
4.2%
033-632-5000 1
 
4.2%
033-334-0038 1
 
4.2%
033-432-7400 1
 
4.2%
033-432-3100 1
 
4.2%
033-374-3771 1
 
4.2%
Other values (14) 14
58.3%
2024-03-15T01:03:59.232622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 74
25.7%
- 48
16.7%
0 46
16.0%
4 24
 
8.3%
7 22
 
7.6%
6 19
 
6.6%
2 17
 
5.9%
5 16
 
5.6%
1 9
 
3.1%
8 9
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240
83.3%
Dash Punctuation 48
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 74
30.8%
0 46
19.2%
4 24
 
10.0%
7 22
 
9.2%
6 19
 
7.9%
2 17
 
7.1%
5 16
 
6.7%
1 9
 
3.8%
8 9
 
3.8%
9 4
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 74
25.7%
- 48
16.7%
0 46
16.0%
4 24
 
8.3%
7 22
 
7.6%
6 19
 
6.6%
2 17
 
5.9%
5 16
 
5.6%
1 9
 
3.1%
8 9
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 74
25.7%
- 48
16.7%
0 46
16.0%
4 24
 
8.3%
7 22
 
7.6%
6 19
 
6.6%
2 17
 
5.9%
5 16
 
5.6%
1 9
 
3.1%
8 9
 
3.1%

팩스번호
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing2
Missing (%)7.7%
Memory size336.0 B
2024-03-15T01:04:00.119932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters288
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

Unique24 ?
Unique (%)100.0%

Sample

1st row033-242-9800
2nd row033-262-4401
3rd row033-653-8887
4th row033-647-4445
5th row033-647-6666
ValueCountFrequency (%)
033-262-4401 1
 
4.2%
033-653-8887 1
 
4.2%
033-553-5539 1
 
4.2%
033-342-7933 1
 
4.2%
033-455-1507 1
 
4.2%
033-631-3007 1
 
4.2%
033-334-0039 1
 
4.2%
033-432-7430 1
 
4.2%
033-435-9890 1
 
4.2%
033-374-5577 1
 
4.2%
Other values (14) 14
58.3%
2024-03-15T01:04:01.378147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 74
25.7%
- 48
16.7%
0 40
13.9%
4 22
 
7.6%
5 19
 
6.6%
7 19
 
6.6%
6 17
 
5.9%
2 15
 
5.2%
1 14
 
4.9%
8 11
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240
83.3%
Dash Punctuation 48
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 74
30.8%
0 40
16.7%
4 22
 
9.2%
5 19
 
7.9%
7 19
 
7.9%
6 17
 
7.1%
2 15
 
6.2%
1 14
 
5.8%
8 11
 
4.6%
9 9
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 74
25.7%
- 48
16.7%
0 40
13.9%
4 22
 
7.6%
5 19
 
6.6%
7 19
 
6.6%
6 17
 
5.9%
2 15
 
5.2%
1 14
 
4.9%
8 11
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 74
25.7%
- 48
16.7%
0 40
13.9%
4 22
 
7.6%
5 19
 
6.6%
7 19
 
6.6%
6 17
 
5.9%
2 15
 
5.2%
1 14
 
4.9%
8 11
 
3.8%

우편번호
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)100.0%
Missing2
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean25478.5
Minimum24025
Maximum26505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-15T01:04:01.766858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24025
5-th percentile24268.45
Q125061.75
median25487
Q326060
95-th percentile26493.7
Maximum26505
Range2480
Interquartile range (IQR)998.25

Descriptive statistics

Standard deviation740.86342
Coefficient of variation (CV)0.029077984
Kurtosis-0.8020448
Mean25478.5
Median Absolute Deviation (MAD)550.5
Skewness-0.28766262
Sum611484
Variance548878.61
MonotonicityNot monotonic
2024-03-15T01:04:02.184954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
24902 1
 
3.8%
26003 1
 
3.8%
25242 1
 
3.8%
24025 1
 
3.8%
24758 1
 
3.8%
25322 1
 
3.8%
25126 1
 
3.8%
25115 1
 
3.8%
26231 1
 
3.8%
25907 1
 
3.8%
Other values (14) 14
53.8%
(Missing) 2
 
7.7%
ValueCountFrequency (%)
24025 1
3.8%
24244 1
3.8%
24407 1
3.8%
24558 1
3.8%
24758 1
3.8%
24902 1
3.8%
25115 1
3.8%
25126 1
3.8%
25242 1
3.8%
25322 1
3.8%
ValueCountFrequency (%)
26505 1
3.8%
26494 1
3.8%
26492 1
3.8%
26372 1
3.8%
26320 1
3.8%
26231 1
3.8%
26003 1
3.8%
25907 1
3.8%
25790 1
3.8%
25705 1
3.8%

소 재 지
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing2
Missing (%)7.7%
Memory size336.0 B
2024-03-15T01:04:03.177201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length14.958333
Min length11

Characters and Unicode

Total characters359
Distinct characters99
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row춘천 영서로 2528(근화동)
2nd row춘천 동내면 동내로 67-1
3rd row강릉 경강로 2408번길 64
4th row강릉 회산로 408번길 4
5th row강릉 성산면 경강로 1377-9
ValueCountFrequency (%)
원주 5
 
5.7%
강릉 4
 
4.5%
홍천 3
 
3.4%
춘천 2
 
2.3%
횡성 2
 
2.3%
흥업면 2
 
2.3%
43 2
 
2.3%
북원로 2
 
2.3%
동해대로 2
 
2.3%
동해 2
 
2.3%
Other values (59) 62
70.5%
2024-03-15T01:04:04.845513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64
 
17.8%
로 16
 
4.5%
동 15
 
4.2%
1 13
 
3.6%
2 12
 
3.3%
길 10
 
2.8%
4 10
 
2.8%
- 9
 
2.5%
0 9
 
2.5%
8 8
 
2.2%
Other values (89) 193
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 187
52.1%
Decimal Number 85
23.7%
Space Separator 64
 
17.8%
Dash Punctuation 9
 
2.5%
Open Punctuation 7
 
1.9%
Close Punctuation 7
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 16
 
8.6%
동 15
 
8.0%
길 10
 
5.3%
천 8
 
4.3%
원 8
 
4.3%
강 6
 
3.2%
면 6
 
3.2%
주 5
 
2.7%
홍 5
 
2.7%
성 5
 
2.7%
Other values (75) 103
55.1%
Decimal Number
ValueCountFrequency (%)
1 13
15.3%
2 12
14.1%
4 10
11.8%
0 9
10.6%
8 8
9.4%
5 8
9.4%
6 7
8.2%
3 7
8.2%
7 6
7.1%
9 5
 
5.9%
Space Separator
ValueCountFrequency (%)
64
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 187
52.1%
Common 172
47.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 16
 
8.6%
동 15
 
8.0%
길 10
 
5.3%
천 8
 
4.3%
원 8
 
4.3%
강 6
 
3.2%
면 6
 
3.2%
주 5
 
2.7%
홍 5
 
2.7%
성 5
 
2.7%
Other values (75) 103
55.1%
Common
ValueCountFrequency (%)
64
37.2%
1 13
 
7.6%
2 12
 
7.0%
4 10
 
5.8%
- 9
 
5.2%
0 9
 
5.2%
8 8
 
4.7%
5 8
 
4.7%
6 7
 
4.1%
3 7
 
4.1%
Other values (4) 25
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 187
52.1%
ASCII 172
47.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64
37.2%
1 13
 
7.6%
2 12
 
7.0%
4 10
 
5.8%
- 9
 
5.2%
0 9
 
5.2%
8 8
 
4.7%
5 8
 
4.7%
6 7
 
4.1%
3 7
 
4.1%
Other values (4) 25
 
14.5%
Hangul
ValueCountFrequency (%)
로 16
 
8.6%
동 15
 
8.0%
길 10
 
5.3%
천 8
 
4.3%
원 8
 
4.3%
강 6
 
3.2%
면 6
 
3.2%
주 5
 
2.7%
홍 5
 
2.7%
성 5
 
2.7%
Other values (75) 103
55.1%

Interactions

2024-03-15T01:03:52.437124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T01:04:05.116549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지역학원명전화번호팩스번호우편번호소 재 지
구분1.0001.0001.0001.0001.0000.0001.000
지역1.0001.0001.0001.0001.0000.9241.000
학원명1.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.000
팩스번호1.0001.0001.0001.0001.0001.0001.000
우편번호0.0000.9241.0001.0001.0001.0001.000
소 재 지1.0001.0001.0001.0001.0001.0001.000
2024-03-15T01:04:05.531296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호구분
우편번호1.0000.000
구분0.0001.000

Missing values

2024-03-15T01:03:52.780654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T01:03:53.131507image/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.
2024-03-15T01:03:53.335421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분지역학원명전화번호팩스번호우편번호소 재 지
0전문학원춘천성문033-241-9100033-242-980024244춘천 영서로 2528(근화동)
1전문학원춘천신촌033-261-3388033-262-440124407춘천 동내면 동내로 67-1
2전문학원강릉신신033-652-4444033-653-888725561강릉 경강로 2408번길 64
3전문학원강릉영동033-648-4444033-647-444525524강릉 회산로 408번길 4
4전문학원강릉용봉033-641-6666033-647-666625450강릉 성산면 경강로 1377-9
5전문학원강릉강릉033-661-7777033-662-111925431강릉 연곡면 동해대로 4100-28
6전문학원원주치악산033-745-2997033-745-298726320원주 북원로 2640(태장동)
7전문학원원주원주033-763-3456033-764-001426505원주 학마을길 60(관설동)
8전문학원원주문막033-734-6525033-734-196026372원주 문막읍 좁은목길 59
9전문학원원주흥업033-763-7300033-762-160026492원주 흥업면 승안동길 43
구분지역학원명전화번호팩스번호우편번호소 재 지
16전문학원영월동남033-374-3771033-374-557726231영월 영월 중앙로 224-21
17전문학원홍천홍천033-432-3100033-435-989025115홍천 북방 홍천로 205-31
18전문학원홍천나래033-432-7400033-432-743025126홍천 홍천 홍천로 753
19전문학원평창미래033-334-0038033-334-003925322평창 진부 간평길 79-28
20전문학원고성설악산033-632-5000033-631-300724758고성 토성 동해대로 5213-16
21전문학원철원동송033-455-1500033-455-150724025철원 동송 상노로 519
22전문학원횡성신진033-342-7932033-342-793325242횡성 횡성 입석로 118
23일반학원태백황지033-553-7777033-553-553926003태백 세곡길 15-3
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