Overview

Dataset statistics

Number of variables11
Number of observations80
Missing cells74
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory94.7 B

Variable types

Numeric3
Text5
Categorical3

Dataset

Description순천시 소재 개인서비스 업체의 판매 품목, 규격, 상호, 소재지, 전화번호, 가격
Author전라남도 순천시
URLhttps://www.data.go.kr/data/15054162/fileData.do

Alerts

증감액 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
증감률(%) is highly overall correlated with 금월가격 and 2 other fieldsHigh correlation
규격 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 규격 and 1 other fieldsHigh correlation
금월가격 is highly overall correlated with 전월가격 and 3 other fieldsHigh correlation
전월가격 is highly overall correlated with 금월가격 and 3 other fieldsHigh correlation
증감액 is highly imbalanced (85.5%)Imbalance
증감률(%) is highly imbalanced (85.5%)Imbalance
전화번호 has 2 (2.5%) missing valuesMissing
비고 has 72 (90.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 10:19:17.201465
Analysis finished2023-12-12 10:19:19.413482
Duration2.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.475
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T19:19:19.500480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q15.75
median10.5
Q324.25
95-th percentile40
Maximum42
Range41
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation12.453432
Coefficient of variation (CV)0.80474522
Kurtosis-0.72903109
Mean15.475
Median Absolute Deviation (MAD)7
Skewness0.76095107
Sum1238
Variance155.08797
MonotonicityIncreasing
2023-12-12T19:19:19.665080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 4
 
5.0%
3 4
 
5.0%
4 4
 
5.0%
5 4
 
5.0%
6 4
 
5.0%
7 4
 
5.0%
8 4
 
5.0%
9 4
 
5.0%
10 4
 
5.0%
11 4
 
5.0%
Other values (32) 40
50.0%
ValueCountFrequency (%)
1 4
5.0%
2 4
5.0%
3 4
5.0%
4 4
5.0%
5 4
5.0%
6 4
5.0%
7 4
5.0%
8 4
5.0%
9 4
5.0%
10 4
5.0%
ValueCountFrequency (%)
42 1
 
1.2%
41 1
 
1.2%
40 3
3.8%
39 1
 
1.2%
38 1
 
1.2%
37 1
 
1.2%
36 1
 
1.2%
35 1
 
1.2%
34 1
 
1.2%
33 1
 
1.2%

품목
Text

Distinct42
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-12T19:19:19.880509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.4375
Min length2

Characters and Unicode

Total characters355
Distinct characters102
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)35.0%

Sample

1st row설렁탕
2nd row설렁탕
3rd row설렁탕
4th row설렁탕
5th row된장찌개백반
ValueCountFrequency (%)
이용료 12
 
12.9%
설렁탕 4
 
4.3%
된장찌개백반 4
 
4.3%
튀김닭 4
 
4.3%
목욕료(성인 4
 
4.3%
미용료 4
 
4.3%
피자 4
 
4.3%
양복 4
 
4.3%
세탁료 4
 
4.3%
공동주택관리비 4
 
4.3%
Other values (34) 45
48.4%
2023-12-12T19:19:20.270170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
료 31
 
8.7%
용 18
 
5.1%
이 15
 
4.2%
13
 
3.7%
장 12
 
3.4%
) 8
 
2.3%
자 8
 
2.3%
( 8
 
2.3%
탕 7
 
2.0%
비 7
 
2.0%
Other values (92) 228
64.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 320
90.1%
Space Separator 13
 
3.7%
Close Punctuation 8
 
2.3%
Open Punctuation 8
 
2.3%
Uppercase Letter 6
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 31
 
9.7%
용 18
 
5.6%
이 15
 
4.7%
장 12
 
3.8%
자 8
 
2.5%
탕 7
 
2.2%
비 7
 
2.2%
면 6
 
1.9%
관 6
 
1.9%
김 6
 
1.9%
Other values (87) 204
63.7%
Uppercase Letter
ValueCountFrequency (%)
C 3
50.0%
P 3
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 320
90.1%
Common 29
 
8.2%
Latin 6
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 31
 
9.7%
용 18
 
5.6%
이 15
 
4.7%
장 12
 
3.8%
자 8
 
2.5%
탕 7
 
2.2%
비 7
 
2.2%
면 6
 
1.9%
관 6
 
1.9%
김 6
 
1.9%
Other values (87) 204
63.7%
Common
ValueCountFrequency (%)
13
44.8%
) 8
27.6%
( 8
27.6%
Latin
ValueCountFrequency (%)
C 3
50.0%
P 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 320
90.1%
ASCII 35
 
9.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
료 31
 
9.7%
용 18
 
5.6%
이 15
 
4.7%
장 12
 
3.8%
자 8
 
2.5%
탕 7
 
2.2%
비 7
 
2.2%
면 6
 
1.9%
관 6
 
1.9%
김 6
 
1.9%
Other values (87) 204
63.7%
ASCII
ValueCountFrequency (%)
13
37.1%
) 8
22.9%
( 8
22.9%
C 3
 
8.6%
P 3
 
8.6%

규격
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
1인분(보통)
21 
기본형(야채,고기)
5 
신사복 상·하 드라이크리닝 1회
 
4
성인
 
4
성인여자(컷트)
 
4
Other values (26)
42 

Length

Max length26
Median length18
Mean length9.9
Min length2

Unique

Unique18 ?
Unique (%)22.5%

Sample

1st row1인분(보통)
2nd row1인분(보통)
3rd row1인분(보통)
4th row1인분(보통)
5th row1인분(보통)

Common Values

ValueCountFrequency (%)
1인분(보통) 21
26.2%
기본형(야채,고기) 5
 
6.2%
신사복 상·하 드라이크리닝 1회 4
 
5.0%
성인 4
 
5.0%
성인여자(컷트) 4
 
5.0%
성인일반대중탕 4
 
5.0%
양념통닭 1마리 4
 
5.0%
아파트, 개별부과금 제외한 전금액 4
 
5.0%
쇠고기 180g 정도 3
 
3.8%
기본 1시간 3
 
3.8%
Other values (21) 24
30.0%

Length

2023-12-12T19:19:20.417589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1인분(보통 22
 
14.5%
1회 6
 
3.9%
성인 6
 
3.9%
기본형(야채,고기 5
 
3.3%
1시간 5
 
3.3%
아파트 4
 
2.6%
정도 4
 
2.6%
쇠고기 4
 
2.6%
전금액 4
 
2.6%
제외한 4
 
2.6%
Other values (49) 88
57.9%

상호
Text

Distinct63
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-12T19:19:20.734044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.075
Min length2

Characters and Unicode

Total characters406
Distinct characters165
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)58.8%

Sample

1st row서울깍두기
2nd row전통진국설렁탕
3rd row국일설렁탕
4th row명장진국설렁탕
5th row신당동 찌개전문
ValueCountFrequency (%)
황금성 3
 
3.4%
백송 2
 
2.3%
신대점 2
 
2.3%
현대세탁소 2
 
2.3%
왕지광양숯불갈비 2
 
2.3%
할리스커피 2
 
2.3%
예미원 2
 
2.3%
백운가든 2
 
2.3%
맛드림김밥나라 2
 
2.3%
워터피아 2
 
2.3%
Other values (59) 67
76.1%
2023-12-12T19:19:21.222154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 10
 
2.5%
아 10
 
2.5%
미 9
 
2.2%
9
 
2.2%
피 8
 
2.0%
이 7
 
1.7%
장 6
 
1.5%
국 6
 
1.5%
점 6
 
1.5%
용 6
 
1.5%
Other values (155) 329
81.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 388
95.6%
Space Separator 9
 
2.2%
Uppercase Letter 6
 
1.5%
Decimal Number 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 10
 
2.6%
아 10
 
2.6%
미 9
 
2.3%
피 8
 
2.1%
이 7
 
1.8%
장 6
 
1.5%
국 6
 
1.5%
점 6
 
1.5%
용 6
 
1.5%
당 6
 
1.5%
Other values (150) 314
80.9%
Uppercase Letter
ValueCountFrequency (%)
C 3
50.0%
P 3
50.0%
Decimal Number
ValueCountFrequency (%)
6 2
66.7%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 388
95.6%
Common 12
 
3.0%
Latin 6
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 10
 
2.6%
아 10
 
2.6%
미 9
 
2.3%
피 8
 
2.1%
이 7
 
1.8%
장 6
 
1.5%
국 6
 
1.5%
점 6
 
1.5%
용 6
 
1.5%
당 6
 
1.5%
Other values (150) 314
80.9%
Common
ValueCountFrequency (%)
9
75.0%
6 2
 
16.7%
3 1
 
8.3%
Latin
ValueCountFrequency (%)
C 3
50.0%
P 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 388
95.6%
ASCII 18
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 10
 
2.6%
아 10
 
2.6%
미 9
 
2.3%
피 8
 
2.1%
이 7
 
1.8%
장 6
 
1.5%
국 6
 
1.5%
점 6
 
1.5%
용 6
 
1.5%
당 6
 
1.5%
Other values (150) 314
80.9%
ASCII
ValueCountFrequency (%)
9
50.0%
C 3
 
16.7%
P 3
 
16.7%
6 2
 
11.1%
3 1
 
5.6%

소재지
Text

Distinct64
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-12T19:19:21.640022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23
Mean length19.4375
Min length9

Characters and Unicode

Total characters1555
Distinct characters86
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)62.5%

Sample

1st row전라남도 순천시 연향번영길 139(연향동)
2nd row전라남도 순천시 해룡면 여순로 1334
3rd row전라남도 순천시 충효로 117
4th row전라남도 순천시 장선배기 1길 10-33
5th row전라남도 순천시 순천대2길 14
ValueCountFrequency (%)
전라남도 79
23.7%
순천시 79
23.7%
해룡면 9
 
2.7%
왕궁길 7
 
2.1%
장선배기길 6
 
1.8%
삼산로 6
 
1.8%
31 4
 
1.2%
중앙로 4
 
1.2%
103-13(용당동 3
 
0.9%
26-1 3
 
0.9%
Other values (101) 134
40.1%
2023-12-12T19:19:22.172030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
254
16.3%
천 84
 
5.4%
도 83
 
5.3%
순 82
 
5.3%
전 80
 
5.1%
라 79
 
5.1%
남 79
 
5.1%
시 79
 
5.1%
1 70
 
4.5%
길 57
 
3.7%
Other values (76) 608
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 987
63.5%
Space Separator 254
 
16.3%
Decimal Number 231
 
14.9%
Open Punctuation 31
 
2.0%
Close Punctuation 31
 
2.0%
Dash Punctuation 18
 
1.2%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 84
 
8.5%
도 83
 
8.4%
순 82
 
8.3%
전 80
 
8.1%
라 79
 
8.0%
남 79
 
8.0%
시 79
 
8.0%
길 57
 
5.8%
동 30
 
3.0%
로 23
 
2.3%
Other values (61) 311
31.5%
Decimal Number
ValueCountFrequency (%)
1 70
30.3%
2 33
14.3%
3 32
13.9%
4 21
 
9.1%
0 17
 
7.4%
6 16
 
6.9%
5 13
 
5.6%
9 10
 
4.3%
8 10
 
4.3%
7 9
 
3.9%
Space Separator
ValueCountFrequency (%)
254
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 987
63.5%
Common 568
36.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 84
 
8.5%
도 83
 
8.4%
순 82
 
8.3%
전 80
 
8.1%
라 79
 
8.0%
남 79
 
8.0%
시 79
 
8.0%
길 57
 
5.8%
동 30
 
3.0%
로 23
 
2.3%
Other values (61) 311
31.5%
Common
ValueCountFrequency (%)
254
44.7%
1 70
 
12.3%
2 33
 
5.8%
3 32
 
5.6%
( 31
 
5.5%
) 31
 
5.5%
4 21
 
3.7%
- 18
 
3.2%
0 17
 
3.0%
6 16
 
2.8%
Other values (5) 45
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 987
63.5%
ASCII 568
36.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
254
44.7%
1 70
 
12.3%
2 33
 
5.8%
3 32
 
5.6%
( 31
 
5.5%
) 31
 
5.5%
4 21
 
3.7%
- 18
 
3.2%
0 17
 
3.0%
6 16
 
2.8%
Other values (5) 45
 
7.9%
Hangul
ValueCountFrequency (%)
천 84
 
8.5%
도 83
 
8.4%
순 82
 
8.3%
전 80
 
8.1%
라 79
 
8.0%
남 79
 
8.0%
시 79
 
8.0%
길 57
 
5.8%
동 30
 
3.0%
로 23
 
2.3%
Other values (61) 311
31.5%

전화번호
Text

MISSING 

Distinct62
Distinct (%)79.5%
Missing2
Missing (%)2.5%
Memory size772.0 B
2023-12-12T19:19:22.479475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters624
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

Unique47 ?
Unique (%)60.3%

Sample

1st row724-6333
2nd row724-5170
3rd row745-7800
4th row727-0775
5th row753-9535
ValueCountFrequency (%)
743-1159 3
 
3.8%
722-1429 2
 
2.6%
721-3891 2
 
2.6%
722-5252 2
 
2.6%
725-1006 2
 
2.6%
727-4001 2
 
2.6%
753-3904 2
 
2.6%
725-3599 2
 
2.6%
724-7159 2
 
2.6%
723-7475 2
 
2.6%
Other values (52) 57
73.1%
2023-12-12T19:19:22.897195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 112
17.9%
2 89
14.3%
- 78
12.5%
5 71
11.4%
3 51
8.2%
1 48
7.7%
4 45
7.2%
0 45
7.2%
9 37
 
5.9%
6 25
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 546
87.5%
Dash Punctuation 78
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 112
20.5%
2 89
16.3%
5 71
13.0%
3 51
9.3%
1 48
8.8%
4 45
8.2%
0 45
8.2%
9 37
 
6.8%
6 25
 
4.6%
8 23
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 112
17.9%
2 89
14.3%
- 78
12.5%
5 71
11.4%
3 51
8.2%
1 48
7.7%
4 45
7.2%
0 45
7.2%
9 37
 
5.9%
6 25
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 112
17.9%
2 89
14.3%
- 78
12.5%
5 71
11.4%
3 51
8.2%
1 48
7.7%
4 45
7.2%
0 45
7.2%
9 37
 
5.9%
6 25
 
4.0%

금월가격
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14341.375
Minimum200
Maximum97420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T19:19:23.034053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile1485
Q15500
median7500
Q315000
95-th percentile70054.5
Maximum97420
Range97220
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation19401.321
Coefficient of variation (CV)1.3528215
Kurtosis8.554615
Mean14341.375
Median Absolute Deviation (MAD)3500
Skewness2.9887933
Sum1147310
Variance3.7641124 × 108
MonotonicityNot monotonic
2023-12-12T19:19:23.447839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7000 10
 
12.5%
12000 8
 
10.0%
6000 8
 
10.0%
8000 6
 
7.5%
15000 6
 
7.5%
5000 4
 
5.0%
4500 3
 
3.8%
17000 3
 
3.8%
10000 3
 
3.8%
5500 2
 
2.5%
Other values (25) 27
33.8%
ValueCountFrequency (%)
200 1
1.2%
700 1
1.2%
1000 1
1.2%
1200 1
1.2%
1500 1
1.2%
2500 1
1.2%
3200 1
1.2%
3400 1
1.2%
4000 2
2.5%
4100 1
1.2%
ValueCountFrequency (%)
97420 1
1.2%
88490 1
1.2%
79810 1
1.2%
71090 1
1.2%
70000 1
1.2%
60000 1
1.2%
32000 1
1.2%
26900 1
1.2%
22000 1
1.2%
20000 2
2.5%

전월가격
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14493.75
Minimum200
Maximum97420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T19:19:23.567678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile1485
Q15500
median7500
Q315000
95-th percentile70256.5
Maximum97420
Range97220
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation19944.527
Coefficient of variation (CV)1.3760778
Kurtosis8.7014749
Mean14493.75
Median Absolute Deviation (MAD)3500
Skewness3.0196308
Sum1159500
Variance3.9778417 × 108
MonotonicityNot monotonic
2023-12-12T19:19:23.702443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7000 10
 
12.5%
12000 8
 
10.0%
6000 8
 
10.0%
8000 6
 
7.5%
15000 6
 
7.5%
5000 4
 
5.0%
4500 3
 
3.8%
17000 3
 
3.8%
10000 3
 
3.8%
5500 2
 
2.5%
Other values (25) 27
33.8%
ValueCountFrequency (%)
200 1
1.2%
700 1
1.2%
1000 1
1.2%
1200 1
1.2%
1500 1
1.2%
2500 1
1.2%
3200 1
1.2%
3400 1
1.2%
4000 2
2.5%
4100 1
1.2%
ValueCountFrequency (%)
97420 1
1.2%
94630 1
1.2%
81820 1
1.2%
75130 1
1.2%
70000 1
1.2%
60000 1
1.2%
32000 1
1.2%
26900 1
1.2%
22000 1
1.2%
20000 2
2.5%

증감액
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
<NA>
77 
-6140
 
1
-4040
 
1
-2010
 
1

Length

Max length5
Median length4
Mean length4.0375
Min length4

Unique

Unique3 ?
Unique (%)3.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 77
96.2%
-6140 1
 
1.2%
-4040 1
 
1.2%
-2010 1
 
1.2%

Length

2023-12-12T19:19:23.821558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:19:23.931698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 77
96.2%
6140 1
 
1.2%
4040 1
 
1.2%
2010 1
 
1.2%

증감률(%)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
0.0
77 
-6.49
 
1
-5.38
 
1
-2.46
 
1

Length

Max length5
Median length3
Mean length3.075
Min length3

Unique

Unique3 ?
Unique (%)3.8%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 77
96.2%
-6.49 1
 
1.2%
-5.38 1
 
1.2%
-2.46 1
 
1.2%

Length

2023-12-12T19:19:24.049243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:19:24.193747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 77
96.2%
6.49 1
 
1.2%
5.38 1
 
1.2%
2.46 1
 
1.2%

비고
Text

MISSING 

Distinct7
Distinct (%)87.5%
Missing72
Missing (%)90.0%
Memory size772.0 B
2023-12-12T19:19:24.403345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.875
Min length3

Characters and Unicode

Total characters55
Distinct characters28
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

Unique6 ?
Unique (%)75.0%

Sample

1st row탕수육 17,000
2nd row바지밑단 4,000
3rd row호주산
4th row등심 32,000
5th row국내산
ValueCountFrequency (%)
호주산 2
15.4%
탕수육 1
7.7%
17,000 1
7.7%
바지밑단 1
7.7%
4,000 1
7.7%
등심 1
7.7%
32,000 1
7.7%
국내산 1
7.7%
주말 1
7.7%
9,000 1
7.7%
Other values (2) 2
15.4%
2023-12-12T19:19:24.837512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15
27.3%
5
 
9.1%
, 5
 
9.1%
산 3
 
5.5%
주 3
 
5.5%
호 2
 
3.6%
심 1
 
1.8%
료 1
 
1.8%
욕 1
 
1.8%
목 1
 
1.8%
Other values (18) 18
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23
41.8%
Decimal Number 22
40.0%
Space Separator 5
 
9.1%
Other Punctuation 5
 
9.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 3
 
13.0%
주 3
 
13.0%
호 2
 
8.7%
심 1
 
4.3%
료 1
 
4.3%
욕 1
 
4.3%
목 1
 
4.3%
말 1
 
4.3%
내 1
 
4.3%
국 1
 
4.3%
Other values (8) 8
34.8%
Decimal Number
ValueCountFrequency (%)
0 15
68.2%
9 1
 
4.5%
2 1
 
4.5%
3 1
 
4.5%
4 1
 
4.5%
7 1
 
4.5%
1 1
 
4.5%
6 1
 
4.5%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32
58.2%
Hangul 23
41.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 3
 
13.0%
주 3
 
13.0%
호 2
 
8.7%
심 1
 
4.3%
료 1
 
4.3%
욕 1
 
4.3%
목 1
 
4.3%
말 1
 
4.3%
내 1
 
4.3%
국 1
 
4.3%
Other values (8) 8
34.8%
Common
ValueCountFrequency (%)
0 15
46.9%
5
 
15.6%
, 5
 
15.6%
9 1
 
3.1%
2 1
 
3.1%
3 1
 
3.1%
4 1
 
3.1%
7 1
 
3.1%
1 1
 
3.1%
6 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
58.2%
Hangul 23
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15
46.9%
5
 
15.6%
, 5
 
15.6%
9 1
 
3.1%
2 1
 
3.1%
3 1
 
3.1%
4 1
 
3.1%
7 1
 
3.1%
1 1
 
3.1%
6 1
 
3.1%
Hangul
ValueCountFrequency (%)
산 3
 
13.0%
주 3
 
13.0%
호 2
 
8.7%
심 1
 
4.3%
료 1
 
4.3%
욕 1
 
4.3%
목 1
 
4.3%
말 1
 
4.3%
내 1
 
4.3%
국 1
 
4.3%
Other values (8) 8
34.8%

Interactions

2023-12-12T19:19:18.680144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.050476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.329581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.762443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.135298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.442588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.855763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.228482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:18.574327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:19:24.988251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번품목규격상호소재지전화번호금월가격전월가격증감액증감률(%)비고
연번1.0001.0000.9850.9220.9030.9110.3720.372NaN0.0001.000
품목1.0001.0001.0000.0000.0000.0000.8800.880NaN0.0000.833
규격0.9851.0001.0000.9210.8860.9240.9160.916NaN0.0000.853
상호0.9220.0000.9211.0000.9991.0000.9480.9481.0001.0000.964
소재지0.9030.0000.8860.9991.0001.0000.0000.0001.0000.7451.000
전화번호0.9110.0000.9241.0001.0001.0000.9430.9431.0001.0000.964
금월가격0.3720.8800.9160.9480.0000.9431.0001.0001.0000.9760.000
전월가격0.3720.8800.9160.9480.0000.9431.0001.0001.0000.9760.000
증감액NaNNaNNaN1.0001.0001.0001.0001.0001.0001.000NaN
증감률(%)0.0000.0000.0001.0000.7451.0000.9760.9761.0001.000NaN
비고1.0000.8330.8530.9641.0000.9640.0000.000NaNNaN1.000
2023-12-12T19:19:25.157721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
증감액증감률(%)규격
증감액1.0001.0001.000
증감률(%)1.0001.0000.000
규격1.0000.0001.000
2023-12-12T19:19:25.283073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번금월가격전월가격규격증감액증감률(%)
연번1.000-0.039-0.0390.7421.0000.000
금월가격-0.0391.0001.0000.5511.0000.772
전월가격-0.0391.0001.0000.5511.0000.772
규격0.7420.5510.5511.0001.0000.000
증감액1.0001.0001.0001.0001.0001.000
증감률(%)0.0000.7720.7720.0001.0001.000

Missing values

2023-12-12T19:19:19.035489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:19:19.222785image/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.
2023-12-12T19:19:19.352006image/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

연번품목규격상호소재지전화번호금월가격전월가격증감액증감률(%)비고
01설렁탕1인분(보통)서울깍두기전라남도 순천시 연향번영길 139(연향동)724-633380008000<NA>0.0<NA>
11설렁탕1인분(보통)전통진국설렁탕전라남도 순천시 해룡면 여순로 1334724-517070007000<NA>0.0<NA>
21설렁탕1인분(보통)국일설렁탕전라남도 순천시 충효로 117745-780070007000<NA>0.0<NA>
31설렁탕1인분(보통)명장진국설렁탕전라남도 순천시 장선배기 1길 10-33727-077570007000<NA>0.0<NA>
42된장찌개백반1인분(보통)신당동 찌개전문전라남도 순천시 순천대2길 14753-953560006000<NA>0.0<NA>
52된장찌개백반1인분(보통)최가네흑두부전라남도 순천시 대석3길 9(연향동)724-512370007000<NA>0.0<NA>
62된장찌개백반1인분(보통)진미청국장전라남도 순천시 왕지2길 4741-664470007000<NA>0.0<NA>
72된장찌개백반1인분(보통)건양식당전라남도 순천시 역전광장2길 5744-957570007000<NA>0.0<NA>
83자장면1인분(보통)우당탕반점전라남도 순천시 환선로 170752-530745004500<NA>0.0<NA>
93자장면1인분(보통)북경반점전라남도 순천시 장자보1길 4(연향동)722-747145004500<NA>0.0<NA>
연번품목규격상호소재지전화번호금월가격전월가격증감액증감률(%)비고
7035당구장 이용료일반인, 저녁시간 1시간 기준태양 당구장전라남도 순천시 해룡면 상대석길 22723-592960006000<NA>0.0<NA>
7136사진촬영료반명함판칼라(3*4cm)길 스튜디오전라남도 순천시 장선배기길 76(조례동)723-74751500015000<NA>0.0<NA>
7237사진인화료디지털사진인화요금, 현상료포함길 스튜디오전라남도 순천시 장선배기길 76(조례동)723-7475200200<NA>0.0<NA>
7338숙박료(호텔)평일, 스탠다드(더블), 부가세 및 봉사료 포함순천로얄관광호텔전라남도 순천시 장천4길 15-17(장천동)741-70007000070000<NA>0.0<NA>
7439숙박료 (여관)독방, 1박, 욕탕부설아이비모텔전라남도 순천시 신월길 8(조례동)724-78786000060000<NA>0.0<NA>
7540PC방 이용료기본 1시간라이온PC전라남도 순천시 해룡면 신대로 118<NA>12001200<NA>0.0<NA>
7640PC방 이용료기본 1시간갤러리PC전라남도 순천시 삼산로 558-1, 3층(용당동)<NA>700700<NA>0.0<NA>
7740PC방 이용료기본 1시간스마일PC전라남도 순천시 연향3로 33, 3층725-614410001000<NA>0.0<NA>
7841택배이용료20kg/140cm이하, 타지역대신택배전라남도 순천시 원가곡길4-31751-321750005000<NA>0.0<NA>
7942찜질방 이용료성인, 찜질복대여료 포함워터피아전라남도 순천시 장선배기1길 20(조례동)722-142980008000<NA>0.0<NA>