Overview

Dataset statistics

Number of variables8
Number of observations52
Missing cells52
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory70.5 B

Variable types

Text2
Numeric3
Categorical2
Unsupported1

Dataset

Description대전광역시 전통시장 현황에 대한 데이터로 시장명, 소재지, 점포수, 종사자, 형태, 개설연도 등의 항목을 제공합니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15062573/fileData.do

Alerts

점포수 is highly overall correlated with 종사자 and 1 other fieldsHigh correlation
종사자 is highly overall correlated with 점포수High correlation
개설연도 is highly overall correlated with 등록High correlation
형태 is highly overall correlated with 점포수 and 1 other fieldsHigh correlation
등록 is highly overall correlated with 개설연도 and 1 other fieldsHigh correlation
Unnamed: 7 has 52 (100.0%) missing valuesMissing
시장명 has unique valuesUnique
소재지 has unique valuesUnique
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 23:44:12.771147
Analysis finished2023-12-11 23:44:14.147390
Duration1.38 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시장명
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-12T08:44:14.308776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.3653846
Min length3

Characters and Unicode

Total characters331
Distinct characters93
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)100.0%

Sample

1st row중앙시장 활성화구역
2nd row중앙메가프라자
3rd row신 중 앙
4th row중앙도매
5th row중앙종합
ValueCountFrequency (%)
신 3
 
3.8%
용 2
 
2.6%
송 2
 
2.6%
동 2
 
2.6%
중 2
 
2.6%
도 2
 
2.6%
상점가 2
 
2.6%
미르길골목형상점가 1
 
1.3%
용두동 1
 
1.3%
선화동음식특화거리골목형상점가 1
 
1.3%
Other values (60) 60
76.9%
2023-12-12T08:44:14.691306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
가 29
 
8.8%
27
 
8.2%
상 25
 
7.6%
점 22
 
6.6%
동 15
 
4.5%
중 13
 
3.9%
형 10
 
3.0%
목 10
 
3.0%
골 10
 
3.0%
앙 9
 
2.7%
Other values (83) 161
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
91.5%
Space Separator 27
 
8.2%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 29
 
9.6%
상 25
 
8.3%
점 22
 
7.3%
동 15
 
5.0%
중 13
 
4.3%
형 10
 
3.3%
목 10
 
3.3%
골 10
 
3.3%
앙 9
 
3.0%
도 9
 
3.0%
Other values (81) 151
49.8%
Space Separator
ValueCountFrequency (%)
27
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
91.5%
Common 28
 
8.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 29
 
9.6%
상 25
 
8.3%
점 22
 
7.3%
동 15
 
5.0%
중 13
 
4.3%
형 10
 
3.3%
목 10
 
3.3%
골 10
 
3.3%
앙 9
 
3.0%
도 9
 
3.0%
Other values (81) 151
49.8%
Common
ValueCountFrequency (%)
27
96.4%
3 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
91.5%
ASCII 28
 
8.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
가 29
 
9.6%
상 25
 
8.3%
점 22
 
7.3%
동 15
 
5.0%
중 13
 
4.3%
형 10
 
3.3%
목 10
 
3.3%
골 10
 
3.3%
앙 9
 
3.0%
도 9
 
3.0%
Other values (81) 151
49.8%
ASCII
ValueCountFrequency (%)
27
96.4%
3 1
 
3.6%

소재지
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-12T08:44:14.961833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length13.096154
Min length8

Characters and Unicode

Total characters681
Distinct characters70
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

Unique52 ?
Unique (%)100.0%

Sample

1st row동구 대전로 783
2nd row동구 중앙로200번길 99
3rd row동구 중앙로200번길 85
4th row동구 중앙로200번길 73
5th row동구 중앙로200번길 45
ValueCountFrequency (%)
동구 22
 
14.1%
중구 15
 
9.6%
중앙로200번길 6
 
3.8%
대덕구 5
 
3.2%
서구 5
 
3.2%
83 2
 
1.3%
28 2
 
1.3%
대전로797번길 2
 
1.3%
충무로 2
 
1.3%
54 2
 
1.3%
Other values (93) 93
59.6%
2023-12-12T08:44:15.358895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108
15.9%
구 51
 
7.5%
로 50
 
7.3%
1 39
 
5.7%
길 35
 
5.1%
번 33
 
4.8%
3 31
 
4.6%
0 30
 
4.4%
중 29
 
4.3%
동 27
 
4.0%
Other values (60) 248
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 344
50.5%
Decimal Number 221
32.5%
Space Separator 108
 
15.9%
Dash Punctuation 8
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 51
14.8%
로 50
14.5%
길 35
10.2%
번 33
9.6%
중 29
 
8.4%
동 27
 
7.8%
대 18
 
5.2%
앙 11
 
3.2%
전 8
 
2.3%
서 7
 
2.0%
Other values (48) 75
21.8%
Decimal Number
ValueCountFrequency (%)
1 39
17.6%
3 31
14.0%
0 30
13.6%
8 23
10.4%
7 22
10.0%
2 19
8.6%
4 18
8.1%
5 16
7.2%
6 12
 
5.4%
9 11
 
5.0%
Space Separator
ValueCountFrequency (%)
108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 344
50.5%
Common 337
49.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 51
14.8%
로 50
14.5%
길 35
10.2%
번 33
9.6%
중 29
 
8.4%
동 27
 
7.8%
대 18
 
5.2%
앙 11
 
3.2%
전 8
 
2.3%
서 7
 
2.0%
Other values (48) 75
21.8%
Common
ValueCountFrequency (%)
108
32.0%
1 39
 
11.6%
3 31
 
9.2%
0 30
 
8.9%
8 23
 
6.8%
7 22
 
6.5%
2 19
 
5.6%
4 18
 
5.3%
5 16
 
4.7%
6 12
 
3.6%
Other values (2) 19
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 344
50.5%
ASCII 337
49.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108
32.0%
1 39
 
11.6%
3 31
 
9.2%
0 30
 
8.9%
8 23
 
6.8%
7 22
 
6.5%
2 19
 
5.6%
4 18
 
5.3%
5 16
 
4.7%
6 12
 
3.6%
Other values (2) 19
 
5.6%
Hangul
ValueCountFrequency (%)
구 51
14.8%
로 50
14.5%
길 35
10.2%
번 33
9.6%
중 29
 
8.4%
동 27
 
7.8%
대 18
 
5.2%
앙 11
 
3.2%
전 8
 
2.3%
서 7
 
2.0%
Other values (48) 75
21.8%

점포수
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.25
Minimum15
Maximum1173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T08:44:15.502292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile35.65
Q153.25
median90
Q3146.75
95-th percentile698.65
Maximum1173
Range1158
Interquartile range (IQR)93.5

Descriptive statistics

Standard deviation229.80861
Coefficient of variation (CV)1.3658758
Kurtosis8.6081756
Mean168.25
Median Absolute Deviation (MAD)40
Skewness2.8786099
Sum8749
Variance52811.995
MonotonicityNot monotonic
2023-12-12T08:44:15.639688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
90 3
 
5.8%
61 3
 
5.8%
100 2
 
3.8%
115 2
 
3.8%
64 2
 
3.8%
39 2
 
3.8%
50 2
 
3.8%
63 2
 
3.8%
818 1
 
1.9%
46 1
 
1.9%
Other values (32) 32
61.5%
ValueCountFrequency (%)
15 1
1.9%
27 1
1.9%
34 1
1.9%
37 1
1.9%
39 2
3.8%
42 1
1.9%
43 1
1.9%
46 1
1.9%
49 1
1.9%
50 2
3.8%
ValueCountFrequency (%)
1173 1
1.9%
870 1
1.9%
818 1
1.9%
601 1
1.9%
465 1
1.9%
395 1
1.9%
300 1
1.9%
290 1
1.9%
250 1
1.9%
240 1
1.9%

종사자
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.82692
Minimum20
Maximum2648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T08:44:15.761245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile39.55
Q175.25
median109
Q3301
95-th percentile1434
Maximum2648
Range2628
Interquartile range (IQR)225.75

Descriptive statistics

Standard deviation545.5389
Coefficient of variation (CV)1.5912954
Kurtosis7.2860944
Mean342.82692
Median Absolute Deviation (MAD)59
Skewness2.6585873
Sum17827
Variance297612.69
MonotonicityNot monotonic
2023-12-12T08:44:15.895785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
90 3
 
5.8%
80 3
 
5.8%
100 2
 
3.8%
120 2
 
3.8%
125 2
 
3.8%
70 2
 
3.8%
50 2
 
3.8%
879 1
 
1.9%
304 1
 
1.9%
1380 1
 
1.9%
Other values (33) 33
63.5%
ValueCountFrequency (%)
20 1
1.9%
35 1
1.9%
39 1
1.9%
40 1
1.9%
45 1
1.9%
48 1
1.9%
50 2
3.8%
53 1
1.9%
60 1
1.9%
69 1
1.9%
ValueCountFrequency (%)
2648 1
1.9%
2108 1
1.9%
1500 1
1.9%
1380 1
1.9%
1130 1
1.9%
1109 1
1.9%
985 1
1.9%
879 1
1.9%
600 1
1.9%
400 1
1.9%

형태
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size548.0 B
시장
28 
상점가
11 
골목형상점가
10 
지하도상점가
 
2
활성화구역
 
1

Length

Max length6
Median length2
Mean length3.1923077
Min length2

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st row활성화구역
2nd row시장
3rd row시장
4th row시장
5th row시장

Common Values

ValueCountFrequency (%)
시장 28
53.8%
상점가 11
 
21.2%
골목형상점가 10
 
19.2%
지하도상점가 2
 
3.8%
활성화구역 1
 
1.9%

Length

2023-12-12T08:44:16.055269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:44:16.198406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시장 28
53.8%
상점가 11
 
21.2%
골목형상점가 10
 
19.2%
지하도상점가 2
 
3.8%
활성화구역 1
 
1.9%

등록
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
등록
25 
인정
16 
지정
10 
<NA>
 
1

Length

Max length4
Median length2
Mean length2.0384615
Min length2

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
등록 25
48.1%
인정 16
30.8%
지정 10
 
19.2%
<NA> 1
 
1.9%

Length

2023-12-12T08:44:16.353208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:44:16.476163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록 25
48.1%
인정 16
30.8%
지정 10
 
19.2%
na 1
 
1.9%

개설연도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1994.1346
Minimum1950
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T08:44:16.585285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1964.75
Q11979.75
median1990.5
Q32012.25
95-th percentile2021
Maximum2022
Range72
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation19.810899
Coefficient of variation (CV)0.0099345846
Kurtosis-1.0682647
Mean1994.1346
Median Absolute Deviation (MAD)16.5
Skewness-0.062332777
Sum103695
Variance392.47172
MonotonicityNot monotonic
2023-12-12T08:44:16.723231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2021 8
 
15.4%
1973 4
 
7.7%
2007 3
 
5.8%
1982 3
 
5.8%
1990 3
 
5.8%
1998 2
 
3.8%
1970 2
 
3.8%
2016 2
 
3.8%
1980 2
 
3.8%
2022 2
 
3.8%
Other values (18) 21
40.4%
ValueCountFrequency (%)
1950 1
 
1.9%
1962 2
3.8%
1967 1
 
1.9%
1970 2
3.8%
1973 4
7.7%
1975 2
3.8%
1979 1
 
1.9%
1980 2
3.8%
1981 1
 
1.9%
1982 3
5.8%
ValueCountFrequency (%)
2022 2
 
3.8%
2021 8
15.4%
2017 1
 
1.9%
2016 2
 
3.8%
2011 1
 
1.9%
2008 1
 
1.9%
2007 3
 
5.8%
2005 1
 
1.9%
2002 1
 
1.9%
2000 1
 
1.9%

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size600.0 B

Interactions

2023-12-12T08:44:13.708010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:13.111251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:13.417502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:13.806576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:13.211618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:13.515143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:13.884851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:13.311621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:13.604382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:44:16.854457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장명소재지점포수종사자형태등록개설연도
시장명1.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.000
점포수1.0001.0001.0000.9740.7440.0000.408
종사자1.0001.0000.9741.0000.5380.0000.604
형태1.0001.0000.7440.5381.0000.7440.634
등록1.0001.0000.0000.0000.7441.0000.918
개설연도1.0001.0000.4080.6040.6340.9181.000
2023-12-12T08:44:16.960635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
형태등록
형태1.0000.783
등록0.7831.000
2023-12-12T08:44:17.058785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
점포수종사자개설연도형태등록
점포수1.0000.876-0.0730.5610.000
종사자0.8761.000-0.0120.3530.000
개설연도-0.073-0.0121.0000.4060.612
형태0.5610.3530.4061.0000.783
등록0.0000.0000.6120.7831.000

Missing values

2023-12-12T08:44:13.995987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:44:14.105108image/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

시장명소재지점포수종사자형태등록개설연도Unnamed: 7
0중앙시장 활성화구역동구 대전로 783818879활성화구역<NA>2007<NA>
1중앙메가프라자동구 중앙로200번길 99104120시장등록1973<NA>
2신 중 앙동구 중앙로200번길 85100111시장등록1975<NA>
3중앙도매동구 중앙로200번길 7390100시장등록1973<NA>
4중앙종합동구 중앙로200번길 45115120시장인정1985<NA>
5자유도매동구 중앙로200번길 369090시장등록1981<NA>
6대전도매동구 대전로791번길 3100100시장등록1975<NA>
7중앙상가동구 대전로797번길 37300300시장인정1962<NA>
8전통중앙도매동구 중앙로204번길 28-1115125시장인정2011<NA>
9정원시장동구 중앙로194번길 33125125시장인정2017<NA>
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