Overview

Dataset statistics

Number of variables19
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory171.6 B

Variable types

Categorical5
Numeric10
DateTime4

Dataset

Description인천광역시 부평구 음식물류폐기물 기간별 제작현황(site코드,스티커시작번호,스티커종류번호,상품코드,상품종류,제작상태,수주전표번호,발주업체코드,제조업체코드,제작시작일시,제작종료일시,스티커제작요청수량,제작완료 수량,묶음수량,묶음당 매수,BOX 당 묶음수량,최종제작스티커번호,입력일,업데이트일)
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15062410&srcSe=7661IVAWM27C61E190

Alerts

site코드 has constant value ""Constant
발주업체코드 has constant value ""Constant
스티커시작번호 is highly overall correlated with 스티커종류번호 and 5 other fieldsHigh correlation
스티커종류번호 is highly overall correlated with 스티커시작번호 and 5 other fieldsHigh correlation
상품코드 is highly overall correlated with 스티커시작번호 and 5 other fieldsHigh correlation
상품종류 is highly overall correlated with 스티커시작번호 and 5 other fieldsHigh correlation
스티커제작요청수량 is highly overall correlated with 제작완료 수량 and 4 other fieldsHigh correlation
제작완료 수량 is highly overall correlated with 스티커제작요청수량 and 4 other fieldsHigh correlation
묶음수량 is highly overall correlated with 스티커제작요청수량 and 2 other fieldsHigh correlation
묶음당 매수 is highly overall correlated with 스티커제작요청수량 and 2 other fieldsHigh correlation
BOX 당 묶음수량 is highly overall correlated with 상품코드 and 4 other fieldsHigh correlation
최종제작스티커번호 is highly overall correlated with 스티커시작번호 and 5 other fieldsHigh correlation
제작상태 is highly overall correlated with 스티커시작번호 and 3 other fieldsHigh correlation
수주전표번호 is highly overall correlated with 스티커시작번호 and 3 other fieldsHigh correlation
제조업체코드 is highly overall correlated with 상품코드 and 4 other fieldsHigh correlation
제작상태 is highly imbalanced (63.8%)Imbalance

Reproduction

Analysis started2024-01-28 12:29:12.964256
Analysis finished2024-01-28 12:29:21.899238
Duration8.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

site코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
306
29 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row306
2nd row306
3rd row306
4th row306
5th row306

Common Values

ValueCountFrequency (%)
306 29
100.0%

Length

2024-01-28T21:29:21.949659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:29:22.023233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
306 29
100.0%

스티커시작번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0031997 × 1016
Minimum2.00102 × 1016
Maximum2.00717 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:22.091401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.00102 × 1016
5-th percentile2.00102 × 1016
Q12.0012 × 1016
median2.00228 × 1016
Q32.00529 × 1016
95-th percentile2.00717 × 1016
Maximum2.00717 × 1016
Range6.15 × 1013
Interquartile range (IQR)4.09 × 1013

Descriptive statistics

Standard deviation2.283047 × 1013
Coefficient of variation (CV)0.0011397002
Kurtosis-1.1765784
Mean2.0031997 × 1016
Median Absolute Deviation (MAD)1.26 × 1013
Skewness0.58090917
Sum5.809279 × 1017
Variance5.2123034 × 1026
MonotonicityNot monotonic
2024-01-28T21:29:22.177056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20010200000000000 5
17.2%
20052900000000000 5
17.2%
20012000000000000 4
13.8%
20040300000000000 4
13.8%
20071700000000000 4
13.8%
20022800000000000 3
10.3%
20011700000000000 2
 
6.9%
20012300000000000 2
 
6.9%
ValueCountFrequency (%)
20010200000000000 5
17.2%
20011700000000000 2
 
6.9%
20012000000000000 4
13.8%
20012300000000000 2
 
6.9%
20022800000000000 3
10.3%
20040300000000000 4
13.8%
20052900000000000 5
17.2%
20071700000000000 4
13.8%
ValueCountFrequency (%)
20071700000000000 4
13.8%
20052900000000000 5
17.2%
20040300000000000 4
13.8%
20022800000000000 3
10.3%
20012300000000000 2
 
6.9%
20012000000000000 4
13.8%
20011700000000000 2
 
6.9%
20010200000000000 5
17.2%

스티커종류번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0031997 × 1016
Minimum2.00102 × 1016
Maximum2.00717 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:22.260868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.00102 × 1016
5-th percentile2.00102 × 1016
Q12.0012 × 1016
median2.00228 × 1016
Q32.00529 × 1016
95-th percentile2.00717 × 1016
Maximum2.00717 × 1016
Range6.15 × 1013
Interquartile range (IQR)4.09 × 1013

Descriptive statistics

Standard deviation2.283047 × 1013
Coefficient of variation (CV)0.0011397002
Kurtosis-1.1765784
Mean2.0031997 × 1016
Median Absolute Deviation (MAD)1.26 × 1013
Skewness0.58090917
Sum5.809279 × 1017
Variance5.2123034 × 1026
MonotonicityNot monotonic
2024-01-28T21:29:22.345990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20010200000000000 5
17.2%
20052900000000000 5
17.2%
20012000000000000 4
13.8%
20040300000000000 4
13.8%
20071700000000000 4
13.8%
20022800000000000 3
10.3%
20011700000000000 2
 
6.9%
20012300000000000 2
 
6.9%
ValueCountFrequency (%)
20010200000000000 5
17.2%
20011700000000000 2
 
6.9%
20012000000000000 4
13.8%
20012300000000000 2
 
6.9%
20022800000000000 3
10.3%
20040300000000000 4
13.8%
20052900000000000 5
17.2%
20071700000000000 4
13.8%
ValueCountFrequency (%)
20071700000000000 4
13.8%
20052900000000000 5
17.2%
20040300000000000 4
13.8%
20022800000000000 3
10.3%
20012300000000000 2
 
6.9%
20012000000000000 4
13.8%
20011700000000000 2
 
6.9%
20010200000000000 5
17.2%

상품코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4998.5172
Minimum1003
Maximum8050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:22.437142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile1011
Q12010
median5100
Q38010
95-th percentile8042
Maximum8050
Range7047
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation2652.8502
Coefficient of variation (CV)0.53072743
Kurtosis-1.3467771
Mean4998.5172
Median Absolute Deviation (MAD)2910
Skewness-0.37032593
Sum144957
Variance7037614.1
MonotonicityNot monotonic
2024-01-28T21:29:22.530982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8010 3
 
10.3%
6020 3
 
10.3%
8030 3
 
10.3%
8050 2
 
6.9%
5050 2
 
6.9%
5010 2
 
6.9%
6125 1
 
3.4%
6060 1
 
3.4%
7020 1
 
3.4%
5005 1
 
3.4%
Other values (10) 10
34.5%
ValueCountFrequency (%)
1003 1
3.4%
1005 1
3.4%
1020 1
3.4%
1060 1
3.4%
1121 1
3.4%
2003 1
3.4%
2005 1
3.4%
2010 1
3.4%
2020 1
3.4%
5005 1
3.4%
ValueCountFrequency (%)
8050 2
6.9%
8030 3
10.3%
8010 3
10.3%
7020 1
 
3.4%
6125 1
 
3.4%
6060 1
 
3.4%
6020 3
10.3%
5100 1
 
3.4%
5050 2
6.9%
5010 2
6.9%

상품종류
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9655172
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:22.614761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.6522599
Coefficient of variation (CV)0.53413568
Kurtosis-1.3406195
Mean4.9655172
Median Absolute Deviation (MAD)3
Skewness-0.36725312
Sum144
Variance7.0344828
MonotonicityNot monotonic
2024-01-28T21:29:22.695800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 8
27.6%
5 6
20.7%
1 5
17.2%
6 5
17.2%
2 4
13.8%
7 1
 
3.4%
ValueCountFrequency (%)
1 5
17.2%
2 4
13.8%
5 6
20.7%
6 5
17.2%
7 1
 
3.4%
8 8
27.6%
ValueCountFrequency (%)
8 8
27.6%
7 1
 
3.4%
6 5
17.2%
5 6
20.7%
2 4
13.8%
1 5
17.2%

제작상태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
3
27 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 27
93.1%
2 2
 
6.9%

Length

2024-01-28T21:29:22.793832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:29:22.867056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 27
93.1%
2 2
 
6.9%

수주전표번호
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
2020010000000000000
13 
2020050000000000000
2020040000000000000
2020070000000000000
2020020000000000000

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020020000000000000
2nd row2020020000000000000
3rd row2020020000000000000
4th row2020010000000000000
5th row2020010000000000000

Common Values

ValueCountFrequency (%)
2020010000000000000 13
44.8%
2020050000000000000 5
 
17.2%
2020040000000000000 4
 
13.8%
2020070000000000000 4
 
13.8%
2020020000000000000 3
 
10.3%

Length

2024-01-28T21:29:22.957056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:29:23.056543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020010000000000000 13
44.8%
2020050000000000000 5
 
17.2%
2020040000000000000 4
 
13.8%
2020070000000000000 4
 
13.8%
2020020000000000000 3
 
10.3%

발주업체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
1001
29 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1001
2nd row1001
3rd row1001
4th row1001
5th row1001

Common Values

ValueCountFrequency (%)
1001 29
100.0%

Length

2024-01-28T21:29:23.165103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:29:23.249423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1001 29
100.0%

제조업체코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
1001
17 
1003
1002
1004

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1001
2nd row1001
3rd row1001
4th row1001
5th row1001

Common Values

ValueCountFrequency (%)
1001 17
58.6%
1003 5
 
17.2%
1002 4
 
13.8%
1004 3
 
10.3%

Length

2024-01-28T21:29:23.324198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T21:29:23.405123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1001 17
58.6%
1003 5
 
17.2%
1002 4
 
13.8%
1004 3
 
10.3%
Distinct16
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum2020-01-02 00:00:00
Maximum2020-07-21 00:00:00
2024-01-28T21:29:23.489015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:23.579581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
Distinct17
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum2020-01-02 00:00:00
Maximum2020-07-25 00:00:00
2024-01-28T21:29:23.681233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:23.775544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

스티커제작요청수량
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269041.38
Minimum100
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:23.886097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile460
Q130000
median50000
Q3300000
95-th percentile1000000
Maximum1000000
Range999900
Interquartile range (IQR)270000

Descriptive statistics

Standard deviation362081.65
Coefficient of variation (CV)1.3458214
Kurtosis0.18616989
Mean269041.38
Median Absolute Deviation (MAD)49000
Skewness1.2975906
Sum7802200
Variance1.3110312 × 1011
MonotonicityNot monotonic
2024-01-28T21:29:23.986168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
30000 8
27.6%
300000 4
13.8%
1000000 4
13.8%
50000 2
 
6.9%
1000 2
 
6.9%
100 2
 
6.9%
250000 1
 
3.4%
10000 1
 
3.4%
400000 1
 
3.4%
900000 1
 
3.4%
Other values (3) 3
 
10.3%
ValueCountFrequency (%)
100 2
 
6.9%
1000 2
 
6.9%
10000 1
 
3.4%
30000 8
27.6%
40000 1
 
3.4%
50000 2
 
6.9%
60000 1
 
3.4%
250000 1
 
3.4%
300000 4
13.8%
400000 1
 
3.4%
ValueCountFrequency (%)
1000000 4
13.8%
900000 1
 
3.4%
600000 1
 
3.4%
400000 1
 
3.4%
300000 4
13.8%
250000 1
 
3.4%
60000 1
 
3.4%
50000 2
 
6.9%
40000 1
 
3.4%
30000 8
27.6%

제작완료 수량
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268006.9
Minimum100
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:24.084749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile460
Q130000
median50000
Q3300000
95-th percentile1000000
Maximum1000000
Range999900
Interquartile range (IQR)270000

Descriptive statistics

Standard deviation362823.1
Coefficient of variation (CV)1.3537827
Kurtosis0.17920297
Mean268006.9
Median Absolute Deviation (MAD)49000
Skewness1.2942678
Sum7772200
Variance1.316406 × 1011
MonotonicityNot monotonic
2024-01-28T21:29:24.183301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
30000 6
20.7%
300000 4
13.8%
1000000 4
13.8%
50000 2
 
6.9%
1000 2
 
6.9%
100 2
 
6.9%
250000 1
 
3.4%
10000 1
 
3.4%
400000 1
 
3.4%
900000 1
 
3.4%
Other values (5) 5
17.2%
ValueCountFrequency (%)
100 2
 
6.9%
1000 2
 
6.9%
3000 1
 
3.4%
10000 1
 
3.4%
27000 1
 
3.4%
30000 6
20.7%
40000 1
 
3.4%
50000 2
 
6.9%
60000 1
 
3.4%
250000 1
 
3.4%
ValueCountFrequency (%)
1000000 4
13.8%
900000 1
 
3.4%
600000 1
 
3.4%
400000 1
 
3.4%
300000 4
13.8%
250000 1
 
3.4%
60000 1
 
3.4%
50000 2
 
6.9%
40000 1
 
3.4%
30000 6
20.7%

묶음수량
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9182.7586
Minimum100
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:24.282320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile140
Q11500
median3000
Q320000
95-th percentile30000
Maximum30000
Range29900
Interquartile range (IQR)18500

Descriptive statistics

Standard deviation10165.455
Coefficient of variation (CV)1.1070154
Kurtosis-0.47893511
Mean9182.7586
Median Absolute Deviation (MAD)2800
Skewness0.99041992
Sum266300
Variance1.0333648 × 108
MonotonicityNot monotonic
2024-01-28T21:29:24.387717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3000 7
24.1%
20000 5
17.2%
30000 3
10.3%
5000 2
 
6.9%
1000 2
 
6.9%
200 2
 
6.9%
100 2
 
6.9%
1500 1
 
3.4%
1200 1
 
3.4%
18000 1
 
3.4%
Other values (3) 3
10.3%
ValueCountFrequency (%)
100 2
 
6.9%
200 2
 
6.9%
1000 2
 
6.9%
1200 1
 
3.4%
1500 1
 
3.4%
3000 7
24.1%
4000 1
 
3.4%
5000 2
 
6.9%
6000 1
 
3.4%
12000 1
 
3.4%
ValueCountFrequency (%)
30000 3
10.3%
20000 5
17.2%
18000 1
 
3.4%
12000 1
 
3.4%
6000 1
 
3.4%
5000 2
 
6.9%
4000 1
 
3.4%
3000 7
24.1%
1500 1
 
3.4%
1200 1
 
3.4%

묶음당 매수
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.551724
Minimum1
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:24.491306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q110
median10
Q350
95-th percentile170
Maximum250
Range249
Interquartile range (IQR)40

Descriptive statistics

Standard deviation62.028557
Coefficient of variation (CV)1.7952377
Kurtosis9.563557
Mean34.551724
Median Absolute Deviation (MAD)0
Skewness3.1612924
Sum1002
Variance3847.5419
MonotonicityNot monotonic
2024-01-28T21:29:24.584811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10 15
51.7%
50 6
 
20.7%
20 2
 
6.9%
250 2
 
6.9%
5 2
 
6.9%
1 2
 
6.9%
ValueCountFrequency (%)
1 2
 
6.9%
5 2
 
6.9%
10 15
51.7%
20 2
 
6.9%
50 6
 
20.7%
250 2
 
6.9%
ValueCountFrequency (%)
250 2
 
6.9%
50 6
 
20.7%
20 2
 
6.9%
10 15
51.7%
5 2
 
6.9%
1 2
 
6.9%

BOX 당 묶음수량
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.72414
Minimum5
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:24.671615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q110
median20
Q320
95-th percentile800
Maximum1000
Range995
Interquartile range (IQR)10

Descriptive statistics

Standard deviation264.62668
Coefficient of variation (CV)2.6014148
Kurtosis8.6429026
Mean101.72414
Median Absolute Deviation (MAD)10
Skewness3.0976813
Sum2950
Variance70027.278
MonotonicityNot monotonic
2024-01-28T21:29:24.765143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 11
37.9%
20 10
34.5%
40 2
 
6.9%
1000 2
 
6.9%
5 2
 
6.9%
500 1
 
3.4%
50 1
 
3.4%
ValueCountFrequency (%)
5 2
 
6.9%
10 11
37.9%
20 10
34.5%
40 2
 
6.9%
50 1
 
3.4%
500 1
 
3.4%
1000 2
 
6.9%
ValueCountFrequency (%)
1000 2
 
6.9%
500 1
 
3.4%
50 1
 
3.4%
40 2
 
6.9%
20 10
34.5%
10 11
37.9%
5 2
 
6.9%

최종제작스티커번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0031997 × 1016
Minimum2.00102 × 1016
Maximum2.00717 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-28T21:29:24.848325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.00102 × 1016
5-th percentile2.00102 × 1016
Q12.0012 × 1016
median2.00228 × 1016
Q32.00529 × 1016
95-th percentile2.00717 × 1016
Maximum2.00717 × 1016
Range6.15 × 1013
Interquartile range (IQR)4.09 × 1013

Descriptive statistics

Standard deviation2.283047 × 1013
Coefficient of variation (CV)0.0011397002
Kurtosis-1.1765784
Mean2.0031997 × 1016
Median Absolute Deviation (MAD)1.26 × 1013
Skewness0.58090917
Sum5.809279 × 1017
Variance5.2123034 × 1026
MonotonicityNot monotonic
2024-01-28T21:29:24.941340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20010200000000000 5
17.2%
20052900000000000 5
17.2%
20012000000000000 4
13.8%
20040300000000000 4
13.8%
20071700000000000 4
13.8%
20022800000000000 3
10.3%
20011700000000000 2
 
6.9%
20012300000000000 2
 
6.9%
ValueCountFrequency (%)
20010200000000000 5
17.2%
20011700000000000 2
 
6.9%
20012000000000000 4
13.8%
20012300000000000 2
 
6.9%
20022800000000000 3
10.3%
20040300000000000 4
13.8%
20052900000000000 5
17.2%
20071700000000000 4
13.8%
ValueCountFrequency (%)
20071700000000000 4
13.8%
20052900000000000 5
17.2%
20040300000000000 4
13.8%
20022800000000000 3
10.3%
20012300000000000 2
 
6.9%
20012000000000000 4
13.8%
20011700000000000 2
 
6.9%
20010200000000000 5
17.2%
Distinct9
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum2019-12-27 00:00:00
Maximum2020-07-16 00:00:00
2024-01-28T21:29:25.033432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:25.121967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
Distinct9
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum2019-12-27 00:00:00
Maximum2020-07-16 00:00:00
2024-01-28T21:29:25.209059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:25.295437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

Interactions

2024-01-28T21:29:20.815856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:13.500687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.271752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.027213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.030593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.810028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.561902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.282349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.028316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.764839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:20.886189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:13.578699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.349919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.102217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.117795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.882046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.635756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.357358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.113160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.838798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:20.978608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:13.671117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.424130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.175420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.195330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.952882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.718594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.430834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.202925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.911259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:21.054643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:13.761521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.508120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.494821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.273549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.033974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.792374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.511063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.288793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.990115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:21.146847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:13.834792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.585578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.570317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.346047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.115312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.869270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.594785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.359900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:20.377437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:21.221890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:13.912701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.658151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.646135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.422793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.183382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.937612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.666735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.425493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:20.462237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:21.297246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:13.984682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.732243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.721371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.505158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.253734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.003735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.735070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.492146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:20.546608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:21.376563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.058746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.807171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.791083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.594355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.338079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.077266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.798527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.560814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:20.618651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:21.445621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.124357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.879605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.864807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.661276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.420308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.144789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.870582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.622657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:20.684108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:21.516728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.197393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:14.947378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:15.936018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:16.734764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:17.487264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.211120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:18.936940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:19.690794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T21:29:20.746099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T21:29:25.380748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
스티커시작번호스티커종류번호상품코드상품종류제작상태수주전표번호제조업체코드제작시작일제작종료일스티커제작요청수량제작완료 수량묶음수량묶음당 매수BOX 당 묶음수량최종제작스티커번호입력일업데이트일
스티커시작번호1.0001.0000.7720.7720.4841.0000.1731.0001.0000.3510.3510.2150.094NaN1.0001.0001.000
스티커종류번호1.0001.0000.7720.7720.4841.0000.1731.0001.0000.3510.3510.2150.094NaN1.0001.0001.000
상품코드0.7720.7721.0001.0000.1800.5530.7770.8840.8590.8890.8890.9200.7590.7900.7720.8400.840
상품종류0.7720.7721.0001.0000.2270.5660.7800.8910.8210.8910.8910.9210.8350.6680.7720.8450.845
제작상태0.4840.4840.1800.2271.0000.5060.0000.6390.6920.0000.0000.0000.0000.0000.4840.5070.507
수주전표번호1.0001.0000.5530.5660.5061.0000.2411.0001.0000.4320.4320.2520.0000.0001.0001.0001.000
제조업체코드0.1730.1730.7770.7800.0000.2411.0000.8970.6210.8140.8140.9300.4800.0000.1730.6990.699
제작시작일1.0001.0000.8840.8910.6391.0000.8971.0000.9800.0000.0000.1780.0000.8211.0000.9800.980
제작종료일1.0001.0000.8590.8210.6921.0000.6210.9801.0000.0000.0000.0000.0001.0001.0000.9790.979
스티커제작요청수량0.3510.3510.8890.8910.0000.4320.8140.0000.0001.0001.0000.9840.9660.0000.3510.4890.489
제작완료 수량0.3510.3510.8890.8910.0000.4320.8140.0000.0001.0001.0000.9840.9660.0000.3510.4890.489
묶음수량0.2150.2150.9200.9210.0000.2520.9300.1780.0000.9840.9841.0000.8670.0000.2150.5010.501
묶음당 매수0.0940.0940.7590.8350.0000.0000.4800.0000.0000.9660.9660.8671.0000.0000.0940.6990.699
BOX 당 묶음수량NaNNaN0.7900.6680.0000.0000.0000.8211.0000.0000.0000.0000.0001.000NaN0.9700.970
최종제작스티커번호1.0001.0000.7720.7720.4841.0000.1731.0001.0000.3510.3510.2150.094NaN1.0001.0001.000
입력일1.0001.0000.8400.8450.5071.0000.6990.9800.9790.4890.4890.5010.6990.9701.0001.0001.000
업데이트일1.0001.0000.8400.8450.5071.0000.6990.9800.9790.4890.4890.5010.6990.9701.0001.0001.000
2024-01-28T21:29:25.846127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제작상태수주전표번호제조업체코드
제작상태1.0000.5760.000
수주전표번호0.5761.0000.181
제조업체코드0.0000.1811.000
2024-01-28T21:29:25.926197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
스티커시작번호스티커종류번호상품코드상품종류스티커제작요청수량제작완료 수량묶음수량묶음당 매수BOX 당 묶음수량최종제작스티커번호제작상태수주전표번호제조업체코드
스티커시작번호1.0001.0000.7590.7670.0760.0340.311-0.099-0.4451.0000.5761.0000.181
스티커종류번호1.0001.0000.7590.7670.0760.0340.311-0.099-0.4451.0000.5761.0000.181
상품코드0.7590.7591.0000.9810.014-0.0160.271-0.132-0.5430.7590.1280.4110.595
상품종류0.7670.7670.9811.0000.0470.0240.260-0.060-0.5320.7670.1280.4110.595
스티커제작요청수량0.0760.0760.0140.0471.0000.9920.8360.8130.5670.0760.0000.2930.637
제작완료 수량0.0340.034-0.0160.0240.9921.0000.8250.8120.5840.0340.0000.2930.637
묶음수량0.3110.3110.2710.2600.8360.8251.0000.4280.3750.3110.0000.1470.789
묶음당 매수-0.099-0.099-0.132-0.0600.8130.8120.4281.0000.659-0.0990.0000.0000.465
BOX 당 묶음수량-0.445-0.445-0.543-0.5320.5670.5840.3750.6591.000-0.4450.0000.0000.000
최종제작스티커번호1.0001.0000.7590.7670.0760.0340.311-0.099-0.4451.0000.5761.0000.181
제작상태0.5760.5760.1280.1280.0000.0000.0000.0000.0000.5761.0000.5760.000
수주전표번호1.0001.0000.4110.4110.2930.2930.1470.0000.0001.0000.5761.0000.181
제조업체코드0.1810.1810.5950.5950.6370.6370.7890.4650.0000.1810.0000.1811.000

Missing values

2024-01-28T21:29:21.633226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T21:29:21.826528image/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

site코드스티커시작번호스티커종류번호상품코드상품종류제작상태수주전표번호발주업체코드제조업체코드제작시작일제작종료일스티커제작요청수량제작완료 수량묶음수량묶음당 매수BOX 당 묶음수량최종제작스티커번호입력일업데이트일
030620022800000000000200228000000000008010832020020000000000000100110012020-03-202020-03-22300003000030001010200228000000000002020-02-272020-02-27
130620022800000000000200228000000000008030832020020000000000000100110012020-03-222020-03-24300003000030001010200228000000000002020-02-272020-02-27
230620022800000000000200228000000000008050832020020000000000000100110012020-03-032020-03-05500005000050001010200228000000000002020-02-272020-02-27
330620010200000000000200102000000000001020132020010000000000000100110012020-01-022020-01-023000030000150020500200102000000000002019-12-272019-12-27
430620010200000000000200102000000000001003132020010000000000000100110012020-01-162020-01-22300000300000120025040200102000000000002020-01-032020-01-03
530620010200000000000200102000000000001005132020010000000000000100110012020-01-152020-01-20250000250000100025040200102000000000002020-01-032020-01-03
630620010200000000000200102000000000001060132020010000000000000100110012020-01-162020-01-1610000100001000101000200102000000000002020-01-032020-01-03
730620010200000000000200102000000000001121132020010000000000000100110012020-01-152020-01-1650000500005000101000200102000000000002020-01-032020-01-03
830620012000000000000200120000000000002003232020010000000000000100110012020-02-072020-02-0710001000200510200120000000000002020-01-202020-01-20
930620012000000000000200120000000000002005232020010000000000000100110012020-02-072020-02-0710001000200510200120000000000002020-01-202020-01-20
site코드스티커시작번호스티커종류번호상품코드상품종류제작상태수주전표번호발주업체코드제조업체코드제작시작일제작종료일스티커제작요청수량제작완료 수량묶음수량묶음당 매수BOX 당 묶음수량최종제작스티커번호입력일업데이트일
1930620040300000000000200403000000000006060632020040000000000000100110032020-04-022020-04-02600006000060001020200403000000000002020-04-022020-04-02
2030620052900000000000200529000000000008010832020050000000000000100110012020-06-052020-06-07300003000030001010200529000000000002020-06-022020-06-02
2130620052900000000000200529000000000008030832020050000000000000100110012020-06-092020-06-10300003000030001010200529000000000002020-06-022020-06-02
2230620052900000000000200529000000000005010532020050000000000000100110032020-06-022020-06-0210000001000000200005020200529000000000002020-06-022020-06-02
2330620052900000000000200529000000000005050532020050000000000000100110042020-06-022020-06-02300000300000300001020200529000000000002020-06-022020-06-02
2430620052900000000000200529000000000006020632020050000000000000100110022020-06-092020-06-0910000001000000200005020200529000000000002020-06-092020-06-09
2530620071700000000000200717000000000008010832020070000000000000100110012020-07-212020-07-24300003000030001010200717000000000002020-07-162020-07-16
2630620071700000000000200717000000000008030822020070000000000000100110012020-07-212020-07-2430000300030001010200717000000000002020-07-162020-07-16
2730620071700000000000200717000000000008050822020070000000000000100110012020-07-212020-07-25300002700030001010200717000000000002020-07-162020-07-16
2830620071700000000000200717000000000006125632020070000000000000100110032020-07-162020-07-16400004000040001010200717000000000002020-07-162020-07-16