Overview

Dataset statistics

Number of variables41
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.2 KiB
Average record size in memory358.1 B

Variable types

Categorical2
Text13
Numeric26

Dataset

Description전통시장 및 대형마트 주요 생필품(채소, 과일, 수산물, 축산물, 곡물, 조미료)의 규격에 따른 가격비교 정보를 알려드립니다.
Author울산광역시
URLhttps://www.data.go.kr/data/3043781/fileData.do

Alerts

조사품목(32개) has unique valuesUnique
남구 홈플러스 현실가격 has unique valuesUnique
남구 홈플러스 환산가격 has unique valuesUnique
북구 홈플러스 현실가격 has unique valuesUnique
북구 홈플러스 환산가격 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:05:46.983289
Analysis finished2023-12-12 14:05:47.594301
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size388.0 B
채소
11 
과일
수산물
축산물
곡물

Length

Max length3
Median length2
Mean length2.375
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
채소 11
34.4%
과일 6
18.8%
수산물 5
15.6%
축산물 5
15.6%
곡물 3
 
9.4%
조미료 2
 
6.2%

Length

2023-12-12T23:05:47.673990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:05:47.810185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
채소 11
34.4%
과일 6
18.8%
수산물 5
15.6%
축산물 5
15.6%
곡물 3
 
9.4%
조미료 2
 
6.2%

조사품목(32개)
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:48.040714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5.5
Mean length2.5625
Min length1

Characters and Unicode

Total characters82
Distinct characters53
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

Unique32 ?
Unique (%)100.0%

Sample

1st row
2nd row배추
3rd row
4th row양파
5th row콩나물
ValueCountFrequency (%)
1
 
3.1%
배추 1
 
3.1%
고추가루 1
 
3.1%
1
 
3.1%
찹쌀 1
 
3.1%
1
 
3.1%
달걀 1
 
3.1%
닭고기 1
 
3.1%
돼지고기 1
 
3.1%
쇠고기(수입 1
 
3.1%
Other values (22) 22
68.8%
2023-12-12T23:05:48.439529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
 
8.5%
6
 
7.3%
4
 
4.9%
3
 
3.7%
2
 
2.4%
( 2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (43) 50
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
95.1%
Open Punctuation 2
 
2.4%
Close Punctuation 2
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7
 
9.0%
6
 
7.7%
4
 
5.1%
3
 
3.8%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (41) 46
59.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
95.1%
Common 4
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7
 
9.0%
6
 
7.7%
4
 
5.1%
3
 
3.8%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (41) 46
59.0%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
95.1%
ASCII 4
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
7
 
9.0%
6
 
7.7%
4
 
5.1%
3
 
3.8%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (41) 46
59.0%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%
Distinct27
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:48.701977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length9.46875
Min length6

Characters and Unicode

Total characters303
Distinct characters66
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

Unique22 ?
Unique (%)68.8%

Sample

1st row잎없는 것_1kg
2nd row통배추_1kg
3rd row대파_1kg
4th row잎없는 것_1kg
5th row신선한 것_100g
ValueCountFrequency (%)
신선한 4
 
7.4%
길이 4
 
7.4%
것_1kg 4
 
7.4%
개당 3
 
5.6%
정도_10개 3
 
5.6%
잎없는 2
 
3.7%
등심 2
 
3.7%
1등급_500g 2
 
3.7%
25cm정도_1마리 2
 
3.7%
것_100g 2
 
3.7%
Other values (26) 26
48.1%
2023-12-12T23:05:49.106779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 33
 
10.9%
1 31
 
10.2%
0 29
 
9.6%
g 26
 
8.6%
22
 
7.3%
k 15
 
5.0%
11
 
3.6%
10
 
3.3%
8
 
2.6%
5 7
 
2.3%
Other values (56) 111
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121
39.9%
Decimal Number 76
25.1%
Lowercase Letter 51
16.8%
Connector Punctuation 33
 
10.9%
Space Separator 22
 
7.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11
 
9.1%
10
 
8.3%
8
 
6.6%
6
 
5.0%
5
 
4.1%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (42) 61
50.4%
Decimal Number
ValueCountFrequency (%)
1 31
40.8%
0 29
38.2%
5 7
 
9.2%
2 4
 
5.3%
6 2
 
2.6%
4 1
 
1.3%
3 1
 
1.3%
8 1
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
g 26
51.0%
k 15
29.4%
m 5
 
9.8%
c 5
 
9.8%
Connector Punctuation
ValueCountFrequency (%)
_ 33
100.0%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 131
43.2%
Hangul 121
39.9%
Latin 51
 
16.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11
 
9.1%
10
 
8.3%
8
 
6.6%
6
 
5.0%
5
 
4.1%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (42) 61
50.4%
Common
ValueCountFrequency (%)
_ 33
25.2%
1 31
23.7%
0 29
22.1%
22
16.8%
5 7
 
5.3%
2 4
 
3.1%
6 2
 
1.5%
4 1
 
0.8%
3 1
 
0.8%
8 1
 
0.8%
Latin
ValueCountFrequency (%)
g 26
51.0%
k 15
29.4%
m 5
 
9.8%
c 5
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 182
60.1%
Hangul 121
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 33
18.1%
1 31
17.0%
0 29
15.9%
g 26
14.3%
22
12.1%
k 15
8.2%
5 7
 
3.8%
m 5
 
2.7%
c 5
 
2.7%
2 4
 
2.2%
Other values (4) 5
 
2.7%
Hangul
ValueCountFrequency (%)
11
 
9.1%
10
 
8.3%
8
 
6.6%
6
 
5.0%
5
 
4.1%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (42) 61
50.4%

중구 태화시장
Real number (ℝ)

Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9353.125
Minimum400
Maximum53800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:49.227486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile887.5
Q12475
median4500
Q310000
95-th percentile32425
Maximum53800
Range53400
Interquartile range (IQR)7525

Descriptive statistics

Standard deviation12004.257
Coefficient of variation (CV)1.2834488
Kurtosis6.3250736
Mean9353.125
Median Absolute Deviation (MAD)3085
Skewness2.4224723
Sum299300
Variance1.4410218 × 108
MonotonicityNot monotonic
2023-12-12T23:05:49.334881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3000 4
 
12.5%
10000 3
 
9.4%
1000 2
 
6.2%
5000 2
 
6.2%
6880 1
 
3.1%
3890 1
 
3.1%
53800 1
 
3.1%
2400 1
 
3.1%
7000 1
 
3.1%
9900 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
400 1
 
3.1%
750 1
 
3.1%
1000 2
6.2%
1330 1
 
3.1%
1500 1
 
3.1%
2000 1
 
3.1%
2400 1
 
3.1%
2500 1
 
3.1%
3000 4
12.5%
3330 1
 
3.1%
ValueCountFrequency (%)
53800 1
 
3.1%
41500 1
 
3.1%
25000 1
 
3.1%
20800 1
 
3.1%
20000 1
 
3.1%
16660 1
 
3.1%
15000 1
 
3.1%
10000 3
9.4%
9900 1
 
3.1%
7000 1
 
3.1%

중구 더프레시
Real number (ℝ)

Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10953.75
Minimum440
Maximum66350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:49.438027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum440
5-th percentile1345
Q12132.5
median4710
Q314850
95-th percentile38635
Maximum66350
Range65910
Interquartile range (IQR)12717.5

Descriptive statistics

Standard deviation14728.453
Coefficient of variation (CV)1.3446038
Kurtosis6.65924
Mean10953.75
Median Absolute Deviation (MAD)2970
Skewness2.4769546
Sum350520
Variance2.1692734 × 108
MonotonicityNot monotonic
2023-12-12T23:05:49.544146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1480 2
 
6.2%
3330 2
 
6.2%
1930 2
 
6.2%
6860 1
 
3.1%
7600 1
 
3.1%
5900 1
 
3.1%
8580 1
 
3.1%
4270 1
 
3.1%
49800 1
 
3.1%
10400 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
440 1
3.1%
1180 1
3.1%
1480 2
6.2%
1660 1
3.1%
1900 1
3.1%
1930 2
6.2%
2200 1
3.1%
2820 1
3.1%
2980 1
3.1%
3330 2
6.2%
ValueCountFrequency (%)
66350 1
3.1%
49800 1
3.1%
29500 1
3.1%
26600 1
3.1%
19800 1
3.1%
19650 1
3.1%
19600 1
3.1%
15000 1
3.1%
14800 1
3.1%
10400 1
3.1%
Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Memory size388.0 B
1kg
12 
100g
1개
1마리
1 개
 
1
Other values (9)

Length

Max length5
Median length3
Mean length3.25
Min length2

Unique

Unique10 ?
Unique (%)31.2%

Sample

1st row1kg
2nd row1kg
3rd row1kg
4th row1kg
5th row100g

Common Values

ValueCountFrequency (%)
1kg 12
37.5%
100g 5
15.6%
1개 3
 
9.4%
1마리 2
 
6.2%
1 개 1
 
3.1%
800g 1
 
3.1%
8kg 1
 
3.1%
500g 1
 
3.1%
5마리 1
 
3.1%
4마리 1
 
3.1%
Other values (4) 4
 
12.5%

Length

2023-12-12T23:05:49.672215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1kg 12
36.4%
100g 5
15.2%
1개 3
 
9.1%
1마리 2
 
6.1%
1 1
 
3.0%
1
 
3.0%
800g 1
 
3.0%
8kg 1
 
3.0%
500g 1
 
3.0%
5마리 1
 
3.0%
Other values (5) 5
15.2%
Distinct22
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9984.375
Minimum500
Maximum63000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:49.776232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile1000
Q13000
median6250
Q310500
95-th percentile34400
Maximum63000
Range62500
Interquartile range (IQR)7500

Descriptive statistics

Standard deviation13514.821
Coefficient of variation (CV)1.3535971
Kurtosis9.6953936
Mean9984.375
Median Absolute Deviation (MAD)3750
Skewness3.0476436
Sum319500
Variance1.8265039 × 108
MonotonicityNot monotonic
2023-12-12T23:05:49.886067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3000 3
 
9.4%
5000 3
 
9.4%
2000 2
 
6.2%
1000 2
 
6.2%
12000 2
 
6.2%
7000 2
 
6.2%
8000 2
 
6.2%
10000 2
 
6.2%
20000 1
 
3.1%
15000 1
 
3.1%
Other values (12) 12
37.5%
ValueCountFrequency (%)
500 1
 
3.1%
1000 2
6.2%
1500 1
 
3.1%
1900 1
 
3.1%
2000 2
6.2%
3000 3
9.4%
4000 1
 
3.1%
4100 1
 
3.1%
5000 3
9.4%
6000 1
 
3.1%
ValueCountFrequency (%)
63000 1
3.1%
52000 1
3.1%
20000 1
3.1%
19000 1
3.1%
15000 1
3.1%
13000 1
3.1%
12000 2
6.2%
10000 2
6.2%
9000 1
3.1%
8000 2
6.2%
Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13387.281
Minimum500
Maximum65000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:49.994311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile887.5
Q12990
median5875
Q315300
95-th percentile52900
Maximum65000
Range64500
Interquartile range (IQR)12310

Descriptive statistics

Standard deviation17045.991
Coefficient of variation (CV)1.2732975
Kurtosis2.5710518
Mean13387.281
Median Absolute Deviation (MAD)4050
Skewness1.8219666
Sum428393
Variance2.9056582 × 108
MonotonicityNot monotonic
2023-12-12T23:05:50.096048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1000 3
 
9.4%
5000 2
 
6.2%
3000 1
 
3.1%
9800 1
 
3.1%
3160 1
 
3.1%
10710 1
 
3.1%
5750 1
 
3.1%
65000 1
 
3.1%
2960 1
 
3.1%
7860 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
500 1
 
3.1%
750 1
 
3.1%
1000 3
9.4%
1900 1
 
3.1%
2500 1
 
3.1%
2960 1
 
3.1%
3000 1
 
3.1%
3130 1
 
3.1%
3160 1
 
3.1%
4330 1
 
3.1%
ValueCountFrequency (%)
65000 1
3.1%
54000 1
3.1%
52000 1
3.1%
36600 1
3.1%
30000 1
3.1%
26000 1
3.1%
25000 1
3.1%
22500 1
3.1%
12900 1
3.1%
10710 1
3.1%
Distinct16
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:50.261487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.46875
Min length2

Characters and Unicode

Total characters111
Distinct characters15
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

Unique12 ?
Unique (%)37.5%

Sample

1st row1kg
2nd row1kg
3rd row100g
4th row1.8kg
5th row100g
ValueCountFrequency (%)
100g 8
25.0%
1kg 7
21.9%
500g 3
 
9.4%
3마리 2
 
6.2%
4kg 2
 
6.2%
1.8kg 1
 
3.1%
2개 1
 
3.1%
1개 1
 
3.1%
150g 1
 
3.1%
10kg 1
 
3.1%
Other values (5) 5
15.6%
2023-12-12T23:05:50.564167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28
25.2%
g 24
21.6%
1 21
18.9%
k 11
 
9.9%
5 4
 
3.6%
4
 
3.6%
4
 
3.6%
2 4
 
3.6%
3
 
2.7%
3 2
 
1.8%
Other values (5) 6
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
55.9%
Lowercase Letter 35
31.5%
Other Letter 11
 
9.9%
Uppercase Letter 2
 
1.8%
Other Punctuation 1
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
45.2%
1 21
33.9%
5 4
 
6.5%
2 4
 
6.5%
3 2
 
3.2%
4 2
 
3.2%
8 1
 
1.6%
Other Letter
ValueCountFrequency (%)
4
36.4%
4
36.4%
3
27.3%
Lowercase Letter
ValueCountFrequency (%)
g 24
68.6%
k 11
31.4%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
G 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63
56.8%
Latin 37
33.3%
Hangul 11
 
9.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
44.4%
1 21
33.3%
5 4
 
6.3%
2 4
 
6.3%
3 2
 
3.2%
4 2
 
3.2%
. 1
 
1.6%
8 1
 
1.6%
Latin
ValueCountFrequency (%)
g 24
64.9%
k 11
29.7%
K 1
 
2.7%
G 1
 
2.7%
Hangul
ValueCountFrequency (%)
4
36.4%
4
36.4%
3
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100
90.1%
Hangul 11
 
9.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
28.0%
g 24
24.0%
1 21
21.0%
k 11
 
11.0%
5 4
 
4.0%
2 4
 
4.0%
3 2
 
2.0%
4 2
 
2.0%
. 1
 
1.0%
8 1
 
1.0%
Other values (2) 2
 
2.0%
Hangul
ValueCountFrequency (%)
4
36.4%
4
36.4%
3
27.3%
Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13267.5
Minimum940
Maximum66900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:50.683621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum940
5-th percentile1132
Q13325
median6390
Q315712.5
95-th percentile48360
Maximum66900
Range65960
Interquartile range (IQR)12387.5

Descriptive statistics

Standard deviation16696.754
Coefficient of variation (CV)1.2584702
Kurtosis3.7226909
Mean13267.5
Median Absolute Deviation (MAD)4140
Skewness2.0119585
Sum424560
Variance2.7878159 × 108
MonotonicityNot monotonic
2023-12-12T23:05:50.793776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3980 2
 
6.2%
10600 2
 
6.2%
7980 2
 
6.2%
1000 1
 
3.1%
3380 1
 
3.1%
3580 1
 
3.1%
6300 1
 
3.1%
59800 1
 
3.1%
3160 1
 
3.1%
11230 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
940 1
3.1%
1000 1
3.1%
1240 1
3.1%
1280 1
3.1%
2320 1
3.1%
2470 1
3.1%
2480 1
3.1%
3160 1
3.1%
3380 1
3.1%
3480 1
3.1%
ValueCountFrequency (%)
66900 1
3.1%
59800 1
3.1%
39000 1
3.1%
33960 1
3.1%
33300 1
3.1%
22900 1
3.1%
22400 1
3.1%
21600 1
3.1%
13750 1
3.1%
11230 1
3.1%
Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13278.75
Minimum620
Maximum66900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:50.907512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum620
5-th percentile973
Q12990
median6985
Q315712.5
95-th percentile48360
Maximum66900
Range66280
Interquartile range (IQR)12722.5

Descriptive statistics

Standard deviation16717.309
Coefficient of variation (CV)1.258952
Kurtosis3.6884709
Mean13278.75
Median Absolute Deviation (MAD)4585
Skewness1.997506
Sum424920
Variance2.7946842 × 108
MonotonicityNot monotonic
2023-12-12T23:05:51.018438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10600 2
 
6.2%
7980 2
 
6.2%
1000 1
 
3.1%
22900 1
 
3.1%
3580 1
 
3.1%
6300 1
 
3.1%
59800 1
 
3.1%
3160 1
 
3.1%
11230 1
 
3.1%
13750 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
620 1
3.1%
940 1
3.1%
1000 1
3.1%
1240 1
3.1%
1280 1
3.1%
2210 1
3.1%
2320 1
3.1%
2480 1
3.1%
3160 1
3.1%
3380 1
3.1%
ValueCountFrequency (%)
66900 1
3.1%
59800 1
3.1%
39000 1
3.1%
33960 1
3.1%
33300 1
3.1%
22900 1
3.1%
22400 1
3.1%
21600 1
3.1%
13750 1
3.1%
11230 1
3.1%
Distinct17
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:51.168677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length3.71875
Min length2

Characters and Unicode

Total characters119
Distinct characters13
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

Unique12 ?
Unique (%)37.5%

Sample

1st row1kg
2nd row2kg
3rd row300g
4th row1kg
5th row250g
ValueCountFrequency (%)
100g 11
34.4%
1kg 3
 
9.4%
2마리 2
 
6.2%
600g 2
 
6.2%
2kg 2
 
6.2%
4.5kg 1
 
3.1%
20kg 1
 
3.1%
10개 1
 
3.1%
1kg1마리 1
 
3.1%
1마리 1
 
3.1%
Other values (7) 7
21.9%
2023-12-12T23:05:51.490980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 31
26.1%
g 25
21.0%
1 21
17.6%
k 10
 
8.4%
2 6
 
5.0%
5
 
4.2%
5
 
4.2%
6 3
 
2.5%
3 3
 
2.5%
5 3
 
2.5%
Other values (3) 7
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
57.1%
Lowercase Letter 35
29.4%
Other Letter 13
 
10.9%
Other Punctuation 3
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
45.6%
1 21
30.9%
2 6
 
8.8%
6 3
 
4.4%
3 3
 
4.4%
5 3
 
4.4%
4 1
 
1.5%
Other Letter
ValueCountFrequency (%)
5
38.5%
5
38.5%
3
23.1%
Lowercase Letter
ValueCountFrequency (%)
g 25
71.4%
k 10
 
28.6%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71
59.7%
Latin 35
29.4%
Hangul 13
 
10.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
43.7%
1 21
29.6%
2 6
 
8.5%
6 3
 
4.2%
3 3
 
4.2%
5 3
 
4.2%
. 3
 
4.2%
4 1
 
1.4%
Hangul
ValueCountFrequency (%)
5
38.5%
5
38.5%
3
23.1%
Latin
ValueCountFrequency (%)
g 25
71.4%
k 10
 
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106
89.1%
Hangul 13
 
10.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
29.2%
g 25
23.6%
1 21
19.8%
k 10
 
9.4%
2 6
 
5.7%
6 3
 
2.8%
3 3
 
2.8%
5 3
 
2.8%
. 3
 
2.8%
4 1
 
0.9%
Hangul
ValueCountFrequency (%)
5
38.5%
5
38.5%
3
23.1%
Distinct27
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10669.188
Minimum500
Maximum62500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:51.601197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile894.25
Q12624.5
median5000
Q311472
95-th percentile36090
Maximum62500
Range62000
Interquartile range (IQR)8847.5

Descriptive statistics

Standard deviation13640.162
Coefficient of variation (CV)1.278463
Kurtosis6.6938494
Mean10669.188
Median Absolute Deviation (MAD)2950
Skewness2.4653434
Sum341414
Variance1.8605401 × 108
MonotonicityNot monotonic
2023-12-12T23:05:51.711706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5000 4
 
12.5%
1000 2
 
6.2%
10000 2
 
6.2%
6666 1
 
3.1%
23000 1
 
3.1%
15888 1
 
3.1%
4000 1
 
3.1%
62500 1
 
3.1%
4300 1
 
3.1%
7500 1
 
3.1%
Other values (17) 17
53.1%
ValueCountFrequency (%)
500 1
3.1%
765 1
3.1%
1000 2
6.2%
1700 1
3.1%
2400 1
3.1%
2480 1
3.1%
2500 1
3.1%
2666 1
3.1%
3500 1
3.1%
4000 1
3.1%
ValueCountFrequency (%)
62500 1
3.1%
45000 1
3.1%
28800 1
3.1%
23333 1
3.1%
23000 1
3.1%
20000 1
3.1%
16666 1
3.1%
15888 1
3.1%
10000 2
6.2%
8500 1
3.1%
Distinct27
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11647.156
Minimum500
Maximum62500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:51.818794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile894.25
Q13458.25
median5000
Q313466.5
95-th percentile43500
Maximum62500
Range62000
Interquartile range (IQR)10008.25

Descriptive statistics

Standard deviation15230.147
Coefficient of variation (CV)1.307628
Kurtosis5.7747067
Mean11647.156
Median Absolute Deviation (MAD)2950
Skewness2.4001397
Sum372709
Variance2.3195739 × 108
MonotonicityNot monotonic
2023-12-12T23:05:51.926867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5000 5
 
15.6%
1000 2
 
6.2%
6666 1
 
3.1%
10000 1
 
3.1%
3333 1
 
3.1%
9930 1
 
3.1%
4000 1
 
3.1%
62500 1
 
3.1%
4300 1
 
3.1%
7500 1
 
3.1%
Other values (17) 17
53.1%
ValueCountFrequency (%)
500 1
3.1%
765 1
3.1%
1000 2
6.2%
1700 1
3.1%
2400 1
3.1%
2666 1
3.1%
3333 1
3.1%
3500 1
3.1%
4000 1
3.1%
4300 1
3.1%
ValueCountFrequency (%)
62500 1
3.1%
60000 1
3.1%
30000 1
3.1%
28800 1
3.1%
23333 1
3.1%
20000 1
3.1%
18000 1
3.1%
16666 1
3.1%
12400 1
3.1%
10000 1
3.1%
Distinct16
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:52.040372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.65625
Min length2

Characters and Unicode

Total characters117
Distinct characters13
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

Unique11 ?
Unique (%)34.4%

Sample

1st row1kg
2nd row2kg
3rd row100g
4th row1.5kg
5th row360g
ValueCountFrequency (%)
100g 13
40.6%
1kg 2
 
6.2%
4kg 2
 
6.2%
2마리 2
 
6.2%
500g 2
 
6.2%
2kg 1
 
3.1%
1.5kg 1
 
3.1%
360g 1
 
3.1%
4개 1
 
3.1%
1개 1
 
3.1%
Other values (6) 6
18.8%
2023-12-12T23:05:52.258942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
28.2%
g 25
21.4%
1 21
17.9%
k 9
 
7.7%
4 5
 
4.3%
2 4
 
3.4%
4
 
3.4%
4
 
3.4%
5 3
 
2.6%
. 3
 
2.6%
Other values (3) 6
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69
59.0%
Lowercase Letter 34
29.1%
Other Letter 11
 
9.4%
Other Punctuation 3
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33
47.8%
1 21
30.4%
4 5
 
7.2%
2 4
 
5.8%
5 3
 
4.3%
3 2
 
2.9%
6 1
 
1.4%
Other Letter
ValueCountFrequency (%)
4
36.4%
4
36.4%
3
27.3%
Lowercase Letter
ValueCountFrequency (%)
g 25
73.5%
k 9
 
26.5%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
61.5%
Latin 34
29.1%
Hangul 11
 
9.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33
45.8%
1 21
29.2%
4 5
 
6.9%
2 4
 
5.6%
5 3
 
4.2%
. 3
 
4.2%
3 2
 
2.8%
6 1
 
1.4%
Hangul
ValueCountFrequency (%)
4
36.4%
4
36.4%
3
27.3%
Latin
ValueCountFrequency (%)
g 25
73.5%
k 9
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106
90.6%
Hangul 11
 
9.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
31.1%
g 25
23.6%
1 21
19.8%
k 9
 
8.5%
4 5
 
4.7%
2 4
 
3.8%
5 3
 
2.8%
. 3
 
2.8%
3 2
 
1.9%
6 1
 
0.9%
Hangul
ValueCountFrequency (%)
4
36.4%
4
36.4%
3
27.3%
Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7844.5625
Minimum444
Maximum49900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:52.362345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum444
5-th percentile500
Q11697.5
median4540
Q39883.5
95-th percentile24300
Maximum49900
Range49456
Interquartile range (IQR)8186

Descriptive statistics

Standard deviation10243.742
Coefficient of variation (CV)1.3058398
Kurtosis8.8550449
Mean7844.5625
Median Absolute Deviation (MAD)3160
Skewness2.7070749
Sum251026
Variance1.0493424 × 108
MonotonicityNot monotonic
2023-12-12T23:05:52.459296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
500 2
 
6.2%
4990 2
 
6.2%
1700 1
 
3.1%
9900 1
 
3.1%
1597 1
 
3.1%
29800 1
 
3.1%
8990 1
 
3.1%
12800 1
 
3.1%
49900 1
 
3.1%
4290 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
444 1
3.1%
500 2
6.2%
563 1
3.1%
1170 1
3.1%
1590 1
3.1%
1597 1
3.1%
1690 1
3.1%
1700 1
3.1%
1745 1
3.1%
1753 1
3.1%
ValueCountFrequency (%)
49900 1
3.1%
29800 1
3.1%
19800 1
3.1%
18000 1
3.1%
15900 1
3.1%
12800 1
3.1%
12600 1
3.1%
9900 1
3.1%
9878 1
3.1%
8990 1
3.1%
Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13624.094
Minimum487
Maximum84950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:52.561545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum487
5-th percentile1092.6
Q13319.5
median5000
Q317587.5
95-th percentile46895
Maximum84950
Range84463
Interquartile range (IQR)14268

Descriptive statistics

Standard deviation18585.458
Coefficient of variation (CV)1.364161
Kurtosis7.3189682
Mean13624.094
Median Absolute Deviation (MAD)3765
Skewness2.583798
Sum435971
Variance3.4541924 × 108
MonotonicityNot monotonic
2023-12-12T23:05:52.652329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5000 2
 
6.2%
31800 2
 
6.2%
4990 2
 
6.2%
1700 1
 
3.1%
3300 1
 
3.1%
8700 1
 
3.1%
5180 1
 
3.1%
11980 1
 
3.1%
2975 1
 
3.1%
62900 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
487 1
3.1%
998 1
3.1%
1170 1
3.1%
1590 1
3.1%
1690 1
3.1%
1700 1
3.1%
2975 1
3.1%
3300 1
3.1%
3326 1
3.1%
3990 1
3.1%
ValueCountFrequency (%)
84950 1
3.1%
62900 1
3.1%
33800 1
3.1%
31800 2
6.2%
21400 1
3.1%
18450 1
3.1%
18000 1
3.1%
17450 1
3.1%
13317 1
3.1%
12600 1
3.1%
Distinct16
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:52.802600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.40625
Min length2

Characters and Unicode

Total characters109
Distinct characters15
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

Unique11 ?
Unique (%)34.4%

Sample

1st row1.5kg
2nd row2.5kg
3rd row500g
4th row2kg
5th row100g
ValueCountFrequency (%)
1kg 7
21.9%
100g 5
15.6%
1마리 5
15.6%
1개 3
9.4%
1.5kg 2
 
6.2%
2.5kg 1
 
3.1%
500g 1
 
3.1%
2kg 1
 
3.1%
3개 1
 
3.1%
1봉 1
 
3.1%
Other values (5) 5
15.6%
2023-12-12T23:05:53.177370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 26
23.9%
g 21
19.3%
k 15
13.8%
0 14
12.8%
5
 
4.6%
5
 
4.6%
5
 
4.6%
. 5
 
4.6%
5 4
 
3.7%
2 3
 
2.8%
Other values (5) 6
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51
46.8%
Lowercase Letter 36
33.0%
Other Letter 16
 
14.7%
Other Punctuation 5
 
4.6%
Space Separator 1
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26
51.0%
0 14
27.5%
5 4
 
7.8%
2 3
 
5.9%
6 2
 
3.9%
3 1
 
2.0%
4 1
 
2.0%
Other Letter
ValueCountFrequency (%)
5
31.2%
5
31.2%
5
31.2%
1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
g 21
58.3%
k 15
41.7%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57
52.3%
Latin 36
33.0%
Hangul 16
 
14.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26
45.6%
0 14
24.6%
. 5
 
8.8%
5 4
 
7.0%
2 3
 
5.3%
6 2
 
3.5%
3 1
 
1.8%
1
 
1.8%
4 1
 
1.8%
Hangul
ValueCountFrequency (%)
5
31.2%
5
31.2%
5
31.2%
1
 
6.2%
Latin
ValueCountFrequency (%)
g 21
58.3%
k 15
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93
85.3%
Hangul 16
 
14.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26
28.0%
g 21
22.6%
k 15
16.1%
0 14
15.1%
. 5
 
5.4%
5 4
 
4.3%
2 3
 
3.2%
6 2
 
2.2%
3 1
 
1.1%
1
 
1.1%
Hangul
ValueCountFrequency (%)
5
31.2%
5
31.2%
5
31.2%
1
 
6.2%
Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11454.188
Minimum667
Maximum64000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:53.348095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum667
5-th percentile910
Q13750
median5750
Q313333.25
95-th percentile34500
Maximum64000
Range63333
Interquartile range (IQR)9583.25

Descriptive statistics

Standard deviation13452.806
Coefficient of variation (CV)1.174488
Kurtosis6.9369952
Mean11454.188
Median Absolute Deviation (MAD)4000
Skewness2.4106506
Sum366534
Variance1.8097798 × 108
MonotonicityNot monotonic
2023-12-12T23:05:53.553052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5000 5
 
15.6%
2000 2
 
6.2%
8000 2
 
6.2%
20000 2
 
6.2%
4000 1
 
3.1%
11000 1
 
3.1%
15833 1
 
3.1%
64000 1
 
3.1%
3800 1
 
3.1%
11667 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
667 1
 
3.1%
800 1
 
3.1%
1000 1
 
3.1%
1500 1
 
3.1%
2000 2
 
6.2%
2500 1
 
3.1%
3600 1
 
3.1%
3800 1
 
3.1%
4000 1
 
3.1%
5000 5
15.6%
ValueCountFrequency (%)
64000 1
3.1%
40000 1
3.1%
30000 1
3.1%
26667 1
3.1%
22000 1
3.1%
20000 2
6.2%
15833 1
3.1%
12500 1
3.1%
11667 1
3.1%
11000 1
3.1%
Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11391.688
Minimum667
Maximum64000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:53.738062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum667
5-th percentile910
Q13450
median5750
Q313333.25
95-th percentile34500
Maximum64000
Range63333
Interquartile range (IQR)9883.25

Descriptive statistics

Standard deviation13488.357
Coefficient of variation (CV)1.1840526
Kurtosis6.8838383
Mean11391.688
Median Absolute Deviation (MAD)4000
Skewness2.4017645
Sum364534
Variance1.8193578 × 108
MonotonicityNot monotonic
2023-12-12T23:05:53.924517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5000 4
 
12.5%
2000 2
 
6.2%
8000 2
 
6.2%
20000 2
 
6.2%
4000 1
 
3.1%
11000 1
 
3.1%
15833 1
 
3.1%
64000 1
 
3.1%
3800 1
 
3.1%
11667 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
667 1
3.1%
800 1
3.1%
1000 1
3.1%
1500 1
3.1%
2000 2
6.2%
2500 1
3.1%
3000 1
3.1%
3600 1
3.1%
3800 1
3.1%
4000 1
3.1%
ValueCountFrequency (%)
64000 1
3.1%
40000 1
3.1%
30000 1
3.1%
26667 1
3.1%
22000 1
3.1%
20000 2
6.2%
15833 1
3.1%
12500 1
3.1%
11667 1
3.1%
11000 1
3.1%
Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:54.190491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6.5
Mean length3.5625
Min length2

Characters and Unicode

Total characters114
Distinct characters17
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)40.6%

Sample

1st row1.5kg
2nd row2kg
3rd row1kg
4th row3kg
5th row340g
ValueCountFrequency (%)
100g 4
12.5%
1마리 4
12.5%
1kg 3
 
9.4%
3kg 3
 
9.4%
2kg 3
 
9.4%
500g 2
 
6.2%
400g 1
 
3.1%
7kg 1
 
3.1%
20kg 1
 
3.1%
10개 1
 
3.1%
Other values (9) 9
28.1%
2023-12-12T23:05:55.016074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 24
21.1%
0 22
19.3%
1 16
14.0%
k 13
11.4%
2 6
 
5.3%
3 5
 
4.4%
5 5
 
4.4%
5
 
4.4%
5
 
4.4%
3
 
2.6%
Other values (7) 10
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59
51.8%
Lowercase Letter 37
32.5%
Other Letter 14
 
12.3%
Other Punctuation 2
 
1.8%
Open Punctuation 1
 
0.9%
Close Punctuation 1
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22
37.3%
1 16
27.1%
2 6
 
10.2%
3 5
 
8.5%
5 5
 
8.5%
4 2
 
3.4%
7 2
 
3.4%
8 1
 
1.7%
Other Letter
ValueCountFrequency (%)
5
35.7%
5
35.7%
3
21.4%
1
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
g 24
64.9%
k 13
35.1%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63
55.3%
Latin 37
32.5%
Hangul 14
 
12.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22
34.9%
1 16
25.4%
2 6
 
9.5%
3 5
 
7.9%
5 5
 
7.9%
4 2
 
3.2%
7 2
 
3.2%
. 2
 
3.2%
8 1
 
1.6%
( 1
 
1.6%
Hangul
ValueCountFrequency (%)
5
35.7%
5
35.7%
3
21.4%
1
 
7.1%
Latin
ValueCountFrequency (%)
g 24
64.9%
k 13
35.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100
87.7%
Hangul 14
 
12.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 24
24.0%
0 22
22.0%
1 16
16.0%
k 13
13.0%
2 6
 
6.0%
3 5
 
5.0%
5 5
 
5.0%
4 2
 
2.0%
7 2
 
2.0%
. 2
 
2.0%
Other values (3) 3
 
3.0%
Hangul
ValueCountFrequency (%)
5
35.7%
5
35.7%
3
21.4%
1
 
7.1%

남구 홈플러스 현실가격
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11672.562
Minimum995
Maximum56333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:55.181338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum995
5-th percentile1271.35
Q13440
median5495
Q314157.5
95-th percentile40021.8
Maximum56333
Range55338
Interquartile range (IQR)10717.5

Descriptive statistics

Standard deviation13586.77
Coefficient of variation (CV)1.163992
Kurtosis3.784973
Mean11672.562
Median Absolute Deviation (MAD)3852.5
Skewness1.99314
Sum373522
Variance1.8460032 × 108
MonotonicityNot monotonic
2023-12-12T23:05:55.300193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1790 1
 
3.1%
4000 1
 
3.1%
13980 1
 
3.1%
23800 1
 
3.1%
6990 1
 
3.1%
10520 1
 
3.1%
47900 1
 
3.1%
3690 1
 
3.1%
9990 1
 
3.1%
2590 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
995 1
3.1%
998 1
3.1%
1495 1
3.1%
1790 1
3.1%
1990 1
3.1%
2590 1
3.1%
3112 1
3.1%
3290 1
3.1%
3490 1
3.1%
3500 1
3.1%
ValueCountFrequency (%)
56333 1
3.1%
47900 1
3.1%
33576 1
3.1%
29983 1
3.1%
23800 1
3.1%
20990 1
3.1%
19980 1
3.1%
14690 1
3.1%
13980 1
3.1%
11000 1
3.1%

남구 홈플러스 환산가격
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14200.625
Minimum995
Maximum64950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:55.436887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum995
5-th percentile1271.35
Q13803.5
median6903.5
Q316995
95-th percentile57938.15
Maximum64950
Range63955
Interquartile range (IQR)13191.5

Descriptive statistics

Standard deviation17287.278
Coefficient of variation (CV)1.2173603
Kurtosis3.0902051
Mean14200.625
Median Absolute Deviation (MAD)4505
Skewness1.9407301
Sum454420
Variance2.9884997 × 108
MonotonicityNot monotonic
2023-12-12T23:05:55.574218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1790 1
 
3.1%
4000 1
 
3.1%
7990 1
 
3.1%
23500 1
 
3.1%
13980 1
 
3.1%
4995 1
 
3.1%
59900 1
 
3.1%
3993 1
 
3.1%
8990 1
 
3.1%
14250 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
995 1
3.1%
998 1
3.1%
1495 1
3.1%
1790 1
3.1%
1990 1
3.1%
3112 1
3.1%
3290 1
3.1%
3490 1
3.1%
3908 1
3.1%
3993 1
3.1%
ValueCountFrequency (%)
64950 1
3.1%
59900 1
3.1%
56333 1
3.1%
33576 1
3.1%
29983 1
3.1%
23500 1
3.1%
20990 1
3.1%
19980 1
3.1%
16000 1
3.1%
14250 1
3.1%
Distinct20
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:55.750269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.84375
Min length2

Characters and Unicode

Total characters123
Distinct characters30
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)46.9%

Sample

1st row1개
2nd row1통
3rd row1단
4th row1바구니
5th row1봉지
ValueCountFrequency (%)
1바구니 8
25.0%
100g 3
 
9.4%
1개 2
 
6.2%
2마리 2
 
6.2%
1마리 2
 
6.2%
1kg 2
 
6.2%
1통 1
 
3.1%
10개 1
 
3.1%
1박스(10kg 1
 
3.1%
5-6kg 1
 
3.1%
Other values (9) 9
28.1%
2023-12-12T23:05:56.061785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 26
21.1%
0 15
12.2%
g 11
 
8.9%
8
 
6.5%
8
 
6.5%
8
 
6.5%
5
 
4.1%
5
 
4.1%
4
 
3.3%
2 3
 
2.4%
Other values (20) 30
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51
41.5%
Other Letter 49
39.8%
Lowercase Letter 14
 
11.4%
Open Punctuation 3
 
2.4%
Close Punctuation 3
 
2.4%
Uppercase Letter 2
 
1.6%
Dash Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8
16.3%
8
16.3%
8
16.3%
5
10.2%
5
10.2%
4
8.2%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
Other values (7) 7
14.3%
Decimal Number
ValueCountFrequency (%)
1 26
51.0%
0 15
29.4%
2 3
 
5.9%
3 2
 
3.9%
5 2
 
3.9%
6 2
 
3.9%
8 1
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
g 11
78.6%
k 3
 
21.4%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58
47.2%
Hangul 49
39.8%
Latin 16
 
13.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8
16.3%
8
16.3%
8
16.3%
5
10.2%
5
10.2%
4
8.2%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
Other values (7) 7
14.3%
Common
ValueCountFrequency (%)
1 26
44.8%
0 15
25.9%
2 3
 
5.2%
( 3
 
5.2%
) 3
 
5.2%
3 2
 
3.4%
5 2
 
3.4%
6 2
 
3.4%
8 1
 
1.7%
- 1
 
1.7%
Latin
ValueCountFrequency (%)
g 11
68.8%
k 3
 
18.8%
K 2
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74
60.2%
Hangul 49
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26
35.1%
0 15
20.3%
g 11
14.9%
2 3
 
4.1%
k 3
 
4.1%
( 3
 
4.1%
) 3
 
4.1%
3 2
 
2.7%
K 2
 
2.7%
5 2
 
2.7%
Other values (3) 4
 
5.4%
Hangul
ValueCountFrequency (%)
8
16.3%
8
16.3%
8
16.3%
5
10.2%
5
10.2%
4
8.2%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
Other values (7) 7
14.3%
Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11696.125
Minimum220
Maximum55000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:56.228742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile1555
Q13475
median6495
Q315000
95-th percentile36725
Maximum55000
Range54780
Interquartile range (IQR)11525

Descriptive statistics

Standard deviation12985.383
Coefficient of variation (CV)1.1102295
Kurtosis3.3816329
Mean11696.125
Median Absolute Deviation (MAD)4100
Skewness1.868247
Sum374276
Variance1.6862017 × 108
MonotonicityNot monotonic
2023-12-12T23:05:56.347035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4000 4
 
12.5%
10000 2
 
6.2%
5000 2
 
6.2%
3000 2
 
6.2%
7000 2
 
6.2%
25000 2
 
6.2%
15000 2
 
6.2%
42500 1
 
3.1%
32000 1
 
3.1%
13000 1
 
3.1%
Other values (13) 13
40.6%
ValueCountFrequency (%)
220 1
 
3.1%
1500 1
 
3.1%
1600 1
 
3.1%
1666 1
 
3.1%
1800 1
 
3.1%
3000 2
6.2%
3400 1
 
3.1%
3500 1
 
3.1%
4000 4
12.5%
5000 2
6.2%
ValueCountFrequency (%)
55000 1
3.1%
42500 1
3.1%
32000 1
3.1%
30000 1
3.1%
25000 2
6.2%
16500 1
3.1%
15000 2
6.2%
13000 1
3.1%
12600 1
3.1%
10000 2
6.2%
Distinct22
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11621.125
Minimum220
Maximum55000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:56.467942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile1555
Q13875
median6000
Q315000
95-th percentile36725
Maximum55000
Range54780
Interquartile range (IQR)11125

Descriptive statistics

Standard deviation12992.09
Coefficient of variation (CV)1.1179718
Kurtosis3.4147997
Mean11621.125
Median Absolute Deviation (MAD)4100
Skewness1.8852528
Sum371876
Variance1.6879441 × 108
MonotonicityNot monotonic
2023-12-12T23:05:56.586546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4000 4
 
12.5%
5000 3
 
9.4%
15000 2
 
6.2%
3000 2
 
6.2%
6000 2
 
6.2%
7000 2
 
6.2%
25000 2
 
6.2%
42500 1
 
3.1%
32000 1
 
3.1%
13000 1
 
3.1%
Other values (12) 12
37.5%
ValueCountFrequency (%)
220 1
 
3.1%
1500 1
 
3.1%
1600 1
 
3.1%
1666 1
 
3.1%
1800 1
 
3.1%
3000 2
6.2%
3500 1
 
3.1%
4000 4
12.5%
5000 3
9.4%
6000 2
6.2%
ValueCountFrequency (%)
55000 1
3.1%
42500 1
3.1%
32000 1
3.1%
30000 1
3.1%
25000 2
6.2%
16500 1
3.1%
15000 2
6.2%
13000 1
3.1%
12600 1
3.1%
10000 1
3.1%
Distinct20
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:56.778624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.15625
Min length2

Characters and Unicode

Total characters133
Distinct characters26
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)50.0%

Sample

1st row1개(1kg내외)
2nd row1통
3rd row1kg
4th row1망
5th row200g
ValueCountFrequency (%)
1마리 5
15.6%
1kg 4
 
12.5%
100g 4
 
12.5%
2kg 3
 
9.4%
1봉지(3kg 1
 
3.1%
1개(1kg내외 1
 
3.1%
4-5kg 1
 
3.1%
500g 1
 
3.1%
20kg 1
 
3.1%
10란 1
 
3.1%
Other values (10) 10
31.2%
2023-12-12T23:05:57.099541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 26
19.5%
g 22
16.5%
0 18
13.5%
k 12
9.0%
( 6
 
4.5%
) 6
 
4.5%
5
 
3.8%
2 5
 
3.8%
5 5
 
3.8%
5
 
3.8%
Other values (16) 23
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59
44.4%
Lowercase Letter 34
25.6%
Other Letter 26
19.5%
Open Punctuation 6
 
4.5%
Close Punctuation 6
 
4.5%
Uppercase Letter 1
 
0.8%
Dash Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
19.2%
5
19.2%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (4) 4
15.4%
Decimal Number
ValueCountFrequency (%)
1 26
44.1%
0 18
30.5%
2 5
 
8.5%
5 5
 
8.5%
3 3
 
5.1%
4 2
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
g 22
64.7%
k 12
35.3%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
54.1%
Latin 35
26.3%
Hangul 26
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5
19.2%
5
19.2%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (4) 4
15.4%
Common
ValueCountFrequency (%)
1 26
36.1%
0 18
25.0%
( 6
 
8.3%
) 6
 
8.3%
2 5
 
6.9%
5 5
 
6.9%
3 3
 
4.2%
4 2
 
2.8%
- 1
 
1.4%
Latin
ValueCountFrequency (%)
g 22
62.9%
k 12
34.3%
K 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107
80.5%
Hangul 26
 
19.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26
24.3%
g 22
20.6%
0 18
16.8%
k 12
11.2%
( 6
 
5.6%
) 6
 
5.6%
2 5
 
4.7%
5 5
 
4.7%
3 3
 
2.8%
4 2
 
1.9%
Other values (2) 2
 
1.9%
Hangul
ValueCountFrequency (%)
5
19.2%
5
19.2%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (4) 4
15.4%
Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12984.031
Minimum426
Maximum62900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:57.217536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum426
5-th percentile1452.85
Q14290
median7875
Q314767.5
95-th percentile43848.5
Maximum62900
Range62474
Interquartile range (IQR)10477.5

Descriptive statistics

Standard deviation14896.266
Coefficient of variation (CV)1.1472759
Kurtosis5.2828681
Mean12984.031
Median Absolute Deviation (MAD)4735
Skewness2.2716246
Sum415489
Variance2.2189874 × 108
MonotonicityNot monotonic
2023-12-12T23:05:57.340537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7990 2
 
6.2%
9990 2
 
6.2%
1790 1
 
3.1%
20000 1
 
3.1%
8990 1
 
3.1%
13980 1
 
3.1%
62900 1
 
3.1%
3990 1
 
3.1%
13450 1
 
3.1%
25000 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
426 1
3.1%
1163 1
3.1%
1690 1
3.1%
1790 1
3.1%
2790 1
3.1%
3490 1
3.1%
3780 1
3.1%
3990 1
3.1%
4390 1
3.1%
4490 1
3.1%
ValueCountFrequency (%)
62900 1
3.1%
58330 1
3.1%
32000 1
3.1%
29900 1
3.1%
25000 1
3.1%
20000 1
3.1%
19990 1
3.1%
15990 1
3.1%
14360 1
3.1%
13980 1
3.1%
Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12671.688
Minimum426
Maximum62900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:57.463124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum426
5-th percentile1452.85
Q14290
median7875
Q314075
95-th percentile43848.5
Maximum62900
Range62474
Interquartile range (IQR)9785

Descriptive statistics

Standard deviation14849.34
Coefficient of variation (CV)1.1718518
Kurtosis5.6125831
Mean12671.688
Median Absolute Deviation (MAD)4240
Skewness2.356654
Sum405494
Variance2.2050289 × 108
MonotonicityNot monotonic
2023-12-12T23:05:57.577777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7990 2
 
6.2%
9990 2
 
6.2%
1790 1
 
3.1%
20000 1
 
3.1%
8990 1
 
3.1%
13980 1
 
3.1%
62900 1
 
3.1%
3990 1
 
3.1%
13450 1
 
3.1%
25000 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
426 1
3.1%
1163 1
3.1%
1690 1
3.1%
1790 1
3.1%
2790 1
3.1%
3490 1
3.1%
3780 1
3.1%
3990 1
3.1%
4390 1
3.1%
4490 1
3.1%
ValueCountFrequency (%)
62900 1
3.1%
58330 1
3.1%
32000 1
3.1%
29900 1
3.1%
25000 1
3.1%
20000 1
3.1%
15990 1
3.1%
14360 1
3.1%
13980 1
3.1%
13450 1
3.1%
Distinct20
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:57.724850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.53125
Min length2

Characters and Unicode

Total characters113
Distinct characters25
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)46.9%

Sample

1st row1kg
2nd row1kg
3rd row1kg
4th row1kg
5th row500g
ValueCountFrequency (%)
1kg 11
34.4%
100g 3
 
9.4%
150g 2
 
6.2%
2kg 2
 
6.2%
1마리 1
 
3.1%
3마리 1
 
3.1%
20kg 1
 
3.1%
30개입 1
 
3.1%
1.5kg 1
 
3.1%
4마리 1
 
3.1%
Other values (8) 8
25.0%
2023-12-12T23:05:58.020578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 22
19.5%
1 18
15.9%
0 14
12.4%
k 11
9.7%
5 6
 
5.3%
K 5
 
4.4%
5
 
4.4%
2 5
 
4.4%
4
 
3.5%
4
 
3.5%
Other values (15) 19
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49
43.4%
Lowercase Letter 33
29.2%
Other Letter 20
17.7%
Uppercase Letter 6
 
5.3%
Open Punctuation 2
 
1.8%
Close Punctuation 2
 
1.8%
Other Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
25.0%
4
20.0%
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Decimal Number
ValueCountFrequency (%)
1 18
36.7%
0 14
28.6%
5 6
 
12.2%
2 5
 
10.2%
3 3
 
6.1%
4 1
 
2.0%
6 1
 
2.0%
8 1
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
g 22
66.7%
k 11
33.3%
Uppercase Letter
ValueCountFrequency (%)
K 5
83.3%
G 1
 
16.7%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 54
47.8%
Latin 39
34.5%
Hangul 20
 
17.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18
33.3%
0 14
25.9%
5 6
 
11.1%
2 5
 
9.3%
3 3
 
5.6%
( 2
 
3.7%
) 2
 
3.7%
4 1
 
1.9%
6 1
 
1.9%
. 1
 
1.9%
Hangul
ValueCountFrequency (%)
5
25.0%
4
20.0%
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Latin
ValueCountFrequency (%)
g 22
56.4%
k 11
28.2%
K 5
 
12.8%
G 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93
82.3%
Hangul 20
 
17.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 22
23.7%
1 18
19.4%
0 14
15.1%
k 11
11.8%
5 6
 
6.5%
K 5
 
5.4%
2 5
 
5.4%
3 3
 
3.2%
( 2
 
2.2%
) 2
 
2.2%
Other values (5) 5
 
5.4%
Hangul
ValueCountFrequency (%)
5
25.0%
4
20.0%
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11366.75
Minimum400
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:58.160064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile820
Q12619.75
median5000
Q315000
95-th percentile37456.5
Maximum60000
Range59600
Interquartile range (IQR)12380.25

Descriptive statistics

Standard deviation13695.078
Coefficient of variation (CV)1.2048368
Kurtosis4.4989946
Mean11366.75
Median Absolute Deviation (MAD)3750
Skewness2.0538442
Sum363736
Variance1.8755517 × 108
MonotonicityNot monotonic
2023-12-12T23:05:58.265109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5000 3
 
9.4%
10000 3
 
9.4%
15000 2
 
6.2%
2000 2
 
6.2%
1500 2
 
6.2%
8000 2
 
6.2%
20000 2
 
6.2%
42500 1
 
3.1%
11000 1
 
3.1%
3500 1
 
3.1%
Other values (13) 13
40.6%
ValueCountFrequency (%)
400 1
3.1%
600 1
3.1%
1000 1
3.1%
1500 2
6.2%
2000 2
6.2%
2280 1
3.1%
2733 1
3.1%
3000 1
3.1%
3333 1
3.1%
3500 1
3.1%
ValueCountFrequency (%)
60000 1
 
3.1%
42500 1
 
3.1%
33330 1
 
3.1%
29000 1
 
3.1%
25000 1
 
3.1%
20000 2
6.2%
15000 2
6.2%
11000 1
 
3.1%
10000 3
9.4%
8000 2
6.2%
Distinct22
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10929.656
Minimum400
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:58.372750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile820
Q12933.25
median5000
Q312000
95-th percentile37456.5
Maximum60000
Range59600
Interquartile range (IQR)9066.75

Descriptive statistics

Standard deviation13540.276
Coefficient of variation (CV)1.2388565
Kurtosis5.1400178
Mean10929.656
Median Absolute Deviation (MAD)3500
Skewness2.2017472
Sum349749
Variance1.8333907 × 108
MonotonicityNot monotonic
2023-12-12T23:05:58.503965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5000 5
15.6%
2000 2
 
6.2%
1500 2
 
6.2%
3333 2
 
6.2%
20000 2
 
6.2%
8000 2
 
6.2%
10000 2
 
6.2%
42500 1
 
3.1%
11000 1
 
3.1%
3500 1
 
3.1%
Other values (12) 12
37.5%
ValueCountFrequency (%)
400 1
3.1%
600 1
3.1%
1000 1
3.1%
1500 2
6.2%
2000 2
6.2%
2733 1
3.1%
3000 1
3.1%
3333 2
6.2%
3500 1
3.1%
4170 1
3.1%
ValueCountFrequency (%)
60000 1
3.1%
42500 1
3.1%
33330 1
3.1%
29000 1
3.1%
20000 2
6.2%
19450 1
3.1%
15000 1
3.1%
11000 1
3.1%
10000 2
6.2%
9400 1
3.1%
Distinct20
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:58.658880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length3.65625
Min length2

Characters and Unicode

Total characters117
Distinct characters24
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)46.9%

Sample

1st row1kg
2nd row2kg
3rd row1kg
4th row2kg
5th row500g
ValueCountFrequency (%)
1kg 7
21.9%
1마리 4
12.5%
500g 3
 
9.4%
2kg 3
 
9.4%
100g 2
 
6.2%
9kg 1
 
3.1%
800g(8개 1
 
3.1%
20kg 1
 
3.1%
30개입 1
 
3.1%
1팩 1
 
3.1%
Other values (8) 8
25.0%
2023-12-12T23:05:58.955310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 22
18.8%
1 19
16.2%
0 18
15.4%
k 10
8.5%
5 6
 
5.1%
6
 
5.1%
4
 
3.4%
4
 
3.4%
2 4
 
3.4%
( 3
 
2.6%
Other values (14) 21
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54
46.2%
Lowercase Letter 32
27.4%
Other Letter 22
18.8%
Open Punctuation 3
 
2.6%
Close Punctuation 3
 
2.6%
Uppercase Letter 3
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
27.3%
4
18.2%
4
18.2%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 19
35.2%
0 18
33.3%
5 6
 
11.1%
2 4
 
7.4%
8 3
 
5.6%
3 2
 
3.7%
7 1
 
1.9%
9 1
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
g 22
68.8%
k 10
31.2%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60
51.3%
Latin 35
29.9%
Hangul 22
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
27.3%
4
18.2%
4
18.2%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Common
ValueCountFrequency (%)
1 19
31.7%
0 18
30.0%
5 6
 
10.0%
2 4
 
6.7%
( 3
 
5.0%
) 3
 
5.0%
8 3
 
5.0%
3 2
 
3.3%
7 1
 
1.7%
9 1
 
1.7%
Latin
ValueCountFrequency (%)
g 22
62.9%
k 10
28.6%
K 3
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95
81.2%
Hangul 22
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 22
23.2%
1 19
20.0%
0 18
18.9%
k 10
10.5%
5 6
 
6.3%
2 4
 
4.2%
( 3
 
3.2%
) 3
 
3.2%
8 3
 
3.2%
K 3
 
3.2%
Other values (3) 4
 
4.2%
Hangul
ValueCountFrequency (%)
6
27.3%
4
18.2%
4
18.2%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%

북구 홈플러스 현실가격
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11878.156
Minimum250
Maximum57800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:59.113991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile1523.6
Q12999.75
median5240
Q314985
95-th percentile42444
Maximum57800
Range57550
Interquartile range (IQR)11985.25

Descriptive statistics

Standard deviation14254.262
Coefficient of variation (CV)1.20004
Kurtosis3.2214662
Mean11878.156
Median Absolute Deviation (MAD)3415
Skewness1.8923193
Sum380101
Variance2.03184 × 108
MonotonicityNot monotonic
2023-12-12T23:05:59.245731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1790 1
 
3.1%
6000 1
 
3.1%
19500 1
 
3.1%
3112 1
 
3.1%
9990 1
 
3.1%
2570 1
 
3.1%
57800 1
 
3.1%
2663 1
 
3.1%
8990 1
 
3.1%
12600 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
250 1
3.1%
1198 1
3.1%
1790 1
3.1%
1860 1
3.1%
1990 1
3.1%
2330 1
3.1%
2570 1
3.1%
2663 1
3.1%
3112 1
3.1%
3330 1
3.1%
ValueCountFrequency (%)
57800 1
3.1%
47900 1
3.1%
37980 1
3.1%
29900 1
3.1%
25433 1
3.1%
24990 1
3.1%
19500 1
3.1%
15000 1
3.1%
14980 1
3.1%
12600 1
3.1%

북구 홈플러스 환산가격
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11878.156
Minimum250
Maximum57800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:05:59.366359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile1523.6
Q12999.75
median5240
Q314985
95-th percentile42444
Maximum57800
Range57550
Interquartile range (IQR)11985.25

Descriptive statistics

Standard deviation14254.262
Coefficient of variation (CV)1.20004
Kurtosis3.2214662
Mean11878.156
Median Absolute Deviation (MAD)3415
Skewness1.8923193
Sum380101
Variance2.03184 × 108
MonotonicityNot monotonic
2023-12-12T23:05:59.499497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1790 1
 
3.1%
6000 1
 
3.1%
19500 1
 
3.1%
3112 1
 
3.1%
9990 1
 
3.1%
2570 1
 
3.1%
57800 1
 
3.1%
2663 1
 
3.1%
8990 1
 
3.1%
12600 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
250 1
3.1%
1198 1
3.1%
1790 1
3.1%
1860 1
3.1%
1990 1
3.1%
2330 1
3.1%
2570 1
3.1%
2663 1
3.1%
3112 1
3.1%
3330 1
3.1%
ValueCountFrequency (%)
57800 1
3.1%
47900 1
3.1%
37980 1
3.1%
29900 1
3.1%
25433 1
3.1%
24990 1
3.1%
19500 1
3.1%
15000 1
3.1%
14980 1
3.1%
12600 1
3.1%
Distinct16
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:05:59.665262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.96875
Min length2

Characters and Unicode

Total characters127
Distinct characters23
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)31.2%

Sample

1st row1kg
2nd row2kg
3rd row1kg
4th row2kg
5th row100g
ValueCountFrequency (%)
100g 7
21.2%
1kg 7
21.2%
2kg 3
9.1%
1마리 3
9.1%
1개 2
 
6.1%
300g2개 1
 
3.0%
600g3개 1
 
3.0%
적포도1kg 1
 
3.0%
600g10개 1
 
3.0%
10kg 1
 
3.0%
Other values (6) 6
18.2%
2023-12-12T23:05:59.961889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28
22.0%
1 26
20.5%
g 24
18.9%
k 12
9.4%
6
 
4.7%
2 5
 
3.9%
3 3
 
2.4%
3
 
2.4%
3
 
2.4%
6 2
 
1.6%
Other values (13) 15
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65
51.2%
Lowercase Letter 36
28.3%
Other Letter 20
 
15.7%
Uppercase Letter 4
 
3.1%
Other Punctuation 1
 
0.8%
Space Separator 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
30.0%
3
15.0%
3
15.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Decimal Number
ValueCountFrequency (%)
0 28
43.1%
1 26
40.0%
2 5
 
7.7%
3 3
 
4.6%
6 2
 
3.1%
5 1
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
g 24
66.7%
k 12
33.3%
Uppercase Letter
ValueCountFrequency (%)
G 2
50.0%
K 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67
52.8%
Latin 40
31.5%
Hangul 20
 
15.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
30.0%
3
15.0%
3
15.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Common
ValueCountFrequency (%)
0 28
41.8%
1 26
38.8%
2 5
 
7.5%
3 3
 
4.5%
6 2
 
3.0%
. 1
 
1.5%
5 1
 
1.5%
1
 
1.5%
Latin
ValueCountFrequency (%)
g 24
60.0%
k 12
30.0%
G 2
 
5.0%
K 2
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107
84.3%
Hangul 20
 
15.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
26.2%
1 26
24.3%
g 24
22.4%
k 12
11.2%
2 5
 
4.7%
3 3
 
2.8%
6 2
 
1.9%
G 2
 
1.9%
K 2
 
1.9%
. 1
 
0.9%
Other values (2) 2
 
1.9%
Hangul
ValueCountFrequency (%)
6
30.0%
3
15.0%
3
15.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10832.812
Minimum1000
Maximum65000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:06:00.123686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1400
Q13225
median6750
Q311750
95-th percentile36150
Maximum65000
Range64000
Interquartile range (IQR)8525

Descriptive statistics

Standard deviation13431.052
Coefficient of variation (CV)1.239849
Kurtosis8.3405113
Mean10832.812
Median Absolute Deviation (MAD)4075
Skewness2.6921353
Sum346650
Variance1.8039316 × 108
MonotonicityNot monotonic
2023-12-12T23:06:00.318471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11000 2
 
6.2%
3500 2
 
6.2%
2000 2
 
6.2%
1400 2
 
6.2%
5000 2
 
6.2%
7000 2
 
6.2%
10000 2
 
6.2%
1700 1
 
3.1%
14000 1
 
3.1%
3400 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
1000 1
3.1%
1400 2
6.2%
1700 1
3.1%
2000 2
6.2%
2850 1
3.1%
3000 1
3.1%
3300 1
3.1%
3400 1
3.1%
3500 2
6.2%
4000 1
3.1%
ValueCountFrequency (%)
65000 1
3.1%
40000 1
3.1%
33000 1
3.1%
25000 1
3.1%
17000 1
3.1%
15000 1
3.1%
14900 1
3.1%
14000 1
3.1%
11000 2
6.2%
10000 2
6.2%
Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12348.438
Minimum1000
Maximum65000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:06:00.452805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1400
Q13225
median6750
Q314225
95-th percentile48775
Maximum65000
Range64000
Interquartile range (IQR)11000

Descriptive statistics

Standard deviation15950.677
Coefficient of variation (CV)1.2917163
Kurtosis4.8864559
Mean12348.438
Median Absolute Deviation (MAD)4075
Skewness2.2795894
Sum395150
Variance2.5442411 × 108
MonotonicityNot monotonic
2023-12-12T23:06:00.592102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3500 2
 
6.2%
2000 2
 
6.2%
1400 2
 
6.2%
5000 2
 
6.2%
7000 2
 
6.2%
10000 2
 
6.2%
1700 1
 
3.1%
17000 1
 
3.1%
14000 1
 
3.1%
3400 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
1000 1
3.1%
1400 2
6.2%
1700 1
3.1%
2000 2
6.2%
2850 1
3.1%
3000 1
3.1%
3300 1
3.1%
3400 1
3.1%
3500 2
6.2%
4000 1
3.1%
ValueCountFrequency (%)
65000 1
3.1%
59500 1
3.1%
40000 1
3.1%
33000 1
3.1%
25000 1
3.1%
17000 1
3.1%
15000 1
3.1%
14900 1
3.1%
14000 1
3.1%
11000 1
3.1%
Distinct24
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:06:00.796914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.71875
Min length2

Characters and Unicode

Total characters119
Distinct characters19
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)62.5%

Sample

1st row1.76kg
2nd row2kg
3rd row700g
4th row1.46kg
5th row300g
ValueCountFrequency (%)
100g 5
 
15.6%
1kg 4
 
12.5%
300g 2
 
6.2%
150g 2
 
6.2%
2마리 1
 
3.1%
1.76kg 1
 
3.1%
20kg 1
 
3.1%
10개 1
 
3.1%
1.05kg 1
 
3.1%
400g 1
 
3.1%
Other values (13) 13
40.6%
2023-12-12T23:06:01.172822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26
21.8%
g 23
19.3%
1 16
13.4%
k 10
 
8.4%
3 5
 
4.2%
5 5
 
4.2%
4
 
3.4%
4
 
3.4%
4
 
3.4%
6 4
 
3.4%
Other values (9) 18
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
56.3%
Lowercase Letter 33
27.7%
Other Letter 12
 
10.1%
Other Punctuation 4
 
3.4%
Uppercase Letter 2
 
1.7%
Space Separator 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26
38.8%
1 16
23.9%
3 5
 
7.5%
5 5
 
7.5%
6 4
 
6.0%
4 3
 
4.5%
2 3
 
4.5%
8 2
 
3.0%
7 2
 
3.0%
9 1
 
1.5%
Other Letter
ValueCountFrequency (%)
4
33.3%
4
33.3%
4
33.3%
Lowercase Letter
ValueCountFrequency (%)
g 23
69.7%
k 10
30.3%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
G 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
60.5%
Latin 35
29.4%
Hangul 12
 
10.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26
36.1%
1 16
22.2%
3 5
 
6.9%
5 5
 
6.9%
6 4
 
5.6%
. 4
 
5.6%
4 3
 
4.2%
2 3
 
4.2%
8 2
 
2.8%
7 2
 
2.8%
Other values (2) 2
 
2.8%
Latin
ValueCountFrequency (%)
g 23
65.7%
k 10
28.6%
K 1
 
2.9%
G 1
 
2.9%
Hangul
ValueCountFrequency (%)
4
33.3%
4
33.3%
4
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107
89.9%
Hangul 12
 
10.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26
24.3%
g 23
21.5%
1 16
15.0%
k 10
 
9.3%
3 5
 
4.7%
5 5
 
4.7%
6 4
 
3.7%
. 4
 
3.7%
4 3
 
2.8%
2 3
 
2.8%
Other values (6) 8
 
7.5%
Hangul
ValueCountFrequency (%)
4
33.3%
4
33.3%
4
33.3%
Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.906
Minimum679
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:06:01.295765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum679
5-th percentile827
Q12687.5
median5462.5
Q311050
95-th percentile42250
Maximum58000
Range57321
Interquartile range (IQR)8362.5

Descriptive statistics

Standard deviation13502.369
Coefficient of variation (CV)1.2838727
Kurtosis5.3534038
Mean10516.906
Median Absolute Deviation (MAD)4150
Skewness2.3429062
Sum336541
Variance1.8231396 × 108
MonotonicityNot monotonic
2023-12-12T23:06:01.422462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
11800 2
 
6.2%
3900 2
 
6.2%
750 1
 
3.1%
9500 1
 
3.1%
3710 1
 
3.1%
10200 1
 
3.1%
5300 1
 
3.1%
58000 1
 
3.1%
3870 1
 
3.1%
8500 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
679 1
3.1%
750 1
3.1%
890 1
3.1%
1200 1
3.1%
1440 1
3.1%
1900 1
3.1%
1980 1
3.1%
2500 1
3.1%
2750 1
3.1%
3500 1
3.1%
ValueCountFrequency (%)
58000 1
3.1%
45000 1
3.1%
40000 1
3.1%
22667 1
3.1%
20000 1
3.1%
14900 1
3.1%
11800 2
6.2%
10800 1
3.1%
10200 1
3.1%
10000 1
3.1%
Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12938.062
Minimum679
Maximum79000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:06:01.532871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum679
5-th percentile827
Q12270
median4600
Q315650
95-th percentile50850
Maximum79000
Range78321
Interquartile range (IQR)13380

Descriptive statistics

Standard deviation18320.773
Coefficient of variation (CV)1.4160368
Kurtosis5.3760576
Mean12938.062
Median Absolute Deviation (MAD)3555
Skewness2.3119983
Sum414018
Variance3.3565071 × 108
MonotonicityNot monotonic
2023-12-12T23:06:01.642708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20000 2
 
6.2%
3900 2
 
6.2%
2360 1
 
3.1%
9500 1
 
3.1%
3710 1
 
3.1%
10200 1
 
3.1%
5300 1
 
3.1%
58000 1
 
3.1%
3870 1
 
3.1%
8500 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
679 1
3.1%
750 1
3.1%
890 1
3.1%
1200 1
3.1%
1237 1
3.1%
1900 1
3.1%
1980 1
3.1%
2000 1
3.1%
2360 1
3.1%
2500 1
3.1%
ValueCountFrequency (%)
79000 1
3.1%
58000 1
3.1%
45000 1
3.1%
40000 1
3.1%
22667 1
3.1%
20000 2
6.2%
17900 1
3.1%
14900 1
3.1%
14400 1
3.1%
10200 1
3.1%

Sample

구분조사품목(32개)규격 및 단위(기본 규격)중구 태화시장중구 더프레시남구 야음시장 현실규격남구 야음시장 현실가격남구 야음시장 환산가격남구 이마트 현실규격남구 이마트 현실가격남구 이마트 환산가격남구 신정시장 현실규격남구 신정시장 현실가격남구 신정시장 환산가격남구 롯데마트 현실규격남구 롯데마트 현실가격남구 롯데마트 환산가격남구 수암시장 현실규격남구 수암시장 현실가격남구 수암시장 환산가격남구 홈플러스 현실규격남구 홈플러스 현실가격남구 홈플러스 환산가격동구 대송농수산물시장현실규격동구 대송농수산물시장현실가격동구 대송농수산물시장환산가격동구 홈플러스 현실규격동구 홈플러스 현실가격동구 홈플러스 환산가격북구호계시장 현실규격북구호계시장 현실가격북구호계시장 환산가격북구 홈플러스 현실규격북구 홈플러스 현실가격북구 홈플러스 환산가격울주군 남창시장 현실규격울주군 남창시장 현실가격울주군 남창시장 환산가격울주군 하나로마트 현실규격울주군 하나로마트 현실가격울주군 하나로마트 환산가격
0채소잎없는 것_1kg100014801kg200010001kg100010001kg100010001kg170017001.5kg200020001.5kg179017901개160016001개(1kg내외)179017901kg100010001kg179017901kg170017001.76kg750750
1채소배추통배추_1kg200019301kg800030001kg248024802kg600060002kg117011702.5kg500050002kg329032901통400040001통449044901kg10000100002kg449044902kg285028502kg27502750
2채소대파_1kg300029801kg30006330100g39807960300g45004500100g47904790500g600060001kg781778171단350035001kg439043901kg200020001kg419041901kg30003000700g56255625
3채소양파잎없는 것_1kg300019301kg300025001.8kg398022101kg266626661.5kg332633262kg250025003kg311231121바구니500050001망529052901kg300030002kg352735272kg350035001.46kg19001900
4채소콩나물신선한 것_100g400440100g1000500100g2470620250g500500360g1753487100g10001000340g9959951봉지220220200g426426500g400400500g250250100g20002000300g679679
5채소상추잎상추_100g10001480100g20001000100g12401240100g10001000100g16901690100g800800100g149514951바구니30003000100g27902790150g20002000180g18601860100g14001400150g19801980
6채소오이길이 25cm_1개75016601개5007502개9409403개7657654개64909983개6676673개9989983개166616661봉지(3입)116311632개(가시오이)6006003개(가시오이)119811981개100010003개12001200
7채소애호박애호박 500g_1개150011801개100019001개128012801개170017001개159015901개150015001개199019901개150015001개169016902개150015001개199019901개20002000360g25002500
8채소감자신선한 것_1kg333037301kg600031301kg34803480100g35003500100g50050001kg500030003kg390839081바구니600060002kg14360143602kg333333331kg39083908100g50005000100g39003900
9채소고구마신선한 것_1kg500044201kg700053801kg64806480100g50005000100g50050001kg500050002kg599059901바구니699069901kg599059902kg417041701Kg59905990100g40004000100g39003900
구분조사품목(32개)규격 및 단위(기본 규격)중구 태화시장중구 더프레시남구 야음시장 현실규격남구 야음시장 현실가격남구 야음시장 환산가격남구 이마트 현실규격남구 이마트 현실가격남구 이마트 환산가격남구 신정시장 현실규격남구 신정시장 현실가격남구 신정시장 환산가격남구 롯데마트 현실규격남구 롯데마트 현실가격남구 롯데마트 환산가격남구 수암시장 현실규격남구 수암시장 현실가격남구 수암시장 환산가격남구 홈플러스 현실규격남구 홈플러스 현실가격남구 홈플러스 환산가격동구 대송농수산물시장현실규격동구 대송농수산물시장현실가격동구 대송농수산물시장환산가격동구 홈플러스 현실규격동구 홈플러스 현실가격동구 홈플러스 환산가격북구호계시장 현실규격북구호계시장 현실가격북구호계시장 환산가격북구 홈플러스 현실규격북구 홈플러스 현실가격북구 홈플러스 환산가격울주군 남창시장 현실규격울주군 남창시장 현실가격울주군 남창시장 환산가격울주군 하나로마트 현실규격울주군 하나로마트 현실가격울주군 하나로마트 환산가격
22축산물쇠고기(국산)등심 1등급_500g4150066350100g1300054000100g6690066900100g850060000100g1980084950100g4000040000100g1469064950100g4250042500100g5833058330100g4250042500100g47900479001등급100g1100059500100g1080079000
23축산물쇠고기(수입)등심 1등급_500g1000019650100g5000225004KG2160021600100g250018000100g298018450100g1250012500100g350016000100g1650016500100g2500025000100g3890194501kg11900119001KG1000010000100g358017900
24축산물돼지고기삼겹살_500g990014800100g300012900100g1375013750100g248012400100g498021400100g1166711667100g259014250100g1260012600100g1345013450100g22809400100g1260012600100g1490014900100g144014400
25축산물닭고기육계_1kg7000104001kg650078601kg11230112301kg1마리750075001.1kg987892101마리800080001마리999089901마리700070001마리799079901kg800080001kg899089901kg900090001.05kg85008500
26축산물달걀특란_10개2400333010개4100296010개3160316010개4300430010개4290399010개3800380010개3690399330란1800180010란3990399030개입2733273330개입26632663특 10개3300330010개38703870
27곡물정미_포장미_20kg538004980020kg630006500020kg598005980020kg625006250020kg499006290020kg640006400020kg479005990020Kg550005500020Kg629006290020kg600006000020kg578005780020kg650006500020kg5800058000
28곡물찹쌀정미_1kg389042701kg700057504kg630063002kg400040004kg1280029751.6kg500050002kg1052049951Kg500050002kg999099901kg350035002Kg257025702kg350035001kg53005300
29곡물백태_1kg688085801.4kg1200010710500g10600106001.6kg158889930500g8990119801.4kg80008000500g699013980500g1300013000500g13980139801kg80008000500g999099901kg820082001kg1020010200
30조미료고추가루중품_100g30005900600g1500031601kg35803580600g2300033331kg2980051801kg1583315833500g2380023500600g70007000400g89908990600g(국산)10000100001kg(국산)31123112100g34003400300g37103710
31조미료마늘깐마늘 중품_1kg1000076001kg120009800200g798079801kg1000010000100g159787001kg11000110001kg1398079901kg32000320001kg799079901kg11000110001kg1950019500300g1400014000900g95009500