Overview

Dataset statistics

Number of variables12
Number of observations75
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory106.8 B

Variable types

Text1
Categorical2
Numeric9

Dataset

Description경상북도 구미시 관내의 물가정보 데이터로 75가지 대표 품목에 대한 평균 가격, 지역별 가격 데이터를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/3033332/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
평균 is highly overall correlated with 선산읍 and 7 other fieldsHigh correlation
선산읍 is highly overall correlated with 평균 and 7 other fieldsHigh correlation
고아읍 is highly overall correlated with 평균 and 7 other fieldsHigh correlation
원평_신평 is highly overall correlated with 평균 and 7 other fieldsHigh correlation
도량_선주원남 is highly overall correlated with 평균 and 7 other fieldsHigh correlation
송정_형곡 is highly overall correlated with 평균 and 7 other fieldsHigh correlation
양포_산동 is highly overall correlated with 평균 and 7 other fieldsHigh correlation
상모사곡_임오 is highly overall correlated with 평균 and 7 other fieldsHigh correlation
인동_진미 is highly overall correlated with 평균 and 7 other fieldsHigh correlation
품목명 has unique valuesUnique
평균 has 2 (2.7%) zerosZeros
선산읍 has 7 (9.3%) zerosZeros
고아읍 has 7 (9.3%) zerosZeros
원평_신평 has 3 (4.0%) zerosZeros
도량_선주원남 has 2 (2.7%) zerosZeros
송정_형곡 has 6 (8.0%) zerosZeros
양포_산동 has 5 (6.7%) zerosZeros
상모사곡_임오 has 7 (9.3%) zerosZeros
인동_진미 has 4 (5.3%) zerosZeros

Reproduction

Analysis started2023-12-11 23:42:43.012631
Analysis finished2023-12-11 23:42:51.806392
Duration8.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-12-12T08:42:51.976804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.3333333
Min length1

Characters and Unicode

Total characters325
Distinct characters146
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

Unique75 ?
Unique (%)100.0%

Sample

1st row설렁탕
2nd row냉면
3rd row비빔밥
4th row갈비탕
5th row삼계탕
ValueCountFrequency (%)
이용료 2
 
2.6%
설렁탕 1
 
1.3%
달걀 1
 
1.3%
닭고기 1
 
1.3%
돼지고기 1
 
1.3%
쇠고기(국산 1
 
1.3%
보리쌀 1
 
1.3%
쌀 1
 
1.3%
찜질방이용료(성인 1
 
1.3%
운동경기관람료(1인 1
 
1.3%
Other values (65) 65
85.5%
2023-12-12T08:42:52.423857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
료 21
 
6.5%
( 18
 
5.5%
) 18
 
5.5%
용 11
 
3.4%
이 9
 
2.8%
기 8
 
2.5%
식 7
 
2.2%
고 7
 
2.2%
장 6
 
1.8%
수 6
 
1.8%
Other values (136) 214
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 281
86.5%
Open Punctuation 18
 
5.5%
Close Punctuation 18
 
5.5%
Uppercase Letter 3
 
0.9%
Space Separator 2
 
0.6%
Lowercase Letter 2
 
0.6%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 21
 
7.5%
용 11
 
3.9%
이 9
 
3.2%
기 8
 
2.8%
식 7
 
2.5%
고 7
 
2.5%
장 6
 
2.1%
수 6
 
2.1%
인 5
 
1.8%
탕 5
 
1.8%
Other values (127) 196
69.8%
Uppercase Letter
ValueCountFrequency (%)
A 1
33.3%
T 1
33.3%
P 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
p 1
50.0%
c 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 281
86.5%
Common 39
 
12.0%
Latin 5
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 21
 
7.5%
용 11
 
3.9%
이 9
 
3.2%
기 8
 
2.8%
식 7
 
2.5%
고 7
 
2.5%
장 6
 
2.1%
수 6
 
2.1%
인 5
 
1.8%
탕 5
 
1.8%
Other values (127) 196
69.8%
Latin
ValueCountFrequency (%)
p 1
20.0%
c 1
20.0%
A 1
20.0%
T 1
20.0%
P 1
20.0%
Common
ValueCountFrequency (%)
( 18
46.2%
) 18
46.2%
2
 
5.1%
1 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 281
86.5%
ASCII 44
 
13.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
료 21
 
7.5%
용 11
 
3.9%
이 9
 
3.2%
기 8
 
2.8%
식 7
 
2.5%
고 7
 
2.5%
장 6
 
2.1%
수 6
 
2.1%
인 5
 
1.8%
탕 5
 
1.8%
Other values (127) 196
69.8%
ASCII
ValueCountFrequency (%)
( 18
40.9%
) 18
40.9%
2
 
4.5%
p 1
 
2.3%
c 1
 
2.3%
1 1
 
2.3%
A 1
 
2.3%
T 1
 
2.3%
P 1
 
2.3%

규격_단위
Categorical

Distinct33
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
1kg
12 
1그릇
10 
1회
8 
1인분
5 
10개
 
3
Other values (28)
37 

Length

Max length10
Median length3
Mean length3.76
Min length2

Unique

Unique20 ?
Unique (%)26.7%

Sample

1st row1그릇
2nd row1그릇
3rd row1그릇
4th row1그릇
5th row1그릇

Common Values

ValueCountFrequency (%)
1kg 12
16.0%
1그릇 10
 
13.3%
1회 8
 
10.7%
1인분 5
 
6.7%
10개 3
 
4.0%
200g 1인분 3
 
4.0%
1개 2
 
2.7%
1마리 2
 
2.7%
1잔 2
 
2.7%
1시간 2
 
2.7%
Other values (23) 26
34.7%

Length

2023-12-12T08:42:52.570914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1kg 12
14.5%
1그릇 10
 
12.0%
1인분 9
 
10.8%
1회 8
 
9.6%
10개 3
 
3.6%
200g 3
 
3.6%
1마리 3
 
3.6%
1병 2
 
2.4%
500g 2
 
2.4%
1인1박 2
 
2.4%
Other values (26) 29
34.9%

평균
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15601.107
Minimum0
Maximum228571
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T08:42:52.720497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1368.1
Q13937.5
median8706
Q314325
95-th percentile41422.5
Maximum228571
Range228571
Interquartile range (IQR)10387.5

Descriptive statistics

Standard deviation29608.486
Coefficient of variation (CV)1.8978453
Kurtosis37.534957
Mean15601.107
Median Absolute Deviation (MAD)4939
Skewness5.6513978
Sum1170083
Variance8.7666246 × 108
MonotonicityNot monotonic
2023-12-12T08:42:52.888216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
2.7%
7500 2
 
2.7%
10025 2
 
2.7%
13331 1
 
1.3%
11600 1
 
1.3%
6925 1
 
1.3%
4923 1
 
1.3%
27316 1
 
1.3%
3260 1
 
1.3%
8706 1
 
1.3%
Other values (62) 62
82.7%
ValueCountFrequency (%)
0 2
2.7%
1075 1
1.3%
1198 1
1.3%
1441 1
1.3%
1589 1
1.3%
1594 1
1.3%
1871 1
1.3%
2030 1
1.3%
2140 1
1.3%
2194 1
1.3%
ValueCountFrequency (%)
228571 1
1.3%
102578 1
1.3%
78000 1
1.3%
50575 1
1.3%
37500 1
1.3%
34785 1
1.3%
28750 1
1.3%
28399 1
1.3%
27316 1
1.3%
26788 1
1.3%

선산읍
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11617.067
Minimum0
Maximum150000
Zeros7
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T08:42:53.017742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13575
median8000
Q313650
95-th percentile26290
Maximum150000
Range150000
Interquartile range (IQR)10075

Descriptive statistics

Standard deviation18469.067
Coefficient of variation (CV)1.5898219
Kurtosis43.474357
Mean11617.067
Median Absolute Deviation (MAD)5000
Skewness5.978446
Sum871280
Variance3.4110642 × 108
MonotonicityNot monotonic
2023-12-12T08:42:53.162904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
9.3%
10000 3
 
4.0%
8000 3
 
4.0%
5000 3
 
4.0%
18000 3
 
4.0%
7000 2
 
2.7%
1980 2
 
2.7%
3000 2
 
2.7%
25000 2
 
2.7%
9000 2
 
2.7%
Other values (44) 46
61.3%
ValueCountFrequency (%)
0 7
9.3%
1000 1
 
1.3%
1450 1
 
1.3%
1700 1
 
1.3%
1800 1
 
1.3%
1980 2
 
2.7%
2200 1
 
1.3%
2500 1
 
1.3%
2700 1
 
1.3%
3000 2
 
2.7%
ValueCountFrequency (%)
150000 1
 
1.3%
52000 1
 
1.3%
30000 1
 
1.3%
29300 1
 
1.3%
25000 2
2.7%
24500 1
 
1.3%
24000 1
 
1.3%
21000 1
 
1.3%
20000 1
 
1.3%
18000 3
4.0%

고아읍
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15320.4
Minimum0
Maximum350000
Zeros7
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T08:42:53.323092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13650
median7000
Q313250
95-th percentile35430
Maximum350000
Range350000
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation41401.353
Coefficient of variation (CV)2.7023676
Kurtosis59.626829
Mean15320.4
Median Absolute Deviation (MAD)5450
Skewness7.415087
Sum1149030
Variance1.714072 × 109
MonotonicityNot monotonic
2023-12-12T08:42:53.475771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7000 8
 
10.7%
0 7
 
9.3%
4000 4
 
5.3%
6000 3
 
4.0%
30000 2
 
2.7%
13000 2
 
2.7%
3800 2
 
2.7%
17000 2
 
2.7%
12000 2
 
2.7%
14000 2
 
2.7%
Other values (41) 41
54.7%
ValueCountFrequency (%)
0 7
9.3%
870 1
 
1.3%
990 1
 
1.3%
1000 1
 
1.3%
1310 1
 
1.3%
1320 1
 
1.3%
1390 1
 
1.3%
1540 1
 
1.3%
1550 1
 
1.3%
1950 1
 
1.3%
ValueCountFrequency (%)
350000 1
1.3%
89880 1
1.3%
52000 1
1.3%
36900 1
1.3%
34800 1
1.3%
30000 2
2.7%
29400 1
1.3%
26000 1
1.3%
20000 1
1.3%
18900 1
1.3%

원평_신평
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15905.333
Minimum0
Maximum280000
Zeros3
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T08:42:53.729496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile918
Q13900
median8000
Q313950
95-th percentile41500
Maximum280000
Range280000
Interquartile range (IQR)10050

Descriptive statistics

Standard deviation34501.026
Coefficient of variation (CV)2.1691482
Kurtosis47.890883
Mean15905.333
Median Absolute Deviation (MAD)5000
Skewness6.4876101
Sum1192900
Variance1.1903208 × 109
MonotonicityNot monotonic
2023-12-12T08:42:53.962266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 5
 
6.7%
10000 4
 
5.3%
13000 4
 
5.3%
3500 3
 
4.0%
0 3
 
4.0%
30000 2
 
2.7%
20000 2
 
2.7%
18000 2
 
2.7%
3000 2
 
2.7%
6000 2
 
2.7%
Other values (46) 46
61.3%
ValueCountFrequency (%)
0 3
4.0%
750 1
 
1.3%
990 1
 
1.3%
1000 1
 
1.3%
1320 1
 
1.3%
1380 1
 
1.3%
1520 1
 
1.3%
1800 1
 
1.3%
1910 1
 
1.3%
2000 1
 
1.3%
ValueCountFrequency (%)
280000 1
1.3%
104290 1
1.3%
60000 1
1.3%
52000 1
1.3%
37000 1
1.3%
30000 2
2.7%
29880 1
1.3%
28000 1
1.3%
25740 1
1.3%
20000 2
2.7%

도량_선주원남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17761.067
Minimum0
Maximum300000
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T08:42:54.115203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1494
Q13900
median9000
Q313500
95-th percentile54400
Maximum300000
Range300000
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation39492.071
Coefficient of variation (CV)2.2235191
Kurtosis36.82917
Mean17761.067
Median Absolute Deviation (MAD)5000
Skewness5.6516897
Sum1332080
Variance1.5596237 × 109
MonotonicityNot monotonic
2023-12-12T08:42:54.292768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 4
 
5.3%
12000 3
 
4.0%
15000 3
 
4.0%
3500 3
 
4.0%
6000 2
 
2.7%
1540 2
 
2.7%
7000 2
 
2.7%
7500 2
 
2.7%
11000 2
 
2.7%
10000 2
 
2.7%
Other values (47) 50
66.7%
ValueCountFrequency (%)
0 2
2.7%
1000 1
1.3%
1480 1
1.3%
1500 1
1.3%
1540 2
2.7%
1780 1
1.3%
1980 1
1.3%
2480 1
1.3%
2500 1
1.3%
2680 1
1.3%
ValueCountFrequency (%)
300000 1
1.3%
132000 1
1.3%
120000 1
1.3%
60000 1
1.3%
52000 1
1.3%
39800 1
1.3%
36800 1
1.3%
32000 1
1.3%
30000 1
1.3%
25000 1
1.3%

송정_형곡
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13240.88
Minimum0
Maximum180000
Zeros6
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T08:42:54.453514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13375
median7500
Q313000
95-th percentile38240
Maximum180000
Range180000
Interquartile range (IQR)9625

Descriptive statistics

Standard deviation23396.829
Coefficient of variation (CV)1.7670147
Kurtosis35.908361
Mean13240.88
Median Absolute Deviation (MAD)4500
Skewness5.4340737
Sum993066
Variance5.4741162 × 108
MonotonicityNot monotonic
2023-12-12T08:42:54.592059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
8.0%
7000 4
 
5.3%
4000 4
 
5.3%
6000 3
 
4.0%
12000 3
 
4.0%
3200 2
 
2.7%
3980 2
 
2.7%
17000 2
 
2.7%
20000 2
 
2.7%
13000 2
 
2.7%
Other values (43) 45
60.0%
ValueCountFrequency (%)
0 6
8.0%
1100 1
 
1.3%
1200 1
 
1.3%
1380 1
 
1.3%
1400 1
 
1.3%
1650 1
 
1.3%
1980 1
 
1.3%
2050 1
 
1.3%
2150 1
 
1.3%
2400 1
 
1.3%
ValueCountFrequency (%)
180000 1
1.3%
80916 1
1.3%
50000 1
1.3%
45800 1
1.3%
35000 1
1.3%
30000 1
1.3%
28000 1
1.3%
25800 1
1.3%
25000 1
1.3%
24750 1
1.3%

양포_산동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14030.8
Minimum0
Maximum160000
Zeros5
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T08:42:54.767416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13450
median8000
Q313000
95-th percentile44100
Maximum160000
Range160000
Interquartile range (IQR)9550

Descriptive statistics

Standard deviation22845.022
Coefficient of variation (CV)1.6282052
Kurtosis23.87376
Mean14030.8
Median Absolute Deviation (MAD)4600
Skewness4.4102107
Sum1052310
Variance5.2189504 × 108
MonotonicityNot monotonic
2023-12-12T08:42:54.934429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 5
 
6.7%
0 5
 
6.7%
10000 4
 
5.3%
12000 4
 
5.3%
7000 3
 
4.0%
6000 2
 
2.7%
9000 2
 
2.7%
17000 2
 
2.7%
1050 1
 
1.3%
10400 1
 
1.3%
Other values (46) 46
61.3%
ValueCountFrequency (%)
0 5
6.7%
1000 1
 
1.3%
1050 1
 
1.3%
1390 1
 
1.3%
1500 1
 
1.3%
1540 1
 
1.3%
1950 1
 
1.3%
1980 1
 
1.3%
2200 1
 
1.3%
2500 1
 
1.3%
ValueCountFrequency (%)
160000 1
1.3%
90700 1
1.3%
70000 1
1.3%
49000 1
1.3%
42000 1
1.3%
40000 1
1.3%
34500 1
1.3%
30000 1
1.3%
28000 1
1.3%
26330 1
1.3%

상모사곡_임오
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13314
Minimum0
Maximum170000
Zeros7
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T08:42:55.083179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13625
median7500
Q314525
95-th percentile37200
Maximum170000
Range170000
Interquartile range (IQR)10900

Descriptive statistics

Standard deviation22579.944
Coefficient of variation (CV)1.6959549
Kurtosis32.686831
Mean13314
Median Absolute Deviation (MAD)4920
Skewness5.1814096
Sum998550
Variance5.0985387 × 108
MonotonicityNot monotonic
2023-12-12T08:42:55.276458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7000 7
 
9.3%
0 7
 
9.3%
12000 3
 
4.0%
15000 3
 
4.0%
9500 2
 
2.7%
20000 2
 
2.7%
6000 2
 
2.7%
4000 2
 
2.7%
19000 2
 
2.7%
1750 2
 
2.7%
Other values (43) 43
57.3%
ValueCountFrequency (%)
0 7
9.3%
1150 1
 
1.3%
1300 1
 
1.3%
1500 1
 
1.3%
1580 1
 
1.3%
1750 2
 
2.7%
1800 1
 
1.3%
1900 1
 
1.3%
2580 1
 
1.3%
3000 1
 
1.3%
ValueCountFrequency (%)
170000 1
1.3%
86840 1
1.3%
49800 1
1.3%
40000 1
1.3%
36000 1
1.3%
32000 1
1.3%
30000 1
1.3%
26000 1
1.3%
24000 1
1.3%
22500 1
1.3%

인동_진미
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13825.533
Minimum0
Maximum160000
Zeros4
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-12T08:42:55.458463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile630
Q13650
median9000
Q315000
95-th percentile38000
Maximum160000
Range160000
Interquartile range (IQR)11350

Descriptive statistics

Standard deviation22118.926
Coefficient of variation (CV)1.5998606
Kurtosis28.139691
Mean13825.533
Median Absolute Deviation (MAD)5650
Skewness4.8542167
Sum1036915
Variance4.892469 × 108
MonotonicityNot monotonic
2023-12-12T08:42:55.893702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000 6
 
8.0%
0 4
 
5.3%
8000 4
 
5.3%
9000 3
 
4.0%
6000 3
 
4.0%
10000 3
 
4.0%
3500 2
 
2.7%
2500 2
 
2.7%
12000 2
 
2.7%
18000 2
 
2.7%
Other values (40) 44
58.7%
ValueCountFrequency (%)
0 4
5.3%
900 1
 
1.3%
1100 1
 
1.3%
1400 2
2.7%
1500 1
 
1.3%
1510 1
 
1.3%
1980 1
 
1.3%
2200 1
 
1.3%
2500 2
2.7%
3200 1
 
1.3%
ValueCountFrequency (%)
160000 1
1.3%
97995 1
1.3%
52000 1
1.3%
45000 1
1.3%
35000 1
1.3%
30000 1
1.3%
28000 1
1.3%
27000 1
1.3%
25000 1
1.3%
20000 2
2.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-06-25
75 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-25
2nd row2023-06-25
3rd row2023-06-25
4th row2023-06-25
5th row2023-06-25

Common Values

ValueCountFrequency (%)
2023-06-25 75
100.0%

Length

2023-12-12T08:42:56.041203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:42:56.144400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-25 75
100.0%

Interactions

2023-12-12T08:42:50.643992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:43.411715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.155377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:45.193033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:46.203866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.113851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:48.055634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.033686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.904733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:50.712220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:43.507013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.226521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:45.267536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:46.311924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.194438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:48.156736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.116344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.978289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:51.003029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:43.613223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.311715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:45.372006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:46.413496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.310510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:48.263902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.206287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:50.057719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:51.091290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:43.689686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.398048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:45.463331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:46.523802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.424191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:48.368327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.324731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:50.132005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:51.167724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:43.756317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.488271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:45.556897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:46.604635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.525809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:48.510383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.449134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:50.202027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:51.253166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:43.829187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.582229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:45.742324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:46.721560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.630244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:48.629598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.569612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:50.274657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:51.332166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:43.904581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.670942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:45.849245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:46.816179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.725698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:48.742419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.660465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:50.357342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:51.414597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:43.989528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.761219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:45.976386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:46.918308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.861226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:48.833938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.741621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:50.458100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:51.491398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.076898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:44.853368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:46.089951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.006979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:47.959998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:48.939250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:49.823576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:42:50.550833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:42:56.211467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목명규격_단위평균선산읍고아읍원평_신평도량_선주원남송정_형곡양포_산동상모사곡_임오인동_진미
품목명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
규격_단위1.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
평균1.0000.0001.0000.9580.9860.9630.9630.9410.9770.8910.988
선산읍1.0000.0000.9581.0000.9470.7000.7000.8140.9250.8070.997
고아읍1.0000.0000.9860.9471.0000.8880.8880.8680.9330.8630.983
원평_신평1.0000.0000.9630.7000.8881.0000.9990.9840.9490.9820.875
도량_선주원남1.0000.0000.9630.7000.8880.9991.0000.9840.9710.9820.875
송정_형곡1.0000.0000.9410.8140.8680.9840.9841.0000.9610.9780.901
양포_산동1.0000.0000.9770.9250.9330.9490.9710.9611.0000.9330.951
상모사곡_임오1.0000.0000.8910.8070.8630.9820.9820.9780.9331.0000.960
인동_진미1.0000.0000.9880.9970.9830.8750.8750.9010.9510.9601.000
2023-12-12T08:42:56.355383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평균선산읍고아읍원평_신평도량_선주원남송정_형곡양포_산동상모사곡_임오인동_진미규격_단위
평균1.0000.7540.8000.9740.9740.8610.8760.8550.9160.000
선산읍0.7541.0000.8280.6970.7360.7430.7530.8910.8120.000
고아읍0.8000.8281.0000.7650.7900.8970.9010.9300.8700.000
원평_신평0.9740.6970.7651.0000.9660.8310.8460.8170.8860.000
도량_선주원남0.9740.7360.7900.9661.0000.8520.8730.8410.8840.000
송정_형곡0.8610.7430.8970.8310.8521.0000.9690.8540.7850.000
양포_산동0.8760.7530.9010.8460.8730.9691.0000.8600.7980.000
상모사곡_임오0.8550.8910.9300.8170.8410.8540.8601.0000.9230.000
인동_진미0.9160.8120.8700.8860.8840.7850.7980.9231.0000.000
규격_단위0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T08:42:51.604904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:42:51.746743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

품목명규격_단위평균선산읍고아읍원평_신평도량_선주원남송정_형곡양포_산동상모사곡_임오인동_진미데이터기준일자
0설렁탕1그릇9125900070001000090009000100009000100002023-06-25
1냉면1그릇7938500070009000900080009000750090002023-06-25
2비빔밥1그릇7500800070008000700095008000700055002023-06-25
3갈비탕1그릇1187511000120001300012000120001200011000120002023-06-25
4삼계탕1그릇1425015000140001500014000120001400015000150002023-06-25
5김치찌개백반1그릇7875800080008000900070008000700080002023-06-25
6불고기200g 1인분1441313500127001200011600170001450019000150002023-06-25
7쇠고기(외식)200g 1인분2678829300260002800021000280002800026000280002023-06-25
8생선초밥(일식집)1인분1562514000120002000015000200001700012000150002023-06-25
9의복수선료신사복바지 밑단1725018000170001800018000170001600017000170002023-06-25
품목명규격_단위평균선산읍고아읍원평_신평도량_선주원남송정_형곡양포_산동상모사곡_임오인동_진미데이터기준일자
65배10개995610000110007900775012000900012000100002023-06-25
66밤1kg87299900990066304100990095008900110002023-06-25
67귤1kg107638900130001130015600790089009500110002023-06-25
68마늘1kg1740018000017000150000019000180002023-06-25
69소주1병1441145013901380154014001390158014002023-06-25
70맥주1병1594170015401520154016501540175015102023-06-25
71참기름1병(320ml)8149940089508980650054808480780096002023-06-25
72식용유1병(1800ml)7838980069806980698088007280790079802023-06-25
73두부1모1589180015502000148013801500150015002023-06-25
74설탕1kg2140270019501800198021501980258019802023-06-25